chore(deps): bump actions/deploy-pages from 4 to 5

Bumps [actions/deploy-pages](https://github.com/actions/deploy-pages) from 4 to 5.
- [Release notes](https://github.com/actions/deploy-pages/releases)
- [Commits](https://github.com/actions/deploy-pages/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/deploy-pages
  dependency-version: '5'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

.github/workflows/CI.yml

@@ -81,7 +81,7 @@ jobs:
     steps:
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v5
 
   # -------------------------------------------------------------------------
   # CI gate — all required jobs must pass before this job succeeds.

.github/workflows/ci-python.yml

@@ -41,10 +41,10 @@ jobs:
         run: pip install uv
 
       - name: Run mypy on ferro_ta via uv
-        run: uv run --with mypy --with numpy mypy python/ferro_ta --ignore-missing-imports --no-error-summary
+        run: uv run --with mypy --with numpy python -m mypy python/ferro_ta --ignore-missing-imports --no-error-summary
 
       - name: Run pyright on ferro_ta via uv
-        run: uv run --with pyright pyright python/ferro_ta
+        run: uv run --with pyright python -m pyright python/ferro_ta
 
   test:
     name: Test (ubuntu-latest / Python ${{ matrix.python-version }})
@@ -63,7 +63,7 @@ jobs:
 
       - name: Install maturin and test dependencies
         run: |
-          pip install maturin numpy pytest pytest-cov pandas polars hypothesis pyyaml
+          pip install maturin numpy pytest pytest-cov pandas polars hypothesis pyyaml mcp
 
       - name: Build and install ferro_ta (dev mode)
         run: |
@@ -73,6 +73,10 @@ jobs:
       - name: Run unit tests with coverage
         run: pytest tests/unit/ tests/integration/ -v --cov=ferro_ta --cov-report=xml --cov-report=term-missing --cov-fail-under=65
 
+      - name: Check API manifest is current
+        if: matrix.python-version == '3.12'
+        run: python scripts/check_api_manifest.py
+
       - name: Upload coverage report
         uses: actions/upload-artifact@v7
         if: matrix.python-version == '3.12'

.github/workflows/ci-wasm.yml

@@ -35,6 +35,9 @@ jobs:
         working-directory: wasm
         run: wasm-pack build --target nodejs --out-dir pkg
 
+      - name: Check API manifest is current
+        run: python3 scripts/check_api_manifest.py
+
       - name: Benchmark WASM package
         working-directory: wasm
         run: node bench.js --json ../wasm_benchmark.json

.github/workflows/release.yml

@@ -1,13 +1,13 @@
 name: Release
 
-# Triggered when a version tag is pushed (e.g. v1.0.2).
+# Triggered when a version tag is pushed (e.g. v1.0.3).
 # Creates a GitHub Release marked as "published", which in turn
 # triggers the build-wheels and publish jobs in CI.yml.
 
 on:
   push:
     tags:
-      - "v[0-9]+.[0-9]+.[0-9]+"
+      - "v*.*.*"
 
 permissions:
   contents: write
@@ -25,6 +25,10 @@ jobs:
         id: version
         run: |
           VERSION="${GITHUB_REF_NAME#v}"
+          if ! [[ "$VERSION" =~ ^[0-9]+\.[0-9]+\.[0-9]+([-.][0-9A-Za-z.]+)?$ ]]; then
+            echo "Expected a semantic-version tag like v1.0.3 or v1.0.3-rc1, got: $GITHUB_REF_NAME"
+            exit 1
+          fi
           echo "version=$VERSION" >> "$GITHUB_OUTPUT"
 
       - name: Extract changelog entry for this version

.gitignore

@@ -31,6 +31,11 @@ env/
 # WASM build output
 wasm/pkg/
 wasm/pkg-web/
+benchmark_vs_talib.json
+wasm_benchmark.json
+.wasm_benchmark.prepush.json
+.coverage
+.coverage.*
 coverage.xml
 .hypothesis/
 
@@ -39,4 +44,4 @@ coverage.xml
 
 # DS Store in all directories
 .DS_Store
-*.DS_Store
+*.DS_Store

.pre-commit-config.yaml

@@ -1,12 +1,12 @@
 # Pre-commit hooks for ferro-ta
-# Install: pre-commit install
+# Install: pre-commit install --hook-type pre-commit --hook-type pre-push
 # Run: pre-commit run --all-files
 default_language_version:
   python: python3
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.0
+    rev: v0.15.7
     hooks:
       - id: ruff
         args: [--fix]
@@ -18,6 +18,7 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: check-yaml
+        exclude: ^conda/meta\.yaml$
       - id: check-added-large-files
         args: [--maxkb=1000]
       - id: check-merge-conflict
@@ -31,3 +32,13 @@ repos:
   #       entry: mypy python/ferro_ta --ignore-missing-imports
   #       language: system
   #       pass_filenames: false
+
+  - repo: local
+    hooks:
+      - id: ci-basic-pre-push
+        name: ci basic pre-push
+        entry: scripts/pre_push_checks.sh
+        language: system
+        pass_filenames: false
+        always_run: true
+        stages: [pre-push]

CHANGELOG.md

@@ -9,6 +9,72 @@ and the project uses [Semantic Versioning](https://semver.org/).
 
