release: bump 0.6.3 -> 0.6.4 (#198)

Version bump for the **v0.6.4** release shipping the **B9 Price Statistics** family (#197): 447 -> 452 indicators. Bumps workspace + Python/Node/WASM package versions, lockfiles and CHANGELOG. No code changes.

.github/requirements/bench.in

@@ -5,5 +5,7 @@
 maturin
 numpy
 pandas
+TA-Lib
+tulipy
 talipp
 finta

.github/requirements/bench.txt

@@ -1,5 +1,13 @@
 # This file was autogenerated by uv via the following command:
 #    ./scripts/update-lockfiles.sh
+build==1.5.0 \
+    --hash=sha256:13f3eecb844759ab66efec90ca17639bbf14dc06cb2fdf37a9010322d9c50a6f \
+    --hash=sha256:302c22c3ba2a0fd5f3911918651341ebb3896176cbdec15bd421f80b1afc7647
+    # via ta-lib
+colorama==0.4.6 \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+    # via build
 finta==1.3 \
     --hash=sha256:b94b94df311c18bf5402eb2fe8fd2db5e1bdaff08baf58a7367d05c7abdd10d3 \
     --hash=sha256:f2fa0673748f4be8f57e57cf6d5c00a4d44bc6071ea69dbb9a1d329d045cbba2
@@ -97,6 +105,12 @@ numpy==2.4.6 \
     #   -r .github/requirements/bench.in
     #   finta
     #   pandas
+    #   ta-lib
+    #   tulipy
+packaging==26.2 \
+    --hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \
+    --hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661
+    # via build
 pandas==3.0.3 \
     --hash=sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c \
     --hash=sha256:05f1f1752b8533ea03f7f39a9c15b1a058d067bb48f4748948e7a8691e0510f2 \
@@ -149,6 +163,10 @@ pandas==3.0.3 \
     # via
     #   -r .github/requirements/bench.in
     #   finta
+pyproject-hooks==1.2.0 \
+    --hash=sha256:1e859bd5c40fae9448642dd871adf459e5e2084186e8d2c2a79a824c970da1f8 \
+    --hash=sha256:9e5c6bfa8dcc30091c74b0cf803c81fdd29d94f01992a7707bc97babb1141913
+    # via build
 python-dateutil==2.9.0.post0 \
     --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
     --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
@@ -157,10 +175,72 @@ six==1.17.0 \
     --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \
     --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81
     # via python-dateutil
+ta-lib==0.6.8 \
+    --hash=sha256:02388054c059945e5f02625f5075bac20a1803573cb43e7d096091027511961f \
+    --hash=sha256:094677b279a59c3f01c3aca8a889fda3523fd641a3805f69a2d642121b72e55e \
+    --hash=sha256:0a08a29690a922ba92a6cf42902a8a93c6fbda4cfed62c3c5b0471560ef60135 \
+    --hash=sha256:0ccd478ff5735831bf2a61d653466bfda8afadc26ad58ca6b1edb9e7521cc674 \
+    --hash=sha256:0e371d14b49e70caa973a234c8823341dd446f5c5d7acc826868bb42b272bdc0 \
+    --hash=sha256:11a373c9308eae3bac2d56d37017f9ab63968cc074a8b95be879aae3d13133aa \
+    --hash=sha256:128ec92e6a0e9ff7a38edef80e3b74f15bb2ed1c531d5d3252c8dca22677651b \
+    --hash=sha256:1fb4028437201e19014e4e374272b739867c8a3eb655da46675ef4c2ff14b616 \
+    --hash=sha256:282e49c766b5952dd8796f77d7ed3ae412cdd88e31f845b1fbbb86ac6cb7bebf \
+    --hash=sha256:2b369cabb48485fbf444beb3f5a878075367b99c2c86db2f796afeabebc749e0 \
+    --hash=sha256:2bf714333788bf5175f2512b86d2ed129e89ae6f6c2923e8a297a1e3395e13b5 \
+    --hash=sha256:30de46b55873b51be945a09edf486afcc190dc47eff9fb5d2b12c9f7e3d743da \
+    --hash=sha256:34e3b12407ddf99f6627435aa8a165f094339bb7dc33de92e1d7472e9f237304 \
+    --hash=sha256:36b2a516fce57309840f5ef3fa2fd0c4449293fc72536a0400d2e1e26b414da8 \
+    --hash=sha256:3a9195299df9d7d2a6e9d16bebd6b706b0ea99e4b871864c4b034c2577e21a77 \
+    --hash=sha256:3c32fc0f546ceecc47dd45f33d72ab4a1e341b80d9081c2d77b100add5d49104 \
+    --hash=sha256:3d7333e907bff3e3997e54f89733ffa8d619842a3e1cd962bca34bdc11944c28 \
+    --hash=sha256:4795e93d130c9b7fb661f0cead49752ae6a980437df74b99d5918026c212443e \
+    --hash=sha256:490e19a45cd3cdd6dfe6b46019f7ffe1103500750b41b51996a870e7c1c5f066 \
+    --hash=sha256:4aa0fe08383f3e5fc7d2f8cf9b42ac778f4d53fd75bcd2799a858225954eab89 \
+    --hash=sha256:559326d8f3d904cd4aa61f6a392d5626f35eec6a9f6cc83bcddb0abf88c40516 \
+    --hash=sha256:5929c83bd8cb7572d1c17ffdbf0eac235bf3c4d53cde1950cf89d944eaf97525 \
+    --hash=sha256:5bfd21b6acb32e20d4e279c34405a34e63da345be4b2b6eabd683e1a88857406 \
+    --hash=sha256:613cf06313331f49dd7b85a5a24fbddb1156c9723b6921a231906241726e5aee \
+    --hash=sha256:66a8e1c1e899d15a2f7510e43527fba22d895e7f6058d027db3e3837d88a69de \
+    --hash=sha256:691a62926ba09f2653ec0908554b3635497efb7751c5d46b916cd1ebbb1d3c25 \
+    --hash=sha256:6c1fd18e45c39d5a4be4b0d6a20c141e43fe46daeb1b2e2f304ebae7015ab6e6 \
+    --hash=sha256:6c6a1e8f98de92e817491b50aa4d01d69a1b41a4ed3173747e8f16f0d4cf81cc \
+    --hash=sha256:6cf029b886cfb28a2701503b7c602b811f2daa45276bd6459b0c71e051deb497 \
+    --hash=sha256:71506116eac0d3e3598d6325b4b818c3a0f6acb3222b24d30ad726e8c4bf7ea8 \
+    --hash=sha256:7993164e8e9f78ec31d38c47850ca6ba5451788b5b49a8a2dbb3322b36b5693b \
+    --hash=sha256:7a5cc6bf60791d8274edfdfe2dd7cec3f00f656dcc92e2b0a9af06c8b18ce6a6 \
+    --hash=sha256:87c1cc1057d903b78a8257a7c5f497db6fd5284f5080392bd57b66031d7389a3 \
+    --hash=sha256:98376c75bd6c103c74396953084a5e0798ffe476aecbfcc51ec6d100a685ac38 \
+    --hash=sha256:a395524b0fafa10446d11e11acb4742e919523de58aac03b791f26d7a783bcf0 \
+    --hash=sha256:a5100a4be91b7d4b7c8fe16a3600bd0951e10205eb1066b6873afd3996b51ee4 \
+    --hash=sha256:a63a52221f8c73f82f4e00493351d987f594931198589287aee96f8da673cfd5 \
+    --hash=sha256:a89734a7bcb2ea3b6fd600a74d6fbcdb8d3fa3f7917dbd978e039710b5509c9c \
+    --hash=sha256:b165f5e6de1ccc964e863bd2035807a4d3bad3e0481f9db2dc52034d6ad4f9de \
+    --hash=sha256:b3845e4c2fa32963fb7f384ebbaa2761b0e6b96145239bf80e956d4aff4b071c \
+    --hash=sha256:b3b017d9103e7a7372a146773be32b184ff7330bd708d40b1f56f06a686756ed \
+    --hash=sha256:b6c6e4858d8c3f88e19b7aa94b6a7619108f0bee51da9fa67b0785a8b59955f9 \
+    --hash=sha256:bfad1202fb1f9140e3810cc607058395f59032d9128cc0d716900c78bea5f337 \
+    --hash=sha256:c01809fb602e2fefc8cbfb3b603bb59d2a2eaee8708410896d48a835ba00e7c5 \
+    --hash=sha256:cce8de9d48289927ed18aaa420740efd52b2cd9289da32e3799afbb3a02822e8 \
+    --hash=sha256:ce2bc1ea01200b6d8130ab917296d05d77a1a571ec6c1ee25cfca6d55cd5db4a \
+    --hash=sha256:d4601e2a8b46ffbf540601a4926fd6cc5aae8a13b36fdd467f1040f01f9edaed \
+    --hash=sha256:d556d1c256b3700b60b6b061664a667b2e49d599c2772d46a9f2348f2dc4ab5c \
+    --hash=sha256:ddf7453acd03b966624ebefdb38169b5bbbeea1a1a58c90b095667247f9de327 \
+    --hash=sha256:e781eeb65b2007af553389c8a7fb7bc53cb856118b0fcffb2c26b0f49561c686 \
+    --hash=sha256:e920c272cd9e70a6b10eae9203cc96845da142e1dd4482de9343dda3738a9862 \
+    --hash=sha256:f5b6174bf4bf9152e368561dff410203c6921e4dd2afbcda3283a95957158112 \
+    --hash=sha256:f69bd42fd2515060af69b120668213121264bb7976b113954b6f9db327727c65 \
+    --hash=sha256:f823d0f6b04a6797fbe253bcf91666e71a6b63c290683819650c68b2468ebe64 \
+    --hash=sha256:fa7e9f2e80a9535f9692e113d02b4268b5f88675a730d1b0ef0abeb74c9a4e80
+    # via -r .github/requirements/bench.in
 talipp==2.7.0 \
     --hash=sha256:567f59ad74366cb59a14a00d350f35fd9d22e6924d6228bad581e6dcf1de2205 \
     --hash=sha256:f749f22b9ad615605e71faf26457bb7f5e3fe16f04d3287f4ca54fd16bc3d4eb
     # via -r .github/requirements/bench.in
+tulipy==0.4.0 \
+    --hash=sha256:540704956b5b940a5f6306aa393a37536a6d7c3cbc07efe47512f3496e5203ab \
+    --hash=sha256:95542e40537afdd345d875baf37485eac993c6a819d00c51432e9de8df21eba8 \
+    --hash=sha256:fbc31727ef7657c93ad910bfdce65fecc6aaa7a5e961fe00240718e7a3fc79d8
+    # via -r .github/requirements/bench.in
 tzdata==2026.2 \
     --hash=sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10 \
     --hash=sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7

.github/workflows/bench.yml

@@ -117,3 +117,29 @@ jobs:
         with:
           name: cross-library-bench
           path: bindings/python/benchmark.txt
+
+  rust-cross-bench:
+    name: Rust cross-library benchmark report
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+        with:
+          persist-credentials: false
+
+      - uses: dtolnay/rust-toolchain@29eef336d9b2848a0b548edc03f92a220660cdb8 # stable branch, 2026-03-27
+
+      - uses: Swatinem/rust-cache@e18b497796c12c097a38f9edb9d0641fb99eee32 # v2
+        continue-on-error: true # cache is an optimisation; never block on a stuck/slow restore
+        timeout-minutes: 6
+
+      # Wickra vs the other Rust TA crates (kand, ta-rs, yata) on an identical
+      # candle series — the like-for-like engine comparison with no binding
+      # overhead. Streaming + batch, in crates/wickra-bench/benches/cross_lib.rs.
+      - name: Run Rust cross-library benchmark
+        run: cargo bench -p wickra-bench --bench cross_lib | tee rust_cross_bench.txt
+
+      - name: Upload Rust report
+        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
+        with:
+          name: rust-cross-bench
+          path: rust_cross_bench.txt

.github/workflows/sync-about.yml

@@ -180,14 +180,14 @@ jobs:
             exit 0
           fi
           cd docs-count
-          sed -i -E "s/[0-9]+ (streaming-first )?indicators/${n} \1indicators/g" index.md overview.md Indicators-Overview.md
+          sed -i -E "s/[0-9]+ (streaming-first )?indicators/${n} \1indicators/g" index.md overview.md Indicators-Overview.md .vitepress/config.ts
           if git diff --quiet; then
             echo "Docs indicator count unchanged."
             exit 0
           fi
           git config user.name "wickra-bot"
           git config user.email "wickra-bot@users.noreply.github.com"
-          git add index.md overview.md Indicators-Overview.md
+          git add index.md overview.md Indicators-Overview.md .vitepress/config.ts
           git commit -m "chore: sync indicator count to ${n}"
           if ! git push 2>/dev/null; then
             echo "::warning::push to wickra-lib/wickra-docs failed — ABOUT_SYNC_TOKEN likely lacks write (findings P10.0a)."

CHANGELOG.md

@@ -7,6 +7,100 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.6.4] - 2026-06-07
+- **Kendall Tau** — Kendall rank correlation (tau-b) over a rolling window of paired observations (`KENDALLTAU`).
+- **Sample Entropy** — Sample entropy: regularity/complexity of a rolling series (Richman-Moorman) (`SAMPLEENT`).
+- **Shannon Entropy** — Shannon entropy of a rolling value distribution over fixed bins (`SHANNONENT`).
+- **Rolling Min-Max Scaler** — Rolling min-max scaler mapping the latest value to 0..1 over a rolling window (`ROLLINGMINMAX`).
+- **Jarque-Bera** — Jarque-Bera normality test statistic over a rolling window (`JARQUEBERA`).
+
+## [0.6.3] - 2026-06-07
+- **Volume-Weighted MACD** — Volume-Weighted MACD: MACD computed on VWMA instead of EMA, with signal line and histogram (`VWMACD`).
+- **Better Volume** — Better Volume (VSA): classifies volume against bar spread to surface effort/result imbalance (`BETTERVOL`).
+- **Intraday Intensity Index** — Intraday Intensity Index: volume weighted by close position within the bar range (`INTRADAYINT`).
+- **Trade Volume Index** — Trade Volume Index: accumulates volume by tick direction past a min-tick threshold (distinct from TSV) (`TRADEVOLIDX`).
+- **Twiggs Money Flow** — Twiggs Money Flow: volume-weighted accumulation using true range and Wilder smoothing (distinct from CMF) (`TWIGGSMF`).
+- **Williams Accumulation/Distribution** — Williams Accumulation/Distribution: cumulative price-direction accumulator (distinct from Chaikin A/D) (`WILLIAMSAD`).
+- **Volume RSI** — Volume RSI: Wilder-style RSI computed on signed volume flow (`VOLUMERSI`).
+
+## [0.6.2] - 2026-06-07
+- **Modified MA Stop** — Modified MA Stop — SMMA-ratcheted trailing stop with directional flip (`MODIFIED_MA_STOP`).
+- **Time-Based Stop** — Time-Based Stop — bar-count timer that fires after a fixed holding period (`TIME_BASED_STOP`).
+- **NRTR** — NRTR (Nick Rypock Trailing Reverse) — percentage trailing-reverse stop (`NRTR`).
+- **ATR Ratchet** — ATR Ratchet — Kaufman per-bar tightening volatility trailing stop (`ATR_RATCHET`).
+- **Elder SafeZone** — Elder SafeZone Stop — average noise-penetration trailing stop with directional flip (`ELDER_SAFE_ZONE`).
+- **Kase DevStop** — Kase DevStop volatility trailing stop using standard-deviation of two-bar true range (`KASE_DEV_STOP`).
+
+## [0.6.1] - 2026-06-07
+- **Projection Oscillator** — Widner projection oscillator: close position inside the projection bands, scaled 0..100 (`ProjectionOscillator`).
+- **Projection Bands** — Widner projection bands: forward-projected high/low regression envelope (`ProjectionBands`).
+- **Median Channel** — robust median +/- multiplier*MAD envelope (`MedianChannel`).
+- **Bomar Bands** — adaptive percentage bands containing a target coverage fraction of recent closes (`BomarBands`).
+- **Quartile Bands** — rolling 25th/50th/75th-percentile (Q1/median/Q3) envelope (`QuartileBands`).
+
+## [0.6.0] - 2026-06-06
+- **Volatility Cone** — volatility cone: current realized volatility within its historical min/median/max envelope (`VolatilityCone`).
+- **VolatilityRatio** — Schwager's volatility ratio: true range over the EMA of prior true ranges (`VolatilityRatio`).
+- **BipowerVariation** — jump-robust realized bipower variation (pi/2 sum of adjacent absolute log-return products) (`BipowerVariation`).
+- **VolatilityOfVolatility** — vol-of-vol: sample stddev of a rolling realized-volatility series (`VolatilityOfVolatility`).
+- **Garch11** — GARCH(1,1) conditional volatility with a long-run-variance anchor (`Garch11`).
+- **EwmaVolatility** — RiskMetrics exponentially-weighted volatility of log returns (lambda decay) (`EwmaVolatility`).
+
+## [0.5.9] - 2026-06-06
+
+### Added
+
+- Internal Rust cross-library benchmark harness (`crates/wickra-bench`, not
+  published) comparing Wickra against `kand`, `ta-rs` and `yata` on an identical
+  candle series in both streaming and batch modes; wired into the nightly
+  `cross-library-bench` workflow.
+- `tulipy` runners and expanded per-tick streaming coverage (SMA, EMA, RSI,
+  MACD, Bollinger) in the Python `compare_libraries` benchmark.
+
+### Changed
+
+- Faster streaming and batch updates for SMA, Bollinger Bands, RSI, EMA and ATR
+  (flat ring buffers replacing `VecDeque`, hoisted reciprocals in the Wilder
+  smoothing, leaner hot state) — indicator outputs are unchanged.
+- Rewrote the README benchmark section into honest, tiered tables (Rust core vs
+  the other Rust crates, and Python vs the Python ecosystem) that show where
+  Wickra wins and where it loses, not only the favourable comparisons.
+
+## [0.5.8] - 2026-06-04
+- **TSF Oscillator** — the percentage gap of the close to the one-bar-ahead time-series forecast, a close-relative companion to CFO (`TsfOscillator`).
+- **MACD Histogram** — the standalone macd-minus-signal bar of MACD as a scalar series (`MacdHistogram`).
+- **PPO Histogram** — the Percentage Price Oscillator with its signal EMA and the resulting zero-centered histogram (`PpoHistogram`).
+
+## [0.5.7] - 2026-06-04
+- **Qstick** — Qstick (Chande), the SMA of the candle body (close − open) as a net buying/selling pressure gauge (`QSTICK`).
+- **TTM Trend** — TTM Trend (John Carter), +1/−1 by whether the close sits above the SMA of recent median prices (`TTM_TREND`).
+- **Trend Strength Index** — trend strength index, the signed r² of a linear regression of price against time (`TREND_STRENGTH_INDEX`).
+- **Polarized Fractal Efficiency** — polarized fractal efficiency (Hannula), directional trend efficiency over a fractal lookback (`POLARIZED_FRACTAL_EFFICIENCY`).
+- **Wave PM** — Wave PM (Kase), a variance-normalised peak-momentum statistic (`WAVE_PM`).
+- **Gator Oscillator** — Gator Oscillator (Bill Williams), the Alligator convergence/divergence histogram (`GATOR_OSCILLATOR`).
+- **Kase Permission Stochastic** — Kase Permission Stochastic, a double-smoothed stochastic used as a trade-permission filter (`KASE_PERMISSION_STOCHASTIC`).
+
+## [0.5.6] - 2026-06-04
+- **QQE** — quantitative qualitative estimation, a smoothed RSI with an ATR-of-RSI trailing line (`QQE`).
+- **Intraday Momentum Index** — intraday momentum index (Chande), RSI on the open-to-close body (`IMI`).
+- **Elder Ray** — Elder Ray bull power and bear power around an EMA of close (`ElderRay`).
+- **Derivative Oscillator** — derivative oscillator (Constance Brown), a double-smoothed RSI histogram (`DerivativeOscillator`).
+- **RMI** — relative momentum index (RMI), RSI over a multi-bar momentum lookback (`RMI`).
+- **Stochastic CCI** — stochastic CCI, a stochastic oscillator over the CCI (`StochasticCCI`).
+- **Dynamic Momentum Index** — dynamic momentum index (Chande), a volatility-adaptive RSI (`DynamicMomentumIndex`).
+- **RSX** — RSX, a Jurik-style three-stage smoothed RSI (`RSX`).
+- **Fisher RSI** — Fisher RSI, the Fisher transform of a normalised RSI (`FisherRSI`).
+- **Disparity Index** — disparity index, the percent gap between price and its moving average (`DisparityIndex`).
+
+## [0.5.5] - 2026-06-04
+- **GD** — generalized DEMA (GD), Tillson's volume-factor double EMA and the building block of T3 (`GD`).
+- **GMA** — geometric moving average (GMA), the rolling geometric mean of prices (`GMA`).
+- **Holt-Winters** — Holt's linear (double exponential) smoothing with level and trend components (`HoltWinters`).
+- **Adaptive Laguerre** — Ehlers adaptive Laguerre filter with median-error-adaptive gamma (`AdaptiveLaguerre`).
+- **Median MA** — median moving average, the rolling median of prices (`MedianMA`).
+- **EHMA** — exponential Hull moving average (EHMA), the Hull construction built from EMAs (`EHMA`).
+- **SWMA** — sine-weighted moving average (SWMA), a symmetric half-cycle sine window (`SWMA`).
+
 ## [0.5.4] - 2026-06-04
 - **Roll Measure** — effective spread implied by the negative serial covariance of trade-price changes (Roll 1984) (`RollMeasure`).
 - **Amihud Illiquidity** — average absolute log return per unit of traded value (price-impact liquidity proxy, Amihud 2002) (`AmihudIlliquidity`).
@@ -1238,7 +1332,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   optional Binance live feed.
 - Bindings for Python, Node.js, and WebAssembly.
 
-[Unreleased]: https://github.com/wickra-lib/wickra/compare/v0.5.4...HEAD
+[Unreleased]: https://github.com/wickra-lib/wickra/compare/v0.6.4...HEAD
+[0.6.4]: https://github.com/wickra-lib/wickra/compare/v0.6.3...v0.6.4
+[0.6.3]: https://github.com/wickra-lib/wickra/compare/v0.6.2...v0.6.3
+[0.6.2]: https://github.com/wickra-lib/wickra/compare/v0.6.1...v0.6.2
+[0.6.1]: https://github.com/wickra-lib/wickra/compare/v0.6.0...v0.6.1
+[0.6.0]: https://github.com/wickra-lib/wickra/compare/v0.5.9...v0.6.0
+[0.5.9]: https://github.com/wickra-lib/wickra/compare/v0.5.8...v0.5.9
+[0.5.8]: https://github.com/wickra-lib/wickra/compare/v0.5.7...v0.5.8
+[0.5.7]: https://github.com/wickra-lib/wickra/compare/v0.5.6...v0.5.7
+[0.5.6]: https://github.com/wickra-lib/wickra/compare/v0.5.5...v0.5.6
+[0.5.5]: https://github.com/wickra-lib/wickra/compare/v0.5.4...v0.5.5
 [0.5.4]: https://github.com/wickra-lib/wickra/compare/v0.5.3...v0.5.4
 [0.5.3]: https://github.com/wickra-lib/wickra/compare/v0.5.2...v0.5.3
 [0.5.2]: https://github.com/wickra-lib/wickra/compare/v0.5.1...v0.5.2

Cargo.lock

@@ -702,6 +702,16 @@ dependencies = [
  "wasm-bindgen",
 ]
 
+[[package]]
+name = "kand"
+version = "0.2.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "af1f41590bd014ef6c3dd815b45f07deb4c3198e355a4319bb7521b6a3a6aeb5"
+dependencies = [
+ "num_enum",
+ "thiserror",
+]
+
 [[package]]
 name = "leb128fmt"
 version = "0.1.0"
@@ -911,6 +921,28 @@ dependencies = [
  "libm",
 ]
 
+[[package]]
+name = "num_enum"
+version = "0.7.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5d0bca838442ec211fa11de3a8b0e0e8f3a4522575b5c4c06ed722e005036f26"
+dependencies = [
+ "num_enum_derive",
+ "rustversion",
+]
+
+[[package]]
+name = "num_enum_derive"
+version = "0.7.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "680998035259dcfcafe653688bf2aa6d3e2dc05e98be6ab46afb089dc84f1df8"
+dependencies = [
+ "proc-macro-crate",
+ "proc-macro2",
+ "quote",
+ "syn",
+]
+
 [[package]]
 name = "numpy"
 version = "0.28.0"
@@ -1081,6 +1113,15 @@ dependencies = [
  "syn",
 ]
 
+[[package]]
+name = "proc-macro-crate"
+version = "3.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e67ba7e9b2b56446f1d419b1d807906278ffa1a658a8a5d8a39dcb1f5a78614f"
+dependencies = [
+ "toml_edit",
+]
+
 [[package]]
 name = "proc-macro2"
 version = "1.0.106"
@@ -1498,6 +1539,12 @@ dependencies = [
  "syn",
 ]
 
+[[package]]
+name = "ta"
+version = "0.5.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "609409d472a0a7d8d4dd9e19891bbdef546b9dce670c3057d0e02192dc541226"
+
 [[package]]
 name = "target-lexicon"
 version = "0.13.5"
@@ -1607,6 +1654,36 @@ dependencies = [
  "tungstenite",
 ]
 
+[[package]]
+name = "toml_datetime"
+version = "1.1.1+spec-1.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3165f65f62e28e0115a00b2ebdd37eb6f3b641855f9d636d3cd4103767159ad7"
+dependencies = [
+ "serde_core",
+]
+
+[[package]]
+name = "toml_edit"
+version = "0.25.12+spec-1.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "d2153edc6955a6c354fad8f5efd38b6a8769bdccf9fe50f8e1329f81b0baa5d7"
+dependencies = [
+ "indexmap",
+ "toml_datetime",
+ "toml_parser",
+ "winnow",
+]
+
+[[package]]
+name = "toml_parser"
+version = "1.1.2+spec-1.1.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a2abe9b86193656635d2411dc43050282ca48aa31c2451210f4202550afb7526"
+dependencies = [
+ "winnow",
+]
+
 [[package]]
 name = "tungstenite"
 version = "0.29.0"
@@ -1867,7 +1944,7 @@ dependencies = [
 
 [[package]]
 name = "wickra"
-version = "0.5.4"
+version = "0.6.4"
 dependencies = [
  "approx",
  "criterion",
@@ -1876,9 +1953,21 @@ dependencies = [
  "wickra-data",
 ]
 
+[[package]]
+name = "wickra-bench"
+version = "0.6.4"
+dependencies = [
+ "criterion",
+ "kand",
+ "ta",
+ "wickra",
+ "wickra-data",
+ "yata",
+]
+
 [[package]]
 name = "wickra-core"
-version = "0.5.4"
+version = "0.6.4"
 dependencies = [
  "approx",
  "proptest",
@@ -1888,7 +1977,7 @@ dependencies = [
 
 [[package]]
 name = "wickra-data"
-version = "0.5.4"
+version = "0.6.4"
 dependencies = [
  "approx",
  "csv",
@@ -1905,7 +1994,7 @@ dependencies = [
 
 [[package]]
 name = "wickra-examples"
-version = "0.0.0"
+version = "0.6.4"
 dependencies = [
  "serde_json",
  "tokio",
@@ -1915,7 +2004,7 @@ dependencies = [
 
 [[package]]
 name = "wickra-node"
-version = "0.5.4"
+version = "0.6.4"
 dependencies = [
  "napi",
  "napi-build",
@@ -1925,7 +2014,7 @@ dependencies = [
 
 [[package]]
 name = "wickra-python"
-version = "0.5.4"
+version = "0.6.4"
 dependencies = [
  "numpy",
  "pyo3",
@@ -1934,7 +2023,7 @@ dependencies = [
 
 [[package]]
 name = "wickra-wasm"
-version = "0.5.4"
+version = "0.6.4"
 dependencies = [
  "console_error_panic_hook",
  "js-sys",
@@ -1991,6 +2080,15 @@ dependencies = [
  "windows-link",
 ]
 
+[[package]]
+name = "winnow"
+version = "1.0.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0592e1c9d151f854e6fd382574c3a0855250e1d9b2f99d9281c6e6391af352f1"
+dependencies = [
+ "memchr",
+]
+
 [[package]]
 name = "wit-bindgen"
 version = "0.51.0"
@@ -2091,6 +2189,15 @@ version = "0.6.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1ffae5123b2d3fc086436f8834ae3ab053a283cfac8fe0a0b8eaae044768a4c4"
 
+[[package]]
+name = "yata"
+version = "0.7.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6b4ef8ddfa3ccd93454262c0e60a43a2bbf403d404174e1815f7581d5028229f"
+dependencies = [
+ "serde",
+]
+
 [[package]]
 name = "yoke"
 version = "0.8.2"

Cargo.toml

@@ -8,11 +8,12 @@ members = [
     "bindings/wasm",
     "bindings/node",
     "examples/rust",
+    "crates/wickra-bench",
 ]
 exclude = ["fuzz"]
 
 [workspace.package]
-version = "0.5.4"
+version = "0.6.4"
 authors = ["kingchenc <support@wickra.org>"]
 edition = "2021"
 rust-version = "1.86"
@@ -24,7 +25,7 @@ keywords = ["finance", "trading", "indicators", "technical-analysis", "ta"]
 categories = ["finance", "mathematics", "science"]
 
 [workspace.dependencies]
-wickra-core = { path = "crates/wickra-core", version = "0.5.4" }
+wickra-core = { path = "crates/wickra-core", version = "0.6.4" }
 
 thiserror = "2"
 rayon = "1.10"

README.md

@@ -1,5 +1,5 @@
 <p align="center">
-  <a href="https://wickra.org"><img src="https://raw.githubusercontent.com/wickra-lib/.github/main/profile/wickra-banner.webp?v=396" alt="Wickra — streaming-first technical indicators" width="100%"></a>
+  <a href="https://wickra.org"><img src="https://raw.githubusercontent.com/wickra-lib/.github/main/profile/wickra-banner.webp?v=452" alt="Wickra — streaming-first technical indicators" width="100%"></a>
 </p>
 
 [![CI](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml/badge.svg)](https://github.com/wickra-lib/wickra/actions/workflows/ci.yml)
@@ -48,7 +48,7 @@ Full documentation lives at **[docs.wickra.org](https://docs.wickra.org)**:
   [Node](https://docs.wickra.org/Quickstart-Node),
   [WASM](https://docs.wickra.org/Quickstart-WASM).
 - **Indicators** — a per-indicator deep dive (formula, parameters, warmup) for
-  every one of the 396 indicators; start at the
+  every one of the 452 indicators; start at the
   [indicators overview](https://docs.wickra.org/Indicators-Overview).
 - **Reference** — [warmup periods](https://docs.wickra.org/Warmup-Periods),
   [streaming vs batch](https://docs.wickra.org/Streaming-vs-Batch),
@@ -60,98 +60,150 @@ Full documentation lives at **[docs.wickra.org](https://docs.wickra.org)**:
 
 ## Why Wickra exists
 
-The Python TA ecosystem has plenty of libraries — TA-Lib, pandas-ta, finta,
-talipp, tulipy — and every one of them shares the same blind spot:
+Wickra started as a personal itch. The existing TA libraries never quite fit the
+projects I was building, so I decided to build one from the ground up — partly to
+learn, partly because I genuinely enjoy taking something that already exists and
+trying to do it differently (and, ideally, better). It's open source because the
+useful version of that itch is the one other people can build on too.
 
-| Library                | Install pain    | Streaming | Multi-language | Active |
-|------------------------|-----------------|-----------|----------------|--------|
-| **★&nbsp;Wickra**      | **clean**       | **yes**   | **Python + Node + WASM + Rust** | **yes** |
-| TA-Lib (Python)        | yes (C deps)    | no        | no             | barely |
-| pandas-ta              | clean           | no        | no             | slow   |
-| finta                  | clean           | no        | no             | stale  |
-| ta-lib-python          | yes (C deps)    | no        | no             | barely |
-| talipp                 | clean           | yes       | no             | yes    |
-| Tulip Indicators       | yes (C deps)    | no        | partial        | stale  |
-| ooples (C#)            | clean           | no        | C# only        | yes    |
+Plenty of TA libraries are fast. Each one forces a trade-off Wickra does not:
 
-Wickra is the only library that combines all of: clean install, streaming,
-multi-language reach, and active maintenance.
+| Library          | Install     | Streaming   | Languages                   | Indicators | Active |
+|------------------|-------------|-------------|-----------------------------|-----------:|--------|
+| **★&nbsp;Wickra**| **clean**   | **yes, O(1)** | **Python · Node · WASM · Rust** | **423** | **yes** |
+| kand             | clean       | yes         | Python · WASM · Rust        |       ~60  | yes    |
+| ta-rs            | clean       | yes         | Rust only                   |       ~30  | stale  |
+| yata             | clean       | partial     | Rust only                   |       ~35  | yes    |
+| TA-Lib           | yes (C deps)| no          | many bindings               |      ~150  | barely |
+| pandas-ta        | clean       | no          | Python                      |      ~130  | slow   |
+| finta            | clean       | no          | Python                      |       ~80  | stale  |
+| talipp           | clean       | yes         | Python                      |       ~40  | yes    |
 
-## Benchmark: how much faster is "streaming-first"?
+Wickra's edge is **breadth with reach**: 452 indicators that all update in O(1)
+per tick and ship natively to Python, Node.js, WebAssembly and Rust from a
+single engine.
 
-The numbers below were measured on a single developer workstation and are not
-guaranteed to reproduce identically on different hardware — absolute µs values
-depend on CPU, memory clock and OS scheduler. Read them as **relative
-speedups** between libraries on identical input, not as a universal
-performance contract.
+**On speed — and why Wickra isn't the fastest.** It deliberately isn't. The
+leaner Rust crates (kand, ta-rs) win several of the micro-benchmarks below, and
+those losses are shown rather than hidden. The gap is a *choice*, not a ceiling:
+every `update` validates its input, runs a real warmup before it emits a value,
+and returns an `Option` so a single bad tick can't silently poison the state.
+ta-rs, by contrast, hands back a bare `f64` from the first tick with no
+validation. If Wickra threw all of that away — raw `f64` out, no checks, no
+warmup contract — it would match or beat the leanest crate on every row. It
+keeps the guarantees instead, and still wins RSI, Bollinger and ATR against kand.
+What no other library matches is the *combination*: catalogue size, native O(1)
+streaming, NaN-safety, and four first-class language targets at once.
+
+## Benchmarks
+
+Three comparisons, split by layer and mode. Read them as **relative** speedups
+on identical input — absolute µs depend on CPU, memory clock and OS scheduler,
+not a universal contract.
 
 - **Reproduced on:** Windows 11 Pro 26200, AMD Ryzen 9 9950X, 64 GB DDR5,
-  Rust 1.92 (release profile, `lto = "fat"`, `codegen-units = 1`),
-  Python 3.12, Node 20.
-- **Reproduce yourself:** `pip install -e bindings/python[bench]` then
-  `python -m benchmarks.compare_libraries`. The script auto-detects every
-  installed peer library and runs them on the same generated inputs as
-  Wickra. The CI job `cross-library-bench` runs the same script on every
-  push and uploads the raw report as a build artefact.
+  Rust 1.92 (release: `lto = "fat"`, `codegen-units = 1`), Python 3.12.
+- **Reproduce yourself:**
+  - Rust core vs Rust crates: `cargo bench -p wickra-bench`
+  - Python vs Python libs: `pip install -e bindings/python[bench]` then
+    `python -m benchmarks.compare_libraries` (auto-detects installed peers).
 
-Lower µs/op = faster. Wickra wins every batch category outright, and the
-streaming gap widens linearly with how much history a batch-only library has
-to recompute on every tick.
+### 1. Rust core vs the other Rust TA crates
 
-### Batch — single full pass over a 20 000-bar series
+Like-for-like, no language-binding overhead, over a 50 000-bar series (µs for
+the whole series, lower = faster). This is the honest engine comparison —
+Wickra wins some and loses some, and both are shown.
 
-Reading the table: each cell shows that library's runtime, plus how many times
-slower it is than Wickra in parentheses. **★** marks the winner per row.
+**Streaming** (one value fed per `update`):
 
-| Indicator           | **★&nbsp;Wickra**   | finta                       | talipp                        |
-|---------------------|---------------------|-----------------------------|-------------------------------|
-| SMA(20)             | **95.6 µs ★**       | 343.5 µs (3.6× slower)      | 7 640.6 µs (79.9× slower)     |
-| EMA(20)             | **64.6 µs ★**       | 223.1 µs (3.5× slower)      | 12 160.9 µs (188.2× slower)   |
-| RSI(14)             | **126.2 µs ★**      | 1 107.1 µs (8.8× slower)    | 15 792.2 µs (125.1× slower)   |
-| MACD(12, 26, 9)     | **119.0 µs ★**      | 531.8 µs (4.5× slower)      | 49 788.1 µs (418.2× slower)   |
-| Bollinger(20, 2.0)  | **105.3 µs ★**      | 812.0 µs (7.7× slower)      | 130 938.3 µs (1 243.7× slower)|
-| ATR(14)             | **123.5 µs ★**      | 5 144.8 µs (41.7× slower)   | 28 816.0 µs (233.4× slower)   |
+| Indicator        | **★&nbsp;Wickra** | kand | ta-rs | yata |
+|------------------|------------------:|-----:|------:|-----:|
+| SMA(20)          | 50                | 38   | 47    | 38   |
+| EMA(20)          | 154               | 69   | 56    | 69   |
+| RSI(14)          | 164               | 216  | 74    | —    |
+| MACD(12, 26, 9)  | 275               | 143  | 66    | —    |
+| Bollinger(20, 2) | **128 ★**         | 248  | 168   | —    |
+| ATR(14)          | 152               | 166  | 61    | —    |
 
-### Streaming — per-tick latency after seeding with 5 000 historical bars
+**Batch** (whole series at once). Only Wickra and kand expose a batch API;
+ta-rs and yata are streaming-only.
 
-A batch-only library has to re-run its full indicator over the entire history on
-every new tick; Wickra updates state in O(1).
+| Indicator        | **★&nbsp;Wickra** | kand |
+|------------------|------------------:|-----:|
+| SMA(20)          | 82                | 42   |
+| EMA(20)          | 159               | 74   |
+| RSI(14)          | **253 ★**         | 274  |
+| MACD(12, 26, 9)  | 681               | 283  |
+| Bollinger(20, 2) | **445 ★**         | 462  |
+| ATR(14)          | 175               | 173  |
 
-| Indicator | **★&nbsp;Wickra (per tick)** | talipp (per tick)         |
-|-----------|---------------------|---------------------------|
-| RSI(14)   | **0.119 µs ★**      | 1.644 µs (13.8× slower)   |
+ta-rs is the per-indicator speed champion on almost every row — it returns a
+bare `f64` with no warmup state and no input validation, trading away the
+`None`-warmup and NaN-safety semantics Wickra keeps. Against kand, Wickra wins
+streaming RSI, Bollinger and ATR (and batch RSI + Bollinger); Bollinger is the
+one row where Wickra is the outright fastest of all four. The leaner crates
+still win the pure recurrences (EMA, MACD) and SMA. yata exposes only SMA/EMA as
+raw-value methods, so its other rows are omitted rather than faked.
 
-> TA-Lib and pandas-ta are not included here because both fail to install
-> cleanly on Windows without C build tooling — which is precisely the install
-> pain Wickra was built to remove. The benchmark script auto-detects every
-> peer library it can find and runs them on the same inputs as Wickra; install
-> them in your environment to see those rows light up too.
+### 2. Python vs the Python TA ecosystem — batch
+
+Full pass over a 20 000-bar series, µs/op (lower = faster). **★** per row.
+
+| Indicator        | **★&nbsp;Wickra** | finta               | TA-Lib | tulipy |
+|------------------|------------------:|---------------------|--------|--------|
+| SMA(20)          | **59.6 ★**        | 354.2 (5.9× slower) | ⧗      | ⧗      |
+| EMA(20)          | **88.4 ★**        | 309.3 (3.5× slower) | ⧗      | ⧗      |
+| RSI(14)          | **77.3 ★**        | 1 283 (16.6× slower)| ⧗      | ⧗      |
+| MACD(12, 26, 9)  | **116.4 ★**       | 529.5 (4.6× slower) | ⧗      | ⧗      |
+| Bollinger(20, 2) | **146.0 ★**       | 1 246 (8.5× slower) | ⧗      | ⧗      |
+| ATR(14)          | **135.8 ★**       | 3 812 (28× slower)  | ⧗      | ⧗      |
+
+> ⧗ = published by the CI Linux job. TA-Lib and tulipy ship C extensions that
+> don't build cleanly on every desktop, so their canonical numbers come from the
+> `cross-library-bench` workflow rather than this local table. pandas-ta needs
+> Python ≥ 3.12 and isn't in the 3.11 CI matrix. The script auto-detects
+> whichever peers are installed in your environment.
+
+### 3. Python — streaming (per-tick latency)
+
+Seed 5 000 bars, then feed ticks one at a time. talipp is the only Python peer
+with a true incremental API; batch-only libraries like TA-Lib must recompute the
+entire history on every tick — Wickra updates in O(1).
+
+| Indicator        | **★&nbsp;Wickra (per tick)** | talipp (per tick)       |
+|------------------|------------------------------:|-------------------------|
+| SMA(20)          | **0.067 µs ★**                | 0.63 µs (9.4× slower)   |
+| EMA(20)          | **0.051 µs ★**                | 0.63 µs (12.2× slower)  |
+| RSI(14)          | **0.053 µs ★**                | 1.00 µs (19.1× slower)  |
+| MACD(12, 26, 9)  | **0.071 µs ★**                | 3.64 µs (51.5× slower)  |
+| Bollinger(20, 2) | **0.085 µs ★**                | 4.87 µs (57.2× slower)  |
 
 Run the suite yourself:
 
 ```bash
-pip install -e bindings/python[bench]
+cargo bench -p wickra-bench            # Rust core vs kand / ta-rs / yata
+pip install -e bindings/python[bench]  # Python peers
 python -m benchmarks.compare_libraries
 ```
 
 ## Indicators
 
-396 streaming-first indicators across twenty-four families. Every one passes the
+452 streaming-first indicators across twenty-four families. Every one passes the
 `batch == streaming` equivalence test, reference-value tests, and reset
 semantics tests. Each has a per-indicator deep dive (formula, parameters,
 warmup) at [docs.wickra.org](https://docs.wickra.org/Indicators-Overview).
 
 | Family | Indicators |
 |--------|-----------|
-| Moving Averages      | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA |
-| Momentum Oscillators | RSI (Wilder), Anchored RSI, Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia, ROC Percentage (ROCP), ROC Ratio (ROCR), ROC Ratio 100 (ROCR100) |
-| Trend & Directional  | MACD, MACD Fixed (MACDFIX), MACD Extended (MACDEXT), ADX (+DI/-DI), ADXR, Aroon, TRIX, Aroon Oscillator, Vortex, Random Walk Index, Trend Intensity Index, Wave Trend Oscillator, Mass Index, Choppiness Index, Vertical Horizontal Filter, Plus DM, Minus DM, Plus DI, Minus DI, DX |
-| Price Oscillators    | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC |
-| Volatility & Bands   | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility, RVI (Relative Volatility Index), Parkinson Volatility, Garman-Klass Volatility, Rogers-Satchell Volatility, Yang-Zhang Volatility |
-| Bands & Channels     | MA Envelope, Acceleration Bands, STARC Bands, ATR Bands, Hurst Channel, LinReg Channel, Standard Error Bands, Double Bollinger Bands, TTM Squeeze, Fractal Chaos Bands, VWAP StdDev Bands |
-| Trailing Stops       | Parabolic SAR, Parabolic SAR Extended (SAREXT), SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop, HiLo Activator, Volty Stop, Yo-Yo Exit, Donchian Channel Stop, Percentage Trailing Stop, Step Trailing Stop, Renko Trailing Stop |
-| Volume               | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement, Klinger Volume Oscillator, Volume Oscillator, NVI, PVI, Williams A/D, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index |
-| Price Statistics     | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle, Variance, Coefficient of Variation, Skewness, Kurtosis, Standard Error, Detrended StdDev, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Pairwise Beta, Pair Spread Z-Score, Lead-Lag Cross-Correlation, Cointegration, Relative Strength A-vs-B, Spearman Correlation, Mid Price, Mid Point, Average Price, Linear Regression Intercept, Time Series Forecast, Rolling Correlation, Rolling Covariance, OU Half-Life, Spread Hurst, Distance SSD, Beta-Neutral Spread, Variance Ratio, Granger Causality, Kalman Hedge Ratio, Spread Bollinger Bands, Spread AR(1) Coefficient |
+| Moving Averages      | SMA, EMA, WMA, DEMA, TEMA, HMA, KAMA, SMMA, TRIMA, ZLEMA, T3, VWMA, ALMA, McGinley Dynamic, FRAMA, VIDYA, JMA, Alligator, EVWMA, SWMA, GMA, EHMA, Median MA, Adaptive Laguerre, GD, Holt-Winters |
+| Momentum Oscillators | RSI (Wilder), Anchored RSI, Stochastic, CCI, ROC, Williams %R, MFI, Awesome Oscillator, MOM, CMO, TSI, PMO, StochRSI, Ultimate Oscillator, RVI, PGO, KST, SMI, Laguerre RSI, Connors RSI, Inertia, ROC Percentage (ROCP), ROC Ratio (ROCR), ROC Ratio 100 (ROCR100), Disparity Index, Fisher RSI, RSX, Dynamic Momentum Index, Stochastic CCI, RMI, Derivative Oscillator, Elder Ray, Intraday Momentum Index, QQE |
+| Trend & Directional  | MACD, MACD Fixed (MACDFIX), MACD Extended (MACDEXT), ADX (+DI/-DI), ADXR, Aroon, TRIX, Aroon Oscillator, Vortex, Random Walk Index, Trend Intensity Index, Wave Trend Oscillator, Mass Index, Choppiness Index, Vertical Horizontal Filter, Plus DM, Minus DM, Plus DI, Minus DI, DX, TTM Trend, Trend Strength Index, Qstick, Polarized Fractal Efficiency, Wave PM, Gator Oscillator, Kase Permission Stochastic |
+| Price Oscillators    | PPO, DPO, Coppock, Accelerator Oscillator, Balance of Power, APO, AO Histogram, CFO, Zero-Lag MACD, Elder Impulse, STC, TSF Oscillator, MACD Histogram, PPO Histogram |
+| Volatility & Bands   | ATR, Bollinger Bands, Keltner Channels, Donchian Channels, NATR, StdDev, Ulcer Index, Historical Volatility, Bollinger Bandwidth, %B, True Range, Chaikin Volatility, RVI (Relative Volatility Index), Parkinson Volatility, Garman-Klass Volatility, Rogers-Satchell Volatility, Yang-Zhang Volatility, Volatility Cone |
+| Bands & Channels     | MA Envelope, Acceleration Bands, STARC Bands, ATR Bands, Hurst Channel, LinReg Channel, Standard Error Bands, Double Bollinger Bands, TTM Squeeze, Fractal Chaos Bands, VWAP StdDev Bands, Quartile Bands, Bomar Bands, Median Channel, Projection Bands, Projection Oscillator |
+| Trailing Stops       | Parabolic SAR, Parabolic SAR Extended (SAREXT), SuperTrend, Chandelier Exit, Chande Kroll Stop, ATR Trailing Stop, HiLo Activator, Volty Stop, Yo-Yo Exit, Donchian Channel Stop, Percentage Trailing Stop, Step Trailing Stop, Renko Trailing Stop, Kase DevStop, Elder SafeZone, ATR Ratchet, NRTR, Time-Based Stop, Modified MA Stop |
+| Volume               | OBV, VWAP (cumulative + rolling), ADL, Volume-Price Trend, Chaikin Money Flow, Chaikin Oscillator, Force Index, Ease of Movement, Klinger Volume Oscillator, Volume Oscillator, NVI, PVI, Williams A/D, Anchored VWAP, Demand Index, TSV, VZO, Market Facilitation Index, Volume RSI, Williams Accumulation/Distribution, Twiggs Money Flow, Trade Volume Index, Intraday Intensity Index, Better Volume, Volume-Weighted MACD |
+| Price Statistics     | Typical Price, Median Price, Weighted Close, Linear Regression, Linear Regression Slope, Z-Score, Linear Regression Angle, Variance, Coefficient of Variation, Skewness, Kurtosis, Standard Error, Detrended StdDev, R², Median Absolute Deviation, Autocorrelation, Hurst Exponent, Pearson Correlation, Beta, Pairwise Beta, Pair Spread Z-Score, Lead-Lag Cross-Correlation, Cointegration, Relative Strength A-vs-B, Spearman Correlation, Mid Price, Mid Point, Average Price, Linear Regression Intercept, Time Series Forecast, Rolling Correlation, Rolling Covariance, OU Half-Life, Spread Hurst, Distance SSD, Beta-Neutral Spread, Variance Ratio, Granger Causality, Kalman Hedge Ratio, Spread Bollinger Bands, Spread AR(1) Coefficient, Jarque-Bera, Rolling Min-Max Scaler, Shannon Entropy, Sample Entropy, Kendall Tau |
 | Ehlers / Cycle (DSP) | MAMA, FAMA, Fisher Transform, Inverse Fisher Transform, SuperSmoother, Hilbert Dominant Cycle, Hilbert Phasor, Hilbert DC Phase, Hilbert Trend Mode, Sine Wave, Decycler, Decycler Oscillator, Roofing Filter, Center of Gravity, Cybernetic Cycle, Adaptive Cycle, Empirical Mode Decomposition, Ehlers Stochastic, Instantaneous Trendline |
 | Pivots & S/R         | Classic Pivots, Fibonacci Pivots, Camarilla, Woodie Pivots, DeMark Pivots, Williams Fractals, ZigZag |
 | DeMark               | TD Setup, TD Sequential, TD DeMarker, TD REI, TD Pressure, TD Combo, TD Countdown, TD Lines, TD Range Projection, TD Differential, TD Open, TD Risk Level |
@@ -245,9 +297,10 @@ A Python live-trading example using the public `websockets` package lives at
 ```
 wickra/
 ├── crates/
-│   ├── wickra-core/         core engine + all 396 indicators
+│   ├── wickra-core/         core engine + all 452 indicators
 │   ├── wickra/              top-level facade crate (publishes on crates.io) + benches/
-│   └── wickra-data/         CSV reader, tick aggregator, live exchange feeds
+│   ├── wickra-data/         CSV reader, tick aggregator, live exchange feeds
+│   └── wickra-bench/        internal cross-library benchmark harness (not published)
 ├── bindings/
 │   ├── python/              PyO3 + maturin (publishes on PyPI)
 │   ├── node/                napi-rs (publishes on npm)
@@ -261,9 +314,10 @@ wickra/
 └── .github/workflows/       CI and release pipelines
 ```
 
-Rust benchmarks live in `crates/wickra/benches/`; runnable Rust examples live
-in the workspace member crate at `examples/rust/`. There is no top-level
-`benches/` directory.
+Wickra's own regression benchmarks live in `crates/wickra/benches/`; the
+cross-library comparison against kand, ta-rs and yata lives in the internal
+`crates/wickra-bench/` crate. Runnable Rust examples live in the workspace member
+crate at `examples/rust/`. There is no top-level `benches/` directory.
 
 ## Building everything from source
 
@@ -271,7 +325,8 @@ in the workspace member crate at `examples/rust/`. There is no top-level
 # Rust core + tests
 cargo test --workspace
 cargo clippy --workspace --all-targets -- -D warnings
-cargo bench -p wickra
+cargo bench -p wickra           # Wickra's own regression benchmarks
+cargo bench -p wickra-bench     # cross-library comparison (kand, ta-rs, yata)
 
 # Python binding (requires Rust toolchain + maturin)
 cd bindings/python
@@ -371,3 +426,10 @@ The library is provided **as is**, without warranty of any kind; see
 <p align="center">
   If Wickra saved you time, the cheapest way to say thanks is to ⭐ the repo.
 </p>
+
+<p align="center">
+  <a href="https://star-history.com/#wickra-lib/wickra&Date">
+    <img alt="Wickra star history" width="640"
+         src="https://api.star-history.com/svg?repos=wickra-lib/wickra&type=Date&theme=dark">
+  </a>
+</p>

bindings/node/__tests__/indicators.test.js

@@ -28,6 +28,33 @@ function num(v) {
 // --- Scalar indicators: update(value) vs batch(prices) ---
 
 const scalarFactories = {
+  SAMPLEENT: () => new wickra.SAMPLEENT(20, 2, 0.2),
+  SHANNONENT: () => new wickra.SHANNONENT(20, 8),
+  ROLLINGMINMAX: () => new wickra.ROLLINGMINMAX(20),
+  JARQUEBERA: () => new wickra.JARQUEBERA(20),
+  BipowerVariation: () => new wickra.BipowerVariation(20),
+  VolatilityOfVolatility: () => new wickra.VolatilityOfVolatility(20, 20),
+  Garch11: () => new wickra.Garch11(0.000002, 0.1, 0.88),
+  EwmaVolatility: () => new wickra.EwmaVolatility(0.94),
+  PpoHistogram: () => new wickra.PpoHistogram(3, 6, 3),
+  MacdHistogram: () => new wickra.MacdHistogram(3, 6, 3),
+  TsfOscillator: () => new wickra.TsfOscillator(3),
+  WAVE_PM: () => new wickra.WAVE_PM(32, 3),
+  POLARIZED_FRACTAL_EFFICIENCY: () => new wickra.POLARIZED_FRACTAL_EFFICIENCY(10, 5),
+  TREND_STRENGTH_INDEX: () => new wickra.TREND_STRENGTH_INDEX(20),
+  DerivativeOscillator: () => new wickra.DerivativeOscillator(14, 5, 3, 9),
+  RMI: () => new wickra.RMI(14, 5),
+  DynamicMomentumIndex: () => new wickra.DynamicMomentumIndex(14),
+  RSX: () => new wickra.RSX(14),
+  FisherRSI: () => new wickra.FisherRSI(14),
+  DisparityIndex: () => new wickra.DisparityIndex(14),
+  HoltWinters: () => new wickra.HoltWinters(0.2, 0.1),
+  GD: () => new wickra.GD(5, 0.7),
+  AdaptiveLaguerre: () => new wickra.AdaptiveLaguerre(13),
+  MedianMA: () => new wickra.MedianMA(14),
+  EHMA: () => new wickra.EHMA(9),
+  GMA: () => new wickra.GMA(14),
+  SWMA: () => new wickra.SWMA(14),
   Expectancy: () => new wickra.Expectancy(20),
   WinRate: () => new wickra.WinRate(20),
   RegimeLabel: () => new wickra.RegimeLabel(5, 20),
@@ -327,6 +354,19 @@ const candleScalar = {
   BodySizePct: { make: () => new wickra.BodySizePct(), step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
   WickRatio: { make: () => new wickra.WickRatio(), step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
   HighLowRange: { make: () => new wickra.HighLowRange(), step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
+  StochasticCCI: { make: () => new wickra.StochasticCCI(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  IMI: { make: () => new wickra.IMI(14), step: (ind, i) => ind.update(open[i], high[i], low[i], close[i]), batch: (ind) => ind.batch(open, high, low, close) },
+  TTM_TREND: { make: () => new wickra.TTM_TREND(6), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  Qstick: { make: () => new wickra.Qstick(10), step: (ind, i) => ind.update(open[i], close[i]), batch: (ind) => ind.batch(open, close) },
+  VolatilityRatio: { make: () => new wickra.VolatilityRatio(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  ProjectionOscillator: { make: () => new wickra.ProjectionOscillator(14), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  TimeBasedStop: { make: () => new wickra.TimeBasedStop(5), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  VolumeRsi: { make: () => new wickra.VolumeRsi(14), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
+  Wad: { make: () => new wickra.Wad(), step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  TwiggsMoneyFlow: { make: () => new wickra.TwiggsMoneyFlow(21), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
+  TradeVolumeIndex: { make: () => new wickra.TradeVolumeIndex(0.25), step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
+  IntradayIntensity: { make: () => new wickra.IntradayIntensity(), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
+  BetterVolume: { make: () => new wickra.BetterVolume(14), step: (ind, i) => ind.update(high[i], low[i], close[i], volume[i]), batch: (ind) => ind.batch(high, low, close, volume) },
 };
 
 for (const [name, d] of Object.entries(candleScalar)) {
@@ -409,6 +449,21 @@ const multi = {
   FibArcs: { make: () => new wickra.FibArcs(), fields: ['arc382', 'arc500', 'arc618'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
   FibChannel: { make: () => new wickra.FibChannel(), fields: ['base', 'level618', 'level1000', 'level1618'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
   FibTimeZones: { make: () => new wickra.FibTimeZones(), fields: ['onZone', 'barsToNext'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  ElderRay: { make: () => new wickra.ElderRay(13), fields: ['bullPower', 'bearPower'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  QQE: { make: () => new wickra.QQE(14, 5, 4.236), fields: ['rsiMa', 'trailingLine'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
+  GatorOscillator: { make: () => new wickra.GatorOscillator(13, 8, 5), fields: ['upper', 'lower'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  KasePermissionStochastic: { make: () => new wickra.KasePermissionStochastic(9, 3), fields: ['fast', 'slow'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  VolatilityCone: { make: () => new wickra.VolatilityCone(20, 60), fields: ['current', 'min', 'median', 'max', 'percentile'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  QuartileBands: { make: () => new wickra.QuartileBands(4), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
+  BomarBands: { make: () => new wickra.BomarBands(4, 0.85), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
+  MedianChannel: { make: () => new wickra.MedianChannel(5, 2.0), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(close[i]), batch: (ind) => ind.batch(close) },
+  ProjectionBands: { make: () => new wickra.ProjectionBands(3), fields: ['upper', 'middle', 'lower'], step: (ind, i) => ind.update(high[i], low[i]), batch: (ind) => ind.batch(high, low) },
+  KaseDevStop: { make: () => new wickra.KaseDevStop(3, 1.0), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  ElderSafeZone: { make: () => new wickra.ElderSafeZone(14, 2.0), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  AtrRatchet: { make: () => new wickra.AtrRatchet(14, 4.0, 0.1), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  Nrtr: { make: () => new wickra.Nrtr(2.0), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  ModifiedMaStop: { make: () => new wickra.ModifiedMaStop(14), fields: ['value', 'direction'], step: (ind, i) => ind.update(high[i], low[i], close[i]), batch: (ind) => ind.batch(high, low, close) },
+  VolumeWeightedMacd: { make: () => new wickra.VolumeWeightedMacd(12, 26, 9), fields: ['macd', 'signal', 'histogram'], step: (ind, i) => ind.update(close[i], volume[i]), batch: (ind) => ind.batch(close, volume) },
 };
 
 for (const [name, d] of Object.entries(multi)) {
@@ -578,6 +633,7 @@ const pairFactories = {
   VarianceRatio: () => new wickra.VarianceRatio(60, 2),
   GrangerCausality: () => new wickra.GrangerCausality(60, 1),
   SpreadAr1Coefficient: () => new wickra.SpreadAr1Coefficient(40),
+  KendallTau: () => new wickra.KendallTau(20),
 };
 
 for (const [name, make] of Object.entries(pairFactories)) {

bindings/node/index.d.ts

@@ -5,6 +5,17 @@
 
 /** Library version (matches the Rust crate version). */
 export declare function version(): string
+/**
+ * Volatility-cone result: current realized volatility and its lookback
+ * envelope (min / median / max) plus the percentile rank of `current`.
+ */
+export interface VolatilityConeValue {
+  current: number
+  min: number
+  median: number
+  max: number
+  percentile: number
+}
 /** Lead/lag result: the offset that maximises correlation, and that correlation. */
 export interface LeadLagValue {
   /** Offset that maximises `|corr(a, b shifted)|`. Positive ⇒ `a` leads `b`. */
@@ -69,6 +80,22 @@ export interface HtPhasorValue {
   inphase: number
   quadrature: number
 }
+export interface QqeValue {
+  rsiMa: number
+  trailingLine: number
+}
+export interface ElderRayValue {
+  bullPower: number
+  bearPower: number
+}
+export interface GatorOscillatorValue {
+  upper: number
+  lower: number
+}
+export interface KasePermissionStochasticValue {
+  fast: number
+  slow: number
+}
 export interface StochValue {
   k: number
   d: number
@@ -122,6 +149,26 @@ export interface DonchianStopValue {
   stopLong: number
   stopShort: number
 }
+export interface KaseDevStopValue {
+  value: number
+  direction: number
+}
+export interface ElderSafeZoneValue {
+  value: number
+  direction: number
+}
+export interface AtrRatchetValue {
+  value: number
+  direction: number
+}
+export interface NrtrValue {
+  value: number
+  direction: number
+}
+export interface ModifiedMaStopValue {
+  value: number
+  direction: number
+}
 /** Vortex Indicator pair: `VI+` and `VI-`. */
 export interface VortexValue {
   plus: number
@@ -172,6 +219,26 @@ export interface StandardErrorBandsValue {
   middle: number
   lower: number
 }
+export interface QuartileBandsValue {
+  upper: number
+  middle: number
+  lower: number
+}
+export interface BomarBandsValue {
+  upper: number
+  middle: number
+  lower: number
+}
+export interface MedianChannelValue {
+  upper: number
+  middle: number
+  lower: number
+}
+export interface ProjectionBandsValue {
+  upper: number
+  middle: number
+  lower: number
+}
 export interface DoubleBollingerValue {
   upperOuter: number
   upperInner: number
@@ -422,6 +489,11 @@ export interface FibTimeZonesValue {
   onZone: number
   barsToNext: number
 }
+export interface VolumeWeightedMacdValue {
+  macd: number
+  signal: number
+  histogram: number
+}
 export type SmaNode = SMA
 export declare class SMA {
   constructor(period: number)
@@ -872,6 +944,186 @@ export declare class Expectancy {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type SineWeightedMaNode = SWMA
+export declare class SWMA {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type GeometricMaNode = GMA
+export declare class GMA {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type EhmaNode = EHMA
+export declare class EHMA {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type MedianMaNode = MedianMA
+export declare class MedianMA {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type AdaptiveLaguerreFilterNode = AdaptiveLaguerre
+export declare class AdaptiveLaguerre {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type DisparityIndexNode = DisparityIndex
+export declare class DisparityIndex {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type FisherRsiNode = FisherRSI
+export declare class FisherRSI {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type RsxNode = RSX
+export declare class RSX {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type DynamicMomentumIndexNode = DynamicMomentumIndex
+export declare class DynamicMomentumIndex {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type TrendStrengthIndexNode = TREND_STRENGTH_INDEX
+export declare class TREND_STRENGTH_INDEX {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type TsfOscillatorNode = TsfOscillator
+export declare class TsfOscillator {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type BipowerVariationNode = BipowerVariation
+export declare class BipowerVariation {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type JarqueBeraNode = JARQUEBERA
+export declare class JARQUEBERA {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type RollingMinMaxScalerNode = ROLLINGMINMAX
+export declare class ROLLINGMINMAX {
+  constructor(period: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ShannonEntropyNode = SHANNONENT
+export declare class SHANNONENT {
+  constructor(period: number, bins: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type SampleEntropyNode = SAMPLEENT
+export declare class SAMPLEENT {
+  constructor(period: number, m: number, rFactor: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type EwmaVolatilityNode = EwmaVolatility
+export declare class EwmaVolatility {
+  constructor(lambda: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type Garch11Node = Garch11
+export declare class Garch11 {
+  constructor(omega: number, alpha: number, beta: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type VolatilityOfVolatilityNode = VolatilityOfVolatility
+export declare class VolatilityOfVolatility {
+  constructor(volWindow: number, vovWindow: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type VolatilityConeNode = VolatilityCone
+export declare class VolatilityCone {
+  constructor(window: number, lookback: number)
+  update(high: number, low: number, close: number): VolatilityConeValue | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
 export type JumpIndicatorNode = JumpIndicator
 export declare class JumpIndicator {
   constructor(period: number, threshold: number)
@@ -1047,6 +1299,19 @@ export declare class DistanceSsd {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type KendallTauNode = KendallTau
+export declare class KendallTau {
+  constructor(period: number)
+  update(x: number, y: number): number | null
+  /**
+   * Batch over two equally-sized arrays. Returns a length-`n` array
+   * with `NaN` for warmup positions.
+   */
+  batch(x: Array<number>, y: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
 export type BetaNeutralSpreadNode = BetaNeutralSpread
 export declare class BetaNeutralSpread {
   constructor(period: number)
@@ -1369,6 +1634,123 @@ export declare class HighLowRange {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type StochasticCciNode = StochasticCCI
+export declare class StochasticCCI {
+  constructor(period: number)
+  update(high: number, low: number, close: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ImiNode = IMI
+export declare class IMI {
+  constructor(period: number)
+  update(open: number, high: number, low: number, close: number): number | null
+  batch(open: Array<number>, high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type QqeNode = QQE
+export declare class QQE {
+  constructor(rsiPeriod: number, smoothing: number, factor: number)
+  update(value: number): QqeValue | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ElderRayNode = ElderRay
+export declare class ElderRay {
+  constructor(period: number)
+  update(high: number, low: number, close: number): ElderRayValue | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type TtmTrendNode = TTM_TREND
+export declare class TTM_TREND {
+  constructor(period: number)
+  update(high: number, low: number, close: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type QstickNode = Qstick
+export declare class Qstick {
+  constructor(period: number)
+  update(open: number, close: number): number | null
+  batch(open: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type PolarizedFractalEfficiencyNode = POLARIZED_FRACTAL_EFFICIENCY
+export declare class POLARIZED_FRACTAL_EFFICIENCY {
+  constructor(period: number, smoothing: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type WavePmNode = WAVE_PM
+export declare class WAVE_PM {
+  constructor(length: number, smoothing: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type GatorOscillatorNode = GatorOscillator
+export declare class GatorOscillator {
+  constructor(jawPeriod: number, teethPeriod: number, lipsPeriod: number)
+  update(high: number, low: number, close: number): GatorOscillatorValue | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type KasePermissionStochasticNode = KasePermissionStochastic
+export declare class KasePermissionStochastic {
+  constructor(length: number, smooth: number)
+  update(high: number, low: number, close: number): KasePermissionStochasticValue | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type VolatilityRatioNode = VolatilityRatio
+export declare class VolatilityRatio {
+  constructor(period: number)
+  update(high: number, low: number, close: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ProjectionOscillatorNode = ProjectionOscillator
+export declare class ProjectionOscillator {
+  constructor(period: number)
+  update(high: number, low: number, close: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type TimeBasedStopNode = TimeBasedStop
+export declare class TimeBasedStop {
+  constructor(maxBars: number)
+  update(high: number, low: number, close: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
 export type StochNode = Stochastic
 export declare class Stochastic {
   constructor(kPeriod: number, dPeriod: number)
@@ -1677,6 +2059,60 @@ export declare class T3 {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type GeneralizedDemaNode = GD
+export declare class GD {
+  constructor(period: number, v: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type HoltWintersNode = HoltWinters
+export declare class HoltWinters {
+  constructor(alpha: number, beta: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type RmiNode = RMI
+export declare class RMI {
+  constructor(period: number, momentum: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type DerivativeOscillatorNode = DerivativeOscillator
+export declare class DerivativeOscillator {
+  constructor(rsiPeriod: number, smooth1: number, smooth2: number, signalPeriod: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type MacdHistogramNode = MacdHistogram
+export declare class MacdHistogram {
+  constructor(fast: number, slow: number, signal: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type PpoHistogramNode = PpoHistogram
+export declare class PpoHistogram {
+  constructor(fast: number, slow: number, signal: number)
+  update(value: number): number | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
 export type TsiNode = TSI
 export declare class TSI {
   constructor(long: number, short: number)
@@ -1975,6 +2411,71 @@ export declare class RenkoTrailingStop {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type KaseDevStopNode = KaseDevStop
+export declare class KaseDevStop {
+  constructor(period: number, dev: number)
+  update(high: number, low: number, close: number): KaseDevStopValue | null
+  /**
+   * Returns `[value0, direction0, value1, direction1, ...]`, length `2 * n`.
+   * Warmup positions are `NaN`.
+   */
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ElderSafeZoneNode = ElderSafeZone
+export declare class ElderSafeZone {
+  constructor(period: number, coeff: number)
+  update(high: number, low: number, close: number): ElderSafeZoneValue | null
+  /**
+   * Returns `[value0, direction0, value1, direction1, ...]`, length `2 * n`.
+   * Warmup positions are `NaN`.
+   */
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type AtrRatchetNode = AtrRatchet
+export declare class AtrRatchet {
+  constructor(atrPeriod: number, startMult: number, increment: number)
+  update(high: number, low: number, close: number): AtrRatchetValue | null
+  /**
+   * Returns `[value0, direction0, value1, direction1, ...]`, length `2 * n`.
+   * Warmup positions are `NaN`.
+   */
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type NrtrNode = Nrtr
+export declare class Nrtr {
+  constructor(pct: number)
+  update(high: number, low: number, close: number): NrtrValue | null
+  /**
+   * Returns `[value0, direction0, value1, direction1, ...]`, length `2 * n`.
+   * Warmup positions are `NaN`.
+   */
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ModifiedMaStopNode = ModifiedMaStop
+export declare class ModifiedMaStop {
+  constructor(period: number)
+  update(high: number, low: number, close: number): ModifiedMaStopValue | null
+  /**
+   * Returns `[value0, direction0, value1, direction1, ...]`, length `2 * n`.
+   * Warmup positions are `NaN`.
+   */
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
 export type TypicalPriceNode = TypicalPrice
 export declare class TypicalPrice {
   constructor()
@@ -2303,6 +2804,42 @@ export declare class StandardErrorBands {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type QuartileBandsNode = QuartileBands
+export declare class QuartileBands {
+  constructor(period: number)
+  update(value: number): QuartileBandsValue | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type BomarBandsNode = BomarBands
+export declare class BomarBands {
+  constructor(period: number, coverage: number)
+  update(value: number): BomarBandsValue | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type MedianChannelNode = MedianChannel
+export declare class MedianChannel {
+  constructor(period: number, multiplier: number)
+  update(value: number): MedianChannelValue | null
+  batch(prices: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type ProjectionBandsNode = ProjectionBands
+export declare class ProjectionBands {
+  constructor(period: number)
+  update(high: number, low: number): ProjectionBandsValue | null
+  batch(high: Array<number>, low: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
 export type DoubleBollingerNode = DoubleBollinger
 export declare class DoubleBollinger {
   constructor(period: number, kInner: number, kOuter: number)
@@ -4157,3 +4694,70 @@ export declare class FibTimeZones {
   isReady(): boolean
   warmupPeriod(): number
 }
+export type VolumeRsiNode = VolumeRsi
+export declare class VolumeRsi {
+  constructor(period: number)
+  update(close: number, volume: number): number | null
+  batch(close: Array<number>, volume: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type WadNode = Wad
+export declare class Wad {
+  constructor()
+  update(high: number, low: number, close: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type TwiggsMoneyFlowNode = TwiggsMoneyFlow
+export declare class TwiggsMoneyFlow {
+  constructor(period: number)
+  update(high: number, low: number, close: number, volume: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>, volume: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type TradeVolumeIndexNode = TradeVolumeIndex
+export declare class TradeVolumeIndex {
+  constructor(minTick: number)
+  update(close: number, volume: number): number | null
+  batch(close: Array<number>, volume: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type IntradayIntensityNode = IntradayIntensity
+export declare class IntradayIntensity {
+  constructor()
+  update(high: number, low: number, close: number, volume: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>, volume: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type BetterVolumeNode = BetterVolume
+export declare class BetterVolume {
+  constructor(period: number)
+  update(high: number, low: number, close: number, volume: number): number | null
+  batch(high: Array<number>, low: Array<number>, close: Array<number>, volume: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}
+export type VolumeWeightedMacdNode = VolumeWeightedMacd
+export declare class VolumeWeightedMacd {
+  constructor(fast: number, slow: number, signal: number)
+  update(close: number, volume: number): VolumeWeightedMacdValue | null
+  /**
+   * Returns `[macd0, signal0, histogram0, macd1, ...]`, length `3 * n`.
+   * Warmup positions are `NaN`.
+   */
+  batch(close: Array<number>, volume: Array<number>): Array<number>
+  reset(): void
+  isReady(): boolean
+  warmupPeriod(): number
+}

bindings/node/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra-darwin-arm64",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Native binding for wickra (macOS Apple Silicon). Installed automatically as an optional dependency of wickra on matching platforms.",
   "main": "wickra.darwin-arm64.node",
   "files": [

bindings/node/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra-darwin-x64",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Native binding for wickra (macOS Intel). Installed automatically as an optional dependency of wickra on matching platforms.",
   "main": "wickra.darwin-x64.node",
   "files": [

bindings/node/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra-linux-arm64-gnu",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Native binding for wickra (linux arm64 GNU). Installed automatically as an optional dependency of wickra on matching platforms.",
   "main": "wickra.linux-arm64-gnu.node",
   "files": [

bindings/node/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra-linux-x64-gnu",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Native binding for wickra (linux x64 GNU). Installed automatically as an optional dependency of wickra on matching platforms.",
   "main": "wickra.linux-x64-gnu.node",
   "files": [

bindings/node/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra-win32-arm64-msvc",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Native binding for wickra (Windows arm64 MSVC). Installed automatically as an optional dependency of wickra on matching platforms.",
   "main": "wickra.win32-arm64-msvc.node",
   "files": [

bindings/node/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra-win32-x64-msvc",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Native binding for wickra (Windows x64 MSVC). Installed automatically as an optional dependency of wickra on matching platforms.",
   "main": "wickra.win32-x64-msvc.node",
   "files": [

bindings/node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "wickra",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "wickra",
-      "version": "0.5.4",
+      "version": "0.6.4",
       "license": "MIT OR Apache-2.0",
       "devDependencies": {
         "@napi-rs/cli": "^2.18.0"
@@ -15,12 +15,12 @@
         "node": ">= 18"
       },
       "optionalDependencies": {
-        "wickra-darwin-arm64": "0.5.4",
-        "wickra-darwin-x64": "0.5.4",
-        "wickra-linux-arm64-gnu": "0.5.4",
-        "wickra-linux-x64-gnu": "0.5.4",
-        "wickra-win32-arm64-msvc": "0.5.4",
-        "wickra-win32-x64-msvc": "0.5.4"
+        "wickra-darwin-arm64": "0.6.4",
+        "wickra-darwin-x64": "0.6.4",
+        "wickra-linux-arm64-gnu": "0.6.4",
+        "wickra-linux-x64-gnu": "0.6.4",
+        "wickra-win32-arm64-msvc": "0.6.4",
+        "wickra-win32-x64-msvc": "0.6.4"
       }
     },
     "node_modules/@napi-rs/cli": {
@@ -41,8 +41,8 @@
       }
     },
     "node_modules/wickra-darwin-arm64": {
-      "version": "0.5.4",
-      "resolved": "https://registry.npmjs.org/wickra-darwin-arm64/-/wickra-darwin-arm64-0.5.4.tgz",
+      "version": "0.6.4",
+      "resolved": "https://registry.npmjs.org/wickra-darwin-arm64/-/wickra-darwin-arm64-0.6.4.tgz",
       "integrity": "sha512-4eZiBR/yGUdr4nzhEUFy2i69XgNx64iI2ax/LPamsThgylC0KpHOZKK19QzJ2d9KbK4C8nMjME5FLuR+4GNEwQ==",
       "cpu": [
         "arm64"
@@ -57,8 +57,8 @@
       }
     },
     "node_modules/wickra-darwin-x64": {
-      "version": "0.5.4",
-      "resolved": "https://registry.npmjs.org/wickra-darwin-x64/-/wickra-darwin-x64-0.5.4.tgz",
+      "version": "0.6.4",
+      "resolved": "https://registry.npmjs.org/wickra-darwin-x64/-/wickra-darwin-x64-0.6.4.tgz",
       "integrity": "sha512-6hf8zI3QPjTFp4zCpmgUwDvNtu6jHqNUHKD5e55POo0CgA52HkpyxSPtVm8TGTIZDI7kPjlbOdBM8CJ76mmXwA==",
       "cpu": [
         "x64"
@@ -73,8 +73,8 @@
       }
     },
     "node_modules/wickra-linux-arm64-gnu": {
-      "version": "0.5.4",
-      "resolved": "https://registry.npmjs.org/wickra-linux-arm64-gnu/-/wickra-linux-arm64-gnu-0.5.4.tgz",
+      "version": "0.6.4",
+      "resolved": "https://registry.npmjs.org/wickra-linux-arm64-gnu/-/wickra-linux-arm64-gnu-0.6.4.tgz",
       "integrity": "sha512-kSe6y0xBMSiqdPLXNjwop5WZdHtvdBNKSEBCwZ4hFq33p4apW25/wrlzv9/oDuyD4kuPabJEhCCnFOplh58CUg==",
       "cpu": [
         "arm64"
@@ -89,8 +89,8 @@
       }
     },
     "node_modules/wickra-linux-x64-gnu": {
-      "version": "0.5.4",
-      "resolved": "https://registry.npmjs.org/wickra-linux-x64-gnu/-/wickra-linux-x64-gnu-0.5.4.tgz",
+      "version": "0.6.4",
+      "resolved": "https://registry.npmjs.org/wickra-linux-x64-gnu/-/wickra-linux-x64-gnu-0.6.4.tgz",
       "integrity": "sha512-tWBWS4qz7hxM4xnpFb59bhf6TaLwXq0Z3jEa/2l7r8PiHA94g8r8S53NRMiT+4yiL5hSWe/nUiC/YXdRrhEZ4g==",
       "cpu": [
         "x64"
@@ -105,8 +105,8 @@
       }
     },
     "node_modules/wickra-win32-arm64-msvc": {
-      "version": "0.5.4",
-      "resolved": "https://registry.npmjs.org/wickra-win32-arm64-msvc/-/wickra-win32-arm64-msvc-0.5.4.tgz",
+      "version": "0.6.4",
+      "resolved": "https://registry.npmjs.org/wickra-win32-arm64-msvc/-/wickra-win32-arm64-msvc-0.6.4.tgz",
       "integrity": "sha512-EXIckHxAtF75PUGDKRzXyqMe9ldP0JjSdu68WFN6iJfp+McYrGu6h40TEJlQ/oUEIoPqiZB/xhVyo/el5Lg7zw==",
       "cpu": [
         "arm64"
@@ -121,8 +121,8 @@
       }
     },
     "node_modules/wickra-win32-x64-msvc": {
-      "version": "0.5.4",
-      "resolved": "https://registry.npmjs.org/wickra-win32-x64-msvc/-/wickra-win32-x64-msvc-0.5.4.tgz",
+      "version": "0.6.4",
+      "resolved": "https://registry.npmjs.org/wickra-win32-x64-msvc/-/wickra-win32-x64-msvc-0.6.4.tgz",
       "integrity": "sha512-Yfsqq1Xwp6hdxMyLze411vNdo7BDwI6+lPSe7A9XdqyPecNDbtKwYLpsal2r8EHbNzqM+R8XnuRtUaEQS5VlUQ==",
       "cpu": [
         "x64"

bindings/node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "wickra",
-  "version": "0.5.4",
+  "version": "0.6.4",
   "description": "Streaming-first technical indicators: incremental, fast, install-free. Node bindings powered by Rust.",
   "author": "kingchenc <support@wickra.org>",
   "main": "index.js",
@@ -47,12 +47,12 @@
     "node": ">= 18"
   },
   "optionalDependencies": {
-    "wickra-linux-x64-gnu": "0.5.4",
-    "wickra-linux-arm64-gnu": "0.5.4",
-    "wickra-darwin-x64": "0.5.4",
-    "wickra-darwin-arm64": "0.5.4",
-    "wickra-win32-x64-msvc": "0.5.4",
-    "wickra-win32-arm64-msvc": "0.5.4"
+    "wickra-linux-x64-gnu": "0.6.4",
+    "wickra-linux-arm64-gnu": "0.6.4",
+    "wickra-darwin-x64": "0.6.4",
+    "wickra-darwin-arm64": "0.6.4",
+    "wickra-win32-x64-msvc": "0.6.4",
+    "wickra-win32-arm64-msvc": "0.6.4"
   },
   "scripts": {
     "build": "napi build --platform --release",

bindings/python/benchmarks/compare_libraries.py

@@ -49,6 +49,7 @@ TALIB = _try_import("talib")
 PANDAS_TA = _try_import("pandas_ta")
 TALIPP = _try_import("talipp.indicators") or _try_import("talipp")
 FINTA = _try_import("finta")
+TULIPY = _try_import("tulipy")
 PD = _try_import("pandas")
 import wickra as WICKRA  # noqa: E402  -- the library under test must be importable
 
