Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 kingchenc
|
654da5722f | ||
|
kingchenc
|
aacb9280f1 |
@@ -5,5 +5,7 @@
|
||||
maturin
|
||||
numpy
|
||||
pandas
|
||||
TA-Lib
|
||||
tulipy
|
||||
talipp
|
||||
finta
|
||||
|
||||
@@ -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 \
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# -r .github/requirements/bench.in
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# finta
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||||
# pandas
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||||
# ta-lib
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||||
# tulipy
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||||
packaging==26.2 \
|
||||
--hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \
|
||||
--hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661
|
||||
# via build
|
||||
pandas==3.0.3 \
|
||||
--hash=sha256:0383c72c75cdcca61a9e116e611143902dbfd08bff356829c2f6d1cf40a9ca8c \
|
||||
--hash=sha256:05f1f1752b8533ea03f7f39a9c15b1a058d067bb48f4748948e7a8691e0510f2 \
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||||
@@ -149,6 +163,10 @@ pandas==3.0.3 \
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||||
# via
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||||
# -r .github/requirements/bench.in
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||||
# finta
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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
|
||||
|
||||
@@ -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
|
||||
|
||||
+22
-1
@@ -7,6 +7,26 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
## [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`).
|
||||
@@ -1273,7 +1293,8 @@ 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/v0.5.8...HEAD
|
||||
[Unreleased]: https://github.com/wickra-lib/wickra/compare/v0.5.9...HEAD
|
||||
[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
|
||||
|
||||
Generated
+114
-7
@@ -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 = "0.5.8"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"approx",
|
||||
"criterion",
|
||||
@@ -1876,9 +1953,21 @@ dependencies = [
|
||||
"wickra-data",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wickra-bench"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"criterion",
|
||||
"kand",
|
||||
"ta",
|
||||
"wickra",
|
||||
"wickra-data",
|
||||
"yata",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wickra-core"
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"approx",
|
||||
"proptest",
|
||||
@@ -1888,7 +1977,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wickra-data"
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"approx",
|
||||
"csv",
|
||||
@@ -1905,7 +1994,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wickra-examples"
|
||||
version = "0.0.0"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"serde_json",
|
||||
"tokio",
|
||||
@@ -1915,7 +2004,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wickra-node"
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"napi",
|
||||
"napi-build",
|
||||
@@ -1925,7 +2014,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wickra-python"
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
dependencies = [
|
||||
"numpy",
|
||||
"pyo3",
|
||||
@@ -1934,7 +2023,7 @@ dependencies = [
|
||||
|
||||
[[package]]
|
||||
name = "wickra-wasm"
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
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"
|
||||
|
||||
+3
-2
@@ -8,11 +8,12 @@ members = [
|
||||
"bindings/wasm",
|
||||
"bindings/node",
|
||||
"examples/rust",
|
||||
"crates/wickra-bench",
|
||||
]
|
||||
exclude = ["fuzz"]
|
||||
|
||||
[workspace.package]
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
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 = "0.5.8" }
|
||||
wickra-core = { path = "crates/wickra-core", version = "0.5.9" }
|
||||
|
||||
thiserror = "2"
|
||||
rayon = "1.10"
|
||||
|
||||
@@ -60,77 +60,129 @@ 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 |
|
||||
|------------------------|-----------------|-----------|----------------|--------|
|
||||
| **★ 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 |
|
||||
|------------------|-------------|-------------|-----------------------------|-----------:|--------|
|
||||