 ## [Unreleased]
 
+## [1.0.6] — 2026-03-24
+
+### Added
+
+- Added a repo-managed pre-push gate that mirrors the core local CI and
+  release checks, including version and changelog validation, Rust formatting
+  and clippy, Python linting and type checks, tests, docs, and the WASM smoke
+  suite.
+- Added generated API manifest tooling and CI coverage so Python and WASM
+  export drift is detected before release candidates are pushed.
+- Expanded the Rust-backed implementation surface for analysis and data-heavy
+  workflows, including backtest signal generation, portfolio loops, payoff and
+  Greeks aggregation, chunked indicator execution, and related helper paths.
+- Expanded the WASM package surface with additional indicator exports such as
+  `WMA`, `ADX`, and `MFI`, along with refreshed Node examples and conformance
+  coverage against the Python package.
+
+### Changed
+
+- Refreshed benchmark wrappers, perf-contract artifacts, and benchmark
+  comparison helpers so the checked-in performance evidence stays aligned with
+  the current feature set.
+- Hardened Python CI and local tooling so they run the same typecheck and test
+  entrypoints, including installing the optional MCP dependency needed by the
+  MCP server tests.
+- Updated local pre-commit integration to match the current Ruff
+  configuration and refreshed locked dependencies to pick up the audited
+  PyJWT security fix.
+
+### Fixed
+
+- One-off benchmark output files produced in the repository root are now
+  ignored so local benchmarking no longer dirties the repo by default.
+- Tightened API typing and MCP helper behavior so the stricter lint and
+  typecheck pipeline passes consistently before release.
+
+## [1.0.4] — 2026-03-24
+
+### Added
+
+- The optional MCP server now exposes the broad public ferro-ta callable
+  surface, including exact top-level exports, non-top-level public analysis and
+  tooling functions, and generic stored-instance tools for stateful classes and
+  returned callables.
+- Added a dedicated `TA_LIB_COMPATIBILITY.md` document so the full TA-Lib
+  coverage matrix remains available without bloating the project homepage.
+
+### Changed
+
+- Reworked the root README into a shorter product-first landing page with a
+  compatibility summary and docs map, and refreshed MCP documentation to match
+  the expanded server behavior.
+- Updated the MCP implementation to use generated tool registration over the
+  public API while keeping the legacy lowercase aliases (`sma`, `ema`, `rsi`,
+  `macd`, `backtest`) available for existing clients.
+- Refreshed locked Python dependency resolutions for the latest low-risk direct
+  updates in this release cycle.
+
+### Fixed
+
+- The repository no longer tracks the stray `.coverage` artifact, and coverage
+  outputs are now ignored consistently.
+- MCP tests now cover generated tool discovery, stored-instance workflows, and
+  callable-reference execution paths so the broader server surface does not
+  regress silently.
+
 ## [1.0.3] — 2026-03-24
 
 ### Added
@@ -32,6 +98,15 @@ and the project uses [Semantic Versioning](https://semver.org/).
   benchmark caveats, support-matrix pages, and more explicit release/version
   consistency guidance.
 
+### Fixed
+
+- Python CI now recognizes the top-level metadata API in type stubs, and the
+  derivatives benchmark smoke test no longer depends on importing the
+  `benchmarks` package from an installed wheel layout.
+- The tag-driven GitHub Release workflow now uses a valid glob trigger and an
+  explicit semantic-version validation step, so pushing `v1.0.3`-style tags
+  correctly creates the release that fans out into the publish jobs.
+
 ## [1.0.2] — 2026-03-24
 
 ### Performance
@@ -316,7 +391,9 @@ and the project uses [Semantic Versioning](https://semver.org/).
 
 ---
 
-[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.3...HEAD
+[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.6...HEAD
+[1.0.6]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.4...v1.0.6
+[1.0.4]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.3...v1.0.4
 [1.0.3]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.2...v1.0.3
 [1.0.2]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.1...v1.0.2
 [1.0.1]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.0...v1.0.1

CONTRIBUTING.md

@@ -36,6 +36,34 @@ uv run ruff check python/ tests/
 uv run mypy python/ferro_ta --ignore-missing-imports
 ```
 
+## Git hooks and pre-push checks
+
+Install the repo-managed git hooks after syncing your environment:
+
+```bash
+make hooks
+```
+
+That installs both the existing `pre-commit` hook and a `pre-push` hook that
+runs the local CI gate before anything is pushed.
+
+To run the same gate manually:
+
+```bash
+make prepush
+```
+
+To run only part of it while iterating:
+
+```bash
+make prepush CHECKS="version changelog python_lint"
+```
+
+The pre-push runner covers the basic required CI categories we can execute
+locally: version/changelog checks, Rust fmt/clippy/core checks, Python
+lint/typecheck/tests, docs, and WASM. It intentionally skips the multi-version
+matrix, audit, and benchmark-regression jobs.
+
 ## Alternative: set up with plain pip
 
 ```bash

Cargo.lock

@@ -207,7 +207,7 @@ checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719"
 
 [[package]]
 name = "ferro_ta"
-version = "1.0.3"
+version = "1.0.6"
 dependencies = [
  "criterion",
  "ferro_ta_core",
@@ -222,7 +222,7 @@ dependencies = [
 
 [[package]]
 name = "ferro_ta_core"
-version = "1.0.3"
+version = "1.0.6"
 dependencies = [
  "criterion",
  "wide",

Cargo.toml

@@ -5,9 +5,16 @@ resolver = "2"
 
 [package]
 name = "ferro_ta"
-version = "1.0.3"
+version = "1.0.6"
 edition = "2021"
+description = "Rust-powered Python technical analysis library with a TA-Lib-compatible API"
 license = "MIT"
+readme = "README.md"
+repository = "https://github.com/pratikbhadane24/ferro-ta"
+homepage = "https://github.com/pratikbhadane24/ferro-ta#readme"
+documentation = "https://pratikbhadane24.github.io/ferro-ta/"
+keywords = ["technical-analysis", "trading", "indicators", "finance", "ta-lib"]
+categories = ["finance", "mathematics"]
 publish = false
 