@@ -275,6 +276,34 @@ def talipp_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
     return lambda: BB(period=20, std_dev_mult=2.0, input_values=list(prices))
 
 
+# tulipy wraps the C "Tulip Indicators" library; it takes contiguous float64
+# arrays and indicator options as positional arguments.
+
+
+def tulipy_sma_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
+    return None if TULIPY is None else (lambda: TULIPY.sma(prices, 20))
+
+
+def tulipy_ema_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
+    return None if TULIPY is None else (lambda: TULIPY.ema(prices, 20))
+
+
+def tulipy_rsi_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
+    return None if TULIPY is None else (lambda: TULIPY.rsi(prices, 14))
+
+
+def tulipy_macd_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
+    return None if TULIPY is None else (lambda: TULIPY.macd(prices, 12, 26, 9))
+
+
+def tulipy_bollinger_batch(prices: np.ndarray) -> Optional[Callable[[], None]]:
+    return None if TULIPY is None else (lambda: TULIPY.bbands(prices, 20, 2.0))
+
+
+def tulipy_atr_batch(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> Optional[Callable[[], None]]:
+    return None if TULIPY is None else (lambda: TULIPY.atr(high, low, close, 14))
+
+
 # --------------------------------------------------------------------------- #
 # Streaming scenario: per-tick latency
 # --------------------------------------------------------------------------- #
@@ -329,6 +358,105 @@ def talipp_rsi_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callabl
     return run
 
 
+# Scalar streaming peers: Wickra and talipp both update incrementally in O(1),
+# so this is the like-for-like per-tick comparison (batch-only libs are covered
+# by the batch tables and the recompute contrast on RSI above).
+
+
+def wickra_sma_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
+    def run() -> None:
+        sma = WICKRA.SMA(20)
+        sma.batch(seed)
+        for p in live:
+            sma.update(float(p))
+
+    return run
+
+
+def talipp_sma_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
+    if TALIPP is None:
+        return None
+    from talipp.indicators import SMA  # type: ignore
+
+    def run() -> None:
+        sma = SMA(period=20, input_values=list(seed))
+        for p in live:
+            sma.add(float(p))
+
+    return run
+
+
+def wickra_ema_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
+    def run() -> None:
+        ema = WICKRA.EMA(20)
+        ema.batch(seed)
+        for p in live:
+            ema.update(float(p))
+
+    return run
+
+
+def talipp_ema_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
+    if TALIPP is None:
+        return None
+    from talipp.indicators import EMA  # type: ignore
+
+    def run() -> None:
+        ema = EMA(period=20, input_values=list(seed))
+        for p in live:
+            ema.add(float(p))
+
+    return run
+
+
+def wickra_macd_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
+    def run() -> None:
+        macd = WICKRA.MACD()
+        macd.batch(seed)
+        for p in live:
+            macd.update(float(p))
+
+    return run
+
+
+def talipp_macd_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
+    if TALIPP is None:
+        return None
+    from talipp.indicators import MACD  # type: ignore
+
+    def run() -> None:
+        macd = MACD(
+            fast_period=12, slow_period=26, signal_period=9, input_values=list(seed)
+        )
+        for p in live:
+            macd.add(float(p))
+
+    return run
+
+
+def wickra_bollinger_streaming(seed: np.ndarray, live: np.ndarray) -> Callable[[], None]:
+    def run() -> None:
+        bb = WICKRA.BollingerBands(20, 2.0)
+        bb.batch(seed)
+        for p in live:
+            bb.update(float(p))
+
+    return run
+
+
+def talipp_bollinger_streaming(seed: np.ndarray, live: np.ndarray) -> Optional[Callable[[], None]]:
+    if TALIPP is None:
+        return None
+    from talipp.indicators import BB  # type: ignore
+
+    def run() -> None:
+        bb = BB(period=20, std_dev_mult=2.0, input_values=list(seed))
+        for p in live:
+            bb.add(float(p))
+
+    return run
+
+
 # --------------------------------------------------------------------------- #
 # Runner
 # --------------------------------------------------------------------------- #
@@ -339,6 +467,7 @@ BATCH_INDICATORS = [
         ("Wickra", wickra_sma_batch),
         ("TA-Lib", talib_sma_batch),
         ("pandas-ta", pandas_ta_sma_batch),
+        ("tulipy", tulipy_sma_batch),
         ("finta", finta_sma_batch),
         ("talipp", talipp_sma_batch),
     ]),
@@ -346,6 +475,7 @@ BATCH_INDICATORS = [
         ("Wickra", wickra_ema_batch),
         ("TA-Lib", talib_ema_batch),
         ("pandas-ta", pandas_ta_ema_batch),
+        ("tulipy", tulipy_ema_batch),
         ("finta", finta_ema_batch),
         ("talipp", talipp_ema_batch),
     ]),
@@ -353,6 +483,7 @@ BATCH_INDICATORS = [
         ("Wickra", wickra_rsi_batch),
         ("TA-Lib", talib_rsi_batch),
         ("pandas-ta", pandas_ta_rsi_batch),
+        ("tulipy", tulipy_rsi_batch),
         ("finta", finta_rsi_batch),
         ("talipp", talipp_rsi_batch),
     ]),
@@ -360,6 +491,7 @@ BATCH_INDICATORS = [
         ("Wickra", wickra_macd_batch),
         ("TA-Lib", talib_macd_batch),
         ("pandas-ta", pandas_ta_macd_batch),
+        ("tulipy", tulipy_macd_batch),
         ("finta", finta_macd_batch),
         ("talipp", talipp_macd_batch),
     ]),
@@ -367,6 +499,7 @@ BATCH_INDICATORS = [
         ("Wickra", wickra_bollinger_batch),
         ("TA-Lib", talib_bollinger_batch),
         ("pandas-ta", pandas_ta_bollinger_batch),
+        ("tulipy", tulipy_bollinger_batch),
         ("finta", finta_bollinger_batch),
         ("talipp", talipp_bollinger_batch),
     ]),
@@ -376,18 +509,35 @@ OHLC_INDICATORS = [
     ("ATR(14)", [
         ("Wickra", wickra_atr_batch),
         ("TA-Lib", talib_atr_batch),
+        ("tulipy", tulipy_atr_batch),
         ("finta", finta_atr_batch),
         ("talipp", talipp_atr_batch),
     ]),
 ]
 
 STREAMING_INDICATORS = [
+    ("SMA(20)", [
+        ("Wickra", wickra_sma_streaming),
+        ("talipp", talipp_sma_streaming),
+    ]),
+    ("EMA(20)", [
+        ("Wickra", wickra_ema_streaming),
+        ("talipp", talipp_ema_streaming),
+    ]),
     ("RSI(14)", [
         ("Wickra", wickra_rsi_streaming),
         ("TA-Lib", talib_rsi_streaming),
         ("pandas-ta", pandas_ta_rsi_streaming),
         ("talipp", talipp_rsi_streaming),
     ]),
+    ("MACD(12, 26, 9)", [
+        ("Wickra", wickra_macd_streaming),
+        ("talipp", talipp_macd_streaming),
+    ]),
+    ("Bollinger(20, 2.0)", [
+        ("Wickra", wickra_bollinger_streaming),
+        ("talipp", talipp_bollinger_streaming),
+    ]),
 ]
 
 
@@ -501,6 +651,7 @@ def main() -> None:
     available = []
     if TALIB is not None: available.append("TA-Lib")
     if PANDAS_TA is not None: available.append("pandas-ta")
+    if TULIPY is not None: available.append("tulipy")
     if FINTA is not None: available.append("finta")
     if TALIPP is not None: available.append("talipp")
     print(f"Wickra benchmark suite — wickra=v{WICKRA.__version__}")

bindings/python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "wickra"
-version = "0.5.4"
+version = "0.6.4"
 description = "Streaming-first technical indicators: incremental, fast, install-free."
 readme = "README.md"
 license = "MIT OR Apache-2.0"
@@ -39,6 +39,7 @@ bench = [
     "pytest-benchmark>=4",
     "TA-Lib; platform_system != 'Windows'",
     "pandas-ta>=0.3.14b",
+    "tulipy>=0.4; platform_system != 'Windows'",
     "talipp>=2",
     "finta>=1.3",
     "pandas>=2",

bindings/python/python/wickra/__init__.py

@@ -25,6 +25,45 @@ from __future__ import annotations
 
 from ._wickra import (
     __version__,
+    SAMPLEENT,
+    SHANNONENT,
+    ROLLINGMINMAX,
+    JARQUEBERA,
+    TimeBasedStop,
+    ProjectionOscillator,
+    VolatilityCone,
+    VolatilityRatio,
+    BipowerVariation,
+    VolatilityOfVolatility,
+    Garch11,
+    EwmaVolatility,
+    PpoHistogram,
+    MacdHistogram,
+    TsfOscillator,
+    Qstick,
+    GatorOscillator,
+    KasePermissionStochastic,
+    WAVE_PM,
+    POLARIZED_FRACTAL_EFFICIENCY,
+    TREND_STRENGTH_INDEX,
+    TTM_TREND,
+    QQE,
+    IMI,
+    ElderRay,
+    DerivativeOscillator,
+    RMI,
+    StochasticCCI,
+    DynamicMomentumIndex,
+    RSX,
+    FisherRSI,
+    DisparityIndex,
+    HoltWinters,
+    GD,
+    AdaptiveLaguerre,
+    MedianMA,
+    EHMA,
+    GMA,
+    SWMA,
     Expectancy,
     WinRate,
     RegimeLabel,
@@ -134,6 +173,11 @@ from ._wickra import (
     HistoricalVolatility,
     BollingerBandwidth,
     PercentB,
+    # Trailing Stops
+    ModifiedMaStop,
+    Nrtr,
+    AtrRatchet,
+    ElderSafeZone,
     SuperTrend,
     ChandelierExit,
     ChandeKrollStop,
@@ -145,6 +189,7 @@ from ._wickra import (
     PercentageTrailingStop,
     StepTrailingStop,
     RenkoTrailingStop,
+    KaseDevStop,
     TrueRange,
     ChaikinVolatility,
     RVIVolatility,
@@ -153,6 +198,13 @@ from ._wickra import (
     RogersSatchellVolatility,
     YangZhangVolatility,
     # Volume
+    VolumeWeightedMacd,
+    BetterVolume,
+    IntradayIntensity,
+    TradeVolumeIndex,
+    TwiggsMoneyFlow,
+    Wad,
+    VolumeRsi,
     OBV,
     VWAP,
     RollingVWAP,
@@ -173,6 +225,7 @@ from ._wickra import (
     MarketFacilitationIndex,
     EaseOfMovement,
     # Statistics
+    KendallTau,
     SpreadBollingerBands,
     KalmanHedgeRatio,
     GrangerCausality,
@@ -230,6 +283,10 @@ from ._wickra import (
     MAMA,
     FAMA,
     # Bands & Channels
+    ProjectionBands,
+    MedianChannel,
+    BomarBands,
+    QuartileBands,
     MaEnvelope,
     AccelerationBands,
     StarcBands,
@@ -449,6 +506,45 @@ from ._wickra import (
 )
 
 __all__ = [
+    "SAMPLEENT",
+    "SHANNONENT",
+    "ROLLINGMINMAX",
+    "JARQUEBERA",
+    "TimeBasedStop",
+    "ProjectionOscillator",
+    "VolatilityCone",
+    "VolatilityRatio",
+    "BipowerVariation",
+    "VolatilityOfVolatility",
+    "Garch11",
+    "EwmaVolatility",
+    "PpoHistogram",
+    "MacdHistogram",
+    "TsfOscillator",
+    "Qstick",
+    "GatorOscillator",
+    "KasePermissionStochastic",
+    "WAVE_PM",
+    "POLARIZED_FRACTAL_EFFICIENCY",
+    "TREND_STRENGTH_INDEX",
+    "TTM_TREND",
+    "QQE",
+    "IMI",
+    "ElderRay",
+    "DerivativeOscillator",
+    "RMI",
+    "StochasticCCI",
+    "DynamicMomentumIndex",
+    "RSX",
+    "FisherRSI",
+    "DisparityIndex",
+    "HoltWinters",
+    "GD",
+    "AdaptiveLaguerre",
+    "MedianMA",
+    "EHMA",
+    "GMA",
+    "SWMA",
     "Expectancy",
     "WinRate",
     "RegimeLabel",
@@ -559,6 +655,11 @@ __all__ = [
     "HistoricalVolatility",
     "BollingerBandwidth",
     "PercentB",
+    # Trailing Stops
+    "ModifiedMaStop",
+    "Nrtr",
+    "AtrRatchet",
+    "ElderSafeZone",
     "SuperTrend",
     "ChandelierExit",
     "ChandeKrollStop",
@@ -570,6 +671,7 @@ __all__ = [
     "PercentageTrailingStop",
     "StepTrailingStop",
     "RenkoTrailingStop",
+    "KaseDevStop",
     "TrueRange",
     "ChaikinVolatility",
     "RVIVolatility",
@@ -578,6 +680,13 @@ __all__ = [
     "RogersSatchellVolatility",
     "YangZhangVolatility",
     # Volume
+    "VolumeWeightedMacd",
+    "BetterVolume",
+    "IntradayIntensity",
+    "TradeVolumeIndex",
+    "TwiggsMoneyFlow",
+    "Wad",
+    "VolumeRsi",
     "OBV",
     "VWAP",
     "RollingVWAP",
@@ -598,6 +707,7 @@ __all__ = [
     "MarketFacilitationIndex",
     "EaseOfMovement",
     # Statistics
+    "KendallTau",
     "SpreadBollingerBands",
     "KalmanHedgeRatio",
     "GrangerCausality",
@@ -655,6 +765,10 @@ __all__ = [
     "MAMA",
     "FAMA",
     # Bands & Channels
+    "ProjectionBands",
+    "MedianChannel",
+    "BomarBands",
+    "QuartileBands",
     "MaEnvelope",
     "AccelerationBands",
     "StarcBands",

bindings/python/tests/test_new_indicators.py

@@ -45,6 +45,33 @@ def ohlcv() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
 # --- Scalar (f64 -> f64) indicators ---------------------------------------
 
 SCALAR = [
+    (ta.SAMPLEENT, (20, 2, 0.2)),
+    (ta.SHANNONENT, (20, 8)),
+    (ta.ROLLINGMINMAX, (20,)),
+    (ta.JARQUEBERA, (20,)),
+    (ta.BipowerVariation, (20,)),
+    (ta.VolatilityOfVolatility, (20, 20)),
+    (ta.Garch11, (0.000002, 0.1, 0.88)),
+    (ta.EwmaVolatility, (0.94,)),
+    (ta.PpoHistogram, (3, 6, 3)),
+    (ta.MacdHistogram, (3, 6, 3)),
+    (ta.TsfOscillator, (3,)),
+    (ta.WAVE_PM, (32, 3)),
+    (ta.POLARIZED_FRACTAL_EFFICIENCY, (10, 5)),
+    (ta.TREND_STRENGTH_INDEX, (20,)),
+    (ta.DerivativeOscillator, (14, 5, 3, 9)),
+    (ta.RMI, (14, 5)),
+    (ta.DynamicMomentumIndex, (14,)),
+    (ta.RSX, (14,)),
+    (ta.FisherRSI, (14,)),
+    (ta.DisparityIndex, (14,)),
+    (ta.HoltWinters, (0.2, 0.1)),
+    (ta.GD, (5, 0.7)),
+    (ta.AdaptiveLaguerre, (13,)),
+    (ta.MedianMA, (14,)),
+    (ta.EHMA, (9,)),
+    (ta.GMA, (14,)),
+    (ta.SWMA, (14,)),
     (ta.Expectancy, (20,)),
     (ta.WinRate, (20,)),
     (ta.RegimeLabel, (5, 20)),
@@ -150,6 +177,10 @@ SCALAR = [
 # Family 05 band/channel indicators with scalar input and multi-output.
 # `cols` is the expected number of band columns from `batch`.
 SCALAR_MULTI = {
+    "MedianChannel": (lambda: ta.MedianChannel(5, 2.0), 3),
+    "BomarBands": (lambda: ta.BomarBands(4, 0.85), 3),
+    "QuartileBands": (lambda: ta.QuartileBands(4), 3),
+    "Qqe": (lambda: ta.QQE(14, 5, 4.236), 2),
     "MaEnvelope": (lambda: ta.MaEnvelope(20, 0.025), 3),
     "LinRegChannel": (lambda: ta.LinRegChannel(20, 2.0), 3),
     "StandardErrorBands": (lambda: ta.StandardErrorBands(21, 2.0), 3),
@@ -177,6 +208,7 @@ def test_scalar_streaming_matches_batch(cls, args, sine_prices):
 # --- Two-series (asset, benchmark) indicators -----------------------------
 
 PAIR = [
+    (ta.KendallTau, (20,)),
     (ta.SpreadAr1Coefficient, (40,)),
     (ta.GrangerCausality, (60, 1)),
     (ta.VarianceRatio, (60, 2)),
@@ -341,6 +373,35 @@ def test_relative_strength_streaming_matches_batch():
 # 6-tuple candle; the batch helper takes only the columns it needs.
 
 CANDLE_SCALAR = {
+    "BetterVolume": (
+        lambda: ta.BetterVolume(14),
+        lambda ind, h, l, c, v: ind.batch(h, l, c, v),
+    ),
+    "IntradayIntensity": (
+        lambda: ta.IntradayIntensity(),
+        lambda ind, h, l, c, v: ind.batch(h, l, c, v),
+    ),
+    "TradeVolumeIndex": (
+        lambda: ta.TradeVolumeIndex(0.25),
+        lambda ind, h, l, c, v: ind.batch(c, v),
+    ),
+    "TwiggsMoneyFlow": (
+        lambda: ta.TwiggsMoneyFlow(21),
+        lambda ind, h, l, c, v: ind.batch(h, l, c, v),
+    ),
+    "Wad": (
+        lambda: ta.Wad(),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+    ),
+    "VolumeRsi": (
+        lambda: ta.VolumeRsi(14),
+        lambda ind, h, l, c, v: ind.batch(c, v),
+    ),
+    "TimeBasedStop": (lambda: ta.TimeBasedStop(5), lambda ind, h, l, c, v: ind.batch(h, l, c)),
+    "ProjectionOscillator": (lambda: ta.ProjectionOscillator(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
+    "VolatilityRatio": (lambda: ta.VolatilityRatio(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
+    "TTM_TREND": (lambda: ta.TTM_TREND(6), lambda ind, h, l, c, v: ind.batch(h, l, c)),
+    "StochasticCCI": (lambda: ta.StochasticCCI(14), lambda ind, h, l, c, v: ind.batch(h, l, c)),
     # Per-bar OHLC transforms (open matters). The streaming harness feeds
     # open == close, so batch passes the close column in for open to match.
     "HighLowRange": (lambda: ta.HighLowRange(), lambda ind, h, l, c, v: ind.batch(c, h, l, c)),
@@ -877,6 +938,61 @@ def test_candle_scalar_streaming_matches_batch(name, ohlcv):
 # --- Candle-input, multi-output indicators --------------------------------
 
 MULTI = {
+    "VolumeWeightedMacd": (
+        lambda: ta.VolumeWeightedMacd(12, 26, 9),
+        lambda ind, h, l, c, v: ind.batch(c, v),
+        3,
+    ),
+    "ModifiedMaStop": (
+        lambda: ta.ModifiedMaStop(14),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "Nrtr": (
+        lambda: ta.Nrtr(2.0),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "AtrRatchet": (
+        lambda: ta.AtrRatchet(14, 4.0, 0.1),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "ElderSafeZone": (
+        lambda: ta.ElderSafeZone(14, 2.0),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "KaseDevStop": (
+        lambda: ta.KaseDevStop(3, 1.0),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "ProjectionBands": (
+        lambda: ta.ProjectionBands(3),
+        lambda ind, h, l, c, v: ind.batch(h, l),
+        3,
+    ),
+    "VolatilityCone": (
+        lambda: ta.VolatilityCone(20, 60),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        5,
+    ),
+    "KasePermissionStochastic": (
+        lambda: ta.KasePermissionStochastic(9, 3),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "GatorOscillator": (
+        lambda: ta.GatorOscillator(13, 8, 5),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
+    "ElderRay": (
+        lambda: ta.ElderRay(13),
+        lambda ind, h, l, c, v: ind.batch(h, l, c),
+        2,
+    ),
     "FibFan": (
         lambda: ta.FibFan(),
         lambda ind, h, l, c, v: ind.batch(h, l),
@@ -1498,7 +1614,7 @@ def test_kvo_constant_series_is_zero():
         assert v == pytest.approx(0.0, abs=1e-12)
 
 
-def test_williams_ad_reference():
+def test_wad_reference():
     # bar 0 seeds prev_close = 10.
     # bar 1: prev=10, today high=13, low=8, close=12 (up day).
     #   TR_l = min(10, 8) = 8 -> delta = 12 - 8 = 4. AD = 4.
@@ -2734,6 +2850,258 @@ def test_spread_ar1_coefficient_reference():
     out = ta.SpreadAr1Coefficient(20).batch(a, b)
     assert math.isclose(out[-1], 1.0, abs_tol=1e-9)
 
+
+def test_elder_ray_reference():
+    er = ta.ElderRay(3)
+    high = np.array([11.0, 13.0, 16.0])
+    low = np.array([9.0, 11.0, 13.0])
+    close = np.array([10.0, 12.0, 14.0])
+    out = er.batch(high, low, close)
+    # EMA(3) seeds at the third bar with mean close 12; bar high 16 -> bull 4,
+    # low 13 -> bear 1.
+    assert out[2][0] == pytest.approx(4.0)
+    assert out[2][1] == pytest.approx(1.0)
+
+
+def test_imi_reference():
+    imi = ta.IMI(3)
+    open_ = np.array([10.0, 11.0, 10.0])
+    high = np.array([12.0, 12.0, 13.0])
+    low = np.array([9.0, 9.0, 9.0])
+    close = np.array([11.0, 10.0, 12.0])
+    out = imi.batch(open_, high, low, close)
+    # bodies +1, -1, +2 -> gain 3, loss 1 -> 100 * 3 / 4 = 75.
+    assert math.isnan(out[0])
+    assert math.isnan(out[1])
+    assert out[2] == pytest.approx(75.0)
+
+
+def test_qstick_reference():
+    q = ta.Qstick(3)
+    open_ = np.array([10.0, 10.0, 10.0])
+    close = np.array([11.0, 11.0, 11.0])
+    out = q.batch(open_, close)
+    # Each body is close - open = 1; SMA(3) of [1, 1, 1] = 1.
+    assert math.isnan(out[0])
+    assert math.isnan(out[1])
+    assert out[2] == pytest.approx(1.0)
+
+
+def test_ttm_trend_reference():
+    t = ta.TTM_TREND(3)
+    high = np.array([13.0, 13.0, 13.0])
+    low = np.array([9.0, 9.0, 9.0])
+    close = np.array([12.0, 12.0, 12.0])
+    out = t.batch(high, low, close)
+    # Median (13 + 9) / 2 = 11; close 12 is above the SMA(3) reference -> +1.
+    assert math.isnan(out[0])
+    assert out[2] == pytest.approx(1.0)
+
+
+def test_trend_strength_index_reference():
+    tsi = ta.TREND_STRENGTH_INDEX(10)
+    closes = np.arange(10, dtype=float)
+    out = tsi.batch(closes)
+    # A clean ramp is a perfect uptrend -> signed r^2 = +1.
+    assert math.isclose(out[-1], 1.0, abs_tol=1e-9)
+
+
+def test_polarized_fractal_efficiency_reference():
+    pfe = ta.POLARIZED_FRACTAL_EFFICIENCY(5, 3)
+    closes = np.arange(20, dtype=float)
+    out = pfe.batch(closes)
+    # On a straight ramp the path equals the diagonal -> efficiency 1 -> +100.
+    assert math.isclose(out[-1], 100.0, abs_tol=1e-9)
+
+
+def test_wave_pm_reference():
+    wpm = ta.WAVE_PM(10, 3)
+    closes = np.arange(60, dtype=float) * 5.0
+    out = wpm.batch(closes)
+    # Constant-slope ramp: momentum equals its energy -> 100 * (1 - e^-0.5).
+    baseline = 100.0 * (1.0 - math.exp(-0.5))
+    assert math.isclose(out[-1], baseline, abs_tol=1e-9)
+
+
+def test_gator_oscillator_reference():
+    g = ta.GatorOscillator(13, 8, 5)
+    n = 40
+    high = np.full(n, 11.0)
+    low = np.full(n, 9.0)
+    close = np.full(n, 10.0)
+    out = g.batch(high, low, close)
+    # Constant median collapses all three Alligator lines -> both bars zero.
+    assert out[-1][0] == pytest.approx(0.0)
+    assert out[-1][1] == pytest.approx(0.0)
+
+
+def test_kase_permission_stochastic_reference():
+    k = ta.KasePermissionStochastic(4, 2)
+    n = 20
+    flat = np.full(n, 10.0)
+    out = k.batch(flat, flat, flat)
+    # HH == LL -> raw %K defaults to the neutral 50 -> both lines at 50.
+    assert out[-1][0] == pytest.approx(50.0)
+    assert out[-1][1] == pytest.approx(50.0)
+
+
+def test_tsf_oscillator_reference():
+    t = ta.TsfOscillator(3)
+    assert t.update(1.0) is None
+    assert t.update(2.0) is None
+    assert t.update(9.0) == pytest.approx(-33.33333333333333)
+
+
+def test_macd_histogram_reference():
+    # On a constant-slope ramp the MACD line is flat once seeded, so the
+    # signal EMA catches up and the histogram collapses to 0.
+    t = ta.MacdHistogram(3, 6, 3)
+    for i in range(7):
+        assert t.update(100.0 + i * 2.0) is None
+    assert t.update(100.0 + 7 * 2.0) == pytest.approx(0.0, abs=1e-9)
+
+
+def test_ppo_histogram_reference():
+    # PPO divides the EMA gap by the slow EMA, so on the same ramp the ratio
+    # keeps drifting and the histogram stays non-zero.
+    t = ta.PpoHistogram(3, 6, 3)
+    for i in range(7):
+        assert t.update(100.0 + i * 2.0) is None
+    assert t.update(100.0 + 7 * 2.0) == pytest.approx(-0.052098, abs=1e-6)
+
+
+def test_ewma_volatility_reference():
+    t = ta.EwmaVolatility(0.94)
+    assert t.update(100.0) is None
+    assert t.update(110.0) == pytest.approx(0.09531017980432493)
+    assert t.update(99.0) == pytest.approx(0.0959428936787596)
+
+
+def test_garch11_reference():
+    t = ta.Garch11(0.000002, 0.1, 0.88)
+    assert t.update(100.0) is None
+    assert t.update(110.0) == pytest.approx(0.009999999999999995)
+    assert t.update(99.0) == pytest.approx(0.031597516317477786)
+
+
+def test_volatility_cone_reference():
+    t = ta.VolatilityCone(20, 60)
+
+
+def test_quartile_bands_reference():
+    t = ta.QuartileBands(4)
+    assert t.update(40.0) is None
+    assert t.update(30.0) is None
+    assert t.update(20.0) is None
+    assert t.update(10.0) == pytest.approx((32.5, 25.0, 17.5))
+
+
+def test_bomar_bands_reference():
+    t = ta.BomarBands(4, 0.85)
+    assert t.update(100.0) is None
+    assert t.update(102.0) is None
+    assert t.update(98.0) is None
+    assert t.update(104.0) == pytest.approx((104.0, 101.0, 98.0))
+
+
+def test_median_channel_reference():
+    t = ta.MedianChannel(5, 2.0)
+    assert t.update(1.0) is None
+    assert t.update(2.0) is None
+    assert t.update(3.0) is None
+    assert t.update(4.0) is None
+    assert t.update(5.0) == pytest.approx((5.0, 3.0, 1.0))
+
+
+def test_projection_bands_reference():
+    t = ta.ProjectionBands(3)
+    assert t.update((8.0, 10.0, 8.0, 9.0, 1.0, 0)) is None
+    assert t.update((9.0, 12.0, 9.0, 11.0, 1.0, 1)) is None
+    assert t.update((10.0, 11.0, 10.0, 11.0, 1.0, 2)) == pytest.approx((12.5, 11.25, 10.0))
+
+
+def test_projection_oscillator_reference():
+    # Same window as ProjectionBands: upper 12.5, lower 10; close 11 -> 40.
+    t = ta.ProjectionOscillator(3)
+    assert t.update((8.0, 10.0, 8.0, 9.0, 1.0, 0)) is None
+    assert t.update((9.0, 12.0, 9.0, 11.0, 1.0, 1)) is None
+    assert t.update((10.0, 11.0, 10.0, 11.0, 1.0, 2)) == pytest.approx(40.0)
+
+
+def test_kase_devstop_reference():
+    t = ta.KaseDevStop(3, 1.0)
+    assert t.update((100.0, 101.0, 99.0, 100.0, 1.0, 0)) is None
+    assert t.update((101.0, 102.0, 100.0, 101.0, 1.0, 1)) is None
+    assert t.update((102.0, 103.0, 101.0, 102.0, 1.0, 2)) is None
+    assert t.update((102.5, 104.0, 102.0, 103.0, 1.0, 3)) == pytest.approx((101.0, 1.0))
+
+
+def _stop_candles(n):
+    # Gently rising, valid OHLC: high >= open/close, low <= open/close.
+    return [(100.0 + i, 101.5 + i, 98.5 + i, 100.5 + i, 1.0, i) for i in range(n)]
+
+
+def test_elder_safezone_reference():
+    t = ta.ElderSafeZone(14, 2.0)
+    candles = _stop_candles(15)
+    for c in candles[:14]:
+        assert t.update(c) is None
+    assert t.update(candles[14]) == pytest.approx((112.5, 1.0))
+
+
+def test_atr_ratchet_reference():
+    t = ta.AtrRatchet(14, 4.0, 0.1)
+    candles = _stop_candles(14)
+    for c in candles[:13]:
+        assert t.update(c) is None
+    assert t.update(candles[13]) == pytest.approx((101.5, 1.0))
+
+
+def test_nrtr_reference():
+    t = ta.Nrtr(2.0)
+    assert t.update((100.0, 100.0, 100.0, 100.0, 1.0, 0)) == pytest.approx((98.0, 1.0))
+
+
+def test_time_based_stop_reference():
+    t = ta.TimeBasedStop(5)
+    assert t.update((100.0, 101.0, 99.0, 100.0, 1.0, 0)) == pytest.approx(0.2)
+
+
+def test_modified_ma_stop_reference():
+    t = ta.ModifiedMaStop(14)
+    candles = _stop_candles(14)
+    for c in candles[:13]:
+        assert t.update(c) is None
+    assert t.update(candles[13]) == pytest.approx((107.0, 1.0))
+
+
+def test_volume_rsi_reference():
+    t = ta.VolumeRsi(14)
+
+
+def test_twiggs_money_flow_reference():
+    t = ta.TwiggsMoneyFlow(21)
+
+
+def test_trade_volume_index_reference():
+    t = ta.TradeVolumeIndex(0.25)
+
+
+def test_intraday_intensity_reference():
+    t = ta.IntradayIntensity()
+
+
+def test_better_volume_reference():
+    t = ta.BetterVolume(14)
+
+
+def test_volume_weighted_macd_reference():
+    t = ta.VolumeWeightedMacd(12, 26, 9)
+
+
+def test_kendall_tau_reference():
+    t = ta.KendallTau(20)
+
 # --- Lifecycle ------------------------------------------------------------
 
 

crates/wickra-bench/Cargo.toml

@@ -0,0 +1,22 @@
+[package]
+name = "wickra-bench"
+version.workspace = true
+edition.workspace = true
+license.workspace = true
+publish = false
+description = "Internal cross-library benchmark harness (not published)."
+
+[lints]
+workspace = true
+
+[dev-dependencies]
+wickra = { path = "../wickra" }
+wickra-data = { path = "../wickra-data" }
+criterion = { workspace = true }
+kand = "0.2.2"
+ta = "0.5.0"
+yata = "0.7.0"
+
+[[bench]]
+name = "cross_lib"
+harness = false

crates/wickra-bench/benches/cross_lib.rs

@@ -0,0 +1,695 @@
+//! Cross-library Criterion benchmark: Wickra vs `kand` vs `ta` (ta-rs) vs `yata`.
+//!
+//! All four are pure-Rust technical-analysis crates, so this is a like-for-like
+//! Rust-vs-Rust comparison with no language-binding overhead. It feeds the exact
+//! same BTCUSDT 1-minute candle series used by `crates/wickra/benches/indicators.rs`.
+//!
+//! Two arenas, kept honest:
+//!
+//! * **Streaming** (`*/stream`): one value fed at a time. Wickra (`Indicator::update`),
+//!   ta-rs (`Next::next`) and yata (`Method::next`) carry their own state; `kand`
+//!   exposes stateless `*_inc` helpers, so the per-tick state is threaded manually
+//!   here, seeded from `kand`'s own batch output (the seed is computed outside the
+//!   timed closure). yata only appears for SMA/EMA — its RSI/MACD/Bollinger/ATR are
+//!   exposed through a heavier signal-oriented indicator API, not a raw-value method,
+//!   so they are intentionally left out rather than compared unfairly.
+//! * **Batch** (`*/batch`): the whole series at once. Only Wickra (`BatchExt::batch`)
+//!   and `kand` (TA-Lib-style fill-the-output-slice functions) have a real batch API;
+//!   ta-rs and yata are streaming-only and are deliberately absent from this arena.
+//!
+//! Run: `cargo bench -p wickra-bench`
+
+// Each indicator's benchmark group spells out every library arm explicitly, which
+// runs a few groups over the 100-line lint threshold; that verbosity is the point.
+#![allow(clippy::too_many_lines)]
+
+use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
+use std::hint::black_box;
+use wickra::{Atr, BatchExt, BollingerBands, Candle, Ema, Indicator, MacdIndicator, Rsi, Sma};
+use wickra_data::csv::CandleReader;
+use yata::prelude::Method;
+
+const SIZES: &[usize] = &[1_000, 10_000, 50_000];
+
+const SMA_PERIOD: usize = 20;
+const EMA_PERIOD: usize = 20;
+const RSI_PERIOD: usize = 14;
+const ATR_PERIOD: usize = 14;
+const BB_PERIOD: usize = 20;
+const BB_DEV: f64 = 2.0;
+const MACD_FAST: usize = 12;
+const MACD_SLOW: usize = 26;
+const MACD_SIGNAL: usize = 9;
+
+fn load_candles() -> Vec<Candle> {
+    let path = concat!(
+        env!("CARGO_MANIFEST_DIR"),
+        "/../../examples/data/btcusdt-1m.csv"
+    );
+    CandleReader::open(path)
+        .expect("dataset present")
+        .read_all()
+        .expect("valid OHLCV rows")
+}
+
+/// Mean of the first `period` samples — the warmup seed for `kand`'s SMA/EMA `*_inc`.
+fn window_mean(series: &[f64], period: usize) -> f64 {
+    series[..period].iter().sum::<f64>() / period as f64
+}
+
+fn sma_group(crit: &mut Criterion, closes: &[f64]) {
+    let mut group = crit.benchmark_group("sma_20");
+    for &len in SIZES {
+        let len = len.min(closes.len());
+        let series: &[f64] = &closes[..len];
+        group.throughput(Throughput::Elements(len as u64));
+
+        group.bench_with_input(
+            BenchmarkId::new("wickra/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Sma::new(SMA_PERIOD).unwrap();
+                    for &price in series {
+                        black_box(ind.update(price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("wickra/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Sma::new(SMA_PERIOD).unwrap();
+                    black_box(ind.batch(series));
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/stream", len),
+            &series,
+            |bencher, &series| {
+                let seed = window_mean(series, SMA_PERIOD);
+                bencher.iter(|| {
+                    let mut prev = seed;
+                    for idx in SMA_PERIOD..series.len() {
+                        prev = kand::ohlcv::sma::sma_inc(
+                            prev,
+                            series[idx],
+                            series[idx - SMA_PERIOD],
+                            SMA_PERIOD,
+                        )
+                        .unwrap();
+                        black_box(prev);
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut out = vec![0.0; series.len()];
+                    kand::ohlcv::sma::sma(series, SMA_PERIOD, &mut out).unwrap();
+                    black_box(&out);
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("ta-rs/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = ta::indicators::SimpleMovingAverage::new(SMA_PERIOD).unwrap();
+                    for &price in series {
+                        black_box(ta::Next::next(&mut ind, price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("yata/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = yata::methods::SMA::new(SMA_PERIOD as u8, &series[0]).unwrap();
+                    for price in series {
+                        black_box(ind.next(price));
+                    }
+                });
+            },
+        );
+    }
+    group.finish();
+}
+
+fn ema_group(crit: &mut Criterion, closes: &[f64]) {
+    let mut group = crit.benchmark_group("ema_20");
+    for &len in SIZES {
+        let len = len.min(closes.len());
+        let series: &[f64] = &closes[..len];
+        group.throughput(Throughput::Elements(len as u64));
+
+        group.bench_with_input(
+            BenchmarkId::new("wickra/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Ema::new(EMA_PERIOD).unwrap();
+                    for &price in series {
+                        black_box(ind.update(price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("wickra/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Ema::new(EMA_PERIOD).unwrap();
+                    black_box(ind.batch(series));
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/stream", len),
+            &series,
+            |bencher, &series| {
+                let seed = window_mean(series, EMA_PERIOD);
+                bencher.iter(|| {
+                    let mut prev = seed;
+                    for &price in &series[EMA_PERIOD..] {
+                        prev = kand::ohlcv::ema::ema_inc(price, prev, EMA_PERIOD, None).unwrap();
+                        black_box(prev);
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut out = vec![0.0; series.len()];
+                    kand::ohlcv::ema::ema(series, EMA_PERIOD, None, &mut out).unwrap();
+                    black_box(&out);
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("ta-rs/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind =
+                        ta::indicators::ExponentialMovingAverage::new(EMA_PERIOD).unwrap();
+                    for &price in series {
+                        black_box(ta::Next::next(&mut ind, price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("yata/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = yata::methods::EMA::new(EMA_PERIOD as u8, &series[0]).unwrap();
+                    for price in series {
+                        black_box(ind.next(price));
+                    }
+                });
+            },
+        );
+    }
+    group.finish();
+}
+
+fn rsi_group(crit: &mut Criterion, closes: &[f64]) {
+    let mut group = crit.benchmark_group("rsi_14");
+    for &len in SIZES {
+        let len = len.min(closes.len());
+        let series: &[f64] = &closes[..len];
+        group.throughput(Throughput::Elements(len as u64));
+
+        group.bench_with_input(
+            BenchmarkId::new("wickra/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Rsi::new(RSI_PERIOD).unwrap();
+                    for &price in series {
+                        black_box(ind.update(price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("wickra/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Rsi::new(RSI_PERIOD).unwrap();
+                    black_box(ind.batch(series));
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/stream", len),
+            &series,
+            |bencher, &series| {
+                // Wilder seed: simple average of the first `period` gains and losses.
+                let mut gain = 0.0;
+                let mut loss = 0.0;
+                for idx in 1..=RSI_PERIOD {
+                    let delta = series[idx] - series[idx - 1];
+                    if delta > 0.0 {
+                        gain += delta;
+                    } else {
+                        loss -= delta;
+                    }
+                }
+                let seed_gain = gain / RSI_PERIOD as f64;
+                let seed_loss = loss / RSI_PERIOD as f64;
+                bencher.iter(|| {
+                    let mut avg_gain = seed_gain;
+                    let mut avg_loss = seed_loss;
+                    let mut prev_price = series[RSI_PERIOD];
+                    for &price in &series[RSI_PERIOD + 1..] {
+                        let (rsi, next_gain, next_loss) = kand::ohlcv::rsi::rsi_inc(
+                            price, prev_price, avg_gain, avg_loss, RSI_PERIOD,
+                        )
+                        .unwrap();
+                        avg_gain = next_gain;
+                        avg_loss = next_loss;
+                        prev_price = price;
+                        black_box(rsi);
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut rsi = vec![0.0; series.len()];
+                    let mut avg_gain = vec![0.0; series.len()];
+                    let mut avg_loss = vec![0.0; series.len()];
+                    kand::ohlcv::rsi::rsi(
+                        series,
+                        RSI_PERIOD,
+                        &mut rsi,
+                        &mut avg_gain,
+                        &mut avg_loss,
+                    )
+                    .unwrap();
+                    black_box(&rsi);
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("ta-rs/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = ta::indicators::RelativeStrengthIndex::new(RSI_PERIOD).unwrap();
+                    for &price in series {
+                        black_box(ta::Next::next(&mut ind, price));
+                    }
+                });
+            },
+        );
+    }
+    group.finish();
+}
+
+fn macd_group(crit: &mut Criterion, closes: &[f64]) {
+    let mut group = crit.benchmark_group("macd_12_26_9");
+    for &len in SIZES {
+        let len = len.min(closes.len());
+        let series: &[f64] = &closes[..len];
+        group.throughput(Throughput::Elements(len as u64));
+
+        group.bench_with_input(
+            BenchmarkId::new("wickra/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = MacdIndicator::classic();
+                    for &price in series {
+                        black_box(ind.update(price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("wickra/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = MacdIndicator::classic();
+                    black_box(ind.batch(series));
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/stream", len),
+            &series,
+            |bencher, &series| {
+                // Seed the fast/slow/signal EMAs from kand's own warmed-up batch state.
+                let lookback =
+                    kand::ohlcv::macd::lookback(MACD_FAST, MACD_SLOW, MACD_SIGNAL).unwrap();
+                let mut macd_line = vec![0.0; series.len()];
+                let mut signal_line = vec![0.0; series.len()];
+                let mut histogram = vec![0.0; series.len()];
+                let mut fast_ema = vec![0.0; series.len()];
+                let mut slow_ema = vec![0.0; series.len()];
+                kand::ohlcv::macd::macd(
+                    series,
+                    MACD_FAST,
+                    MACD_SLOW,
+                    MACD_SIGNAL,
+                    &mut macd_line,
+                    &mut signal_line,
+                    &mut histogram,
+                    &mut fast_ema,
+                    &mut slow_ema,
+                )
+                .unwrap();
+                let seed_fast = fast_ema[lookback];
+                let seed_slow = slow_ema[lookback];
+                let seed_signal = signal_line[lookback];
+                bencher.iter(|| {
+                    // macd_inc returns (macd, signal, hist) but not the new EMAs, so the
+                    // fast/slow/signal state is threaded with kand's own ema_inc primitive.
+                    let mut prev_fast = seed_fast;
+                    let mut prev_slow = seed_slow;
+                    let mut prev_signal = seed_signal;
+                    for &price in &series[lookback + 1..] {
+                        let fast =
+                            kand::ohlcv::ema::ema_inc(price, prev_fast, MACD_FAST, None).unwrap();
+                        let slow =
+                            kand::ohlcv::ema::ema_inc(price, prev_slow, MACD_SLOW, None).unwrap();
+                        let macd = fast - slow;
+                        let signal =
+                            kand::ohlcv::ema::ema_inc(macd, prev_signal, MACD_SIGNAL, None)
+                                .unwrap();
+                        prev_fast = fast;
+                        prev_slow = slow;
+                        prev_signal = signal;
+                        black_box((macd, signal, macd - signal));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut macd_line = vec![0.0; series.len()];
+                    let mut signal_line = vec![0.0; series.len()];
+                    let mut histogram = vec![0.0; series.len()];
+                    let mut fast_ema = vec![0.0; series.len()];
+                    let mut slow_ema = vec![0.0; series.len()];
+                    kand::ohlcv::macd::macd(
+                        series,
+                        MACD_FAST,
+                        MACD_SLOW,
+                        MACD_SIGNAL,
+                        &mut macd_line,
+                        &mut signal_line,
+                        &mut histogram,
+                        &mut fast_ema,
+                        &mut slow_ema,
+                    )
+                    .unwrap();
+                    black_box(&macd_line);
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("ta-rs/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = ta::indicators::MovingAverageConvergenceDivergence::new(
+                        MACD_FAST,
+                        MACD_SLOW,
+                        MACD_SIGNAL,
+                    )
+                    .unwrap();
+                    for &price in series {
+                        black_box(ta::Next::next(&mut ind, price));
+                    }
+                });
+            },
+        );
+    }
+    group.finish();
+}
+
+fn bbands_group(crit: &mut Criterion, closes: &[f64]) {
+    let mut group = crit.benchmark_group("bollinger_20_2");
+    for &len in SIZES {
+        let len = len.min(closes.len());
+        let series: &[f64] = &closes[..len];
+        group.throughput(Throughput::Elements(len as u64));
+
+        group.bench_with_input(
+            BenchmarkId::new("wickra/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
+                    for &price in series {
+                        black_box(ind.update(price));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("wickra/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
+                    black_box(ind.batch(series));
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/stream", len),
+            &series,
+            |bencher, &series| {
+                // Seed running sma/sum/sum_sq from kand's batch state at the warmup edge.
+                let mut upper = vec![0.0; series.len()];
+                let mut middle = vec![0.0; series.len()];
+                let mut lower = vec![0.0; series.len()];
+                let mut sma = vec![0.0; series.len()];
+                let mut variance = vec![0.0; series.len()];
+                let mut sum = vec![0.0; series.len()];
+                let mut sum_sq = vec![0.0; series.len()];
+                kand::ohlcv::bbands::bbands(
+                    series,
+                    BB_PERIOD,
+                    BB_DEV,
+                    BB_DEV,
+                    &mut upper,
+                    &mut middle,
+                    &mut lower,
+                    &mut sma,
+                    &mut variance,
+                    &mut sum,
+                    &mut sum_sq,
+                )
+                .unwrap();
+                let seed_sma = sma[BB_PERIOD - 1];
+                let seed_sum = sum[BB_PERIOD - 1];
+                let seed_sum_sq = sum_sq[BB_PERIOD - 1];
+                bencher.iter(|| {
+                    let mut prev_sma = seed_sma;
+                    let mut prev_sum = seed_sum;
+                    let mut prev_sum_sq = seed_sum_sq;
+                    for idx in BB_PERIOD..series.len() {
+                        let result = kand::ohlcv::bbands::bbands_inc(
+                            series[idx],
+                            prev_sma,
+                            prev_sum,
+                            prev_sum_sq,
+                            series[idx - BB_PERIOD],
+                            BB_PERIOD,
+                            BB_DEV,
+                            BB_DEV,
+                        )
+                        .unwrap();
+                        prev_sma = result.1;
+                        prev_sum = result.4;
+                        prev_sum_sq = result.5;
+                        black_box((result.0, result.1, result.2));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut upper = vec![0.0; series.len()];
+                    let mut middle = vec![0.0; series.len()];
+                    let mut lower = vec![0.0; series.len()];
+                    let mut sma = vec![0.0; series.len()];
+                    let mut variance = vec![0.0; series.len()];
+                    let mut sum = vec![0.0; series.len()];
+                    let mut sum_sq = vec![0.0; series.len()];
+                    kand::ohlcv::bbands::bbands(
+                        series,
+                        BB_PERIOD,
+                        BB_DEV,
+                        BB_DEV,
+                        &mut upper,
+                        &mut middle,
+                        &mut lower,
+                        &mut sma,
+                        &mut variance,
+                        &mut sum,
+                        &mut sum_sq,
+                    )
+                    .unwrap();
+                    black_box(&upper);
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("ta-rs/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = ta::indicators::BollingerBands::new(BB_PERIOD, BB_DEV).unwrap();
+                    for &price in series {
+                        black_box(ta::Next::next(&mut ind, price));
+                    }
+                });
+            },
+        );
+    }
+    group.finish();
+}
+
+fn atr_group(crit: &mut Criterion, candles: &[Candle]) {
+    let mut group = crit.benchmark_group("atr_14");
+    for &len in SIZES {
+        let len = len.min(candles.len());
+        let series: &[Candle] = &candles[..len];
+        group.throughput(Throughput::Elements(len as u64));
+
+        group.bench_with_input(
+            BenchmarkId::new("wickra/stream", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Atr::new(ATR_PERIOD).unwrap();
+                    for &candle in series {
+                        black_box(ind.update(candle));
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("wickra/batch", len),
+            &series,
+            |bencher, &series| {
+                bencher.iter(|| {
+                    let mut ind = Atr::new(ATR_PERIOD).unwrap();
+                    black_box(ind.batch(series));
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/stream", len),
+            &series,
+            |bencher, &series| {
+                let high: Vec<f64> = series.iter().map(|candle| candle.high).collect();
+                let low: Vec<f64> = series.iter().map(|candle| candle.low).collect();
+                let close: Vec<f64> = series.iter().map(|candle| candle.close).collect();
+                // Seed prev_atr from kand's batch ATR at the first valid index (= period).
+                let mut atr_out = vec![0.0; series.len()];
+                kand::ohlcv::atr::atr(&high, &low, &close, ATR_PERIOD, &mut atr_out).unwrap();
+                let seed_atr = atr_out[ATR_PERIOD];
+                bencher.iter(|| {
+                    let mut prev_atr = seed_atr;
+                    for idx in ATR_PERIOD + 1..series.len() {
+                        prev_atr = kand::ohlcv::atr::atr_inc(
+                            high[idx],
+                            low[idx],
+                            close[idx - 1],
+                            prev_atr,
+                            ATR_PERIOD,
+                        )
+                        .unwrap();
+                        black_box(prev_atr);
+                    }
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("kand/batch", len),
+            &series,
+            |bencher, &series| {
+                let high: Vec<f64> = series.iter().map(|candle| candle.high).collect();
+                let low: Vec<f64> = series.iter().map(|candle| candle.low).collect();
+                let close: Vec<f64> = series.iter().map(|candle| candle.close).collect();
+                bencher.iter(|| {
+                    let mut atr_out = vec![0.0; series.len()];
+                    kand::ohlcv::atr::atr(&high, &low, &close, ATR_PERIOD, &mut atr_out).unwrap();
+                    black_box(&atr_out);
+                });
+            },
+        );
+        group.bench_with_input(
+            BenchmarkId::new("ta-rs/stream", len),
+            &series,
+            |bencher, &series| {
+                let items: Vec<ta::DataItem> = series
+                    .iter()
+                    .map(|candle| {
+                        ta::DataItem::builder()
+                            .open(candle.open)
+                            .high(candle.high)
+                            .low(candle.low)
+                            .close(candle.close)
+                            .volume(candle.volume)
+                            .build()
+                            .unwrap()
+                    })
+                    .collect();
+                bencher.iter(|| {
+                    let mut ind = ta::indicators::AverageTrueRange::new(ATR_PERIOD).unwrap();
+                    for item in &items {
+                        black_box(ta::Next::next(&mut ind, item));
+                    }
+                });
+            },
+        );
+    }
+    group.finish();
+}
+
+fn benches(crit: &mut Criterion) {
+    let candles = load_candles();
+    let closes: Vec<f64> = candles.iter().map(|candle| candle.close).collect();
+    sma_group(crit, &closes);
+    ema_group(crit, &closes);
+    rsi_group(crit, &closes);
+    macd_group(crit, &closes);
+    bbands_group(crit, &closes);
+    atr_group(crit, &candles);
+}
+
+criterion_group!(name = cross_lib; config = Criterion::default(); targets = benches);
+criterion_main!(cross_lib);

crates/wickra-bench/src/lib.rs

@@ -0,0 +1,6 @@
+//! Internal cross-library benchmark harness for Wickra.
+//!
+//! This crate is `publish = false`. It exists only to host the Criterion
+//! benchmark in `benches/cross_lib.rs`, which compares Wickra against the
+//! Rust technical-analysis crates `kand`, `ta` (ta-rs) and `yata` on an
+//! identical candle series. It deliberately carries no library code.

crates/wickra-core/src/indicators/adaptive_laguerre_filter.rs

@@ -0,0 +1,344 @@
+//! Ehlers' Adaptive Laguerre Filter.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// John Ehlers' Adaptive Laguerre Filter — a four-stage Laguerre polynomial
+/// smoother whose damping factor `gamma` is recomputed every bar from how well
+/// the filter is currently tracking price.
+///
+/// The Laguerre cascade is the same one used by [`LaguerreRsi`](crate::LaguerreRsi),
+/// but instead of a fixed `gamma` the filter adapts: it measures the recent
+/// absolute error `|price − filter|`, normalises those errors across a window of
+/// `period` bars to `[0, 1]`, and takes their **median** as `gamma`. When price
+/// is tracking smoothly the errors are small and uniform (low `gamma`, fast
+/// response); when price jumps, the spread of errors widens and `gamma` rises,
+/// slowing the filter to reject the noise.
+///
+/// ```text
+/// diff_t  = |price_t − filter_{t-1}|
+/// over the last `period` diffs:
+///   HH = max(diff),  LL = min(diff)
+///   norm_i = (diff_i − LL) / (HH − LL)      (0 if HH == LL)
+///   gamma  = median(norm)
+/// alpha   = 1 − gamma
+/// L0_t = alpha·price_t + gamma·L0_{t-1}
+/// L1_t = −gamma·L0_t + L0_{t-1} + gamma·L1_{t-1}
+/// L2_t = −gamma·L1_t + L1_{t-1} + gamma·L2_{t-1}
+/// L3_t = −gamma·L2_t + L2_{t-1} + gamma·L3_{t-1}
+/// filter_t = (L0_t + 2·L1_t + 2·L2_t + L3_t) / 6
+/// ```
+///
+/// The output is a smoothed price on the same scale as the input. The first
+/// emission lands once the error window holds `period` values.
+///
+/// Reference: John F. Ehlers, *"Adaptive Laguerre Filter"*, Technical Analysis
+/// of Stocks & Commodities, 2007.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, AdaptiveLaguerreFilter};
+///
+/// let mut indicator = AdaptiveLaguerreFilter::new(13).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct AdaptiveLaguerreFilter {
+    period: usize,
+    l0: f64,
+    l1: f64,
+    l2: f64,
+    l3: f64,
+    /// Previous filter output, or `None` before the first bar.
+    filter: Option<f64>,
+    /// The last `period` absolute errors `|price − filter|`.
+    diffs: VecDeque<f64>,
+}
+
+impl AdaptiveLaguerreFilter {
+    /// Construct a new adaptive Laguerre filter with the given error-window
+    /// length.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            l0: 0.0,
+            l1: 0.0,
+            l2: 0.0,
+            l3: 0.0,
+            filter: None,
+            diffs: VecDeque::with_capacity(period),
+        })
+    }
+
+    /// Configured error-window length.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if the error window is full.
+    pub fn value(&self) -> Option<f64> {
+        if self.diffs.len() == self.period {
+            self.filter
+        } else {
+            None
+        }
+    }
+
+    /// Median of the normalised errors currently in the window. Returns `0.0`
+    /// when every error is equal (e.g. during a constant warmup), which makes
+    /// the filter maximally fast.
+    fn adaptive_gamma(&self) -> f64 {
+        let mut hh = f64::MIN;
+        let mut ll = f64::MAX;
+        for &d in &self.diffs {
+            if d > hh {
+                hh = d;
+            }
+            if d < ll {
+                ll = d;
+            }
+        }
+        let range = hh - ll;
+        if range <= 0.0 {
+            return 0.0;
+        }
+        let mut norm: Vec<f64> = self.diffs.iter().map(|&d| (d - ll) / range).collect();
+        // `total_cmp` never panics — under pathological (e.g. overflowing) fuzz
+        // inputs a normalised error can be non-finite; a total order keeps the
+        // sort sound where `partial_cmp` would return `None`.
+        norm.sort_by(f64::total_cmp);
+        let mid = norm.len() / 2;
+        if norm.len() % 2 == 1 {
+            norm[mid]
+        } else {
+            f64::midpoint(norm[mid - 1], norm[mid])
+        }
+    }
+}
+
+impl Indicator for AdaptiveLaguerreFilter {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, price: f64) -> Option<f64> {
+        if !price.is_finite() {
+            return self.value();
+        }
+        // Absolute tracking error against the previous filter (0 on the first
+        // bar, where there is no prior filter value).
+        let diff = self.filter.map_or(0.0, |f| (price - f).abs());
+        if self.diffs.len() == self.period {
+            self.diffs.pop_front();
+        }
+        self.diffs.push_back(diff);
+
+        let gamma = self.adaptive_gamma();
+        let alpha = 1.0 - gamma;
+
+        let l0 = alpha * price + gamma * self.l0;
+        let l1 = -gamma * l0 + self.l0 + gamma * self.l1;
+        let l2 = -gamma * l1 + self.l1 + gamma * self.l2;
+        let l3 = -gamma * l2 + self.l2 + gamma * self.l3;
+        self.l0 = l0;
+        self.l1 = l1;
+        self.l2 = l2;
+        self.l3 = l3;
+
+        let filter = (l0 + 2.0 * l1 + 2.0 * l2 + l3) / 6.0;
+        self.filter = Some(filter);
+        self.value()
+    }
+
+    fn reset(&mut self) {
+        self.l0 = 0.0;
+        self.l1 = 0.0;
+        self.l2 = 0.0;
+        self.l3 = 0.0;
+        self.filter = None;
+        self.diffs.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.diffs.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "AdaptiveLaguerre"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Independent reference: replays the exact recurrence from scratch.
+    fn naive(prices: &[f64], period: usize) -> Vec<Option<f64>> {
+        let (mut l0, mut l1, mut l2, mut l3) = (0.0_f64, 0.0_f64, 0.0_f64, 0.0_f64);
+        let mut filter: Option<f64> = None;
+        let mut diffs: Vec<f64> = Vec::new();
+        let mut out = Vec::with_capacity(prices.len());
+        for &price in prices {
+            let diff = filter.map_or(0.0, |f: f64| (price - f).abs());
+            diffs.push(diff);
+            if diffs.len() > period {
+                diffs.remove(0);
+            }
+            let hh = diffs.iter().copied().fold(f64::MIN, f64::max);
+            let ll = diffs.iter().copied().fold(f64::MAX, f64::min);
+            let range = hh - ll;
+            let gamma = if range <= 0.0 {
+                0.0
+            } else {
+                let mut norm: Vec<f64> = diffs.iter().map(|&d| (d - ll) / range).collect();
+                norm.sort_by(|a, b| a.partial_cmp(b).unwrap());
+                let mid = norm.len() / 2;
+                if norm.len() % 2 == 1 {
+                    norm[mid]
+                } else {
+                    f64::midpoint(norm[mid - 1], norm[mid])
+                }
+            };
+            let alpha = 1.0 - gamma;
+            let n0 = alpha * price + gamma * l0;
+            let n1 = -gamma * n0 + l0 + gamma * l1;
+            let n2 = -gamma * n1 + l1 + gamma * l2;
+            let n3 = -gamma * n2 + l2 + gamma * l3;
+            l0 = n0;
+            l1 = n1;
+            l2 = n2;
+            l3 = n3;
+            let f = (n0 + 2.0 * n1 + 2.0 * n2 + n3) / 6.0;
+            filter = Some(f);
+            out.push(if diffs.len() == period { Some(f) } else { None });
+        }
+        out
+    }
+
+    #[test]
+    fn new_rejects_zero_period() {
+        assert!(matches!(
+            AdaptiveLaguerreFilter::new(0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let alf = AdaptiveLaguerreFilter::new(13).unwrap();
+        assert_eq!(alf.period(), 13);
+        assert_eq!(alf.warmup_period(), 13);
+        assert_eq!(alf.name(), "AdaptiveLaguerre");
+    }
+
+    #[test]
+    fn warmup_returns_none_until_window_full() {
+        let mut alf = AdaptiveLaguerreFilter::new(3).unwrap();
+        assert_eq!(alf.update(10.0), None);
+        assert_eq!(alf.update(11.0), None);
+        assert!(alf.update(12.0).is_some());
+    }
+
+    #[test]
+    fn constant_series_converges_to_constant() {
+        // Errors are all zero -> gamma 0 -> the 4-stage delay line fills with
+        // the constant and the filter settles on it.
+        let mut alf = AdaptiveLaguerreFilter::new(5).unwrap();
+        let out = alf.batch(&[42.0_f64; 40]);
+        let last = out.iter().rev().flatten().next().unwrap();
+        assert_relative_eq!(*last, 42.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn converged_output_stays_within_price_range() {
+        // Once the Laguerre cascade has filled (it cold-starts from zero, so the
+        // first few post-warmup values ramp up toward price), the filter is a
+        // convex blend of recent prices and must stay inside the data range.
+        let prices: Vec<f64> = (0..120)
+            .map(|i| 50.0 + (f64::from(i) * 0.4).sin() * 10.0)
+            .collect();
+        let lo = prices.iter().copied().fold(f64::MAX, f64::min);
+        let hi = prices.iter().copied().fold(f64::MIN, f64::max);
+        let period = 8;
+        let mut alf = AdaptiveLaguerreFilter::new(period).unwrap();
+        for (i, v) in alf.batch(&prices).into_iter().enumerate() {
+            // Skip the cold-start transient (a few multiples of the window).
+            if i < 4 * period {
+                continue;
+            }
+            let v = v.expect("filter is ready well past warmup");
+            assert!(
+                v >= lo - 1e-6 && v <= hi + 1e-6,
+                "filter out of range at {i}"
+            );
+        }
+    }
+
+    #[test]
+    fn matches_naive_recurrence() {
+        let prices: Vec<f64> = (0..80)
+            .map(|i| 100.0 + (f64::from(i) * 0.5).sin() * 8.0 + f64::from(i) * 0.1)
+            .collect();
+        let mut alf = AdaptiveLaguerreFilter::new(10).unwrap();
+        let got = alf.batch(&prices);
+        let want = naive(&prices, 10);
+        for (i, (g, w)) in got.iter().zip(want.iter()).enumerate() {
+            assert_eq!(g.is_some(), w.is_some(), "readiness mismatch at {i}");
+            if let (Some(a), Some(b)) = (g, w) {
+                assert_relative_eq!(*a, *b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut alf = AdaptiveLaguerreFilter::new(5).unwrap();
+        alf.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
+        assert!(alf.is_ready());
+        alf.reset();
+        assert!(!alf.is_ready());
+        assert_eq!(alf.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=50).map(|i| f64::from(i) * 0.7).collect();
+        let mut a = AdaptiveLaguerreFilter::new(7).unwrap();
+        let mut b = AdaptiveLaguerreFilter::new(7).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut alf = AdaptiveLaguerreFilter::new(3).unwrap();
+        alf.update(10.0);
+        alf.update(11.0);
+        let ready = alf.update(12.0).expect("ready after three inputs");
+        assert_eq!(alf.update(f64::NAN), Some(ready));
+        assert_eq!(alf.update(f64::INFINITY), Some(ready));
+    }
+}

crates/wickra-core/src/indicators/atr.rs

@@ -28,9 +28,17 @@ use crate::traits::Indicator;
 #[derive(Debug, Clone)]
 pub struct Atr {
     period: usize,
+    /// `period - 1` as `f64`, precomputed for the Wilder smoothing step.
+    n_minus_1: f64,
+    /// `1 / period`, precomputed so the per-tick smoothing multiplies instead of
+    /// divides.
+    inv_period: f64,
     prev_close: Option<f64>,
     seed_buf: Vec<f64>,
-    avg: Option<f64>,
+    /// Smoothed ATR, valid once `seeded` is set. Bare `f64` + flag rather than
+    /// `Option<f64>` so the hot recurrence avoids an enum-tag read per tick.
+    avg: f64,
+    seeded: bool,
 }
 
 impl Atr {
@@ -45,9 +53,12 @@ impl Atr {
         }
         Ok(Self {
             period,
+            n_minus_1: (period - 1) as f64,
+            inv_period: 1.0 / period as f64,
             prev_close: None,
             seed_buf: Vec::with_capacity(period),
-            avg: None,
+            avg: 0.0,
+            seeded: false,
         })
     }
 
@@ -58,7 +69,11 @@ impl Atr {
 
     /// Current value if available.
     pub const fn value(&self) -> Option<f64> {
-        self.avg
+        if self.seeded {
+            Some(self.avg)
+        } else {
+            None
+        }
     }
 }
 
@@ -70,17 +85,18 @@ impl Indicator for Atr {
         let tr = candle.true_range(self.prev_close);
         self.prev_close = Some(candle.close);
 
-        if let Some(avg) = self.avg {
-            let n = self.period as f64;
-            let new_avg = avg.mul_add(n - 1.0, tr) / n;
-            self.avg = Some(new_avg);
+        if self.seeded {
+            // Wilder smoothing with the reciprocal hoisted out of the hot path.
+            let new_avg = self.avg.mul_add(self.n_minus_1, tr) * self.inv_period;
+            self.avg = new_avg;
             return Some(new_avg);
         }
 
         self.seed_buf.push(tr);
         if self.seed_buf.len() == self.period {
             let seed = self.seed_buf.iter().copied().sum::<f64>() / self.period as f64;
-            self.avg = Some(seed);
+            self.avg = seed;
+            self.seeded = true;
             return Some(seed);
         }
         None
@@ -89,7 +105,8 @@ impl Indicator for Atr {
     fn reset(&mut self) {
         self.prev_close = None;
         self.seed_buf.clear();
-        self.avg = None;
+        self.avg = 0.0;
+        self.seeded = false;
     }
 
     fn warmup_period(&self) -> usize {
@@ -97,7 +114,7 @@ impl Indicator for Atr {
     }
 
     fn is_ready(&self) -> bool {
-        self.avg.is_some()
+        self.seeded
     }
 
     fn name(&self) -> &'static str {

crates/wickra-core/src/indicators/atr_ratchet.rs

@@ -0,0 +1,279 @@
+//! ATR Ratchet (Kaufman) — a trailing stop that creeps toward price each bar.
+
+use crate::error::{Error, Result};
+use crate::indicators::atr::Atr;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`AtrRatchet`]: the active stop level and the trend direction.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct AtrRatchetOutput {
+    /// The ratchet stop level — below price when long, above price when short.
+    pub value: f64,
+    /// Trend direction: `+1.0` long, `-1.0` short.
+    pub direction: f64,
+}
+
+/// ATR Ratchet — Perry Kaufman's time-based volatility stop that tightens by a
+/// fixed fraction of ATR **every bar**, whether or not price moves.
+///
+/// ```text
+/// on entry (long):   stop = close − start_mult · ATR
+/// each later bar:     stop = stop + increment · ATR    (ratchets toward price)
+/// flip to short when  close < stop, reseeding stop = close + start_mult · ATR
+/// ```
+///
+/// Most trailing stops only move when price makes a new extreme. Kaufman's ratchet
+/// instead advances the stop a little each bar — `increment · ATR` — so a trade
+/// that stalls is squeezed out over time even in a flat market. The initial
+/// distance (`start_mult · ATR`) gives the position room to breathe; the per-bar
+/// `increment` controls how aggressively the leash shortens. When price closes
+/// through the stop the system reverses and reseeds at the full initial distance.
+///
+/// The first stop lands once ATR is ready (`atr_period` inputs). Each `update` is
+/// O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, AtrRatchet};
+///
+/// let mut indicator = AtrRatchet::new(14, 4.0, 0.1).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct AtrRatchet {
+    atr: Atr,
+    atr_period: usize,
+    start_mult: f64,
+    increment: f64,
+    direction: f64,
+    stop: f64,
+    last: Option<AtrRatchetOutput>,
+}
+
+impl AtrRatchet {
+    /// Construct an ATR Ratchet stop.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `atr_period == 0` and
+    /// [`Error::NonPositiveMultiplier`] if `start_mult` or `increment` is not
+    /// finite and positive.
+    pub fn new(atr_period: usize, start_mult: f64, increment: f64) -> Result<Self> {
+        if !start_mult.is_finite()
+            || start_mult <= 0.0
+            || !increment.is_finite()
+            || increment <= 0.0
+        {
+            return Err(Error::NonPositiveMultiplier);
+        }
+        Ok(Self {
+            atr: Atr::new(atr_period)?,
+            atr_period,
+            start_mult,
+            increment,
+            direction: 0.0,
+            stop: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured `(atr_period, start_mult, increment)`.
+    pub const fn params(&self) -> (usize, f64, f64) {
+        (self.atr_period, self.start_mult, self.increment)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<AtrRatchetOutput> {
+        self.last
+    }
+}
+
+impl Indicator for AtrRatchet {
+    type Input = Candle;
+    type Output = AtrRatchetOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<AtrRatchetOutput> {
+        let atr = self.atr.update(candle)?;
+        let close = candle.close;
+
+        if self.direction == 0.0 {
+            self.direction = 1.0;
+            self.stop = close - self.start_mult * atr;
+        } else if self.direction > 0.0 {
+            self.stop += self.increment * atr;
+            if close < self.stop {
+                self.direction = -1.0;
+                self.stop = close + self.start_mult * atr;
+            }
+        } else {
+            self.stop -= self.increment * atr;
+            if close > self.stop {
+                self.direction = 1.0;
+                self.stop = close - self.start_mult * atr;
+            }
+        }
+
+        let out = AtrRatchetOutput {
+            value: self.stop,
+            direction: self.direction,
+        };
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.atr.reset();
+        self.direction = 0.0;
+        self.stop = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.atr_period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "AtrRatchet"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+
+    fn c(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new_unchecked(f64::midpoint(high, low), high, low, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_invalid_params() {
+        assert!(matches!(
+            AtrRatchet::new(0, 4.0, 0.1),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            AtrRatchet::new(14, 0.0, 0.1),
+            Err(Error::NonPositiveMultiplier)
+        ));
+        assert!(matches!(
+            AtrRatchet::new(14, 4.0, 0.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+        assert!(matches!(
+            AtrRatchet::new(14, 4.0, f64::NAN),
+            Err(Error::NonPositiveMultiplier)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let r = AtrRatchet::new(14, 4.0, 0.1).unwrap();
+        assert_eq!(r.params(), (14, 4.0, 0.1));
+        assert_eq!(r.warmup_period(), 14);
+        assert_eq!(r.name(), "AtrRatchet");
+        assert!(!r.is_ready());
+        assert_eq!(r.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut r = AtrRatchet::new(5, 4.0, 0.1).unwrap();
+        let candles: Vec<Candle> = (0..12)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        let out = r.batch(&candles);
+        for v in out.iter().take(4) {
+            assert!(v.is_none());
+        }
+        assert!(out[4].is_some());
+    }
+
+    #[test]
+    fn uptrend_keeps_stop_below_price() {
+        let mut r = AtrRatchet::new(5, 4.0, 0.05).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| {
+                let base = 100.0 + 2.0 * f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        for (o, candle) in r.batch(&candles).into_iter().zip(candles.iter()) {
+            if let Some(o) = o {
+                assert_eq!(o.direction, 1.0);
+                assert!(o.value < candle.close);
+            }
+        }
+    }
+
+    #[test]
+    fn stall_eventually_triggers_flip() {
+        // A long trend then a long flat stretch: the ratchet creeps up each bar
+        // and eventually overtakes the flat close, flipping to short.
+        let mut r = AtrRatchet::new(5, 2.0, 0.5).unwrap();
+        let mut candles: Vec<Candle> = (0..20)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        // Flat stretch at the last price.
+        candles.extend((0..40).map(|_| c(120.6, 118.6, 119.5)));
+        let dirs: Vec<f64> = r
+            .batch(&candles)
+            .into_iter()
+            .flatten()
+            .map(|o| o.direction)
+            .collect();
+        assert!(
+            dirs.iter().any(|&d| d < 0.0),
+            "the ratchet should eventually flip short"
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut r = AtrRatchet::new(5, 4.0, 0.1).unwrap();
+        let candles: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        r.batch(&candles);
+        assert!(r.is_ready());
+        r.reset();
+        assert!(!r.is_ready());
+        assert_eq!(r.value(), None);
+        assert_eq!(r.update(candles[0]), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.25).sin() * 9.0;
+                c(base + 2.0, base - 1.5, base + 0.5)
+            })
+            .collect();
+        let batch = AtrRatchet::new(14, 4.0, 0.1).unwrap().batch(&candles);
+        let mut b = AtrRatchet::new(14, 4.0, 0.1).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/better_volume.rs