| **★ 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**: 423 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 | **★ 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 | **★ 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 | **★ 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 | **★ 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 | **★ 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 | **★ 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
|
||||
```
|
||||
|
||||
@@ -247,7 +299,8 @@ wickra/
|
||||
├── crates/
|
||||
│ ├── wickra-core/ core engine + all 423 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://github.com/wickra-lib/wickra">
|
||||
<img alt="Star Wickra on GitHub"
|
||||
src="https://img.shields.io/badge/%E2%AD%90%20Star%20Wickra%20on%20GitHub-1f2328?style=for-the-badge&logo=github&logoColor=ffd866&labelColor=1f2328">
|
||||
</a>
|
||||
</p>
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra-darwin-arm64",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"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": [
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra-darwin-x64",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"description": "Native binding for wickra (macOS Intel). Installed automatically as an optional dependency of wickra on matching platforms.",
|
||||
"main": "wickra.darwin-x64.node",
|
||||
"files": [
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra-linux-arm64-gnu",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"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": [
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra-linux-x64-gnu",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"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": [
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra-win32-arm64-msvc",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"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": [
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra-win32-x64-msvc",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"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": [
|
||||
|
||||
Generated
+20
-20
@@ -1,12 +1,12 @@
|
||||
{
|
||||
"name": "wickra",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"lockfileVersion": 3,
|
||||
"requires": true,
|
||||
"packages": {
|
||||
"": {
|
||||
"name": "wickra",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"license": "MIT OR Apache-2.0",
|
||||
"devDependencies": {
|
||||
"@napi-rs/cli": "^2.18.0"
|
||||
@@ -15,12 +15,12 @@
|
||||
"node": ">= 18"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"wickra-darwin-arm64": "0.5.8",
|
||||
"wickra-darwin-x64": "0.5.8",
|
||||
"wickra-linux-arm64-gnu": "0.5.8",
|
||||
"wickra-linux-x64-gnu": "0.5.8",
|
||||
"wickra-win32-arm64-msvc": "0.5.8",
|
||||
"wickra-win32-x64-msvc": "0.5.8"
|
||||
"wickra-darwin-arm64": "0.5.9",
|
||||
"wickra-darwin-x64": "0.5.9",
|
||||
"wickra-linux-arm64-gnu": "0.5.9",
|
||||
"wickra-linux-x64-gnu": "0.5.9",
|
||||
"wickra-win32-arm64-msvc": "0.5.9",
|
||||
"wickra-win32-x64-msvc": "0.5.9"
|
||||
}
|
||||
},
|
||||
"node_modules/@napi-rs/cli": {
|
||||
@@ -41,8 +41,8 @@
|
||||
}
|
||||
},
|
||||
"node_modules/wickra-darwin-arm64": {
|
||||
"version": "0.5.8",
|
||||
"resolved": "https://registry.npmjs.org/wickra-darwin-arm64/-/wickra-darwin-arm64-0.5.8.tgz",
|
||||
"version": "0.5.9",
|
||||
"resolved": "https://registry.npmjs.org/wickra-darwin-arm64/-/wickra-darwin-arm64-0.5.9.tgz",
|
||||
"integrity": "sha512-4eZiBR/yGUdr4nzhEUFy2i69XgNx64iI2ax/LPamsThgylC0KpHOZKK19QzJ2d9KbK4C8nMjME5FLuR+4GNEwQ==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
@@ -57,8 +57,8 @@
|
||||
}
|
||||
},
|
||||
"node_modules/wickra-darwin-x64": {
|
||||
"version": "0.5.8",
|
||||
"resolved": "https://registry.npmjs.org/wickra-darwin-x64/-/wickra-darwin-x64-0.5.8.tgz",
|
||||
"version": "0.5.9",
|
||||
"resolved": "https://registry.npmjs.org/wickra-darwin-x64/-/wickra-darwin-x64-0.5.9.tgz",
|
||||
"integrity": "sha512-6hf8zI3QPjTFp4zCpmgUwDvNtu6jHqNUHKD5e55POo0CgA52HkpyxSPtVm8TGTIZDI7kPjlbOdBM8CJ76mmXwA==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
@@ -73,8 +73,8 @@
|
||||
}
|
||||
},
|
||||
"node_modules/wickra-linux-arm64-gnu": {
|
||||
"version": "0.5.8",
|
||||
"resolved": "https://registry.npmjs.org/wickra-linux-arm64-gnu/-/wickra-linux-arm64-gnu-0.5.8.tgz",
|
||||
"version": "0.5.9",
|