 [lib]
@@ -23,7 +30,7 @@ ndarray = "0.16"
 rayon = "1.10"
 log = "0.4"
 pyo3-log = "0.12"
-ferro_ta_core = { path = "crates/ferro_ta_core", version = "1.0.3" }
+ferro_ta_core = { path = "crates/ferro_ta_core", version = "1.0.6" }
 
 [dev-dependencies]
 criterion = { version = "0.8", features = ["html_reports"] }

Makefile

@@ -1,7 +1,7 @@
 # ferro-ta development Makefile
 # Usage: make <target>
 
-.PHONY: help dev build test lint typecheck fmt docs clean bench version
+.PHONY: help dev build test lint typecheck fmt docs clean bench version audit prepush hooks
 
 # Default target
 help:
@@ -17,12 +17,14 @@ help:
 	@echo "  make bench      Run Rust criterion benchmarks (ferro_ta_core)"
 	@echo "  make version    Bump tracked version strings (set VERSION=X.Y.Z)"
 	@echo "  make audit      Run cargo-audit + pip-audit"
+	@echo "  make prepush    Run the local pre-push CI gate (set CHECKS='version rust_fmt' to scope it)"
+	@echo "  make hooks      Install pre-commit and pre-push git hooks"
 	@echo "  make clean      Remove build artefacts"
 
 dev:
 	pip install uv
 	uv pip install --system maturin numpy pytest pytest-cov pandas polars hypothesis pyyaml \
-	    sphinx sphinx-rtd-theme ruff mypy pyright
+	    sphinx sphinx-rtd-theme ruff mypy pyright pre-commit
 
 build:
 	maturin develop --release
@@ -57,6 +59,12 @@ audit:
 	cargo audit
 	uv run --with pip-audit pip-audit
 
+prepush:
+	bash scripts/pre_push_checks.sh $(CHECKS)
+
+hooks:
+	uv run --with pre-commit pre-commit install --hook-type pre-commit --hook-type pre-push
+
 clean:
 	cargo clean
 	rm -rf dist/ docs/_build/ coverage.xml .coverage *.egg-info

PERFORMANCE_ROADMAP.md

@@ -70,23 +70,23 @@ rustflags = ["-C", "target-cpu=native"]
 ### Phase 3: Algorithm-Level Optimizations (Target: 5-10x improvement)
 
 #### SMA — O(n) running sum
-Current: recomputes each window.  
+Current: recomputes each window.
 Target: single-pass running sum (already done in Rust — verify SIMD path is hit).
 
 #### BBANDS — Welford's algorithm
-Current: compute mean, then variance in two passes.  
+Current: compute mean, then variance in two passes.
 Target: Welford's online algorithm — single pass, better cache utilization.
 
 #### ATR/ADX — Avoid redundant True Range calculations
-Current: ATR → ADX each compute TR independently.  
+Current: ATR → ADX each compute TR independently.
 Target: Compute TR once, share with ATR, NATR, +DI, -DI, ADX in a single pass.
 
 #### MACD — Reuse EMA computations
-Current: Compute fast EMA and slow EMA separately.  
+Current: Compute fast EMA and slow EMA separately.
 Target: Single function computes both EMAs in one pass.
 
 #### Candlestick Patterns — Batch lookup table
-Current: Sequential condition checks per bar.  
+Current: Sequential condition checks per bar.
 Target: Pre-compute body/shadow ratios, vectorized pattern matching.
 