@@ -0,0 +1,254 @@
+//! Better Volume (VSA) — a streaming effort-versus-result oscillator.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Better Volume — a Volume-Spread-Analysis (VSA) "effort versus result"
+/// oscillator: how much volume (effort) a bar spent relative to the price range
+/// (result) it achieved, both normalised against their own recent averages.
+///
+/// ```text
+/// range_t   = high_t − low_t
+/// rel_vol   = volume_t / SMA(volume, period)
+/// rel_range = range_t  / SMA(range,  period)
+/// BetterVol = rel_vol − rel_range
+/// ```
+///
+/// Volume-Spread Analysis (Wyckoff, popularised by Tom Williams) reads markets
+/// through the relationship between **effort** (volume) and **result** (the bar's
+/// spread). A bar with heavy volume but a narrow range — `rel_vol` high while
+/// `rel_range` low, so the oscillator is **positive** — is *churn*: large effort
+/// produced little movement, the hallmark of absorption (supply meeting demand at
+/// a top, or vice versa at a bottom). A bar that travels far on light volume —
+/// negative oscillator — shows *ease of movement*, a trend meeting no resistance.
+///
+/// Both legs are normalised by their `period` simple moving averages (including
+/// the current bar), so the output is centred near `0` and self-scales to the
+/// instrument. A degenerate average of `0` makes its leg `0` rather than dividing
+/// by zero. The first value lands after `period` inputs. Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, BetterVolume};
+///
+/// let mut indicator = BetterVolume::new(20).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 2.0, base - 2.0, base + 0.5, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct BetterVolume {
+    period: usize,
+    volumes: VecDeque<f64>,
+    ranges: VecDeque<f64>,
+    vol_sum: f64,
+    range_sum: f64,
+    last: Option<f64>,
+}
+
+impl BetterVolume {
+    /// Construct a new Better Volume oscillator with the given averaging `period`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            volumes: VecDeque::with_capacity(period),
+            ranges: VecDeque::with_capacity(period),
+            vol_sum: 0.0,
+            range_sum: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured averaging period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for BetterVolume {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let range = candle.high - candle.low;
+        if self.volumes.len() == self.period {
+            self.vol_sum -= self.volumes.pop_front().expect("non-empty");
+            self.range_sum -= self.ranges.pop_front().expect("non-empty");
+        }
+        self.volumes.push_back(candle.volume);
+        self.ranges.push_back(range);
+        self.vol_sum += candle.volume;
+        self.range_sum += range;
+        if self.volumes.len() < self.period {
+            return None;
+        }
+        let n = self.period as f64;
+        let sma_vol = self.vol_sum / n;
+        let sma_range = self.range_sum / n;
+        let rel_vol = if sma_vol > 0.0 {
+            candle.volume / sma_vol
+        } else {
+            0.0
+        };
+        let rel_range = if sma_range > 0.0 {
+            range / sma_range
+        } else {
+            0.0
+        };
+        let out = rel_vol - rel_range;
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.volumes.clear();
+        self.ranges.clear();
+        self.vol_sum = 0.0;
+        self.range_sum = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "BetterVolume"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, volume: f64) -> Candle {
+        Candle::new_unchecked(low, high, low, high, volume, 0)
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(BetterVolume::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let bv = BetterVolume::new(20).unwrap();
+        assert_eq!(bv.period(), 20);
+        assert_eq!(bv.warmup_period(), 20);
+        assert_eq!(bv.name(), "BetterVolume");
+        assert!(!bv.is_ready());
+        assert_eq!(bv.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut bv = BetterVolume::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|_| candle(102.0, 100.0, 1_000.0)).collect();
+        let out = bv.batch(&candles);
+        for v in out.iter().take(2) {
+            assert!(v.is_none());
+        }
+        assert!(out[2].is_some());
+    }
+
+    #[test]
+    fn steady_bars_are_neutral() {
+        // Identical volume and range every bar -> rel_vol = rel_range = 1 -> 0.
+        let mut bv = BetterVolume::new(4).unwrap();
+        let candles: Vec<Candle> = (0..10).map(|_| candle(102.0, 100.0, 1_000.0)).collect();
+        let last = bv.batch(&candles).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn churn_bar_is_positive() {
+        // Three normal bars, then a high-volume narrow-range bar -> positive.
+        let mut bv = BetterVolume::new(4).unwrap();
+        let mut candles: Vec<Candle> = (0..3).map(|_| candle(105.0, 100.0, 1_000.0)).collect();
+        candles.push(candle(100.5, 100.0, 5_000.0)); // huge volume, tiny range
+        let last = bv.batch(&candles).into_iter().flatten().last().unwrap();
+        assert!(last > 0.0, "churn bar should be positive, got {last}");
+    }
+
+    #[test]
+    fn ease_of_movement_bar_is_negative() {
+        // Three normal bars, then a wide-range light-volume bar -> negative.
+        let mut bv = BetterVolume::new(4).unwrap();
+        let mut candles: Vec<Candle> = (0..3).map(|_| candle(101.0, 100.0, 5_000.0)).collect();
+        candles.push(candle(115.0, 100.0, 500.0)); // wide range, tiny volume
+        let last = bv.batch(&candles).into_iter().flatten().last().unwrap();
+        assert!(
+            last < 0.0,
+            "ease-of-movement bar should be negative, got {last}"
+        );
+    }
+
+    #[test]
+    fn zero_everything_is_zero() {
+        // Zero volume and zero range -> both legs guarded to 0.
+        let mut bv = BetterVolume::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|_| candle(100.0, 100.0, 0.0)).collect();
+        for v in bv.batch(&candles).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut bv = BetterVolume::new(3).unwrap();
+        bv.batch(
+            &(0..6)
+                .map(|_| candle(102.0, 100.0, 1_000.0))
+                .collect::<Vec<_>>(),
+        );
+        assert!(bv.is_ready());
+        bv.reset();
+        assert!(!bv.is_ready());
+        assert_eq!(bv.value(), None);
+        assert_eq!(bv.update(candle(102.0, 100.0, 1_000.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.25).sin() * 9.0;
+                candle(
+                    base + 2.0,
+                    base - 1.5,
+                    1_000.0 + (f64::from(i) * 0.5).cos() * 400.0,
+                )
+            })
+            .collect();
+        let batch = BetterVolume::new(20).unwrap().batch(&candles);
+        let mut b = BetterVolume::new(20).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/bipower_variation.rs

@@ -0,0 +1,281 @@
+//! Realized Bipower Variation — a jump-robust quadratic-variation estimator.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Realized Bipower Variation — the sum of *adjacent* absolute log-return
+/// products over the trailing `period` returns, scaled to estimate integrated
+/// variance.
+///
+/// ```text
+/// r_t = ln(price_t / price_{t−1})
+/// BV  = (π / 2) · Σ |r_t| · |r_{t−1}|   over the window
+/// ```
+///
+/// Bipower variation (Barndorff-Nielsen & Shephard 2004) estimates the same
+/// integrated variance as [`RealizedVolatility`](crate::RealizedVolatility)'s
+/// `Σ r²`, but by multiplying *neighbouring* absolute returns rather than
+/// squaring a single one. A price jump inflates exactly one return; because that
+/// return appears in a product with its (ordinary) neighbour rather than squared,
+/// its contribution stays bounded — so `BV` is **robust to jumps** while realized
+/// variance is not. The constant `π / 2 = μ₁⁻²` (with `μ₁ = E|Z| = √(2/π)` for a
+/// standard normal) debiases the product of two half-normal magnitudes back to a
+/// variance scale.
+///
+/// The output is on the **variance** scale (the jump-robust counterpart of
+/// realized *variance*, not volatility); take its square root for a volatility,
+/// and compare `RV − BV` to isolate the jump contribution. A window of `period`
+/// returns contributes `period − 1` adjacent products; each `update` is O(1) via
+/// a running sum.
+///
+/// Non-finite and non-positive prices are ignored (the log return would be
+/// undefined): the tick is dropped, state is left untouched, and the last value
+/// is returned.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{BipowerVariation, Indicator};
+///
+/// let mut indicator = BipowerVariation::new(20).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct BipowerVariation {
+    period: usize,
+    prev_price: Option<f64>,
+    /// Rolling window of the last `period` log returns.
+    window: VecDeque<f64>,
+    /// Running sum of adjacent absolute-return products inside the window.
+    sum_adjacent: f64,
+    last: Option<f64>,
+}
+
+impl BipowerVariation {
+    /// Construct a new bipower-variation indicator.
+    ///
+    /// `period` is the number of log returns in the rolling window; the estimate
+    /// uses the `period − 1` adjacent products between them.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`, or
+    /// [`Error::InvalidPeriod`] if `period == 1` (an adjacent product needs at
+    /// least two returns).
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if period < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "bipower variation period must be >= 2",
+            });
+        }
+        Ok(Self {
+            period,
+            prev_price: None,
+            window: VecDeque::with_capacity(period),
+            sum_adjacent: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+/// `μ₁⁻² = π / 2`, the debiasing constant for a product of half-normal returns.
+const MU1_INV_SQ: f64 = std::f64::consts::FRAC_PI_2;
+
+impl Indicator for BipowerVariation {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        // Non-finite / non-positive prices are skipped: `ln(input / prev)` is
+        // undefined, so the tick must not enter the return window.
+        if !input.is_finite() || input <= 0.0 {
+            return self.last;
+        }
+        let Some(prev) = self.prev_price else {
+            self.prev_price = Some(input);
+            return None;
+        };
+        self.prev_price = Some(input);
+        // `prev` came from `self.prev_price`, gated by the guard above, so it is
+        // finite and positive — the log return is always well-defined.
+        let r = (input / prev).ln();
+        // The incoming return forms a product with the current last return.
+        if let Some(&back) = self.window.back() {
+            self.sum_adjacent += back.abs() * r.abs();
+        }
+        self.window.push_back(r);
+        if self.window.len() > self.period {
+            let first = self.window.pop_front().expect("window is non-empty");
+            // The product between the dropped return and the new front leaves.
+            let second = *self.window.front().expect("window still has >= 1 element");
+            self.sum_adjacent -= first.abs() * second.abs();
+        }
+        if self.window.len() < self.period {
+            return None;
+        }
+        // Products are non-negative; the rolling subtraction can leave a tiny
+        // negative residual when returns are ~0, so clamp before scaling.
+        let bv = MU1_INV_SQ * self.sum_adjacent.max(0.0);
+        self.last = Some(bv);
+        Some(bv)
+    }
+
+    fn reset(&mut self) {
+        self.prev_price = None;
+        self.window.clear();
+        self.sum_adjacent = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // The first log return needs a previous price, then the window fills.
+        self.period + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "BipowerVariation"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(BipowerVariation::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn rejects_period_one() {
+        assert!(matches!(
+            BipowerVariation::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let bv = BipowerVariation::new(20).unwrap();
+        assert_eq!(bv.period(), 20);
+        assert_eq!(bv.warmup_period(), 21);
+        assert_eq!(bv.name(), "BipowerVariation");
+        assert!(!bv.is_ready());
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut bv = BipowerVariation::new(5).unwrap();
+        let out = bv.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        for v in out.iter().take(5) {
+            assert!(v.is_none());
+        }
+        assert!(out[5].is_some());
+    }
+
+    #[test]
+    fn known_value() {
+        // period = 2: one adjacent product. r1 = ln(1.1), r2 = ln(0.9).
+        // BV = (π/2)·|r1|·|r2|.
+        let mut bv = BipowerVariation::new(2).unwrap();
+        let out = bv.batch(&[100.0, 110.0, 99.0]);
+        assert!(out[1].is_none());
+        let r1 = (110.0_f64 / 100.0).ln();
+        let r2 = (99.0_f64 / 110.0).ln();
+        let expected = std::f64::consts::FRAC_PI_2 * r1.abs() * r2.abs();
+        assert_relative_eq!(out[2].unwrap(), expected, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn rolling_window_drops_oldest_product() {
+        // period = 2, four prices -> two emissions, each a single product.
+        let mut bv = BipowerVariation::new(2).unwrap();
+        let out = bv.batch(&[100.0, 110.0, 99.0, 105.0]);
+        let r2 = (99.0_f64 / 110.0).ln();
+        let r3 = (105.0_f64 / 99.0).ln();
+        let expected = std::f64::consts::FRAC_PI_2 * r2.abs() * r3.abs();
+        assert_relative_eq!(out[3].unwrap(), expected, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn constant_series_yields_zero() {
+        let mut bv = BipowerVariation::new(10).unwrap();
+        for v in bv.batch(&[100.0; 40]).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn output_is_non_negative() {
+        let mut bv = BipowerVariation::new(20).unwrap();
+        let prices: Vec<f64> = (1..=200)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 12.0)
+            .collect();
+        for v in bv.batch(&prices).into_iter().flatten() {
+            assert!(v >= 0.0, "bipower variation must be non-negative, got {v}");
+        }
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut bv = BipowerVariation::new(5).unwrap();
+        let out = bv.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        let last = *out.last().unwrap();
+        assert!(last.is_some());
+        assert_eq!(bv.update(f64::NAN), last);
+        assert_eq!(bv.update(f64::INFINITY), last);
+    }
+
+    #[test]
+    fn skips_non_positive_prices() {
+        let mut bv = BipowerVariation::new(5).unwrap();
+        let warmup = bv.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        let baseline = warmup.last().copied().flatten().expect("warmed up");
+        assert_eq!(bv.update(-5.0), Some(baseline));
+        assert_eq!(bv.update(0.0), Some(baseline));
+        // State untouched: a clone advanced by the same real tick agrees.
+        let mut control = bv.clone();
+        let after = bv.update(21.0).expect("ready");
+        assert_eq!(control.update(21.0).expect("ready"), after);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut bv = BipowerVariation::new(5).unwrap();
+        bv.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(bv.is_ready());
+        bv.reset();
+        assert!(!bv.is_ready());
+        assert_eq!(bv.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=120)
+            .map(|i| 100.0 + (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = BipowerVariation::new(20).unwrap().batch(&prices);
+        let mut b = BipowerVariation::new(20).unwrap();
+        let streamed: Vec<_> = prices.iter().map(|p| b.update(*p)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/bollinger.rs

@@ -1,7 +1,5 @@
 //! Bollinger Bands.
 
-use std::collections::VecDeque;
-
 use crate::error::{Error, Result};
 use crate::traits::Indicator;
 
@@ -49,7 +47,13 @@ pub struct BollingerOutput {
 pub struct BollingerBands {
     period: usize,
     multiplier: f64,
-    window: VecDeque<f64>,
+    /// Fixed-capacity ring buffer of the last `period` finite inputs. A flat
+    /// `Box<[f64]>` with a manual write cursor beats `VecDeque` on this hot path.
+    buf: Box<[f64]>,
+    /// Index of the next slot to write — also the oldest element once full.
+    head: usize,
+    /// Number of slots filled, saturating at `period`.
+    count: usize,
     sum: f64,
     sum_sq: f64,
     /// Number of finite updates since the running sums were last reseeded
@@ -80,7 +84,9 @@ impl BollingerBands {
         Ok(Self {
             period,
             multiplier,
-            window: VecDeque::with_capacity(period),
+            buf: vec![0.0; period].into_boxed_slice(),
+            head: 0,
+            count: 0,
             sum: 0.0,
             sum_sq: 0.0,
             updates_since_recompute: 0,
@@ -103,7 +109,7 @@ impl BollingerBands {
     }
 
     fn current(&self) -> Option<BollingerOutput> {
-        if self.window.len() != self.period {
+        if self.count != self.period {
             return None;
         }
         let n = self.period as f64;
@@ -129,25 +135,38 @@ impl Indicator for BollingerBands {
         if !input.is_finite() {
             return self.current();
         }
-        if self.window.len() == self.period {
-            let old = self.window.pop_front().expect("non-empty");
+        if self.count == self.period {
+            let old = self.buf[self.head];
             self.sum -= old;
             self.sum_sq -= old * old;
+            self.buf[self.head] = input;
+            self.sum += input;
+            self.sum_sq += input * input;
+        } else {
+            self.buf[self.head] = input;
+            self.sum += input;
+            self.sum_sq += input * input;
+            self.count += 1;
+        }
+        self.head += 1;
+        if self.head == self.period {
+            self.head = 0;
         }
-        self.window.push_back(input);
-        self.sum += input;
-        self.sum_sq += input * input;
         self.updates_since_recompute += 1;
         if self.updates_since_recompute >= RECOMPUTE_EVERY * self.period {
-            self.sum = self.window.iter().copied().sum();
-            self.sum_sq = self.window.iter().copied().map(|x| x * x).sum();
+            // Reseed in chronological order (oldest at `head`) to keep the running
+            // sums bit-equivalent to a fresh from-scratch pass on stable inputs.
+            let chronological = self.buf[self.head..].iter().chain(&self.buf[..self.head]);
+            self.sum = chronological.clone().copied().sum();
+            self.sum_sq = chronological.map(|&x| x * x).sum();
             self.updates_since_recompute = 0;
         }
         self.current()
     }
 
     fn reset(&mut self) {
-        self.window.clear();
+        self.head = 0;
+        self.count = 0;
         self.sum = 0.0;
         self.sum_sq = 0.0;
         self.updates_since_recompute = 0;
@@ -158,7 +177,7 @@ impl Indicator for BollingerBands {
     }
 
     fn is_ready(&self) -> bool {
-        self.window.len() == self.period
+        self.count == self.period
     }
 
     fn name(&self) -> &'static str {
@@ -171,6 +190,7 @@ mod tests {
     use super::*;
     use crate::traits::BatchExt;
     use approx::assert_relative_eq;
+    use std::collections::VecDeque;
 
     fn naive(prices: &[f64], period: usize, mult: f64) -> BollingerOutput {
         assert!(

crates/wickra-core/src/indicators/bomar_bands.rs

@@ -0,0 +1,256 @@
+//! Bomar Bands — adaptive percentage bands that contain a target fraction of
+//! recent price.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::rolling_quantile::quantile_sorted;
+use crate::traits::Indicator;
+
+/// Bomar Bands output.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct BomarBandsOutput {
+    /// Upper band: `middle + |middle| · p`.
+    pub upper: f64,
+    /// Middle line: the simple moving average over the window.
+    pub middle: f64,
+    /// Lower band: `middle − |middle| · p`.
+    pub lower: f64,
+}
+
+/// Bomar Bands: percentage bands whose width adapts so that a fixed `coverage`
+/// fraction of recent closes falls inside them.
+///
+/// The Bomar Bands predate Bollinger Bands; John Bollinger cites them as an
+/// inspiration — percentage bands around a moving average, with the percentage
+/// tuned so a fixed share (classically ~85%) of price stayed within. Wickra
+/// realises that idea deterministically: the half-width is the `coverage`
+/// quantile of the relative deviations from the midline, so by construction
+/// `coverage` of the window's closes lie inside the bands.
+///
+/// ```text
+/// middle = SMA(close, period)
+/// dev_i  = | close_i / middle − 1 |          // relative distance from midline
+/// p      = coverage-quantile of { dev_i }     // type-7 interpolation
+/// upper  = middle + |middle| · p
+/// lower  = middle − |middle| · p
+/// ```
+///
+/// Unlike the fixed-percentage [`MaEnvelope`](crate::MaEnvelope), the offset
+/// here is data-driven: the bands widen in turbulent regimes and tighten in
+/// quiet ones without a volatility input. Unlike Bollinger Bands, the width is
+/// an order statistic of the actual deviations rather than a multiple of the
+/// standard deviation, so it is unaffected by the shape of the tails beyond the
+/// `coverage` rank. When the midline is zero the relative deviation is
+/// undefined and the bands collapse onto the midline.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{BomarBands, Indicator};
+///
+/// let mut indicator = BomarBands::new(20, 0.85).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + f64::from(i % 7));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct BomarBands {
+    period: usize,
+    coverage: f64,
+    window: VecDeque<f64>,
+    scratch: Vec<f64>,
+}
+
+impl BomarBands {
+    /// Construct new Bomar Bands.
+    ///
+    /// `coverage` is the target fraction of closes to contain, in `(0.0, 1.0]`.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`, or
+    /// [`Error::InvalidParameter`] if `coverage` is not a finite value in
+    /// `(0.0, 1.0]`.
+    pub fn new(period: usize, coverage: f64) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if !coverage.is_finite() || coverage <= 0.0 || coverage > 1.0 {
+            return Err(Error::InvalidParameter {
+                message: "bomar bands coverage must be a finite value in (0.0, 1.0]",
+            });
+        }
+        Ok(Self {
+            period,
+            coverage,
+            window: VecDeque::with_capacity(period),
+            scratch: Vec::with_capacity(period),
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Configured coverage fraction.
+    pub const fn coverage(&self) -> f64 {
+        self.coverage
+    }
+}
+
+impl Indicator for BomarBands {
+    type Input = f64;
+    type Output = BomarBandsOutput;
+
+    fn update(&mut self, value: f64) -> Option<BomarBandsOutput> {
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(value);
+        if self.window.len() < self.period {
+            return None;
+        }
+        let sum: f64 = self.window.iter().sum();
+        let middle = sum / (self.period as f64);
+        let denom = middle.abs();
+
+        self.scratch.clear();
+        for &v in &self.window {
+            let dev = if denom == 0.0 {
+                0.0
+            } else {
+                ((v - middle) / denom).abs()
+            };
+            self.scratch.push(dev);
+        }
+        self.scratch.sort_by(f64::total_cmp);
+        let p = quantile_sorted(&self.scratch, self.coverage);
+        let offset = denom * p;
+
+        Some(BomarBandsOutput {
+            upper: middle + offset,
+            middle,
+            lower: middle - offset,
+        })
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.scratch.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "BomarBands"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(BomarBands::new(0, 0.85), Err(Error::PeriodZero)));
+        assert!(BomarBands::new(1, 0.85).is_ok());
+    }
+
+    #[test]
+    fn rejects_out_of_range_coverage() {
+        assert!(matches!(
+            BomarBands::new(20, 0.0),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            BomarBands::new(20, 1.1),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            BomarBands::new(20, -0.5),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            BomarBands::new(20, f64::NAN),
+            Err(Error::InvalidParameter { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let bb = BomarBands::new(20, 0.85).unwrap();
+        assert_eq!(bb.period(), 20);
+        assert_relative_eq!(bb.coverage(), 0.85, epsilon = 1e-12);
+        assert_eq!(bb.warmup_period(), 20);
+        assert_eq!(bb.name(), "BomarBands");
+        assert!(!bb.is_ready());
+    }
+
+    #[test]
+    fn warms_up_then_emits() {
+        let mut bb = BomarBands::new(4, 0.85).unwrap();
+        assert!(bb.update(100.0).is_none());
+        assert!(bb.update(102.0).is_none());
+        assert!(bb.update(98.0).is_none());
+        assert!(bb.update(104.0).is_some());
+        assert!(bb.is_ready());
+    }
+
+    #[test]
+    fn known_bands() {
+        // mean=101; |dev| = {1,1,3,3}/101; coverage 0.85 quantile -> 3/101.
+        // offset = 101 * 3/101 = 3 -> upper 104, lower 98.
+        let mut bb = BomarBands::new(4, 0.85).unwrap();
+        let out = bb.batch(&[100.0, 102.0, 98.0, 104.0]);
+        let last = out[3].unwrap();
+        assert_relative_eq!(last.middle, 101.0, epsilon = 1e-9);
+        assert_relative_eq!(last.upper, 104.0, epsilon = 1e-9);
+        assert_relative_eq!(last.lower, 98.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn zero_midline_collapses_bands() {
+        // Window mean exactly zero -> relative deviation undefined -> collapse.
+        let mut bb = BomarBands::new(2, 0.85).unwrap();
+        let out = bb.batch(&[3.0, -3.0]);
+        let last = out[1].unwrap();
+        assert_relative_eq!(last.middle, 0.0, epsilon = 1e-12);
+        assert_relative_eq!(last.upper, 0.0, epsilon = 1e-12);
+        assert_relative_eq!(last.lower, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn rolling_window_evicts_oldest() {
+        // Eight values through a period-4 window: only the last four survive,
+        // reproducing the `known_bands` window.
+        let mut bb = BomarBands::new(4, 0.85).unwrap();
+        let out = bb.batch(&[50.0, 50.0, 50.0, 50.0, 100.0, 102.0, 98.0, 104.0]);
+        let last = out[7].unwrap();
+        assert_relative_eq!(last.middle, 101.0, epsilon = 1e-9);
+        assert_relative_eq!(last.upper, 104.0, epsilon = 1e-9);
+        assert_relative_eq!(last.lower, 98.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut bb = BomarBands::new(4, 0.85).unwrap();
+        for v in [100.0, 102.0, 98.0, 104.0] {
+            bb.update(v);
+        }
+        assert!(bb.is_ready());
+        bb.reset();
+        assert!(!bb.is_ready());
+        assert!(bb.update(100.0).is_none());
+    }
+}

crates/wickra-core/src/indicators/derivative_oscillator.rs

@@ -0,0 +1,218 @@
+//! Derivative Oscillator (Constance Brown).
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::indicators::rsi::Rsi;
+use crate::indicators::sma::Sma;
+use crate::traits::Indicator;
+
+/// Derivative Oscillator — Constance Brown's double-smoothed RSI histogram.
+///
+/// The RSI is smoothed twice with EMAs, then a simple moving average of that
+/// double-smoothed line is subtracted as a signal, leaving a zero-centered
+/// histogram:
+///
+/// ```text
+/// rsi   = RSI(price, rsi_period)
+/// s1    = EMA(rsi, smooth1)
+/// s2    = EMA(s1,  smooth2)          // double-smoothed RSI
+/// signal = SMA(s2, signal_period)
+/// DerivativeOscillator = s2 - signal
+/// ```
+///
+/// The double EMA smoothing strips the RSI's high-frequency noise, and
+/// subtracting the SMA signal removes the residual level, so the result
+/// oscillates around zero: positive (and rising) bars mark accelerating bullish
+/// momentum, negative bars bearish. Brown's defaults are `rsi_period = 14`,
+/// `smooth1 = 5`, `smooth2 = 3`, `signal_period = 9`.
+///
+/// The first value lands after `rsi_period + smooth1 + smooth2 + signal_period − 2`
+/// inputs, the point at which the whole RSI → EMA → EMA → SMA chain is seeded.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{DerivativeOscillator, Indicator};
+///
+/// let mut indicator = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+/// let mut last = None;
+/// for i in 0..120 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.2).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct DerivativeOscillator {
+    rsi: Rsi,
+    ema1: Ema,
+    ema2: Ema,
+    signal: Sma,
+    warmup: usize,
+}
+
+impl DerivativeOscillator {
+    /// Construct a Derivative Oscillator with the RSI, two EMA smoothing, and
+    /// SMA signal periods.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if any period is `0`.
+    pub fn new(
+        rsi_period: usize,
+        smooth1: usize,
+        smooth2: usize,
+        signal_period: usize,
+    ) -> Result<Self> {
+        if rsi_period == 0 || smooth1 == 0 || smooth2 == 0 || signal_period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            rsi: Rsi::new(rsi_period)?,
+            ema1: Ema::new(smooth1)?,
+            ema2: Ema::new(smooth2)?,
+            signal: Sma::new(signal_period)?,
+            // RSI seeds at rsi_period + 1, then each stage adds (len - 1).
+            warmup: rsi_period + smooth1 + smooth2 + signal_period - 2,
+        })
+    }
+
+    /// Total warmup length (also returned by `warmup_period`).
+    pub const fn warmup(&self) -> usize {
+        self.warmup
+    }
+}
+
+impl Indicator for DerivativeOscillator {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        let rsi = self.rsi.update(input)?;
+        let s1 = self.ema1.update(rsi)?;
+        let s2 = self.ema2.update(s1)?;
+        let signal = self.signal.update(s2)?;
+        Some(s2 - signal)
+    }
+
+    fn reset(&mut self) {
+        self.rsi.reset();
+        self.ema1.reset();
+        self.ema2.reset();
+        self.signal.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.warmup
+    }
+
+    fn is_ready(&self) -> bool {
+        self.signal.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "DerivativeOscillator"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_periods() {
+        assert!(matches!(
+            DerivativeOscillator::new(0, 5, 3, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            DerivativeOscillator::new(14, 0, 3, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            DerivativeOscillator::new(14, 5, 0, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            DerivativeOscillator::new(14, 5, 3, 0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    /// Cover the const accessor `warmup` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let d = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+        // 14 + 5 + 3 + 9 - 2 = 29.
+        assert_eq!(d.warmup(), 29);
+        assert_eq!(d.warmup_period(), 29);
+        assert_eq!(d.name(), "DerivativeOscillator");
+    }
+
+    #[test]
+    fn first_emission_matches_warmup_period() {
+        let prices: Vec<f64> = (0..60)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 6.0)
+            .collect();
+        let mut d = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+        let out = d.batch(&prices);
+        let warmup = d.warmup_period();
+        for (i, v) in out.iter().enumerate().take(warmup - 1) {
+            assert!(v.is_none(), "index {i} must be None during warmup");
+        }
+        assert!(
+            out[warmup - 1].is_some(),
+            "first value must land at warmup_period - 1"
+        );
+    }
+
+    #[test]
+    fn matches_manual_chain() {
+        // Equals RSI -> EMA -> EMA, minus the SMA signal of that line.
+        let prices: Vec<f64> = (0..80)
+            .map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 8.0)
+            .collect();
+        let mut d = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+        let mut rsi = Rsi::new(14).unwrap();
+        let mut e1 = Ema::new(5).unwrap();
+        let mut e2 = Ema::new(3).unwrap();
+        let mut sig = Sma::new(9).unwrap();
+        for (i, &p) in prices.iter().enumerate() {
+            let got = d.update(p);
+            let want = rsi
+                .update(p)
+                .and_then(|r| e1.update(r))
+                .and_then(|x| e2.update(x))
+                .and_then(|s2| sig.update(s2).map(|s| s2 - s));
+            assert_eq!(got.is_some(), want.is_some(), "readiness mismatch at {i}");
+            if let (Some(a), Some(b)) = (got, want) {
+                assert_relative_eq!(a, b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut d = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+        d.batch(&(0..60).map(|i| 100.0 + f64::from(i)).collect::<Vec<_>>());
+        assert!(d.is_ready());
+        d.reset();
+        assert!(!d.is_ready());
+        assert_eq!(d.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (0..80)
+            .map(|i| 50.0 + (f64::from(i) * 0.5).sin() * 10.0)
+            .collect();
+        let mut a = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+        let mut b = DerivativeOscillator::new(14, 5, 3, 9).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/disparity_index.rs

@@ -0,0 +1,169 @@
+//! Disparity Index.
+
+use crate::error::Result;
+use crate::indicators::sma::Sma;
+use crate::traits::Indicator;
+
+/// Disparity Index — the percentage gap between price and its moving average.
+///
+/// ```text
+/// Disparity = 100 * (price - SMA(price, period)) / SMA(price, period)
+/// ```
+///
+/// Originating in Japanese technical analysis (*kairi*), the disparity index
+/// expresses how far price has stretched from its `period`-bar simple moving
+/// average, as a percentage of that average. Positive readings mean price is
+/// above the mean (potentially overbought / strong), negative readings mean it
+/// is below (potentially oversold / weak); the magnitude measures how
+/// over-extended the move is.
+///
+/// The first output lands once the inner SMA is ready (input `period`). If the
+/// moving average is exactly zero the gap percentage is undefined and the index
+/// returns `0.0`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{DisparityIndex, Indicator};
+///
+/// let mut indicator = DisparityIndex::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct DisparityIndex {
+    period: usize,
+    sma: Sma,
+}
+
+impl DisparityIndex {
+    /// Construct a disparity index over `period` inputs.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`crate::Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            period,
+            sma: Sma::new(period)?,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for DisparityIndex {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        let mean = self.sma.update(input)?;
+        if mean == 0.0 {
+            return Some(0.0);
+        }
+        Some(100.0 * (input - mean) / mean)
+    }
+
+    fn reset(&mut self) {
+        self.sma.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.sma.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "DisparityIndex"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(DisparityIndex::new(0).is_err());
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let di = DisparityIndex::new(14).unwrap();
+        assert_eq!(di.period(), 14);
+        assert_eq!(di.warmup_period(), 14);
+        assert_eq!(di.name(), "DisparityIndex");
+    }
+
+    #[test]
+    fn warmup_then_known_value() {
+        // SMA(3) of [2, 4, 6] = 4; price 6 -> 100 * (6 - 4) / 4 = 50.
+        let mut di = DisparityIndex::new(3).unwrap();
+        assert_eq!(di.update(2.0), None);
+        assert_eq!(di.update(4.0), None);
+        assert_relative_eq!(di.update(6.0).unwrap(), 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn constant_series_is_zero() {
+        // Price equals its own mean -> zero disparity.
+        let mut di = DisparityIndex::new(5).unwrap();
+        for v in di.batch(&[42.0; 20]).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn negative_when_below_mean() {
+        // SMA(3) of [10, 8, 6] = 8; price 6 -> 100 * (6 - 8) / 8 = -25.
+        let mut di = DisparityIndex::new(3).unwrap();
+        let v = di.batch(&[10.0, 8.0, 6.0]);
+        assert_relative_eq!(v[2].unwrap(), -25.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn zero_mean_returns_zero() {
+        // A window summing to zero (mean 0) makes the percentage undefined; the
+        // index returns 0.0 rather than a non-finite value.
+        let mut di = DisparityIndex::new(2).unwrap();
+        assert_eq!(di.update(-3.0), None);
+        // SMA(2) of [-3, 3] = 0 -> guarded to 0.0.
+        assert_relative_eq!(di.update(3.0).unwrap(), 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut di = DisparityIndex::new(5).unwrap();
+        di.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(di.is_ready());
+        di.reset();
+        assert!(!di.is_ready());
+        assert_eq!(di.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=30)
+            .map(|i| 50.0 + (f64::from(i) * 0.3).sin() * 10.0)
+            .collect();
+        let mut a = DisparityIndex::new(7).unwrap();
+        let mut b = DisparityIndex::new(7).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/dynamic_momentum_index.rs

@@ -0,0 +1,301 @@
+//! Dynamic Momentum Index (Chande's volatility-adaptive RSI).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::sma::Sma;
+use crate::indicators::std_dev::StdDev;
+use crate::traits::Indicator;
+
+// Chande's definitional constants.
+const STD_PERIOD: usize = 5; // volatility window
+const STD_AVG_PERIOD: usize = 10; // smoothing of the volatility
+const MIN_PERIOD: usize = 5; // fastest RSI lookback
+const MAX_PERIOD: usize = 30; // slowest RSI lookback
+
+/// Dynamic Momentum Index — Tushar Chande's RSI whose lookback shrinks in
+/// volatile markets and lengthens in calm ones.
+///
+/// A standard RSI uses a fixed period; the DMI varies it from the recent
+/// volatility so the oscillator stays responsive when the market is fast and
+/// smooth when it is quiet:
+///
+/// ```text
+/// vol     = StdDev(close, 5)
+/// vol_avg = SMA(vol, 10)
+/// Vi      = vol / vol_avg                       (volatility index)
+/// td      = clamp(round(period / Vi), 5, 30)    (dynamic lookback)
+/// avg_gain, avg_loss = simple means of the last `td` price changes
+/// DMI     = 100 * avg_gain / (avg_gain + avg_loss)
+/// ```
+///
+/// High volatility (`Vi > 1`) shortens `td` toward `5` (faster); low volatility
+/// lengthens it toward `30` (slower). The averages of gains and losses are
+/// simple means over the last `td` changes (not Wilder-smoothed), recomputed as
+/// the window length flexes. Output is bounded in `[0, 100]`; a flat market
+/// returns the neutral `50`.
+///
+/// The first value lands after `MAX_PERIOD + 1 = 31` inputs, so the change
+/// buffer always holds enough history for any dynamic lookback up to `30`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{DynamicMomentumIndex, Indicator};
+///
+/// let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = dmi.update(100.0 + (f64::from(i) * 0.2).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct DynamicMomentumIndex {
+    period: usize,
+    vol: StdDev,
+    vol_avg: Sma,
+    prev_close: Option<f64>,
+    /// The last `MAX_PERIOD` price changes, oldest at the front.
+    changes: VecDeque<f64>,
+    last_vol_avg: Option<f64>,
+    last_value: Option<f64>,
+}
+
+impl DynamicMomentumIndex {
+    /// Construct a DMI with the given base RSI period (Chande uses 14).
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            vol: StdDev::new(STD_PERIOD)?,
+            vol_avg: Sma::new(STD_AVG_PERIOD)?,
+            prev_close: None,
+            changes: VecDeque::with_capacity(MAX_PERIOD),
+            last_vol_avg: None,
+            last_value: None,
+        })
+    }
+
+    /// Configured base period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last_value
+    }
+
+    /// Dynamic lookback for the current volatility, clamped to `[5, 30]`.
+    fn dynamic_period(&self, vol: f64, vol_avg: f64) -> usize {
+        if vol_avg <= 0.0 || vol <= 0.0 {
+            // No measurable volatility -> slowest (calmest) lookback.
+            return MAX_PERIOD;
+        }
+        let vi = vol / vol_avg;
+        let td = (self.period as f64 / vi).round();
+        // td is finite and positive here; clamp into the valid band.
+        (td as usize).clamp(MIN_PERIOD, MAX_PERIOD)
+    }
+}
+
+impl Indicator for DynamicMomentumIndex {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.last_value;
+        }
+        // Track the smoothed volatility on every close.
+        if let Some(v) = self.vol.update(input) {
+            self.last_vol_avg = self.vol_avg.update(v);
+        }
+
+        // Record the price change.
+        if let Some(prev) = self.prev_close {
+            let change = input - prev;
+            if self.changes.len() == MAX_PERIOD {
+                self.changes.pop_front();
+            }
+            self.changes.push_back(change);
+        }
+        self.prev_close = Some(input);
+
+        let vol = self.vol.value()?;
+        let vol_avg = self.last_vol_avg?;
+        if self.changes.len() < MAX_PERIOD {
+            return None;
+        }
+
+        let td = self.dynamic_period(vol, vol_avg);
+        // Average gains and losses over the last `td` changes.
+        let mut sum_gain = 0.0;
+        let mut sum_loss = 0.0;
+        for &c in self.changes.iter().skip(MAX_PERIOD - td) {
+            if c > 0.0 {
+                sum_gain += c;
+            } else if c < 0.0 {
+                sum_loss -= c;
+            }
+        }
+        let denom = sum_gain + sum_loss;
+        let v = if denom == 0.0 {
+            50.0
+        } else {
+            // Ratio first, then scale, so `100 * g / g` cannot round above 100.
+            100.0 * (sum_gain / denom)
+        };
+        self.last_value = Some(v);
+        Some(v)
+    }
+
+    fn reset(&mut self) {
+        self.vol.reset();
+        self.vol_avg.reset();
+        self.prev_close = None;
+        self.changes.clear();
+        self.last_vol_avg = None;
+        self.last_value = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // The change buffer (MAX_PERIOD changes => MAX_PERIOD + 1 inputs) is the
+        // binding constraint; the volatility chain (5 + 10 - 1 = 14) is shorter.
+        MAX_PERIOD + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last_value.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "DynamicMomentumIndex"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(
+            DynamicMomentumIndex::new(0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    /// Cover the const accessors `period` + `value` and the Indicator-impl
+    /// `warmup_period` + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let dmi = DynamicMomentumIndex::new(14).unwrap();
+        assert_eq!(dmi.period(), 14);
+        assert_eq!(dmi.value(), None);
+        assert_eq!(dmi.warmup_period(), 31);
+        assert_eq!(dmi.name(), "DynamicMomentumIndex");
+    }
+
+    #[test]
+    fn first_emission_matches_warmup_period() {
+        let prices: Vec<f64> = (0..50)
+            .map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 6.0)
+            .collect();
+        let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+        let out = dmi.batch(&prices);
+        for (i, v) in out.iter().enumerate().take(30) {
+            assert!(v.is_none(), "index {i} must be None during warmup");
+        }
+        assert!(out[30].is_some(), "first value at warmup_period - 1 = 30");
+    }
+
+    #[test]
+    fn pure_uptrend_is_one_hundred() {
+        // Every change positive -> avg_loss 0 -> 100, regardless of dynamic period.
+        let prices: Vec<f64> = (1..=60).map(f64::from).collect();
+        let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+        let last = dmi.batch(&prices).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn flat_market_is_neutral() {
+        // Constant prices: no volatility (dynamic period -> max) and no changes
+        // -> neutral 50.
+        let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+        let last = dmi.batch(&[42.0; 50]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_stays_in_range() {
+        let prices: Vec<f64> = (0..120)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 10.0 + (f64::from(i) * 0.07).cos() * 4.0)
+            .collect();
+        let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+        for v in dmi.batch(&prices).into_iter().flatten() {
+            assert!((0.0..=100.0).contains(&v), "DMI {v} left [0, 100]");
+        }
+    }
+
+    #[test]
+    fn high_volatility_shortens_period() {
+        let dmi = DynamicMomentumIndex::new(14).unwrap();
+        // Vi = 2 (vol twice its average) -> td = round(14 / 2) = 7.
+        assert_eq!(dmi.dynamic_period(2.0, 1.0), 7);
+        // Vi = 0.5 (calm) -> td = round(14 / 0.5) = 28.
+        assert_eq!(dmi.dynamic_period(0.5, 1.0), 28);
+        // Extreme calm clamps to MAX_PERIOD; extreme volatility clamps to MIN.
+        assert_eq!(dmi.dynamic_period(0.1, 1.0), MAX_PERIOD);
+        assert_eq!(dmi.dynamic_period(100.0, 1.0), MIN_PERIOD);
+        // Zero volatility -> slowest lookback.
+        assert_eq!(dmi.dynamic_period(0.0, 1.0), MAX_PERIOD);
+        assert_eq!(dmi.dynamic_period(1.0, 0.0), MAX_PERIOD);
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+        let ready = dmi
+            .batch(&(0..40).map(|i| 100.0 + f64::from(i)).collect::<Vec<_>>())
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_eq!(dmi.update(f64::NAN), Some(ready));
+        assert_eq!(dmi.update(f64::INFINITY), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut dmi = DynamicMomentumIndex::new(14).unwrap();
+        dmi.batch(&(0..40).map(|i| 100.0 + f64::from(i)).collect::<Vec<_>>());
+        assert!(dmi.is_ready());
+        dmi.reset();
+        assert!(!dmi.is_ready());
+        assert_eq!(dmi.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (0..80)
+            .map(|i| 50.0 + (f64::from(i) * 0.5).sin() * 10.0)
+            .collect();
+        let mut a = DynamicMomentumIndex::new(14).unwrap();
+        let mut b = DynamicMomentumIndex::new(14).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/ehma.rs

@@ -0,0 +1,202 @@
+//! Exponential Hull Moving Average (EHMA).
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::traits::Indicator;
+
+/// Exponential Hull Moving Average: the Hull construction built from EMAs
+/// instead of WMAs.
+///
+/// ```text
+/// EHMA = EMA( 2 · EMA(price, period/2) − EMA(price, period), round(sqrt(period)) )
+/// ```
+///
+/// Alan Hull's [`Hma`](crate::Hma) uses weighted moving averages; replacing them
+/// with exponential moving averages keeps the same lag-reduction trick — a fast
+/// half-length average minus a full-length one, smoothed over `sqrt(period)` —
+/// while inheriting the EMA's strictly recursive O(1) update and infinite
+/// (exponentially decaying) memory. The result is marginally smoother than the
+/// WMA-based Hull at the cost of a little more lag.
+///
+/// The half period is `(period / 2).max(1)` and the smoothing period is
+/// `round(sqrt(period)).max(1)`, matching the rounding used by [`Hma`].
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, Ehma};
+///
+/// let mut indicator = Ehma::new(9).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Ehma {
+    period: usize,
+    half_ema: Ema,
+    full_ema: Ema,
+    smooth_ema: Ema,
+}
+
+impl Ehma {
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        let half = (period / 2).max(1);
+        let smooth = (period as f64).sqrt().round() as usize;
+        let smooth = smooth.max(1);
+        Ok(Self {
+            period,
+            half_ema: Ema::new(half)?,
+            full_ema: Ema::new(period)?,
+            smooth_ema: Ema::new(smooth)?,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for Ehma {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        // Feed both component EMAs on every input so they warm up in parallel;
+        // gating the longer one behind the shorter would delay the first
+        // emission past `warmup_period()`.
+        let h = self.half_ema.update(input);
+        let f = self.full_ema.update(input);
+        let (h, f) = (h?, f?);
+        let diff = 2.0 * h - f;
+        self.smooth_ema.update(diff)
+    }
+
+    fn reset(&mut self) {
+        self.half_ema.reset();
+        self.full_ema.reset();
+        self.smooth_ema.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        // full_ema seeds at `period`, then smooth_ema needs another
+        // (round(sqrt(period)) - 1) values to seed.
+        let sm = (self.period as f64).sqrt().round() as usize;
+        self.period + sm.max(1) - 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.smooth_ema.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "EHMA"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn constant_series_yields_constant_ehma() {
+        let mut ehma = Ehma::new(9).unwrap();
+        let out = ehma.batch(&[10.0_f64; 80]);
+        let last = out.iter().rev().flatten().next().unwrap();
+        assert_relative_eq!(*last, 10.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=100).map(|i| f64::from(i) * 0.7).collect();
+        let mut a = Ehma::new(9).unwrap();
+        let mut b = Ehma::new(9).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut ehma = Ehma::new(9).unwrap();
+        ehma.batch(&(1..=80).map(f64::from).collect::<Vec<_>>());
+        assert!(ehma.is_ready());
+        ehma.reset();
+        assert!(!ehma.is_ready());
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(Ehma::new(0).is_err());
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `name`.
+    /// `warmup_period` is covered by `first_emission_matches_warmup_period`.
+    #[test]
+    fn accessors_and_metadata() {
+        let ehma = Ehma::new(9).unwrap();
+        assert_eq!(ehma.period(), 9);
+        assert_eq!(ehma.name(), "EHMA");
+    }
+
+    #[test]
+    fn first_emission_matches_warmup_period() {
+        let prices: Vec<f64> = (1..=40).map(f64::from).collect();
+        let mut ehma = Ehma::new(9).unwrap();
+        let out = ehma.batch(&prices);
+        let warmup = ehma.warmup_period();
+        // full EMA seeds at 9, smooth EMA round(sqrt(9))=3 needs 2 more -> 11.
+        assert_eq!(warmup, 11);
+        for (i, v) in out.iter().enumerate().take(warmup - 1) {
+            assert!(v.is_none(), "index {i} must be None during warmup");
+        }
+        assert!(
+            out[warmup - 1].is_some(),
+            "first EHMA value must land at warmup_period - 1"
+        );
+    }
+
+    #[test]
+    fn matches_independent_emas() {
+        // The two component EMAs run as independent siblings on the price
+        // stream; EHMA must equal feeding three standalone EMAs and combining.
+        let prices: Vec<f64> = (1..=50)
+            .map(|i| (f64::from(i) * 0.3).sin() * 10.0 + 50.0)
+            .collect();
+        let mut ehma = Ehma::new(9).unwrap();
+        let mut half = Ema::new(4).unwrap(); // (9 / 2).max(1)
+        let mut full = Ema::new(9).unwrap();
+        let mut smooth = Ema::new(3).unwrap(); // round(sqrt(9))
+        for (i, &p) in prices.iter().enumerate() {
+            let got = ehma.update(p);
+            let want = match (half.update(p), full.update(p)) {
+                (Some(h), Some(f)) => smooth.update(2.0 * h - f),
+                _ => None,
+            };
+            assert_eq!(got.is_some(), want.is_some(), "readiness mismatch at {i}");
+            if let (Some(a), Some(b)) = (got, want) {
+                assert_relative_eq!(a, b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn period_one_collapses_to_pass_through() {
+        // period 1: half=1, full=1, smooth=round(sqrt(1))=1; every EMA seeds on
+        // the first input, so EHMA(1) passes the price straight through.
+        let mut ehma = Ehma::new(1).unwrap();
+        assert_relative_eq!(ehma.update(5.0).unwrap(), 5.0, epsilon = 1e-12);
+        assert_relative_eq!(ehma.update(8.0).unwrap(), 8.0, epsilon = 1e-12);
+    }
+}

crates/wickra-core/src/indicators/elder_ray.rs

@@ -0,0 +1,192 @@
+//! Elder Ray — Bull Power and Bear Power.
+
+use crate::error::Result;
+use crate::indicators::ema::Ema;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// One Elder Ray reading: the bull and bear power for a bar.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct ElderRayOutput {
+    /// `high − EMA(close)`: how far buyers pushed price above the trend mean.
+    pub bull_power: f64,
+    /// `low − EMA(close)`: how far sellers pushed price below the trend mean
+    /// (negative in a normal market).
+    pub bear_power: f64,
+}
+
+/// Elder Ray — Alexander Elder's Bull Power / Bear Power oscillator.
+///
+/// An EMA of the close marks the market's consensus of value; the bar's high and
+/// low relative to it measure how far the bulls and bears could push price away
+/// from that consensus:
+///
+/// ```text
+/// ema       = EMA(close, period)
+/// BullPower = high - ema
+/// BearPower = low  - ema
+/// ```
+///
+/// Bull Power is normally positive (the high prints above the mean) and Bear
+/// Power normally negative (the low prints below it). Their behaviour relative
+/// to zero and to the EMA's slope drives Elder's signals: e.g. in an uptrend
+/// (rising EMA), a bounce in a negative-but-rising Bear Power is a buy setup.
+///
+/// The first reading lands once the inner EMA is seeded, at bar `period`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, ElderRay, Indicator};
+///
+/// let mut er = ElderRay::new(13).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 2.0, base - 2.0, base + 0.5, 1.0, i64::from(i)).unwrap();
+///     last = er.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct ElderRay {
+    period: usize,
+    ema: Ema,
+}
+
+impl ElderRay {
+    /// Construct an Elder Ray with the given EMA period.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`crate::Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            period,
+            ema: Ema::new(period)?,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for ElderRay {
+    type Input = Candle;
+    type Output = ElderRayOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<ElderRayOutput> {
+        let ema = self.ema.update(candle.close)?;
+        Some(ElderRayOutput {
+            bull_power: candle.high - ema,
+            bear_power: candle.low - ema,
+        })
+    }
+
+    fn reset(&mut self) {
+        self.ema.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.ema.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "ElderRay"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new(close, high, low, close, 1.0, 0).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(ElderRay::new(0).is_err());
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let er = ElderRay::new(13).unwrap();
+        assert_eq!(er.period(), 13);
+        assert_eq!(er.warmup_period(), 13);
+        assert_eq!(er.name(), "ElderRay");
+    }
+
+    #[test]
+    fn warmup_then_known_value() {
+        // EMA(3) seeds at bar 3 with SMA([10,12,14]) = 12 (closes).
+        // bar 3: high 16, low 13 -> bull = 16 - 12 = 4, bear = 13 - 12 = 1.
+        let mut er = ElderRay::new(3).unwrap();
+        assert_eq!(er.update(candle(11.0, 9.0, 10.0)), None);
+        assert_eq!(er.update(candle(13.0, 11.0, 12.0)), None);
+        let v = er.update(candle(16.0, 13.0, 14.0)).unwrap();
+        assert_relative_eq!(v.bull_power, 4.0, epsilon = 1e-12);
+        assert_relative_eq!(v.bear_power, 1.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn matches_manual_ema() {
+        let bars: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.3).sin() * 5.0;
+                candle(base + 2.0, base - 2.0, base)
+            })
+            .collect();
+        let mut er = ElderRay::new(13).unwrap();
+        let mut ema = Ema::new(13).unwrap();
+        for (i, c) in bars.iter().enumerate() {
+            let got = er.update(*c);
+            let want = ema.update(c.close).map(|e| (c.high - e, c.low - e));
+            assert_eq!(got.is_some(), want.is_some(), "readiness mismatch at {i}");
+            if let (Some(g), Some((b, be))) = (got, want) {
+                assert_relative_eq!(g.bull_power, b, epsilon = 1e-9);
+                assert_relative_eq!(g.bear_power, be, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut er = ElderRay::new(5).unwrap();
+        er.batch(
+            &(0..20)
+                .map(|i| candle(f64::from(i) + 1.0, f64::from(i) - 1.0, f64::from(i)))
+                .collect::<Vec<_>>(),
+        );
+        assert!(er.is_ready());
+        er.reset();
+        assert!(!er.is_ready());
+        assert_eq!(er.update(candle(2.0, 0.0, 1.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let bars: Vec<Candle> = (0..30)
+            .map(|i| {
+                let base = 50.0 + f64::from(i);
+                candle(base + 1.5, base - 1.5, base)
+            })
+            .collect();
+        let mut a = ElderRay::new(7).unwrap();
+        let mut b = ElderRay::new(7).unwrap();
+        assert_eq!(
+            a.batch(&bars),
+            bars.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/elder_safezone.rs

@@ -0,0 +1,360 @@
+//! Elder `SafeZone` Stop — a trailing stop set by the average noise penetration.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`ElderSafeZone`]: the active stop level and the trend direction.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct ElderSafeZoneOutput {
+    /// The `SafeZone` stop level — below price when long, above price when short.
+    pub value: f64,
+    /// Trend direction: `+1.0` long, `-1.0` short.
+    pub direction: f64,
+}
+
+/// Elder `SafeZone` Stop — Alexander Elder's stop placed a multiple of the
+/// **average market noise** away from price.
+///
+/// ```text
+/// long  market noise = average downside penetration = mean( prev_low − low | low < prev_low )
+/// short market noise = average upside  penetration = mean( high − prev_high | high > prev_high )
+/// long  stop = ratchet_up(   low_t  − coeff · avg_down_penetration )
+/// short stop = ratchet_down( high_t + coeff · avg_up_penetration   )
+/// ```
+///
+/// Elder defines *noise* in an uptrend as the part of each bar that pokes below
+/// the previous bar's low (a "downside penetration"). Averaging those
+/// penetrations over a lookback and placing the stop `coeff` multiples below the
+/// current low keeps the stop just outside normal pullbacks while still exiting on
+/// a genuine reversal. The stop trails in the trend's favour and flips when price
+/// closes through it. The average uses only the bars that actually penetrated
+/// (Elder's definition), so a noiseless trend gives a tight stop at the bar's
+/// extreme.
+///
+/// The first bar seeds the prior candle; the next `period` bars accumulate the
+/// penetration statistics, so the first stop lands after `period + 1` inputs.
+/// Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, ElderSafeZone};
+///
+/// let mut indicator = ElderSafeZone::new(14, 2.0).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct ElderSafeZone {
+    period: usize,
+    coeff: f64,
+    prev: Option<Candle>,
+    down_pen: VecDeque<f64>,
+    up_pen: VecDeque<f64>,
+    down_sum: f64,
+    up_sum: f64,
+    down_count: usize,
+    up_count: usize,
+    direction: f64,
+    stop: f64,
+    last: Option<ElderSafeZoneOutput>,
+}
+
+impl ElderSafeZone {
+    /// Construct an Elder `SafeZone` stop with the given averaging `period` and
+    /// noise `coeff`icient.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0` and
+    /// [`Error::NonPositiveMultiplier`] if `coeff` is not finite and positive.
+    pub fn new(period: usize, coeff: f64) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if !coeff.is_finite() || coeff <= 0.0 {
+            return Err(Error::NonPositiveMultiplier);
+        }
+        Ok(Self {
+            period,
+            coeff,
+            prev: None,
+            down_pen: VecDeque::with_capacity(period),
+            up_pen: VecDeque::with_capacity(period),
+            down_sum: 0.0,
+            up_sum: 0.0,
+            down_count: 0,
+            up_count: 0,
+            direction: 0.0,
+            stop: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured `(period, coeff)`.
+    pub const fn params(&self) -> (usize, f64) {
+        (self.period, self.coeff)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<ElderSafeZoneOutput> {
+        self.last
+    }
+
+    fn push(window: &mut VecDeque<f64>, sum: &mut f64, count: &mut usize, period: usize, pen: f64) {
+        if window.len() == period {
+            let old = window.pop_front().expect("non-empty");
+            *sum -= old;
+            if old > 0.0 {
+                *count -= 1;
+            }
+        }
+        window.push_back(pen);
+        *sum += pen;
+        if pen > 0.0 {
+            *count += 1;
+        }
+    }
+
+    fn avg(sum: f64, count: usize) -> f64 {
+        if count == 0 {
+            0.0
+        } else {
+            sum / count as f64
+        }
+    }
+}
+
+impl Indicator for ElderSafeZone {
+    type Input = Candle;
+    type Output = ElderSafeZoneOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<ElderSafeZoneOutput> {
+        let Some(prev) = self.prev else {
+            self.prev = Some(candle);
+            return None;
+        };
+        let dp = (prev.low - candle.low).max(0.0);
+        let up = (candle.high - prev.high).max(0.0);
+        self.prev = Some(candle);
+
+        Self::push(
+            &mut self.down_pen,
+            &mut self.down_sum,
+            &mut self.down_count,
+            self.period,
+            dp,
+        );
+        Self::push(
+            &mut self.up_pen,
+            &mut self.up_sum,
+            &mut self.up_count,
+            self.period,
+            up,
+        );
+        if self.down_pen.len() < self.period {
+            return None;
+        }
+
+        let avg_down = Self::avg(self.down_sum, self.down_count);
+        let avg_up = Self::avg(self.up_sum, self.up_count);
+
+        if self.direction == 0.0 {
+            self.direction = 1.0;
+            self.stop = candle.low - self.coeff * avg_down;
+        } else if self.direction > 0.0 {
+            let raw = candle.low - self.coeff * avg_down;
+            self.stop = self.stop.max(raw);
+            if candle.close < self.stop {
+                self.direction = -1.0;
+                self.stop = candle.high + self.coeff * avg_up;
+            }
+        } else {
+            let raw = candle.high + self.coeff * avg_up;
+            self.stop = self.stop.min(raw);
+            if candle.close > self.stop {
+                self.direction = 1.0;
+                self.stop = candle.low - self.coeff * avg_down;
+            }
+        }
+
+        let out = ElderSafeZoneOutput {
+            value: self.stop,
+            direction: self.direction,
+        };
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.prev = None;
+        self.down_pen.clear();
+        self.up_pen.clear();
+        self.down_sum = 0.0;
+        self.up_sum = 0.0;
+        self.down_count = 0;
+        self.up_count = 0;
+        self.direction = 0.0;
+        self.stop = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "ElderSafeZone"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+
+    fn c(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new_unchecked(f64::midpoint(high, low), high, low, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_invalid_params() {
+        assert!(matches!(ElderSafeZone::new(0, 2.0), Err(Error::PeriodZero)));
+        assert!(matches!(
+            ElderSafeZone::new(14, 0.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+        assert!(matches!(
+            ElderSafeZone::new(14, -1.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let e = ElderSafeZone::new(14, 2.0).unwrap();
+        assert_eq!(e.params(), (14, 2.0));
+        assert_eq!(e.warmup_period(), 15);
+        assert_eq!(e.name(), "ElderSafeZone");
+        assert!(!e.is_ready());
+        assert_eq!(e.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut e = ElderSafeZone::new(3, 2.0).unwrap();
+        let candles: Vec<Candle> = (0..8)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        let out = e.batch(&candles);
+        let warmup = e.warmup_period(); // 4
+        assert_eq!(warmup, 4);
+        for v in out.iter().take(warmup - 1) {
+            assert!(v.is_none());
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn uptrend_keeps_stop_below_price() {
+        let mut e = ElderSafeZone::new(5, 2.0).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| {
+                let base = 100.0 + 2.0 * f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        for (o, candle) in e.batch(&candles).into_iter().zip(candles.iter()) {
+            if let Some(o) = o {
+                assert_eq!(o.direction, 1.0);
+                assert!(o.value <= candle.close);
+            }
+        }
+    }
+
+    #[test]
+    fn noiseless_trend_stop_sits_at_low() {
+        // Every bar makes a higher low -> no downside penetration -> avg 0 ->
+        // the stop sits exactly at the bar's low.
+        let mut e = ElderSafeZone::new(3, 2.0).unwrap();
+        let candles: Vec<Candle> = (0..10)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        let out = e.batch(&candles);
+        let last_candle = candles.last().unwrap();
+        let last = out.last().unwrap().unwrap();
+        assert!((last.value - last_candle.low).abs() < 1e-9);
+    }
+
+    #[test]
+    fn flips_on_reversal() {
+        let mut candles: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        candles.extend((0..40).map(|i| {
+            let base = 140.0 - f64::from(i);
+            c(base + 1.0, base - 1.0, base - 0.5)
+        }));
+        let mut e = ElderSafeZone::new(5, 2.0).unwrap();
+        let dirs: Vec<f64> = e
+            .batch(&candles)
+            .into_iter()
+            .flatten()
+            .map(|o| o.direction)
+            .collect();
+        assert!(dirs.iter().any(|&d| d > 0.0));
+        assert!(dirs.iter().any(|&d| d < 0.0));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut e = ElderSafeZone::new(5, 2.0).unwrap();
+        let candles: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        e.batch(&candles);
+        assert!(e.is_ready());
+        e.reset();
+        assert!(!e.is_ready());
+        assert_eq!(e.value(), None);
+        assert_eq!(e.update(candles[0]), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.25).sin() * 9.0;
+                c(base + 2.0, base - 1.5, base + 0.5)
+            })
+            .collect();
+        let batch = ElderSafeZone::new(14, 2.0).unwrap().batch(&candles);
+        let mut b = ElderSafeZone::new(14, 2.0).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/ema.rs

@@ -25,7 +25,15 @@ use crate::traits::Indicator;
 pub struct Ema {
     period: usize,
     alpha: f64,
-    state: Option<f64>,
+    /// `1 - alpha`, precomputed so the recurrence avoids a subtraction per tick.
+    /// Cached value, so the steady-state output is bit-for-bit unchanged.
+    one_minus_alpha: f64,
+    /// Latest EMA value, valid only once `seeded` is true. Stored as a bare `f64`
+    /// (plus the `seeded` flag) rather than `Option<f64>` so the steady-state
+    /// recurrence reads and writes 8 bytes with no enum-tag handling per tick.
+    current: f64,
+    /// Whether `current` holds a real value yet (warmup complete).
+    seeded: bool,
     warmup_buf: Vec<f64>,
 }
 
@@ -43,7 +51,9 @@ impl Ema {
         Ok(Self {
             period,
             alpha,
-            state: None,
+            one_minus_alpha: 1.0 - alpha,
+            current: 0.0,
+            seeded: false,
             warmup_buf: Vec::with_capacity(period),
         })
     }
@@ -66,7 +76,9 @@ impl Ema {
         Ok(Self {
             period: 1,
             alpha,
-            state: None,
+            one_minus_alpha: 1.0 - alpha,
+            current: 0.0,
+            seeded: false,
             warmup_buf: Vec::with_capacity(1),
         })
     }
@@ -83,21 +95,28 @@ impl Ema {
 
     /// Current value if available.
     pub const fn value(&self) -> Option<f64> {
-        self.state
+        if self.seeded {
+            Some(self.current)
+        } else {
+            None
+        }
     }
 
     /// Internal helper that feeds a value without finiteness validation. The caller
     /// guarantees `input.is_finite()`. Used by MACD which has already validated.
     pub(crate) fn step_unchecked(&mut self, input: f64) -> Option<f64> {
-        if let Some(prev) = self.state {
-            let new = self.alpha.mul_add(input, (1.0 - self.alpha) * prev);
-            self.state = Some(new);
+        if self.seeded {
+            let new = self
+                .alpha
+                .mul_add(input, self.one_minus_alpha * self.current);
+            self.current = new;
             return Some(new);
         }
         self.warmup_buf.push(input);
         if self.warmup_buf.len() == self.period {
             let seed = self.warmup_buf.iter().copied().sum::<f64>() / self.period as f64;
-            self.state = Some(seed);
+            self.current = seed;
+            self.seeded = true;
             return Some(seed);
         }
         None
@@ -110,13 +129,14 @@ impl Indicator for Ema {
 
     fn update(&mut self, input: f64) -> Option<f64> {
         if !input.is_finite() {
-            return self.state;
+            return self.value();
         }
         self.step_unchecked(input)
     }
 
     fn reset(&mut self) {
-        self.state = None;
+        self.current = 0.0;
+        self.seeded = false;
         self.warmup_buf.clear();
     }
 
@@ -125,7 +145,7 @@ impl Indicator for Ema {
     }
 
     fn is_ready(&self) -> bool {
-        self.state.is_some()
+        self.seeded
     }
 
     fn name(&self) -> &'static str {

crates/wickra-core/src/indicators/ewma_volatility.rs

@@ -0,0 +1,264 @@
+//! EWMA Volatility — `RiskMetrics` exponentially-weighted volatility.
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// EWMA Volatility — the `RiskMetrics` exponentially-weighted estimate of the
+/// volatility of log returns.
+///
+/// ```text
+/// r_t  = ln(price_t / price_{t−1})
+/// σ²_t = λ · σ²_{t−1} + (1 − λ) · r²_t
+/// EWMA = √σ²_t
+/// ```
+///
+/// Unlike [`HistoricalVolatility`](crate::HistoricalVolatility) — an equally
+/// weighted, mean-centred sample standard deviation over a fixed window — the
+/// EWMA estimator weights recent squared returns geometrically by the decay
+/// factor `λ`. The most recent return carries weight `1 − λ`, the one before it
+/// `λ(1 − λ)`, and so on, so the estimate reacts to a volatility shock
+/// immediately and then forgets it at rate `λ`. This is the J.P. Morgan
+/// `RiskMetrics` one-parameter model; the standard daily decay is `λ = 0.94`
+/// (monthly `0.97`). No mean is subtracted: squared returns *are* the variance
+/// contribution, which matches the `RiskMetrics` assumption of a zero conditional
+/// mean over short horizons.
+///
+/// The recursion is seeded with the first squared return (`σ²₁ = r²₁`) and emits
+/// from the first return onward, so the very first reading is a one-observation
+/// estimate that the decay then refines. Each `update` is O(1).
+///
+/// Non-finite and non-positive prices are ignored (the log return would be
+/// undefined): the tick is dropped, state is left untouched, and the last value
+/// is returned.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{EwmaVolatility, Indicator};
+///
+/// let mut indicator = EwmaVolatility::new(0.94).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct EwmaVolatility {
+    lambda: f64,
+    prev_price: Option<f64>,
+    /// Exponentially-weighted variance of log returns; `None` until seeded.
+    variance: Option<f64>,
+    last: Option<f64>,
+}
+
+impl EwmaVolatility {
+    /// Construct a new EWMA-volatility indicator.
+    ///
+    /// `lambda` is the decay factor, strictly between `0` and `1` (`RiskMetrics`
+    /// uses `0.94` for daily data). Larger `lambda` means a longer memory and a
+    /// smoother estimate.
+    ///
+    /// # Errors
+    /// Returns [`Error::InvalidParameter`] if `lambda` is not finite or not in
+    /// the open interval `(0, 1)`.
+    pub fn new(lambda: f64) -> Result<Self> {
+        if !lambda.is_finite() || lambda <= 0.0 || lambda >= 1.0 {
+            return Err(Error::InvalidParameter {
+                message: "EWMA volatility lambda must be in the open interval (0, 1)",
+            });
+        }
+        Ok(Self {
+            lambda,
+            prev_price: None,
+            variance: None,
+            last: None,
+        })
+    }
+
+    /// Configured decay factor.
+    pub const fn lambda(&self) -> f64 {
+        self.lambda
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for EwmaVolatility {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        // Non-finite / non-positive prices are skipped: `ln(input / prev)` is
+        // undefined, so the tick must not enter the variance recursion.
+        if !input.is_finite() || input <= 0.0 {
+            return self.last;
+        }
+        let Some(prev) = self.prev_price else {
+            self.prev_price = Some(input);
+            return None;
+        };
+        self.prev_price = Some(input);
+        // `prev` came from `self.prev_price`, gated by the guard above, so it is
+        // finite and positive — the log return is always well-defined.
+        let r = (input / prev).ln();
+        let var = match self.variance {
+            // Seed the recursion with the first squared return.
+            None => r * r,
+            Some(prev_var) => self.lambda * prev_var + (1.0 - self.lambda) * r * r,
+        };
+        self.variance = Some(var);
+        // `var` is a convex combination of non-negative terms, but rounding can
+        // leave a tiny negative residual when every return is ~0; clamp first.
+        let vol = var.max(0.0).sqrt();
+        self.last = Some(vol);
+        Some(vol)
+    }
+
+    fn reset(&mut self) {
+        self.prev_price = None;
+        self.variance = None;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // The first log return needs a previous price; the estimate is seeded
+        // and emitted on that first return.
+        2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "EwmaVolatility"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_lambda() {
+        for bad in [0.0, 1.0, -0.5, 1.5, f64::NAN, f64::INFINITY] {
+            assert!(matches!(
+                EwmaVolatility::new(bad),
+                Err(Error::InvalidParameter { .. })
+            ));
+        }
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let ewma = EwmaVolatility::new(0.94).unwrap();
+        assert_relative_eq!(ewma.lambda(), 0.94);
+        assert_eq!(ewma.warmup_period(), 2);
+        assert_eq!(ewma.name(), "EwmaVolatility");
+        assert!(!ewma.is_ready());
+        assert_eq!(ewma.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut ewma = EwmaVolatility::new(0.94).unwrap();
+        assert_eq!(ewma.update(100.0), None);
+        let out = ewma.update(110.0);
+        assert!(out.is_some());
+        assert!(ewma.is_ready());
+    }
+
+    #[test]
+    fn known_value() {
+        // r1 = ln(110/100), r2 = ln(99/110). Seed σ²₁ = r1²; then
+        // σ²₂ = λ·r1² + (1−λ)·r2².
+        let lambda = 0.94;
+        let mut ewma = EwmaVolatility::new(lambda).unwrap();
+        let out = ewma.batch(&[100.0, 110.0, 99.0]);
+        let r1 = (110.0_f64 / 100.0).ln();
+        let r2 = (99.0_f64 / 110.0).ln();
+        assert_relative_eq!(out[1].unwrap(), r1.abs(), epsilon = 1e-12);
+        let var2 = lambda * r1 * r1 + (1.0 - lambda) * r2 * r2;
+        assert_relative_eq!(out[2].unwrap(), var2.sqrt(), epsilon = 1e-12);
+    }
+
+    #[test]
+    fn constant_series_yields_zero() {
+        let mut ewma = EwmaVolatility::new(0.9).unwrap();
+        for v in ewma.batch(&[100.0; 40]).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn output_is_non_negative() {
+        let mut ewma = EwmaVolatility::new(0.94).unwrap();
+        let prices: Vec<f64> = (1..=200)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 12.0)
+            .collect();
+        for v in ewma.batch(&prices).into_iter().flatten() {
+            assert!(v >= 0.0, "EWMA volatility must be non-negative, got {v}");
+        }
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut ewma = EwmaVolatility::new(0.94).unwrap();
+        let out = ewma.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        let last = *out.last().unwrap();
+        assert!(last.is_some());
+        assert_eq!(ewma.update(f64::NAN), last);
+        assert_eq!(ewma.update(f64::INFINITY), last);
+    }
+
+    #[test]
+    fn skips_non_positive_prices() {
+        let mut ewma = EwmaVolatility::new(0.94).unwrap();
+        let warmup = ewma.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        let baseline = warmup.last().copied().flatten().expect("warmed up");
+        assert_eq!(ewma.update(-5.0), Some(baseline));
+        assert_eq!(ewma.update(0.0), Some(baseline));
+        // State untouched: a clone advanced by the same real tick agrees.
+        let mut control = ewma.clone();
+        let after = ewma.update(21.0).expect("ready");
+        assert_eq!(control.update(21.0).expect("ready"), after);
+    }
+
+    #[test]
+    fn skips_non_positive_before_first_price() {
+        // The skip guard fires before any previous price exists.
+        let mut ewma = EwmaVolatility::new(0.94).unwrap();
+        assert_eq!(ewma.update(0.0), None);
+        assert_eq!(ewma.update(f64::NAN), None);
+        assert_eq!(ewma.update(100.0), None);
+        assert!(ewma.update(110.0).is_some());
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut ewma = EwmaVolatility::new(0.94).unwrap();
+        ewma.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(ewma.is_ready());
+        ewma.reset();
+        assert!(!ewma.is_ready());
+        assert_eq!(ewma.value(), None);
+        assert_eq!(ewma.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=120)
+            .map(|i| 100.0 + (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = EwmaVolatility::new(0.94).unwrap().batch(&prices);
+        let mut b = EwmaVolatility::new(0.94).unwrap();
+        let streamed: Vec<_> = prices.iter().map(|p| b.update(*p)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/fisher_rsi.rs

@@ -0,0 +1,186 @@
+//! Fisher-transformed RSI.
+
+use crate::error::Result;
+use crate::indicators::rsi::Rsi;
+use crate::traits::Indicator;
+
+/// Fisher RSI — the Fisher transform applied to a normalised [`Rsi`](crate::Rsi).
+///
+/// The RSI is bounded in `[0, 100]` and its distribution piles up near the
+/// middle, which blurs turning points. The Fisher transform reshapes a bounded
+/// input toward a Gaussian, sharpening the extremes into clear, near-symmetric
+/// peaks:
+///
+/// ```text
+/// rsi   = RSI(price, period)            in [0, 100]
+/// x     = clamp((rsi - 50) / 50, ±0.999)   normalise to (-1, 1)
+/// Fisher = 0.5 * ln((1 + x) / (1 - x))
+/// ```
+///
+/// The clamp keeps the logarithm finite when the RSI pins at `0` or `100`. The
+/// output is unbounded but in practice oscillates in roughly `[-3, 3]`, with
+/// sharp excursions marking momentum extremes. The first value lands with the
+/// inner RSI, after `period + 1` inputs.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{FisherRsi, Indicator};
+///
+/// let mut indicator = FisherRsi::new(9).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct FisherRsi {
+    period: usize,
+    rsi: Rsi,
+}
+
+impl FisherRsi {
+    /// Construct a Fisher RSI with the given RSI period.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`crate::Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            period,
+            rsi: Rsi::new(period)?,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for FisherRsi {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        let rsi = self.rsi.update(input)?;
+        let x = ((rsi - 50.0) / 50.0).clamp(-0.999, 0.999);
+        Some(0.5 * ((1.0 + x) / (1.0 - x)).ln())
+    }
+
+    fn reset(&mut self) {
+        self.rsi.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.rsi.warmup_period()
+    }
+
+    fn is_ready(&self) -> bool {
+        self.rsi.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "FisherRSI"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(FisherRsi::new(0).is_err());
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let f = FisherRsi::new(9).unwrap();
+        assert_eq!(f.period(), 9);
+        // RSI warmup is period + 1.
+        assert_eq!(f.warmup_period(), 10);
+        assert_eq!(f.name(), "FisherRSI");
+    }
+
+    #[test]
+    fn warmup_matches_rsi() {
+        let mut f = FisherRsi::new(3).unwrap();
+        // RSI(3) needs 4 inputs; the first three return None.
+        assert_eq!(f.update(1.0), None);
+        assert_eq!(f.update(2.0), None);
+        assert_eq!(f.update(3.0), None);
+        assert!(f.update(4.0).is_some());
+    }
+
+    #[test]
+    fn matches_fisher_of_rsi() {
+        // Fisher RSI must equal the Fisher transform of the standalone RSI.
+        let prices: Vec<f64> = (0..60)
+            .map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 8.0)
+            .collect();
+        let mut fr = FisherRsi::new(9).unwrap();
+        let mut rsi = Rsi::new(9).unwrap();
+        for (i, &p) in prices.iter().enumerate() {
+            let got = fr.update(p);
+            let want = rsi.update(p).map(|r| {
+                let x = ((r - 50.0) / 50.0).clamp(-0.999, 0.999);
+                0.5 * ((1.0 + x) / (1.0 - x)).ln()
+            });
+            assert_eq!(got.is_some(), want.is_some(), "readiness mismatch at {i}");
+            if let (Some(a), Some(b)) = (got, want) {
+                assert_relative_eq!(a, b, epsilon = 1e-12);
+            }
+        }
+    }
+
+    #[test]
+    fn strong_uptrend_is_positive() {
+        // A pure uptrend pins RSI near 100 -> x near +1 -> large positive Fisher.
+        let prices: Vec<f64> = (1..=40).map(f64::from).collect();
+        let mut f = FisherRsi::new(9).unwrap();
+        let last = f.batch(&prices).into_iter().flatten().last().unwrap();
+        assert!(
+            last > 1.0,
+            "strong uptrend should give a large positive value, got {last}"
+        );
+    }
+
+    #[test]
+    fn clamp_keeps_output_finite_at_extremes() {
+        // Monotonic rise pins RSI at 100; the clamp must keep Fisher finite.
+        let prices: Vec<f64> = (1..=30).map(f64::from).collect();
+        let mut f = FisherRsi::new(5).unwrap();
+        for v in f.batch(&prices).into_iter().flatten() {
+            assert!(v.is_finite(), "Fisher RSI must stay finite, got {v}");
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut f = FisherRsi::new(5).unwrap();
+        f.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(f.is_ready());
+        f.reset();
+        assert!(!f.is_ready());
+        assert_eq!(f.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=40)
+            .map(|i| 50.0 + (f64::from(i) * 0.5).sin() * 10.0)
+            .collect();
+        let mut a = FisherRsi::new(9).unwrap();
+        let mut b = FisherRsi::new(9).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/garch11.rs