||||
"resolved": "https://registry.npmjs.org/wickra-linux-arm64-gnu/-/wickra-linux-arm64-gnu-0.5.9.tgz",
|
||||
"integrity": "sha512-kSe6y0xBMSiqdPLXNjwop5WZdHtvdBNKSEBCwZ4hFq33p4apW25/wrlzv9/oDuyD4kuPabJEhCCnFOplh58CUg==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
@@ -89,8 +89,8 @@
|
||||
}
|
||||
},
|
||||
"node_modules/wickra-linux-x64-gnu": {
|
||||
"version": "0.5.8",
|
||||
"resolved": "https://registry.npmjs.org/wickra-linux-x64-gnu/-/wickra-linux-x64-gnu-0.5.8.tgz",
|
||||
"version": "0.5.9",
|
||||
"resolved": "https://registry.npmjs.org/wickra-linux-x64-gnu/-/wickra-linux-x64-gnu-0.5.9.tgz",
|
||||
"integrity": "sha512-tWBWS4qz7hxM4xnpFb59bhf6TaLwXq0Z3jEa/2l7r8PiHA94g8r8S53NRMiT+4yiL5hSWe/nUiC/YXdRrhEZ4g==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
@@ -105,8 +105,8 @@
|
||||
}
|
||||
},
|
||||
"node_modules/wickra-win32-arm64-msvc": {
|
||||
"version": "0.5.8",
|
||||
"resolved": "https://registry.npmjs.org/wickra-win32-arm64-msvc/-/wickra-win32-arm64-msvc-0.5.8.tgz",
|
||||
"version": "0.5.9",
|
||||
"resolved": "https://registry.npmjs.org/wickra-win32-arm64-msvc/-/wickra-win32-arm64-msvc-0.5.9.tgz",
|
||||
"integrity": "sha512-EXIckHxAtF75PUGDKRzXyqMe9ldP0JjSdu68WFN6iJfp+McYrGu6h40TEJlQ/oUEIoPqiZB/xhVyo/el5Lg7zw==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
@@ -121,8 +121,8 @@
|
||||
}
|
||||
},
|
||||
"node_modules/wickra-win32-x64-msvc": {
|
||||
"version": "0.5.8",
|
||||
"resolved": "https://registry.npmjs.org/wickra-win32-x64-msvc/-/wickra-win32-x64-msvc-0.5.8.tgz",
|
||||
"version": "0.5.9",
|
||||
"resolved": "https://registry.npmjs.org/wickra-win32-x64-msvc/-/wickra-win32-x64-msvc-0.5.9.tgz",
|
||||
"integrity": "sha512-Yfsqq1Xwp6hdxMyLze411vNdo7BDwI6+lPSe7A9XdqyPecNDbtKwYLpsal2r8EHbNzqM+R8XnuRtUaEQS5VlUQ==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "wickra",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"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": "0.5.8",
|
||||
"wickra-linux-arm64-gnu": "0.5.8",
|
||||
"wickra-darwin-x64": "0.5.8",
|
||||
"wickra-darwin-arm64": "0.5.8",
|
||||
"wickra-win32-x64-msvc": "0.5.8",
|
||||
"wickra-win32-arm64-msvc": "0.5.8"
|
||||
"wickra-linux-x64-gnu": "0.5.9",
|
||||
"wickra-linux-arm64-gnu": "0.5.9",
|
||||
"wickra-darwin-x64": "0.5.9",
|
||||
"wickra-darwin-arm64": "0.5.9",
|
||||
"wickra-win32-x64-msvc": "0.5.9",
|
||||
"wickra-win32-arm64-msvc": "0.5.9"
|
||||
},
|
||||
"scripts": {
|
||||
"build": "napi build --platform --release",
|
||||
|
||||
@@ -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__}")
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "maturin"
|
||||
|
||||
[project]
|
||||
name = "wickra"
|
||||
version = "0.5.8"
|
||||
version = "0.5.9"
|
||||
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",
|
||||
|
||||
@@ -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
|
||||
@@ -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);
|
||||
@@ -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.
|
||||
@@ -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 {
|
||||
|
||||
@@ -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!(
|
||||
|
||||
@@ -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 {
|
||||
|
||||
@@ -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;
|
||||
}
|
||||
|
||||
|
||||
@@ -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() {
|
||||
|
||||
Generated
+7
-7
@@ -17,7 +17,7 @@
|
||||
},
|
||||
"../../bindings/node": {
|
||||
"name": "wickra",
|
||||
"version": "0.5.8",
|
||||
"version": "0.5.9",
|
||||
"license": "MIT OR Apache-2.0",
|
||||
"devDependencies": {
|
||||
"@napi-rs/cli": "^2.18.0"
|
||||
@@ -26,12 +26,12 @@
|
||||
"node": ">= 18"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"wickra-darwin-arm64": "0.5.8",
|
||||
"wickra-darwin-x64": "0.5.8",
|
||||
"wickra-linux-arm64-gnu": "0.5.8",
|
||||
"wickra-linux-x64-gnu": "0.5.8",
|
||||
"wickra-win32-arm64-msvc": "0.5.8",
|
||||
"wickra-win32-x64-msvc": "0.5.8"
|
||||
"wickra-darwin-arm64": "0.5.9",
|
||||
"wickra-darwin-x64": "0.5.9",
|
||||
"wickra-linux-arm64-gnu": "0.5.9",
|
||||
"wickra-linux-x64-gnu": "0.5.9",
|
||||
"wickra-win32-arm64-msvc": "0.5.9",
|
||||
"wickra-win32-x64-msvc": "0.5.9"
|
||||
}
|
||||
},
|
||||
"node_modules/wickra": {
|
||||
|
||||
@@ -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
|
||||
|
||||
Reference in New Issue
Block a user