 ### Phase 4: Streaming Precomputation (Target: 100x for incremental updates)

TA_LIB_COMPATIBILITY.md

@@ -0,0 +1,261 @@
+# TA-Lib Compatibility
+
+`ferro-ta` covers **100% of TA-Lib's function set** (`162+` indicators). This
+file keeps the full GitHub-facing parity matrix in one place so the root
+`README.md` can stay product-focused.
+
+See also:
+
+- [docs/migration_talib.rst](docs/migration_talib.rst)
+- [docs/compatibility/talib.md](docs/compatibility/talib.md)
+- [docs/support_matrix.rst](docs/support_matrix.rst)
+
+## Legend
+
+
+| Symbol   | Meaning                                                                                                |
+| -------- | ------------------------------------------------------------------------------------------------------ |
+| ✅ Exact  | Values match TA-Lib to floating-point precision                                                        |
+| ✅ Close  | Values match after a short convergence window (EMA-seed difference)                                    |
+| ⚠️ Corr  | Strong correlation (> 0.95) but not numerically identical (Wilder smoothing seed or algorithm variant) |
+| ⚠️ Shape | Same output shape / NaN structure; values differ due to algorithm variant                              |
+| ❌        | Not yet implemented                                                                                    |
+
+
+## Overlap Studies
+
+
+| TA-Lib Function | ferro-ta | Accuracy | Notes                                                 |
+| --------------- | -------- | -------- | ----------------------------------------------------- |
+| `BBANDS`        | ✅        | ✅ Exact  | Bollinger Bands                                       |
+| `DEMA`          | ✅        | ✅ Close  | Double EMA; converges after ~20 bars                  |
+| `EMA`           | ✅        | ✅ Close  | Exponential Moving Average; converges after ~20 bars  |
+| `KAMA`          | ✅        | ✅ Exact  | Kaufman Adaptive MA (values match after seed bar)     |
+| `MA`            | ✅        | ✅ Exact  | Moving average (generic, type-selectable)             |
+| `MAMA`          | ✅        | ⚠️ Corr  | MESA Adaptive MA                                      |
+| `MAVP`          | ✅        | ✅ Exact  | MA with variable period                               |
+| `MIDPOINT`      | ✅        | ✅ Exact  | Midpoint over period                                  |
+| `MIDPRICE`      | ✅        | ✅ Exact  | Midpoint price over period                            |
+| `SAR`           | ✅        | ⚠️ Shape | Parabolic SAR (same shape; reversal history diverges) |
+| `SAREXT`        | ✅        | ⚠️ Shape | Parabolic SAR Extended                                |
+| `SMA`           | ✅        | ✅ Exact  | Simple Moving Average                                 |
+| `T3`            | ✅        | ✅ Close  | Triple Exponential MA (T3); converges after ~50 bars  |
+| `TEMA`          | ✅        | ✅ Close  | Triple EMA; converges after ~20 bars                  |
+| `TRIMA`         | ✅        | ✅ Exact  | Triangular Moving Average                             |
+| `WMA`           | ✅        | ✅ Exact  | Weighted Moving Average                               |
+
+
+## Momentum Indicators
+
+
+| TA-Lib Function | ferro-ta | Accuracy | Notes                                                                        |
+| --------------- | -------- | -------- | ---------------------------------------------------------------------------- |
+| `ADX`           | ✅        | ✅ Close  | Avg Directional Movement Index (TA-Lib Wilder sum-seeding)                   |
+| `ADXR`          | ✅        | ✅ Close  | ADX Rating (inherits ADX; TA-Lib seeding)                                    |
+| `APO`           | ✅        | ✅ Close  | Absolute Price Oscillator (EMA-based)                                        |
+| `AROON`         | ✅        | ✅ Exact  | Aroon Up/Down                                                                |
+| `AROONOSC`      | ✅        | ✅ Exact  | Aroon Oscillator                                                             |
+| `BOP`           | ✅        | ✅ Exact  | Balance Of Power                                                             |
+| `CCI`           | ✅        | ✅ Exact  | Commodity Channel Index (TA-Lib-compatible MAD formula)                      |
+| `CMO`           | ✅        | ✅ Close  | Chande Momentum Oscillator (rolling window, TA-Lib-compatible)               |
+| `DX`            | ✅        | ✅ Close  | Directional Movement Index (TA-Lib Wilder sum-seeding)                       |
+| `MACD`          | ✅        | ✅ Close  | MACD (EMA-based; converges after ~30 bars)                                   |
+| `MACDEXT`       | ✅        | ✅ Close  | MACD with controllable MA type (EMA-based; converges)                        |
+| `MACDFIX`       | ✅        | ✅ Close  | MACD Fixed 12/26 (EMA-based; converges)                                      |
+| `MFI`           | ✅        | ✅ Exact  | Money Flow Index                                                             |
+| `MINUS_DI`      | ✅        | ✅ Close  | Minus Directional Indicator (TA-Lib Wilder sum-seeding)                      |
+| `MINUS_DM`      | ✅        | ✅ Close  | Minus Directional Movement (TA-Lib Wilder sum-seeding)                       |
+| `MOM`           | ✅        | ✅ Exact  | Momentum                                                                     |
+| `PLUS_DI`       | ✅        | ✅ Close  | Plus Directional Indicator (TA-Lib Wilder sum-seeding)                       |
+| `PLUS_DM`       | ✅        | ✅ Close  | Plus Directional Movement (TA-Lib Wilder sum-seeding)                        |
+| `PPO`           | ✅        | ✅ Close  | Percentage Price Oscillator (EMA-based)                                      |
+| `ROC`           | ✅        | ✅ Exact  | Rate of Change                                                               |
+| `ROCP`          | ✅        | ✅ Exact  | Rate of Change Percentage                                                    |
+| `ROCR`          | ✅        | ✅ Exact  | Rate of Change Ratio                                                         |
+| `ROCR100`       | ✅        | ✅ Exact  | Rate of Change Ratio × 100                                                   |
+| `RSI`           | ✅        | ✅ Close  | Relative Strength Index (TA-Lib Wilder seeding; converges after ~1 seed bar) |