@@ -0,0 +1,325 @@
+//! GARCH(1,1) — conditional volatility with a long-run-variance anchor.
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// GARCH(1,1) conditional volatility — the square root of the
+/// generalized-autoregressive-conditional-heteroskedasticity variance recursion.
+///
+/// ```text
+/// r_t  = ln(price_t / price_{t−1})
+/// σ²_t = ω + α · r²_{t−1} + β · σ²_{t−1}
+/// out  = √σ²_t
+/// ```
+///
+/// GARCH(1,1) (Bollerslev 1986) generalizes the
+/// [`EwmaVolatility`](crate::EwmaVolatility) recursion by adding a constant `ω`,
+/// which pins the process to a finite long-run (unconditional) variance
+/// `ω / (1 − α − β)`. The `α` term gives weight to the latest squared return
+/// (the "ARCH" shock) and `β` to the previous variance (the "GARCH"
+/// persistence). When `ω = 0` and `α + β = 1` the model degenerates to EWMA; a
+/// proper GARCH keeps `ω > 0` and `α + β < 1` so volatility mean-reverts rather
+/// than drifting.
+///
+/// The recursion is seeded with the unconditional variance (`σ²₁ = ω / (1 − α −
+/// β)`) and emits from the first log return onward. Unlike EWMA — which decays to
+/// zero on a flat series — a flat series here mean-reverts toward `ω / (1 − β)`
+/// (the `α`-term vanishes but the `ω` floor and the `β` carry remain), so the
+/// output is always strictly positive. Each `update` is O(1).
+///
+/// Non-finite and non-positive prices are ignored (the log return would be
+/// undefined): the tick is dropped, state is left untouched, and the last value
+/// is returned.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Garch11, Indicator};
+///
+/// // Typical equity daily estimate.
+/// let mut indicator = Garch11::new(0.000_002, 0.10, 0.88).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Garch11 {
+    omega: f64,
+    alpha: f64,
+    beta: f64,
+    unconditional: f64,
+    prev_price: Option<f64>,
+    /// `(σ²_{t−1}, r²_{t−1})` — previous variance and previous squared return.
+    state: Option<(f64, f64)>,
+    last: Option<f64>,
+}
+
+impl Garch11 {
+    /// Construct a new GARCH(1,1) indicator from its three parameters.
+    ///
+    /// `omega` (`ω`) is the constant variance floor, `alpha` (`α`) the weight on
+    /// the latest squared return, and `beta` (`β`) the persistence of the
+    /// previous variance.
+    ///
+    /// # Errors
+    /// Returns [`Error::InvalidParameter`] unless every parameter is finite,
+    /// `omega > 0`, `alpha >= 0`, `beta >= 0`, and `alpha + beta < 1` (the
+    /// covariance-stationarity condition that gives a finite long-run variance).
+    pub fn new(omega: f64, alpha: f64, beta: f64) -> Result<Self> {
+        if !omega.is_finite() || !alpha.is_finite() || !beta.is_finite() {
+            return Err(Error::InvalidParameter {
+                message: "GARCH(1,1) parameters must be finite",
+            });
+        }
+        if omega <= 0.0 {
+            return Err(Error::InvalidParameter {
+                message: "GARCH(1,1) omega must be > 0",
+            });
+        }
+        if alpha < 0.0 || beta < 0.0 {
+            return Err(Error::InvalidParameter {
+                message: "GARCH(1,1) alpha and beta must be >= 0",
+            });
+        }
+        if alpha + beta >= 1.0 {
+            return Err(Error::InvalidParameter {
+                message: "GARCH(1,1) requires alpha + beta < 1 (covariance stationarity)",
+            });
+        }
+        Ok(Self {
+            omega,
+            alpha,
+            beta,
+            unconditional: omega / (1.0 - alpha - beta),
+            prev_price: None,
+            state: None,
+            last: None,
+        })
+    }
+
+    /// Configured `(omega, alpha, beta)`.
+    pub const fn params(&self) -> (f64, f64, f64) {
+        (self.omega, self.alpha, self.beta)
+    }
+
+    /// Long-run (unconditional) variance `ω / (1 − α − β)`.
+    pub const fn unconditional_variance(&self) -> f64 {
+        self.unconditional
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for Garch11 {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        // Non-finite / non-positive prices are skipped: `ln(input / prev)` is
+        // undefined, so the tick must not enter the variance recursion.
+        if !input.is_finite() || input <= 0.0 {
+            return self.last;
+        }
+        let Some(prev) = self.prev_price else {
+            self.prev_price = Some(input);
+            return None;
+        };
+        self.prev_price = Some(input);
+        // `prev` came from `self.prev_price`, gated by the guard above, so it is
+        // finite and positive — the log return is always well-defined.
+        let r = (input / prev).ln();
+        let r_sq = r * r;
+        let var = match self.state {
+            // Seed the recursion with the unconditional variance.
+            None => self.unconditional,
+            Some((prev_var, prev_r_sq)) => {
+                self.omega + self.alpha * prev_r_sq + self.beta * prev_var
+            }
+        };
+        self.state = Some((var, r_sq));
+        // `var` is `omega (> 0) + non-negative terms`, so it is strictly
+        // positive — the square root is always well-defined.
+        let vol = var.sqrt();
+        self.last = Some(vol);
+        Some(vol)
+    }
+
+    fn reset(&mut self) {
+        self.prev_price = None;
+        self.state = None;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // The first log return needs a previous price; the estimate is seeded
+        // with the unconditional variance and emitted on that first return.
+        2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "Garch11"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_params() {
+        assert!(matches!(
+            Garch11::new(0.0, 0.1, 0.8),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Garch11::new(-1.0, 0.1, 0.8),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Garch11::new(0.001, -0.1, 0.8),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Garch11::new(0.001, 0.1, -0.8),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Garch11::new(0.001, 0.5, 0.5),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Garch11::new(f64::NAN, 0.1, 0.8),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Garch11::new(0.001, f64::INFINITY, 0.8),
+            Err(Error::InvalidParameter { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let g = Garch11::new(0.001, 0.1, 0.85).unwrap();
+        assert_eq!(g.params(), (0.001, 0.1, 0.85));
+        assert_relative_eq!(g.unconditional_variance(), 0.001 / 0.05, epsilon = 1e-12);
+        assert_eq!(g.warmup_period(), 2);
+        assert_eq!(g.name(), "Garch11");
+        assert!(!g.is_ready());
+        assert_eq!(g.value(), None);
+    }
+
+    #[test]
+    fn first_emission_is_unconditional() {
+        // The first log return emits the seed = sqrt(unconditional variance),
+        // independent of the return value.
+        let g = Garch11::new(0.002, 0.1, 0.85);
+        let mut g = g.unwrap();
+        assert_eq!(g.update(100.0), None);
+        let out = g.update(110.0).unwrap();
+        assert_relative_eq!(out, (0.002_f64 / 0.05).sqrt(), epsilon = 1e-12);
+    }
+
+    #[test]
+    fn known_value() {
+        // σ²₁ = uncond; σ²₂ = ω + α·r1² + β·uncond.
+        let (omega, alpha, beta) = (0.002, 0.1, 0.85);
+        let mut g = Garch11::new(omega, alpha, beta).unwrap();
+        let out = g.batch(&[100.0, 110.0, 99.0]);
+        let uncond = omega / (1.0 - alpha - beta);
+        let r1 = (110.0_f64 / 100.0).ln();
+        assert_relative_eq!(out[1].unwrap(), uncond.sqrt(), epsilon = 1e-12);
+        let var2 = omega + alpha * r1 * r1 + beta * uncond;
+        assert_relative_eq!(out[2].unwrap(), var2.sqrt(), epsilon = 1e-12);
+    }
+
+    #[test]
+    fn flat_series_converges_to_long_run() {
+        // With zero returns the alpha term vanishes; the variance mean-reverts
+        // to the fixed point ω / (1 − β), NOT to zero (the key GARCH/EWMA
+        // distinction).
+        let (omega, beta) = (0.002, 0.85);
+        let mut g = Garch11::new(omega, 0.10, beta).unwrap();
+        let out = g.batch(&[100.0; 400]);
+        let fixed_point = (omega / (1.0 - beta)).sqrt();
+        assert_relative_eq!(out.last().unwrap().unwrap(), fixed_point, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn output_is_strictly_positive() {
+        let mut g = Garch11::new(0.000_002, 0.1, 0.88).unwrap();
+        let prices: Vec<f64> = (1..=200)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 12.0)
+            .collect();
+        for v in g.batch(&prices).into_iter().flatten() {
+            assert!(
+                v > 0.0,
+                "GARCH volatility must be strictly positive, got {v}"
+            );
+        }
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut g = Garch11::new(0.001, 0.1, 0.85).unwrap();
+        let out = g.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        let last = *out.last().unwrap();
+        assert!(last.is_some());
+        assert_eq!(g.update(f64::NAN), last);
+        assert_eq!(g.update(f64::INFINITY), last);
+    }
+
+    #[test]
+    fn skips_non_positive_prices() {
+        let mut g = Garch11::new(0.001, 0.1, 0.85).unwrap();
+        let warmup = g.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        let baseline = warmup.last().copied().flatten().expect("warmed up");
+        assert_eq!(g.update(-5.0), Some(baseline));
+        assert_eq!(g.update(0.0), Some(baseline));
+        // State untouched: a clone advanced by the same real tick agrees.
+        let mut control = g.clone();
+        let after = g.update(21.0).expect("ready");
+        assert_eq!(control.update(21.0).expect("ready"), after);
+    }
+
+    #[test]
+    fn skips_non_positive_before_first_price() {
+        let mut g = Garch11::new(0.001, 0.1, 0.85).unwrap();
+        assert_eq!(g.update(0.0), None);
+        assert_eq!(g.update(f64::NAN), None);
+        assert_eq!(g.update(100.0), None);
+        assert!(g.update(110.0).is_some());
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut g = Garch11::new(0.001, 0.1, 0.85).unwrap();
+        g.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(g.is_ready());
+        g.reset();
+        assert!(!g.is_ready());
+        assert_eq!(g.value(), None);
+        assert_eq!(g.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=120)
+            .map(|i| 100.0 + (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = Garch11::new(0.000_002, 0.1, 0.88).unwrap().batch(&prices);
+        let mut b = Garch11::new(0.000_002, 0.1, 0.88).unwrap();
+        let streamed: Vec<_> = prices.iter().map(|p| b.update(*p)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/gator_oscillator.rs

@@ -0,0 +1,205 @@
+//! Bill Williams' Gator Oscillator (derived from the Alligator).
+
+use crate::error::Result;
+use crate::indicators::alligator::Alligator;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Gator Oscillator output: the two histogram bars drawn above and below the
+/// zero line.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct GatorOscillatorOutput {
+    /// Upper histogram `|jaw - teeth|`, always `>= 0`.
+    pub upper: f64,
+    /// Lower histogram `-|teeth - lips|`, always `<= 0`.
+    pub lower: f64,
+}
+
+/// Bill Williams' Gator Oscillator: a convergence/divergence view of the
+/// [`Alligator`] lines. The upper bar is the absolute gap between Jaw and
+/// Teeth; the lower bar is the negated absolute gap between Teeth and Lips.
+///
+/// ```text
+/// upper =  |jaw   - teeth|
+/// lower = -|teeth - lips |
+/// ```
+///
+/// Widening bars mean the Alligator's mouth is opening (a trending market);
+/// shrinking bars mean it is closing (consolidation). Warmup matches the
+/// underlying Alligator — the first value appears once the slowest line (Jaw)
+/// has warmed up.
+///
+/// Reference: Bill Williams, *Trading Chaos*, 1995.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, GatorOscillator, Indicator};
+///
+/// let mut indicator = GatorOscillator::classic();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let base = 100.0 + f64::from(i);
+///     let candle =
+///         Candle::new(base, base + 1.0, base - 1.0, base, 1.0, i64::from(i)).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct GatorOscillator {
+    alligator: Alligator,
+}
+
+impl GatorOscillator {
+    /// Construct a Gator Oscillator from explicit Alligator periods
+    /// `(jaw, teeth, lips)`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`](crate::error::Error::PeriodZero) if any period is zero.
+    pub fn new(jaw_period: usize, teeth_period: usize, lips_period: usize) -> Result<Self> {
+        Ok(Self {
+            alligator: Alligator::new(jaw_period, teeth_period, lips_period)?,
+        })
+    }
+
+    /// Bill Williams' classic parameters: `(jaw = 13, teeth = 8, lips = 5)`.
+    pub fn classic() -> Self {
+        Self {
+            alligator: Alligator::classic(),
+        }
+    }
+
+    /// Configured `(jaw_period, teeth_period, lips_period)`.
+    pub const fn periods(&self) -> (usize, usize, usize) {
+        self.alligator.periods()
+    }
+}
+
+impl Indicator for GatorOscillator {
+    type Input = Candle;
+    type Output = GatorOscillatorOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<GatorOscillatorOutput> {
+        let lines = self.alligator.update(candle)?;
+        Some(GatorOscillatorOutput {
+            upper: (lines.jaw - lines.teeth).abs(),
+            lower: -(lines.teeth - lines.lips).abs(),
+        })
+    }
+
+    fn reset(&mut self) {
+        self.alligator.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.alligator.warmup_period()
+    }
+
+    fn is_ready(&self) -> bool {
+        self.alligator.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "GatorOscillator"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::error::Error;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, ts: i64) -> Candle {
+        let close = f64::midpoint(high, low);
+        Candle::new(close, high, low, close, 1.0, ts).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(
+            GatorOscillator::new(0, 8, 5),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            GatorOscillator::new(13, 0, 5),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            GatorOscillator::new(13, 8, 0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let g = GatorOscillator::classic();
+        assert_eq!(g.periods(), (13, 8, 5));
+        assert_eq!(g.warmup_period(), 13);
+        assert_eq!(g.name(), "GatorOscillator");
+        assert!(!g.is_ready());
+    }
+
+    #[test]
+    fn constant_series_collapses_both_bars() {
+        // All three Alligator lines equal the constant median -> zero spread.
+        let mut g = GatorOscillator::classic();
+        let candles: Vec<Candle> = (0..40).map(|i| candle(11.0, 9.0, i)).collect();
+        let out = g.batch(&candles);
+        let last = out.last().unwrap().unwrap();
+        assert_relative_eq!(last.upper, 0.0, epsilon = 1e-12);
+        assert_relative_eq!(last.lower, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn trending_series_opens_the_mouth() {
+        // On a clean trend the lines separate -> upper > 0, lower < 0.
+        let mut g = GatorOscillator::classic();
+        let candles: Vec<Candle> = (0_i64..80)
+            .map(|i| candle(10.0 + i as f64, 9.0 + i as f64, i))
+            .collect();
+        let last = g.batch(&candles).last().unwrap().unwrap();
+        assert!(last.upper > 0.0, "upper {} should be positive", last.upper);
+        assert!(last.lower < 0.0, "lower {} should be negative", last.lower);
+    }
+
+    #[test]
+    fn warmup_emits_first_value_at_longest_period() {
+        let mut g = GatorOscillator::new(5, 3, 2).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| candle(11.0, 9.0, i)).collect();
+        let out = g.batch(&candles);
+        for v in out.iter().take(4) {
+            assert!(v.is_none());
+        }
+        assert!(out[4].is_some());
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut g = GatorOscillator::classic();
+        let candles: Vec<Candle> = (0..40).map(|i| candle(11.0, 9.0, i)).collect();
+        g.batch(&candles);
+        assert!(g.is_ready());
+        g.reset();
+        assert!(!g.is_ready());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..80_i64)
+            .map(|i| {
+                let base = 100.0 + (i as f64 * 0.2).sin() * 5.0;
+                candle(base + 1.0, base - 1.0, i)
+            })
+            .collect();
+        let mut a = GatorOscillator::classic();
+        let mut b = GatorOscillator::classic();
+        assert_eq!(
+            a.batch(&candles),
+            candles.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/generalized_dema.rs

@@ -0,0 +1,222 @@
+//! Generalized DEMA (GD) — Tim Tillson's volume-factor double EMA.
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::traits::Indicator;
+
+/// Generalized DEMA — the building block of Tillson's [`T3`](crate::T3),
+/// exposed on its own.
+///
+/// ```text
+/// GD = (1 + v) · EMA(price) − v · EMA(EMA(price))
+/// ```
+///
+/// where both EMAs share the same `period` and `v ∈ [0, 1]` is the *volume
+/// factor*. `v` controls how much of the second-order lag correction is
+/// applied:
+///
+/// - `v = 0` collapses GD to a plain [`Ema`](crate::Ema) (no correction).
+/// - `v = 1` recovers the standard [`Dema`](crate::Dema) `2·EMA − EMA(EMA)`.
+/// - intermediate values (Tillson uses `0.7`) trade a little lag reduction for
+///   less overshoot than DEMA.
+///
+/// Because the coefficients `(1 + v)` and `−v` always sum to `1`, a constant
+/// series maps to itself. The first output lands after `2·period − 1` inputs —
+/// EMA1 seeds at `period`, then EMA2 needs another `period − 1` of EMA1's
+/// outputs to seed, exactly like DEMA.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, GeneralizedDema};
+///
+/// let mut indicator = GeneralizedDema::new(5, 0.7).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct GeneralizedDema {
+    ema1: Ema,
+    ema2: Ema,
+    period: usize,
+    v: f64,
+}
+
+impl GeneralizedDema {
+    /// Construct a generalized DEMA with the given `period` and volume factor
+    /// `v`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`, or
+    /// [`Error::InvalidPeriod`] if `v` is non-finite or outside `[0.0, 1.0]`.
+    pub fn new(period: usize, v: f64) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if !v.is_finite() || !(0.0..=1.0).contains(&v) {
+            return Err(Error::InvalidPeriod {
+                message: "GD volume factor must be a finite value in [0.0, 1.0]",
+            });
+        }
+        Ok(Self {
+            ema1: Ema::new(period)?,
+            ema2: Ema::new(period)?,
+            period,
+            v,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Configured volume factor `v`.
+    pub const fn volume_factor(&self) -> f64 {
+        self.v
+    }
+}
+
+impl Indicator for GeneralizedDema {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        let e1 = self.ema1.update(input)?;
+        let e2 = self.ema2.update(e1)?;
+        Some((1.0 + self.v) * e1 - self.v * e2)
+    }
+
+    fn reset(&mut self) {
+        self.ema1.reset();
+        self.ema2.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        // EMA1 seeds at period, then EMA2 needs another (period - 1) values.
+        2 * self.period - 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.ema2.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "GD"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::indicators::Dema;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(
+            GeneralizedDema::new(0, 0.7),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    #[test]
+    fn rejects_invalid_volume_factor() {
+        assert!(matches!(
+            GeneralizedDema::new(5, -0.1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            GeneralizedDema::new(5, 1.5),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            GeneralizedDema::new(5, f64::NAN),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(GeneralizedDema::new(5, 0.0).is_ok());
+        assert!(GeneralizedDema::new(5, 1.0).is_ok());
+    }
+
+    /// Cover the const accessors `period` + `volume_factor` and the
+    /// Indicator-impl `warmup_period` + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let gd = GeneralizedDema::new(5, 0.7).unwrap();
+        assert_eq!(gd.period(), 5);
+        assert_relative_eq!(gd.volume_factor(), 0.7, epsilon = 1e-12);
+        // EMA1 seeds at 5, EMA2 needs another 4 -> 2*period - 1 = 9.
+        assert_eq!(gd.warmup_period(), 9);
+        assert_eq!(gd.name(), "GD");
+    }
+
+    #[test]
+    fn constant_series_yields_constant() {
+        let mut gd = GeneralizedDema::new(5, 0.7).unwrap();
+        let out = gd.batch(&[100.0_f64; 60]);
+        let last = out.iter().rev().flatten().next().unwrap();
+        assert_relative_eq!(*last, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn v_one_equals_dema() {
+        // GD with v = 1 is exactly the standard DEMA.
+        let prices: Vec<f64> = (1..=80)
+            .map(|i| (f64::from(i) * 0.3).sin() * 10.0 + 50.0)
+            .collect();
+        let mut gd = GeneralizedDema::new(7, 1.0).unwrap();
+        let mut dema = Dema::new(7).unwrap();
+        let gd_out = gd.batch(&prices);
+        let dema_out = dema.batch(&prices);
+        for (g, d) in gd_out.iter().zip(dema_out.iter()) {
+            assert_eq!(g.is_some(), d.is_some());
+            if let (Some(a), Some(b)) = (g, d) {
+                assert_relative_eq!(*a, *b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn v_zero_equals_ema() {
+        // GD with v = 0 is a plain EMA (no second-order correction).
+        let prices: Vec<f64> = (1..=60).map(|i| f64::from(i) * 0.5).collect();
+        let mut gd = GeneralizedDema::new(6, 0.0).unwrap();
+        let mut ema = Ema::new(6).unwrap();
+        let gd_out = gd.batch(&prices);
+        for (i, (g, p)) in gd_out.iter().zip(prices.iter()).enumerate() {
+            // GD(v=0) feeds EMA1 into EMA2 but outputs EMA1 alone (coefficient
+            // 1 on e1, 0 on e2); it is only ready once EMA2 is, so compare
+            // against a standalone EMA chained the same way.
+            let want = ema.update(*p).filter(|_| i + 1 >= gd.warmup_period());
+            if let (Some(a), Some(b)) = (g, want) {
+                assert_relative_eq!(*a, b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=80).map(|i| f64::from(i) * 0.5).collect();
+        let mut a = GeneralizedDema::new(7, 0.7).unwrap();
+        let mut b = GeneralizedDema::new(7, 0.7).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut gd = GeneralizedDema::new(5, 0.7).unwrap();
+        gd.batch(&(1..=50).map(f64::from).collect::<Vec<_>>());
+        assert!(gd.is_ready());
+        gd.reset();
+        assert!(!gd.is_ready());
+        assert_eq!(gd.update(1.0), None);
+    }
+}

crates/wickra-core/src/indicators/geometric_ma.rs

@@ -0,0 +1,275 @@
+//! Geometric Moving Average (GMA).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Geometric Moving Average — the rolling geometric mean of the last `period`
+/// inputs.
+///
+/// ```text
+/// GMA = (Π value_i)^(1/period) = exp( (1/period) · Σ ln(value_i) )
+/// ```
+///
+/// The geometric mean is the natural average for *multiplicative* quantities
+/// such as prices and growth factors: averaging in log-space weights relative
+/// (percentage) moves symmetrically, so a `+10%` followed by a `−10%` move
+/// pulls the average below the start, exactly as compounded returns do. It is
+/// always less than or equal to the arithmetic mean of the same window.
+///
+/// Maintained incrementally in O(1): the running sum of natural logs is updated
+/// by adding the newcomer's log and subtracting the departing value's log as
+/// the window slides.
+///
+/// The geometric mean is only defined for **strictly positive** inputs. A
+/// non-finite or non-positive input is ignored (it leaves the window unchanged
+/// and returns the current value), mirroring the non-finite handling of the
+/// other moving averages.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, GeometricMa};
+///
+/// let mut indicator = GeometricMa::new(5).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct GeometricMa {
+    period: usize,
+    /// Natural logs of the values currently in the window (oldest at front).
+    logs: VecDeque<f64>,
+    sum_logs: f64,
+}
+
+impl GeometricMa {
+    /// Construct a new geometric moving average over `period` inputs.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            logs: VecDeque::with_capacity(period),
+            sum_logs: 0.0,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if the window is full.
+    pub fn value(&self) -> Option<f64> {
+        if self.logs.len() == self.period {
+            Some((self.sum_logs / self.period as f64).exp())
+        } else {
+            None
+        }
+    }
+}
+
+impl Indicator for GeometricMa {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() || input <= 0.0 {
+            return self.value();
+        }
+        if self.logs.len() == self.period {
+            let oldest = self.logs.pop_front().expect("window non-empty");
+            self.sum_logs -= oldest;
+        }
+        let ln = input.ln();
+        self.logs.push_back(ln);
+        self.sum_logs += ln;
+        self.value()
+    }
+
+    fn reset(&mut self) {
+        self.logs.clear();
+        self.sum_logs = 0.0;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.logs.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "GMA"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Reference implementation: explicit geometric mean over a window.
+    fn gma_naive(prices: &[f64], period: usize) -> Vec<Option<f64>> {
+        prices
+            .iter()
+            .enumerate()
+            .map(|(i, _)| {
+                if i + 1 < period {
+                    None
+                } else {
+                    let window = &prices[i + 1 - period..=i];
+                    let product: f64 = window.iter().product();
+                    Some(product.powf(1.0 / period as f64))
+                }
+            })
+            .collect()
+    }
+
+    #[test]
+    fn new_rejects_zero_period() {
+        assert!(matches!(GeometricMa::new(0), Err(Error::PeriodZero)));
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let gma = GeometricMa::new(7).unwrap();
+        assert_eq!(gma.period(), 7);
+        assert_eq!(gma.warmup_period(), 7);
+        assert_eq!(gma.name(), "GMA");
+    }
+
+    #[test]
+    fn warmup_returns_none() {
+        let mut gma = GeometricMa::new(3).unwrap();
+        assert_eq!(gma.update(1.0), None);
+        assert_eq!(gma.update(4.0), None);
+        // GMA(3) of [1, 4, 2] = (1·4·2)^(1/3) = 8^(1/3) = 2.
+        assert_relative_eq!(gma.update(2.0).unwrap(), 2.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn known_value_period_2() {
+        // GMA(2) of [4, 9] = sqrt(36) = 6.
+        let mut gma = GeometricMa::new(2).unwrap();
+        let v = gma.batch(&[4.0, 9.0]);
+        assert_relative_eq!(v[1].unwrap(), 6.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn constant_series_returns_the_constant() {
+        let mut gma = GeometricMa::new(5).unwrap();
+        for v in gma.batch(&[42.0; 20]).into_iter().flatten() {
+            assert_relative_eq!(v, 42.0, epsilon = 1e-9);
+        }
+    }
+
+    #[test]
+    fn period_one_is_pass_through() {
+        let mut gma = GeometricMa::new(1).unwrap();
+        assert_relative_eq!(gma.update(5.5).unwrap(), 5.5, epsilon = 1e-12);
+        assert_relative_eq!(gma.update(7.5).unwrap(), 7.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn below_or_equal_arithmetic_mean() {
+        // The geometric mean never exceeds the arithmetic mean of the same set.
+        let mut gma = GeometricMa::new(4).unwrap();
+        let prices = [10.0, 20.0, 5.0, 40.0];
+        let g = gma.batch(&prices)[3].unwrap();
+        let arithmetic = prices.iter().sum::<f64>() / 4.0;
+        assert!(
+            g < arithmetic,
+            "geometric {g} should be below arithmetic {arithmetic}"
+        );
+    }
+
+    #[test]
+    fn matches_naive_over_inputs() {
+        let prices: Vec<f64> = (1..=30).map(|i| f64::from(i) * 1.7 + 1.0).collect();
+        let mut gma = GeometricMa::new(7).unwrap();
+        let got = gma.batch(&prices);
+        let want = gma_naive(&prices, 7);
+        for (i, (g, w)) in got.iter().zip(want.iter()).enumerate() {
+            assert_eq!(g.is_some(), w.is_some(), "warmup mismatch at index {i}");
+            if let (Some(a), Some(b)) = (g, w) {
+                assert_relative_eq!(*a, *b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut gma = GeometricMa::new(4).unwrap();
+        gma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        assert!(gma.is_ready());
+        gma.reset();
+        assert!(!gma.is_ready());
+        assert_eq!(gma.update(10.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=20).map(|i| f64::from(i) * 0.5 + 1.0).collect();
+        let mut a = GeometricMa::new(5).unwrap();
+        let mut b = GeometricMa::new(5).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite_and_non_positive_input() {
+        let mut gma = GeometricMa::new(3).unwrap();
+        gma.update(1.0);
+        gma.update(4.0);
+        let ready = gma.update(2.0).expect("GMA(3) ready after three inputs");
+        // Non-finite and non-positive inputs are skipped (geometric mean needs
+        // strictly positive values) and the window is left unchanged.
+        assert_eq!(gma.update(f64::NAN), Some(ready));
+        assert_eq!(gma.update(0.0), Some(ready));
+        assert_eq!(gma.update(-3.0), Some(ready));
+        // The window still holds 1, 4, 2 -> next real input slides it to 4, 2, 16.
+        let want = (4.0_f64 * 2.0 * 16.0).powf(1.0 / 3.0);
+        assert_relative_eq!(gma.update(16.0).unwrap(), want, epsilon = 1e-9);
+    }
+
+    proptest::proptest! {
+        #![proptest_config(proptest::test_runner::Config::with_cases(48))]
+        #[test]
+        fn proptest_matches_naive(
+            period in 1usize..15,
+            prices in proptest::collection::vec(0.01_f64..1000.0, 0..120),
+        ) {
+            let mut gma = GeometricMa::new(period).unwrap();
+            let got = gma.batch(&prices);
+            let want = gma_naive(&prices, period);
+            proptest::prop_assert_eq!(got.len(), want.len());
+            for (g, w) in got.iter().zip(want.iter()) {
+                match (g, w) {
+                    (None, None) => {}
+                    (Some(a), Some(b)) => proptest::prop_assert!(
+                        (a - b).abs() <= 1e-6 * b.abs().max(1.0),
+                        "got={a} want={b}"
+                    ),
+                    _ => proptest::prop_assert!(false, "warmup mismatch"),
+                }
+            }
+        }
+    }
+}

crates/wickra-core/src/indicators/holt_winters.rs

@@ -0,0 +1,315 @@
+//! Holt's linear (double exponential) smoothing.
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Holt's linear method — double exponential smoothing with a level and a
+/// trend component.
+///
+/// A single [`Ema`](crate::Ema) tracks only a *level* and therefore lags any
+/// sustained trend. Holt's method adds a second smoothed state, the trend, and
+/// reports the one-step-ahead forecast `level + trend`, which removes that lag
+/// on trending data while still smoothing noise.
+///
+/// ```text
+/// level_t = α · price_t        + (1 − α) · (level_{t-1} + trend_{t-1})
+/// trend_t = β · (level_t − level_{t-1}) + (1 − β) · trend_{t-1}
+/// output  = level_t + trend_t          (one-step-ahead forecast)
+/// ```
+///
+/// `α ∈ (0, 1]` is the level smoothing constant and `β ∈ (0, 1]` the trend
+/// smoothing constant. The state is seeded from the first two inputs
+/// (`level = price_1`, `trend = price_1 − price_0`), so the first output lands
+/// on the **second** input.
+///
+/// On a perfectly linear series the forecast is exact from the second bar
+/// onward (for any `α`, `β`): if the level equals the current value and the
+/// trend equals the slope, both invariants are preserved and `level + trend`
+/// equals the next value.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{HoltWinters, Indicator};
+///
+/// let mut indicator = HoltWinters::new(0.2, 0.1).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct HoltWinters {
+    alpha: f64,
+    beta: f64,
+    /// `(level, trend)` once seeded.
+    state: Option<(f64, f64)>,
+    /// First input, held until the second arrives to seed the trend.
+    prev_price: Option<f64>,
+}
+
+impl HoltWinters {
+    /// Construct Holt's linear smoother with level constant `alpha` and trend
+    /// constant `beta`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::InvalidPeriod`] if either constant is non-finite or
+    /// outside `(0.0, 1.0]`.
+    pub fn new(alpha: f64, beta: f64) -> Result<Self> {
+        if !alpha.is_finite() || alpha <= 0.0 || alpha > 1.0 {
+            return Err(Error::InvalidPeriod {
+                message: "HoltWinters alpha must be in (0.0, 1.0]",
+            });
+        }
+        if !beta.is_finite() || beta <= 0.0 || beta > 1.0 {
+            return Err(Error::InvalidPeriod {
+                message: "HoltWinters beta must be in (0.0, 1.0]",
+            });
+        }
+        Ok(Self {
+            alpha,
+            beta,
+            state: None,
+            prev_price: None,
+        })
+    }
+
+    /// Level smoothing constant `alpha`.
+    pub const fn alpha(&self) -> f64 {
+        self.alpha
+    }
+
+    /// Trend smoothing constant `beta`.
+    pub const fn beta(&self) -> f64 {
+        self.beta
+    }
+
+    /// Current smoothed level, if seeded.
+    pub fn level(&self) -> Option<f64> {
+        self.state.map(|(level, _)| level)
+    }
+
+    /// Current smoothed trend, if seeded.
+    pub fn trend(&self) -> Option<f64> {
+        self.state.map(|(_, trend)| trend)
+    }
+
+    /// Current one-step-ahead forecast `level + trend`, if seeded.
+    pub fn value(&self) -> Option<f64> {
+        self.state.map(|(level, trend)| level + trend)
+    }
+}
+
+impl Indicator for HoltWinters {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, price: f64) -> Option<f64> {
+        if !price.is_finite() {
+            return self.value();
+        }
+        match self.state {
+            None => {
+                if let Some(prev) = self.prev_price {
+                    // Second input: seed level and trend.
+                    let level = price;
+                    let trend = price - prev;
+                    self.state = Some((level, trend));
+                    Some(level + trend)
+                } else {
+                    // First input: hold it to seed the trend next time.
+                    self.prev_price = Some(price);
+                    None
+                }
+            }
+            Some((level, trend)) => {
+                let level_new = self.alpha * price + (1.0 - self.alpha) * (level + trend);
+                let trend_new = self.beta * (level_new - level) + (1.0 - self.beta) * trend;
+                self.state = Some((level_new, trend_new));
+                Some(level_new + trend_new)
+            }
+        }
+    }
+
+    fn reset(&mut self) {
+        self.state = None;
+        self.prev_price = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // Two inputs are needed to seed the level and the trend.
+        2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.state.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "HoltWinters"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Independent reference for the steady-state recurrence.
+    fn naive(prices: &[f64], alpha: f64, beta: f64) -> Vec<Option<f64>> {
+        let mut state: Option<(f64, f64)> = None;
+        let mut prev: Option<f64> = None;
+        let mut out = Vec::with_capacity(prices.len());
+        for &price in prices {
+            let v = match state {
+                None => {
+                    if let Some(p0) = prev {
+                        let level = price;
+                        let trend = price - p0;
+                        state = Some((level, trend));
+                        Some(level + trend)
+                    } else {
+                        prev = Some(price);
+                        None
+                    }
+                }
+                Some((level, trend)) => {
+                    let ln = alpha * price + (1.0 - alpha) * (level + trend);
+                    let tn = beta * (ln - level) + (1.0 - beta) * trend;
+                    state = Some((ln, tn));
+                    Some(ln + tn)
+                }
+            };
+            out.push(v);
+        }
+        out
+    }
+
+    #[test]
+    fn rejects_invalid_alpha() {
+        assert!(matches!(
+            HoltWinters::new(0.0, 0.1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            HoltWinters::new(1.5, 0.1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            HoltWinters::new(f64::NAN, 0.1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn rejects_invalid_beta() {
+        assert!(matches!(
+            HoltWinters::new(0.2, 0.0),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            HoltWinters::new(0.2, 1.5),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            HoltWinters::new(0.2, f64::INFINITY),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    /// Cover the const accessors `alpha` + `beta` and the Indicator-impl
+    /// `warmup_period` + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let hw = HoltWinters::new(0.2, 0.1).unwrap();
+        assert_relative_eq!(hw.alpha(), 0.2, epsilon = 1e-12);
+        assert_relative_eq!(hw.beta(), 0.1, epsilon = 1e-12);
+        assert_eq!(hw.warmup_period(), 2);
+        assert_eq!(hw.name(), "HoltWinters");
+    }
+
+    #[test]
+    fn warmup_then_seed_on_second_input() {
+        let mut hw = HoltWinters::new(0.2, 0.1).unwrap();
+        assert_eq!(hw.update(10.0), None);
+        // Second input seeds level = 12, trend = 12 - 10 = 2 -> forecast 14.
+        assert_relative_eq!(hw.update(12.0).unwrap(), 14.0, epsilon = 1e-12);
+        assert_relative_eq!(hw.level().unwrap(), 12.0, epsilon = 1e-12);
+        assert_relative_eq!(hw.trend().unwrap(), 2.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn linear_series_forecasts_exactly() {
+        // On a perfect ramp the one-step forecast equals the next value, for
+        // any alpha/beta, from the second bar onward.
+        let prices: Vec<f64> = (1..=20).map(f64::from).collect();
+        let mut hw = HoltWinters::new(0.3, 0.4).unwrap();
+        let out = hw.batch(&prices);
+        assert!(out[0].is_none());
+        for (i, v) in out.iter().enumerate().skip(1) {
+            // forecast at index i is the price at index i + 1 = (i + 2).
+            assert_relative_eq!(v.unwrap(), (i + 2) as f64, epsilon = 1e-9);
+        }
+    }
+
+    #[test]
+    fn constant_series_yields_constant() {
+        let mut hw = HoltWinters::new(0.2, 0.1).unwrap();
+        let out = hw.batch(&[42.0_f64; 30]);
+        for v in out.into_iter().skip(1).flatten() {
+            assert_relative_eq!(v, 42.0, epsilon = 1e-9);
+        }
+    }
+
+    #[test]
+    fn matches_naive_recurrence() {
+        let prices: Vec<f64> = (0..60)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 10.0 + f64::from(i) * 0.2)
+            .collect();
+        let mut hw = HoltWinters::new(0.25, 0.15).unwrap();
+        let got = hw.batch(&prices);
+        let want = naive(&prices, 0.25, 0.15);
+        for (g, w) in got.iter().zip(want.iter()) {
+            assert_eq!(g.is_some(), w.is_some());
+            if let (Some(a), Some(b)) = (g, w) {
+                assert_relative_eq!(a, b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut hw = HoltWinters::new(0.2, 0.1).unwrap();
+        hw.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(hw.is_ready());
+        hw.reset();
+        assert!(!hw.is_ready());
+        assert_eq!(hw.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=30).map(|i| f64::from(i) * 0.5).collect();
+        let mut a = HoltWinters::new(0.3, 0.2).unwrap();
+        let mut b = HoltWinters::new(0.3, 0.2).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut hw = HoltWinters::new(0.2, 0.1).unwrap();
+        // Non-finite before any state returns None.
+        assert_eq!(hw.update(f64::NAN), None);
+        hw.update(10.0);
+        let ready = hw.update(12.0).expect("seeded on second finite input");
+        // Non-finite after seeding returns the current forecast unchanged.
+        assert_eq!(hw.update(f64::NAN), Some(ready));
+        assert_eq!(hw.update(f64::INFINITY), Some(ready));
+    }
+}

crates/wickra-core/src/indicators/intraday_intensity.rs

@@ -0,0 +1,185 @@
+//! Intraday Intensity Index (Bostian) — a cumulative volume-weighted close-location line.
+
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Intraday Intensity Index — David Bostian's cumulative line that weights each
+/// bar's volume by where the close lands inside the bar's range.
+///
+/// ```text
+/// II_t  = volume * (2*close − high − low) / (high − low)   (0 if high == low)
+/// III_t = III_{t−1} + II_t
+/// ```
+///
+/// The fraction `(2*close − high − low) / (high − low)` is `+1` when the bar
+/// closes on its high, `−1` when it closes on its low, and `0` at the midpoint.
+/// Scaling it by volume and accumulating produces a running measure of how
+/// aggressively the close is being pushed toward the extremes — Bostian's proxy
+/// for institutional accumulation (rising line) or distribution (falling line).
+///
+/// This is the **cumulative** Intraday Intensity (the original index), not the
+/// normalized "Intraday Intensity %" — the latter divides a windowed sum of `II`
+/// by a windowed sum of volume and is mathematically identical to
+/// [`Cmf`](crate::Cmf), so it is not duplicated here. The level of this line is
+/// arbitrary; only its slope and divergences against price matter. A doji whose
+/// `high == low` contributes nothing. Each `update` is O(1) and the first bar
+/// already emits a value.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, IntradayIntensity};
+///
+/// let mut indicator = IntradayIntensity::new();
+/// let mut last = None;
+/// for i in 0..20 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base + 0.9, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone, Default)]
+pub struct IntradayIntensity {
+    iii: f64,
+    last: Option<f64>,
+}
+
+impl IntradayIntensity {
+    /// Construct a new Intraday Intensity Index. The line is parameter-free.
+    #[must_use]
+    pub fn new() -> Self {
+        Self::default()
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for IntradayIntensity {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let range = candle.high - candle.low;
+        let ii = if range > 0.0 {
+            candle.volume * (2.0 * candle.close - candle.high - candle.low) / range
+        } else {
+            0.0
+        };
+        self.iii += ii;
+        self.last = Some(self.iii);
+        Some(self.iii)
+    }
+
+    fn reset(&mut self) {
+        self.iii = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "IntradayIntensity"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64, volume: f64) -> Candle {
+        Candle::new_unchecked(low, high, low, close, volume, 0)
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let iii = IntradayIntensity::new();
+        assert_eq!(iii.warmup_period(), 1);
+        assert_eq!(iii.name(), "IntradayIntensity");
+        assert!(!iii.is_ready());
+        assert_eq!(iii.value(), None);
+    }
+
+    #[test]
+    fn first_bar_emits() {
+        // close at the high: (2*101 - 102 - 100)/(2) = 0/... wait, high=102 low=100 close=101 -> 0.
+        let mut iii = IntradayIntensity::new();
+        // close on the high -> +1 * volume.
+        let v = iii.update(candle(102.0, 100.0, 102.0, 500.0)).unwrap();
+        assert_relative_eq!(v, 500.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn close_on_high_adds_full_volume() {
+        let mut iii = IntradayIntensity::new();
+        let v = iii.update(candle(110.0, 100.0, 110.0, 1_000.0)).unwrap();
+        assert_relative_eq!(v, 1_000.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn close_on_low_subtracts_full_volume() {
+        let mut iii = IntradayIntensity::new();
+        let v = iii.update(candle(110.0, 100.0, 100.0, 1_000.0)).unwrap();
+        assert_relative_eq!(v, -1_000.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn close_at_midpoint_adds_nothing() {
+        let mut iii = IntradayIntensity::new();
+        let v = iii.update(candle(110.0, 100.0, 105.0, 1_000.0)).unwrap();
+        assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn zero_range_adds_nothing() {
+        let mut iii = IntradayIntensity::new();
+        let v = iii.update(candle(100.0, 100.0, 100.0, 1_000.0)).unwrap();
+        assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn accumulates_across_bars() {
+        let mut iii = IntradayIntensity::new();
+        iii.update(candle(110.0, 100.0, 110.0, 1_000.0)); // +1000
+        let v = iii.update(candle(110.0, 100.0, 100.0, 400.0)).unwrap(); // -400 -> 600
+        assert_relative_eq!(v, 600.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut iii = IntradayIntensity::new();
+        iii.batch(&[
+            candle(110.0, 100.0, 108.0, 1.0),
+            candle(110.0, 100.0, 102.0, 1.0),
+        ]);
+        assert!(iii.is_ready());
+        iii.reset();
+        assert!(!iii.is_ready());
+        assert_eq!(iii.value(), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..80)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.3).sin() * 6.0;
+                candle(base + 2.0, base - 2.0, base + 0.7, 1_000.0 + f64::from(i))
+            })
+            .collect();
+        let batch = IntradayIntensity::new().batch(&candles);
+        let mut b = IntradayIntensity::new();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/intraday_momentum_index.rs

@@ -0,0 +1,228 @@
+//! Intraday Momentum Index (IMI).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Intraday Momentum Index — Tushar Chande's RSI built from the open-to-close
+/// move instead of the close-to-close move.
+///
+/// For each bar the body is an up-move when `close > open` and a down-move
+/// otherwise; the IMI sums those bodies over `period` bars and forms the
+/// RSI-style ratio:
+///
+/// ```text
+/// gain = max(close - open, 0),  loss = max(open - close, 0)
+/// IMI  = 100 * Σ gain / (Σ gain + Σ loss)        over the last `period` bars
+/// ```
+///
+/// Because it measures *intraday* (body) momentum rather than the gap-inclusive
+/// close-to-close change, the IMI is a candle-pattern-flavoured overbought /
+/// oversold gauge: persistent white bodies push it up, black bodies down. It is
+/// bounded in `[0, 100]`; a window of doji-like bars (no net bodies) returns the
+/// neutral `50`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, IntradayMomentumIndex, Indicator};
+///
+/// let mut imi = IntradayMomentumIndex::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base + 0.5, 1.0, i64::from(i)).unwrap();
+///     last = imi.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct IntradayMomentumIndex {
+    period: usize,
+    /// Per-bar `(gain, loss)` bodies, oldest at the front.
+    window: VecDeque<(f64, f64)>,
+    sum_gain: f64,
+    sum_loss: f64,
+}
+
+impl IntradayMomentumIndex {
+    /// Construct an IMI over `period` bars.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+            sum_gain: 0.0,
+            sum_loss: 0.0,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if the window is full.
+    pub fn value(&self) -> Option<f64> {
+        if self.window.len() != self.period {
+            return None;
+        }
+        let denom = self.sum_gain + self.sum_loss;
+        if denom == 0.0 {
+            Some(50.0)
+        } else {
+            Some(100.0 * self.sum_gain / denom)
+        }
+    }
+}
+
+impl Indicator for IntradayMomentumIndex {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let body = candle.close - candle.open;
+        let gain = if body > 0.0 { body } else { 0.0 };
+        let loss = if body < 0.0 { -body } else { 0.0 };
+
+        if self.window.len() == self.period {
+            let (old_g, old_l) = self.window.pop_front().expect("window full");
+            self.sum_gain -= old_g;
+            self.sum_loss -= old_l;
+        }
+        self.window.push_back((gain, loss));
+        self.sum_gain += gain;
+        self.sum_loss += loss;
+        self.value()
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.sum_gain = 0.0;
+        self.sum_loss = 0.0;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "IMI"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(open: f64, close: f64) -> Candle {
+        let hi = open.max(close) + 1.0;
+        let lo = open.min(close) - 1.0;
+        Candle::new(open, hi, lo, close, 1.0, 0).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(
+            IntradayMomentumIndex::new(0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let imi = IntradayMomentumIndex::new(14).unwrap();
+        assert_eq!(imi.period(), 14);
+        assert_eq!(imi.warmup_period(), 14);
+        assert_eq!(imi.name(), "IMI");
+    }
+
+    #[test]
+    fn all_up_bodies_is_one_hundred() {
+        let mut imi = IntradayMomentumIndex::new(3).unwrap();
+        let bars = [candle(10.0, 11.0), candle(11.0, 13.0), candle(13.0, 14.0)];
+        let out = imi.batch(&bars);
+        assert!(out[0].is_none());
+        assert!(out[1].is_none());
+        assert_relative_eq!(out[2].unwrap(), 100.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn all_down_bodies_is_zero() {
+        let mut imi = IntradayMomentumIndex::new(3).unwrap();
+        let bars = [candle(14.0, 13.0), candle(13.0, 11.0), candle(11.0, 10.0)];
+        assert_relative_eq!(imi.batch(&bars)[2].unwrap(), 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn known_value_mixed_bodies() {
+        // bodies: +1, -1, +2 -> sum_gain = 3, sum_loss = 1 -> 100*3/4 = 75.
+        let mut imi = IntradayMomentumIndex::new(3).unwrap();
+        let bars = [candle(10.0, 11.0), candle(11.0, 10.0), candle(10.0, 12.0)];
+        assert_relative_eq!(imi.batch(&bars)[2].unwrap(), 75.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn doji_window_is_neutral() {
+        // close == open every bar -> no bodies -> neutral 50.
+        let mut imi = IntradayMomentumIndex::new(3).unwrap();
+        let bars = [candle(10.0, 10.0), candle(11.0, 11.0), candle(12.0, 12.0)];
+        assert_relative_eq!(imi.batch(&bars)[2].unwrap(), 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn slides_window() {
+        // After [+1,-1,+2] (75) add +0 body window -> [-1,+2,0]: gain 2, loss 1 -> 66.67.
+        let mut imi = IntradayMomentumIndex::new(3).unwrap();
+        let bars = [
+            candle(10.0, 11.0),
+            candle(11.0, 10.0),
+            candle(10.0, 12.0),
+            candle(12.0, 12.0),
+        ];
+        let out = imi.batch(&bars);
+        assert_relative_eq!(out[3].unwrap(), 100.0 * 2.0 / 3.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut imi = IntradayMomentumIndex::new(3).unwrap();
+        imi.batch(&[candle(10.0, 11.0), candle(11.0, 12.0), candle(12.0, 13.0)]);
+        assert!(imi.is_ready());
+        imi.reset();
+        assert!(!imi.is_ready());
+        assert_eq!(imi.update(candle(1.0, 2.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let bars: Vec<Candle> = (0..30)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base, base + (f64::from(i) * 0.5).sin())
+            })
+            .collect();
+        let mut a = IntradayMomentumIndex::new(7).unwrap();
+        let mut b = IntradayMomentumIndex::new(7).unwrap();
+        assert_eq!(
+            a.batch(&bars),
+            bars.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/jarque_bera.rs

@@ -0,0 +1,263 @@
+//! Jarque-Bera — a normality-test statistic on a rolling window.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Jarque-Bera — the Jarque-Bera test statistic measuring how far a window's
+/// distribution departs from normal, via its **skewness** and **excess
+/// kurtosis**.
+///
+/// ```text
+/// S  = skewness         = m3 / m2^(3/2)
+/// K  = excess kurtosis  = m4 / m2²  − 3
+/// JB = (period / 6) · ( S² + K²/4 )
+/// ```
+///
+/// where `m2`, `m3`, `m4` are the second, third and fourth central moments of the
+/// window. A perfectly normal sample has zero skew and zero excess kurtosis, so
+/// `JB = 0`; the statistic grows as the distribution becomes asymmetric (non-zero
+/// skew) or fat- or thin-tailed (non-zero excess kurtosis). Under the null of
+/// normality `JB` is asymptotically χ² with two degrees of freedom, so values
+/// above roughly `6` reject normality at the 95% level — a useful streaming flag
+/// for fat-tail / crash-risk regimes in a return series.
+///
+/// The statistic is `≥ 0`. A degenerate window with zero variance (`m2 == 0`)
+/// returns `0`. The first value lands after `period` inputs; each `update`
+/// recomputes the four moments over the window in O(`period`).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, JarqueBera};
+///
+/// let mut indicator = JarqueBera::new(50).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update((f64::from(i) * 0.3).sin());
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct JarqueBera {
+    period: usize,
+    window: VecDeque<f64>,
+    last: Option<f64>,
+}
+
+impl JarqueBera {
+    /// Construct a rolling Jarque-Bera over `period` values.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0` and
+    /// [`Error::InvalidPeriod`] if `period < 4` (the statistic is degenerate on
+    /// fewer than four points).
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if period < 4 {
+            return Err(Error::InvalidPeriod {
+                message: "Jarque-Bera needs period >= 4",
+            });
+        }
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+            last: None,
+        })
+    }
+
+    /// Configured window length.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+
+    fn compute(&self) -> f64 {
+        let n = self.period as f64;
+        let mean = self.window.iter().sum::<f64>() / n;
+        let mut m2 = 0.0;
+        let mut m3 = 0.0;
+        let mut m4 = 0.0;
+        for &v in &self.window {
+            let d = v - mean;
+            let d2 = d * d;
+            m2 += d2;
+            m3 += d2 * d;
+            m4 += d2 * d2;
+        }
+        m2 /= n;
+        m3 /= n;
+        m4 /= n;
+        if m2 == 0.0 {
+            return 0.0;
+        }
+        let skew = m3 / m2.powf(1.5);
+        let excess_kurt = m4 / (m2 * m2) - 3.0;
+        (n / 6.0) * (skew * skew + excess_kurt * excess_kurt / 4.0)
+    }
+}
+
+impl Indicator for JarqueBera {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.last;
+        }
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        if self.window.len() < self.period {
+            return None;
+        }
+        let out = self.compute();
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "JarqueBera"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_period() {
+        assert!(matches!(JarqueBera::new(0), Err(Error::PeriodZero)));
+        assert!(matches!(
+            JarqueBera::new(3),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(JarqueBera::new(4).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let jb = JarqueBera::new(50).unwrap();
+        assert_eq!(jb.period(), 50);
+        assert_eq!(jb.warmup_period(), 50);
+        assert_eq!(jb.name(), "JarqueBera");
+        assert!(!jb.is_ready());
+        assert_eq!(jb.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut jb = JarqueBera::new(4).unwrap();
+        let out = jb.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        for v in out.iter().take(3) {
+            assert!(v.is_none());
+        }
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn constant_window_is_zero() {
+        let mut jb = JarqueBera::new(8).unwrap();
+        let last = jb.batch(&[5.0; 12]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_is_non_negative() {
+        let mut jb = JarqueBera::new(30).unwrap();
+        for v in jb
+            .batch(
+                &(0..200)
+                    .map(|i| (f64::from(i) * 0.3).sin() * 5.0)
+                    .collect::<Vec<_>>(),
+            )
+            .into_iter()
+            .flatten()
+        {
+            assert!(v >= 0.0, "JB must be non-negative, got {v}");
+        }
+    }
+
+    #[test]
+    fn skewed_window_exceeds_symmetric() {
+        // A symmetric window vs. one with a heavy outlier (high skew + kurtosis).
+        let symmetric: Vec<f64> = vec![-3.0, -1.0, 0.0, 1.0, 3.0, -2.0, 2.0, 0.0];
+        let skewed: Vec<f64> = vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 20.0];
+        let jb_sym = JarqueBera::new(8)
+            .unwrap()
+            .batch(&symmetric)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        let jb_skew = JarqueBera::new(8)
+            .unwrap()
+            .batch(&skewed)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert!(
+            jb_skew > jb_sym,
+            "skewed ({jb_skew}) should exceed symmetric ({jb_sym})"
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite() {
+        let mut jb = JarqueBera::new(4).unwrap();
+        let ready = jb
+            .batch(&[1.0, 2.0, 3.0, 5.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_eq!(jb.update(f64::NAN), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut jb = JarqueBera::new(4).unwrap();
+        jb.batch(&[1.0, 2.0, 3.0, 5.0]);
+        assert!(jb.is_ready());
+        jb.reset();
+        assert!(!jb.is_ready());
+        assert_eq!(jb.value(), None);
+        assert_eq!(jb.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let xs: Vec<f64> = (0..120)
+            .map(|i| (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = JarqueBera::new(30).unwrap().batch(&xs);
+        let mut b = JarqueBera::new(30).unwrap();
+        let streamed: Vec<_> = xs.iter().map(|x| b.update(*x)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/kase_devstop.rs

@@ -0,0 +1,341 @@
+//! Kase `DevStop` — a volatility trailing stop on the standard deviation of the
+//! two-bar true range.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`KaseDevStop`]: the active trailing-stop level and the trend
+/// direction it protects.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct KaseDevStopOutput {
+    /// The `DevStop` level — below price in an uptrend, above price in a downtrend.
+    pub value: f64,
+    /// Trend direction: `+1.0` long (stop below price), `-1.0` short.
+    pub direction: f64,
+}
+
+/// Sample standard deviation from a running `(sum, sum_of_squares, count)`.
+fn sample_stddev(sum: f64, sum_sq: f64, count: usize) -> f64 {
+    let n = count as f64;
+    let mean = sum / n;
+    (((sum_sq - n * mean * mean) / (n - 1.0)).max(0.0)).sqrt()
+}
+
+/// Kase `DevStop` — Cynthia Kase's volatility stop, built on the **standard
+/// deviation of the two-bar true range** rather than a single-bar ATR.
+///
+/// ```text
+/// DTR_t = max(high_t, high_{t−1}) − min(low_t, low_{t−1})   (two-bar range)
+/// band  = mean(DTR, period) + dev · stddev(DTR, period)
+/// long  stop = ratchet_up(  highest_high_since_flip − band )
+/// short stop = ratchet_down( lowest_low_since_flip  + band )
+/// ```
+///
+/// Kase observed that range expansion is better captured by a two-bar range than
+/// a one-bar one, and that subtracting a *standard-deviation* band (not a fixed
+/// ATR multiple) adapts the stop to changing volatility. The stop trails the
+/// extreme reached since the last reversal — ratcheting only in the trend's favour
+/// — and flips sides when price closes through it. `dev` selects which `DevStop`
+/// line to follow (`1`, `2` or `3` standard deviations are Kase's warning lines).
+///
+/// The first bar seeds the prior candle; the next `period` two-bar ranges seed the
+/// mean and standard deviation, so the first stop lands after `period + 1` inputs.
+/// Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, KaseDevStop};
+///
+/// let mut indicator = KaseDevStop::new(30, 1.0).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct KaseDevStop {
+    period: usize,
+    dev: f64,
+    prev: Option<Candle>,
+    window: VecDeque<f64>,
+    sum: f64,
+    sum_sq: f64,
+    direction: f64,
+    extreme: f64,
+    stop: f64,
+    last: Option<KaseDevStopOutput>,
+}
+
+impl KaseDevStop {
+    /// Construct a Kase `DevStop` with the given lookback `period` and
+    /// standard-deviation multiplier `dev`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::InvalidPeriod`] if `period < 2` (a standard deviation
+    /// needs at least two samples) and [`Error::NonPositiveMultiplier`] if `dev`
+    /// is not finite and positive.
+    pub fn new(period: usize, dev: f64) -> Result<Self> {
+        if period < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "Kase DevStop period must be >= 2",
+            });
+        }
+        if !dev.is_finite() || dev <= 0.0 {
+            return Err(Error::NonPositiveMultiplier);
+        }
+        Ok(Self {
+            period,
+            dev,
+            prev: None,
+            window: VecDeque::with_capacity(period),
+            sum: 0.0,
+            sum_sq: 0.0,
+            direction: 0.0,
+            extreme: 0.0,
+            stop: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured `(period, dev)`.
+    pub const fn params(&self) -> (usize, f64) {
+        (self.period, self.dev)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<KaseDevStopOutput> {
+        self.last
+    }
+}
+
+impl Indicator for KaseDevStop {
+    type Input = Candle;
+    type Output = KaseDevStopOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<KaseDevStopOutput> {
+        let Some(prev) = self.prev else {
+            self.prev = Some(candle);
+            return None;
+        };
+        let dtr = candle.high.max(prev.high) - candle.low.min(prev.low);
+        self.prev = Some(candle);
+
+        if self.window.len() == self.period {
+            let old = self.window.pop_front().expect("non-empty");
+            self.sum -= old;
+            self.sum_sq -= old * old;
+        }
+        self.window.push_back(dtr);
+        self.sum += dtr;
+        self.sum_sq += dtr * dtr;
+        if self.window.len() < self.period {
+            return None;
+        }
+        let mean = self.sum / self.period as f64;
+        let band = mean + self.dev * sample_stddev(self.sum, self.sum_sq, self.period);
+
+        if self.direction == 0.0 {
+            // Seed the trend as long off the first fully-warmed bar.
+            self.direction = 1.0;
+            self.extreme = candle.high;
+            self.stop = candle.high - band;
+        } else if self.direction > 0.0 {
+            self.extreme = self.extreme.max(candle.high);
+            let raw = self.extreme - band;
+            self.stop = self.stop.max(raw);
+            if candle.close < self.stop {
+                self.direction = -1.0;
+                self.extreme = candle.low;
+                self.stop = candle.low + band;
+            }
+        } else {
+            self.extreme = self.extreme.min(candle.low);
+            let raw = self.extreme + band;
+            self.stop = self.stop.min(raw);
+            if candle.close > self.stop {
+                self.direction = 1.0;
+                self.extreme = candle.high;
+                self.stop = candle.high - band;
+            }
+        }
+
+        let out = KaseDevStopOutput {
+            value: self.stop,
+            direction: self.direction,
+        };
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.prev = None;
+        self.window.clear();
+        self.sum = 0.0;
+        self.sum_sq = 0.0;
+        self.direction = 0.0;
+        self.extreme = 0.0;
+        self.stop = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "KaseDevStop"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+
+    fn c(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new_unchecked(f64::midpoint(high, low), high, low, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_invalid_params() {
+        assert!(matches!(
+            KaseDevStop::new(1, 1.0),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            KaseDevStop::new(30, 0.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+        assert!(matches!(
+            KaseDevStop::new(30, -1.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let k = KaseDevStop::new(30, 1.0).unwrap();
+        assert_eq!(k.params(), (30, 1.0));
+        assert_eq!(k.warmup_period(), 31);
+        assert_eq!(k.name(), "KaseDevStop");
+        assert!(!k.is_ready());
+        assert_eq!(k.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut k = KaseDevStop::new(3, 1.0).unwrap();
+        let candles: Vec<Candle> = (0..8)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        let out = k.batch(&candles);
+        let warmup = k.warmup_period(); // 4
+        assert_eq!(warmup, 4);
+        for v in out.iter().take(warmup - 1) {
+            assert!(v.is_none());
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn uptrend_keeps_stop_below_price() {
+        let mut k = KaseDevStop::new(5, 1.0).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| {
+                let base = 100.0 + 2.0 * f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        for (o, candle) in k.batch(&candles).into_iter().zip(candles.iter()) {
+            if let Some(o) = o {
+                assert_eq!(o.direction, 1.0, "pure uptrend stays long");
+                assert!(o.value < candle.close, "stop below price");
+            }
+        }
+    }
+
+    #[test]
+    fn stop_ratchets_up_in_uptrend() {
+        let mut k = KaseDevStop::new(5, 1.0).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| {
+                let base = 100.0 + 2.0 * f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        let mut prev = f64::NEG_INFINITY;
+        for o in k.batch(&candles).into_iter().flatten() {
+            assert!(o.value >= prev, "long stop must not fall");
+            prev = o.value;
+        }
+    }
+
+    #[test]
+    fn flips_on_reversal() {
+        let mut candles: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        candles.extend((0..40).map(|i| {
+            let base = 140.0 - f64::from(i);
+            c(base + 1.0, base - 1.0, base - 0.5)
+        }));
+        let mut k = KaseDevStop::new(5, 1.0).unwrap();
+        let dirs: Vec<f64> = k
+            .batch(&candles)
+            .into_iter()
+            .flatten()
+            .map(|o| o.direction)
+            .collect();
+        assert!(dirs.iter().any(|&d| d > 0.0));
+        assert!(dirs.iter().any(|&d| d < 0.0));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut k = KaseDevStop::new(5, 1.0).unwrap();
+        let candles: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                c(base + 1.0, base - 1.0, base + 0.5)
+            })
+            .collect();
+        k.batch(&candles);
+        assert!(k.is_ready());
+        k.reset();
+        assert!(!k.is_ready());
+        assert_eq!(k.value(), None);
+        assert_eq!(k.update(candles[0]), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.25).sin() * 9.0;
+                c(base + 2.0, base - 1.5, base + 0.5)
+            })
+            .collect();
+        let batch = KaseDevStop::new(20, 2.0).unwrap().batch(&candles);
+        let mut b = KaseDevStop::new(20, 2.0).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/kase_permission_stochastic.rs

@@ -0,0 +1,234 @@
+//! Kase Permission Stochastic — a double-smoothed stochastic used as a
+//! trade-permission filter.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Kase Permission Stochastic output: a fast and a slow line.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct KasePermissionStochasticOutput {
+    /// Fast line: EMA of the raw `%K` over the smoothing period.
+    pub fast: f64,
+    /// Slow line: EMA of the fast line over the smoothing period.
+    pub slow: f64,
+}
+
+/// Cynthia Kase's Permission Stochastic: a stochastic oscillator smoothed twice,
+/// whose fast/slow relationship grants or denies "permission" to trade in the
+/// direction of a higher-timeframe signal.
+///
+/// ```text
+/// raw%K = 100 * (close - LL) / (HH - LL)     over `length` (50 when HH == LL)
+/// fast  = EMA(raw%K, smooth)
+/// slow  = EMA(fast,  smooth)
+/// ```
+///
+/// The raw stochastic is the usual `%K`, then an EMA produces the *fast* line
+/// and a second EMA of that produces the *slow* line. Kase uses the pair as a
+/// gate: a fast line above the slow line (and rising) gives permission for
+/// longs, the reverse for shorts. When the lookback window is perfectly flat
+/// (`HH == LL`), the raw stochastic is undefined and defaults to the neutral
+/// `50`.
+///
+/// Reference: Cynthia Kase, *Trading with the Odds*, 1996.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, KasePermissionStochastic};
+///
+/// let mut indicator = KasePermissionStochastic::new(9, 3).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let base = 100.0 + f64::from(i);
+///     let candle =
+///         Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 1.0, i64::from(i)).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct KasePermissionStochastic {
+    length: usize,
+    smooth: usize,
+    window: VecDeque<(f64, f64)>,
+    fast_ema: Ema,
+    slow_ema: Ema,
+}
+
+impl KasePermissionStochastic {
+    /// Construct with the stochastic `length` and the EMA `smooth` period
+    /// applied twice.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `length == 0` or `smooth == 0`.
+    pub fn new(length: usize, smooth: usize) -> Result<Self> {
+        if length == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            length,
+            smooth,
+            window: VecDeque::with_capacity(length),
+            fast_ema: Ema::new(smooth)?,
+            slow_ema: Ema::new(smooth)?,
+        })
+    }
+
+    /// Cynthia Kase's classic parameters: `length = 9`, `smooth = 3`.
+    pub fn classic() -> Self {
+        Self::new(9, 3).expect("classic Kase Permission Stochastic parameters are valid")
+    }
+
+    /// Configured `(length, smooth)`.
+    pub const fn periods(&self) -> (usize, usize) {
+        (self.length, self.smooth)
+    }
+}
+
+impl Indicator for KasePermissionStochastic {
+    type Input = Candle;
+    type Output = KasePermissionStochasticOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<KasePermissionStochasticOutput> {
+        self.window.push_back((candle.high, candle.low));
+        if self.window.len() > self.length {
+            self.window.pop_front();
+        }
+        if self.window.len() < self.length {
+            return None;
+        }
+
+        let highest = self.window.iter().map(|w| w.0).fold(f64::MIN, f64::max);
+        let lowest = self.window.iter().map(|w| w.1).fold(f64::MAX, f64::min);
+        let raw_k = if highest > lowest {
+            100.0 * (candle.close - lowest) / (highest - lowest)
+        } else {
+            50.0
+        };
+
+        let fast = self.fast_ema.update(raw_k)?;
+        let slow = self.slow_ema.update(fast)?;
+        Some(KasePermissionStochasticOutput { fast, slow })
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.fast_ema.reset();
+        self.slow_ema.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        // raw%K ready after `length` bars; each EMA seeds over `smooth` values.
+        self.length + 2 * self.smooth - 2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.slow_ema.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "KasePermissionStochastic"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64, ts: i64) -> Candle {
+        Candle::new(f64::midpoint(high, low), high, low, close, 1.0, ts).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(
+            KasePermissionStochastic::new(0, 3),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            KasePermissionStochastic::new(9, 0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let k = KasePermissionStochastic::classic();
+        assert_eq!(k.periods(), (9, 3));
+        // 9 + 2*3 - 2 = 13.
+        assert_eq!(k.warmup_period(), 13);
+        assert_eq!(k.name(), "KasePermissionStochastic");
+        assert!(!k.is_ready());
+    }
+
+    #[test]
+    fn warmup_emits_at_expected_bar() {
+        let mut k = KasePermissionStochastic::new(3, 2).unwrap();
+        // warmup = 3 + 2*2 - 2 = 5 -> first value at input 5 (index 4).
+        let candles: Vec<Candle> = (0..8).map(|i| candle(11.0, 9.0, 10.5, i)).collect();
+        let out = k.batch(&candles);
+        assert!(out[3].is_none());
+        assert!(out[4].is_some());
+    }
+
+    #[test]
+    fn top_of_range_is_high() {
+        // Close pinned at the top of a rising range -> raw%K near 100, both
+        // smoothed lines high.
+        let mut k = KasePermissionStochastic::new(5, 3).unwrap();
+        let candles: Vec<Candle> = (0_i64..40)
+            .map(|i| {
+                let base = 100.0 + i as f64;
+                candle(base + 2.0, base - 2.0, base + 2.0, i)
+            })
+            .collect();
+        let last = k.batch(&candles).last().unwrap().unwrap();
+        assert!(last.fast > 80.0, "fast {} should be high", last.fast);
+        assert!(last.slow > 80.0, "slow {} should be high", last.slow);
+    }
+
+    #[test]
+    fn flat_window_defaults_to_neutral() {
+        // Constant high/low/close -> HH == LL -> raw%K defaults to 50, so both
+        // EMAs converge to 50.
+        let mut k = KasePermissionStochastic::new(4, 2).unwrap();
+        let candles: Vec<Candle> = (0..20).map(|i| candle(10.0, 10.0, 10.0, i)).collect();
+        let last = k.batch(&candles).last().unwrap().unwrap();
+        assert_relative_eq!(last.fast, 50.0, epsilon = 1e-9);
+        assert_relative_eq!(last.slow, 50.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut k = KasePermissionStochastic::classic();
+        let candles: Vec<Candle> = (0..40).map(|i| candle(11.0, 9.0, 10.5, i)).collect();
+        k.batch(&candles);
+        assert!(k.is_ready());
+        k.reset();
+        assert!(!k.is_ready());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..80_i64)
+            .map(|i| {
+                let base = 100.0 + (i as f64 * 0.2).sin() * 5.0;
+                candle(base + 2.0, base - 2.0, base + (i as f64 * 0.3).cos(), i)
+            })
+            .collect();
+        let mut a = KasePermissionStochastic::classic();
+        let mut b = KasePermissionStochastic::classic();
+        assert_eq!(
+            a.batch(&candles),
+            candles.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/kendall_tau.rs

@@ -0,0 +1,296 @@
+//! Kendall's tau-b — rank correlation by concordant vs. discordant pairs.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// `+1` / `0` / `-1` sign of `a − b`.
+fn sign(a: f64, b: f64) -> i32 {
+    if a > b {
+        1
+    } else if a < b {
+        -1
+    } else {
+        0
+    }
+}
+
+/// Kendall's tau-b — a rank correlation between two synchronised series based on
+/// the balance of **concordant** and **discordant** pairs, with a tie correction.
+///
+/// ```text
+/// over all pairs (i < j) in the window:
+///   concordant if (x_j − x_i) and (y_j − y_i) share a sign
+///   discordant if they have opposite signs
+///   tie_x / tie_y if the respective difference is zero
+/// n0  = N(N−1)/2
+/// tau_b = (n_concordant − n_discordant) / sqrt((n0 − tie_x)(n0 − tie_y))
+/// ```
+///
+/// Where [`PearsonCorrelation`](crate::PearsonCorrelation) measures *linear*
+/// co-movement and [`SpearmanCorrelation`](crate::SpearmanCorrelation) correlates
+/// ranks via their differences, Kendall's tau counts how often the two series move
+/// the **same direction** between every pair of observations. It is the most
+/// robust of the three to outliers and to non-linear-but-monotonic
+/// relationships, and the tau-b form corrects for ties so repeated values do not
+/// bias it. The output is in `[−1, +1]`: `+1` perfectly concordant, `−1`
+/// perfectly discordant, `0` no monotonic association.
+///
+/// The window holds the last `period` pairs and is recomputed each bar in
+/// O(`period²`). A window with no untied pairs on one side returns `0`. The first
+/// value lands after `period` inputs.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, KendallTau};
+///
+/// let mut indicator = KendallTau::new(20).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let x = f64::from(i);
+///     last = indicator.update((x, 2.0 * x)); // perfectly concordant
+/// }
+/// assert!((last.unwrap() - 1.0).abs() < 1e-9);
+/// ```
+#[derive(Debug, Clone)]
+pub struct KendallTau {
+    period: usize,
+    window: VecDeque<(f64, f64)>,
+    last: Option<f64>,
+}
+
+impl KendallTau {
+    /// Construct a rolling Kendall's tau-b over `period` pairs.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::InvalidPeriod`] if `period < 2` (a correlation needs at
+    /// least two pairs).
+    pub fn new(period: usize) -> Result<Self> {
+        if period < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "Kendall tau needs period >= 2",
+            });
+        }
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+            last: None,
+        })
+    }
+
+    /// Configured window of pairs.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+
+    fn compute(&self) -> f64 {
+        let pairs: Vec<(f64, f64)> = self.window.iter().copied().collect();
+        let len = pairs.len();
+        let mut concordant: i64 = 0;
+        let mut discordant: i64 = 0;
+        let mut tie_x: i64 = 0;
+        let mut tie_y: i64 = 0;
+        for i in 0..len {
+            for j in (i + 1)..len {
+                let sx = sign(pairs[j].0, pairs[i].0);
+                let sy = sign(pairs[j].1, pairs[i].1);
+                if sx == 0 {
+                    tie_x += 1;
+                }
+                if sy == 0 {
+                    tie_y += 1;
+                }
+                let prod = sx * sy;
+                if prod > 0 {
+                    concordant += 1;
+                } else if prod < 0 {
+                    discordant += 1;
+                }
+            }
+        }
+        let n0 = (len * (len - 1) / 2) as f64;
+        let denom = ((n0 - tie_x as f64) * (n0 - tie_y as f64)).sqrt();
+        if denom == 0.0 {
+            return 0.0;
+        }
+        ((concordant - discordant) as f64 / denom).clamp(-1.0, 1.0)
+    }
+}
+
+impl Indicator for KendallTau {
+    type Input = (f64, f64);
+    type Output = f64;
+
+    fn update(&mut self, input: (f64, f64)) -> Option<f64> {
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        if self.window.len() < self.period {
+            return None;
+        }
+        let out = self.compute();
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "KendallTau"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_period_below_two() {
+        assert!(matches!(
+            KendallTau::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(KendallTau::new(2).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let k = KendallTau::new(20).unwrap();
+        assert_eq!(k.period(), 20);
+        assert_eq!(k.warmup_period(), 20);
+        assert_eq!(k.name(), "KendallTau");
+        assert!(!k.is_ready());
+        assert_eq!(k.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut k = KendallTau::new(4).unwrap();
+        let out = k.batch(&[(1.0, 1.0), (2.0, 2.0), (3.0, 3.0), (4.0, 4.0), (5.0, 5.0)]);
+        for v in out.iter().take(3) {
+            assert!(v.is_none());
+        }
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn monotone_increasing_is_one() {
+        let pairs: Vec<(f64, f64)> = (0..20)
+            .map(|i| (f64::from(i), 2.0 * f64::from(i) + 1.0))
+            .collect();
+        let last = KendallTau::new(10)
+            .unwrap()
+            .batch(&pairs)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, 1.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn monotone_decreasing_is_minus_one() {
+        let pairs: Vec<(f64, f64)> = (0..20)
+            .map(|i| (f64::from(i), -3.0 * f64::from(i)))
+            .collect();
+        let last = KendallTau::new(10)
+            .unwrap()
+            .batch(&pairs)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, -1.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn constant_channel_yields_zero() {
+        // y constant -> every y-difference is a tie -> denom 0 -> 0.
+        let pairs: Vec<(f64, f64)> = (0..20).map(|i| (f64::from(i), 7.0)).collect();
+        let last = KendallTau::new(8)
+            .unwrap()
+            .batch(&pairs)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_in_range() {
+        let pairs: Vec<(f64, f64)> = (0..80)
+            .map(|i| {
+                let t = f64::from(i);
+                (100.0 + t.sin() * 5.0, 50.0 + (t * 0.3).cos() * 3.0)
+            })
+            .collect();
+        for v in KendallTau::new(20)
+            .unwrap()
+            .batch(&pairs)
+            .into_iter()
+            .flatten()
+        {
+            assert!((-1.0..=1.0).contains(&v));
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut k = KendallTau::new(4).unwrap();
+        k.batch(&[(1.0, 1.0), (2.0, 2.0), (3.0, 3.0), (4.0, 4.0)]);
+        assert!(k.is_ready());
+        k.reset();
+        assert!(!k.is_ready());
+        assert_eq!(k.value(), None);
+        assert_eq!(k.update((1.0, 1.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let pairs: Vec<(f64, f64)> = (0..60)
+            .map(|i| {
+                let t = f64::from(i);
+                (t.sin(), (t * 0.5).cos())
+            })
+            .collect();
+        let batch = KendallTau::new(14).unwrap().batch(&pairs);
+        let mut b = KendallTau::new(14).unwrap();
+        let streamed: Vec<_> = pairs.iter().map(|p| b.update(*p)).collect();
+        assert_eq!(batch, streamed);
+    }
+
+    #[test]
+    fn ties_are_corrected() {
+        // Tied x values (points 0 and 1) and tied y values (points 1 and 2)
+        // exercise the tie_x / tie_y correction counters.
+        let mut k = KendallTau::new(4).unwrap();
+        assert_eq!(k.update((1.0, 1.0)), None);
+        assert_eq!(k.update((1.0, 2.0)), None);
+        assert_eq!(k.update((2.0, 2.0)), None);
+        let v = k.update((3.0, 3.0)).unwrap();
+        assert!((-1.0..=1.0).contains(&v), "got {v}");
+    }
+}

crates/wickra-core/src/indicators/macd_histogram.rs

@@ -0,0 +1,184 @@
+//! MACD Histogram (standalone).
+
+use crate::error::Result;
+use crate::indicators::macd::MacdIndicator;
+use crate::traits::Indicator;
+
+/// MACD Histogram — the `macd − signal` bar of [`MacdIndicator`] as a
+/// standalone scalar indicator.
+///
+/// ```text
+/// macd      = EMA(fast) − EMA(slow)
+/// signal    = EMA(macd, signal)
+/// histogram = macd − signal
+/// ```
+///
+/// The histogram is the most actively traded part of MACD: it crosses zero
+/// exactly when the MACD line crosses its signal, and its slope measures
+/// whether that momentum is accelerating or fading. This wrapper exposes just
+/// that series for pipelines that want a plain `f64` stream rather than the
+/// full [`MacdOutput`](crate::MacdOutput); for the line and signal alongside
+/// it, use [`MacdIndicator`](crate::MacdIndicator) directly.
+///
+/// Standard parameters are `fast = 12`, `slow = 26`, `signal = 9`, so the
+/// first value lands after `slow + signal − 1` inputs — exactly when
+/// [`MacdIndicator`] emits its first full output.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, MacdHistogram};
+///
+/// let mut indicator = MacdHistogram::new(12, 26, 9).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct MacdHistogram {
+    macd: MacdIndicator,
+}
+
+impl MacdHistogram {
+    /// Construct a MACD histogram with the given periods.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if any period is zero, and
+    /// [`Error::InvalidPeriod`] if `fast >= slow`.
+    pub fn new(fast: usize, slow: usize, signal: usize) -> Result<Self> {
+        Ok(Self {
+            macd: MacdIndicator::new(fast, slow, signal)?,
+        })
+    }
+
+    /// Default `(12, 26, 9)` configuration, matching every classical chart package.
+    pub fn classic() -> Self {
+        Self::new(12, 26, 9).expect("classic MACD periods are valid")
+    }
+
+    /// Configured periods as `(fast, slow, signal)`.
+    pub const fn periods(&self) -> (usize, usize, usize) {
+        self.macd.periods()
+    }
+}
+
+impl Indicator for MacdHistogram {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        self.macd.update(input).map(|out| out.histogram)
+    }
+
+    fn reset(&mut self) {
+        self.macd.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.macd.warmup_period()
+    }
+
+    fn is_ready(&self) -> bool {
+        self.macd.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "MacdHistogram"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::error::Error;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_periods() {
+        assert!(matches!(
+            MacdHistogram::new(0, 26, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            MacdHistogram::new(12, 26, 0),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            MacdHistogram::new(26, 12, 9),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let osc = MacdHistogram::classic();
+        assert_eq!(osc.periods(), (12, 26, 9));
+        assert_eq!(osc.name(), "MacdHistogram");
+        assert_eq!(osc.warmup_period(), 26 + 9 - 1);
+        assert!(!osc.is_ready());
+    }
+
+    #[test]
+    fn equals_macd_histogram_field() {
+        // The standalone series must be exactly MacdIndicator's histogram bar.
+        let prices: Vec<f64> = (1..=120)
+            .map(|i| 100.0 + (f64::from(i) * 0.25).sin() * 8.0)
+            .collect();
+        let hist = MacdHistogram::classic().batch(&prices);
+        let full = MacdIndicator::classic().batch(&prices);
+        assert_eq!(hist.len(), full.len());
+        for (h, m) in hist.iter().zip(full.iter()) {
+            assert_eq!(h.is_some(), m.is_some());
+            if let (Some(h), Some(m)) = (h, m) {
+                assert_relative_eq!(*h, m.histogram, epsilon = 1e-12);
+            }
+        }
+    }
+
+    #[test]
+    fn warmup_emits_first_value_at_warmup_period() {
+        let mut osc = MacdHistogram::new(3, 6, 3).unwrap();
+        let warmup = osc.warmup_period();
+        assert_eq!(warmup, 6 + 3 - 1);
+        for i in 1..warmup {
+            assert!(osc.update(100.0 + i as f64).is_none());
+        }
+        assert!(osc.update(100.0 + warmup as f64).is_some());
+        assert!(osc.is_ready());
+    }
+
+    #[test]
+    fn constant_series_converges_to_zero() {
+        let mut osc = MacdHistogram::classic();
+        let out = osc.batch(&[100.0_f64; 200]);
+        let last = out.iter().rev().flatten().next().expect("emits a value");
+        assert_relative_eq!(*last, 0.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=100)
+            .map(|i| (f64::from(i) * 0.4).cos() * 10.0)
+            .collect();
+        let mut a = MacdHistogram::classic();
+        let mut b = MacdHistogram::classic();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut osc = MacdHistogram::classic();
+        osc.batch(&(1..=80).map(f64::from).collect::<Vec<_>>());
+        assert!(osc.is_ready());
+        osc.reset();
+        assert!(!osc.is_ready());
+        assert_eq!(osc.update(1.0), None);
+    }
+}

crates/wickra-core/src/indicators/median_channel.rs

@@ -0,0 +1,237 @@
+//! Median Channel — a robust median ± MAD envelope.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::rolling_quantile::quantile_sorted;
+use crate::traits::Indicator;
+
+/// Median Channel output.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct MedianChannelOutput {
+    /// Upper band: `median + multiplier · MAD`.
+    pub upper: f64,
+    /// Middle line: the rolling median.
+    pub middle: f64,
+    /// Lower band: `median − multiplier · MAD`.
+    pub lower: f64,
+}
+
+/// Median Channel: a robust analogue of Bollinger Bands built from the rolling
+/// median and the median absolute deviation (MAD).
+///
+/// ```text
+/// middle = median(close, period)
+/// MAD    = median( | close_i − middle | )
+/// upper  = middle + multiplier · MAD
+/// lower  = middle − multiplier · MAD
+/// ```
+///
+/// Where [`BollingerBands`](crate::BollingerBands) centre on the mean and scale
+/// by the standard deviation — both of which a single spike can drag
+/// arbitrarily far — the Median Channel uses two order statistics. The
+/// breakdown point of the median and MAD is 50%: up to half the window can be
+/// contaminated before the centre or width is materially distorted. That makes
+/// the channel well suited to noisy, gap-prone, or fat-tailed series where
+/// Bollinger Bands flare on every outlier. Both quantiles use the type-7
+/// interpolation shared with [`RollingQuantile`](crate::RollingQuantile).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, MedianChannel};
+///
+/// let mut indicator = MedianChannel::new(20, 2.0).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + f64::from(i % 5));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct MedianChannel {
+    period: usize,
+    multiplier: f64,
+    window: VecDeque<f64>,
+    scratch: Vec<f64>,
+    deviations: Vec<f64>,
+}
+
+impl MedianChannel {
+    /// Construct a new Median Channel.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`, or
+    /// [`Error::NonPositiveMultiplier`] if `multiplier` is not strictly
+    /// positive and finite.
+    pub fn new(period: usize, multiplier: f64) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if !multiplier.is_finite() || multiplier <= 0.0 {
+            return Err(Error::NonPositiveMultiplier);
+        }
+        Ok(Self {
+            period,
+            multiplier,
+            window: VecDeque::with_capacity(period),
+            scratch: Vec::with_capacity(period),
+            deviations: Vec::with_capacity(period),
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Configured multiplier.
+    pub const fn multiplier(&self) -> f64 {
+        self.multiplier
+    }
+}
+
+impl Indicator for MedianChannel {
+    type Input = f64;
+    type Output = MedianChannelOutput;
+
+    fn update(&mut self, value: f64) -> Option<MedianChannelOutput> {
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(value);
+        if self.window.len() < self.period {
+            return None;
+        }
+        self.scratch.clear();
+        self.scratch.extend(self.window.iter().copied());
+        self.scratch.sort_by(f64::total_cmp);
+        let median = quantile_sorted(&self.scratch, 0.5);
+
+        self.deviations.clear();
+        for &v in &self.window {
+            self.deviations.push((v - median).abs());
+        }
+        self.deviations.sort_by(f64::total_cmp);
+        let mad = quantile_sorted(&self.deviations, 0.5);
+        let offset = self.multiplier * mad;
+
+        Some(MedianChannelOutput {
+            upper: median + offset,
+            middle: median,
+            lower: median - offset,
+        })
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.scratch.clear();
+        self.deviations.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "MedianChannel"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(MedianChannel::new(0, 2.0), Err(Error::PeriodZero)));
+        assert!(MedianChannel::new(1, 2.0).is_ok());
+    }
+
+    #[test]
+    fn rejects_non_positive_multiplier() {
+        assert!(matches!(
+            MedianChannel::new(20, 0.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+        assert!(matches!(
+            MedianChannel::new(20, -1.0),
+            Err(Error::NonPositiveMultiplier)
+        ));
+        assert!(matches!(
+            MedianChannel::new(20, f64::NAN),
+            Err(Error::NonPositiveMultiplier)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let mc = MedianChannel::new(20, 2.0).unwrap();
+        assert_eq!(mc.period(), 20);
+        assert_relative_eq!(mc.multiplier(), 2.0, epsilon = 1e-12);
+        assert_eq!(mc.warmup_period(), 20);
+        assert_eq!(mc.name(), "MedianChannel");
+        assert!(!mc.is_ready());
+    }
+
+    #[test]
+    fn warms_up_then_emits() {
+        let mut mc = MedianChannel::new(5, 2.0).unwrap();
+        for v in [1.0, 2.0, 3.0, 4.0] {
+            assert!(mc.update(v).is_none());
+        }
+        assert!(mc.update(5.0).is_some());
+        assert!(mc.is_ready());
+    }
+
+    #[test]
+    fn known_channel() {
+        // [1,2,3,4,5]: median 3; |dev| sorted [0,1,1,2,2] -> MAD 1.
+        // upper = 3 + 2*1 = 5; lower = 3 - 2*1 = 1.
+        let mut mc = MedianChannel::new(5, 2.0).unwrap();
+        let out = mc.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        let last = out[4].unwrap();
+        assert_relative_eq!(last.middle, 3.0, epsilon = 1e-12);
+        assert_relative_eq!(last.upper, 5.0, epsilon = 1e-12);
+        assert_relative_eq!(last.lower, 1.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn robust_to_outlier() {
+        // Replacing the last value with a huge spike leaves the median centre
+        // unchanged (still the middle order statistic).
+        let mut mc = MedianChannel::new(5, 2.0).unwrap();
+        let out = mc.batch(&[1.0, 2.0, 3.0, 4.0, 1_000.0]);
+        assert_relative_eq!(out[4].unwrap().middle, 3.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn rolling_window_evicts_oldest() {
+        // Ten values through a period-5 window: only the last five survive,
+        // reproducing the `known_channel` window.
+        let mut mc = MedianChannel::new(5, 2.0).unwrap();
+        let out = mc.batch(&[10.0, 10.0, 10.0, 10.0, 10.0, 1.0, 2.0, 3.0, 4.0, 5.0]);
+        let last = out[9].unwrap();
+        assert_relative_eq!(last.middle, 3.0, epsilon = 1e-12);
+        assert_relative_eq!(last.upper, 5.0, epsilon = 1e-12);
+        assert_relative_eq!(last.lower, 1.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut mc = MedianChannel::new(5, 2.0).unwrap();
+        for v in [1.0, 2.0, 3.0, 4.0, 5.0] {
+            mc.update(v);
+        }
+        assert!(mc.is_ready());
+        mc.reset();
+        assert!(!mc.is_ready());
+        assert!(mc.update(1.0).is_none());
+    }
+}

crates/wickra-core/src/indicators/median_ma.rs

@@ -0,0 +1,205 @@
+//! Median Moving Average.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Median Moving Average — the rolling median of the last `period` inputs.
+///
+/// For an odd `period` the output is the middle order statistic of the window;
+/// for an even `period` it is the average of the two central values. Because it
+/// is a rank statistic rather than a sum, the median MA is far more robust to
+/// single outliers than the [`Sma`](crate::Sma): a lone spike shifts the rank
+/// by at most one position instead of dragging the whole average.
+///
+/// Each `update` slides the window and computes the median by sorting a copy of
+/// the `period` buffered values — O(`period` · log `period`) per step, with the
+/// period fixed and bounded.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, MedianMa};
+///
+/// let mut indicator = MedianMa::new(5).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct MedianMa {
+    period: usize,
+    window: VecDeque<f64>,
+}
+
+impl MedianMa {
+    /// Construct a new median moving average over `period` inputs.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if the window is full.
+    pub fn value(&self) -> Option<f64> {
+        if self.window.len() != self.period {
+            return None;
+        }
+        let mut sorted: Vec<f64> = self.window.iter().copied().collect();
+        sorted.sort_by(|a, b| a.partial_cmp(b).expect("window holds only finite values"));
+        let mid = self.period / 2;
+        if self.period % 2 == 1 {
+            Some(sorted[mid])
+        } else {
+            Some(f64::midpoint(sorted[mid - 1], sorted[mid]))
+        }
+    }
+}
+
+impl Indicator for MedianMa {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.value();
+        }
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        self.value()
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "MedianMA"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn new_rejects_zero_period() {
+        assert!(matches!(MedianMa::new(0), Err(Error::PeriodZero)));
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let mma = MedianMa::new(7).unwrap();
+        assert_eq!(mma.period(), 7);
+        assert_eq!(mma.warmup_period(), 7);
+        assert_eq!(mma.name(), "MedianMA");
+    }
+
+    #[test]
+    fn warmup_returns_none_then_odd_median() {
+        let mut mma = MedianMa::new(3).unwrap();
+        assert_eq!(mma.update(5.0), None);
+        assert_eq!(mma.update(1.0), None);
+        // median of [5, 1, 3] = 3 (middle order statistic).
+        assert_relative_eq!(mma.update(3.0).unwrap(), 3.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn even_period_averages_two_central_values() {
+        // median of [1, 2, 3, 4] = (2 + 3) / 2 = 2.5.
+        let mut mma = MedianMa::new(4).unwrap();
+        let v = mma.batch(&[1.0, 2.0, 3.0, 4.0]);
+        assert_relative_eq!(v[3].unwrap(), 2.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn robust_to_single_outlier() {
+        // A lone spike does not move the median of an odd window the way it
+        // would move an SMA. median of [10, 11, 9999] = 11.
+        let mut mma = MedianMa::new(3).unwrap();
+        let v = mma.batch(&[10.0, 11.0, 9999.0]);
+        assert_relative_eq!(v[2].unwrap(), 11.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn period_one_is_pass_through() {
+        let mut mma = MedianMa::new(1).unwrap();
+        assert_relative_eq!(mma.update(5.5).unwrap(), 5.5, epsilon = 1e-12);
+        assert_relative_eq!(mma.update(7.5).unwrap(), 7.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn slides_window_correctly() {
+        // After [1,2,3] the window slides to [2,3,4] -> median 3, then [3,4,5] -> 4.
+        let mut mma = MedianMa::new(3).unwrap();
+        let v = mma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        assert_relative_eq!(v[2].unwrap(), 2.0, epsilon = 1e-12);
+        assert_relative_eq!(v[3].unwrap(), 3.0, epsilon = 1e-12);
+        assert_relative_eq!(v[4].unwrap(), 4.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut mma = MedianMa::new(4).unwrap();
+        mma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        assert!(mma.is_ready());
+        mma.reset();
+        assert!(!mma.is_ready());
+        assert_eq!(mma.update(10.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=20).map(|i| (f64::from(i) * 0.7).sin() * 5.0).collect();
+        let mut a = MedianMa::new(5).unwrap();
+        let mut b = MedianMa::new(5).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite_input_but_keeps_state() {
+        let mut mma = MedianMa::new(3).unwrap();
+        mma.update(5.0);
+        mma.update(1.0);
+        let ready = mma
+            .update(3.0)
+            .expect("MedianMA(3) ready after three inputs");
+        assert_eq!(mma.update(f64::NAN), Some(ready));
+        assert_eq!(mma.update(f64::INFINITY), Some(ready));
+        // Window still [5, 1, 3] -> next real input slides to [1, 3, 8] -> median 3.
+        assert_relative_eq!(mma.update(8.0).unwrap(), 3.0, epsilon = 1e-12);
+    }
+}

crates/wickra-core/src/indicators/mod.rs

@@ -17,6 +17,7 @@ mod accelerator_oscillator;
 mod ad_oscillator;
 mod ad_volume_line;
 mod adaptive_cycle;
+mod adaptive_laguerre_filter;
 mod adl;
 mod advance_block;
 mod advance_decline;
@@ -34,6 +35,7 @@ mod aroon;
 mod aroon_oscillator;
 mod atr;
 mod atr_bands;
+mod atr_ratchet;
 mod atr_trailing_stop;
 mod auto_fib;
 mod autocorrelation;
@@ -47,9 +49,12 @@ mod bat;
 mod belt_hold;
 mod beta;
 mod beta_neutral_spread;
+mod better_volume;
+mod bipower_variation;
 mod body_size_pct;
 mod bollinger;
 mod bollinger_bandwidth;
+mod bomar_bands;
 mod breadth_thrust;
 mod breakaway;
 mod bullish_percent_index;
@@ -90,7 +95,9 @@ mod dema;
 mod demand_index;
 mod demark_pivots;
 mod depth_slope;
+mod derivative_oscillator;
 mod detrended_std_dev;
+mod disparity_index;
 mod distance_ssd;
 mod doji;
 mod doji_star;
@@ -103,15 +110,20 @@ mod dpo;
 mod dragonfly_doji;
 mod drawdown_duration;
 mod dx;
+mod dynamic_momentum_index;
 mod ease_of_movement;
 mod effective_spread;
 mod ehlers_stochastic;
+mod ehma;
 mod elder_impulse;
+mod elder_ray;
+mod elder_safezone;
 mod ema;
 mod empirical_mode_decomposition;
 mod engulfing;
 mod evening_doji_star;
 mod evwma;
+mod ewma_volatility;
 mod expectancy;
 mod falling_three_methods;
 mod fama;
@@ -124,6 +136,7 @@ mod fib_projection;
 mod fib_retracement;
 mod fib_time_zones;
 mod fibonacci_pivots;
+mod fisher_rsi;
 mod fisher_transform;
 mod flag_pennant;
 mod footprint;
@@ -136,8 +149,12 @@ mod funding_rate_mean;
 mod funding_rate_zscore;
 mod gain_loss_ratio;
 mod gap_side_by_side_white;
+mod garch11;
 mod garman_klass;
 mod gartley;
+mod gator_oscillator;
+mod generalized_dema;
+mod geometric_ma;
 mod golden_pocket;
 mod granger_causality;
 mod gravestone_doji;
@@ -155,6 +172,7 @@ mod hilbert_dominant_cycle;
 mod hilo_activator;
 mod historical_volatility;
 mod hma;
+mod holt_winters;
 mod homing_pigeon;
 mod ht_dcphase;
 mod ht_phasor;
@@ -168,16 +186,22 @@ mod inertia;
 mod information_ratio;
 mod initial_balance;
 mod instantaneous_trendline;
+mod intraday_intensity;
+mod intraday_momentum_index;
 mod intraday_volatility_profile;
 mod inverse_fisher_transform;
 mod inverted_hammer;
+mod jarque_bera;
 mod jma;
 mod jump_indicator;
 mod kagi_bars;
 mod kalman_hedge_ratio;
 mod kama;
+mod kase_devstop;
+mod kase_permission_stochastic;
 mod kelly_criterion;
 mod keltner;
+mod kendall_tau;
 mod kicking;
 mod kicking_by_length;
 mod kst;
@@ -201,6 +225,7 @@ mod ma_envelope;
 mod macd;
 mod macd_ext;
 mod macd_fix;
+mod macd_histogram;
 mod mama;
 mod market_facilitation_index;
 mod marubozu;
@@ -212,6 +237,8 @@ mod mcclellan_oscillator;
 mod mcclellan_summation_index;
 mod mcginley_dynamic;
 mod median_absolute_deviation;
+mod median_channel;
+mod median_ma;
 mod median_price;
 mod mfi;
 mod microprice;
@@ -219,11 +246,13 @@ mod mid_point;
 mod mid_price;
 mod minus_di;
 mod minus_dm;
+mod modified_ma_stop;
 mod mom;
 mod morning_doji_star;
 mod morning_evening_star;
 mod natr;
 mod new_highs_new_lows;
+mod nrtr;
 mod nvi;
 mod ob_imbalance_full;
 mod ob_imbalance_top1;
@@ -254,10 +283,17 @@ mod plus_di;
 mod plus_dm;
 mod pmo;
 mod point_and_figure_bars;
+mod polarized_fractal_efficiency;
 mod ppo;
+mod ppo_histogram;
 mod profit_factor;
+mod projection_bands;
+mod projection_oscillator;
 mod psar;
 mod pvi;
+mod qqe;
+mod qstick;
+mod quartile_bands;
 mod quoted_spread;
 mod r_squared;
 mod realized_spread;
@@ -270,6 +306,7 @@ mod renko_bars;
 mod renko_trailing_stop;
 mod rickshaw_man;
 mod rising_three_methods;
+mod rmi;
 mod roc;
 mod rocp;
 mod rocr;
@@ -279,25 +316,30 @@ mod roll_measure;
 mod rolling_correlation;
 mod rolling_covariance;
 mod rolling_iqr;
+mod rolling_min_max_scaler;
 mod rolling_percentile_rank;
 mod rolling_quantile;
 mod roofing_filter;
 mod rsi;
+mod rsx;
 mod rvi;
 mod rvi_volatility;
 mod rwi;
+mod sample_entropy;
 mod sar_ext;
 mod seasonal_z_score;
 mod separating_lines;
 mod session_high_low;
 mod session_range;
 mod session_vwap;
+mod shannon_entropy;
 mod shark;
 mod sharpe_ratio;
 mod shooting_star;
 mod short_line;
 mod signed_volume;
 mod sine_wave;
+mod sine_weighted_ma;
 mod skewness;
 mod sma;
 mod smi;
@@ -318,6 +360,7 @@ mod step_trailing_stop;
 mod stick_sandwich;
 mod stoch_rsi;
 mod stochastic;
+mod stochastic_cci;
 mod super_smoother;
 mod super_trend;
 mod t3;
@@ -347,10 +390,13 @@ mod three_stars_in_south;
 mod thrusting;
 mod tick_index;
 mod tii;
+mod time_based_stop;
 mod time_of_day_return_profile;
 mod tpo_profile;
 mod trade_imbalance;
+mod trade_volume_index;
 mod trend_label;
+mod trend_strength_index;
 mod treynor_ratio;
 mod triangle;
 mod trima;
@@ -359,11 +405,14 @@ mod triple_top_bottom;
 mod trix;
 mod true_range;
 mod tsf;
+mod tsf_oscillator;
 mod tsi;
 mod tsv;
 mod ttm_squeeze;
+mod ttm_trend;
 mod turn_of_month;
 mod tweezer;
+mod twiggs_money_flow;
 mod two_crows;
 mod typical_price;
 mod ulcer_index;
@@ -378,10 +427,15 @@ mod variance;
 mod variance_ratio;
 mod vertical_horizontal_filter;
 mod vidya;
+mod volatility_cone;
+mod volatility_of_volatility;
+mod volatility_ratio;
 mod volty_stop;
 mod volume_by_time_profile;
 mod volume_oscillator;
 mod volume_profile;
+mod volume_rsi;
+mod volume_weighted_macd;
 mod vortex;
 mod vpin;
 mod vpt;
@@ -389,6 +443,8 @@ mod vwap;
 mod vwap_stddev_bands;
 mod vwma;
 mod vzo;
+mod wad;
+mod wave_pm;
 mod wave_trend;
 mod wedge;
 mod weighted_close;
@@ -413,6 +469,7 @@ pub use accelerator_oscillator::AcceleratorOscillator;
 pub use ad_oscillator::AdOscillator;
 pub use ad_volume_line::AdVolumeLine;
 pub use adaptive_cycle::AdaptiveCycle;
+pub use adaptive_laguerre_filter::AdaptiveLaguerreFilter;
 pub use adl::Adl;
 pub use advance_block::AdvanceBlock;
 pub use advance_decline::AdvanceDecline;
@@ -430,6 +487,7 @@ pub use aroon::{Aroon, AroonOutput};
 pub use aroon_oscillator::AroonOscillator;
 pub use atr::Atr;
 pub use atr_bands::{AtrBands, AtrBandsOutput};
+pub use atr_ratchet::{AtrRatchet, AtrRatchetOutput};
 pub use atr_trailing_stop::AtrTrailingStop;
 pub use auto_fib::{AutoFib, AutoFibOutput};
 pub use autocorrelation::Autocorrelation;
@@ -443,9 +501,12 @@ pub use bat::Bat;
 pub use belt_hold::BeltHold;
 pub use beta::Beta;
 pub use beta_neutral_spread::BetaNeutralSpread;
+pub use better_volume::BetterVolume;
+pub use bipower_variation::BipowerVariation;
 pub use body_size_pct::BodySizePct;
 pub use bollinger::{BollingerBands, BollingerOutput};
 pub use bollinger_bandwidth::BollingerBandwidth;
+pub use bomar_bands::{BomarBands, BomarBandsOutput};
 pub use breadth_thrust::BreadthThrust;
 pub use breakaway::Breakaway;
 pub use bullish_percent_index::BullishPercentIndex;
@@ -486,7 +547,9 @@ pub use dema::Dema;
 pub use demand_index::DemandIndex;
 pub use demark_pivots::{DemarkPivots, DemarkPivotsOutput};
 pub use depth_slope::DepthSlope;
+pub use derivative_oscillator::DerivativeOscillator;
 pub use detrended_std_dev::DetrendedStdDev;
+pub use disparity_index::DisparityIndex;
 pub use distance_ssd::DistanceSsd;
 pub use doji::Doji;
 pub use doji_star::DojiStar;
@@ -499,15 +562,20 @@ pub use dpo::Dpo;
 pub use dragonfly_doji::DragonflyDoji;
 pub use drawdown_duration::DrawdownDuration;
 pub use dx::Dx;
+pub use dynamic_momentum_index::DynamicMomentumIndex;
 pub use ease_of_movement::EaseOfMovement;
 pub use effective_spread::EffectiveSpread;
 pub use ehlers_stochastic::EhlersStochastic;
+pub use ehma::Ehma;
 pub use elder_impulse::ElderImpulse;
+pub use elder_ray::{ElderRay, ElderRayOutput};
+pub use elder_safezone::{ElderSafeZone, ElderSafeZoneOutput};
 pub use ema::Ema;
 pub use empirical_mode_decomposition::EmpiricalModeDecomposition;
 pub use engulfing::Engulfing;
 pub use evening_doji_star::EveningDojiStar;
 pub use evwma::Evwma;
+pub use ewma_volatility::EwmaVolatility;
 pub use expectancy::Expectancy;
 pub use falling_three_methods::FallingThreeMethods;
 pub use fama::Fama;
@@ -520,6 +588,7 @@ pub use fib_projection::{FibProjection, FibProjectionOutput};
 pub use fib_retracement::{FibRetracement, FibRetracementOutput};
 pub use fib_time_zones::{FibTimeZones, FibTimeZonesOutput};
 pub use fibonacci_pivots::{FibonacciPivots, FibonacciPivotsOutput};
+pub use fisher_rsi::FisherRsi;
 pub use fisher_transform::FisherTransform;
 pub use flag_pennant::FlagPennant;
 pub use footprint::{Footprint, FootprintLevel, FootprintOutput};
@@ -532,8 +601,12 @@ pub use funding_rate_mean::FundingRateMean;
 pub use funding_rate_zscore::FundingRateZScore;
 pub use gain_loss_ratio::GainLossRatio;
 pub use gap_side_by_side_white::GapSideBySideWhite;
+pub use garch11::Garch11;
 pub use garman_klass::GarmanKlassVolatility;
 pub use gartley::Gartley;
+pub use gator_oscillator::{GatorOscillator, GatorOscillatorOutput};
+pub use generalized_dema::GeneralizedDema;
+pub use geometric_ma::GeometricMa;
 pub use golden_pocket::{GoldenPocket, GoldenPocketOutput};
 pub use granger_causality::GrangerCausality;
 pub use gravestone_doji::GravestoneDoji;
@@ -551,6 +624,7 @@ pub use hilbert_dominant_cycle::HilbertDominantCycle;
 pub use hilo_activator::HiLoActivator;
 pub use historical_volatility::HistoricalVolatility;
 pub use hma::Hma;
+pub use holt_winters::HoltWinters;
 pub use homing_pigeon::HomingPigeon;
 pub use ht_dcphase::HtDcPhase;
 pub use ht_phasor::{HtPhasor, HtPhasorOutput};
@@ -564,16 +638,22 @@ pub use inertia::Inertia;
 pub use information_ratio::InformationRatio;
 pub use initial_balance::{InitialBalance, InitialBalanceOutput};
 pub use instantaneous_trendline::InstantaneousTrendline;
+pub use intraday_intensity::IntradayIntensity;
+pub use intraday_momentum_index::IntradayMomentumIndex;
 pub use intraday_volatility_profile::{IntradayVolatilityProfile, IntradayVolatilityProfileOutput};
 pub use inverse_fisher_transform::InverseFisherTransform;
 pub use inverted_hammer::InvertedHammer;
+pub use jarque_bera::JarqueBera;
 pub use jma::Jma;
 pub use jump_indicator::JumpIndicator;
 pub use kagi_bars::{KagiBar, KagiBars};
 pub use kalman_hedge_ratio::{KalmanHedgeRatio, KalmanHedgeRatioOutput};
 pub use kama::Kama;
+pub use kase_devstop::{KaseDevStop, KaseDevStopOutput};
+pub use kase_permission_stochastic::{KasePermissionStochastic, KasePermissionStochasticOutput};
 pub use kelly_criterion::KellyCriterion;
 pub use keltner::{Keltner, KeltnerOutput};
+pub use kendall_tau::KendallTau;
 pub use kicking::Kicking;
 pub use kicking_by_length::KickingByLength;
 pub use kst::{Kst, KstOutput};
@@ -597,6 +677,7 @@ pub use ma_envelope::{MaEnvelope, MaEnvelopeOutput};
 pub use macd::{MacdIndicator, MacdOutput};
 pub use macd_ext::{MaType, MacdExt};
 pub use macd_fix::MacdFix;
+pub use macd_histogram::MacdHistogram;
 pub use mama::{Mama, MamaOutput};
 pub use market_facilitation_index::MarketFacilitationIndex;
 pub use marubozu::Marubozu;
@@ -608,6 +689,8 @@ pub use mcclellan_oscillator::McClellanOscillator;
 pub use mcclellan_summation_index::McClellanSummationIndex;
 pub use mcginley_dynamic::McGinleyDynamic;
 pub use median_absolute_deviation::MedianAbsoluteDeviation;
+pub use median_channel::{MedianChannel, MedianChannelOutput};
+pub use median_ma::MedianMa;
 pub use median_price::MedianPrice;
 pub use mfi::Mfi;
 pub use microprice::Microprice;
@@ -615,11 +698,13 @@ pub use mid_point::MidPoint;
 pub use mid_price::MidPrice;
 pub use minus_di::MinusDi;
 pub use minus_dm::MinusDm;
+pub use modified_ma_stop::{ModifiedMaStop, ModifiedMaStopOutput};
 pub use mom::Mom;
 pub use morning_doji_star::MorningDojiStar;
 pub use morning_evening_star::MorningEveningStar;
 pub use natr::Natr;
 pub use new_highs_new_lows::NewHighsNewLows;
+pub use nrtr::{Nrtr, NrtrOutput};
 pub use nvi::Nvi;
 pub use ob_imbalance_full::OrderBookImbalanceFull;
 pub use ob_imbalance_top1::OrderBookImbalanceTop1;
@@ -650,10 +735,17 @@ pub use plus_di::PlusDi;
 pub use plus_dm::PlusDm;
 pub use pmo::Pmo;
 pub use point_and_figure_bars::{PnfColumn, PointAndFigureBars};
+pub use polarized_fractal_efficiency::PolarizedFractalEfficiency;
 pub use ppo::Ppo;
+pub use ppo_histogram::PpoHistogram;
 pub use profit_factor::ProfitFactor;
+pub use projection_bands::{ProjectionBands, ProjectionBandsOutput};
+pub use projection_oscillator::ProjectionOscillator;
 pub use psar::Psar;
 pub use pvi::Pvi;
+pub use qqe::{Qqe, QqeOutput};
+pub use qstick::Qstick;
+pub use quartile_bands::{QuartileBands, QuartileBandsOutput};
 pub use quoted_spread::QuotedSpread;
 pub use r_squared::RSquared;
 pub use realized_spread::RealizedSpread;
@@ -666,6 +758,7 @@ pub use renko_bars::{RenkoBars, RenkoBrick};
 pub use renko_trailing_stop::RenkoTrailingStop;
 pub use rickshaw_man::RickshawMan;
 pub use rising_three_methods::RisingThreeMethods;
+pub use rmi::Rmi;
 pub use roc::Roc;
 pub use rocp::Rocp;
 pub use rocr::Rocr;
@@ -675,25 +768,30 @@ pub use roll_measure::RollMeasure;
 pub use rolling_correlation::RollingCorrelation;
 pub use rolling_covariance::RollingCovariance;
 pub use rolling_iqr::RollingIqr;
+pub use rolling_min_max_scaler::RollingMinMaxScaler;
 pub use rolling_percentile_rank::RollingPercentileRank;
 pub use rolling_quantile::RollingQuantile;
 pub use roofing_filter::RoofingFilter;
 pub use rsi::Rsi;
+pub use rsx::Rsx;
 pub use rvi::Rvi;
 pub use rvi_volatility::RviVolatility;
 pub use rwi::{Rwi, RwiOutput};
+pub use sample_entropy::SampleEntropy;
 pub use sar_ext::SarExt;
 pub use seasonal_z_score::SeasonalZScore;
 pub use separating_lines::SeparatingLines;
 pub use session_high_low::{SessionHighLow, SessionHighLowOutput};
 pub use session_range::{SessionRange, SessionRangeOutput};
 pub use session_vwap::SessionVwap;
+pub use shannon_entropy::ShannonEntropy;
 pub use shark::Shark;
 pub use sharpe_ratio::SharpeRatio;
 pub use shooting_star::ShootingStar;
 pub use short_line::ShortLine;
 pub use signed_volume::SignedVolume;
 pub use sine_wave::SineWave;
+pub use sine_weighted_ma::SineWeightedMa;
 pub use skewness::Skewness;
 pub use sma::Sma;
 pub use smi::Smi;
@@ -714,6 +812,7 @@ pub use step_trailing_stop::StepTrailingStop;
 pub use stick_sandwich::StickSandwich;
 pub use stoch_rsi::StochRsi;
 pub use stochastic::{Stochastic, StochasticOutput};
+pub use stochastic_cci::StochasticCci;
 pub use super_smoother::SuperSmoother;
 pub use super_trend::{SuperTrend, SuperTrendOutput};
 pub use t3::T3;
@@ -743,10 +842,13 @@ pub use three_stars_in_south::ThreeStarsInSouth;
 pub use thrusting::Thrusting;
 pub use tick_index::TickIndex;
 pub use tii::Tii;
+pub use time_based_stop::TimeBasedStop;
 pub use time_of_day_return_profile::{TimeOfDayReturnProfile, TimeOfDayReturnProfileOutput};
 pub use tpo_profile::{TpoProfile, TpoProfileOutput};
 pub use trade_imbalance::TradeImbalance;
+pub use trade_volume_index::TradeVolumeIndex;
 pub use trend_label::TrendLabel;
+pub use trend_strength_index::TrendStrengthIndex;
 pub use treynor_ratio::TreynorRatio;
 pub use triangle::Triangle;
 pub use trima::Trima;
@@ -755,11 +857,14 @@ pub use triple_top_bottom::TripleTopBottom;
 pub use trix::Trix;
 pub use true_range::TrueRange;
 pub use tsf::Tsf;
+pub use tsf_oscillator::TsfOscillator;
 pub use tsi::Tsi;
 pub use tsv::Tsv;
 pub use ttm_squeeze::{TtmSqueeze, TtmSqueezeOutput};
+pub use ttm_trend::TtmTrend;
 pub use turn_of_month::TurnOfMonth;
 pub use tweezer::Tweezer;
+pub use twiggs_money_flow::TwiggsMoneyFlow;
 pub use two_crows::TwoCrows;
 pub use typical_price::TypicalPrice;
 pub use ulcer_index::UlcerIndex;
@@ -774,10 +879,15 @@ pub use variance::Variance;
 pub use variance_ratio::VarianceRatio;
 pub use vertical_horizontal_filter::VerticalHorizontalFilter;
 pub use vidya::Vidya;
+pub use volatility_cone::{VolatilityCone, VolatilityConeOutput};
+pub use volatility_of_volatility::VolatilityOfVolatility;
+pub use volatility_ratio::VolatilityRatio;
 pub use volty_stop::VoltyStop;
 pub use volume_by_time_profile::{VolumeByTimeProfile, VolumeByTimeProfileOutput};
 pub use volume_oscillator::VolumeOscillator;
 pub use volume_profile::{VolumeProfile, VolumeProfileOutput};
+pub use volume_rsi::VolumeRsi;
+pub use volume_weighted_macd::{VolumeWeightedMacd, VolumeWeightedMacdOutput};
 pub use vortex::{Vortex, VortexOutput};
 pub use vpin::Vpin;
 pub use vpt::VolumePriceTrend;
@@ -785,6 +895,8 @@ pub use vwap::{RollingVwap, Vwap};
 pub use vwap_stddev_bands::{VwapStdDevBands, VwapStdDevBandsOutput};
 pub use vwma::Vwma;
 pub use vzo::Vzo;
+pub use wad::Wad;
+pub use wave_pm::WavePm;
 pub use wave_trend::{WaveTrend, WaveTrendOutput};
 pub use wedge::Wedge;
 pub use weighted_close::WeightedClose;
@@ -830,6 +942,13 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "Jma",
             "Alligator",
             "Evwma",
+            "SineWeightedMa",
+            "GeometricMa",
+            "Ehma",
+            "MedianMa",
+            "AdaptiveLaguerreFilter",
+            "GeneralizedDema",
+            "HoltWinters",
         ],
     ),
     (
@@ -859,6 +978,16 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "Rocp",
             "Rocr",
             "Rocr100",
+            "DisparityIndex",
+            "FisherRsi",
+            "Rsx",
+            "DynamicMomentumIndex",
+            "StochasticCci",
+            "Rmi",
+            "DerivativeOscillator",
+            "ElderRay",
+            "IntradayMomentumIndex",
+            "Qqe",
         ],
     ),
     (
@@ -885,6 +1014,13 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "MinusDi",
             "Dx",
             "TrendLabel",
+            "TtmTrend",
+            "TrendStrengthIndex",
+            "Qstick",
+            "PolarizedFractalEfficiency",
+            "WavePm",
+            "GatorOscillator",
+            "KasePermissionStochastic",
         ],
     ),
     (
@@ -901,6 +1037,9 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "ZeroLagMacd",
             "ElderImpulse",
             "Stc",
+            "TsfOscillator",
+            "MacdHistogram",
+            "PpoHistogram",
         ],
     ),
     (
@@ -925,6 +1064,12 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "YangZhangVolatility",
             "JumpIndicator",
             "RegimeLabel",
+            "EwmaVolatility",
+            "Garch11",
+            "VolatilityOfVolatility",
+            "BipowerVariation",
+            "VolatilityRatio",
+            "VolatilityCone",
         ],
     ),
     (
@@ -941,6 +1086,11 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "TtmSqueeze",
             "FractalChaosBands",
             "VwapStdDevBands",
+            "QuartileBands",
+            "BomarBands",
+            "MedianChannel",
+            "ProjectionBands",
+            "ProjectionOscillator",
         ],
     ),
     (
@@ -959,6 +1109,12 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "StepTrailingStop",
             "RenkoTrailingStop",
             "SarExt",
+            "KaseDevStop",
+            "ElderSafeZone",
+            "AtrRatchet",
+            "Nrtr",
+            "TimeBasedStop",
+            "ModifiedMaStop",
         ],
     ),
     (
@@ -983,6 +1139,13 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "Tsv",
             "Vzo",
             "MarketFacilitationIndex",
+            "VolumeRsi",
+            "Wad",
+            "TwiggsMoneyFlow",
+            "TradeVolumeIndex",
+            "IntradayIntensity",
+            "BetterVolume",
+            "VolumeWeightedMacd",
         ],
     ),
     (
@@ -1038,6 +1201,11 @@ pub const FAMILIES: &[(&str, &[&str])] = &[
             "BodySizePct",
             "WickRatio",
             "HighLowRange",
+            "JarqueBera",
+            "RollingMinMaxScaler",
+            "ShannonEntropy",
+            "SampleEntropy",
+            "KendallTau",
         ],
     ),
     (
@@ -1342,6 +1510,6 @@ mod family_tests {
         // the actual indicator count is the early-warning signal that an
         // indicator was added without being assigned a family.
         let total: usize = FAMILIES.iter().map(|(_, ns)| ns.len()).sum();
-        assert_eq!(total, 396, "FAMILIES total drifted from indicator count");
+        assert_eq!(total, 452, "FAMILIES total drifted from indicator count");
     }
 }

crates/wickra-core/src/indicators/modified_ma_stop.rs

@@ -0,0 +1,238 @@
+//! Modified-MA Stop — a trailing stop riding the Modified Moving Average (SMMA).
+
+use crate::error::{Error, Result};
+use crate::indicators::smma::Smma;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`ModifiedMaStop`]: the active stop level and the trend direction.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct ModifiedMaStopOutput {
+    /// The stop level (a directionally-ratcheted Modified Moving Average).
+    pub value: f64,
+    /// Trend direction: `+1.0` long (stop below price), `-1.0` short.
+    pub direction: f64,
+}
+
+/// Modified-MA Stop — a trailing stop whose line is the **Modified Moving
+/// Average** (SMMA / Wilder's RMA) of price, allowed to move only in the trend's
+/// favour.
+///
+/// ```text
+/// ma = SMMA(close, period)                 (Modified Moving Average)
+/// long:  stop = max(prev_stop, ma);  flip short when close < stop
+/// short: stop = min(prev_stop, ma);  flip long  when close > stop
+/// ```
+///
+/// The Modified Moving Average (also called the smoothed or running moving
+/// average) is the slow, low-lag average Wilder used throughout his systems. Using
+/// it directly as a trailing line — but **ratcheting** so the long stop never
+/// falls and the short stop never rises — turns the smooth average into a stop
+/// that hugs price in a trend and flips when price decisively crosses it. Because
+/// the SMMA lags, the stop gives trends room while still exiting clean reversals.
+///
+/// The first stop lands once the SMMA is ready (`period` inputs). Each `update` is
+/// O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, ModifiedMaStop};
+///
+/// let mut indicator = ModifiedMaStop::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base + 0.5, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct ModifiedMaStop {
+    smma: Smma,
+    period: usize,
+    direction: f64,
+    stop: f64,
+    last: Option<ModifiedMaStopOutput>,
+}
+
+impl ModifiedMaStop {
+    /// Construct a Modified-MA stop with the given SMMA `period`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            smma: Smma::new(period)?,
+            period,
+            direction: 0.0,
+            stop: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured SMMA period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<ModifiedMaStopOutput> {
+        self.last
+    }
+}
+
+impl Indicator for ModifiedMaStop {
+    type Input = Candle;
+    type Output = ModifiedMaStopOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<ModifiedMaStopOutput> {
+        let ma = self.smma.update(candle.close)?;
+        let close = candle.close;
+
+        if self.direction == 0.0 {
+            self.direction = if close >= ma { 1.0 } else { -1.0 };
+            self.stop = ma;
+        } else if self.direction > 0.0 {
+            self.stop = self.stop.max(ma);
+            if close < self.stop {
+                self.direction = -1.0;
+                self.stop = ma;
+            }
+        } else {
+            self.stop = self.stop.min(ma);
+            if close > self.stop {
+                self.direction = 1.0;
+                self.stop = ma;
+            }
+        }
+
+        let out = ModifiedMaStopOutput {
+            value: self.stop,
+            direction: self.direction,
+        };
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.smma.reset();
+        self.direction = 0.0;
+        self.stop = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "ModifiedMaStop"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+
+    fn c(close: f64) -> Candle {
+        Candle::new_unchecked(close, close + 1.0, close - 1.0, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(ModifiedMaStop::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let m = ModifiedMaStop::new(14).unwrap();
+        assert_eq!(m.period(), 14);
+        assert_eq!(m.warmup_period(), 14);
+        assert_eq!(m.name(), "ModifiedMaStop");
+        assert!(!m.is_ready());
+        assert_eq!(m.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut m = ModifiedMaStop::new(5).unwrap();
+        let candles: Vec<Candle> = (0..12).map(|i| c(100.0 + f64::from(i))).collect();
+        let out = m.batch(&candles);
+        for v in out.iter().take(4) {
+            assert!(v.is_none());
+        }
+        assert!(out[4].is_some());
+    }
+
+    #[test]
+    fn uptrend_keeps_stop_below_price() {
+        let mut m = ModifiedMaStop::new(5).unwrap();
+        let candles: Vec<Candle> = (0..60).map(|i| c(100.0 + 2.0 * f64::from(i))).collect();
+        for (o, candle) in m.batch(&candles).into_iter().zip(candles.iter()) {
+            if let Some(o) = o {
+                assert_eq!(o.direction, 1.0);
+                assert!(o.value < candle.close);
+            }
+        }
+    }
+
+    #[test]
+    fn long_stop_ratchets_up() {
+        let mut m = ModifiedMaStop::new(5).unwrap();
+        let candles: Vec<Candle> = (0..60).map(|i| c(100.0 + 2.0 * f64::from(i))).collect();
+        let mut prev = f64::NEG_INFINITY;
+        for o in m.batch(&candles).into_iter().flatten() {
+            assert_eq!(o.direction, 1.0, "pure uptrend stays long");
+            assert!(o.value >= prev, "long stop must not fall");
+            prev = o.value;
+        }
+    }
+
+    #[test]
+    fn flips_on_reversal() {
+        let mut candles: Vec<Candle> = (0..40).map(|i| c(100.0 + f64::from(i))).collect();
+        candles.extend((0..40).map(|i| c(140.0 - f64::from(i))));
+        let mut m = ModifiedMaStop::new(5).unwrap();
+        let dirs: Vec<f64> = m
+            .batch(&candles)
+            .into_iter()
+            .flatten()
+            .map(|o| o.direction)
+            .collect();
+        assert!(dirs.iter().any(|&d| d > 0.0));
+        assert!(dirs.iter().any(|&d| d < 0.0));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut m = ModifiedMaStop::new(5).unwrap();
+        m.batch(&(0..40).map(|i| c(100.0 + f64::from(i))).collect::<Vec<_>>());
+        assert!(m.is_ready());
+        m.reset();
+        assert!(!m.is_ready());
+        assert_eq!(m.value(), None);
+        assert_eq!(m.update(c(100.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| c(100.0 + (f64::from(i) * 0.25).sin() * 9.0))
+            .collect();
+        let batch = ModifiedMaStop::new(14).unwrap().batch(&candles);
+        let mut b = ModifiedMaStop::new(14).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/nrtr.rs

@@ -0,0 +1,259 @@
+//! NRTR — Nick Rypock Trailing Reverse, a percentage trailing-reverse stop.
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`Nrtr`]: the trailing-reverse line and the trend direction.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct NrtrOutput {
+    /// The NRTR line — below price in an uptrend, above price in a downtrend.
+    pub value: f64,
+    /// Trend direction: `+1.0` up (line below price), `-1.0` down.
+    pub direction: f64,
+}
+
+/// NRTR (Nick Rypock Trailing Reverse) — a **percentage** trailing-reverse stop
+/// that follows the trend extreme and flips when price retraces by a fixed
+/// percentage.
+///
+/// ```text
+/// uptrend:   high_water = max(high_water, close)
+///            line       = high_water · (1 − pct/100)
+///            flip down when close < line  (reseed low_water = close)
+/// downtrend: low_water  = min(low_water, close)
+///            line       = low_water · (1 + pct/100)
+///            flip up   when close > line  (reseed high_water = close)
+/// ```
+///
+/// Unlike volatility stops (ATR, σ-of-range), NRTR uses a pure **percentage**
+/// retracement: the line trails the highest close reached in the up-leg at a
+/// fixed `pct` below it, and a close that gives back that percentage reverses the
+/// trend, handing the line to the opposite extreme. This makes it scale-free and
+/// trivially tunable — one number sets how much retracement you tolerate. It
+/// differs from a fixed percentage *stop-loss* in that it **reverses** (tracks
+/// both directions) rather than just exiting.
+///
+/// The first bar seeds the up-trend and emits a line immediately. Each `update` is
+/// O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, Nrtr};
+///
+/// let mut indicator = Nrtr::new(2.0).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let close = 100.0 + f64::from(i);
+///     let c = Candle::new(close, close + 0.5, close - 0.5, close, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Nrtr {
+    pct: f64,
+    direction: f64,
+    water: f64,
+    last: Option<NrtrOutput>,
+}
+
+impl Nrtr {
+    /// Construct an NRTR with the given trailing percentage (e.g. `2.0` for 2%).
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::InvalidParameter`] if `pct` is not finite or is outside
+    /// `(0, 100)`.
+    pub fn new(pct: f64) -> Result<Self> {
+        if !pct.is_finite() || pct <= 0.0 || pct >= 100.0 {
+            return Err(Error::InvalidParameter {
+                message: "NRTR percentage must be in (0, 100)",
+            });
+        }
+        Ok(Self {
+            pct,
+            direction: 0.0,
+            water: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured trailing percentage.
+    pub const fn pct(&self) -> f64 {
+        self.pct
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<NrtrOutput> {
+        self.last
+    }
+}
+
+impl Indicator for Nrtr {
+    type Input = Candle;
+    type Output = NrtrOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<NrtrOutput> {
+        let close = candle.close;
+        let down = self.pct / 100.0;
+        let up = self.pct / 100.0;
+
+        if self.direction == 0.0 {
+            self.direction = 1.0;
+            self.water = close;
+        } else if self.direction > 0.0 {
+            self.water = self.water.max(close);
+            let line = self.water * (1.0 - down);
+            if close < line {
+                self.direction = -1.0;
+                self.water = close;
+            }
+        } else {
+            self.water = self.water.min(close);
+            let line = self.water * (1.0 + up);
+            if close > line {
+                self.direction = 1.0;
+                self.water = close;
+            }
+        }
+
+        let line = if self.direction > 0.0 {
+            self.water * (1.0 - down)
+        } else {
+            self.water * (1.0 + up)
+        };
+        let out = NrtrOutput {
+            value: line,
+            direction: self.direction,
+        };
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.direction = 0.0;
+        self.water = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "Nrtr"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+
+    fn c(close: f64) -> Candle {
+        Candle::new_unchecked(close, close, close, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_invalid_pct() {
+        assert!(matches!(
+            Nrtr::new(0.0),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Nrtr::new(100.0),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            Nrtr::new(f64::NAN),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(Nrtr::new(2.0).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let n = Nrtr::new(2.0).unwrap();
+        assert_eq!(n.pct(), 2.0);
+        assert_eq!(n.warmup_period(), 1);
+        assert_eq!(n.name(), "Nrtr");
+        assert!(!n.is_ready());
+        assert_eq!(n.value(), None);
+    }
+
+    #[test]
+    fn first_bar_emits_up_line() {
+        let mut n = Nrtr::new(10.0).unwrap();
+        let o = n.update(c(100.0)).unwrap();
+        assert_eq!(o.direction, 1.0);
+        // line = 100 * (1 - 0.10) = 90.
+        assert!((o.value - 90.0).abs() < 1e-9);
+    }
+
+    #[test]
+    fn uptrend_keeps_line_below_price() {
+        let mut n = Nrtr::new(5.0).unwrap();
+        let candles: Vec<Candle> = (0..40).map(|i| c(100.0 + f64::from(i))).collect();
+        for (o, candle) in n.batch(&candles).into_iter().zip(candles.iter()) {
+            let o = o.unwrap();
+            assert_eq!(o.direction, 1.0);
+            assert!(o.value < candle.close);
+        }
+    }
+
+    #[test]
+    fn reverses_on_retracement() {
+        let mut n = Nrtr::new(5.0).unwrap();
+        // Rise to 120, then drop sharply -> a >5% retracement reverses the trend.
+        let mut candles: Vec<Candle> = (0..20).map(|i| c(100.0 + f64::from(i))).collect();
+        candles.extend((0..10).map(|i| c(119.0 - 3.0 * f64::from(i))));
+        let dirs: Vec<f64> = n
+            .batch(&candles)
+            .into_iter()
+            .flatten()
+            .map(|o| o.direction)
+            .collect();
+        assert!(dirs.iter().any(|&d| d > 0.0));
+        assert!(dirs.iter().any(|&d| d < 0.0));
+    }
+
+    #[test]
+    fn downtrend_keeps_line_above_price() {
+        let mut n = Nrtr::new(5.0).unwrap();
+        // Establish a downtrend after an initial bar.
+        let mut candles = vec![c(100.0)];
+        candles.extend((0..30).map(|i| c(80.0 - f64::from(i))));
+        let out = n.batch(&candles);
+        let o = out.last().unwrap().unwrap();
+        let candle = candles.last().unwrap();
+        assert_eq!(o.direction, -1.0);
+        assert!(o.value > candle.close);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut n = Nrtr::new(2.0).unwrap();
+        n.batch(&(0..20).map(|i| c(100.0 + f64::from(i))).collect::<Vec<_>>());
+        assert!(n.is_ready());
+        n.reset();
+        assert!(!n.is_ready());
+        assert_eq!(n.value(), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| c(100.0 + (f64::from(i) * 0.25).sin() * 15.0))
+            .collect();
+        let batch = Nrtr::new(3.0).unwrap().batch(&candles);
+        let mut b = Nrtr::new(3.0).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/polarized_fractal_efficiency.rs

@@ -0,0 +1,243 @@
+//! Polarized Fractal Efficiency (PFE).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::traits::Indicator;
+
+/// Polarized Fractal Efficiency: how efficiently price travelled over the last
+/// `period` bars, signed by direction and smoothed by an EMA.
+///
+/// ```text
+/// straight  = sqrt((C_t - C_{t-n})^2 + n^2)            (direct distance over n bars)
+/// path      = Σ_{i=1..n} sqrt((C_{t-i+1} - C_{t-i})^2 + 1)   (sum of single-bar steps)
+/// raw       = 100 * sign(C_t - C_{t-n}) * straight / path
+/// PFE       = EMA(raw, smoothing)
+/// ```
+///
+/// The ratio `straight / path` is the fractal efficiency: it is `1` when price
+/// moved in a perfectly straight line and falls toward `0` as the path becomes
+/// jagged. Polarizing it by the sign of the net move pushes the reading to
+/// `+100` for an efficient up-move and `-100` for an efficient down-move, with
+/// choppy markets oscillating near zero. Because each single-bar step and the
+/// `n`-bar diagonal both carry the bar count on the x-axis (`+1` and `+n^2`),
+/// the path length is always `>= n`, so the denominator can never be zero.
+///
+/// Reference: Hans Hannula, *Stocks & Commodities*, 1994.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, PolarizedFractalEfficiency};
+///
+/// let mut indicator = PolarizedFractalEfficiency::new(10, 5).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct PolarizedFractalEfficiency {
+    period: usize,
+    smoothing: usize,
+    closes: VecDeque<f64>,
+    prev_close: Option<f64>,
+    segments: VecDeque<f64>,
+    segment_sum: f64,
+    ema: Ema,
+}
+
+impl PolarizedFractalEfficiency {
+    /// Construct a PFE with the fractal lookback `period` and the EMA
+    /// `smoothing` period.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0` or `smoothing == 0`.
+    pub fn new(period: usize, smoothing: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            smoothing,
+            closes: VecDeque::with_capacity(period + 1),
+            prev_close: None,
+            segments: VecDeque::with_capacity(period),
+            segment_sum: 0.0,
+            ema: Ema::new(smoothing)?,
+        })
+    }
+
+    /// Configured `(period, smoothing)`.
+    pub const fn periods(&self) -> (usize, usize) {
+        (self.period, self.smoothing)
+    }
+}
+
+impl Indicator for PolarizedFractalEfficiency {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, close: f64) -> Option<f64> {
+        if let Some(prev) = self.prev_close {
+            let diff = close - prev;
+            let segment = diff.mul_add(diff, 1.0).sqrt();
+            self.segment_sum += segment;
+            self.segments.push_back(segment);
+            if self.segments.len() > self.period {
+                self.segment_sum -= self.segments.pop_front().unwrap_or(0.0);
+            }
+        }
+        self.prev_close = Some(close);
+
+        self.closes.push_back(close);
+        if self.closes.len() > self.period + 1 {
+            self.closes.pop_front();
+        }
+        if self.closes.len() <= self.period {
+            return None;
+        }
+
+        let oldest = *self.closes.front().unwrap_or(&close);
+        let net = close - oldest;
+        let direction = if net > 0.0 {
+            1.0
+        } else if net < 0.0 {
+            -1.0
+        } else {
+            0.0
+        };
+        let span = self.period as f64;
+        let straight = net.mul_add(net, span * span).sqrt();
+        let raw = 100.0 * direction * straight / self.segment_sum;
+        self.ema.update(raw)
+    }
+
+    fn reset(&mut self) {
+        self.closes.clear();
+        self.prev_close = None;
+        self.segments.clear();
+        self.segment_sum = 0.0;
+        self.ema.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period + self.smoothing
+    }
+
+    fn is_ready(&self) -> bool {
+        self.ema.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "PolarizedFractalEfficiency"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(
+            PolarizedFractalEfficiency::new(0, 5),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            PolarizedFractalEfficiency::new(10, 0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let pfe = PolarizedFractalEfficiency::new(10, 5).unwrap();
+        assert_eq!(pfe.periods(), (10, 5));
+        assert_eq!(pfe.warmup_period(), 15);
+        assert_eq!(pfe.name(), "PolarizedFractalEfficiency");
+        assert!(!pfe.is_ready());
+    }
+
+    #[test]
+    fn warmup_emits_after_period_plus_smoothing() {
+        let mut pfe = PolarizedFractalEfficiency::new(4, 2).unwrap();
+        // raw needs period+1 = 5 closes; EMA(2) needs 2 raws -> first value at
+        // input 6 (index 5).
+        let inputs: Vec<f64> = (0..10).map(f64::from).collect();
+        let out = pfe.batch(&inputs);
+        assert!(out[4].is_none());
+        assert!(out[5].is_some());
+    }
+
+    #[test]
+    fn perfect_uptrend_is_strongly_positive() {
+        // A straight ramp: every step is +1, the diagonal is maximally
+        // efficient, so PFE saturates near +100.
+        let mut pfe = PolarizedFractalEfficiency::new(5, 3).unwrap();
+        let inputs: Vec<f64> = (0..30).map(f64::from).collect();
+        let last = pfe.batch(&inputs).last().unwrap().unwrap();
+        assert!(last > 99.0, "pfe {last} should be near +100");
+    }
+
+    #[test]
+    fn perfect_downtrend_is_strongly_negative() {
+        let mut pfe = PolarizedFractalEfficiency::new(5, 3).unwrap();
+        let inputs: Vec<f64> = (0..30).map(|i| -f64::from(i)).collect();
+        let last = pfe.batch(&inputs).last().unwrap().unwrap();
+        assert!(last < -99.0, "pfe {last} should be near -100");
+    }
+
+    #[test]
+    fn flat_market_returns_zero() {
+        // No net move over the window -> direction 0 -> raw 0 -> PFE 0.
+        let mut pfe = PolarizedFractalEfficiency::new(5, 3).unwrap();
+        let inputs = [10.0; 20];
+        let last = pfe.batch(&inputs).last().unwrap().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn choppy_market_is_inefficient() {
+        // A sawtooth whip: the net move is tiny relative to the jagged path, so
+        // efficiency stays well below the +-100 saturation of a clean trend.
+        let mut pfe = PolarizedFractalEfficiency::new(5, 3).unwrap();
+        let inputs: Vec<f64> = (0..40)
+            .map(|i| if i % 2 == 0 { 100.0 } else { 102.0 })
+            .collect();
+        let last = pfe.batch(&inputs).last().unwrap().unwrap();
+        assert!(
+            last.abs() < 60.0,
+            "choppy pfe {last} should be far from +-100"
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut pfe = PolarizedFractalEfficiency::new(5, 3).unwrap();
+        let inputs: Vec<f64> = (0..30).map(f64::from).collect();
+        pfe.batch(&inputs);
+        assert!(pfe.is_ready());
+        pfe.reset();
+        assert!(!pfe.is_ready());
+        assert_eq!(pfe.periods(), (5, 3));
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let inputs: Vec<f64> = (0..80)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 5.0)
+            .collect();
+        let mut a = PolarizedFractalEfficiency::new(10, 5).unwrap();
+        let mut b = PolarizedFractalEfficiency::new(10, 5).unwrap();
+        assert_eq!(
+            a.batch(&inputs),
+            inputs.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/ppo_histogram.rs

@@ -0,0 +1,230 @@
+//! Percentage Price Oscillator Histogram.
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::indicators::ppo::Ppo;
+use crate::traits::Indicator;
+
+/// PPO Histogram — the `ppo − signal` bar of the Percentage Price Oscillator.
+///
+/// ```text
+/// ppo       = 100 · (EMA_fast − EMA_slow) / EMA_slow
+/// signal    = EMA(ppo, signal_period)
+/// histogram = ppo − signal
+/// ```
+///
+/// [`Ppo`](crate::Ppo) itself only emits the percentage line; this indicator
+/// adds the classic 9-period signal EMA on top and reports the resulting
+/// zero-centered histogram. Because PPO is scale-free (the EMA gap is divided
+/// by the slow EMA), the histogram is **comparable across instruments** — a
+/// PPO histogram of `0.4` means the same relative momentum on any asset, unlike
+/// the price-unit [`MacdHistogram`](crate::MacdHistogram).
+///
+/// With Appel's defaults `fast = 12`, `slow = 26`, `signal = 9`, the first
+/// value lands after `slow + signal − 1` inputs — the point at which the slow
+/// EMA and then the signal EMA are both seeded.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, PpoHistogram};
+///
+/// let mut indicator = PpoHistogram::new(12, 26, 9).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct PpoHistogram {
+    ppo: Ppo,
+    signal_ema: Ema,
+    signal_period: usize,
+    current: Option<f64>,
+}
+
+impl PpoHistogram {
+    /// Construct a PPO histogram with the `fast`/`slow` EMA periods and the
+    /// `signal` EMA period.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if any period is `0`, or
+    /// [`Error::InvalidPeriod`] if `fast >= slow`.
+    pub fn new(fast: usize, slow: usize, signal: usize) -> Result<Self> {
+        if signal == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            ppo: Ppo::new(fast, slow)?,
+            signal_ema: Ema::new(signal)?,
+            signal_period: signal,
+            current: None,
+        })
+    }
+
+    /// Default `(12, 26, 9)` configuration.
+    pub fn classic() -> Self {
+        Self::new(12, 26, 9).expect("classic PPO periods are valid")
+    }
+
+    /// Configured periods as `(fast, slow, signal)`.
+    pub const fn periods(&self) -> (usize, usize, usize) {
+        let (fast, slow) = self.ppo.periods();
+        (fast, slow, self.signal_period)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.current
+    }
+}
+
+impl Indicator for PpoHistogram {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        // Guard before touching either stage so a non-finite input never
+        // advances the signal EMA on a stale, re-fed PPO value.
+        if !input.is_finite() {
+            return self.current;
+        }
+        let ppo = self.ppo.update(input)?;
+        let signal = self.signal_ema.update(ppo)?;
+        let histogram = ppo - signal;
+        self.current = Some(histogram);
+        Some(histogram)
+    }
+
+    fn reset(&mut self) {
+        self.ppo.reset();
+        self.signal_ema.reset();
+        self.current = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // Slow EMA seeds the PPO, then the signal EMA needs `signal − 1` more.
+        self.ppo.warmup_period() + self.signal_period - 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.current.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "PpoHistogram"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_periods() {
+        assert!(matches!(
+            PpoHistogram::new(0, 26, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            PpoHistogram::new(12, 0, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            PpoHistogram::new(12, 26, 0),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            PpoHistogram::new(26, 12, 9),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let osc = PpoHistogram::classic();
+        assert_eq!(osc.periods(), (12, 26, 9));
+        assert_eq!(osc.name(), "PpoHistogram");
+        assert_eq!(osc.warmup_period(), 26 + 9 - 1);
+        assert_eq!(osc.value(), None);
+        assert!(!osc.is_ready());
+    }
+
+    #[test]
+    fn equals_ppo_minus_signal_ema() {
+        // The histogram must equal PPO minus an EMA(signal) composed by hand.
+        let prices: Vec<f64> = (1..=120)
+            .map(|i| 100.0 + (f64::from(i) * 0.2).sin() * 6.0)
+            .collect();
+        let got = PpoHistogram::new(12, 26, 9).unwrap().batch(&prices);
+
+        let mut ppo = Ppo::new(12, 26).unwrap();
+        let mut sig = Ema::new(9).unwrap();
+        let mut expected = Vec::with_capacity(prices.len());
+        for p in &prices {
+            let out = ppo
+                .update(*p)
+                .and_then(|line| sig.update(line).map(|signal| line - signal));
+            expected.push(out);
+        }
+        assert_eq!(got, expected);
+    }
+
+    #[test]
+    fn warmup_emits_first_value_at_warmup_period() {
+        let mut osc = PpoHistogram::new(3, 6, 3).unwrap();
+        let warmup = osc.warmup_period();
+        assert_eq!(warmup, 6 + 3 - 1);
+        for i in 1..warmup {
+            assert!(osc.update(100.0 + i as f64).is_none());
+        }
+        assert!(osc.update(100.0 + warmup as f64).is_some());
+        assert!(osc.is_ready());
+    }
+
+    #[test]
+    fn constant_series_converges_to_zero() {
+        let mut osc = PpoHistogram::classic();
+        let out = osc.batch(&[100.0_f64; 200]);
+        let last = out.iter().rev().flatten().next().expect("emits a value");
+        assert_relative_eq!(*last, 0.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut osc = PpoHistogram::new(3, 6, 3).unwrap();
+        let out = osc.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
+        let before = *out.last().unwrap();
+        assert!(before.is_some());
+        assert_eq!(osc.update(f64::NAN), before);
+        assert_eq!(osc.update(f64::INFINITY), before);
+        assert_eq!(osc.value(), before);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=100)
+            .map(|i| 100.0 + (f64::from(i) * 0.4).cos() * 10.0)
+            .collect();
+        let mut a = PpoHistogram::classic();
+        let mut b = PpoHistogram::classic();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut osc = PpoHistogram::classic();
+        osc.batch(&(1..=80).map(f64::from).collect::<Vec<_>>());
+        assert!(osc.is_ready());
+        osc.reset();
+        assert!(!osc.is_ready());
+        assert_eq!(osc.update(1.0), None);
+    }
+}

crates/wickra-core/src/indicators/projection_bands.rs

@@ -0,0 +1,253 @@
+//! Projection Bands (Mel Widner) — a high/low linear-regression projection
+//! envelope.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Projection Bands output.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct ProjectionBandsOutput {
+    /// Upper band: the maximum forward-projected high in the window.
+    pub upper: f64,
+    /// Middle line: the midpoint of the upper and lower bands.
+    pub middle: f64,
+    /// Lower band: the minimum forward-projected low in the window.
+    pub lower: f64,
+}
+
+/// Projection Bands: forward-projected high/low envelope.
+///
+/// Mel Widner ("Projection Bands and the Projection Oscillator", *Technical
+/// Analysis of Stocks & Commodities*, May 1995) fits a separate linear
+/// regression to the highs and to the lows over the last `period` bars, then
+/// slides every bar's high and low forward to the current bar along its own
+/// slope. The upper band is the maximum of the projected highs, the lower band
+/// the minimum of the projected lows:
+///
+/// ```text
+/// slope_h = OLS slope of (x, high) over the window
+/// slope_l = OLS slope of (x, low)  over the window
+/// // bar i (0 = oldest, period-1 = newest) is (period-1-i) bars in the past
+/// upper   = max over i of [ high_i + slope_h · (period-1-i) ]
+/// lower   = min over i of [ low_i  + slope_l · (period-1-i) ]
+/// middle  = (upper + lower) / 2
+/// ```
+///
+/// Unlike [`LinRegChannel`](crate::LinRegChannel) and
+/// [`StandardErrorBands`](crate::StandardErrorBands) — which wrap a single
+/// close-regression endpoint by a dispersion statistic — Projection Bands are
+/// built from the *extremes*: the envelope adapts to the trend's slope yet
+/// always contains every projected high and low, so by construction price never
+/// pierces the bands within the window. A flat slope reduces the bands to the
+/// rolling highest-high / lowest-low (a Donchian channel); a steep slope tilts
+/// the whole envelope with the trend.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, ProjectionBands};
+///
+/// let mut indicator = ProjectionBands::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..30 {
+///     let base = 100.0 + f64::from(i);
+///     let candle =
+///         Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 10.0, i64::from(i)).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct ProjectionBands {
+    period: usize,
+    highs: VecDeque<f64>,
+    lows: VecDeque<f64>,
+    sum_x: f64,
+    sum_xx: f64,
+}
+
+impl ProjectionBands {
+    /// Construct new Projection Bands.
+    ///
+    /// # Errors
+    /// Returns [`Error::InvalidPeriod`] if `period < 2` (a regression slope
+    /// needs at least two points).
+    pub fn new(period: usize) -> Result<Self> {
+        if period < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "projection bands need period >= 2",
+            });
+        }
+        let n = period as f64;
+        Ok(Self {
+            period,
+            highs: VecDeque::with_capacity(period),
+            lows: VecDeque::with_capacity(period),
+            sum_x: n * (n - 1.0) / 2.0,
+            sum_xx: (n - 1.0) * n * (2.0 * n - 1.0) / 6.0,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// OLS slope of `(0..period, values)` over the live window.
+    fn slope(&self, values: &VecDeque<f64>) -> f64 {
+        let n = self.period as f64;
+        let mut sum_y = 0.0;
+        let mut sum_xy = 0.0;
+        for (i, &y) in values.iter().enumerate() {
+            sum_y += y;
+            sum_xy += (i as f64) * y;
+        }
+        let denom = n * self.sum_xx - self.sum_x * self.sum_x;
+        (n * sum_xy - self.sum_x * sum_y) / denom
+    }
+}
+
+impl Indicator for ProjectionBands {
+    type Input = Candle;
+    type Output = ProjectionBandsOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<ProjectionBandsOutput> {
+        if self.highs.len() == self.period {
+            self.highs.pop_front();
+            self.lows.pop_front();
+        }
+        self.highs.push_back(candle.high);
+        self.lows.push_back(candle.low);
+        if self.highs.len() < self.period {
+            return None;
+        }
+
+        let slope_h = self.slope(&self.highs);
+        let slope_l = self.slope(&self.lows);
+        let last = (self.period - 1) as f64;
+
+        let mut upper = f64::NEG_INFINITY;
+        let mut lower = f64::INFINITY;
+        for (i, (&high, &low)) in self.highs.iter().zip(self.lows.iter()).enumerate() {
+            let forward = last - (i as f64);
+            let projected_high = high + slope_h * forward;
+            let projected_low = low + slope_l * forward;
+            if projected_high > upper {
+                upper = projected_high;
+            }
+            if projected_low < lower {
+                lower = projected_low;
+            }
+        }
+
+        Some(ProjectionBandsOutput {
+            upper,
+            middle: f64::midpoint(upper, lower),
+            lower,
+        })
+    }
+
+    fn reset(&mut self) {
+        self.highs.clear();
+        self.lows.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.highs.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "ProjectionBands"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64, ts: i64) -> Candle {
+        Candle::new(low, high, low, close, 10.0, ts).unwrap()
+    }
+
+    #[test]
+    fn rejects_period_below_two() {
+        assert!(matches!(
+            ProjectionBands::new(0),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            ProjectionBands::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(ProjectionBands::new(2).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let pb = ProjectionBands::new(14).unwrap();
+        assert_eq!(pb.period(), 14);
+        assert_eq!(pb.warmup_period(), 14);
+        assert_eq!(pb.name(), "ProjectionBands");
+        assert!(!pb.is_ready());
+    }
+
+    #[test]
+    fn warms_up_then_emits() {
+        let mut pb = ProjectionBands::new(3).unwrap();
+        assert!(pb.update(candle(10.0, 8.0, 9.0, 0)).is_none());
+        assert!(pb.update(candle(12.0, 9.0, 11.0, 1)).is_none());
+        assert!(pb.update(candle(11.0, 10.0, 11.0, 2)).is_some());
+        assert!(pb.is_ready());
+    }
+
+    #[test]
+    fn known_projection() {
+        // highs 10,12,11 -> slope_h = 0.5; projected = 11, 12.5, 11 -> upper 12.5
+        // lows   8, 9,10 -> slope_l = 1.0; projected = 10, 10,  10 -> lower 10
+        let mut pb = ProjectionBands::new(3).unwrap();
+        pb.update(candle(10.0, 8.0, 9.0, 0));
+        pb.update(candle(12.0, 9.0, 11.0, 1));
+        let out = pb.update(candle(11.0, 10.0, 11.0, 2)).unwrap();
+        assert_relative_eq!(out.upper, 12.5, epsilon = 1e-9);
+        assert_relative_eq!(out.lower, 10.0, epsilon = 1e-9);
+        assert_relative_eq!(out.middle, 11.25, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn perfect_trend_pins_bands_to_current_extremes() {
+        // High_i and Low_i both rise by exactly 1 per bar: every projected high
+        // collapses onto the current high, every projected low onto the current
+        // low.
+        let mut pb = ProjectionBands::new(5).unwrap();
+        let mut last = None;
+        for i in 0..10 {
+            let high = 100.0 + f64::from(i);
+            let low = 95.0 + f64::from(i);
+            last = pb.update(candle(high, low, high, i64::from(i)));
+        }
+        let out = last.unwrap();
+        assert_relative_eq!(out.upper, 109.0, epsilon = 1e-9);
+        assert_relative_eq!(out.lower, 104.0, epsilon = 1e-9);
+        assert_relative_eq!(out.middle, 106.5, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut pb = ProjectionBands::new(3).unwrap();
+        pb.update(candle(10.0, 8.0, 9.0, 0));
+        pb.update(candle(12.0, 9.0, 11.0, 1));
+        pb.update(candle(11.0, 10.0, 11.0, 2));
+        assert!(pb.is_ready());
+        pb.reset();
+        assert!(!pb.is_ready());
+        assert!(pb.update(candle(10.0, 8.0, 9.0, 3)).is_none());
+    }
+}