+| `STOCH`         | ✅        | ✅ Close  | Stochastic (TA-Lib-compatible SMA smoothing for slowk and slowd)             |
+| `STOCHF`        | ✅        | ✅ Exact  | Stochastic Fast (%K exact; %D NaN offset ±2)                                 |
+| `STOCHRSI`      | ✅        | ✅ Close  | Stochastic RSI (TA-Lib-compatible; SMA fastd, Wilder-seeded RSI)             |
+| `TRIX`          | ✅        | ✅ Close  | 1-day ROC of Triple EMA (EMA-based; converges)                               |
+| `ULTOSC`        | ✅        | ✅ Exact  | Ultimate Oscillator                                                          |
+| `WILLR`         | ✅        | ✅ Exact  | Williams' %R                                                                 |
+
+
+## Volume Indicators
+
+
+| TA-Lib Function | ferro-ta | Accuracy | Notes                                                              |
+| --------------- | -------- | -------- | ------------------------------------------------------------------ |
+| `AD`            | ✅        | ✅ Exact  | Chaikin A/D Line                                                   |
+| `ADOSC`         | ✅        | ✅ Exact  | Chaikin A/D Oscillator                                             |
+| `OBV`           | ✅        | ✅ Exact  | On Balance Volume (increments identical; constant offset at bar 0) |
+
+
+## Volatility Indicators
+
+
+| TA-Lib Function | ferro-ta | Accuracy | Notes                                                                   |
+| --------------- | -------- | -------- | ----------------------------------------------------------------------- |
+| `ATR`           | ✅        | ✅ Close  | Average True Range (TA-Lib Wilder seeding; matches from bar timeperiod) |
+| `NATR`          | ✅        | ✅ Close  | Normalized ATR (TA-Lib Wilder seeding)                                  |
+| `TRANGE`        | ✅        | ✅ Exact  | True Range (bar 0 differs; all others identical)                        |
+
+
+## Cycle Indicators
+
+
+| TA-Lib Function | ferro-ta | Accuracy | Notes                                                      |
+| --------------- | -------- | -------- | ---------------------------------------------------------- |
+| `HT_DCPERIOD`   | ✅        | ⚠️ Shape | Hilbert Transform Dominant Cycle Period (Ehlers algorithm) |
+| `HT_DCPHASE`    | ✅        | ⚠️ Shape | Hilbert Transform Dominant Cycle Phase                     |
+| `HT_PHASOR`     | ✅        | ⚠️ Shape | Hilbert Transform Phasor Components (inphase, quadrature)  |
+| `HT_SINE`       | ✅        | ⚠️ Shape | Hilbert Transform SineWave (sine, leadsine)                |
+| `HT_TRENDLINE`  | ✅        | ⚠️ Shape | Hilbert Transform Instantaneous Trendline                  |
+| `HT_TRENDMODE`  | ✅        | ⚠️ Shape | Hilbert Transform Trend vs Cycle Mode (1=trend, 0=cycle)   |
+
+
+## Price Transformations
+
+
+| TA-Lib Function | ferro-ta | Accuracy | Notes                |
+| --------------- | -------- | -------- | -------------------- |
+| `AVGPRICE`      | ✅        | ✅ Exact  | Average Price        |
+| `MEDPRICE`      | ✅        | ✅ Exact  | Median Price         |
+| `TYPPRICE`      | ✅        | ✅ Exact  | Typical Price        |
+| `WCLPRICE`      | ✅        | ✅ Exact  | Weighted Close Price |
+
+
+## Statistic Functions
+
+
+| TA-Lib Function       | ferro-ta | Accuracy | Notes                                                       |
+| --------------------- | -------- | -------- | ----------------------------------------------------------- |
+| `BETA`                | ✅        | ✅ Close  | Beta coefficient (returns-based regression matching TA-Lib) |
+| `CORREL`              | ✅        | ✅ Exact  | Pearson Correlation Coefficient                             |
+| `LINEARREG`           | ✅        | ✅ Exact  | Linear Regression                                           |
+| `LINEARREG_ANGLE`     | ✅        | ✅ Exact  | Linear Regression Angle                                     |
+| `LINEARREG_INTERCEPT` | ✅        | ✅ Exact  | Linear Regression Intercept                                 |
+| `LINEARREG_SLOPE`     | ✅        | ✅ Exact  | Linear Regression Slope                                     |
+| `STDDEV`              | ✅        | ✅ Exact  | Standard Deviation                                          |
+| `TSF`                 | ✅        | ✅ Exact  | Time Series Forecast                                        |
+| `VAR`                 | ✅        | ✅ Exact  | Variance                                                    |
+
+
+## Pattern Recognition
+
+`ferro-ta` implements all 61 candlestick patterns. All return the same
+`{-100, 0, 100}` convention as TA-Lib. Pattern thresholds may differ slightly
+from the full TA-Lib implementation.
+
+
+| TA-Lib Function       | ferro-ta | Notes                                               |
+| --------------------- | -------- | --------------------------------------------------- |
+| `CDL2CROWS`           | ✅        | Two Crows                                           |
+| `CDL3BLACKCROWS`      | ✅        | Three Black Crows                                   |
+| `CDL3INSIDE`          | ✅        | Three Inside Up/Down                                |
+| `CDL3LINESTRIKE`      | ✅        | Three-Line Strike                                   |
+| `CDL3OUTSIDE`         | ✅        | Three Outside Up/Down                               |
+| `CDL3STARSINSOUTH`    | ✅        | Three Stars In The South                            |
+| `CDL3WHITESOLDIERS`   | ✅        | Three Advancing White Soldiers                      |
+| `CDLABANDONEDBABY`    | ✅        | Abandoned Baby                                      |
+| `CDLADVANCEBLOCK`     | ✅        | Advance Block                                       |
+| `CDLBELTHOLD`         | ✅        | Belt-hold                                           |
+| `CDLBREAKAWAY`        | ✅        | Breakaway                                           |
+| `CDLCLOSINGMARUBOZU`  | ✅        | Closing Marubozu                                    |
+| `CDLCONCEALBABYSWALL` | ✅        | Concealing Baby Swallow                             |
+| `CDLCOUNTERATTACK`    | ✅        | Counterattack                                       |
+| `CDLDARKCLOUDCOVER`   | ✅        | Dark Cloud Cover                                    |
+| `CDLDOJI`             | ✅        | Doji                                                |
+| `CDLDOJISTAR`         | ✅        | Doji Star                                           |