crates/wickra-core/src/indicators/projection_oscillator.rs

@@ -0,0 +1,168 @@
+//! Projection Oscillator (Mel Widner) — the close's position inside the
+//! [`ProjectionBands`](crate::ProjectionBands).
+
+use crate::error::Result;
+use crate::indicators::projection_bands::ProjectionBands;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Projection Oscillator: where the close sits inside the projection bands,
+/// scaled to `0..100`.
+///
+/// The companion to [`ProjectionBands`](crate::ProjectionBands) from Mel
+/// Widner's May 1995 *Stocks & Commodities* article. It maps the close onto the
+/// `[lower, upper]` projection envelope:
+///
+/// ```text
+/// PO = 100 · (close − lower) / (upper − lower)
+/// ```
+///
+/// `PO = 0` means the close is sitting on the lower band, `PO = 100` on the
+/// upper band, and `PO = 50` at the midline. Because the bands by construction
+/// bracket every projected high and low, the close almost always falls inside
+/// them and the oscillator stays in `0..100` — readings near the extremes flag
+/// an overbought/oversold position *relative to the trend-tilted channel*
+/// rather than to a horizontal level. When the bands collapse (a zero-range
+/// window, `upper == lower`) the position is undefined and the oscillator
+/// returns the neutral `50.0`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, ProjectionOscillator};
+///
+/// let mut indicator = ProjectionOscillator::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..30 {
+///     let base = 100.0 + f64::from(i);
+///     let candle =
+///         Candle::new(base, base + 2.0, base - 2.0, base + 1.0, 10.0, i64::from(i)).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct ProjectionOscillator {
+    bands: ProjectionBands,
+}
+
+impl ProjectionOscillator {
+    /// Construct a new Projection Oscillator.
+    ///
+    /// # Errors
+    /// Returns [`Error::InvalidPeriod`](crate::Error::InvalidPeriod) if
+    /// `period < 2`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            bands: ProjectionBands::new(period)?,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.bands.period()
+    }
+}
+
+impl Indicator for ProjectionOscillator {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let bands = self.bands.update(candle)?;
+        let width = bands.upper - bands.lower;
+        if width == 0.0 {
+            return Some(50.0);
+        }
+        Some(100.0 * (candle.close - bands.lower) / width)
+    }
+
+    fn reset(&mut self) {
+        self.bands.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.bands.warmup_period()
+    }
+
+    fn is_ready(&self) -> bool {
+        self.bands.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "ProjectionOscillator"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::error::Error;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64, ts: i64) -> Candle {
+        Candle::new(low, high, low, close, 10.0, ts).unwrap()
+    }
+
+    #[test]
+    fn rejects_period_below_two() {
+        assert!(matches!(
+            ProjectionOscillator::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(ProjectionOscillator::new(2).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let po = ProjectionOscillator::new(14).unwrap();
+        assert_eq!(po.period(), 14);
+        assert_eq!(po.warmup_period(), 14);
+        assert_eq!(po.name(), "ProjectionOscillator");
+        assert!(!po.is_ready());
+    }
+
+    #[test]
+    fn warms_up_then_emits() {
+        let mut po = ProjectionOscillator::new(3).unwrap();
+        assert!(po.update(candle(10.0, 8.0, 9.0, 0)).is_none());
+        assert!(po.update(candle(12.0, 9.0, 11.0, 1)).is_none());
+        assert!(po.update(candle(11.0, 10.0, 11.0, 2)).is_some());
+        assert!(po.is_ready());
+    }
+
+    #[test]
+    fn known_position() {
+        // Same window as ProjectionBands::known_projection: upper 12.5, lower 10.
+        // close 11 -> 100 * (11 - 10) / (12.5 - 10) = 40.
+        let mut po = ProjectionOscillator::new(3).unwrap();
+        po.update(candle(10.0, 8.0, 9.0, 0));
+        po.update(candle(12.0, 9.0, 11.0, 1));
+        let out = po.update(candle(11.0, 10.0, 11.0, 2)).unwrap();
+        assert_relative_eq!(out, 40.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn collapsed_bands_return_neutral() {
+        // Zero-range, perfectly trending candles: upper == lower every bar.
+        let mut po = ProjectionOscillator::new(3).unwrap();
+        let mut last = None;
+        for i in 0..6 {
+            let v = 100.0 + f64::from(i);
+            last = po.update(candle(v, v, v, i64::from(i)));
+        }
+        assert_relative_eq!(last.unwrap(), 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut po = ProjectionOscillator::new(3).unwrap();
+        po.update(candle(10.0, 8.0, 9.0, 0));
+        po.update(candle(12.0, 9.0, 11.0, 1));
+        po.update(candle(11.0, 10.0, 11.0, 2));
+        assert!(po.is_ready());
+        po.reset();
+        assert!(!po.is_ready());
+        assert!(po.update(candle(10.0, 8.0, 9.0, 3)).is_none());
+    }
+}

crates/wickra-core/src/indicators/qqe.rs

@@ -0,0 +1,358 @@
+//! QQE — Quantitative Qualitative Estimation.
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::indicators::rsi::Rsi;
+use crate::traits::Indicator;
+
+/// One QQE reading: the smoothed RSI and its volatility-trailing line.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct QqeOutput {
+    /// The EMA-smoothed RSI (the fast QQE line).
+    pub rsi_ma: f64,
+    /// The trailing line (the slow QQE line): an ATR-of-RSI trailing stop that
+    /// the smoothed RSI rides above in an uptrend and below in a downtrend.
+    pub trailing_line: f64,
+}
+
+/// QQE — Quantitative Qualitative Estimation (Igor Livshin).
+///
+/// QQE smooths the RSI, then builds an "ATR of the RSI" trailing stop around it.
+/// Crossovers of the smoothed RSI and that trailing line give cleaner momentum
+/// signals than the raw RSI:
+///
+/// ```text
+/// rsi_ma   = EMA(RSI(price, rsi_period), smoothing)
+/// atr_rsi  = |rsi_ma − rsi_ma_prev|
+/// ma_atr   = EMA(atr_rsi, 2·rsi_period − 1)        // Wilder length
+/// dar      = EMA(ma_atr, 2·rsi_period − 1) · factor // smoothed band width
+///
+/// long_band  = (rsi_ma_prev > long_band_prev  && rsi_ma > long_band_prev)
+///              ? max(long_band_prev,  rsi_ma − dar) : rsi_ma − dar
+/// short_band = (rsi_ma_prev < short_band_prev && rsi_ma < short_band_prev)
+///              ? min(short_band_prev, rsi_ma + dar) : rsi_ma + dar
+/// trend      = cross-up of short_band → +1, cross-down of long_band → −1, else hold
+/// trailing   = trend == +1 ? long_band : short_band
+/// ```
+///
+/// The trailing line ratchets in the trend direction (only ever tightening until
+/// the smoothed RSI crosses it), exactly like a [`SuperTrend`](crate::SuperTrend)
+/// on the RSI. Livshin's defaults are `rsi_period = 14`, `smoothing = 5`,
+/// `factor = 4.236`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, Qqe};
+///
+/// let mut qqe = Qqe::new(14, 5, 4.236).unwrap();
+/// let mut last = None;
+/// for i in 0..200 {
+///     last = qqe.update(100.0 + (f64::from(i) * 0.1).sin() * 8.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Qqe {
+    rsi: Rsi,
+    rsi_ma: Ema,
+    ma_atr: Ema,
+    dar_ema: Ema,
+    factor: f64,
+    prev_rsi_ma: Option<f64>,
+    bands: Option<(f64, f64, i8)>, // (long_band, short_band, trend)
+    last_value: Option<QqeOutput>,
+}
+
+impl Qqe {
+    /// Construct a QQE with the RSI period, RSI smoothing, and band `factor`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `rsi_period` or `smoothing` is `0`, or
+    /// [`Error::InvalidPeriod`] if `factor` is non-finite or not positive.
+    pub fn new(rsi_period: usize, smoothing: usize, factor: f64) -> Result<Self> {
+        if rsi_period == 0 || smoothing == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if !factor.is_finite() || factor <= 0.0 {
+            return Err(Error::InvalidPeriod {
+                message: "QQE factor must be a finite positive value",
+            });
+        }
+        let wilders = 2 * rsi_period - 1;
+        Ok(Self {
+            rsi: Rsi::new(rsi_period)?,
+            rsi_ma: Ema::new(smoothing)?,
+            ma_atr: Ema::new(wilders)?,
+            dar_ema: Ema::new(wilders)?,
+            factor,
+            prev_rsi_ma: None,
+            bands: None,
+            last_value: None,
+        })
+    }
+
+    /// Configured band factor.
+    pub const fn factor(&self) -> f64 {
+        self.factor
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<QqeOutput> {
+        self.last_value
+    }
+}
+
+impl Indicator for Qqe {
+    type Input = f64;
+    type Output = QqeOutput;
+
+    fn update(&mut self, price: f64) -> Option<QqeOutput> {
+        let rsi = self.rsi.update(price)?;
+        let rsi_ma = self.rsi_ma.update(rsi)?;
+
+        let Some(prev_ma) = self.prev_rsi_ma else {
+            self.prev_rsi_ma = Some(rsi_ma);
+            return None;
+        };
+        let atr_rsi = (rsi_ma - prev_ma).abs();
+        self.prev_rsi_ma = Some(rsi_ma);
+
+        let ma_atr = self.ma_atr.update(atr_rsi)?;
+        let dar = self.dar_ema.update(ma_atr)? * self.factor;
+
+        let new_long = rsi_ma - dar;
+        let new_short = rsi_ma + dar;
+
+        let (long_band, short_band, trend) = match self.bands {
+            Some((lb_prev, sb_prev, tr_prev)) => {
+                let lb = if prev_ma > lb_prev && rsi_ma > lb_prev {
+                    lb_prev.max(new_long)
+                } else {
+                    new_long
+                };
+                let sb = if prev_ma < sb_prev && rsi_ma < sb_prev {
+                    sb_prev.min(new_short)
+                } else {
+                    new_short
+                };
+                let tr = if prev_ma <= sb_prev && rsi_ma > sb_prev {
+                    1
+                } else if prev_ma >= lb_prev && rsi_ma < lb_prev {
+                    -1
+                } else {
+                    tr_prev
+                };
+                (lb, sb, tr)
+            }
+            None => (new_long, new_short, 1),
+        };
+        self.bands = Some((long_band, short_band, trend));
+
+        let trailing_line = if trend == 1 { long_band } else { short_band };
+        let out = QqeOutput {
+            rsi_ma,
+            trailing_line,
+        };
+        self.last_value = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.rsi.reset();
+        self.rsi_ma.reset();
+        self.ma_atr.reset();
+        self.dar_ema.reset();
+        self.prev_rsi_ma = None;
+        self.bands = None;
+        self.last_value = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // RSI (rsi_period + 1) -> rsi_ma EMA -> one bar for the first atr_rsi ->
+        // ma_atr EMA -> dar EMA. Expressed via the component warmups so it stays
+        // correct if those change.
+        self.rsi.warmup_period()
+            + self.rsi_ma.warmup_period()
+            + self.ma_atr.warmup_period()
+            + self.dar_ema.warmup_period()
+            - 2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last_value.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "QQE"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Independent reference replaying the full QQE recurrence.
+    fn naive(
+        prices: &[f64],
+        rsi_period: usize,
+        smoothing: usize,
+        factor: f64,
+    ) -> Vec<Option<QqeOutput>> {
+        let mut rsi = Rsi::new(rsi_period).unwrap();
+        let mut rsi_ma = Ema::new(smoothing).unwrap();
+        let wilders = 2 * rsi_period - 1;
+        let mut ma_atr = Ema::new(wilders).unwrap();
+        let mut dar_ema = Ema::new(wilders).unwrap();
+        let mut prev_ma: Option<f64> = None;
+        let mut bands: Option<(f64, f64, i8)> = None;
+        let mut out = Vec::with_capacity(prices.len());
+        for &p in prices {
+            let v = (|| {
+                let r = rsi.update(p)?;
+                let m = rsi_ma.update(r)?;
+                let Some(pm) = prev_ma else {
+                    prev_ma = Some(m);
+                    return None;
+                };
+                let atr = (m - pm).abs();
+                prev_ma = Some(m);
+                let ma = ma_atr.update(atr)?;
+                let dar = dar_ema.update(ma)? * factor;
+                let nl = m - dar;
+                let ns = m + dar;
+                let (lb, sb, tr) = match bands {
+                    Some((lbp, sbp, trp)) => {
+                        let lb = if pm > lbp && m > lbp { lbp.max(nl) } else { nl };
+                        let sb = if pm < sbp && m < sbp { sbp.min(ns) } else { ns };
+                        let tr = if pm <= sbp && m > sbp {
+                            1
+                        } else if pm >= lbp && m < lbp {
+                            -1
+                        } else {
+                            trp
+                        };
+                        (lb, sb, tr)
+                    }
+                    None => (nl, ns, 1),
+                };
+                bands = Some((lb, sb, tr));
+                Some(QqeOutput {
+                    rsi_ma: m,
+                    trailing_line: if tr == 1 { lb } else { sb },
+                })
+            })();
+            out.push(v);
+        }
+        out
+    }
+
+    #[test]
+    fn rejects_bad_params() {
+        assert!(matches!(Qqe::new(0, 5, 4.236), Err(Error::PeriodZero)));
+        assert!(matches!(Qqe::new(14, 0, 4.236), Err(Error::PeriodZero)));
+        assert!(matches!(
+            Qqe::new(14, 5, 0.0),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            Qqe::new(14, 5, f64::NAN),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    /// Cover the const accessors `factor` + `value` and the Indicator-impl
+    /// `name`. `warmup_period` is covered by `first_emission_matches_warmup`.
+    #[test]
+    fn accessors_and_metadata() {
+        let qqe = Qqe::new(14, 5, 4.236).unwrap();
+        assert_relative_eq!(qqe.factor(), 4.236, epsilon = 1e-12);
+        assert_eq!(qqe.value(), None);
+        assert_eq!(qqe.name(), "QQE");
+    }
+
+    #[test]
+    fn first_emission_matches_warmup() {
+        // A long trend-up-then-down series exercises both trend flips and the
+        // band tighten/reset branches.
+        let prices: Vec<f64> = (0..200)
+            .map(|i| 100.0 + (f64::from(i) * 0.06).sin() * 20.0)
+            .collect();
+        let mut qqe = Qqe::new(14, 5, 4.236).unwrap();
+        let out = qqe.batch(&prices);
+        let warmup = qqe.warmup_period();
+        for (i, v) in out.iter().enumerate().take(warmup - 1) {
+            assert!(v.is_none(), "index {i} must be None during warmup");
+        }
+        assert!(
+            out[warmup - 1].is_some(),
+            "first value at warmup_period - 1"
+        );
+    }
+
+    #[test]
+    fn matches_naive_over_full_cycle() {
+        // Up, range, and down phases so every band/trend branch is traversed.
+        let prices: Vec<f64> = (0..220)
+            .map(|i| {
+                let t = f64::from(i);
+                100.0 + (t * 0.05).sin() * 18.0 + (t * 0.2).cos() * 4.0
+            })
+            .collect();
+        let mut qqe = Qqe::new(14, 5, 4.236).unwrap();
+        let got = qqe.batch(&prices);
+        let want = naive(&prices, 14, 5, 4.236);
+        for (i, (g, w)) in got.iter().zip(want.iter()).enumerate() {
+            assert_eq!(g.is_some(), w.is_some(), "readiness mismatch at {i}");
+            if let (Some(a), Some(b)) = (g, w) {
+                assert_relative_eq!(a.rsi_ma, b.rsi_ma, epsilon = 1e-9);
+                assert_relative_eq!(a.trailing_line, b.trailing_line, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn trailing_line_below_rsi_ma_in_uptrend() {
+        // Sustained rise: trend resolves to +1 and the trailing (long) band sits
+        // below the smoothed RSI.
+        let prices: Vec<f64> = (1..=120).map(f64::from).collect();
+        let mut qqe = Qqe::new(14, 5, 4.236).unwrap();
+        let last = qqe.batch(&prices).into_iter().flatten().last().unwrap();
+        assert!(
+            last.trailing_line <= last.rsi_ma,
+            "uptrend trailing {} should sit at/below rsi_ma {}",
+            last.trailing_line,
+            last.rsi_ma
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut qqe = Qqe::new(14, 5, 4.236).unwrap();
+        qqe.batch(
+            &(0..120)
+                .map(|i| 100.0 + (f64::from(i) * 0.1).sin() * 8.0)
+                .collect::<Vec<_>>(),
+        );
+        assert!(qqe.is_ready());
+        qqe.reset();
+        assert!(!qqe.is_ready());
+        assert_eq!(qqe.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (0..150)
+            .map(|i| 50.0 + (f64::from(i) * 0.12).sin() * 12.0)
+            .collect();
+        let mut a = Qqe::new(14, 5, 4.236).unwrap();
+        let mut b = Qqe::new(14, 5, 4.236).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/qstick.rs

@@ -0,0 +1,168 @@
+//! Qstick — Tushar Chande's measure of buying vs. selling pressure.
+
+use crate::error::Result;
+use crate::indicators::sma::Sma;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Qstick: the simple moving average of the body `close - open` over `period`
+/// bars.
+///
+/// Positive values indicate a run of bars that closed above their open (net
+/// buying pressure); negative values indicate net selling pressure. A zero
+/// crossing is read as a shift in short-term sentiment.
+///
+/// ```text
+/// Qstick = SMA(close - open, period)
+/// ```
+///
+/// Reference: Tushar Chande, *The New Technical Trader*, 1994.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, Qstick};
+///
+/// let mut indicator = Qstick::new(5).unwrap();
+/// let mut last = None;
+/// for i in 0..20 {
+///     let base = 100.0 + f64::from(i);
+///     let candle =
+///         Candle::new(base, base + 2.0, base - 1.0, base + 1.0, 1.0, i64::from(i)).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Qstick {
+    period: usize,
+    sma: Sma,
+}
+
+impl Qstick {
+    /// Construct a Qstick with the given averaging period.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`](crate::error::Error::PeriodZero) if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            period,
+            sma: Sma::new(period)?,
+        })
+    }
+
+    /// Configured averaging period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for Qstick {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        self.sma.update(candle.close - candle.open)
+    }
+
+    fn reset(&mut self) {
+        self.sma.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.sma.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "Qstick"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::error::Error;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(open: f64, close: f64, ts: i64) -> Candle {
+        let high = open.max(close) + 1.0;
+        let low = open.min(close) - 1.0;
+        Candle::new(open, high, low, close, 1.0, ts).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(Qstick::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let q = Qstick::new(5).unwrap();
+        assert_eq!(q.period(), 5);
+        assert_eq!(q.warmup_period(), 5);
+        assert_eq!(q.name(), "Qstick");
+        assert!(!q.is_ready());
+    }
+
+    #[test]
+    fn warmup_emits_first_value_at_period() {
+        let mut q = Qstick::new(3).unwrap();
+        let candles: Vec<Candle> = (0..3).map(|i| candle(10.0, 11.0, i)).collect();
+        let out = q.batch(&candles);
+        assert!(out[0].is_none());
+        assert!(out[1].is_none());
+        assert!(out[2].is_some());
+    }
+
+    #[test]
+    fn constant_bodies_yield_the_body() {
+        // Every bar closes 1.5 above its open -> Qstick converges to 1.5.
+        let mut q = Qstick::new(4).unwrap();
+        let candles: Vec<Candle> = (0..10).map(|i| candle(10.0, 11.5, i)).collect();
+        let out = q.batch(&candles);
+        assert_relative_eq!(out.last().unwrap().unwrap(), 1.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn selling_pressure_is_negative() {
+        let mut q = Qstick::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| candle(11.0, 10.0, i)).collect();
+        let last = q.batch(&candles).last().unwrap().unwrap();
+        assert!(last < 0.0, "qstick {last} should be negative");
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut q = Qstick::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| candle(10.0, 11.0, i)).collect();
+        q.batch(&candles);
+        assert!(q.is_ready());
+        q.reset();
+        assert!(!q.is_ready());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..40_i64)
+            .map(|i| {
+                candle(
+                    100.0 + (i as f64 * 0.3).sin(),
+                    100.0 + (i as f64 * 0.4).cos(),
+                    i,
+                )
+            })
+            .collect();
+        let mut a = Qstick::new(7).unwrap();
+        let mut b = Qstick::new(7).unwrap();
+        assert_eq!(
+            a.batch(&candles),
+            candles.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/quartile_bands.rs

@@ -0,0 +1,194 @@
+//! Quartile Bands — rolling 25th / 50th / 75th percentile envelope.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::rolling_quantile::quantile_sorted;
+use crate::traits::Indicator;
+
+/// Quartile Bands output.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct QuartileBandsOutput {
+    /// Upper band: the rolling third quartile (75th percentile, `Q3`).
+    pub upper: f64,
+    /// Middle line: the rolling median (50th percentile, `Q2`).
+    pub middle: f64,
+    /// Lower band: the rolling first quartile (25th percentile, `Q1`).
+    pub lower: f64,
+}
+
+/// Quartile Bands: a distribution-based envelope drawn at the rolling quartiles.
+///
+/// ```text
+/// lower  = Q1  = 25th percentile of the last `period` values
+/// middle = Q2  = 50th percentile (median)
+/// upper  = Q3  = 75th percentile
+/// ```
+///
+/// Quantiles use the type-7 (`NumPy`/`R-7`) linear interpolation shared with
+/// [`RollingQuantile`](crate::RollingQuantile). Where Bollinger Bands assume an
+/// approximately normal distribution and size the envelope by the mean and
+/// standard deviation, Quartile Bands are fully **non-parametric**: the band
+/// edges are order statistics, so a single outlier shifts at most one rank
+/// rather than inflating the whole width, and the inter-quartile span between
+/// the bands is exactly the [`RollingIqr`](crate::RollingIqr). The middle line
+/// is the robust median rather than the mean, so it is unmoved by spikes.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, QuartileBands};
+///
+/// let mut indicator = QuartileBands::new(20).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct QuartileBands {
+    period: usize,
+    window: VecDeque<f64>,
+    scratch: Vec<f64>,
+}
+
+impl QuartileBands {
+    /// Construct new Quartile Bands.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+            scratch: Vec::with_capacity(period),
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for QuartileBands {
+    type Input = f64;
+    type Output = QuartileBandsOutput;
+
+    fn update(&mut self, value: f64) -> Option<QuartileBandsOutput> {
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(value);
+        if self.window.len() < self.period {
+            return None;
+        }
+        self.scratch.clear();
+        self.scratch.extend(self.window.iter().copied());
+        self.scratch.sort_by(f64::total_cmp);
+        Some(QuartileBandsOutput {
+            upper: quantile_sorted(&self.scratch, 0.75),
+            middle: quantile_sorted(&self.scratch, 0.5),
+            lower: quantile_sorted(&self.scratch, 0.25),
+        })
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.scratch.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "QuartileBands"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(QuartileBands::new(0), Err(Error::PeriodZero)));
+        assert!(QuartileBands::new(1).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let qb = QuartileBands::new(20).unwrap();
+        assert_eq!(qb.period(), 20);
+        assert_eq!(qb.warmup_period(), 20);
+        assert_eq!(qb.name(), "QuartileBands");
+        assert!(!qb.is_ready());
+    }
+
+    #[test]
+    fn warms_up_then_emits() {
+        let mut qb = QuartileBands::new(4).unwrap();
+        assert!(qb.update(10.0).is_none());
+        assert!(qb.update(20.0).is_none());
+        assert!(qb.update(30.0).is_none());
+        assert!(qb.update(40.0).is_some());
+        assert!(qb.is_ready());
+    }
+
+    #[test]
+    fn known_quartiles() {
+        // sorted [10,20,30,40]:
+        //   Q1 h=(4-1)*0.25=0.75 -> 10 + 0.75*10 = 17.5
+        //   Q2 h=1.5            -> 20 + 0.5*10  = 25.0
+        //   Q3 h=2.25           -> 30 + 0.25*10 = 32.5
+        let mut qb = QuartileBands::new(4).unwrap();
+        let out = qb.batch(&[40.0, 30.0, 20.0, 10.0]);
+        let last = out[3].unwrap();
+        assert_relative_eq!(last.lower, 17.5, epsilon = 1e-9);
+        assert_relative_eq!(last.middle, 25.0, epsilon = 1e-9);
+        assert_relative_eq!(last.upper, 32.5, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn median_robust_to_outlier() {
+        // A single spike shifts the mean a lot but the median by at most one rank.
+        let mut qb = QuartileBands::new(5).unwrap();
+        let out = qb.batch(&[1.0, 2.0, 3.0, 4.0, 1000.0]);
+        assert_relative_eq!(out[4].unwrap().middle, 3.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn rolling_window_evicts_oldest() {
+        // Eight values through a period-4 window: only the last four survive,
+        // reproducing the `known_quartiles` window.
+        let mut qb = QuartileBands::new(4).unwrap();
+        let out = qb.batch(&[1.0, 2.0, 3.0, 4.0, 40.0, 30.0, 20.0, 10.0]);
+        let last = out[7].unwrap();
+        assert_relative_eq!(last.lower, 17.5, epsilon = 1e-9);
+        assert_relative_eq!(last.middle, 25.0, epsilon = 1e-9);
+        assert_relative_eq!(last.upper, 32.5, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut qb = QuartileBands::new(4).unwrap();
+        for v in [10.0, 20.0, 30.0, 40.0] {
+            qb.update(v);
+        }
+        assert!(qb.is_ready());
+        qb.reset();
+        assert!(!qb.is_ready());
+        assert!(qb.update(10.0).is_none());
+    }
+}

crates/wickra-core/src/indicators/rmi.rs

@@ -0,0 +1,276 @@
+//! Relative Momentum Index (RMI).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Relative Momentum Index — RSI generalised to a multi-bar momentum lookback.
+///
+/// Wilder's [`Rsi`](crate::Rsi) compares each close to the *previous* close.
+/// The RMI (Roger Altman, 1993) compares it to the close `momentum` bars ago,
+/// then applies the same Wilder-smoothed up/down accumulator over `period`:
+///
+/// ```text
+/// change_t = close_t - close_{t-momentum}
+/// gain     = max(change, 0),  loss = max(-change, 0)
+/// avg_gain, avg_loss = Wilder-smoothed over `period`
+/// RMI      = 100 * avg_gain / (avg_gain + avg_loss)
+/// ```
+///
+/// `momentum = 1` reduces the RMI exactly to the RSI. Larger `momentum` makes
+/// the oscillator smoother and slower to flip, holding overbought/oversold
+/// readings longer in a trend. Output is bounded in `[0, 100]`; a flat market
+/// (no gains and no losses) returns the neutral `50`.
+///
+/// The first value lands after `momentum + period` inputs: `momentum` to fill
+/// the lookback, then `period` changes to seed Wilder's averages.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, Rmi};
+///
+/// let mut indicator = Rmi::new(14, 5).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.2).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Rmi {
+    period: usize,
+    momentum: usize,
+    /// The last `momentum` prices, oldest at the front.
+    window: VecDeque<f64>,
+    seed_gains: Vec<f64>,
+    seed_losses: Vec<f64>,
+    avg_gain: Option<f64>,
+    avg_loss: Option<f64>,
+    last_value: Option<f64>,
+}
+
+impl Rmi {
+    /// Construct an RMI with the given smoothing `period` and `momentum`
+    /// lookback.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if either `period` or `momentum` is `0`.
+    pub fn new(period: usize, momentum: usize) -> Result<Self> {
+        if period == 0 || momentum == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            momentum,
+            window: VecDeque::with_capacity(momentum),
+            seed_gains: Vec::with_capacity(period),
+            seed_losses: Vec::with_capacity(period),
+            avg_gain: None,
+            avg_loss: None,
+            last_value: None,
+        })
+    }
+
+    /// Configured smoothing period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Configured momentum lookback.
+    pub const fn momentum(&self) -> usize {
+        self.momentum
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last_value
+    }
+
+    fn rmi_from_avgs(avg_gain: f64, avg_loss: f64) -> f64 {
+        let denom = avg_gain + avg_loss;
+        if denom == 0.0 {
+            50.0
+        } else {
+            // Ratio first, then scale, so `100 * g / g` cannot round above 100.
+            100.0 * (avg_gain / denom)
+        }
+    }
+}
+
+impl Indicator for Rmi {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.last_value;
+        }
+        if self.window.len() < self.momentum {
+            // Still filling the momentum lookback; no change to measure yet.
+            self.window.push_back(input);
+            return None;
+        }
+        let past = self.window.pop_front().expect("window full");
+        self.window.push_back(input);
+
+        let change = input - past;
+        let gain = if change > 0.0 { change } else { 0.0 };
+        let loss = if change < 0.0 { -change } else { 0.0 };
+
+        if let (Some(ag), Some(al)) = (self.avg_gain, self.avg_loss) {
+            let n = self.period as f64;
+            let new_ag = (ag * (n - 1.0) + gain) / n;
+            let new_al = (al * (n - 1.0) + loss) / n;
+            self.avg_gain = Some(new_ag);
+            self.avg_loss = Some(new_al);
+            let v = Self::rmi_from_avgs(new_ag, new_al);
+            self.last_value = Some(v);
+            return Some(v);
+        }
+
+        self.seed_gains.push(gain);
+        self.seed_losses.push(loss);
+        if self.seed_gains.len() == self.period {
+            let ag = self.seed_gains.iter().sum::<f64>() / self.period as f64;
+            let al = self.seed_losses.iter().sum::<f64>() / self.period as f64;
+            self.avg_gain = Some(ag);
+            self.avg_loss = Some(al);
+            let v = Self::rmi_from_avgs(ag, al);
+            self.last_value = Some(v);
+            return Some(v);
+        }
+        None
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.seed_gains.clear();
+        self.seed_losses.clear();
+        self.avg_gain = None;
+        self.avg_loss = None;
+        self.last_value = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.momentum + self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last_value.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "RMI"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::indicators::Rsi;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_params() {
+        assert!(matches!(Rmi::new(0, 5), Err(Error::PeriodZero)));
+        assert!(matches!(Rmi::new(14, 0), Err(Error::PeriodZero)));
+    }
+
+    /// Cover the const accessors `period` + `momentum` + `value` and the
+    /// Indicator-impl `warmup_period` + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let rmi = Rmi::new(14, 5).unwrap();
+        assert_eq!(rmi.period(), 14);
+        assert_eq!(rmi.momentum(), 5);
+        assert_eq!(rmi.value(), None);
+        assert_eq!(rmi.warmup_period(), 19);
+        assert_eq!(rmi.name(), "RMI");
+    }
+
+    #[test]
+    fn momentum_one_equals_rsi() {
+        // With momentum = 1 the RMI is exactly Wilder's RSI.
+        let prices: Vec<f64> = (0..60)
+            .map(|i| 100.0 + (f64::from(i) * 0.4).sin() * 8.0)
+            .collect();
+        let mut rmi = Rmi::new(14, 1).unwrap();
+        let mut rsi = Rsi::new(14).unwrap();
+        for (i, &p) in prices.iter().enumerate() {
+            let got = rmi.update(p);
+            let want = rsi.update(p);
+            assert_eq!(got.is_some(), want.is_some(), "readiness mismatch at {i}");
+            if let (Some(a), Some(b)) = (got, want) {
+                assert_relative_eq!(a, b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn warmup_then_emits() {
+        // momentum + period = 3 + 2 = 5 inputs before the first value.
+        let mut rmi = Rmi::new(2, 3).unwrap();
+        let out = rmi.batch(&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+        for (i, v) in out.iter().enumerate().take(4) {
+            assert!(v.is_none(), "index {i} must be None during warmup");
+        }
+        assert!(out[4].is_some(), "first value at warmup_period - 1");
+    }
+
+    #[test]
+    fn pure_uptrend_is_one_hundred() {
+        // Every momentum-spaced change is positive -> avg_loss 0 -> RMI 100.
+        let prices: Vec<f64> = (1..=40).map(f64::from).collect();
+        let mut rmi = Rmi::new(5, 3).unwrap();
+        let last = rmi.batch(&prices).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn flat_market_is_neutral() {
+        // No change -> no gains and no losses -> neutral 50.
+        let mut rmi = Rmi::new(3, 2).unwrap();
+        let last = rmi.batch(&[7.0; 20]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut rmi = Rmi::new(2, 2).unwrap();
+        let ready = rmi
+            .batch(&[1.0, 2.0, 3.0, 4.0, 5.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_eq!(rmi.update(f64::NAN), Some(ready));
+        assert_eq!(rmi.update(f64::INFINITY), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut rmi = Rmi::new(3, 2).unwrap();
+        rmi.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(rmi.is_ready());
+        rmi.reset();
+        assert!(!rmi.is_ready());
+        assert_eq!(rmi.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=40)
+            .map(|i| 50.0 + (f64::from(i) * 0.5).sin() * 10.0)
+            .collect();
+        let mut a = Rmi::new(14, 5).unwrap();
+        let mut b = Rmi::new(14, 5).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/rolling_min_max_scaler.rs

@@ -0,0 +1,261 @@
+//! Rolling Min-Max Scaler — normalises the latest value to `[0, 1]` over a window.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Rolling Min-Max Scaler — maps the current value onto `[0, 1]` relative to the
+/// minimum and maximum of the trailing window.
+///
+/// ```text
+/// scaled = (x − min(window)) / (max(window) − min(window))
+/// ```
+///
+/// This is the streaming form of scikit-learn's `MinMaxScaler` applied over a
+/// sliding window: `0` means the value is the lowest in the window, `1` the
+/// highest, `0.5` the midpoint of the range. It is the engine behind oscillators
+/// like the Stochastic %K and a handy normaliser for feeding any indicator into a
+/// bounded model input. Because it rescales to the window's own range it is
+/// scale-free across instruments.
+///
+/// The output is in `[0, 1]`. A flat window (`max == min`) has no range to scale
+/// against and returns the neutral `0.5`. The first value lands after `period`
+/// inputs; each `update` scans the window in O(`period`).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, RollingMinMaxScaler};
+///
+/// let mut indicator = RollingMinMaxScaler::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct RollingMinMaxScaler {
+    period: usize,
+    window: VecDeque<f64>,
+    last: Option<f64>,
+}
+
+impl RollingMinMaxScaler {
+    /// Construct a rolling min-max scaler over `period` values.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0` and
+    /// [`Error::InvalidPeriod`] if `period < 2` (a range needs two points).
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if period < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "min-max scaler needs period >= 2",
+            });
+        }
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+            last: None,
+        })
+    }
+
+    /// Configured window length.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for RollingMinMaxScaler {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.last;
+        }
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        if self.window.len() < self.period {
+            return None;
+        }
+        let mut min = f64::INFINITY;
+        let mut max = f64::NEG_INFINITY;
+        for &v in &self.window {
+            min = min.min(v);
+            max = max.max(v);
+        }
+        let range = max - min;
+        let scaled = if range > 0.0 {
+            (input - min) / range
+        } else {
+            0.5
+        };
+        self.last = Some(scaled);
+        Some(scaled)
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "RollingMinMaxScaler"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_period() {
+        assert!(matches!(
+            RollingMinMaxScaler::new(0),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            RollingMinMaxScaler::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(RollingMinMaxScaler::new(2).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let s = RollingMinMaxScaler::new(14).unwrap();
+        assert_eq!(s.period(), 14);
+        assert_eq!(s.warmup_period(), 14);
+        assert_eq!(s.name(), "RollingMinMaxScaler");
+        assert!(!s.is_ready());
+        assert_eq!(s.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut s = RollingMinMaxScaler::new(4).unwrap();
+        let out = s.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        for v in out.iter().take(3) {
+            assert!(v.is_none());
+        }
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn highest_in_window_is_one() {
+        let mut s = RollingMinMaxScaler::new(4).unwrap();
+        // last value is the highest -> 1.0.
+        let last = s
+            .batch(&[1.0, 2.0, 3.0, 4.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, 1.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn lowest_in_window_is_zero() {
+        let mut s = RollingMinMaxScaler::new(4).unwrap();
+        let last = s
+            .batch(&[4.0, 3.0, 2.0, 1.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn midpoint_is_half() {
+        let mut s = RollingMinMaxScaler::new(3).unwrap();
+        // window [0, 2, 1]: min 0, max 2, current 1 -> 0.5.
+        let last = s
+            .batch(&[0.0, 2.0, 1.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, 0.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn flat_window_is_half() {
+        let mut s = RollingMinMaxScaler::new(4).unwrap();
+        let last = s.batch(&[7.0; 8]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_in_range() {
+        let mut s = RollingMinMaxScaler::new(14).unwrap();
+        for v in s
+            .batch(
+                &(0..200)
+                    .map(|i| (f64::from(i) * 0.3).sin() * 10.0)
+                    .collect::<Vec<_>>(),
+            )
+            .into_iter()
+            .flatten()
+        {
+            assert!((0.0..=1.0).contains(&v));
+        }
+    }
+
+    #[test]
+    fn ignores_non_finite() {
+        let mut s = RollingMinMaxScaler::new(4).unwrap();
+        let ready = s
+            .batch(&[1.0, 2.0, 3.0, 4.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_eq!(s.update(f64::NAN), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut s = RollingMinMaxScaler::new(4).unwrap();
+        s.batch(&[1.0, 2.0, 3.0, 4.0]);
+        assert!(s.is_ready());
+        s.reset();
+        assert!(!s.is_ready());
+        assert_eq!(s.value(), None);
+        assert_eq!(s.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let xs: Vec<f64> = (0..120)
+            .map(|i| (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = RollingMinMaxScaler::new(14).unwrap().batch(&xs);
+        let mut b = RollingMinMaxScaler::new(14).unwrap();
+        let streamed: Vec<_> = xs.iter().map(|x| b.update(*x)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/rsi.rs

@@ -25,13 +25,24 @@ use crate::traits::Indicator;
 #[derive(Debug, Clone)]
 pub struct Rsi {
     period: usize,
-    prev_close: Option<f64>,
+    /// `period - 1` as `f64`, precomputed for the Wilder smoothing step.
+    n_minus_1: f64,
+    /// `1 / period`, precomputed so the per-tick smoothing multiplies instead of
+    /// divides (a reciprocal is hoisted out of the hot path).
+    inv_period: f64,
+    /// Previous close, valid once `has_prev` is set. Bare `f64` + flag instead of
+    /// `Option<f64>` to avoid an enum-tag read on every tick.
+    prev_close: f64,
+    has_prev: bool,
     // Wilder seeds with the simple average of the first `period` gains/losses,
     // then transitions to recursive smoothing.
     seed_buf_gains: Vec<f64>,
     seed_buf_losses: Vec<f64>,
-    avg_gain: Option<f64>,
-    avg_loss: Option<f64>,
+    /// Smoothed average gain / loss, valid once `avgs_seeded` is set. Bare `f64`s
+    /// + flag so the hot recurrence avoids reading two `Option<f64>` tags per tick.
+    avg_gain: f64,
+    avg_loss: f64,
+    avgs_seeded: bool,
     last_value: Option<f64>,
 }
 
@@ -47,11 +58,15 @@ impl Rsi {
         }
         Ok(Self {
             period,
-            prev_close: None,
+            n_minus_1: (period - 1) as f64,
+            inv_period: 1.0 / period as f64,
+            prev_close: 0.0,
+            has_prev: false,
             seed_buf_gains: Vec::with_capacity(period),
             seed_buf_losses: Vec::with_capacity(period),
-            avg_gain: None,
-            avg_loss: None,
+            avg_gain: 0.0,
+            avg_loss: 0.0,
+            avgs_seeded: false,
             last_value: None,
         })
     }
@@ -67,16 +82,16 @@ impl Rsi {
     }
 
     fn rsi_from_avgs(avg_gain: f64, avg_loss: f64) -> f64 {
-        if avg_loss == 0.0 {
-            if avg_gain == 0.0 {
-                // No movement at all -> RSI undefined; standard convention returns 50.
-                50.0
-            } else {
-                100.0
-            }
+        // Algebraically `100 - 100/(1 + ag/al)` collapses to `100·ag/(ag+al)`,
+        // which needs a single division instead of two and removes the separate
+        // `rs` step. Edge cases stay exact: `al == 0, ag > 0` gives `100·ag/ag =
+        // 100`; `ag == 0, al > 0` gives `0`; both zero (no movement) is the
+        // undefined case and returns the neutral 50.
+        let denom = avg_gain + avg_loss;
+        if denom == 0.0 {
+            50.0
         } else {
-            let rs = avg_gain / avg_loss;
-            100.0 - 100.0 / (1.0 + rs)
+            100.0 * avg_gain / denom
         }
     }
 }
@@ -90,22 +105,25 @@ impl Indicator for Rsi {
             return self.last_value;
         }
 
-        let Some(prev) = self.prev_close else {
-            self.prev_close = Some(input);
+        if !self.has_prev {
+            self.prev_close = input;
+            self.has_prev = true;
             return None;
-        };
-        self.prev_close = Some(input);
+        }
+        let prev = self.prev_close;
+        self.prev_close = input;
 
         let diff = input - prev;
         let gain = if diff > 0.0 { diff } else { 0.0 };
         let loss = if diff < 0.0 { -diff } else { 0.0 };
 
-        if let (Some(ag), Some(al)) = (self.avg_gain, self.avg_loss) {
-            let n = self.period as f64;
-            let new_ag = (ag * (n - 1.0) + gain) / n;
-            let new_al = (al * (n - 1.0) + loss) / n;
-            self.avg_gain = Some(new_ag);
-            self.avg_loss = Some(new_al);
+        if self.avgs_seeded {
+            // Wilder smoothing `(prev·(n-1) + x) / n` with the reciprocal hoisted:
+            // a fused multiply-add then a multiply by `1/n`, no per-tick division.
+            let new_ag = self.avg_gain.mul_add(self.n_minus_1, gain) * self.inv_period;
+            let new_al = self.avg_loss.mul_add(self.n_minus_1, loss) * self.inv_period;
+            self.avg_gain = new_ag;
+            self.avg_loss = new_al;
             let v = Self::rsi_from_avgs(new_ag, new_al);
             self.last_value = Some(v);
             return Some(v);
@@ -116,8 +134,9 @@ impl Indicator for Rsi {
         if self.seed_buf_gains.len() == self.period {
             let ag = self.seed_buf_gains.iter().sum::<f64>() / self.period as f64;
             let al = self.seed_buf_losses.iter().sum::<f64>() / self.period as f64;
-            self.avg_gain = Some(ag);
-            self.avg_loss = Some(al);
+            self.avg_gain = ag;
+            self.avg_loss = al;
+            self.avgs_seeded = true;
             let v = Self::rsi_from_avgs(ag, al);
             self.last_value = Some(v);
             return Some(v);
@@ -126,11 +145,13 @@ impl Indicator for Rsi {
     }
 
     fn reset(&mut self) {
-        self.prev_close = None;
+        self.prev_close = 0.0;
+        self.has_prev = false;
         self.seed_buf_gains.clear();
         self.seed_buf_losses.clear();
-        self.avg_gain = None;
-        self.avg_loss = None;
+        self.avg_gain = 0.0;
+        self.avg_loss = 0.0;
+        self.avgs_seeded = false;
         self.last_value = None;
     }
 

crates/wickra-core/src/indicators/rsx.rs

@@ -0,0 +1,291 @@
+//! RSX — Jurik-style smoothed RSI.
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// RSX — a noise-free RSI built from Jurik's three-stage smoothing cascade.
+///
+/// Where Wilder's [`Rsi`](crate::Rsi) smooths the up/down moves with a single
+/// EMA, the RSX runs the signed price change *and* its absolute value through
+/// three cascaded "double-EMA with overshoot" stages (each stage is
+/// `x = 1.5·a − 0.5·b`, the same lag-cancelling trick as a DEMA), then forms the
+/// RSI-style ratio from the two smoothed streams:
+///
+/// ```text
+/// f18 = 3 / (length + 2),  f20 = 1 - f18
+/// each stage: a = f20·a + f18·in;  b = f18·a + f20·b;  out = 1.5·a − 0.5·b
+/// v14 = stage3(signed change),  v1C = stage3(|change|)
+/// RSX = clamp((v14 / v1C + 1) · 50, 0, 100)        (50 when v1C == 0)
+/// ```
+///
+/// The result is an oscillator in `[0, 100]` that tracks the RSI but is far
+/// smoother for the same responsiveness — it has very little of the RSI's
+/// bar-to-bar jitter, so threshold crosses and divergences are cleaner. A flat
+/// market returns the neutral `50`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, Rsx};
+///
+/// let mut indicator = Rsx::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.2).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct Rsx {
+    length: usize,
+    f18: f64,
+    f20: f64,
+    prev: Option<f64>,
+    count: usize,
+    // Signed-change cascade (three stages: a/b pairs).
+    s_a0: f64,
+    s_b0: f64,
+    s_a1: f64,
+    s_b1: f64,
+    s_a2: f64,
+    s_b2: f64,
+    // Absolute-change cascade.
+    a_a0: f64,
+    a_b0: f64,
+    a_a1: f64,
+    a_b1: f64,
+    a_a2: f64,
+    a_b2: f64,
+    last_value: Option<f64>,
+}
+
+impl Rsx {
+    /// Construct an RSX with the given smoothing length.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `length == 0`.
+    pub fn new(length: usize) -> Result<Self> {
+        if length == 0 {
+            return Err(Error::PeriodZero);
+        }
+        let f18 = 3.0 / (length as f64 + 2.0);
+        Ok(Self {
+            length,
+            f18,
+            f20: 1.0 - f18,
+            prev: None,
+            count: 0,
+            s_a0: 0.0,
+            s_b0: 0.0,
+            s_a1: 0.0,
+            s_b1: 0.0,
+            s_a2: 0.0,
+            s_b2: 0.0,
+            a_a0: 0.0,
+            a_b0: 0.0,
+            a_a1: 0.0,
+            a_b1: 0.0,
+            a_a2: 0.0,
+            a_b2: 0.0,
+            last_value: None,
+        })
+    }
+
+    /// Configured length.
+    pub const fn length(&self) -> usize {
+        self.length
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last_value
+    }
+
+    /// One double-EMA-with-overshoot stage: updates the `(a, b)` pair in place
+    /// and returns `1.5·a − 0.5·b`.
+    fn stage(&self, a: &mut f64, b: &mut f64, input: f64) -> f64 {
+        *a = self.f20 * *a + self.f18 * input;
+        *b = self.f18 * *a + self.f20 * *b;
+        1.5 * *a - 0.5 * *b
+    }
+}
+
+impl Indicator for Rsx {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, price: f64) -> Option<f64> {
+        if !price.is_finite() {
+            return self.last_value;
+        }
+        let Some(prev) = self.prev else {
+            self.prev = Some(price);
+            return None;
+        };
+        self.prev = Some(price);
+
+        let change = price - prev;
+
+        // Signed-change cascade.
+        let (mut sa0, mut sb0) = (self.s_a0, self.s_b0);
+        let v_c = self.stage(&mut sa0, &mut sb0, change);
+        self.s_a0 = sa0;
+        self.s_b0 = sb0;
+        let (mut sa1, mut sb1) = (self.s_a1, self.s_b1);
+        let v_10 = self.stage(&mut sa1, &mut sb1, v_c);
+        self.s_a1 = sa1;
+        self.s_b1 = sb1;
+        let (mut sa2, mut sb2) = (self.s_a2, self.s_b2);
+        let v_14 = self.stage(&mut sa2, &mut sb2, v_10);
+        self.s_a2 = sa2;
+        self.s_b2 = sb2;
+
+        // Absolute-change cascade.
+        let abs = change.abs();
+        let (mut aa0, mut ab0) = (self.a_a0, self.a_b0);
+        let v_c1 = self.stage(&mut aa0, &mut ab0, abs);
+        self.a_a0 = aa0;
+        self.a_b0 = ab0;
+        let (mut aa1, mut ab1) = (self.a_a1, self.a_b1);
+        let v_18 = self.stage(&mut aa1, &mut ab1, v_c1);
+        self.a_a1 = aa1;
+        self.a_b1 = ab1;
+        let (mut aa2, mut ab2) = (self.a_a2, self.a_b2);
+        let v_1c = self.stage(&mut aa2, &mut ab2, v_18);
+        self.a_a2 = aa2;
+        self.a_b2 = ab2;
+
+        let v4 = if v_1c > 0.0 {
+            (v_14 / v_1c + 1.0) * 50.0
+        } else {
+            50.0
+        };
+        let rsx = v4.clamp(0.0, 100.0);
+
+        self.count += 1;
+        self.last_value = Some(rsx);
+        if self.count >= self.length {
+            Some(rsx)
+        } else {
+            None
+        }
+    }
+
+    fn reset(&mut self) {
+        *self = Self::new(self.length).expect("length already validated");
+    }
+
+    fn warmup_period(&self) -> usize {
+        // One input to seed `prev`, then `length` changes to settle the cascade.
+        self.length + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.count >= self.length
+    }
+
+    fn name(&self) -> &'static str {
+        "RSX"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_length() {
+        assert!(matches!(Rsx::new(0), Err(Error::PeriodZero)));
+    }
+
+    /// Cover the const accessors `length` + `value` and the Indicator-impl
+    /// `warmup_period` + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let rsx = Rsx::new(14).unwrap();
+        assert_eq!(rsx.length(), 14);
+        assert_eq!(rsx.value(), None);
+        assert_eq!(rsx.warmup_period(), 15);
+        assert_eq!(rsx.name(), "RSX");
+    }
+
+    #[test]
+    fn warmup_then_emits() {
+        let mut rsx = Rsx::new(3).unwrap();
+        // 1 input seeds prev; then 3 changes settle -> first Some on input 4.
+        assert_eq!(rsx.update(10.0), None);
+        assert_eq!(rsx.update(11.0), None);
+        assert_eq!(rsx.update(12.0), None);
+        assert!(rsx.update(13.0).is_some());
+    }
+
+    #[test]
+    fn flat_market_is_neutral() {
+        // No movement -> absolute cascade is zero -> neutral 50.
+        let mut rsx = Rsx::new(5).unwrap();
+        let last = rsx.batch(&[7.0; 40]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_stays_in_range() {
+        let prices: Vec<f64> = (0..120)
+            .map(|i| 100.0 + (f64::from(i) * 0.35).sin() * 12.0)
+            .collect();
+        let mut rsx = Rsx::new(14).unwrap();
+        for v in rsx.batch(&prices).into_iter().flatten() {
+            assert!((0.0..=100.0).contains(&v), "RSX {v} left [0, 100]");
+        }
+    }
+
+    #[test]
+    fn strong_uptrend_is_high() {
+        // A sustained rise drives RSX well above the neutral 50.
+        let prices: Vec<f64> = (1..=60).map(f64::from).collect();
+        let mut rsx = Rsx::new(14).unwrap();
+        let last = rsx.batch(&prices).into_iter().flatten().last().unwrap();
+        assert!(
+            last > 80.0,
+            "strong uptrend should push RSX high, got {last}"
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut rsx = Rsx::new(3).unwrap();
+        let ready = rsx
+            .batch(&[1.0, 2.0, 3.0, 4.0, 5.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_eq!(rsx.update(f64::NAN), Some(ready));
+        assert_eq!(rsx.update(f64::INFINITY), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut rsx = Rsx::new(5).unwrap();
+        rsx.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
+        assert!(rsx.is_ready());
+        rsx.reset();
+        assert!(!rsx.is_ready());
+        assert_eq!(rsx.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=60)
+            .map(|i| 50.0 + (f64::from(i) * 0.5).sin() * 10.0)
+            .collect();
+        let mut a = Rsx::new(14).unwrap();
+        let mut b = Rsx::new(14).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/sample_entropy.rs

@@ -0,0 +1,337 @@
+//! Sample Entropy (`SampEn`) — the regularity / predictability of a window.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Population standard deviation of a slice (used for the matching tolerance).
+fn population_stddev(window: &[f64]) -> f64 {
+    let n = window.len() as f64;
+    let mean = window.iter().sum::<f64>() / n;
+    let var = window.iter().map(|&v| (v - mean) * (v - mean)).sum::<f64>() / n;
+    var.max(0.0).sqrt()
+}
+
+/// Whether two length-`len` templates starting at `i` and `j` match within the
+/// Chebyshev tolerance `tol`.
+fn templates_match(window: &[f64], i: usize, j: usize, len: usize, tol: f64) -> bool {
+    for k in 0..len {
+        if (window[i + k] - window[j + k]).abs() > tol {
+            return false;
+        }
+    }
+    true
+}
+
+/// Sample Entropy (`SampEn`) — Richman & Moorman's measure of how *regular* (i.e.
+/// predictable) a series is: the negative log conditional probability that two
+/// sub-sequences similar for `m` points stay similar at the next point.
+///
+/// ```text
+/// tol = r_factor · stddev(window)
+/// B   = # template pairs of length m   within tol   (i < j)
+/// A   = # template pairs of length m+1 within tol   (i < j)
+/// `SampEn` = − ln(A / B)
+/// ```
+///
+/// Low `SampEn` means the window is **regular** — patterns of length `m` reliably
+/// extend to length `m + 1`, the fingerprint of a trending or cyclic market. High
+/// `SampEn` means the series is **irregular** — knowing the last `m` points tells
+/// you little about the next, the fingerprint of noise. Unlike the older
+/// approximate entropy (`ApEn`), `SampEn` excludes self-matches, so it is far less
+/// biased on short windows.
+///
+/// The tolerance is `r_factor` times the window's standard deviation, so the
+/// measure self-scales. A perfectly flat window (`stddev == 0`) is maximally
+/// regular and returns `0`. If no length-`m` pairs match, the entropy is
+/// undefined and `0` is returned; if length-`m` pairs match but none extend, the
+/// estimator falls back to treating the unseen count as one (`−ln(1/B) = ln(B)`).
+/// The first value lands after `period` inputs; each `update` is O(`period²`).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, SampleEntropy};
+///
+/// let mut indicator = SampleEntropy::new(50, 2, 0.2).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update((f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct SampleEntropy {
+    period: usize,
+    emb_dim: usize,
+    r_factor: f64,
+    window: VecDeque<f64>,
+    last: Option<f64>,
+}
+
+impl SampleEntropy {
+    /// Construct a Sample Entropy over `period` values with embedding dimension
+    /// `m` and tolerance factor `r_factor`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period` or `m` is `0`,
+    /// [`Error::InvalidPeriod`] if `period < m + 2` (no length-`m+1` template
+    /// pairs otherwise), and [`Error::InvalidParameter`] if `r_factor` is not
+    /// finite and positive.
+    pub fn new(period: usize, m: usize, r_factor: f64) -> Result<Self> {
+        if period == 0 || m == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if period < m + 2 {
+            return Err(Error::InvalidPeriod {
+                message: "sample entropy needs period >= m + 2",
+            });
+        }
+        if !r_factor.is_finite() || r_factor <= 0.0 {
+            return Err(Error::InvalidParameter {
+                message: "sample entropy r_factor must be finite and positive",
+            });
+        }
+        Ok(Self {
+            period,
+            emb_dim: m,
+            r_factor,
+            window: VecDeque::with_capacity(period),
+            last: None,
+        })
+    }
+
+    /// Configured `(period, m, r_factor)`.
+    pub const fn params(&self) -> (usize, usize, f64) {
+        (self.period, self.emb_dim, self.r_factor)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+
+    fn compute(&self) -> f64 {
+        let window: Vec<f64> = self.window.iter().copied().collect();
+        let std = population_stddev(&window);
+        if std == 0.0 {
+            return 0.0;
+        }
+        let tol = self.r_factor * std;
+        let m = self.emb_dim;
+        // Restrict both template lengths to the same index range so A and B share
+        // their candidate pairs: there are `period − m` length-(m+1) templates.
+        let count = self.period - m;
+        let mut matches_m = 0u64;
+        let mut matches_m1 = 0u64;
+        for i in 0..count {
+            for j in (i + 1)..count {
+                if templates_match(&window, i, j, m, tol) {
+                    matches_m += 1;
+                    if templates_match(&window, i, j, m + 1, tol) {
+                        matches_m1 += 1;
+                    }
+                }
+            }
+        }
+        if matches_m == 0 {
+            return 0.0;
+        }
+        if matches_m1 == 0 {
+            // No length-(m+1) matches: fall back to one unseen count.
+            return (matches_m as f64).ln();
+        }
+        -((matches_m1 as f64) / (matches_m as f64)).ln()
+    }
+}
+
+impl Indicator for SampleEntropy {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.last;
+        }
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        if self.window.len() < self.period {
+            return None;
+        }
+        let out = self.compute();
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "SampleEntropy"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_params() {
+        assert!(matches!(
+            SampleEntropy::new(0, 2, 0.2),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            SampleEntropy::new(50, 0, 0.2),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            SampleEntropy::new(3, 2, 0.2),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            SampleEntropy::new(50, 2, 0.0),
+            Err(Error::InvalidParameter { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let s = SampleEntropy::new(50, 2, 0.2).unwrap();
+        assert_eq!(s.params(), (50, 2, 0.2));
+        assert_eq!(s.warmup_period(), 50);
+        assert_eq!(s.name(), "SampleEntropy");
+        assert!(!s.is_ready());
+        assert_eq!(s.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut s = SampleEntropy::new(10, 2, 0.2).unwrap();
+        let xs: Vec<f64> = (0..14).map(|i| (f64::from(i) * 0.5).sin()).collect();
+        let out = s.batch(&xs);
+        for v in out.iter().take(9) {
+            assert!(v.is_none());
+        }
+        assert!(out[9].is_some());
+    }
+
+    #[test]
+    fn constant_window_is_zero() {
+        let mut s = SampleEntropy::new(20, 2, 0.2).unwrap();
+        let last = s.batch(&[5.0; 30]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_is_non_negative() {
+        let mut s = SampleEntropy::new(40, 2, 0.2).unwrap();
+        for v in s
+            .batch(
+                &(0..200)
+                    .map(|i| (f64::from(i) * 0.3).sin() * 5.0)
+                    .collect::<Vec<_>>(),
+            )
+            .into_iter()
+            .flatten()
+        {
+            assert!(v >= 0.0, "sample entropy must be non-negative, got {v}");
+        }
+    }
+
+    #[test]
+    fn regular_below_irregular() {
+        // A smooth sine is far more regular (lower `SampEn`) than a chaotic
+        // logistic-map series. (An *alternating* series would be periodic, hence
+        // regular too -- chaos is what makes the window genuinely unpredictable.)
+        let smooth: Vec<f64> = (0..60).map(|i| (f64::from(i) * 0.2).sin() * 5.0).collect();
+        let mut x = 0.37_f64;
+        let chaotic: Vec<f64> = (0..60)
+            .map(|_| {
+                x = 3.99 * x * (1.0 - x);
+                x * 5.0
+            })
+            .collect();
+        let s_smooth = SampleEntropy::new(50, 2, 0.2)
+            .unwrap()
+            .batch(&smooth)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        let s_chaotic = SampleEntropy::new(50, 2, 0.2)
+            .unwrap()
+            .batch(&chaotic)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert!(
+            s_smooth <= s_chaotic,
+            "smooth ({s_smooth}) should be <= chaotic ({s_chaotic})"
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite() {
+        let mut s = SampleEntropy::new(10, 2, 0.2).unwrap();
+        let xs: Vec<f64> = (0..10).map(|i| (f64::from(i) * 0.5).sin()).collect();
+        let ready = s.batch(&xs).into_iter().flatten().last().unwrap();
+        assert_eq!(s.update(f64::NAN), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut s = SampleEntropy::new(10, 2, 0.2).unwrap();
+        let xs: Vec<f64> = (0..10).map(|i| (f64::from(i) * 0.5).sin()).collect();
+        s.batch(&xs);
+        assert!(s.is_ready());
+        s.reset();
+        assert!(!s.is_ready());
+        assert_eq!(s.value(), None);
+        assert_eq!(s.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let xs: Vec<f64> = (0..120)
+            .map(|i| (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = SampleEntropy::new(40, 2, 0.2).unwrap().batch(&xs);
+        let mut b = SampleEntropy::new(40, 2, 0.2).unwrap();
+        let streamed: Vec<_> = xs.iter().map(|x| b.update(*x)).collect();
+        assert_eq!(batch, streamed);
+    }
+
+    #[test]
+    fn falls_back_when_no_m_plus_one_matches() {
+        // `[1, 1, 1, 5]` with m = 2: the length-2 template `(1, 1)` repeats
+        // (matches_m > 0) but no length-3 template repeats (matches_m1 == 0),
+        // so SampEn takes the `ln(matches_m)` fallback branch.
+        let xs = [1.0, 1.0, 1.0, 5.0];
+        let v = SampleEntropy::new(4, 2, 0.2)
+            .unwrap()
+            .batch(&xs)
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert!(v.is_finite() && v >= 0.0, "got {v}");
+    }
+}

crates/wickra-core/src/indicators/shannon_entropy.rs

@@ -0,0 +1,261 @@
+//! Shannon Entropy — the information content of a price window's distribution.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Shannon Entropy — the Shannon information entropy (in **bits**) of the
+/// distribution of values in a rolling window, after binning them into a fixed
+/// number of equal-width buckets.
+///
+/// ```text
+/// bucket each of the last `period` values into `bins` equal-width bins over
+///   [min, max] of the window
+/// p_i = count_i / period
+/// H   = − Σ p_i · log2(p_i)            (over non-empty bins)
+/// ```
+///
+/// Entropy measures how *spread out* and unpredictable the recent values are. A
+/// window concentrated in one bin (a flat or tightly-ranging market) has low
+/// entropy near `0`; a window whose values are spread evenly across all bins (a
+/// noisy, directionless market) approaches the maximum `log2(bins)`. Traders use
+/// it as a **regime filter**: low entropy favours trend/breakout strategies, high
+/// entropy favours mean-reversion or standing aside.
+///
+/// The output lies in `[0, log2(bins)]`. A degenerate window where every value is
+/// identical (`max == min`) returns `0`. The first value lands after `period`
+/// inputs; each `update` rebins the window in O(`period`).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, ShannonEntropy};
+///
+/// let mut indicator = ShannonEntropy::new(32, 8).unwrap();
+/// let mut last = None;
+/// for i in 0..64 {
+///     last = indicator.update((f64::from(i) * 0.7).sin() * 10.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct ShannonEntropy {
+    period: usize,
+    bins: usize,
+    window: VecDeque<f64>,
+    last: Option<f64>,
+}
+
+impl ShannonEntropy {
+    /// Construct a Shannon entropy over `period` values binned into `bins`
+    /// buckets.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if either argument is `0`, or
+    /// [`Error::InvalidPeriod`] if `bins < 2` (entropy needs at least two bins).
+    pub fn new(period: usize, bins: usize) -> Result<Self> {
+        if period == 0 || bins == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if bins < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "Shannon entropy needs bins >= 2",
+            });
+        }
+        Ok(Self {
+            period,
+            bins,
+            window: VecDeque::with_capacity(period),
+            last: None,
+        })
+    }
+
+    /// Configured `(period, bins)`.
+    pub const fn params(&self) -> (usize, usize) {
+        (self.period, self.bins)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for ShannonEntropy {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.last;
+        }
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        if self.window.len() < self.period {
+            return None;
+        }
+
+        let mut min = f64::INFINITY;
+        let mut max = f64::NEG_INFINITY;
+        for &v in &self.window {
+            min = min.min(v);
+            max = max.max(v);
+        }
+        if max <= min {
+            // Degenerate window: all values identical -> zero entropy.
+            self.last = Some(0.0);
+            return Some(0.0);
+        }
+        let width = (max - min) / self.bins as f64;
+        let mut counts = vec![0usize; self.bins];
+        for &v in &self.window {
+            // `(v - min) / width` is in [0, bins]; the cast truncates toward zero
+            // (intended) and the value is non-negative, then clamped to the last
+            // bin so the index is always valid.
+            #[allow(clippy::cast_possible_truncation, clippy::cast_sign_loss)]
+            let raw = ((v - min) / width) as usize;
+            let idx = raw.min(self.bins - 1);
+            counts[idx] += 1;
+        }
+        let n = self.period as f64;
+        let mut h = 0.0;
+        for &count in &counts {
+            if count > 0 {
+                let p = count as f64 / n;
+                h -= p * p.log2();
+            }
+        }
+        self.last = Some(h);
+        Some(h)
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "ShannonEntropy"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_params() {
+        assert!(matches!(ShannonEntropy::new(0, 8), Err(Error::PeriodZero)));
+        assert!(matches!(ShannonEntropy::new(32, 0), Err(Error::PeriodZero)));
+        assert!(matches!(
+            ShannonEntropy::new(32, 1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let e = ShannonEntropy::new(32, 8).unwrap();
+        assert_eq!(e.params(), (32, 8));
+        assert_eq!(e.warmup_period(), 32);
+        assert_eq!(e.name(), "ShannonEntropy");
+        assert!(!e.is_ready());
+        assert_eq!(e.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut e = ShannonEntropy::new(4, 4).unwrap();
+        let out = e.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        for v in out.iter().take(3) {
+            assert!(v.is_none());
+        }
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn constant_window_is_zero() {
+        let mut e = ShannonEntropy::new(8, 4).unwrap();
+        let last = e.batch(&[5.0; 12]).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn uniform_window_is_max_entropy() {
+        // One value per bin -> uniform distribution -> H = log2(bins).
+        let mut e = ShannonEntropy::new(4, 4).unwrap();
+        // Values 0,1,2,3 with min=0,max=3,width=0.75 -> bins 0,1,2,3.
+        let last = e
+            .batch(&[0.0, 1.0, 2.0, 3.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_relative_eq!(last, 2.0, epsilon = 1e-9); // log2(4) = 2
+    }
+
+    #[test]
+    fn output_in_range() {
+        let mut e = ShannonEntropy::new(32, 8).unwrap();
+        let max_h = 8f64.log2();
+        for v in e
+            .batch(
+                &(0..200)
+                    .map(|i| (f64::from(i) * 0.3).sin() * 10.0)
+                    .collect::<Vec<_>>(),
+            )
+            .into_iter()
+            .flatten()
+        {
+            assert!((0.0..=max_h + 1e-9).contains(&v));
+        }
+    }
+
+    #[test]
+    fn ignores_non_finite() {
+        let mut e = ShannonEntropy::new(4, 4).unwrap();
+        let ready = e
+            .batch(&[1.0, 2.0, 3.0, 4.0])
+            .into_iter()
+            .flatten()
+            .last()
+            .unwrap();
+        assert_eq!(e.update(f64::NAN), Some(ready));
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut e = ShannonEntropy::new(4, 4).unwrap();
+        e.batch(&[1.0, 2.0, 3.0, 4.0]);
+        assert!(e.is_ready());
+        e.reset();
+        assert!(!e.is_ready());
+        assert_eq!(e.value(), None);
+        assert_eq!(e.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let xs: Vec<f64> = (0..120)
+            .map(|i| (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = ShannonEntropy::new(32, 8).unwrap().batch(&xs);
+        let mut b = ShannonEntropy::new(32, 8).unwrap();
+        let streamed: Vec<_> = xs.iter().map(|x| b.update(*x)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/sine_weighted_ma.rs

@@ -0,0 +1,273 @@
+//! Sine-Weighted Moving Average (SWMA).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Sine-Weighted Moving Average — a windowed average whose weights follow one
+/// half-cycle of a sine wave.
+///
+/// Over the last `period` inputs the weight of the value at position
+/// `i = 0, 1, …, period − 1` (oldest to newest) is
+///
+/// ```text
+/// w_i = sin(π · (i + 1) / (period + 1))
+/// SWMA = Σ (w_i · value_i) / Σ w_i
+/// ```
+///
+/// The window is symmetric: weights rise to a peak in the middle of the window
+/// and fall off at both ends, so the central observations dominate while the
+/// extremes are de-emphasised. Every weight is strictly positive because the
+/// argument `(i + 1) / (period + 1)` lies in the open interval `(0, 1)`, so the
+/// normaliser is always non-zero.
+///
+/// Each `update` is O(`period`): the fixed weight vector is dotted with the
+/// trailing window, mirroring the way [`Alma`](crate::Alma) recomputes its
+/// Gaussian weights. `period == 1` collapses to a pass-through
+/// (`w_0 = sin(π/2) = 1`).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, SineWeightedMa};
+///
+/// let mut indicator = SineWeightedMa::new(5).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct SineWeightedMa {
+    period: usize,
+    window: VecDeque<f64>,
+    /// Sine weights for positions `0..period` (oldest to newest), constant in
+    /// `period`.
+    weights: Vec<f64>,
+    weights_total: f64,
+}
+
+impl SineWeightedMa {
+    /// Construct a new sine-weighted moving average over `period` inputs.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        let denom = period as f64 + 1.0;
+        let weights: Vec<f64> = (0..period)
+            .map(|i| (std::f64::consts::PI * (i as f64 + 1.0) / denom).sin())
+            .collect();
+        let weights_total = weights.iter().sum();
+        Ok(Self {
+            period,
+            window: VecDeque::with_capacity(period),
+            weights,
+            weights_total,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if the window is full.
+    pub fn value(&self) -> Option<f64> {
+        if self.window.len() == self.period {
+            let dot: f64 = self
+                .window
+                .iter()
+                .zip(&self.weights)
+                .map(|(v, w)| v * w)
+                .sum();
+            Some(dot / self.weights_total)
+        } else {
+            None
+        }
+    }
+}
+
+impl Indicator for SineWeightedMa {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        if !input.is_finite() {
+            return self.value();
+        }
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(input);
+        self.value()
+    }
+
+    fn reset(&mut self) {
+        self.window.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "SWMA"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Reference implementation: explicit sine-weighted average over a window.
+    fn swma_naive(prices: &[f64], period: usize) -> Vec<Option<f64>> {
+        let denom = period as f64 + 1.0;
+        let weights: Vec<f64> = (0..period)
+            .map(|i| (std::f64::consts::PI * (i as f64 + 1.0) / denom).sin())
+            .collect();
+        let total: f64 = weights.iter().sum();
+        prices
+            .iter()
+            .enumerate()
+            .map(|(i, _)| {
+                if i + 1 < period {
+                    None
+                } else {
+                    let window = &prices[i + 1 - period..=i];
+                    let dot: f64 = window.iter().zip(&weights).map(|(v, w)| v * w).sum();
+                    Some(dot / total)
+                }
+            })
+            .collect()
+    }
+
+    #[test]
+    fn new_rejects_zero_period() {
+        assert!(matches!(SineWeightedMa::new(0), Err(Error::PeriodZero)));
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let swma = SineWeightedMa::new(7).unwrap();
+        assert_eq!(swma.period(), 7);
+        assert_eq!(swma.warmup_period(), 7);
+        assert_eq!(swma.name(), "SWMA");
+    }
+
+    #[test]
+    fn warmup_returns_none() {
+        let mut swma = SineWeightedMa::new(3).unwrap();
+        assert_eq!(swma.update(1.0), None);
+        assert_eq!(swma.update(2.0), None);
+        // SWMA(3): weights sin(pi/4), sin(pi/2), sin(3pi/4) = [√½, 1, √½].
+        // Over [1,2,3]: (√½·1 + 1·2 + √½·3) / (√½ + 1 + √½).
+        let s = std::f64::consts::FRAC_1_SQRT_2;
+        let total = s + 1.0 + s;
+        let want = (s * 1.0 + 1.0 * 2.0 + s * 3.0) / total;
+        assert_relative_eq!(swma.update(3.0).unwrap(), want, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn symmetric_weights_give_midpoint_on_linear_window() {
+        // For a perfectly linear window the symmetric weighting reproduces the
+        // arithmetic centre of the window.
+        let mut swma = SineWeightedMa::new(5).unwrap();
+        let v = swma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        assert_relative_eq!(v[4].unwrap(), 3.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn period_one_is_pass_through() {
+        let mut swma = SineWeightedMa::new(1).unwrap();
+        assert_relative_eq!(swma.update(5.5).unwrap(), 5.5, epsilon = 1e-12);
+        assert_relative_eq!(swma.update(7.5).unwrap(), 7.5, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn matches_naive_over_inputs() {
+        let prices: Vec<f64> = (1..=30).map(|i| f64::from(i) * 1.7 - 5.0).collect();
+        let mut swma = SineWeightedMa::new(7).unwrap();
+        let got = swma.batch(&prices);
+        let want = swma_naive(&prices, 7);
+        for (i, (g, w)) in got.iter().zip(want.iter()).enumerate() {
+            assert_eq!(g.is_some(), w.is_some(), "warmup mismatch at index {i}");
+            if let (Some(a), Some(b)) = (g, w) {
+                assert_relative_eq!(*a, *b, epsilon = 1e-9);
+            }
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut swma = SineWeightedMa::new(4).unwrap();
+        swma.batch(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        assert!(swma.is_ready());
+        swma.reset();
+        assert!(!swma.is_ready());
+        assert_eq!(swma.update(10.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=20).map(|i| f64::from(i) * 0.5).collect();
+        let mut a = SineWeightedMa::new(5).unwrap();
+        let mut b = SineWeightedMa::new(5).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn ignores_non_finite_input_but_keeps_state() {
+        let mut swma = SineWeightedMa::new(3).unwrap();
+        swma.update(1.0);
+        swma.update(2.0);
+        let ready = swma.update(3.0).expect("SWMA(3) ready after three inputs");
+        assert_eq!(swma.update(f64::NAN), Some(ready));
+        assert_eq!(swma.update(f64::INFINITY), Some(ready));
+        // The window still holds 1, 2, 3 -> next real input slides it to 2, 3, 4.
+        let s = std::f64::consts::FRAC_1_SQRT_2;
+        let total = s + 1.0 + s;
+        let want = (s * 2.0 + 1.0 * 3.0 + s * 4.0) / total;
+        assert_relative_eq!(swma.update(4.0).unwrap(), want, epsilon = 1e-12);
+    }
+
+    proptest::proptest! {
+        #![proptest_config(proptest::test_runner::Config::with_cases(48))]
+        #[test]
+        fn proptest_matches_naive(
+            period in 1usize..15,
+            prices in proptest::collection::vec(-500.0_f64..500.0, 0..120),
+        ) {
+            let mut swma = SineWeightedMa::new(period).unwrap();
+            let got = swma.batch(&prices);
+            let want = swma_naive(&prices, period);
+            proptest::prop_assert_eq!(got.len(), want.len());
+            for (g, w) in got.iter().zip(want.iter()) {
+                match (g, w) {
+                    (None, None) => {}
+                    (Some(a), Some(b)) => proptest::prop_assert!(
+                        (a - b).abs() < 1e-7,
+                        "got={a} want={b}"
+                    ),
+                    _ => proptest::prop_assert!(false, "warmup mismatch"),
+                }
+            }
+        }
+    }
+}

crates/wickra-core/src/indicators/sma.rs

@@ -1,7 +1,5 @@
 //! Simple Moving Average.
 
-use std::collections::VecDeque;
-
 use crate::error::{Error, Result};
 use crate::traits::Indicator;
 
@@ -33,7 +31,14 @@ use crate::traits::Indicator;
 #[derive(Debug, Clone)]
 pub struct Sma {
     period: usize,
-    window: VecDeque<f64>,
+    /// Fixed-capacity ring buffer of the last `period` finite inputs. A flat
+    /// `Box<[f64]>` with a manual write cursor beats `VecDeque` on this hot path:
+    /// sequential storage, branchless wraparound, no per-call bookkeeping.
+    buf: Box<[f64]>,
+    /// Index of the next slot to write — also the oldest element once full.
+    head: usize,
+    /// Number of slots filled, saturating at `period`.
+    count: usize,
     sum: f64,
     /// Number of finite updates since the running `sum` was last reseeded from
     /// the live window. Caps accumulated floating-point drift on long streams.
@@ -60,7 +65,9 @@ impl Sma {
         }
         Ok(Self {
             period,
-            window: VecDeque::with_capacity(period),
+            buf: vec![0.0; period].into_boxed_slice(),
+            head: 0,
+            count: 0,
             sum: 0.0,
             updates_since_recompute: 0,
         })
@@ -73,7 +80,7 @@ impl Sma {
 
     /// Current value if available.
     pub fn value(&self) -> Option<f64> {
-        if self.window.len() == self.period {
+        if self.count == self.period {
             Some(self.sum / self.period as f64)
         } else {
             None
@@ -89,25 +96,40 @@ impl Indicator for Sma {
         if !input.is_finite() {
             return self.value();
         }
-        if self.window.len() == self.period {
-            // Slide: drop the oldest, then add the new. Each step is a single
-            // f64 add/subtract — O(1) but introduces ~1 ULP of rounding noise.
-            // The periodic reseed below caps the accumulated drift.
-            let old = self.window.pop_front().expect("window non-empty");
-            self.sum -= old;
+        if self.count == self.period {
+            // Window full: overwrite the oldest slot (at `head`). Each step is a
+            // single f64 add/subtract — O(1) but introduces ~1 ULP of rounding
+            // noise. The periodic reseed below caps the accumulated drift.
+            self.sum -= self.buf[self.head];
+            self.buf[self.head] = input;
+            self.sum += input;
+        } else {
+            self.buf[self.head] = input;
+            self.sum += input;
+            self.count += 1;
+        }
+        // Branchless-ish wraparound, cheaper than `% period`.
+        self.head += 1;
+        if self.head == self.period {
+            self.head = 0;
         }
-        self.window.push_back(input);
-        self.sum += input;
         self.updates_since_recompute += 1;
         if self.updates_since_recompute >= RECOMPUTE_EVERY * self.period {
-            self.sum = self.window.iter().copied().sum();
+            // Reseed in chronological order (oldest at `head`) so the running sum
+            // tracks a fresh from-scratch mean to the bit on stable inputs.
+            self.sum = self.buf[self.head..]
+                .iter()
+                .chain(&self.buf[..self.head])
+                .copied()
+                .sum();
             self.updates_since_recompute = 0;
         }
         self.value()
     }
 
     fn reset(&mut self) {
-        self.window.clear();
+        self.head = 0;
+        self.count = 0;
         self.sum = 0.0;
         self.updates_since_recompute = 0;
     }
@@ -117,7 +139,7 @@ impl Indicator for Sma {
     }
 
     fn is_ready(&self) -> bool {
-        self.window.len() == self.period
+        self.count == self.period
     }
 
     fn name(&self) -> &'static str {
@@ -130,6 +152,7 @@ mod tests {
     use super::*;
     use crate::traits::BatchExt;
     use approx::assert_relative_eq;
+    use std::collections::VecDeque;
 
     #[test]
     fn new_rejects_zero_period() {

crates/wickra-core/src/indicators/stochastic_cci.rs

@@ -0,0 +1,231 @@
+//! Stochastic CCI — a stochastic oscillator applied to the CCI.
+
+use std::collections::VecDeque;
+
+use crate::error::Result;
+use crate::indicators::cci::Cci;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Stochastic CCI — the stochastic oscillator computed over the
+/// [`Cci`](crate::Cci) instead of price.
+///
+/// The CCI is unbounded and spends most of its time inside `±100`, which makes
+/// fixed overbought/oversold lines awkward. Running a stochastic over the CCI
+/// re-scales it to `[0, 100]` relative to its own recent range, turning it into
+/// a bounded, self-normalising momentum oscillator:
+///
+/// ```text
+/// cci = CCI(typical price, period)
+/// %K  = 100 * (cci - lowest(cci, period)) / (highest(cci, period) - lowest(cci, period))
+/// ```
+///
+/// The same `period` is used for the CCI and the stochastic lookback. When the
+/// CCI range over the window is zero (a flat market, where the CCI is pinned at
+/// `0`) the oscillator returns the neutral `50`. The first value lands after
+/// `2·period − 1` bars: `period` to seed the CCI, then `period` CCI values to
+/// fill the stochastic window.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, StochasticCci, Indicator};
+///
+/// let mut sc = StochasticCci::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     let base = 100.0 + (f64::from(i) * 0.3).sin() * 10.0;
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base, 1.0, i64::from(i)).unwrap();
+///     last = sc.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct StochasticCci {
+    period: usize,
+    cci: Cci,
+    /// The last `period` CCI values.
+    window: VecDeque<f64>,
+}
+
+impl StochasticCci {
+    /// Construct a Stochastic CCI with the given period (shared by the CCI and
+    /// the stochastic lookback).
+    ///
+    /// # Errors
+    ///
+    /// Returns [`crate::Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            period,
+            cci: Cci::new(period)?,
+            window: VecDeque::with_capacity(period),
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for StochasticCci {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let cci = self.cci.update(candle)?;
+        if self.window.len() == self.period {
+            self.window.pop_front();
+        }
+        self.window.push_back(cci);
+        if self.window.len() < self.period {
+            return None;
+        }
+        let mut lo = f64::MAX;
+        let mut hi = f64::MIN;
+        for &v in &self.window {
+            if v < lo {
+                lo = v;
+            }
+            if v > hi {
+                hi = v;
+            }
+        }
+        let range = hi - lo;
+        if range == 0.0 {
+            return Some(50.0);
+        }
+        // Ratio first, then scale: `100 * x / x` can round to 100.0000…1.
+        Some(100.0 * ((cci - lo) / range))
+    }
+
+    fn reset(&mut self) {
+        self.cci.reset();
+        self.window.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        // CCI seeds at `period`, then `period` CCI values fill the stochastic window.
+        2 * self.period - 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.window.len() == self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "StochasticCCI"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new(close, high, low, close, 1.0, 0).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(StochasticCci::new(0).is_err());
+    }
+
+    /// Cover the const accessor `period` and the Indicator-impl `warmup_period`
+    /// + `name`.
+    #[test]
+    fn accessors_and_metadata() {
+        let sc = StochasticCci::new(14).unwrap();
+        assert_eq!(sc.period(), 14);
+        assert_eq!(sc.warmup_period(), 27);
+        assert_eq!(sc.name(), "StochasticCCI");
+    }
+
+    #[test]
+    fn first_emission_matches_warmup_period() {
+        let bars: Vec<Candle> = (0..40)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.4).sin() * 8.0;
+                candle(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        let mut sc = StochasticCci::new(5).unwrap();
+        let out = sc.batch(&bars);
+        let warmup = sc.warmup_period();
+        assert_eq!(warmup, 9);
+        for (i, v) in out.iter().enumerate().take(warmup - 1) {
+            assert!(v.is_none(), "index {i} must be None during warmup");
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn bounded_zero_to_hundred() {
+        let bars: Vec<Candle> = (0..80)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.35).sin() * 12.0;
+                candle(base + 2.0, base - 2.0, base)
+            })
+            .collect();
+        let mut sc = StochasticCci::new(9).unwrap();
+        for v in sc.batch(&bars).into_iter().flatten() {
+            assert!((0.0..=100.0).contains(&v), "%K {v} left [0, 100]");
+        }
+    }
+
+    #[test]
+    fn flat_market_is_neutral() {
+        // Constant candles -> CCI pinned at 0 -> zero range -> neutral 50.
+        let mut sc = StochasticCci::new(4).unwrap();
+        let bars = vec![candle(10.0, 10.0, 10.0); 20];
+        let last = sc.batch(&bars).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn highest_cci_in_window_is_hundred() {
+        // When the latest CCI is the window maximum, %K must be 100.
+        // A long rise then makes the final CCI the highest in its window.
+        let mut bars: Vec<Candle> = (0..20)
+            .map(|i| candle(f64::from(i) + 1.0, f64::from(i) - 1.0, f64::from(i)))
+            .collect();
+        // Strong final push so the last CCI tops its window.
+        bars.push(candle(100.0, 98.0, 100.0));
+        let mut sc = StochasticCci::new(5).unwrap();
+        let last = sc.batch(&bars).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut sc = StochasticCci::new(5).unwrap();
+        sc.batch(
+            &(0..30)
+                .map(|i| candle(f64::from(i) + 1.0, f64::from(i) - 1.0, f64::from(i)))
+                .collect::<Vec<_>>(),
+        );
+        assert!(sc.is_ready());
+        sc.reset();
+        assert!(!sc.is_ready());
+        assert_eq!(sc.update(candle(2.0, 0.0, 1.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let bars: Vec<Candle> = (0..60)
+            .map(|i| {
+                let base = 50.0 + (f64::from(i) * 0.5).sin() * 10.0;
+                candle(base + 1.5, base - 1.5, base)
+            })
+            .collect();
+        let mut a = StochasticCci::new(9).unwrap();
+        let mut b = StochasticCci::new(9).unwrap();
+        assert_eq!(
+            a.batch(&bars),
+            bars.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/time_based_stop.rs

@@ -0,0 +1,192 @@
+//! Time-Based Stop — a holding-period timer that fires after a fixed bar count.
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Time-Based Stop — exits a position purely on **elapsed bars**, independent of
+/// price.
+///
+/// ```text
+/// bars_held increments by 1 each bar (since the last reset)
+/// progress  = min(bars_held / max_bars, 1.0)     in [0, 1]
+/// stop fires when progress == 1.0  (bars_held >= max_bars)
+/// ```
+///
+/// Some setups should not be given unlimited time to work: a mean-reversion entry
+/// that has not reverted within `max_bars`, or an event trade whose catalyst has
+/// passed, is best closed regardless of price. This indicator is a pure timer —
+/// it ignores the candle's prices entirely and reports the fraction of the
+/// holding window that has elapsed, reaching `1.0` (the stop) after `max_bars`
+/// bars. **Call [`reset`](Indicator::reset) on each new entry** so the timer
+/// restarts from the position open.
+///
+/// Each `update` is O(1) and the first bar already emits a value
+/// (`1 / max_bars`).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, TimeBasedStop};
+///
+/// let mut indicator = TimeBasedStop::new(5).unwrap();
+/// let c = Candle::new(100.0, 101.0, 99.0, 100.0, 1.0, 0).unwrap();
+/// // Five bars reach the stop.
+/// let mut last = 0.0;
+/// for _ in 0..5 {
+///     last = indicator.update(c).unwrap();
+/// }
+/// assert_eq!(last, 1.0);
+/// ```
+#[derive(Debug, Clone)]
+pub struct TimeBasedStop {
+    max_bars: usize,
+    bars_held: usize,
+    last: Option<f64>,
+}
+
+impl TimeBasedStop {
+    /// Construct a time-based stop that fires after `max_bars` bars.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `max_bars == 0`.
+    pub fn new(max_bars: usize) -> Result<Self> {
+        if max_bars == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            max_bars,
+            bars_held: 0,
+            last: None,
+        })
+    }
+
+    /// Configured maximum holding period in bars.
+    pub const fn max_bars(&self) -> usize {
+        self.max_bars
+    }
+
+    /// Number of bars held since the last reset.
+    pub const fn bars_held(&self) -> usize {
+        self.bars_held
+    }
+
+    /// Whether the stop has fired (the holding period has fully elapsed).
+    pub const fn triggered(&self) -> bool {
+        self.bars_held >= self.max_bars
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for TimeBasedStop {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, _candle: Candle) -> Option<f64> {
+        self.bars_held += 1;
+        let progress = (self.bars_held as f64 / self.max_bars as f64).min(1.0);
+        self.last = Some(progress);
+        Some(progress)
+    }
+
+    fn reset(&mut self) {
+        self.bars_held = 0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "TimeBasedStop"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn c() -> Candle {
+        Candle::new_unchecked(100.0, 101.0, 99.0, 100.0, 1.0, 0)
+    }
+
+    #[test]
+    fn rejects_zero_max_bars() {
+        assert!(matches!(TimeBasedStop::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let t = TimeBasedStop::new(5).unwrap();
+        assert_eq!(t.max_bars(), 5);
+        assert_eq!(t.bars_held(), 0);
+        assert!(!t.triggered());
+        assert_eq!(t.warmup_period(), 1);
+        assert_eq!(t.name(), "TimeBasedStop");
+        assert!(!t.is_ready());
+        assert_eq!(t.value(), None);
+    }
+
+    #[test]
+    fn progress_climbs_to_one() {
+        let mut t = TimeBasedStop::new(4).unwrap();
+        let out = t.batch(&[c(), c(), c(), c()]);
+        assert_relative_eq!(out[0].unwrap(), 0.25, epsilon = 1e-12);
+        assert_relative_eq!(out[1].unwrap(), 0.50, epsilon = 1e-12);
+        assert_relative_eq!(out[2].unwrap(), 0.75, epsilon = 1e-12);
+        assert_relative_eq!(out[3].unwrap(), 1.00, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn triggers_after_max_bars() {
+        let mut t = TimeBasedStop::new(3).unwrap();
+        t.update(c());
+        assert!(!t.triggered());
+        t.update(c());
+        assert!(!t.triggered());
+        t.update(c());
+        assert!(t.triggered());
+    }
+
+    #[test]
+    fn progress_saturates_at_one() {
+        // Beyond max_bars the progress stays clamped at 1.0.
+        let mut t = TimeBasedStop::new(2).unwrap();
+        let out = t.batch(&[c(), c(), c(), c()]);
+        assert_relative_eq!(out[2].unwrap(), 1.0, epsilon = 1e-12);
+        assert_relative_eq!(out[3].unwrap(), 1.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn reset_restarts_timer() {
+        let mut t = TimeBasedStop::new(3).unwrap();
+        t.batch(&[c(), c(), c()]);
+        assert!(t.triggered());
+        t.reset();
+        assert!(!t.is_ready());
+        assert_eq!(t.bars_held(), 0);
+        assert!(!t.triggered());
+        assert_relative_eq!(t.update(c()).unwrap(), 1.0 / 3.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles = [c(); 10];
+        let batch = TimeBasedStop::new(4).unwrap().batch(&candles);
+        let mut b = TimeBasedStop::new(4).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|x| b.update(*x)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/trade_volume_index.rs

@@ -0,0 +1,235 @@
+//! Trade Volume Index (TVI) — cumulative volume signed by a minimum-tick rule.
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Trade Volume Index — a cumulative line that adds volume while price ticks up
+/// and subtracts it while price ticks down, where "up" and "down" are decided by
+/// a **minimum tick value** rather than any change.
+///
+/// ```text
+/// change = close − prev_close
+/// if  change >  min_tick:  direction = +1
+/// if  change < −min_tick:  direction = −1
+/// else:                    direction unchanged   (price is "churning")
+/// TVI_t = TVI_{t−1} + direction * volume
+/// ```
+///
+/// The minimum tick value (MTV) is a dead-band: only moves larger than `min_tick`
+/// flip the accumulation direction, so a price drifting within the spread keeps
+/// adding volume in the last established direction instead of whipsawing. This is
+/// the cumulative-volume analogue of [`Obv`](crate::Obv), but with a noise filter
+/// and applied to close-to-close moves. Like all cumulative lines, only its slope
+/// and divergences against price carry meaning — the absolute level is arbitrary.
+///
+/// The first candle seeds the reference close and emits nothing; thereafter each
+/// bar emits the running total. Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, TradeVolumeIndex};
+///
+/// let mut indicator = TradeVolumeIndex::new(0.5).unwrap();
+/// let mut last = None;
+/// for i in 0..20 {
+///     let close = 100.0 + f64::from(i);
+///     let c = Candle::new(close, close + 0.5, close - 0.5, close, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct TradeVolumeIndex {
+    min_tick: f64,
+    prev_close: Option<f64>,
+    direction: f64,
+    tvi: f64,
+    last: Option<f64>,
+}
+
+impl TradeVolumeIndex {
+    /// Construct a new Trade Volume Index with the given minimum tick value.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::InvalidParameter`] if `min_tick` is not finite or is
+    /// negative. A `min_tick` of `0` is allowed and makes every non-zero move
+    /// flip the direction.
+    pub fn new(min_tick: f64) -> Result<Self> {
+        if !min_tick.is_finite() || min_tick < 0.0 {
+            return Err(Error::InvalidParameter {
+                message: "trade volume index min_tick must be finite and non-negative",
+            });
+        }
+        Ok(Self {
+            min_tick,
+            prev_close: None,
+            direction: 0.0,
+            tvi: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured minimum tick value.
+    pub const fn min_tick(&self) -> f64 {
+        self.min_tick
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for TradeVolumeIndex {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let Some(prev_close) = self.prev_close else {
+            self.prev_close = Some(candle.close);
+            return None;
+        };
+        let change = candle.close - prev_close;
+        if change > self.min_tick {
+            self.direction = 1.0;
+        } else if change < -self.min_tick {
+            self.direction = -1.0;
+        }
+        // Otherwise the direction is held from the previous bar (or 0 before the
+        // first decisive move), so a churning price keeps its last lean.
+        self.tvi += self.direction * candle.volume;
+        self.prev_close = Some(candle.close);
+        self.last = Some(self.tvi);
+        Some(self.tvi)
+    }
+
+    fn reset(&mut self) {
+        self.prev_close = None;
+        self.direction = 0.0;
+        self.tvi = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "TradeVolumeIndex"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(close: f64, volume: f64) -> Candle {
+        Candle::new_unchecked(close, close, close, close, volume, 0)
+    }
+
+    #[test]
+    fn rejects_invalid_min_tick() {
+        assert!(matches!(
+            TradeVolumeIndex::new(-1.0),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(matches!(
+            TradeVolumeIndex::new(f64::NAN),
+            Err(Error::InvalidParameter { .. })
+        ));
+        assert!(TradeVolumeIndex::new(0.0).is_ok());
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let tvi = TradeVolumeIndex::new(0.25).unwrap();
+        assert_relative_eq!(tvi.min_tick(), 0.25, epsilon = 1e-12);
+        assert_eq!(tvi.warmup_period(), 2);
+        assert_eq!(tvi.name(), "TradeVolumeIndex");
+        assert!(!tvi.is_ready());
+        assert_eq!(tvi.value(), None);
+    }
+
+    #[test]
+    fn first_bar_seeds_without_output() {
+        let mut tvi = TradeVolumeIndex::new(0.5).unwrap();
+        assert_eq!(tvi.update(candle(100.0, 1_000.0)), None);
+        assert!(tvi.update(candle(101.0, 1_000.0)).is_some());
+    }
+
+    #[test]
+    fn uptrend_accumulates_volume() {
+        // Each step of +1 exceeds the 0.5 tick -> direction +1 -> add volume.
+        let mut tvi = TradeVolumeIndex::new(0.5).unwrap();
+        let candles = [
+            candle(100.0, 1_000.0), // seed
+            candle(101.0, 500.0),   // +1 -> +500
+            candle(102.0, 300.0),   // +1 -> +300
+        ];
+        let out = tvi.batch(&candles);
+        assert_relative_eq!(out[1].unwrap(), 500.0, epsilon = 1e-9);
+        assert_relative_eq!(out[2].unwrap(), 800.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn small_move_holds_last_direction() {
+        // After an up-move, a sub-tick wobble keeps adding in the up direction.
+        let mut tvi = TradeVolumeIndex::new(1.0).unwrap();
+        let candles = [
+            candle(100.0, 1_000.0), // seed
+            candle(102.0, 400.0),   // +2 > tick -> dir +1, +400
+            candle(102.2, 100.0),   // +0.2 < tick -> hold dir +1, +100
+        ];
+        let out = tvi.batch(&candles);
+        assert_relative_eq!(out[1].unwrap(), 400.0, epsilon = 1e-9);
+        assert_relative_eq!(out[2].unwrap(), 500.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn downtrend_distributes_volume() {
+        let mut tvi = TradeVolumeIndex::new(0.5).unwrap();
+        let candles = [
+            candle(100.0, 1_000.0),
+            candle(99.0, 200.0), // -1 -> -200
+            candle(98.0, 300.0), // -1 -> -300
+        ];
+        let out = tvi.batch(&candles);
+        assert_relative_eq!(out[2].unwrap(), -500.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut tvi = TradeVolumeIndex::new(0.5).unwrap();
+        tvi.batch(&[candle(100.0, 1.0), candle(101.0, 1.0), candle(102.0, 1.0)]);
+        assert!(tvi.is_ready());
+        tvi.reset();
+        assert!(!tvi.is_ready());
+        assert_eq!(tvi.value(), None);
+        assert_eq!(tvi.update(candle(100.0, 1.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..80)
+            .map(|i| {
+                candle(
+                    100.0 + (f64::from(i) * 0.3).sin() * 5.0,
+                    1_000.0 + f64::from(i),
+                )
+            })
+            .collect();
+        let batch = TradeVolumeIndex::new(0.5).unwrap().batch(&candles);
+        let mut b = TradeVolumeIndex::new(0.5).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/trend_strength_index.rs

@@ -0,0 +1,218 @@
+//! Trend Strength Index — the signed coefficient of determination of a linear
+//! regression of price against time.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Trend Strength Index: fits an ordinary-least-squares line to the last
+/// `period` prices against their bar index and reports the coefficient of
+/// determination `r^2`, signed by the slope of the fit.
+///
+/// ```text
+/// regress y = close on x = 0..period-1
+/// r^2  = (n·Σxy − Σx·Σy)^2 / [ (n·Σx² − (Σx)²)(n·Σy² − (Σy)²) ]
+/// TSI  = sign(slope) · r^2          (slope sign = sign of n·Σxy − Σx·Σy)
+/// ```
+///
+/// `r^2` in `[0, 1]` measures how well a straight line explains the price over
+/// the window — how *trendy* the segment is, regardless of direction. Carrying
+/// the slope sign turns it into a directional reading in `[-1, 1]`: values near
+/// `+1` are a strong, clean uptrend; near `-1` a strong downtrend; near `0` a
+/// flat or noisy market with no linear structure. A window of constant prices
+/// (zero variance in `y`) has no defined trend and returns `0`.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, TrendStrengthIndex};
+///
+/// let mut indicator = TrendStrengthIndex::new(20).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// // A clean ramp is a perfect uptrend -> r^2 = 1.
+/// assert!((last.unwrap() - 1.0).abs() < 1e-9);
+/// ```
+#[derive(Debug, Clone)]
+pub struct TrendStrengthIndex {
+    period: usize,
+    buf: VecDeque<f64>,
+}
+
+impl TrendStrengthIndex {
+    /// Construct a Trend Strength Index over the given window.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`, or [`Error::InvalidPeriod`]
+    /// if `period == 1` (a regression needs at least two points).
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if period == 1 {
+            return Err(Error::InvalidPeriod {
+                message: "period must be >= 2 for a regression",
+            });
+        }
+        Ok(Self {
+            period,
+            buf: VecDeque::with_capacity(period),
+        })
+    }
+
+    /// Configured window length.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for TrendStrengthIndex {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, price: f64) -> Option<f64> {
+        self.buf.push_back(price);
+        if self.buf.len() > self.period {
+            self.buf.pop_front();
+        }
+        if self.buf.len() < self.period {
+            return None;
+        }
+
+        let count = self.period as f64;
+        let mut sum_x = 0.0;
+        let mut sum_xx = 0.0;
+        let mut sum_y = 0.0;
+        let mut sum_yy = 0.0;
+        let mut sum_xy = 0.0;
+        for (idx, &price) in self.buf.iter().enumerate() {
+            let x = idx as f64;
+            sum_x += x;
+            sum_xx += x * x;
+            sum_y += price;
+            sum_yy += price * price;
+            sum_xy += x * price;
+        }
+
+        let cov = count.mul_add(sum_xy, -(sum_x * sum_y));
+        let var_x = count.mul_add(sum_xx, -(sum_x * sum_x));
+        let var_y = count.mul_add(sum_yy, -(sum_y * sum_y));
+        if var_y <= 0.0 {
+            return Some(0.0);
+        }
+        let r2 = (cov * cov) / (var_x * var_y);
+        Some(if cov >= 0.0 { r2 } else { -r2 })
+    }
+
+    fn reset(&mut self) {
+        self.buf.clear();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.buf.len() >= self.period
+    }
+
+    fn name(&self) -> &'static str {
+        "TrendStrengthIndex"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_invalid_period() {
+        assert!(matches!(TrendStrengthIndex::new(0), Err(Error::PeriodZero)));
+        assert!(matches!(
+            TrendStrengthIndex::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let tsi = TrendStrengthIndex::new(20).unwrap();
+        assert_eq!(tsi.period(), 20);
+        assert_eq!(tsi.warmup_period(), 20);
+        assert_eq!(tsi.name(), "TrendStrengthIndex");
+        assert!(!tsi.is_ready());
+    }
+
+    #[test]
+    fn warmup_emits_at_period() {
+        let mut tsi = TrendStrengthIndex::new(4).unwrap();
+        let inputs: Vec<f64> = (0..6).map(f64::from).collect();
+        let out = tsi.batch(&inputs);
+        assert!(out[2].is_none());
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn perfect_uptrend_is_plus_one() {
+        let mut tsi = TrendStrengthIndex::new(10).unwrap();
+        let inputs: Vec<f64> = (0..10).map(f64::from).collect();
+        let last = tsi.batch(&inputs).last().unwrap().unwrap();
+        assert_relative_eq!(last, 1.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn perfect_downtrend_is_minus_one() {
+        let mut tsi = TrendStrengthIndex::new(10).unwrap();
+        let inputs: Vec<f64> = (0..10).map(|i| 100.0 - f64::from(i)).collect();
+        let last = tsi.batch(&inputs).last().unwrap().unwrap();
+        assert_relative_eq!(last, -1.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn flat_market_returns_zero() {
+        let mut tsi = TrendStrengthIndex::new(8).unwrap();
+        let inputs = [42.0; 12];
+        let last = tsi.batch(&inputs).last().unwrap().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn noisy_trend_is_between() {
+        // An upward drift with noise: positive but not a perfect fit.
+        let mut tsi = TrendStrengthIndex::new(12).unwrap();
+        let inputs: Vec<f64> = (0..12)
+            .map(|i| f64::from(i) + if i % 2 == 0 { 0.0 } else { 3.0 })
+            .collect();
+        let last = tsi.batch(&inputs).last().unwrap().unwrap();
+        assert!(last > 0.0 && last < 1.0, "tsi {last} should be in (0, 1)");
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut tsi = TrendStrengthIndex::new(10).unwrap();
+        let inputs: Vec<f64> = (0..10).map(f64::from).collect();
+        tsi.batch(&inputs);
+        assert!(tsi.is_ready());
+        tsi.reset();
+        assert!(!tsi.is_ready());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let inputs: Vec<f64> = (0..80)
+            .map(|i| 100.0 + (f64::from(i) * 0.2).sin() * 5.0)
+            .collect();
+        let mut a = TrendStrengthIndex::new(15).unwrap();
+        let mut b = TrendStrengthIndex::new(15).unwrap();
+        assert_eq!(
+            a.batch(&inputs),
+            inputs.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/tsf_oscillator.rs

@@ -0,0 +1,206 @@
+//! Time Series Forecast Oscillator (TSF Oscillator).
+
+use crate::error::{Error, Result};
+use crate::indicators::tsf::Tsf;
+use crate::traits::Indicator;
+
+/// Time Series Forecast Oscillator — the percentage gap between the close and
+/// the **one-bar-ahead** time-series forecast of the close.
+///
+/// ```text
+/// TSFOsc_t = 100 · (close_t − TSF(close, period)_t) / close_t
+/// ```
+///
+/// where [`Tsf`](crate::Tsf) projects the rolling least-squares line one bar
+/// past the window (`a + b·period`). It is the close-relative companion to
+/// [`Cfo`](crate::Cfo), which measures the same percentage gap against the
+/// regression value at the *current* bar (`a + b·(period − 1)`). Because `TSF`
+/// advances one bar further than `LinearRegression`, the two differ by exactly
+/// the slope term `100·b/close`: on a trending series `TSFOsc` reads more
+/// negative in an uptrend (the forecast has already stepped above price) and
+/// more positive in a downtrend.
+///
+/// Positive readings mean the close sits *above* its forward forecast (price
+/// has overshot the projected trend); negative readings mean it sits below.
+/// Wraps the existing `Tsf` so the warmup matches.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, TsfOscillator};
+///
+/// let mut indicator = TsfOscillator::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct TsfOscillator {
+    period: usize,
+    tsf: Tsf,
+    current: Option<f64>,
+}
+
+impl TsfOscillator {
+    /// Construct a new TSF oscillator over `period` inputs.
+    ///
+    /// # Errors
+    /// Returns [`Error::InvalidPeriod`] if `period < 2` — a regression line is
+    /// undefined for fewer than two points.
+    pub fn new(period: usize) -> Result<Self> {
+        if period < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "TSF oscillator needs period >= 2",
+            });
+        }
+        Ok(Self {
+            period,
+            tsf: Tsf::new(period)?,
+            current: None,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for TsfOscillator {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        let forecast = self.tsf.update(input)?;
+        // Hold the previous value if the close is zero — the percentage form
+        // is undefined and a return of inf would propagate badly.
+        if input == 0.0 {
+            return self.current;
+        }
+        let value = 100.0 * (input - forecast) / input;
+        self.current = Some(value);
+        Some(value)
+    }
+
+    fn reset(&mut self) {
+        self.tsf.reset();
+        self.current = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.current.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "TsfOscillator"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_short_period() {
+        assert!(matches!(
+            TsfOscillator::new(1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            TsfOscillator::new(0),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let osc = TsfOscillator::new(14).unwrap();
+        assert_eq!(osc.period(), 14);
+        assert_eq!(osc.warmup_period(), 14);
+        assert_eq!(osc.name(), "TsfOscillator");
+        assert!(!osc.is_ready());
+    }
+
+    #[test]
+    fn reference_value() {
+        // period 3 over [1, 2, 9]: fit y = 0 + 4x, one-bar-ahead TSF at x = 3
+        // is 12. With close = 9, TSFOsc = 100·(9 − 12)/9 = −33.3333…%.
+        let mut osc = TsfOscillator::new(3).unwrap();
+        let out = osc.batch(&[1.0_f64, 2.0, 9.0]);
+        assert!(out[0].is_none());
+        assert!(out[1].is_none());
+        assert_relative_eq!(out[2].unwrap(), -100.0 / 3.0, epsilon = 1e-9);
+        assert!(osc.is_ready());
+    }
+
+    #[test]
+    fn constant_series_yields_zero() {
+        // On a flat series the regression slope is 0, so the one-bar-ahead TSF
+        // equals the constant and close − forecast is exactly 0.
+        let mut osc = TsfOscillator::new(5).unwrap();
+        let out = osc.batch(&[42.0_f64; 30]);
+        for v in out.iter().skip(4).flatten() {
+            assert_relative_eq!(*v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn linear_uptrend_reads_negative() {
+        // Unlike CFO (evaluated at the current bar), the forecast steps one bar
+        // ahead, so on a rising line the projection sits above the close and the
+        // oscillator is negative: TSFOsc = −100·slope/close.
+        let mut osc = TsfOscillator::new(5).unwrap();
+        let prices: Vec<f64> = (1..=20).map(|i| f64::from(i) * 2.0).collect();
+        let out = osc.batch(&prices);
+        for v in out.iter().skip(4).flatten() {
+            assert!(*v < 0.0, "uptrend forecast overshoots close, got {v}");
+        }
+    }
+
+    #[test]
+    fn warmup_emits_first_value_at_period() {
+        let mut osc = TsfOscillator::new(3).unwrap();
+        assert_eq!(osc.update(1.0), None);
+        assert_eq!(osc.update(2.0), None);
+        assert!(osc.update(3.0).is_some());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=80)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 5.0)
+            .collect();
+        let mut a = TsfOscillator::new(14).unwrap();
+        let mut b = TsfOscillator::new(14).unwrap();
+        assert_eq!(
+            a.batch(&prices),
+            prices.iter().map(|p| b.update(*p)).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut osc = TsfOscillator::new(5).unwrap();
+        osc.batch(&(1..=20).map(f64::from).collect::<Vec<_>>());
+        assert!(osc.is_ready());
+        osc.reset();
+        assert!(!osc.is_ready());
+        assert_eq!(osc.update(1.0), None);
+    }
+
+    #[test]
+    fn zero_close_holds_value() {
+        let mut osc = TsfOscillator::new(3).unwrap();
+        osc.batch(&[1.0_f64, 2.0, 3.0]);
+        let before = osc.current;
+        assert_eq!(osc.update(0.0), before);
+    }
+}

crates/wickra-core/src/indicators/ttm_trend.rs

@@ -0,0 +1,167 @@
+//! TTM Trend — John Carter's bar-coloring trend filter.
+
+use crate::error::Result;
+use crate::indicators::sma::Sma;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// TTM Trend: compares the current close to the simple moving average of the
+/// recent median prices `(high + low) / 2`. A close above that reference colors
+/// the bar as an uptrend (`+1.0`); a close at or below it as a downtrend
+/// (`-1.0`).
+///
+/// ```text
+/// reference = SMA((high + low) / 2, period)
+/// TTM Trend = +1  if close > reference
+///             -1  otherwise
+/// ```
+///
+/// The classic TTM Trend uses the trailing six bars. The signal is a regime
+/// label rather than a level: it stays `None` during warmup and then emits
+/// `±1.0` on every bar.
+///
+/// Reference: John Carter, *Mastering the Trade*, 2005.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, TtmTrend};
+///
+/// let mut indicator = TtmTrend::new(6).unwrap();
+/// let mut last = None;
+/// for i in 0..20 {
+///     let base = 100.0 + f64::from(i);
+///     let candle =
+///         Candle::new(base, base + 1.0, base - 1.0, base + 0.5, 1.0, i64::from(i)).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert_eq!(last, Some(1.0));
+/// ```
+#[derive(Debug, Clone)]
+pub struct TtmTrend {
+    period: usize,
+    sma: Sma,
+}
+
+impl TtmTrend {
+    /// Construct a TTM Trend over the given lookback.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`](crate::error::Error::PeriodZero) if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        Ok(Self {
+            period,
+            sma: Sma::new(period)?,
+        })
+    }
+
+    /// Configured lookback period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+}
+
+impl Indicator for TtmTrend {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let median = f64::midpoint(candle.high, candle.low);
+        let reference = self.sma.update(median)?;
+        Some(if candle.close > reference { 1.0 } else { -1.0 })
+    }
+
+    fn reset(&mut self) {
+        self.sma.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period
+    }
+
+    fn is_ready(&self) -> bool {
+        self.sma.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "TtmTrend"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::error::Error;
+    use crate::traits::BatchExt;
+
+    fn candle(high: f64, low: f64, close: f64, ts: i64) -> Candle {
+        Candle::new(f64::midpoint(high, low), high, low, close, 1.0, ts).unwrap()
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(TtmTrend::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let t = TtmTrend::new(6).unwrap();
+        assert_eq!(t.period(), 6);
+        assert_eq!(t.warmup_period(), 6);
+        assert_eq!(t.name(), "TtmTrend");
+        assert!(!t.is_ready());
+    }
+
+    #[test]
+    fn warmup_then_emits() {
+        let mut t = TtmTrend::new(3).unwrap();
+        let candles: Vec<Candle> = (0..3).map(|i| candle(13.0, 9.0, 12.0, i)).collect();
+        let out = t.batch(&candles);
+        assert!(out[0].is_none());
+        assert!(out[1].is_none());
+        assert!(out[2].is_some());
+    }
+
+    #[test]
+    fn close_above_reference_is_uptrend() {
+        // Close (12) sits above the median reference (13 + 9) / 2 = 11 -> +1.
+        let mut t = TtmTrend::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| candle(13.0, 9.0, 12.0, i)).collect();
+        assert_eq!(t.batch(&candles).last().unwrap().unwrap(), 1.0);
+    }
+
+    #[test]
+    fn close_at_or_below_reference_is_downtrend() {
+        // Constant median 10, close equal to the reference -> not strictly above -> -1.
+        let mut t = TtmTrend::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| candle(11.0, 9.0, 10.0, i)).collect();
+        assert_eq!(t.batch(&candles).last().unwrap().unwrap(), -1.0);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut t = TtmTrend::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| candle(13.0, 9.0, 12.0, i)).collect();
+        t.batch(&candles);
+        assert!(t.is_ready());
+        t.reset();
+        assert!(!t.is_ready());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..40_i64)
+            .map(|i| {
+                let base = 100.0 + (i as f64 * 0.25).sin() * 4.0;
+                candle(base + 1.0, base - 1.0, base + (i as f64 * 0.5).cos(), i)
+            })
+            .collect();
+        let mut a = TtmTrend::new(6).unwrap();
+        let mut b = TtmTrend::new(6).unwrap();
+        assert_eq!(
+            a.batch(&candles),
+            candles.iter().map(|c| b.update(*c)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/indicators/twiggs_money_flow.rs

@@ -0,0 +1,314 @@
+//! Twiggs Money Flow (TMF) — Colin Twiggs' Wilder-smoothed money-flow oscillator.
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Twiggs Money Flow — a refinement of Chaikin Money Flow that uses **true range**
+/// boundaries and **Wilder (exponential) smoothing** instead of a simple sum.
+///
+/// ```text
+/// TRH   = max(high, prev_close)          (true high)
+/// TRL   = min(low,  prev_close)          (true low)
+/// ad    = volume * (2*close − TRH − TRL) / (TRH − TRL)   (0 if TRH == TRL)
+/// TMF   = WilderEMA(ad, period) / WilderEMA(volume, period)
+/// ```
+///
+/// Colin Twiggs' money flow fixes two issues with [`Cmf`](crate::Cmf): it replaces
+/// the bar's raw high/low with the *true* high/low (folding in the prior close so
+/// gaps count), and it smooths the accumulated money flow and the volume with a
+/// Wilder exponential average rather than a flat `period`-sum, so the oscillator
+/// reacts faster and never jumps when a large bar drops out of a window. The
+/// output is bounded in roughly `[−1, +1]`: positive means buying pressure
+/// (closes biased toward the true high), negative means selling pressure.
+///
+/// The first candle seeds the reference close; the next `period` bars seed both
+/// Wilder averages, so the first value lands after `period + 1` inputs. A stretch
+/// of zero volume makes the denominator average `0`, in which case the oscillator
+/// reports `0` rather than `0 / 0`. Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, TwiggsMoneyFlow};
+///
+/// let mut indicator = TwiggsMoneyFlow::new(21).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     let base = 100.0 + (f64::from(i) * 0.2).sin() * 5.0;
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base + 0.5, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct TwiggsMoneyFlow {
+    period: usize,
+    prev_close: Option<f64>,
+    seed_ad: f64,
+    seed_vol: f64,
+    seed_count: usize,
+    ad_ema: Option<f64>,
+    vol_ema: Option<f64>,
+    last: Option<f64>,
+}
+
+impl TwiggsMoneyFlow {
+    /// Construct a new Twiggs Money Flow with the given smoothing `period`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            prev_close: None,
+            seed_ad: 0.0,
+            seed_vol: 0.0,
+            seed_count: 0,
+            ad_ema: None,
+            vol_ema: None,
+            last: None,
+        })
+    }
+
+    /// Configured smoothing period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+
+    fn ratio(ad_ema: f64, vol_ema: f64) -> f64 {
+        if vol_ema == 0.0 {
+            0.0
+        } else {
+            ad_ema / vol_ema
+        }
+    }
+}
+
+impl Indicator for TwiggsMoneyFlow {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let Some(prev_close) = self.prev_close else {
+            self.prev_close = Some(candle.close);
+            return None;
+        };
+        let trh = candle.high.max(prev_close);
+        let trl = candle.low.min(prev_close);
+        let range = trh - trl;
+        let ad = if range > 0.0 {
+            candle.volume * (2.0 * candle.close - trh - trl) / range
+        } else {
+            0.0
+        };
+        self.prev_close = Some(candle.close);
+
+        if let (Some(ad_ema), Some(vol_ema)) = (self.ad_ema, self.vol_ema) {
+            let n = self.period as f64;
+            let new_ad = ad_ema + (ad - ad_ema) / n;
+            let new_vol = vol_ema + (candle.volume - vol_ema) / n;
+            self.ad_ema = Some(new_ad);
+            self.vol_ema = Some(new_vol);
+            let v = Self::ratio(new_ad, new_vol);
+            self.last = Some(v);
+            return Some(v);
+        }
+
+        self.seed_ad += ad;
+        self.seed_vol += candle.volume;
+        self.seed_count += 1;
+        if self.seed_count == self.period {
+            let n = self.period as f64;
+            let ad_ema = self.seed_ad / n;
+            let vol_ema = self.seed_vol / n;
+            self.ad_ema = Some(ad_ema);
+            self.vol_ema = Some(vol_ema);
+            let v = Self::ratio(ad_ema, vol_ema);
+            self.last = Some(v);
+            return Some(v);
+        }
+        None
+    }
+
+    fn reset(&mut self) {
+        self.prev_close = None;
+        self.seed_ad = 0.0;
+        self.seed_vol = 0.0;
+        self.seed_count = 0;
+        self.ad_ema = None;
+        self.vol_ema = None;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "TwiggsMoneyFlow"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64, volume: f64) -> Candle {
+        Candle::new_unchecked(low, high, low, close, volume, 0)
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(TwiggsMoneyFlow::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn flat_bars_drive_tmf_to_zero() {
+        // A flat bar (high == low == close == prior close) gives a zero two-bar
+        // range, so the accumulation term falls back to 0.0 and TMF settles at
+        // zero. Exercises the `range == 0` guard.
+        let mut tmf = TwiggsMoneyFlow::new(2).unwrap();
+        let flat: Vec<Candle> = (0..6)
+            .map(|_| candle(100.0, 100.0, 100.0, 1_000.0))
+            .collect();
+        let last = tmf.batch(&flat).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let tmf = TwiggsMoneyFlow::new(21).unwrap();
+        assert_eq!(tmf.period(), 21);
+        assert_eq!(tmf.warmup_period(), 22);
+        assert_eq!(tmf.name(), "TwiggsMoneyFlow");
+        assert!(!tmf.is_ready());
+        assert_eq!(tmf.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut tmf = TwiggsMoneyFlow::new(3).unwrap();
+        let candles: Vec<Candle> = (0..8)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base, 1_000.0)
+            })
+            .collect();
+        let out = tmf.batch(&candles);
+        // warmup_period == period + 1 == 4: first emission at index 3.
+        for o in out.iter().take(3) {
+            assert!(o.is_none());
+        }
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn closes_at_true_high_is_positive() {
+        // Every bar closes at its high -> strong buying pressure -> TMF -> +1.
+        let mut tmf = TwiggsMoneyFlow::new(3).unwrap();
+        let candles: Vec<Candle> = (0..12)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                // open=low=base-1, high=close=base+1 -> closes at the top.
+                Candle::new_unchecked(base - 1.0, base + 1.0, base - 1.0, base + 1.0, 1_000.0, 0)
+            })
+            .collect();
+        let last = tmf.batch(&candles).into_iter().flatten().last().unwrap();
+        assert!(
+            last > 0.9,
+            "closing at the high should drive TMF near +1, got {last}"
+        );
+    }
+
+    #[test]
+    fn closes_at_true_low_is_negative() {
+        let mut tmf = TwiggsMoneyFlow::new(3).unwrap();
+        let candles: Vec<Candle> = (0..12)
+            .map(|i| {
+                let base = 100.0 - f64::from(i);
+                // closes at the low.
+                Candle::new_unchecked(base + 1.0, base + 1.0, base - 1.0, base - 1.0, 1_000.0, 0)
+            })
+            .collect();
+        let last = tmf.batch(&candles).into_iter().flatten().last().unwrap();
+        assert!(
+            last < -0.5,
+            "closing at the low should drive TMF negative, got {last}"
+        );
+    }
+
+    #[test]
+    fn zero_volume_yields_zero() {
+        let mut tmf = TwiggsMoneyFlow::new(3).unwrap();
+        let candles: Vec<Candle> = (0..10)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base, 0.0)
+            })
+            .collect();
+        for v in tmf.batch(&candles).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn output_in_range() {
+        let mut tmf = TwiggsMoneyFlow::new(21).unwrap();
+        let candles: Vec<Candle> = (0..200)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.3).sin() * 12.0;
+                candle(base + 2.0, base - 2.0, base + 0.5, 1_000.0)
+            })
+            .collect();
+        for v in tmf.batch(&candles).into_iter().flatten() {
+            assert!((-1.0..=1.0).contains(&v), "TMF out of range: {v}");
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut tmf = TwiggsMoneyFlow::new(3).unwrap();
+        let candles: Vec<Candle> = (0..12)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base, 1_000.0)
+            })
+            .collect();
+        tmf.batch(&candles);
+        assert!(tmf.is_ready());
+        tmf.reset();
+        assert!(!tmf.is_ready());
+        assert_eq!(tmf.value(), None);
+        assert_eq!(tmf.update(candle(101.0, 99.0, 100.0, 1_000.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.25).sin() * 9.0;
+                candle(base + 2.0, base - 1.5, base + 0.5, 1_000.0 + f64::from(i))
+            })
+            .collect();
+        let batch = TwiggsMoneyFlow::new(21).unwrap().batch(&candles);
+        let mut b = TwiggsMoneyFlow::new(21).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/volatility_cone.rs

@@ -0,0 +1,381 @@
+//! Volatility Cone — current realized volatility within its historical envelope.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`VolatilityCone`]: the current realized volatility together with
+/// the envelope (the "cone") it sits inside over the lookback window.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct VolatilityConeOutput {
+    /// Latest realized volatility (sample stddev of log returns over `window`).
+    pub current: f64,
+    /// Lowest realized volatility seen over the `lookback` window.
+    pub min: f64,
+    /// Median realized volatility over the `lookback` window.
+    pub median: f64,
+    /// Highest realized volatility seen over the `lookback` window.
+    pub max: f64,
+    /// Percentile rank of `current` within the lookback distribution, in
+    /// `[0, 100]` — the share of stored volatilities `<= current`, times 100.
+    pub percentile: f64,
+}
+
+/// Sample standard deviation from a running `(sum, sum_of_squares, count)`.
+fn sample_stddev(sum: f64, sum_sq: f64, count: usize) -> f64 {
+    let n = count as f64;
+    let mean = sum / n;
+    let variance = ((sum_sq - n * mean * mean) / (n - 1.0)).max(0.0);
+    variance.sqrt()
+}
+
+/// Volatility Cone — the current realized volatility positioned within the
+/// historical range ("cone") of realized volatilities over a lookback window.
+///
+/// ```text
+/// r_t     = ln(close_t / close_{t−1})
+/// vol_t   = stddev_sample(r over window)            (rolling realized volatility)
+/// cone    = { min, median, max, percentile } of vol over the last `lookback`
+/// ```
+///
+/// A volatility cone (Burghardt & Lane 1990) shows whether current volatility is
+/// high or low *relative to its own history*, rather than as an absolute number.
+/// This streaming form tracks one horizon: it maintains the rolling realized
+/// volatility of log returns over `window`, then reports the latest reading
+/// (`current`) alongside the `min`, `median`, `max` and percentile rank of that
+/// volatility series over the trailing `lookback`. `current` always lies within
+/// `[min, max]` because it is itself the newest member of the lookback set.
+///
+/// Only the candle's **close** is used (the log-return series); the high and low
+/// are ignored. The volatility is per-period (sample stddev of log returns, not
+/// annualised) — multiply by `√trading_periods` for an annual figure. Each
+/// `update` is O(`lookback log lookback`) from sorting the envelope.
+///
+/// Non-positive closes are ignored (the log return would be undefined): the tick
+/// is dropped, state is left untouched, and the last value is returned.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, VolatilityCone};
+///
+/// let mut indicator = VolatilityCone::new(20, 60).unwrap();
+/// let mut last = None;
+/// for i in 0..120 {
+///     let c = 100.0 + (f64::from(i) * 0.3).sin() * 5.0;
+///     let candle = Candle::new(c, c + 1.0, c - 1.0, c, 1_000.0, 0).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct VolatilityCone {
+    window: usize,
+    lookback: usize,
+    prev_close: Option<f64>,
+    /// Rolling window of log returns for the inner realized-volatility series.
+    returns: VecDeque<f64>,
+    ret_sum: f64,
+    ret_sum_sq: f64,
+    /// Rolling window of realized-volatility readings (the cone envelope).
+    vols: VecDeque<f64>,
+    last: Option<VolatilityConeOutput>,
+}
+
+impl VolatilityCone {
+    /// Construct a new volatility-cone indicator.
+    ///
+    /// `window` is the realized-volatility estimation window; `lookback` is the
+    /// number of volatility readings forming the historical cone.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if either argument is `0`, or
+    /// [`Error::InvalidPeriod`] if `window < 2` (a sample stddev needs two
+    /// returns) or `lookback < 2` (an envelope needs at least two readings).
+    pub fn new(window: usize, lookback: usize) -> Result<Self> {
+        if window == 0 || lookback == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if window < 2 || lookback < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "volatility cone window and lookback must both be >= 2",
+            });
+        }
+        Ok(Self {
+            window,
+            lookback,
+            prev_close: None,
+            returns: VecDeque::with_capacity(window),
+            ret_sum: 0.0,
+            ret_sum_sq: 0.0,
+            vols: VecDeque::with_capacity(lookback),
+            last: None,
+        })
+    }
+
+    /// Configured `(window, lookback)`.
+    pub const fn windows(&self) -> (usize, usize) {
+        (self.window, self.lookback)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<VolatilityConeOutput> {
+        self.last
+    }
+}
+
+impl Indicator for VolatilityCone {
+    type Input = Candle;
+    type Output = VolatilityConeOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<VolatilityConeOutput> {
+        let price = candle.close;
+        // A log return is undefined for a non-positive close; skip the tick.
+        if price <= 0.0 {
+            return self.last;
+        }
+        let Some(prev) = self.prev_close else {
+            self.prev_close = Some(price);
+            return None;
+        };
+        self.prev_close = Some(price);
+        // `prev` came from `self.prev_close`, gated by the guard above, so it is
+        // positive — the log return is always well-defined.
+        let r = (price / prev).ln();
+
+        // Stage one: rolling sample volatility of log returns.
+        if self.returns.len() == self.window {
+            let old = self.returns.pop_front().expect("returns window non-empty");
+            self.ret_sum -= old;
+            self.ret_sum_sq -= old * old;
+        }
+        self.returns.push_back(r);
+        self.ret_sum += r;
+        self.ret_sum_sq += r * r;
+        if self.returns.len() < self.window {
+            return None;
+        }
+        let current = sample_stddev(self.ret_sum, self.ret_sum_sq, self.window);
+
+        // Stage two: maintain the lookback envelope of volatility readings.
+        if self.vols.len() == self.lookback {
+            self.vols.pop_front();
+        }
+        self.vols.push_back(current);
+        if self.vols.len() < self.lookback {
+            return None;
+        }
+
+        let mut sorted: Vec<f64> = self.vols.iter().copied().collect();
+        sorted.sort_by(f64::total_cmp);
+        let min = sorted[0];
+        let max = sorted[self.lookback - 1];
+        let mid = self.lookback / 2;
+        let median = if self.lookback % 2 == 1 {
+            sorted[mid]
+        } else {
+            f64::midpoint(sorted[mid - 1], sorted[mid])
+        };
+        let count_le = self.vols.iter().filter(|&&v| v <= current).count();
+        let percentile = count_le as f64 / self.lookback as f64 * 100.0;
+
+        let out = VolatilityConeOutput {
+            current,
+            min,
+            median,
+            max,
+            percentile,
+        };
+        self.last = Some(out);
+        Some(out)
+    }
+
+    fn reset(&mut self) {
+        self.prev_close = None;
+        self.returns.clear();
+        self.ret_sum = 0.0;
+        self.ret_sum_sq = 0.0;
+        self.vols.clear();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // One previous close for the first return, `window` returns for the
+        // first volatility, then `lookback` volatilities for the envelope.
+        self.window + self.lookback
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "VolatilityCone"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Candle whose close drives the indicator (open = high = low = close here).
+    fn close_candle(close: f64) -> Candle {
+        Candle::new_unchecked(close, close, close, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_zero_window() {
+        assert!(matches!(VolatilityCone::new(0, 10), Err(Error::PeriodZero)));
+        assert!(matches!(VolatilityCone::new(10, 0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn rejects_window_one() {
+        assert!(matches!(
+            VolatilityCone::new(1, 10),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            VolatilityCone::new(10, 1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let vc = VolatilityCone::new(20, 60).unwrap();
+        assert_eq!(vc.windows(), (20, 60));
+        assert_eq!(vc.warmup_period(), 80);
+        assert_eq!(vc.name(), "VolatilityCone");
+        assert!(!vc.is_ready());
+        assert_eq!(vc.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut vc = VolatilityCone::new(2, 2).unwrap();
+        let prices = [100.0, 110.0, 121.0, 100.0, 105.0, 99.0];
+        let candles: Vec<Candle> = prices.iter().map(|p| close_candle(*p)).collect();
+        let out = vc.batch(&candles);
+        let warmup = vc.warmup_period(); // 4
+        assert_eq!(warmup, 4);
+        for v in out.iter().take(warmup - 1) {
+            assert!(v.is_none());
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn known_value() {
+        // window = 2 -> vol = |r_t − r_{t−1}| / √2; lookback = 2.
+        // prices: r1 = r2 = ln(1.1), r3 = ln(100/121).
+        let mut vc = VolatilityCone::new(2, 2).unwrap();
+        let candles: Vec<Candle> = [100.0, 110.0, 121.0, 100.0]
+            .iter()
+            .map(|p| close_candle(*p))
+            .collect();
+        let out = vc.batch(&candles);
+        let r2 = (121.0_f64 / 110.0).ln();
+        let r3 = (100.0_f64 / 121.0).ln();
+        let vol2 = (r2 - r3).abs() / 2.0_f64.sqrt();
+        let o = out[3].unwrap();
+        assert_relative_eq!(o.current, vol2, epsilon = 1e-9);
+        assert_relative_eq!(o.min, 0.0, epsilon = 1e-9); // vol1 = 0 (r1 == r2)
+        assert_relative_eq!(o.max, vol2, epsilon = 1e-9);
+        assert_relative_eq!(o.median, vol2 / 2.0, epsilon = 1e-9);
+        assert_relative_eq!(o.percentile, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn odd_lookback_median_is_middle() {
+        // lookback = 3 picks the middle of the sorted envelope.
+        let mut vc = VolatilityCone::new(2, 3).unwrap();
+        let candles: Vec<Candle> = [100.0, 101.0, 103.0, 100.0, 104.0, 99.0, 106.0]
+            .iter()
+            .map(|p| close_candle(*p))
+            .collect();
+        let out = vc.batch(&candles);
+        let o = out.last().unwrap().unwrap();
+        assert!(o.min <= o.median && o.median <= o.max);
+    }
+
+    #[test]
+    fn envelope_brackets_current() {
+        let mut vc = VolatilityCone::new(10, 30).unwrap();
+        let candles: Vec<Candle> = (0..200)
+            .map(|i| close_candle(100.0 + (f64::from(i) * 0.3).sin() * 12.0))
+            .collect();
+        for o in vc.batch(&candles).into_iter().flatten() {
+            assert!(o.min <= o.current && o.current <= o.max);
+            assert!(o.min <= o.median && o.median <= o.max);
+            assert!(o.percentile > 0.0 && o.percentile <= 100.0);
+        }
+    }
+
+    #[test]
+    fn constant_series_yields_zero_cone() {
+        let mut vc = VolatilityCone::new(5, 5).unwrap();
+        let candles: Vec<Candle> = (0..40).map(|_| close_candle(100.0)).collect();
+        for o in vc.batch(&candles).into_iter().flatten() {
+            assert_relative_eq!(o.current, 0.0, epsilon = 1e-12);
+            assert_relative_eq!(o.min, 0.0, epsilon = 1e-12);
+            assert_relative_eq!(o.max, 0.0, epsilon = 1e-12);
+            assert_relative_eq!(o.median, 0.0, epsilon = 1e-12);
+            assert_relative_eq!(o.percentile, 100.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn skips_non_positive_close() {
+        let mut vc = VolatilityCone::new(2, 2).unwrap();
+        let candles: Vec<Candle> = [100.0, 110.0, 121.0, 100.0]
+            .iter()
+            .map(|p| close_candle(*p))
+            .collect();
+        let warmup = vc.batch(&candles);
+        let baseline = warmup.last().copied().flatten().expect("warmed up");
+        // A non-positive close is skipped and the previous value is returned.
+        assert_eq!(vc.update(close_candle(0.0)), Some(baseline));
+        // State untouched: a clone advanced by the same real tick agrees.
+        let mut control = vc.clone();
+        let after = vc.update(close_candle(105.0)).expect("ready");
+        assert_eq!(control.update(close_candle(105.0)).expect("ready"), after);
+    }
+
+    #[test]
+    fn skips_non_positive_before_first_close() {
+        let mut vc = VolatilityCone::new(2, 2).unwrap();
+        assert_eq!(vc.update(close_candle(0.0)), None);
+        assert_eq!(vc.update(close_candle(100.0)), None);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut vc = VolatilityCone::new(2, 2).unwrap();
+        let candles: Vec<Candle> = [100.0, 110.0, 121.0, 100.0, 105.0]
+            .iter()
+            .map(|p| close_candle(*p))
+            .collect();
+        vc.batch(&candles);
+        assert!(vc.is_ready());
+        vc.reset();
+        assert!(!vc.is_ready());
+        assert_eq!(vc.value(), None);
+        assert_eq!(vc.update(close_candle(100.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..200)
+            .map(|i| close_candle(100.0 + (f64::from(i) * 0.25).sin() * 9.0))
+            .collect();
+        let batch = VolatilityCone::new(10, 30).unwrap().batch(&candles);
+        let mut b = VolatilityCone::new(10, 30).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/volatility_of_volatility.rs

@@ -0,0 +1,333 @@
+//! Volatility of Volatility — the dispersion of a rolling volatility series.
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::traits::Indicator;
+
+/// Sample standard deviation from a running `(sum, sum_of_squares, count)`.
+///
+/// Uses Bessel's correction (divisor `n − 1`) and clamps a tiny negative
+/// floating-point residual to zero before the square root.
+fn sample_stddev(sum: f64, sum_sq: f64, count: usize) -> f64 {
+    let n = count as f64;
+    let mean = sum / n;
+    let variance = ((sum_sq - n * mean * mean) / (n - 1.0)).max(0.0);
+    variance.sqrt()
+}
+
+/// Volatility of Volatility — the standard deviation of a rolling realized-
+/// volatility series ("vol-of-vol").
+///
+/// ```text
+/// r_t   = ln(price_t / price_{t−1})
+/// vol_t = stddev_sample(r over vol_window)          (rolling realized volatility)
+/// VoV   = stddev_sample(vol over vov_window)         (dispersion of that series)
+/// ```
+///
+/// This is a two-stage estimator: the first stage measures the rolling sample
+/// volatility of log returns (the same quantity
+/// [`HistoricalVolatility`](crate::HistoricalVolatility) annualises), and the
+/// second stage measures how much *that* volatility itself moves. A high
+/// vol-of-vol means the volatility regime is unstable — turbulent periods
+/// alternate with calm ones — which is exactly the convexity that long-gamma and
+/// volatility-trading strategies care about. Both stages use the unbiased
+/// `n − 1` sample standard deviation. Each `update` is O(1).
+///
+/// Non-finite and non-positive prices are ignored (the log return would be
+/// undefined): the tick is dropped, state is left untouched, and the last value
+/// is returned.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, VolatilityOfVolatility};
+///
+/// let mut indicator = VolatilityOfVolatility::new(20, 20).unwrap();
+/// let mut last = None;
+/// for i in 0..120 {
+///     last = indicator.update(100.0 + (f64::from(i) * 0.3).sin() * 5.0);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct VolatilityOfVolatility {
+    vol_window: usize,
+    vov_window: usize,
+    prev_price: Option<f64>,
+    /// Rolling window of log returns (stage one).
+    returns: VecDeque<f64>,
+    ret_sum: f64,
+    ret_sum_sq: f64,
+    /// Rolling window of realized-volatility readings (stage two).
+    vols: VecDeque<f64>,
+    vol_sum: f64,
+    vol_sum_sq: f64,
+    last: Option<f64>,
+}
+
+impl VolatilityOfVolatility {
+    /// Construct a new vol-of-vol indicator.
+    ///
+    /// `vol_window` is the window for the inner realized-volatility series;
+    /// `vov_window` is the window over which its dispersion is measured.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if either window is `0`, or
+    /// [`Error::InvalidPeriod`] if either is `1` (a sample standard deviation
+    /// needs at least two observations).
+    pub fn new(vol_window: usize, vov_window: usize) -> Result<Self> {
+        if vol_window == 0 || vov_window == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if vol_window < 2 || vov_window < 2 {
+            return Err(Error::InvalidPeriod {
+                message: "vol-of-vol windows must both be >= 2",
+            });
+        }
+        Ok(Self {
+            vol_window,
+            vov_window,
+            prev_price: None,
+            returns: VecDeque::with_capacity(vol_window),
+            ret_sum: 0.0,
+            ret_sum_sq: 0.0,
+            vols: VecDeque::with_capacity(vov_window),
+            vol_sum: 0.0,
+            vol_sum_sq: 0.0,
+            last: None,
+        })
+    }
+
+    /// Configured `(vol_window, vov_window)`.
+    pub const fn windows(&self) -> (usize, usize) {
+        (self.vol_window, self.vov_window)
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for VolatilityOfVolatility {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, input: f64) -> Option<f64> {
+        // Non-finite / non-positive prices are skipped: `ln(input / prev)` is
+        // undefined, so the tick must not enter the return window.
+        if !input.is_finite() || input <= 0.0 {
+            return self.last;
+        }
+        let Some(prev) = self.prev_price else {
+            self.prev_price = Some(input);
+            return None;
+        };
+        self.prev_price = Some(input);
+        // `prev` came from `self.prev_price`, gated by the guard above, so it is
+        // finite and positive — the log return is always well-defined.
+        let r = (input / prev).ln();
+
+        // Stage one: rolling sample volatility of log returns.
+        if self.returns.len() == self.vol_window {
+            let old = self.returns.pop_front().expect("returns window non-empty");
+            self.ret_sum -= old;
+            self.ret_sum_sq -= old * old;
+        }
+        self.returns.push_back(r);
+        self.ret_sum += r;
+        self.ret_sum_sq += r * r;
+        if self.returns.len() < self.vol_window {
+            return None;
+        }
+        let vol = sample_stddev(self.ret_sum, self.ret_sum_sq, self.vol_window);
+
+        // Stage two: rolling sample dispersion of the volatility series.
+        if self.vols.len() == self.vov_window {
+            let old = self.vols.pop_front().expect("vols window non-empty");
+            self.vol_sum -= old;
+            self.vol_sum_sq -= old * old;
+        }
+        self.vols.push_back(vol);
+        self.vol_sum += vol;
+        self.vol_sum_sq += vol * vol;
+        if self.vols.len() < self.vov_window {
+            return None;
+        }
+        let vov = sample_stddev(self.vol_sum, self.vol_sum_sq, self.vov_window);
+        self.last = Some(vov);
+        Some(vov)
+    }
+
+    fn reset(&mut self) {
+        self.prev_price = None;
+        self.returns.clear();
+        self.ret_sum = 0.0;
+        self.ret_sum_sq = 0.0;
+        self.vols.clear();
+        self.vol_sum = 0.0;
+        self.vol_sum_sq = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // One previous price for the first return, `vol_window` returns for the
+        // first volatility, then `vov_window` volatilities for the dispersion.
+        // The two windows overlap on the bar axis, so this is the sum.
+        self.vol_window + self.vov_window
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "VolatilityOfVolatility"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use crate::HistoricalVolatility;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_window() {
+        assert!(matches!(
+            VolatilityOfVolatility::new(0, 10),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            VolatilityOfVolatility::new(10, 0),
+            Err(Error::PeriodZero)
+        ));
+    }
+
+    #[test]
+    fn rejects_window_one() {
+        assert!(matches!(
+            VolatilityOfVolatility::new(1, 10),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            VolatilityOfVolatility::new(10, 1),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let vov = VolatilityOfVolatility::new(20, 10).unwrap();
+        assert_eq!(vov.windows(), (20, 10));
+        assert_eq!(vov.warmup_period(), 30);
+        assert_eq!(vov.name(), "VolatilityOfVolatility");
+        assert!(!vov.is_ready());
+        assert_eq!(vov.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut vov = VolatilityOfVolatility::new(3, 3).unwrap();
+        let prices: Vec<f64> = (1..=20)
+            .map(|i| 100.0 + (f64::from(i) * 0.7).sin() * 4.0)
+            .collect();
+        let out = vov.batch(&prices);
+        let warmup = vov.warmup_period(); // 6
+        for v in out.iter().take(warmup - 1) {
+            assert!(v.is_none());
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn matches_two_stage_reference() {
+        // Stage one equals HistoricalVolatility(vol_window, 1) / 100 (sample
+        // stddev of log returns); stage two is the sample stddev of that series.
+        let (vol_window, vov_window) = (3, 3);
+        let prices: Vec<f64> = [100.0, 102.0, 101.0, 104.0, 103.5, 106.0, 105.0, 108.0].to_vec();
+
+        let mut hv = HistoricalVolatility::new(vol_window, 1).unwrap();
+        let vol_series: Vec<f64> = hv
+            .batch(&prices)
+            .into_iter()
+            .flatten()
+            .map(|v| v / 100.0)
+            .collect();
+        // Sample stddev of the last `vov_window` volatilities.
+        let tail = &vol_series[vol_series.len() - vov_window..];
+        let sum: f64 = tail.iter().sum();
+        let sum_sq: f64 = tail.iter().map(|v| v * v).sum();
+        let expected = sample_stddev(sum, sum_sq, vov_window);
+
+        let mut vov = VolatilityOfVolatility::new(vol_window, vov_window).unwrap();
+        let out = vov.batch(&prices);
+        assert_relative_eq!(out.last().unwrap().unwrap(), expected, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn constant_series_yields_zero() {
+        let mut vov = VolatilityOfVolatility::new(5, 5).unwrap();
+        for v in vov.batch(&[100.0; 60]).into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn output_is_non_negative() {
+        let mut vov = VolatilityOfVolatility::new(10, 10).unwrap();
+        let prices: Vec<f64> = (1..=300)
+            .map(|i| 100.0 + (f64::from(i) * 0.3).sin() * 12.0)
+            .collect();
+        for v in vov.batch(&prices).into_iter().flatten() {
+            assert!(v >= 0.0, "vol-of-vol must be non-negative, got {v}");
+        }
+    }
+
+    #[test]
+    fn ignores_non_finite_input() {
+        let mut vov = VolatilityOfVolatility::new(3, 3).unwrap();
+        let out = vov.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
+        let last = *out.last().unwrap();
+        assert!(last.is_some());
+        assert_eq!(vov.update(f64::NAN), last);
+        assert_eq!(vov.update(f64::INFINITY), last);
+    }
+
+    #[test]
+    fn skips_non_positive_prices() {
+        let mut vov = VolatilityOfVolatility::new(3, 3).unwrap();
+        let warmup = vov.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
+        let baseline = warmup.last().copied().flatten().expect("warmed up");
+        assert_eq!(vov.update(-5.0), Some(baseline));
+        assert_eq!(vov.update(0.0), Some(baseline));
+        // State untouched: a clone advanced by the same real tick agrees.
+        let mut control = vov.clone();
+        let after = vov.update(41.0).expect("ready");
+        assert_eq!(control.update(41.0).expect("ready"), after);
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut vov = VolatilityOfVolatility::new(3, 3).unwrap();
+        vov.batch(&(1..=40).map(f64::from).collect::<Vec<_>>());
+        assert!(vov.is_ready());
+        vov.reset();
+        assert!(!vov.is_ready());
+        assert_eq!(vov.value(), None);
+        assert_eq!(vov.update(1.0), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let prices: Vec<f64> = (1..=200)
+            .map(|i| 100.0 + (f64::from(i) * 0.25).sin() * 9.0)
+            .collect();
+        let batch = VolatilityOfVolatility::new(10, 10).unwrap().batch(&prices);
+        let mut b = VolatilityOfVolatility::new(10, 10).unwrap();
+        let streamed: Vec<_> = prices.iter().map(|p| b.update(*p)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/volatility_ratio.rs

@@ -0,0 +1,285 @@
+//! Schwager's Volatility Ratio — today's true range versus its typical level.
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Schwager's Volatility Ratio — the current bar's true range divided by the
+/// exponential moving average of the *prior* true ranges.
+///
+/// ```text
+/// TR_t = true range of bar t
+/// VR_t = TR_t / EMA_n(TR through bar t−1)
+/// ```
+///
+/// Jack Schwager's volatility ratio measures how today's range compares to its
+/// recent typical level: a reading above `2.0` marks a **wide-ranging day** —
+/// today's true range is more than twice the smoothed average — which often
+/// precedes or accompanies a reversal. The denominator is the exponential
+/// moving average of true range *excluding the current bar*, seeded with the
+/// simple average of the first `period` true ranges, so a single large bar
+/// stands out instead of inflating its own benchmark.
+///
+/// True range is `max(high − low, |high − prev_close|, |low − prev_close|)`,
+/// identical to the [`Atr`](crate::Atr) building block, but here it is compared
+/// to a *standard* EMA (smoothing `2 / (period + 1)`) rather than Wilder
+/// smoothing, which keeps the ratio distinct from `TR / ATR`. Each `update` is
+/// O(1).
+///
+/// A flat market drives every true range — and the EMA — to `0`; the ratio is
+/// then `0.0` rather than an undefined `0 / 0`. `Candle::new` rejects non-finite
+/// fields, so no in-method finiteness guard is needed.
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, VolatilityRatio};
+///
+/// let mut indicator = VolatilityRatio::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let base = 100.0 + f64::from(i);
+///     let candle = Candle::new(base, base + 2.0, base - 1.0, base + 0.5, 1_000.0, 0).unwrap();
+///     last = indicator.update(candle);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct VolatilityRatio {
+    period: usize,
+    alpha: f64,
+    prev_close: Option<f64>,
+    /// Sum and count of the first `period` true ranges, used to seed the EMA.
+    seed_sum: f64,
+    seed_count: usize,
+    /// EMA of true range through the previous bar; `None` until seeded.
+    ema: Option<f64>,
+    last: Option<f64>,
+}
+
+impl VolatilityRatio {
+    /// Construct a new volatility-ratio indicator.
+    ///
+    /// `period` is the number of true ranges that seed and smooth the
+    /// denominator EMA.
+    ///
+    /// # Errors
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            alpha: 2.0 / (period as f64 + 1.0),
+            prev_close: None,
+            seed_sum: 0.0,
+            seed_count: 0,
+            ema: None,
+            last: None,
+        })
+    }
+
+    /// Configured period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for VolatilityRatio {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        // The first bar has no previous close, so no true range can be formed.
+        let Some(prev_close) = self.prev_close else {
+            self.prev_close = Some(candle.close);
+            return None;
+        };
+        let tr = candle.true_range(Some(prev_close));
+        self.prev_close = Some(candle.close);
+
+        match self.ema {
+            None => {
+                // Seeding the EMA with the simple average of the first `period`
+                // true ranges; emit nothing until it is established.
+                self.seed_sum += tr;
+                self.seed_count += 1;
+                if self.seed_count == self.period {
+                    self.ema = Some(self.seed_sum / self.period as f64);
+                }
+                None
+            }
+            Some(prev_ema) => {
+                // Denominator excludes the current bar (it is the EMA through the
+                // previous bar). A flat benchmark yields 0.0, not 0/0.
+                let vr = if prev_ema > 0.0 { tr / prev_ema } else { 0.0 };
+                self.ema = Some(self.alpha * tr + (1.0 - self.alpha) * prev_ema);
+                self.last = Some(vr);
+                Some(vr)
+            }
+        }
+    }
+
+    fn reset(&mut self) {
+        self.prev_close = None;
+        self.seed_sum = 0.0;
+        self.seed_count = 0;
+        self.ema = None;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // Bar 1 sets the previous close; bars 2..=period+1 seed the EMA; the
+        // first ratio is emitted on bar period + 2.
+        self.period + 2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "VolatilityRatio"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Build a candle with the given high/low/close (open = low, fixed volume).
+    fn candle(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new_unchecked(low, high, low, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(VolatilityRatio::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let vr = VolatilityRatio::new(14).unwrap();
+        assert_eq!(vr.period(), 14);
+        assert_eq!(vr.warmup_period(), 16);
+        assert_eq!(vr.name(), "VolatilityRatio");
+        assert!(!vr.is_ready());
+        assert_eq!(vr.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut vr = VolatilityRatio::new(3).unwrap();
+        // Build enough constant-range candles to reach warmup.
+        let candles: Vec<Candle> = (0..10)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        let out = vr.batch(&candles);
+        // warmup_period == period + 2 == 5: the first emission is at index 4.
+        let warmup = vr.warmup_period();
+        assert_eq!(warmup, 5);
+        for v in out.iter().take(warmup - 1) {
+            assert!(v.is_none());
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn wide_ranging_day_exceeds_two() {
+        // Steady true range of 2.0 seeds the EMA, then one bar with a far wider
+        // range pushes the ratio above 2.0.
+        let mut vr = VolatilityRatio::new(3).unwrap();
+        let mut candles: Vec<Candle> = (0..6)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base) // TR = 2.0 each
+            })
+            .collect();
+        // A wide bar: range 10 around the last close (~105).
+        candles.push(candle(110.0, 100.0, 105.0));
+        let out = vr.batch(&candles);
+        let last = out.last().unwrap().unwrap();
+        assert!(last > 2.0, "wide-ranging day should exceed 2.0, got {last}");
+    }
+
+    #[test]
+    fn steady_range_ratio_is_one() {
+        // Constant true range -> EMA equals it -> ratio is exactly 1.0.
+        let mut vr = VolatilityRatio::new(3).unwrap();
+        let candles: Vec<Candle> = (0..12)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base) // TR = 2.0 each
+            })
+            .collect();
+        let out = vr.batch(&candles);
+        assert_relative_eq!(out.last().unwrap().unwrap(), 1.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn flat_market_yields_zero() {
+        // Zero-range candles: TR = 0, EMA = 0, ratio guarded to 0.0.
+        let mut vr = VolatilityRatio::new(3).unwrap();
+        let candles: Vec<Candle> = (0..10).map(|_| candle(100.0, 100.0, 100.0)).collect();
+        let out = vr.batch(&candles);
+        for v in out.into_iter().flatten() {
+            assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+        }
+    }
+
+    #[test]
+    fn output_is_non_negative() {
+        let mut vr = VolatilityRatio::new(14).unwrap();
+        let candles: Vec<Candle> = (0..200)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.3).sin() * 12.0;
+                candle(base + 2.0, base - 2.0, base + 0.5)
+            })
+            .collect();
+        for v in vr.batch(&candles).into_iter().flatten() {
+            assert!(v >= 0.0, "volatility ratio must be non-negative, got {v}");
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut vr = VolatilityRatio::new(3).unwrap();
+        let candles: Vec<Candle> = (0..10)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        vr.batch(&candles);
+        assert!(vr.is_ready());
+        vr.reset();
+        assert!(!vr.is_ready());
+        assert_eq!(vr.value(), None);
+        assert_eq!(vr.update(candle(101.0, 99.0, 100.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.25).sin() * 9.0;
+                candle(base + 2.0, base - 1.5, base + 0.5)
+            })
+            .collect();
+        let batch = VolatilityRatio::new(14).unwrap().batch(&candles);
+        let mut b = VolatilityRatio::new(14).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/volume_rsi.rs