+| `CDLDRAGONFLYDOJI`    | ✅        | Dragonfly Doji                                      |
+| `CDLENGULFING`        | ✅        | Engulfing Pattern                                   |
+| `CDLEVENINGDOJISTAR`  | ✅        | Evening Doji Star                                   |
+| `CDLEVENINGSTAR`      | ✅        | Evening Star                                        |
+| `CDLGAPSIDESIDEWHITE` | ✅        | Up/Down-gap side-by-side white lines                |
+| `CDLGRAVESTONEDOJI`   | ✅        | Gravestone Doji                                     |
+| `CDLHAMMER`           | ✅        | Hammer                                              |
+| `CDLHANGINGMAN`       | ✅        | Hanging Man                                         |
+| `CDLHARAMI`           | ✅        | Harami Pattern                                      |
+| `CDLHARAMICROSS`      | ✅        | Harami Cross Pattern                                |
+| `CDLHIGHWAVE`         | ✅        | High-Wave Candle                                    |
+| `CDLHIKKAKE`          | ✅        | Hikkake Pattern                                     |
+| `CDLHIKKAKEMOD`       | ✅        | Modified Hikkake Pattern                            |
+| `CDLHOMINGPIGEON`     | ✅        | Homing Pigeon                                       |
+| `CDLIDENTICAL3CROWS`  | ✅        | Identical Three Crows                               |
+| `CDLINNECK`           | ✅        | In-Neck Pattern                                     |
+| `CDLINVERTEDHAMMER`   | ✅        | Inverted Hammer                                     |
+| `CDLKICKING`          | ✅        | Kicking                                             |
+| `CDLKICKINGBYLENGTH`  | ✅        | Kicking by the longer Marubozu                      |
+| `CDLLADDERBOTTOM`     | ✅        | Ladder Bottom                                       |
+| `CDLLONGLEGGEDDOJI`   | ✅        | Long Legged Doji                                    |
+| `CDLLONGLINE`         | ✅        | Long Line Candle                                    |
+| `CDLMARUBOZU`         | ✅        | Marubozu                                            |
+| `CDLMATCHINGLOW`      | ✅        | Matching Low                                        |
+| `CDLMATHOLD`          | ✅        | Mat Hold                                            |
+| `CDLMORNINGDOJISTAR`  | ✅        | Morning Doji Star                                   |
+| `CDLMORNINGSTAR`      | ✅        | Morning Star                                        |
+| `CDLONNECK`           | ✅        | On-Neck Pattern                                     |
+| `CDLPIERCING`         | ✅        | Piercing Pattern                                    |
+| `CDLRICKSHAWMAN`      | ✅        | Rickshaw Man                                        |
+| `CDLRISEFALL3METHODS` | ✅        | Rising/Falling Three Methods                        |
+| `CDLSEPARATINGLINES`  | ✅        | Separating Lines                                    |
+| `CDLSHOOTINGSTAR`     | ✅        | Shooting Star                                       |
+| `CDLSHORTLINE`        | ✅        | Short Line Candle                                   |
+| `CDLSPINNINGTOP`      | ✅        | Spinning Top                                        |
+| `CDLSTALLEDPATTERN`   | ✅        | Stalled Pattern                                     |
+| `CDLSTICKSANDWICH`    | ✅        | Stick Sandwich                                      |
+| `CDLTAKURI`           | ✅        | Takuri (Dragonfly Doji with very long lower shadow) |
+| `CDLTASUKIGAP`        | ✅        | Tasuki Gap                                          |
+| `CDLTHRUSTING`        | ✅        | Thrusting Pattern                                   |
+| `CDLTRISTAR`          | ✅        | Tristar Pattern                                     |
+| `CDLUNIQUE3RIVER`     | ✅        | Unique 3 River                                      |
+| `CDLUPSIDEGAP2CROWS`  | ✅        | Upside Gap Two Crows                                |
+| `CDLXSIDEGAP3METHODS` | ✅        | Upside/Downside Gap Three Methods                   |
+
+
+## Math Operators / Math Transforms
+
+`ferro-ta` provides TA-Lib-compatible wrappers for all arithmetic and
+math-transform functions. Rolling functions (`SUM`, `MAX`, `MIN`) produce `NaN`
+for the first `timeperiod - 1` bars.
+
+
+| TA-Lib Function          | ferro-ta | Notes                         |
+| ------------------------ | -------- | ----------------------------- |
+| `ADD`                    | ✅        | Element-wise addition         |
+| `SUB`                    | ✅        | Element-wise subtraction      |
+| `MULT`                   | ✅        | Element-wise multiplication   |
+| `DIV`                    | ✅        | Element-wise division         |
+| `SUM`                    | ✅        | Rolling sum over *timeperiod* |
+| `MAX` / `MAXINDEX`       | ✅        | Rolling maximum / index       |
+| `MIN` / `MININDEX`       | ✅        | Rolling minimum / index       |
+| `ACOS` / `ASIN` / `ATAN` | ✅        | Arc trig transforms           |
+| `CEIL` / `FLOOR`         | ✅        | Round up / down               |
+| `COS` / `SIN` / `TAN`    | ✅        | Trig transforms               |
+| `COSH` / `SINH` / `TANH` | ✅        | Hyperbolic transforms         |
+| `EXP` / `LN` / `LOG10`   | ✅        | Exponential / log transforms  |
+| `SQRT`                   | ✅        | Square root                   |
+
+
+## Implementation Coverage Summary
+
+
+| Category                    | Implemented | Not Implemented |
+| --------------------------- | ----------- | --------------- |
+| Overlap Studies             | 19          | 0               |
+| Momentum Indicators         | 28          | 0               |
+| Volume Indicators           | 3           | 0               |
+| Volatility Indicators       | 3           | 0               |
+| Cycle Indicators            | 6           | 0               |
+| Price Transforms            | 4           | 0               |
+| Statistic Functions         | 9           | 0               |
+| Pattern Recognition         | 61          | 0               |
+| Math Operators / Transforms | 24          | 0               |
+| Extended Indicators         | 10          | -               |
+| Streaming Classes           | 9           | -               |
+| **Total**                   | **162+**    | **0**           |
+
+
+> `ferro-ta` implements 100% of TA-Lib's function set. NaN values are placed
+> at the beginning of each output array for the warmup period.