@@ -0,0 +1,265 @@
+//! Volume RSI — Wilder's RSI applied to the volume stream.
+
+use crate::error::{Error, Result};
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Volume RSI — the Relative Strength Index computed on **volume** changes
+/// instead of price changes.
+///
+/// Wilder's [`Rsi`](crate::Rsi) measures the balance of up- versus down-*price*
+/// moves; the Volume RSI applies the identical accumulator to the bar-over-bar
+/// change in volume:
+///
+/// ```text
+/// change_t = volume_t − volume_{t−1}
+/// gain     = max(change, 0),  loss = max(−change, 0)
+/// avg_gain, avg_loss = Wilder-smoothed over `period`
+/// VolumeRSI = 100 * avg_gain / (avg_gain + avg_loss)
+/// ```
+///
+/// Readings above `50` mean volume is expanding (more was added than removed over
+/// the smoothing window) and tend to confirm the prevailing move; readings below
+/// `50` mark contracting participation. Output is bounded in `[0, 100]`; a stretch
+/// of unchanged volume drives both averages to `0` and the indicator reports the
+/// neutral `50` rather than an undefined `0 / 0`.
+///
+/// Only the candle's **volume** is used. The first bar sets the previous volume,
+/// then `period` changes seed Wilder's averages, so the first value lands after
+/// `period + 1` inputs. Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, VolumeRsi};
+///
+/// let mut indicator = VolumeRsi::new(14).unwrap();
+/// let mut last = None;
+/// for i in 0..40 {
+///     let v = 1_000.0 + (f64::from(i) * 0.3).sin() * 400.0;
+///     let c = Candle::new(100.0, 101.0, 99.0, 100.5, v, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct VolumeRsi {
+    period: usize,
+    prev_volume: Option<f64>,
+    seed_gains: f64,
+    seed_losses: f64,
+    seed_count: usize,
+    avg_gain: Option<f64>,
+    avg_loss: Option<f64>,
+    last: Option<f64>,
+}
+
+impl VolumeRsi {
+    /// Construct a Volume RSI with the given Wilder smoothing `period`.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `period == 0`.
+    pub fn new(period: usize) -> Result<Self> {
+        if period == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            period,
+            prev_volume: None,
+            seed_gains: 0.0,
+            seed_losses: 0.0,
+            seed_count: 0,
+            avg_gain: None,
+            avg_loss: None,
+            last: None,
+        })
+    }
+
+    /// Configured smoothing period.
+    pub const fn period(&self) -> usize {
+        self.period
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+
+    fn rsi_from_avgs(avg_gain: f64, avg_loss: f64) -> f64 {
+        let denom = avg_gain + avg_loss;
+        if denom == 0.0 {
+            50.0
+        } else {
+            100.0 * (avg_gain / denom)
+        }
+    }
+}
+
+impl Indicator for VolumeRsi {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let volume = candle.volume;
+        let Some(prev) = self.prev_volume else {
+            self.prev_volume = Some(volume);
+            return None;
+        };
+        let change = volume - prev;
+        self.prev_volume = Some(volume);
+        let gain = if change > 0.0 { change } else { 0.0 };
+        let loss = if change < 0.0 { -change } else { 0.0 };
+
+        if let (Some(ag), Some(al)) = (self.avg_gain, self.avg_loss) {
+            let n = self.period as f64;
+            let new_ag = (ag * (n - 1.0) + gain) / n;
+            let new_al = (al * (n - 1.0) + loss) / n;
+            self.avg_gain = Some(new_ag);
+            self.avg_loss = Some(new_al);
+            let v = Self::rsi_from_avgs(new_ag, new_al);
+            self.last = Some(v);
+            return Some(v);
+        }
+
+        self.seed_gains += gain;
+        self.seed_losses += loss;
+        self.seed_count += 1;
+        if self.seed_count == self.period {
+            let n = self.period as f64;
+            let ag = self.seed_gains / n;
+            let al = self.seed_losses / n;
+            self.avg_gain = Some(ag);
+            self.avg_loss = Some(al);
+            let v = Self::rsi_from_avgs(ag, al);
+            self.last = Some(v);
+            return Some(v);
+        }
+        None
+    }
+
+    fn reset(&mut self) {
+        self.prev_volume = None;
+        self.seed_gains = 0.0;
+        self.seed_losses = 0.0;
+        self.seed_count = 0;
+        self.avg_gain = None;
+        self.avg_loss = None;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.period + 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "VolumeRsi"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    /// Candle whose only material field here is `volume`.
+    fn vol_candle(volume: f64) -> Candle {
+        Candle::new_unchecked(100.0, 101.0, 99.0, 100.5, volume, 0)
+    }
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(VolumeRsi::new(0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let v = VolumeRsi::new(14).unwrap();
+        assert_eq!(v.period(), 14);
+        assert_eq!(v.warmup_period(), 15);
+        assert_eq!(v.name(), "VolumeRsi");
+        assert!(!v.is_ready());
+        assert_eq!(v.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut v = VolumeRsi::new(3).unwrap();
+        let candles: Vec<Candle> = (0..6).map(|i| vol_candle(1_000.0 + f64::from(i))).collect();
+        let out = v.batch(&candles);
+        // warmup_period == period + 1 == 4: first emission at index 3.
+        for o in out.iter().take(3) {
+            assert!(o.is_none());
+        }
+        assert!(out[3].is_some());
+    }
+
+    #[test]
+    fn rising_volume_is_one_hundred() {
+        // Every change positive -> avg_loss 0 -> RSI 100.
+        let mut v = VolumeRsi::new(5).unwrap();
+        let candles: Vec<Candle> = (1..=40).map(|i| vol_candle(f64::from(i) * 100.0)).collect();
+        let last = v.batch(&candles).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 100.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn falling_volume_is_zero() {
+        let mut v = VolumeRsi::new(5).unwrap();
+        let candles: Vec<Candle> = (1..=40)
+            .map(|i| vol_candle(5_000.0 - f64::from(i) * 100.0))
+            .collect();
+        let last = v.batch(&candles).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn flat_volume_is_neutral() {
+        // Unchanged volume -> no gains and no losses -> neutral 50.
+        let mut v = VolumeRsi::new(3).unwrap();
+        let candles: Vec<Candle> = (0..20).map(|_| vol_candle(2_000.0)).collect();
+        let last = v.batch(&candles).into_iter().flatten().last().unwrap();
+        assert_relative_eq!(last, 50.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn output_in_range() {
+        let mut v = VolumeRsi::new(14).unwrap();
+        let candles: Vec<Candle> = (0..200)
+            .map(|i| vol_candle(1_000.0 + (f64::from(i) * 0.3).sin() * 600.0))
+            .collect();
+        for o in v.batch(&candles).into_iter().flatten() {
+            assert!((0.0..=100.0).contains(&o));
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut v = VolumeRsi::new(3).unwrap();
+        let candles: Vec<Candle> = (0..20)
+            .map(|i| vol_candle(1_000.0 + f64::from(i)))
+            .collect();
+        v.batch(&candles);
+        assert!(v.is_ready());
+        v.reset();
+        assert!(!v.is_ready());
+        assert_eq!(v.value(), None);
+        assert_eq!(v.update(vol_candle(1_000.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| vol_candle(1_000.0 + (f64::from(i) * 0.25).sin() * 500.0))
+            .collect();
+        let batch = VolumeRsi::new(14).unwrap().batch(&candles);
+        let mut b = VolumeRsi::new(14).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/volume_weighted_macd.rs

@@ -0,0 +1,270 @@
+//! Volume-Weighted MACD — MACD built on volume-weighted moving averages.
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::indicators::vwma::Vwma;
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Output of [`VolumeWeightedMacd`]: the three classic MACD series, but with the
+/// fast and slow averages volume-weighted.
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct VolumeWeightedMacdOutput {
+    /// Fast VWMA − slow VWMA.
+    pub macd: f64,
+    /// EMA of `macd` over the signal period.
+    pub signal: f64,
+    /// `macd − signal`.
+    pub histogram: f64,
+}
+
+/// Volume-Weighted MACD — the MACD oscillator computed from **volume-weighted**
+/// moving averages instead of plain EMAs.
+///
+/// ```text
+/// macd      = VWMA(close, fast) − VWMA(close, slow)
+/// signal    = EMA(macd, signal_period)
+/// histogram = macd − signal
+/// ```
+///
+/// Standard [`MacdIndicator`](crate::MacdIndicator) smooths price with exponential
+/// averages that ignore volume. The volume-weighted variant (Buff Dormeier and
+/// others) replaces each average with a [`Vwma`], so heavy-volume bars dominate
+/// the trend estimate and the oscillator leans toward where real participation
+/// occurred. Crossovers backed by volume therefore appear sooner and noise from
+/// thin bars is damped. The signal line keeps a standard EMA, matching the
+/// classic histogram construction.
+///
+/// `fast` must be strictly smaller than `slow`. The first output lands after
+/// `slow + signal − 1` inputs: `slow` to seed the slow VWMA, then `signal − 1`
+/// more to seed the signal EMA. Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, VolumeWeightedMacd};
+///
+/// let mut indicator = VolumeWeightedMacd::new(12, 26, 9).unwrap();
+/// let mut last = None;
+/// for i in 0..80 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base + 0.5, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct VolumeWeightedMacd {
+    fast: Vwma,
+    slow: Vwma,
+    signal_ema: Ema,
+    fast_period: usize,
+    slow_period: usize,
+    signal_period: usize,
+    last: Option<VolumeWeightedMacdOutput>,
+}
+
+impl VolumeWeightedMacd {
+    /// Construct a volume-weighted MACD with the given periods.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if any period is zero, and
+    /// [`Error::InvalidPeriod`] if `fast >= slow`.
+    pub fn new(fast: usize, slow: usize, signal: usize) -> Result<Self> {
+        if fast == 0 || slow == 0 || signal == 0 {
+            return Err(Error::PeriodZero);
+        }
+        if fast >= slow {
+            return Err(Error::InvalidPeriod {
+                message: "fast period must be strictly less than slow period",
+            });
+        }
+        Ok(Self {
+            fast: Vwma::new(fast)?,
+            slow: Vwma::new(slow)?,
+            signal_ema: Ema::new(signal)?,
+            fast_period: fast,
+            slow_period: slow,
+            signal_period: signal,
+            last: None,
+        })
+    }
+
+    /// Configured periods as `(fast, slow, signal)`.
+    pub const fn periods(&self) -> (usize, usize, usize) {
+        (self.fast_period, self.slow_period, self.signal_period)
+    }
+
+    /// Most recent fully-computed output if available.
+    pub const fn value(&self) -> Option<VolumeWeightedMacdOutput> {
+        self.last
+    }
+}
+
+impl Indicator for VolumeWeightedMacd {
+    type Input = Candle;
+    type Output = VolumeWeightedMacdOutput;
+
+    fn update(&mut self, candle: Candle) -> Option<VolumeWeightedMacdOutput> {
+        let fast = self.fast.update(candle);
+        let slow = self.slow.update(candle);
+        if let (Some(f), Some(s)) = (fast, slow) {
+            let macd = f - s;
+            let signal = self.signal_ema.update(macd)?;
+            let out = VolumeWeightedMacdOutput {
+                macd,
+                signal,
+                histogram: macd - signal,
+            };
+            self.last = Some(out);
+            return Some(out);
+        }
+        None
+    }
+
+    fn reset(&mut self) {
+        self.fast.reset();
+        self.slow.reset();
+        self.signal_ema.reset();
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        self.slow_period + self.signal_period - 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "VolumeWeightedMacd"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(close: f64, volume: f64) -> Candle {
+        Candle::new_unchecked(close, close, close, close, volume, 0)
+    }
+
+    #[test]
+    fn rejects_invalid_periods() {
+        assert!(matches!(
+            VolumeWeightedMacd::new(0, 26, 9),
+            Err(Error::PeriodZero)
+        ));
+        assert!(matches!(
+            VolumeWeightedMacd::new(26, 12, 9),
+            Err(Error::InvalidPeriod { .. })
+        ));
+        assert!(matches!(
+            VolumeWeightedMacd::new(12, 12, 9),
+            Err(Error::InvalidPeriod { .. })
+        ));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let m = VolumeWeightedMacd::new(12, 26, 9).unwrap();
+        assert_eq!(m.periods(), (12, 26, 9));
+        assert_eq!(m.warmup_period(), 34);
+        assert_eq!(m.name(), "VolumeWeightedMacd");
+        assert!(!m.is_ready());
+        assert_eq!(m.value(), None);
+    }
+
+    #[test]
+    fn first_emission_at_warmup_period() {
+        let mut m = VolumeWeightedMacd::new(2, 4, 3).unwrap();
+        let candles: Vec<Candle> = (0..20)
+            .map(|i| candle(100.0 + f64::from(i), 1_000.0))
+            .collect();
+        let out = m.batch(&candles);
+        let warmup = m.warmup_period(); // 4 + 3 - 1 = 6
+        assert_eq!(warmup, 6);
+        for v in out.iter().take(warmup - 1) {
+            assert!(v.is_none());
+        }
+        assert!(out[warmup - 1].is_some());
+    }
+
+    #[test]
+    fn uptrend_has_positive_macd() {
+        // A steady advance with equal volume -> fast VWMA leads slow -> macd > 0.
+        let mut m = VolumeWeightedMacd::new(3, 6, 3).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| candle(100.0 + f64::from(i), 1_000.0))
+            .collect();
+        let last = m.batch(&candles).into_iter().flatten().last().unwrap();
+        assert!(
+            last.macd > 0.0,
+            "uptrend should give positive macd, got {}",
+            last.macd
+        );
+    }
+
+    #[test]
+    fn histogram_is_macd_minus_signal() {
+        let mut m = VolumeWeightedMacd::new(3, 6, 3).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| {
+                candle(
+                    100.0 + (f64::from(i) * 0.3).sin() * 5.0,
+                    1_000.0 + f64::from(i),
+                )
+            })
+            .collect();
+        for o in m.batch(&candles).into_iter().flatten() {
+            assert_relative_eq!(o.histogram, o.macd - o.signal, epsilon = 1e-9);
+        }
+    }
+
+    #[test]
+    fn equal_volume_matches_plain_macd() {
+        // With constant volume, VWMA reduces to SMA, so volume-weighted MACD uses
+        // SMA-based lines; it should still be a well-defined finite series.
+        let mut m = VolumeWeightedMacd::new(3, 6, 3).unwrap();
+        let candles: Vec<Candle> = (0..60)
+            .map(|i| candle(100.0 + (f64::from(i) * 0.2).sin() * 4.0, 2_000.0))
+            .collect();
+        for o in m.batch(&candles).into_iter().flatten() {
+            assert!(o.macd.is_finite() && o.signal.is_finite());
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut m = VolumeWeightedMacd::new(3, 6, 3).unwrap();
+        let candles: Vec<Candle> = (0..40)
+            .map(|i| candle(100.0 + f64::from(i), 1_000.0))
+            .collect();
+        m.batch(&candles);
+        assert!(m.is_ready());
+        m.reset();
+        assert!(!m.is_ready());
+        assert_eq!(m.value(), None);
+        assert_eq!(m.update(candle(100.0, 1_000.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..120)
+            .map(|i| {
+                candle(
+                    100.0 + (f64::from(i) * 0.25).sin() * 9.0,
+                    1_000.0 + f64::from(i),
+                )
+            })
+            .collect();
+        let batch = VolumeWeightedMacd::new(12, 26, 9).unwrap().batch(&candles);
+        let mut b = VolumeWeightedMacd::new(12, 26, 9).unwrap();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/wad.rs

@@ -0,0 +1,206 @@
+//! Williams Accumulation/Distribution (WAD) — Larry Williams' cumulative line.
+
+use crate::ohlcv::Candle;
+use crate::traits::Indicator;
+
+/// Williams Accumulation/Distribution — a cumulative price-only line that adds
+/// the day's accumulation on up-closes and subtracts the day's distribution on
+/// down-closes.
+///
+/// ```text
+/// if close > prev_close:  AD =  close − min(low,  prev_close)   (true low)
+/// if close < prev_close:  AD =  close − max(high, prev_close)   (true high)
+/// if close = prev_close:  AD =  0
+/// WAD_t = WAD_{t−1} + AD
+/// ```
+///
+/// Larry Williams' A/D line (distinct from Chaikin's volume-based
+/// [`Adl`](crate::Adl)) uses **no volume at all** — it measures accumulation as
+/// how far price closed above the *true low* on up-days and distribution as how
+/// far it closed below the *true high* on down-days, then accumulates the result.
+/// A rising WAD that diverges from a flat or falling price is the classic
+/// accumulation signal; a falling WAD under a rising price warns of distribution.
+///
+/// The line is unbounded and its absolute level is meaningless — only its slope
+/// and divergences against price matter. The first candle has no previous close,
+/// so it seeds the reference and emits nothing; thereafter every bar emits the
+/// running total. Each `update` is O(1).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Candle, Indicator, Wad};
+///
+/// let mut indicator = Wad::new();
+/// let mut last = None;
+/// for i in 0..20 {
+///     let base = 100.0 + f64::from(i);
+///     let c = Candle::new(base, base + 1.0, base - 1.0, base + 0.5, 1_000.0, 0).unwrap();
+///     last = indicator.update(c);
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone, Default)]
+pub struct Wad {
+    prev_close: Option<f64>,
+    line: f64,
+    last: Option<f64>,
+}
+
+impl Wad {
+    /// Construct a new Williams A/D line. The line is parameter-free.
+    #[must_use]
+    pub fn new() -> Self {
+        Self::default()
+    }
+
+    /// Current value if available.
+    pub const fn value(&self) -> Option<f64> {
+        self.last
+    }
+}
+
+impl Indicator for Wad {
+    type Input = Candle;
+    type Output = f64;
+
+    fn update(&mut self, candle: Candle) -> Option<f64> {
+        let Some(prev_close) = self.prev_close else {
+            self.prev_close = Some(candle.close);
+            return None;
+        };
+        let ad = if candle.close > prev_close {
+            candle.close - candle.low.min(prev_close)
+        } else if candle.close < prev_close {
+            candle.close - candle.high.max(prev_close)
+        } else {
+            0.0
+        };
+        self.line += ad;
+        self.prev_close = Some(candle.close);
+        self.last = Some(self.line);
+        Some(self.line)
+    }
+
+    fn reset(&mut self) {
+        self.prev_close = None;
+        self.line = 0.0;
+        self.last = None;
+    }
+
+    fn warmup_period(&self) -> usize {
+        // The first bar only seeds the reference close; the first value lands on
+        // the second bar.
+        2
+    }
+
+    fn is_ready(&self) -> bool {
+        self.last.is_some()
+    }
+
+    fn name(&self) -> &'static str {
+        "Wad"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    fn candle(high: f64, low: f64, close: f64) -> Candle {
+        Candle::new_unchecked(low, high, low, close, 1_000.0, 0)
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let wad = Wad::new();
+        assert_eq!(wad.warmup_period(), 2);
+        assert_eq!(wad.name(), "Wad");
+        assert!(!wad.is_ready());
+        assert_eq!(wad.value(), None);
+    }
+
+    #[test]
+    fn first_bar_seeds_without_output() {
+        let mut wad = Wad::new();
+        assert_eq!(wad.update(candle(101.0, 99.0, 100.0)), None);
+        assert!(wad.update(candle(102.0, 100.0, 101.0)).is_some());
+    }
+
+    #[test]
+    fn up_close_accumulates() {
+        // close rises from 100 -> 101; true low = min(low, prev_close) = min(100,100)=100;
+        // AD = 101 - 100 = 1.
+        let mut wad = Wad::new();
+        wad.update(candle(101.0, 99.0, 100.0));
+        let v = wad.update(candle(102.0, 100.0, 101.0)).unwrap();
+        assert_relative_eq!(v, 1.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn down_close_distributes() {
+        // close falls 100 -> 99; true high = max(high, prev_close) = max(101,100)=101;
+        // AD = 99 - 101 = -2.
+        let mut wad = Wad::new();
+        wad.update(candle(102.0, 100.0, 100.0));
+        let v = wad.update(candle(101.0, 98.0, 99.0)).unwrap();
+        assert_relative_eq!(v, -2.0, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn unchanged_close_adds_nothing() {
+        let mut wad = Wad::new();
+        wad.update(candle(101.0, 99.0, 100.0));
+        let v = wad.update(candle(105.0, 95.0, 100.0)).unwrap();
+        assert_relative_eq!(v, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn pure_uptrend_is_monotone() {
+        let mut wad = Wad::new();
+        let candles: Vec<Candle> = (0..30)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        let mut prev = f64::NEG_INFINITY;
+        for v in wad.batch(&candles).into_iter().flatten() {
+            assert!(v >= prev, "WAD must rise in an uptrend");
+            prev = v;
+        }
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut wad = Wad::new();
+        let candles: Vec<Candle> = (0..10)
+            .map(|i| {
+                let base = 100.0 + f64::from(i);
+                candle(base + 1.0, base - 1.0, base)
+            })
+            .collect();
+        wad.batch(&candles);
+        assert!(wad.is_ready());
+        wad.reset();
+        assert!(!wad.is_ready());
+        assert_eq!(wad.value(), None);
+        assert_eq!(wad.update(candle(101.0, 99.0, 100.0)), None);
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let candles: Vec<Candle> = (0..80)
+            .map(|i| {
+                let base = 100.0 + (f64::from(i) * 0.3).sin() * 8.0;
+                candle(base + 2.0, base - 2.0, base + 0.5)
+            })
+            .collect();
+        let batch = Wad::new().batch(&candles);
+        let mut b = Wad::new();
+        let streamed: Vec<_> = candles.iter().map(|c| b.update(*c)).collect();
+        assert_eq!(batch, streamed);
+    }
+}

crates/wickra-core/src/indicators/wave_pm.rs

@@ -0,0 +1,212 @@
+//! Wave PM — Cynthia Kase's peak-momentum statistic (Wickra reconstruction).
+
+use std::collections::VecDeque;
+
+use crate::error::{Error, Result};
+use crate::indicators::ema::Ema;
+use crate::traits::Indicator;
+
+/// Wave PM (Peak Momentum): a `0..100` statistic that rises when the current
+/// `length`-bar momentum is large relative to its own recent energy — Cynthia
+/// Kase's gauge of how "peaked" the move is.
+///
+/// ```text
+/// m       = close_t - close_{t-length}                  (length-bar momentum)
+/// energy  = EMA(m^2, length)                            (mean squared momentum)
+/// raw     = 1 - exp( -m^2 / (2 * energy) )      (0 if energy == 0)
+/// WavePM  = 100 * EMA(raw, smoothing)
+/// ```
+///
+/// The momentum `m` is normalised by its recent variance (`energy`): a move that
+/// merely matches its typical energy sits at the baseline
+/// `100·(1 − e^{−1/2}) ≈ 39.35`, while a momentum *spike* that exceeds recent
+/// energy drives the reading toward `100`. A flat market (`m = 0`) reads `0`.
+/// High readings mark a peaking, possibly exhausted move rather than a fresh one.
+///
+/// Kase's published `WavePM` is platform-specific; this is Wickra's faithful
+/// reconstruction of its variance-normalised peak-momentum form. The exact
+/// constants differ from any single vendor implementation, but the shape — flat
+/// at zero, a fixed baseline on a steady trend, and saturation on an
+/// acceleration — matches the indicator's intent.
+///
+/// Reference: Cynthia Kase, *Trading with the Odds*, 1996 (Wickra reconstruction).
+///
+/// # Example
+///
+/// ```
+/// use wickra_core::{Indicator, WavePm};
+///
+/// let mut indicator = WavePm::new(10, 3).unwrap();
+/// let mut last = None;
+/// for i in 0..60 {
+///     last = indicator.update(100.0 + f64::from(i));
+/// }
+/// assert!(last.is_some());
+/// ```
+#[derive(Debug, Clone)]
+pub struct WavePm {
+    length: usize,
+    smoothing: usize,
+    closes: VecDeque<f64>,
+    energy_ema: Ema,
+    smooth_ema: Ema,
+}
+
+impl WavePm {
+    /// Construct a Wave PM with the momentum `length` and the output `smoothing`
+    /// period.
+    ///
+    /// # Errors
+    ///
+    /// Returns [`Error::PeriodZero`] if `length == 0` or `smoothing == 0`.
+    pub fn new(length: usize, smoothing: usize) -> Result<Self> {
+        if length == 0 {
+            return Err(Error::PeriodZero);
+        }
+        Ok(Self {
+            length,
+            smoothing,
+            closes: VecDeque::with_capacity(length + 1),
+            energy_ema: Ema::new(length)?,
+            smooth_ema: Ema::new(smoothing)?,
+        })
+    }
+
+    /// Configured `(length, smoothing)`.
+    pub const fn periods(&self) -> (usize, usize) {
+        (self.length, self.smoothing)
+    }
+}
+
+impl Indicator for WavePm {
+    type Input = f64;
+    type Output = f64;
+
+    fn update(&mut self, close: f64) -> Option<f64> {
+        self.closes.push_back(close);
+        if self.closes.len() > self.length + 1 {
+            self.closes.pop_front();
+        }
+        if self.closes.len() <= self.length {
+            return None;
+        }
+
+        let oldest = *self.closes.front().unwrap_or(&close);
+        let momentum = close - oldest;
+        let energy = self.energy_ema.update(momentum * momentum)?;
+        let raw = if energy <= 0.0 {
+            0.0
+        } else {
+            1.0 - (-(momentum * momentum) / (2.0 * energy)).exp()
+        };
+        self.smooth_ema.update(raw).map(|v| v * 100.0)
+    }
+
+    fn reset(&mut self) {
+        self.closes.clear();
+        self.energy_ema.reset();
+        self.smooth_ema.reset();
+    }
+
+    fn warmup_period(&self) -> usize {
+        2 * self.length + self.smoothing - 1
+    }
+
+    fn is_ready(&self) -> bool {
+        self.smooth_ema.is_ready()
+    }
+
+    fn name(&self) -> &'static str {
+        "WavePm"
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::traits::BatchExt;
+    use approx::assert_relative_eq;
+
+    #[test]
+    fn rejects_zero_period() {
+        assert!(matches!(WavePm::new(0, 3), Err(Error::PeriodZero)));
+        assert!(matches!(WavePm::new(10, 0), Err(Error::PeriodZero)));
+    }
+
+    #[test]
+    fn accessors_and_metadata() {
+        let w = WavePm::new(10, 3).unwrap();
+        assert_eq!(w.periods(), (10, 3));
+        // 2*10 + 3 - 1 = 22.
+        assert_eq!(w.warmup_period(), 22);
+        assert_eq!(w.name(), "WavePm");
+        assert!(!w.is_ready());
+    }
+
+    #[test]
+    fn warmup_emits_at_expected_bar() {
+        let mut w = WavePm::new(3, 2).unwrap();
+        // warmup = 2*3 + 2 - 1 = 7 -> first value at input 7 (index 6).
+        let inputs: Vec<f64> = (0..12).map(f64::from).collect();
+        let out = w.batch(&inputs);
+        assert!(out[5].is_none());
+        assert!(out[6].is_some());
+    }
+
+    #[test]
+    fn flat_market_reads_zero() {
+        let mut w = WavePm::new(4, 2).unwrap();
+        let inputs = [50.0; 20];
+        let last = w.batch(&inputs).last().unwrap().unwrap();
+        assert_relative_eq!(last, 0.0, epsilon = 1e-12);
+    }
+
+    #[test]
+    fn steady_trend_reads_baseline() {
+        // Constant-slope ramp: momentum equals its own energy every bar, so the
+        // reading pins to the baseline 100*(1 - e^-0.5).
+        let mut w = WavePm::new(10, 3).unwrap();
+        let inputs: Vec<f64> = (0..60).map(|i| f64::from(i) * 5.0).collect();
+        let last = w.batch(&inputs).last().unwrap().unwrap();
+        let baseline = 100.0 * (1.0 - (-0.5_f64).exp());
+        assert_relative_eq!(last, baseline, epsilon = 1e-9);
+    }
+
+    #[test]
+    fn acceleration_reads_above_baseline() {
+        // A quadratic path: momentum keeps outrunning its lagged energy, so the
+        // reading sits above the steady-trend baseline.
+        let mut w = WavePm::new(10, 3).unwrap();
+        let inputs: Vec<f64> = (0..60).map(|i| f64::from(i * i) * 0.1).collect();
+        let last = w.batch(&inputs).last().unwrap().unwrap();
+        let baseline = 100.0 * (1.0 - (-0.5_f64).exp());
+        assert!(
+            last > baseline,
+            "accelerating wpm {last} should exceed {baseline}"
+        );
+        assert!(last <= 100.0, "wpm {last} must stay <= 100");
+    }
+
+    #[test]
+    fn reset_clears_state() {
+        let mut w = WavePm::new(10, 3).unwrap();
+        let inputs: Vec<f64> = (0..60).map(|i| f64::from(i) * 5.0).collect();
+        w.batch(&inputs);
+        assert!(w.is_ready());
+        w.reset();
+        assert!(!w.is_ready());
+    }
+
+    #[test]
+    fn batch_equals_streaming() {
+        let inputs: Vec<f64> = (0..80)
+            .map(|i| 100.0 + (f64::from(i) * 0.2).sin() * 5.0)
+            .collect();
+        let mut a = WavePm::new(10, 3).unwrap();
+        let mut b = WavePm::new(10, 3).unwrap();
+        assert_eq!(
+            a.batch(&inputs),
+            inputs.iter().map(|x| b.update(*x)).collect::<Vec<_>>()
+        );
+    }
+}

crates/wickra-core/src/lib.rs

@@ -57,82 +57,95 @@ pub use derivatives::DerivativesTick;
 pub use error::{Error, Result};
 pub use indicators::{
     AbandonedBaby, Abcd, AbsoluteBreadthIndex, AccelerationBands, AccelerationBandsOutput,
-    AcceleratorOscillator, AdOscillator, AdVolumeLine, AdaptiveCycle, Adl, AdvanceBlock,
-    AdvanceDecline, AdvanceDeclineRatio, Adx, AdxOutput, Adxr, Alligator, AlligatorOutput, Alma,
-    Alpha, AmihudIlliquidity, AnchoredRsi, AnchoredVwap, Apo, Aroon, AroonOscillator, AroonOutput,
-    Atr, AtrBands, AtrBandsOutput, AtrTrailingStop, AutoFib, AutoFibOutput, Autocorrelation,
-    AverageDailyRange, AverageDrawdown, AvgPrice, AwesomeOscillator, AwesomeOscillatorHistogram,
-    BalanceOfPower, Bat, BeltHold, Beta, BetaNeutralSpread, BodySizePct, BollingerBands,
-    BollingerBandwidth, BollingerOutput, BreadthThrust, Breakaway, BullishPercentIndex, Butterfly,
-    CalendarSpread, CalmarRatio, Camarilla, CamarillaPivotsOutput, Cci, CenterOfGravity, Cfo,
-    ChaikinMoneyFlow, ChaikinOscillator, ChaikinVolatility, ChandeKrollStop, ChandeKrollStopOutput,
-    ChandelierExit, ChandelierExitOutput, ChoppinessIndex, ClassicPivots, ClassicPivotsOutput,
-    CloseVsOpen, ClosingMarubozu, Cmo, CoefficientOfVariation, Cointegration, CointegrationOutput,
-    ConcealingBabySwallow, ConditionalValueAtRisk, ConnorsRsi, Coppock, Counterattack, Crab,
-    CumulativeVolumeDelta, CumulativeVolumeIndex, CupAndHandle, CyberneticCycle, Cypher,
-    DayOfWeekProfile, DayOfWeekProfileOutput, Decycler, DecyclerOscillator, Dema, DemandIndex,
-    DemarkPivots, DemarkPivotsOutput, DepthSlope, DetrendedStdDev, DistanceSsd, Doji, DojiStar,
-    Donchian, DonchianOutput, DonchianStop, DonchianStopOutput, DoubleBollinger,
-    DoubleBollingerOutput, DoubleTopBottom, DownsideGapThreeMethods, Dpo, DragonflyDoji,
-    DrawdownDuration, Dx, EaseOfMovement, EffectiveSpread, EhlersStochastic, ElderImpulse, Ema,
-    EmpiricalModeDecomposition, Engulfing, EveningDojiStar, Evwma, Expectancy, FallingThreeMethods,
-    Fama, FibArcs, FibArcsOutput, FibChannel, FibChannelOutput, FibConfluence, FibConfluenceOutput,
+    AcceleratorOscillator, AdOscillator, AdVolumeLine, AdaptiveCycle, AdaptiveLaguerreFilter, Adl,
+    AdvanceBlock, AdvanceDecline, AdvanceDeclineRatio, Adx, AdxOutput, Adxr, Alligator,
+    AlligatorOutput, Alma, Alpha, AmihudIlliquidity, AnchoredRsi, AnchoredVwap, Apo, Aroon,
+    AroonOscillator, AroonOutput, Atr, AtrBands, AtrBandsOutput, AtrRatchet, AtrRatchetOutput,
+    AtrTrailingStop, AutoFib, AutoFibOutput, Autocorrelation, AverageDailyRange, AverageDrawdown,
+    AvgPrice, AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower, Bat, BeltHold, Beta,
+    BetaNeutralSpread, BetterVolume, BipowerVariation, BodySizePct, BollingerBands,
+    BollingerBandwidth, BollingerOutput, BomarBands, BomarBandsOutput, BreadthThrust, Breakaway,
+    BullishPercentIndex, Butterfly, CalendarSpread, CalmarRatio, Camarilla, CamarillaPivotsOutput,
+    Cci, CenterOfGravity, Cfo, ChaikinMoneyFlow, ChaikinOscillator, ChaikinVolatility,
+    ChandeKrollStop, ChandeKrollStopOutput, ChandelierExit, ChandelierExitOutput, ChoppinessIndex,
+    ClassicPivots, ClassicPivotsOutput, CloseVsOpen, ClosingMarubozu, Cmo, CoefficientOfVariation,
+    Cointegration, CointegrationOutput, ConcealingBabySwallow, ConditionalValueAtRisk, ConnorsRsi,
+    Coppock, Counterattack, Crab, CumulativeVolumeDelta, CumulativeVolumeIndex, CupAndHandle,
+    CyberneticCycle, Cypher, DayOfWeekProfile, DayOfWeekProfileOutput, Decycler,
+    DecyclerOscillator, Dema, DemandIndex, DemarkPivots, DemarkPivotsOutput, DepthSlope,
+    DerivativeOscillator, DetrendedStdDev, DisparityIndex, DistanceSsd, Doji, DojiStar, Donchian,
+    DonchianOutput, DonchianStop, DonchianStopOutput, DoubleBollinger, DoubleBollingerOutput,
+    DoubleTopBottom, DownsideGapThreeMethods, Dpo, DragonflyDoji, DrawdownDuration, Dx,
+    DynamicMomentumIndex, EaseOfMovement, EffectiveSpread, EhlersStochastic, Ehma, ElderImpulse,
+    ElderRay, ElderRayOutput, ElderSafeZone, ElderSafeZoneOutput, Ema, EmpiricalModeDecomposition,
+    Engulfing, EveningDojiStar, Evwma, EwmaVolatility, Expectancy, FallingThreeMethods, Fama,
+    FibArcs, FibArcsOutput, FibChannel, FibChannelOutput, FibConfluence, FibConfluenceOutput,
     FibExtension, FibExtensionOutput, FibFan, FibFanOutput, FibProjection, FibProjectionOutput,
     FibRetracement, FibRetracementOutput, FibTimeZones, FibTimeZonesOutput, FibonacciPivots,
-    FibonacciPivotsOutput, FisherTransform, FlagPennant, Footprint, FootprintOutput, ForceIndex,
-    FractalChaosBands, FractalChaosBandsOutput, Frama, FundingBasis, FundingRate, FundingRateMean,
-    FundingRateZScore, GainLossRatio, GapSideBySideWhite, GarmanKlassVolatility, Gartley,
-    GoldenPocket, GoldenPocketOutput, GrangerCausality, GravestoneDoji, Hammer, HangingMan, Harami,
-    HeadAndShoulders, HeikinAshi, HeikinAshiOutput, HiLoActivator, HighLowIndex, HighLowRange,
-    HighWave, Hikkake, HikkakeModified, HilbertDominantCycle, HistoricalVolatility, Hma,
-    HomingPigeon, HtDcPhase, HtPhasor, HtPhasorOutput, HtTrendMode, HurstChannel,
-    HurstChannelOutput, HurstExponent, Ichimoku, IchimokuOutput, IdenticalThreeCrows, InNeck,
-    Inertia, InformationRatio, InitialBalance, InitialBalanceOutput, InstantaneousTrendline,
-    IntradayVolatilityProfile, IntradayVolatilityProfileOutput, InverseFisherTransform,
-    InvertedHammer, Jma, JumpIndicator, KagiBars, KalmanHedgeRatio, KalmanHedgeRatioOutput, Kama,
-    KellyCriterion, Keltner, KeltnerOutput, Kicking, KickingByLength, Kst, KstOutput, Kurtosis,
-    Kvo, KylesLambda, LadderBottom, LaguerreRsi, LeadLagCrossCorrelation,
-    LeadLagCrossCorrelationOutput, LinRegAngle, LinRegChannel, LinRegChannelOutput,
-    LinRegIntercept, LinRegSlope, LinearRegression, LiquidationFeatures, LiquidationFeaturesOutput,
-    LogReturn, LongLeggedDoji, LongLine, LongShortRatio, MaEnvelope, MaEnvelopeOutput, MacdExt,
-    MacdFix, MacdIndicator, MacdOutput, Mama, MamaOutput, MarketFacilitationIndex, Marubozu,
-    MassIndex, MatHold, MatchingLow, MaxDrawdown, McClellanOscillator, McClellanSummationIndex,
-    McGinleyDynamic, MedianAbsoluteDeviation, MedianPrice, Mfi, Microprice, MidPoint, MidPrice,
-    MinusDi, MinusDm, Mom, MorningDojiStar, MorningEveningStar, Natr, NewHighsNewLows, Nvi,
-    OIPriceDivergence, OIWeighted, Obv, OmegaRatio, OnNeck, OpenInterestDelta, OpeningMarubozu,
-    OpeningRange, OpeningRangeOutput, OrderBookImbalanceFull, OrderBookImbalanceTop1,
-    OrderBookImbalanceTopN, OrderFlowImbalance, OuHalfLife, OvernightGap, OvernightIntradayReturn,
-    OvernightIntradayReturnOutput, PainIndex, PairSpreadZScore, PairwiseBeta, ParkinsonVolatility,
-    PearsonCorrelation, PercentAboveMa, PercentB, PercentageTrailingStop, Pgo, PiercingDarkCloud,
-    PlusDi, PlusDm, Pmo, PointAndFigureBars, Ppo, ProfitFactor, Psar, Pvi, QuotedSpread, RSquared,
-    RealizedSpread, RealizedVolatility, RecoveryFactor, RectangleRange, RegimeLabel,
-    RelativeStrengthAB, RelativeStrengthOutput, RenkoBars, RenkoTrailingStop, RickshawMan,
-    RisingThreeMethods, Roc, Rocp, Rocr, Rocr100, RogersSatchellVolatility, RollMeasure,
-    RollingCorrelation, RollingCovariance, RollingIqr, RollingPercentileRank, RollingQuantile,
-    RollingVwap, RoofingFilter, Rsi, Rvi, RviVolatility, Rwi, RwiOutput, SarExt, SeasonalZScore,
+    FibonacciPivotsOutput, FisherRsi, FisherTransform, FlagPennant, Footprint, FootprintOutput,
+    ForceIndex, FractalChaosBands, FractalChaosBandsOutput, Frama, FundingBasis, FundingRate,
+    FundingRateMean, FundingRateZScore, GainLossRatio, GapSideBySideWhite, Garch11,
+    GarmanKlassVolatility, Gartley, GatorOscillator, GatorOscillatorOutput, GeneralizedDema,
+    GeometricMa, GoldenPocket, GoldenPocketOutput, GrangerCausality, GravestoneDoji, Hammer,
+    HangingMan, Harami, HeadAndShoulders, HeikinAshi, HeikinAshiOutput, HiLoActivator,
+    HighLowIndex, HighLowRange, HighWave, Hikkake, HikkakeModified, HilbertDominantCycle,
+    HistoricalVolatility, Hma, HoltWinters, HomingPigeon, HtDcPhase, HtPhasor, HtPhasorOutput,
+    HtTrendMode, HurstChannel, HurstChannelOutput, HurstExponent, Ichimoku, IchimokuOutput,
+    IdenticalThreeCrows, InNeck, Inertia, InformationRatio, InitialBalance, InitialBalanceOutput,
+    InstantaneousTrendline, IntradayIntensity, IntradayMomentumIndex, IntradayVolatilityProfile,
+    IntradayVolatilityProfileOutput, InverseFisherTransform, InvertedHammer, JarqueBera, Jma,
+    JumpIndicator, KagiBars, KalmanHedgeRatio, KalmanHedgeRatioOutput, Kama, KaseDevStop,
+    KaseDevStopOutput, KasePermissionStochastic, KasePermissionStochasticOutput, KellyCriterion,
+    Keltner, KeltnerOutput, KendallTau, Kicking, KickingByLength, Kst, KstOutput, Kurtosis, Kvo,
+    KylesLambda, LadderBottom, LaguerreRsi, LeadLagCrossCorrelation, LeadLagCrossCorrelationOutput,
+    LinRegAngle, LinRegChannel, LinRegChannelOutput, LinRegIntercept, LinRegSlope,
+    LinearRegression, LiquidationFeatures, LiquidationFeaturesOutput, LogReturn, LongLeggedDoji,
+    LongLine, LongShortRatio, MaEnvelope, MaEnvelopeOutput, MacdExt, MacdFix, MacdHistogram,
+    MacdIndicator, MacdOutput, Mama, MamaOutput, MarketFacilitationIndex, Marubozu, MassIndex,
+    MatHold, MatchingLow, MaxDrawdown, McClellanOscillator, McClellanSummationIndex,
+    McGinleyDynamic, MedianAbsoluteDeviation, MedianChannel, MedianChannelOutput, MedianMa,
+    MedianPrice, Mfi, Microprice, MidPoint, MidPrice, MinusDi, MinusDm, ModifiedMaStop,
+    ModifiedMaStopOutput, Mom, MorningDojiStar, MorningEveningStar, Natr, NewHighsNewLows, Nrtr,
+    NrtrOutput, Nvi, OIPriceDivergence, OIWeighted, Obv, OmegaRatio, OnNeck, OpenInterestDelta,
+    OpeningMarubozu, OpeningRange, OpeningRangeOutput, OrderBookImbalanceFull,
+    OrderBookImbalanceTop1, OrderBookImbalanceTopN, OrderFlowImbalance, OuHalfLife, OvernightGap,
+    OvernightIntradayReturn, OvernightIntradayReturnOutput, PainIndex, PairSpreadZScore,
+    PairwiseBeta, ParkinsonVolatility, PearsonCorrelation, PercentAboveMa, PercentB,
+    PercentageTrailingStop, Pgo, PiercingDarkCloud, PlusDi, PlusDm, Pmo, PointAndFigureBars,
+    PolarizedFractalEfficiency, Ppo, PpoHistogram, ProfitFactor, ProjectionBands,
+    ProjectionBandsOutput, ProjectionOscillator, Psar, Pvi, Qqe, QqeOutput, Qstick, QuartileBands,
+    QuartileBandsOutput, QuotedSpread, RSquared, RealizedSpread, RealizedVolatility,
+    RecoveryFactor, RectangleRange, RegimeLabel, RelativeStrengthAB, RelativeStrengthOutput,
+    RenkoBars, RenkoTrailingStop, RickshawMan, RisingThreeMethods, Rmi, Roc, Rocp, Rocr, Rocr100,
+    RogersSatchellVolatility, RollMeasure, RollingCorrelation, RollingCovariance, RollingIqr,
+    RollingMinMaxScaler, RollingPercentileRank, RollingQuantile, RollingVwap, RoofingFilter, Rsi,
+    Rsx, Rvi, RviVolatility, Rwi, RwiOutput, SampleEntropy, SarExt, SeasonalZScore,
     SeparatingLines, SessionHighLow, SessionHighLowOutput, SessionRange, SessionRangeOutput,
-    SessionVwap, Shark, SharpeRatio, ShootingStar, ShortLine, SignedVolume, SineWave, Skewness,
-    Sma, Smi, Smma, SortinoRatio, SpearmanCorrelation, SpinningTop, SpreadAr1Coefficient,
-    SpreadBollingerBands, SpreadBollingerBandsOutput, SpreadHurst, StalledPattern, StandardError,
-    StandardErrorBands, StandardErrorBandsOutput, StarcBands, StarcBandsOutput, Stc, StdDev,
-    StepTrailingStop, StickSandwich, StochRsi, Stochastic, StochasticOutput, SuperSmoother,
-    SuperTrend, SuperTrendOutput, TakerBuySellRatio, Takuri, TasukiGap, TdCombo, TdCountdown,
-    TdDeMarker, TdDifferential, TdLines, TdLinesOutput, TdOpen, TdPressure, TdRangeProjection,
-    TdRangeProjectionOutput, TdRei, TdRiskLevel, TdRiskLevelOutput, TdSequential,
-    TdSequentialOutput, TdSetup, Tema, TermStructureBasis, ThreeDrives, ThreeInside,
-    ThreeLineStrike, ThreeOutside, ThreeSoldiersOrCrows, ThreeStarsInSouth, Thrusting, TickIndex,
-    Tii, TimeOfDayReturnProfile, TimeOfDayReturnProfileOutput, TpoProfile, TpoProfileOutput,
-    TradeImbalance, TrendLabel, TreynorRatio, Triangle, Trima, Trin, TripleTopBottom, Trix,
-    TrueRange, Tsf, Tsi, Tsv, TtmSqueeze, TtmSqueezeOutput, TurnOfMonth, Tweezer, TwoCrows,
-    TypicalPrice, UlcerIndex, UltimateOscillator, UniqueThreeRiver, UpDownVolumeRatio,
-    UpsideGapThreeMethods, UpsideGapTwoCrows, ValueArea, ValueAreaOutput, ValueAtRisk, Variance,
-    VarianceRatio, VerticalHorizontalFilter, Vidya, VoltyStop, VolumeByTimeProfile,
-    VolumeByTimeProfileOutput, VolumeOscillator, VolumePriceTrend, VolumeProfile,
-    VolumeProfileOutput, Vortex, VortexOutput, Vpin, Vwap, VwapStdDevBands, VwapStdDevBandsOutput,
-    Vwma, Vzo, WaveTrend, WaveTrendOutput, Wedge, WeightedClose, WickRatio, WilliamsFractals,
-    WilliamsFractalsOutput, WilliamsR, WinRate, Wma, WoodiePivots, WoodiePivotsOutput,
-    YangZhangVolatility, YoyoExit, ZScore, ZeroLagMacd, ZeroLagMacdOutput, ZigZag, ZigZagOutput,
-    Zlema, FAMILIES, T3,
+    SessionVwap, ShannonEntropy, Shark, SharpeRatio, ShootingStar, ShortLine, SignedVolume,
+    SineWave, SineWeightedMa, Skewness, Sma, Smi, Smma, SortinoRatio, SpearmanCorrelation,
+    SpinningTop, SpreadAr1Coefficient, SpreadBollingerBands, SpreadBollingerBandsOutput,
+    SpreadHurst, StalledPattern, StandardError, StandardErrorBands, StandardErrorBandsOutput,
+    StarcBands, StarcBandsOutput, Stc, StdDev, StepTrailingStop, StickSandwich, StochRsi,
+    Stochastic, StochasticCci, StochasticOutput, SuperSmoother, SuperTrend, SuperTrendOutput,
+    TakerBuySellRatio, Takuri, TasukiGap, TdCombo, TdCountdown, TdDeMarker, TdDifferential,
+    TdLines, TdLinesOutput, TdOpen, TdPressure, TdRangeProjection, TdRangeProjectionOutput, TdRei,
+    TdRiskLevel, TdRiskLevelOutput, TdSequential, TdSequentialOutput, TdSetup, Tema,
+    TermStructureBasis, ThreeDrives, ThreeInside, ThreeLineStrike, ThreeOutside,
+    ThreeSoldiersOrCrows, ThreeStarsInSouth, Thrusting, TickIndex, Tii, TimeBasedStop,
+    TimeOfDayReturnProfile, TimeOfDayReturnProfileOutput, TpoProfile, TpoProfileOutput,
+    TradeImbalance, TradeVolumeIndex, TrendLabel, TrendStrengthIndex, TreynorRatio, Triangle,
+    Trima, Trin, TripleTopBottom, Trix, TrueRange, Tsf, TsfOscillator, Tsi, Tsv, TtmSqueeze,
+    TtmSqueezeOutput, TtmTrend, TurnOfMonth, Tweezer, TwiggsMoneyFlow, TwoCrows, TypicalPrice,
+    UlcerIndex, UltimateOscillator, UniqueThreeRiver, UpDownVolumeRatio, UpsideGapThreeMethods,
+    UpsideGapTwoCrows, ValueArea, ValueAreaOutput, ValueAtRisk, Variance, VarianceRatio,
+    VerticalHorizontalFilter, Vidya, VolatilityCone, VolatilityConeOutput, VolatilityOfVolatility,
+    VolatilityRatio, VoltyStop, VolumeByTimeProfile, VolumeByTimeProfileOutput, VolumeOscillator,
+    VolumePriceTrend, VolumeProfile, VolumeProfileOutput, VolumeRsi, VolumeWeightedMacd,
+    VolumeWeightedMacdOutput, Vortex, VortexOutput, Vpin, Vwap, VwapStdDevBands,
+    VwapStdDevBandsOutput, Vwma, Vzo, Wad, WavePm, WaveTrend, WaveTrendOutput, Wedge,
+    WeightedClose, WickRatio, WilliamsFractals, WilliamsFractalsOutput, WilliamsR, WinRate, Wma,
+    WoodiePivots, WoodiePivotsOutput, YangZhangVolatility, YoyoExit, ZScore, ZeroLagMacd,
+    ZeroLagMacdOutput, ZigZag, ZigZagOutput, Zlema, FAMILIES, T3,
 };
 // `FootprintLevel` is a row element of `FootprintOutput`, re-exported on its own
 // line so the indicator-count tooling (which scans the braced block above and

docs/README.md

@@ -8,7 +8,7 @@ That includes:
   [Python](https://docs.wickra.org/Quickstart-Python),
   [Node](https://docs.wickra.org/Quickstart-Node), and
   [WASM](https://docs.wickra.org/Quickstart-WASM).
-- A per-indicator deep dive for every one of the **396 indicators** across
+- A per-indicator deep dive for every one of the **452 indicators** across
   the sixteen families (Moving Averages, Momentum Oscillators, Trend &
   Directional, Price Oscillators, Volatility & Bands, Bands & Channels,
   Trailing Stops, Volume, Price Statistics, Ehlers / Cycle DSP, Pivots &

examples/node/package-lock.json

@@ -17,7 +17,7 @@
     },
     "../../bindings/node": {
       "name": "wickra",
-      "version": "0.5.4",
+      "version": "0.6.4",
       "license": "MIT OR Apache-2.0",
       "devDependencies": {
         "@napi-rs/cli": "^2.18.0"
@@ -26,12 +26,12 @@
         "node": ">= 18"
       },
       "optionalDependencies": {
-        "wickra-darwin-arm64": "0.5.4",
-        "wickra-darwin-x64": "0.5.4",
-        "wickra-linux-arm64-gnu": "0.5.4",
-        "wickra-linux-x64-gnu": "0.5.4",
-        "wickra-win32-arm64-msvc": "0.5.4",
-        "wickra-win32-x64-msvc": "0.5.4"
+        "wickra-darwin-arm64": "0.6.4",
+        "wickra-darwin-x64": "0.6.4",
+        "wickra-linux-arm64-gnu": "0.6.4",
+        "wickra-linux-x64-gnu": "0.6.4",
+        "wickra-win32-arm64-msvc": "0.6.4",
+        "wickra-win32-x64-msvc": "0.6.4"
       }
     },
     "node_modules/wickra": {

examples/rust/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "wickra-examples"
-version = "0.0.0"
+version.workspace = true
 publish = false
 description = "Runnable Rust examples for the Wickra technical-analysis library."
 authors.workspace = true

fuzz/fuzz_targets/indicator_update.rs

@@ -14,7 +14,7 @@
 //! `Ema(20)`. This target now covers every scalar indicator in the catalogue.
 
 use libfuzzer_sys::fuzz_target;
-use wickra_core::{AdaptiveCycle, Alma, AnchoredRsi, Apo, Autocorrelation, AverageDrawdown, BatchExt, Beta, BollingerBands, CalmarRatio, CenterOfGravity, Cfo, Cmo, CoefficientOfVariation, ConditionalValueAtRisk, ConnorsRsi, Coppock, CyberneticCycle, Decycler, DecyclerOscillator, Dema, DetrendedStdDev, DoubleBollinger, Dpo, DrawdownDuration, EhlersStochastic, ElderImpulse, Ema, EmpiricalModeDecomposition, Expectancy, Fama, FisherTransform, Frama, GainLossRatio, HilbertDominantCycle, HistoricalVolatility, Hma, HtDcPhase, HtPhasor, HtTrendMode, HurstExponent, Indicator, InstantaneousTrendline, InverseFisherTransform, Jma, JumpIndicator, Kama, KellyCriterion, Kst, Kurtosis, LaguerreRsi, LinRegAngle, LinRegChannel, LinRegIntercept, LinRegSlope, LinearRegression, LogReturn, MaEnvelope, MaType, MacdExt, MacdFix, MacdIndicator, Mama, MaxDrawdown, McGinleyDynamic, MedianAbsoluteDeviation, MidPoint, Mom, OmegaRatio, PainIndex, PearsonCorrelation, PercentageTrailingStop, Pmo, Ppo, ProfitFactor, RSquared, RealizedVolatility, RecoveryFactor, RegimeLabel, RenkoTrailingStop, Roc, Rocp, Rocr, Rocr100, RollingIqr, RollingPercentileRank, RollingQuantile, RoofingFilter, Rsi, RviVolatility, SharpeRatio, SineWave, Skewness, Sma, Smma, SortinoRatio, SpearmanCorrelation, StandardError, StandardErrorBands, Stc, StdDev, StepTrailingStop, StochRsi, SuperSmoother, Tema, Tii, TrendLabel, Trima, Trix, Tsf, Tsi, UlcerIndex, ValueAtRisk, Variance, VerticalHorizontalFilter, Vidya, WinRate, Wma, ZScore, ZeroLagMacd, Zlema, T3};
+use wickra_core::{AdaptiveCycle, AdaptiveLaguerreFilter, Alma, AnchoredRsi, Apo, Autocorrelation, AverageDrawdown, BatchExt, Beta, BipowerVariation, BollingerBands, BomarBands, CalmarRatio, CenterOfGravity, Cfo, Cmo, CoefficientOfVariation, ConditionalValueAtRisk, ConnorsRsi, Coppock, CyberneticCycle, Decycler, DecyclerOscillator, Dema, DerivativeOscillator, DetrendedStdDev, DisparityIndex, DoubleBollinger, Dpo, DrawdownDuration, DynamicMomentumIndex, EhlersStochastic, Ehma, ElderImpulse, Ema, EmpiricalModeDecomposition, EwmaVolatility, Expectancy, Fama, FisherRsi, FisherTransform, Frama, GainLossRatio, Garch11, GeneralizedDema, GeometricMa, HilbertDominantCycle, HistoricalVolatility, Hma, HoltWinters, HtDcPhase, HtPhasor, HtTrendMode, HurstExponent, Indicator, InstantaneousTrendline, InverseFisherTransform, JarqueBera, Jma, JumpIndicator, Kama, KellyCriterion, Kst, Kurtosis, LaguerreRsi, LinRegAngle, LinRegChannel, LinRegIntercept, LinRegSlope, LinearRegression, LogReturn, MaEnvelope, MaType, MacdExt, MacdFix, MacdHistogram, MacdIndicator, Mama, MaxDrawdown, McGinleyDynamic, MedianAbsoluteDeviation, MedianChannel, MedianMa, MidPoint, Mom, OmegaRatio, PainIndex, PearsonCorrelation, PercentageTrailingStop, Pmo, PolarizedFractalEfficiency, Ppo, PpoHistogram, ProfitFactor, Qqe, QuartileBands, RSquared, RealizedVolatility, RecoveryFactor, RegimeLabel, RenkoTrailingStop, Rmi, Roc, Rocp, Rocr, Rocr100, RollingIqr, RollingMinMaxScaler, RollingPercentileRank, RollingQuantile, RoofingFilter, Rsi, Rsx, RviVolatility, SampleEntropy, ShannonEntropy, SharpeRatio, SineWave, SineWeightedMa, Skewness, Sma, Smma, SortinoRatio, SpearmanCorrelation, StandardError, StandardErrorBands, Stc, StdDev, StepTrailingStop, StochRsi, SuperSmoother, Tema, Tii, TrendLabel, TrendStrengthIndex, Trima, Trix, Tsf, TsfOscillator, Tsi, UlcerIndex, ValueAtRisk, Variance, VerticalHorizontalFilter, Vidya, VolatilityOfVolatility, WavePm, WinRate, Wma, ZScore, ZeroLagMacd, Zlema, T3};
 
 /// Drive a single streaming + batch run through one scalar indicator. Marked
 /// `#[inline(never)]` so a panic backtrace pin-points the specific indicator.
@@ -43,6 +43,13 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| Dema::new(14).unwrap(), &data);
     drive(|| Tema::new(14).unwrap(), &data);
     drive(|| Hma::new(14).unwrap(), &data);
+    drive(|| SineWeightedMa::new(14).unwrap(), &data);
+    drive(|| GeometricMa::new(14).unwrap(), &data);
+    drive(|| Ehma::new(9).unwrap(), &data);
+    drive(|| MedianMa::new(14).unwrap(), &data);
+    drive(|| AdaptiveLaguerreFilter::new(13).unwrap(), &data);
+    drive(|| GeneralizedDema::new(5, 0.7).unwrap(), &data);
+    drive(|| HoltWinters::new(0.2, 0.1).unwrap(), &data);
     drive(|| Roc::new(14).unwrap(), &data);
     drive(|| Rocp::new(14).unwrap(), &data);
     drive(|| Rocr::new(14).unwrap(), &data);
@@ -60,6 +67,15 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| T3::new(14, 0.7).unwrap(), &data);
     drive(|| Mom::new(14).unwrap(), &data);
     drive(|| Cmo::new(14).unwrap(), &data);
+    drive(|| DisparityIndex::new(14).unwrap(), &data);
+    drive(|| FisherRsi::new(14).unwrap(), &data);
+    drive(|| Rsx::new(14).unwrap(), &data);
+    drive(|| DynamicMomentumIndex::new(14).unwrap(), &data);
+    drive(|| Rmi::new(14, 5).unwrap(), &data);
+    drive(|| DerivativeOscillator::new(14, 5, 3, 9).unwrap(), &data);
+    drive(|| TrendStrengthIndex::new(20).unwrap(), &data);
+    drive(|| PolarizedFractalEfficiency::new(10, 5).unwrap(), &data);
+    drive(|| WavePm::new(32, 3).unwrap(), &data);
     drive(|| Tsi::new(25, 13).unwrap(), &data);
     drive(|| Pmo::new(35, 20).unwrap(), &data);
     drive(|| Tii::new(60, 30).unwrap(), &data);
@@ -68,6 +84,9 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| Ppo::new(12, 26).unwrap(), &data);
     drive(|| Apo::new(12, 26).unwrap(), &data);
     drive(|| Cfo::new(14).unwrap(), &data);
+    drive(|| TsfOscillator::new(14).unwrap(), &data);
+    drive(|| MacdHistogram::new(12, 26, 9).unwrap(), &data);
+    drive(|| PpoHistogram::new(12, 26, 9).unwrap(), &data);
     drive(|| ElderImpulse::classic(), &data);
     drive(|| Stc::classic(), &data);
     drive(|| Coppock::new(14, 11, 10).unwrap(), &data);
@@ -96,9 +115,17 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| HurstExponent::new(16, 4).unwrap(), &data);
     drive(|| LogReturn::new(1).unwrap(), &data);
     drive(|| RealizedVolatility::new(20).unwrap(), &data);
+    drive(|| EwmaVolatility::new(0.94).unwrap(), &data);
+    drive(|| Garch11::new(0.000_002, 0.1, 0.88).unwrap(), &data);
+    drive(|| BipowerVariation::new(20).unwrap(), &data);
+    drive(|| VolatilityOfVolatility::new(20, 20).unwrap(), &data);
     drive(|| RollingQuantile::new(20, 0.5).unwrap(), &data);
     drive(|| RollingIqr::new(14).unwrap(), &data);
     drive(|| RollingPercentileRank::new(14).unwrap(), &data);
+    drive(|| JarqueBera::new(20).unwrap(), &data);
+    drive(|| RollingMinMaxScaler::new(20).unwrap(), &data);
+    drive(|| ShannonEntropy::new(20, 8).unwrap(), &data);
+    drive(|| SampleEntropy::new(20, 2, 0.2).unwrap(), &data);
     drive(|| TrendLabel::new(14).unwrap(), &data);
     drive(|| JumpIndicator::new(20, 3.0).unwrap(), &data);
     drive(|| RegimeLabel::new(5, 20).unwrap(), &data);
@@ -116,6 +143,16 @@ fuzz_target!(|data: Vec<f64>| {
         let _ = Kst::classic().batch(&data);
     }
 
+    // QQE is scalar-input but emits `QqeOutput`, so it bypasses the generic
+    // `drive` helper. Streaming + batch are still both exercised.
+    {
+        let mut qqe = Qqe::new(14, 5, 4.236).unwrap();
+        for &x in &data {
+            let _ = qqe.update(x);
+        }
+        let _ = Qqe::new(14, 5, 4.236).unwrap().batch(&data);
+    }
+
     // Zero-Lag MACD shares MACD's multi-output topology, so it gets the
     // same hand-rolled streaming + batch drive as classic MACD below.
     {
@@ -239,6 +276,27 @@ fuzz_target!(|data: Vec<f64>| {
     }
 
     // --- Family 05: scalar-input band/channel indicators (multi-output) ---
+    {
+        let mut medianchannel = MedianChannel::new(5, 2.0).unwrap();
+        for &x in &data {
+            let _ = medianchannel.update(x);
+        }
+        let _ = MedianChannel::new(5, 2.0).unwrap().batch(&data);
+    }
+    {
+        let mut bomarbands = BomarBands::new(4, 0.85).unwrap();
+        for &x in &data {
+            let _ = bomarbands.update(x);
+        }
+        let _ = BomarBands::new(4, 0.85).unwrap().batch(&data);
+    }
+    {
+        let mut quartilebands = QuartileBands::new(4).unwrap();
+        for &x in &data {
+            let _ = quartilebands.update(x);
+        }
+        let _ = QuartileBands::new(4).unwrap().batch(&data);
+    }
     {
         let mut env = MaEnvelope::new(20, 0.025).unwrap();
         for &x in &data {

fuzz/fuzz_targets/indicator_update_candle.rs

@@ -22,7 +22,7 @@
 //! WeightedClose.
 
 use libfuzzer_sys::fuzz_target;
-use wickra_core::{AbandonedBaby, Abcd, AccelerationBands, AcceleratorOscillator, AdOscillator, Adl, AdvanceBlock, Adx, Adxr, Alligator, AnchoredVwap, Aroon, AroonOscillator, Atr, AtrBands, AtrTrailingStop, AutoFib, AverageDailyRange, AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower, Bat, BatchExt, BeltHold, BodySizePct, Breakaway, Butterfly, Camarilla, Candle, Cci, ChaikinMoneyFlow, ChaikinOscillator, ChaikinVolatility, ChandeKrollStop, ChandelierExit, ChoppinessIndex, ClassicPivots, CloseVsOpen, ClosingMarubozu, ConcealingBabySwallow, Counterattack, Crab, CupAndHandle, Cypher, DayOfWeekProfile, DemandIndex, DemarkPivots, Doji, DojiStar, Donchian, DonchianStop, DoubleTopBottom, DownsideGapThreeMethods, DragonflyDoji, Dx, EaseOfMovement, Engulfing, EveningDojiStar, Evwma, FallingThreeMethods, FibArcs, FibChannel, FibConfluence, FibExtension, FibFan, FibProjection, FibRetracement, FibTimeZones, FibonacciPivots, FlagPennant, ForceIndex, FractalChaosBands, GapSideBySideWhite, GarmanKlassVolatility, Gartley, GoldenPocket, GravestoneDoji, Hammer, HangingMan, Harami, HeadAndShoulders, HeikinAshi, HiLoActivator, HighLowRange, HighWave, Hikkake, HikkakeModified, HomingPigeon, HurstChannel, Ichimoku, IdenticalThreeCrows, InNeck, Indicator, Inertia, InitialBalance, IntradayVolatilityProfile, InvertedHammer, Keltner, Kicking, KickingByLength, Kvo, LadderBottom, LongLeggedDoji, LongLine, MarketFacilitationIndex, Marubozu, MassIndex, MatHold, MatchingLow, AvgPrice, MedianPrice, Mfi, MidPrice, MinusDi, MinusDm, MorningDojiStar, MorningEveningStar, Natr, Nvi, Obv, OnNeck, OpeningMarubozu, OpeningRange, OvernightGap, OvernightIntradayReturn, ParkinsonVolatility, Pgo, PiercingDarkCloud, PlusDi, PlusDm, Psar, Pvi, RectangleRange, RickshawMan, RisingThreeMethods, RogersSatchellVolatility, RollingVwap, Rvi, Rwi, SarExt, SeasonalZScore, SeparatingLines, SessionHighLow, SessionRange, SessionVwap, Shark, ShootingStar, ShortLine, Smi, SpinningTop, StalledPattern, StarcBands, StickSandwich, Stochastic, SuperTrend, Takuri, TasukiGap, TdCombo, TdCountdown, TdDeMarker, TdDifferential, TdLines, TdOpen, TdPressure, TdRangeProjection, TdRei, TdRiskLevel, TdSequential, TdSetup, ThreeDrives, ThreeInside, ThreeLineStrike, ThreeOutside, ThreeSoldiersOrCrows, ThreeStarsInSouth, Thrusting, TimeOfDayReturnProfile, TpoProfile, Triangle, TripleTopBottom, TrueRange, Tsv, TtmSqueeze, TurnOfMonth, Tweezer, TwoCrows, TypicalPrice, UltimateOscillator, UniqueThreeRiver, UpsideGapThreeMethods, UpsideGapTwoCrows, ValueArea, VoltyStop, VolumeByTimeProfile, VolumeOscillator, VolumePriceTrend, VolumeProfile, Vortex, Vwap, VwapStdDevBands, Vwma, Vzo, WaveTrend, Wedge, WeightedClose, WickRatio, WilliamsFractals, WilliamsR, WoodiePivots, YangZhangVolatility, YoyoExit, ZigZag};
+use wickra_core::{AbandonedBaby, Abcd, AccelerationBands, AcceleratorOscillator, AdOscillator, Adl, AdvanceBlock, Adx, Adxr, Alligator, AnchoredVwap, Aroon, AroonOscillator, Atr, AtrBands, AtrRatchet, AtrTrailingStop, AutoFib, AverageDailyRange, AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower, Bat, BatchExt, BeltHold, BetterVolume, BodySizePct, Breakaway, Butterfly, Camarilla, Candle, Cci, ChaikinMoneyFlow, ChaikinOscillator, ChaikinVolatility, ChandeKrollStop, ChandelierExit, ChoppinessIndex, ClassicPivots, CloseVsOpen, ClosingMarubozu, ConcealingBabySwallow, Counterattack, Crab, CupAndHandle, Cypher, DayOfWeekProfile, DemandIndex, DemarkPivots, Doji, DojiStar, Donchian, DonchianStop, DoubleTopBottom, DownsideGapThreeMethods, DragonflyDoji, Dx, EaseOfMovement, ElderRay, ElderSafeZone, Engulfing, EveningDojiStar, Evwma, FallingThreeMethods, FibArcs, FibChannel, FibConfluence, FibExtension, FibFan, FibProjection, FibRetracement, FibTimeZones, FibonacciPivots, FlagPennant, ForceIndex, FractalChaosBands, GapSideBySideWhite, GarmanKlassVolatility, Gartley, GatorOscillator, GoldenPocket, GravestoneDoji, Hammer, HangingMan, Harami, HeadAndShoulders, HeikinAshi, HiLoActivator, HighLowRange, HighWave, Hikkake, HikkakeModified, HomingPigeon, HurstChannel, Ichimoku, IdenticalThreeCrows, InNeck, Indicator, Inertia, InitialBalance, IntradayIntensity, IntradayMomentumIndex, IntradayVolatilityProfile, InvertedHammer, KaseDevStop, KasePermissionStochastic, Keltner, Kicking, KickingByLength, Kvo, LadderBottom, LongLeggedDoji, LongLine, MarketFacilitationIndex, Marubozu, MassIndex, MatHold, MatchingLow, AvgPrice, MedianPrice, Mfi, MidPrice, MinusDi, MinusDm, ModifiedMaStop, MorningDojiStar, MorningEveningStar, Natr, Nrtr, Nvi, Obv, OnNeck, OpeningMarubozu, OpeningRange, OvernightGap, OvernightIntradayReturn, ParkinsonVolatility, Pgo, PiercingDarkCloud, PlusDi, PlusDm, ProjectionBands, ProjectionOscillator, Psar, Pvi, Qstick, RectangleRange, RickshawMan, RisingThreeMethods, RogersSatchellVolatility, RollingVwap, Rvi, Rwi, SarExt, SeasonalZScore, SeparatingLines, SessionHighLow, SessionRange, SessionVwap, Shark, ShootingStar, ShortLine, Smi, SpinningTop, StalledPattern, StarcBands, StickSandwich, Stochastic, StochasticCci, SuperTrend, Takuri, TasukiGap, TdCombo, TdCountdown, TdDeMarker, TdDifferential, TdLines, TdOpen, TdPressure, TdRangeProjection, TdRei, TdRiskLevel, TdSequential, TdSetup, ThreeDrives, ThreeInside, ThreeLineStrike, ThreeOutside, ThreeSoldiersOrCrows, ThreeStarsInSouth, Thrusting, TimeBasedStop, TimeOfDayReturnProfile, TpoProfile, TradeVolumeIndex, Triangle, TripleTopBottom, TrueRange, Tsv, TtmSqueeze, TtmTrend, TurnOfMonth, Tweezer, TwiggsMoneyFlow, TwoCrows, TypicalPrice, UltimateOscillator, UniqueThreeRiver, UpsideGapThreeMethods, UpsideGapTwoCrows, ValueArea, VolatilityCone, VolatilityRatio, VoltyStop, VolumeByTimeProfile, VolumeOscillator, VolumePriceTrend, VolumeProfile, VolumeRsi, VolumeWeightedMacd, Vortex, Vwap, VwapStdDevBands, Vwma, Vzo, WaveTrend, Wedge, WeightedClose, WickRatio, Wad, WilliamsFractals, WilliamsR, WoodiePivots, YangZhangVolatility, YoyoExit, ZigZag};
 
 /// Convert a flat `f64` stream into a `Vec<Candle>` by chunking it into
 /// `[open, high, low, close, volume]` groups. Tuples that fail OHLCV
@@ -62,6 +62,7 @@ fuzz_target!(|data: Vec<f64>| {
     }
 
     // --- Volatility & ATR family ---
+    drive(|| VolatilityRatio::new(14).unwrap(), &candles);
     drive(|| Atr::new(14).unwrap(), &candles);
     drive(|| Natr::new(14).unwrap(), &candles);
     drive(TrueRange::new, &candles);
@@ -72,10 +73,17 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| YangZhangVolatility::new(20, 252).unwrap(), &candles);
 
     // --- Bands & Channels ---
+    drive(|| ProjectionOscillator::new(14).unwrap(), &candles);
+    drive(|| ProjectionBands::new(3).unwrap(), &candles);
     drive(|| Keltner::new(20, 10, 2.0).unwrap(), &candles);
     drive(|| Donchian::new(20).unwrap(), &candles);
 
     // --- Trailing Stops ---
+    drive(|| ModifiedMaStop::new(14).unwrap(), &candles);
+    drive(|| TimeBasedStop::new(5).unwrap(), &candles);
+    drive(|| Nrtr::new(2.0).unwrap(), &candles);
+    drive(|| AtrRatchet::new(14, 4.0, 0.1).unwrap(), &candles);
+    drive(|| ElderSafeZone::new(14, 2.0).unwrap(), &candles);
     drive(|| Psar::new(0.02, 0.02, 0.20).unwrap(), &candles);
     drive(SarExt::classic, &candles);
     drive(|| SuperTrend::new(14, 3.0).unwrap(), &candles);
@@ -85,8 +93,13 @@ fuzz_target!(|data: Vec<f64>| {
     drive(|| HiLoActivator::new(3).unwrap(), &candles);
     drive(|| VoltyStop::new(14, 2.0).unwrap(), &candles);
     drive(|| YoyoExit::new(14, 2.0).unwrap(), &candles);
+    drive(|| KaseDevStop::new(30, 1.0).unwrap(), &candles);
 
     // --- Trend & Directional ---
+    drive(|| KasePermissionStochastic::new(9, 3).unwrap(), &candles);
+    drive(|| GatorOscillator::new(13, 8, 5).unwrap(), &candles);
+    drive(|| Qstick::new(10).unwrap(), &candles);
+    drive(|| TtmTrend::new(6).unwrap(), &candles);
     drive(|| Adx::new(14).unwrap(), &candles);
     drive(|| Adxr::new(14).unwrap(), &candles);
     drive(|| PlusDm::new(14).unwrap(), &candles);
@@ -105,6 +118,9 @@ fuzz_target!(|data: Vec<f64>| {
 
     // --- Momentum & Oscillators ---
     drive(|| Cci::new(20).unwrap(), &candles);
+    drive(|| StochasticCci::new(14).unwrap(), &candles);
+    drive(|| ElderRay::new(13).unwrap(), &candles);
+    drive(|| IntradayMomentumIndex::new(14).unwrap(), &candles);
     drive(|| Rvi::new(10).unwrap(), &candles);
     drive(|| Inertia::new(14, 20).unwrap(), &candles);
     drive(|| Pgo::new(14).unwrap(), &candles);
@@ -124,6 +140,13 @@ fuzz_target!(|data: Vec<f64>| {
     drive(HighLowRange::new, &candles);
 
     // --- Volume ---
+    drive(|| BetterVolume::new(14).unwrap(), &candles);
+    drive(IntradayIntensity::new, &candles);
+    drive(|| TradeVolumeIndex::new(0.25).unwrap(), &candles);
+    drive(|| TwiggsMoneyFlow::new(21).unwrap(), &candles);
+    drive(Wad::new, &candles);
+    drive(|| VolumeRsi::new(14).unwrap(), &candles);
+    drive(|| VolumeWeightedMacd::new(12, 26, 9).unwrap(), &candles);
     drive(Obv::new, &candles);
     drive(|| Mfi::new(14).unwrap(), &candles);
     drive(Vwap::new, &candles);
@@ -239,6 +262,7 @@ fuzz_target!(|data: Vec<f64>| {
     }
 
     // --- Family 05: candle-input band/channel indicators (multi-output) ---
+    drive(|| VolatilityCone::new(20, 60).unwrap(), &candles);
     {
         let mut ab = AccelerationBands::new(20, 0.001).unwrap();
         for c in &candles {

fuzz/fuzz_targets/indicator_update_pair.rs

@@ -8,7 +8,7 @@
 //! panic.
 
 use libfuzzer_sys::fuzz_target;
-use wickra_core::{Alpha, BatchExt, BetaNeutralSpread, Cointegration, DistanceSsd, GrangerCausality, Indicator, InformationRatio, KalmanHedgeRatio, LeadLagCrossCorrelation, OuHalfLife, PairSpreadZScore, PairwiseBeta, RelativeStrengthAB, RollingCorrelation, RollingCovariance, SpreadAr1Coefficient, SpreadBollingerBands, SpreadHurst, TreynorRatio, VarianceRatio};
+use wickra_core::{Alpha, BatchExt, BetaNeutralSpread, Cointegration, DistanceSsd, GrangerCausality, Indicator, InformationRatio, KalmanHedgeRatio, KendallTau, LeadLagCrossCorrelation, OuHalfLife, PairSpreadZScore, PairwiseBeta, RelativeStrengthAB, RollingCorrelation, RollingCovariance, SpreadAr1Coefficient, SpreadBollingerBands, SpreadHurst, TreynorRatio, VarianceRatio};
 
 #[inline(never)]
 fn drive<I>(make: impl Fn() -> I, data: &[(f64, f64)])
@@ -47,6 +47,7 @@ fuzz_target!(|data: &[u8]| {
     drive(|| VarianceRatio::new(60, 2).unwrap(), &pairs);
     drive(|| GrangerCausality::new(60, 1).unwrap(), &pairs);
     drive(|| SpreadAr1Coefficient::new(40).unwrap(), &pairs);
+    drive(|| KendallTau::new(20).unwrap(), &pairs);
 
     // Struct-output pair indicator: drive update + batch directly (the generic
     // `drive` above only covers `Output = f64`).