api/main.py

@@ -77,7 +77,7 @@ from __future__ import annotations
 
 import math
 import os
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 import numpy as np
 
@@ -117,12 +117,12 @@ app = FastAPI(
 # ---------------------------------------------------------------------------
 
 
-def _nan_to_none(arr: np.ndarray) -> List[Optional[float]]:
+def _nan_to_none(arr: np.ndarray) -> list[float | None]:
     """Convert numpy array to list, replacing NaN/Inf with None."""
     return [None if not math.isfinite(v) else float(v) for v in arr]
 
 
-def _validate_series(close: List[float]) -> np.ndarray:
+def _validate_series(close: list[float]) -> np.ndarray:
     if len(close) > MAX_SERIES_LENGTH:
         raise HTTPException(
             status_code=413,
@@ -142,54 +142,54 @@ def _validate_series(close: List[float]) -> np.ndarray:
 
 
 class IndicatorRequest(BaseModel):
-    close: List[float] = Field(..., description="Close price series")
+    close: list[float] = Field(..., description="Close price series")
     timeperiod: int = Field(default=14, ge=1, description="Look-back period")
 
     @field_validator("close")
     @classmethod
-    def close_must_be_finite(cls, v: List[float]) -> List[float]:
+    def close_must_be_finite(cls, v: list[float]) -> list[float]:
         if not all(math.isfinite(x) for x in v):
             raise ValueError("close series must contain only finite values")
         return v
 
 
 class MACDRequest(BaseModel):
-    close: List[float] = Field(..., description="Close price series")
+    close: list[float] = Field(..., description="Close price series")
     fastperiod: int = Field(default=12, ge=1)
     slowperiod: int = Field(default=26, ge=1)
     signalperiod: int = Field(default=9, ge=1)
 
     @field_validator("close")
     @classmethod
-    def close_must_be_finite(cls, v: List[float]) -> List[float]:
+    def close_must_be_finite(cls, v: list[float]) -> list[float]:
         if not all(math.isfinite(x) for x in v):
             raise ValueError("close series must contain only finite values")
         return v
 
 
 class BBANDSRequest(BaseModel):
-    close: List[float] = Field(..., description="Close price series")
+    close: list[float] = Field(..., description="Close price series")
     timeperiod: int = Field(default=5, ge=2)
     nbdevup: float = Field(default=2.0, gt=0)
     nbdevdn: float = Field(default=2.0, gt=0)
 
     @field_validator("close")
     @classmethod
-    def close_must_be_finite(cls, v: List[float]) -> List[float]:
+    def close_must_be_finite(cls, v: list[float]) -> list[float]:
         if not all(math.isfinite(x) for x in v):
             raise ValueError("close series must contain only finite values")
         return v
 
 
 class BacktestRequest(BaseModel):
-    close: List[float] = Field(..., description="Close price series")
+    close: list[float] = Field(..., description="Close price series")
     strategy: str = Field(default="rsi_30_70")
     commission_per_trade: float = Field(default=0.0, ge=0.0)
     slippage_bps: float = Field(default=0.0, ge=0.0)
 
     @field_validator("close")
     @classmethod
-    def close_must_be_finite(cls, v: List[float]) -> List[float]:
+    def close_must_be_finite(cls, v: list[float]) -> list[float]:
         if not all(math.isfinite(x) for x in v):
             raise ValueError("close series must contain only finite values")
         return v
@@ -201,13 +201,13 @@ class BacktestRequest(BaseModel):
 
 
 @app.get("/health", summary="Health check")
-def health() -> Dict[str, str]:
+def health() -> dict[str, str]:
     """Readiness / liveness probe."""
     return {"status": "ok", "version": app.version}
 
 
 @app.post("/indicators/sma", summary="Simple Moving Average")
-def compute_sma(req: IndicatorRequest) -> Dict[str, Any]:
+def compute_sma(req: IndicatorRequest) -> dict[str, Any]:
     """Compute Simple Moving Average (SMA).
 
     Returns ``result``: list of floats (null for warm-up bars).
@@ -218,7 +218,7 @@ def compute_sma(req: IndicatorRequest) -> Dict[str, Any]:
 
 
 @app.post("/indicators/ema", summary="Exponential Moving Average")
-def compute_ema(req: IndicatorRequest) -> Dict[str, Any]:
+def compute_ema(req: IndicatorRequest) -> dict[str, Any]:
     """Compute Exponential Moving Average (EMA)."""
     c = _validate_series(req.close)
     out = np.asarray(ft.EMA(c, timeperiod=req.timeperiod), dtype=np.float64)
@@ -226,7 +226,7 @@ def compute_ema(req: IndicatorRequest) -> Dict[str, Any]:
 
 
 @app.post("/indicators/rsi", summary="Relative Strength Index")
-def compute_rsi(req: IndicatorRequest) -> Dict[str, Any]:
+def compute_rsi(req: IndicatorRequest) -> dict[str, Any]:
     """Compute Relative Strength Index (RSI)."""
     c = _validate_series(req.close)
     out = np.asarray(ft.RSI(c, timeperiod=req.timeperiod), dtype=np.float64)
@@ -234,7 +234,7 @@ def compute_rsi(req: IndicatorRequest) -> Dict[str, Any]:
 
 
 @app.post("/indicators/macd", summary="MACD")
-def compute_macd(req: MACDRequest) -> Dict[str, Any]:
+def compute_macd(req: MACDRequest) -> dict[str, Any]:
     """Compute MACD (line, signal, histogram).
 
     Returns ``result`` with keys ``macd``, ``signal``, ``hist``.
@@ -256,7 +256,7 @@ def compute_macd(req: MACDRequest) -> Dict[str, Any]:
 
 
 @app.post("/indicators/bbands", summary="Bollinger Bands")
-def compute_bbands(req: BBANDSRequest) -> Dict[str, Any]:
+def compute_bbands(req: BBANDSRequest) -> dict[str, Any]:
     """Compute Bollinger Bands (upper, middle, lower).
 
     Returns ``result`` with keys ``upper``, ``middle``, ``lower``.
@@ -278,7 +278,7 @@ def compute_bbands(req: BBANDSRequest) -> Dict[str, Any]:
 
 
 @app.post("/backtest", summary="Vectorized backtest")
-def run_backtest(req: BacktestRequest) -> Dict[str, Any]:
+def run_backtest(req: BacktestRequest) -> dict[str, Any]:
     """Run a vectorized backtest using a named strategy.
 
     Strategies: ``rsi_30_70``, ``sma_crossover``, ``macd_crossover``.

benchmarks/artifacts/latest/batch.json

@@ -68,4 +68,4 @@
       "speedup_vs_separate": 2.2811
     }
   ]
-}
+}

benchmarks/artifacts/latest/benchmark_derivatives_compare.json

@@ -1159,4 +1159,4 @@
       }
     ]
   }
-}
+}

benchmarks/artifacts/latest/benchmark_vs_talib.json

@@ -1379,4 +1379,4 @@
       "outcome": "ferro_ta_win"
     }
   ]
-}
+}

benchmarks/artifacts/latest/indicator_latency.json

@@ -160,4 +160,4 @@
       "elapsed_ms": 0.0026
     }
   ]
-}
+}

benchmarks/artifacts/latest/manifest.json

@@ -59,4 +59,4 @@
       "sha256": "f31fd871990c44e24a2259d618ae40a52866d20b95aa6047af3d38b9371c2ab7"
     }
   }
-}
+}

benchmarks/artifacts/latest/runtime_hotspots.json

@@ -93,4 +93,4 @@
       "share_of_suite_pct": 0.09
     }
   ]
-}
+}

benchmarks/artifacts/latest/simd.json

@@ -282,4 +282,4 @@
       ]
     }
   }
-}
+}

benchmarks/artifacts/latest/streaming.json

@@ -70,4 +70,4 @@
       "stream_over_batch_ratio": 121.7603
     }
   ]
-}
+}

benchmarks/bench_batch.py

@@ -48,13 +48,17 @@ def run_batch_benchmark(
             "SMA",
             lambda: ferro_ta.batch.batch_sma(close2d, timeperiod=14, parallel=True),
             lambda: ferro_ta.batch.batch_sma(close2d, timeperiod=14, parallel=False),
-            lambda: [ferro_ta.SMA(close2d[:, j], timeperiod=14) for j in range(n_series)],
+            lambda: [
+                ferro_ta.SMA(close2d[:, j], timeperiod=14) for j in range(n_series)
+            ],
         ),
         (
             "RSI",
             lambda: ferro_ta.batch.batch_rsi(close2d, timeperiod=14, parallel=True),
             lambda: ferro_ta.batch.batch_rsi(close2d, timeperiod=14, parallel=False),
-            lambda: [ferro_ta.RSI(close2d[:, j], timeperiod=14) for j in range(n_series)],
+            lambda: [
+                ferro_ta.RSI(close2d[:, j], timeperiod=14) for j in range(n_series)
+            ],
         ),
         (
             "ATR",
@@ -201,7 +205,9 @@ def main() -> int:
     if payload["grouped_results"]:
         print("\nGrouped Multi-Indicator Calls")
         print("-" * 64)
-        print(f"{'Case':<18} {'Grouped (ms)':>14} {'Separate (ms)':>16} {'Speedup':>12}")
+        print(
+            f"{'Case':<18} {'Grouped (ms)':>14} {'Separate (ms)':>16} {'Speedup':>12}"
+        )
         print("-" * 64)
         for row in payload["grouped_results"]:
             print(

benchmarks/bench_derivatives_compare.py

@@ -26,9 +26,10 @@ import math
 import sys
 import time
 import tracemalloc
+from collections.abc import Callable
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Callable
+from typing import Any
 
 import numpy as np
 

benchmarks/bench_simd.py

@@ -70,9 +70,7 @@ def run_simd_benchmark(
         for label, args in variants
     }
 
-    portable_rows = {
-        row["name"]: row for row in reports["portable_release"]["results"]
-    }
+    portable_rows = {row["name"]: row for row in reports["portable_release"]["results"]}
     simd_rows = {row["name"]: row for row in reports["simd_release"]["results"]}
 
     comparison: list[dict[str, Any]] = []
@@ -136,9 +134,7 @@ def main() -> int:
         window=args.window,
     )
 
-    print(
-        f"{'Case':<20} {'Portable (ms)':>14} {'SIMD (ms)':>12} {'SIMD speedup':>14}"
-    )
+    print(f"{'Case':<20} {'Portable (ms)':>14} {'SIMD (ms)':>12} {'SIMD speedup':>14}")
     print("-" * 64)
     for row in payload["results"]:
         print(

benchmarks/bench_streaming.py

@@ -57,7 +57,9 @@ def _stream_hlcv(
 ) -> float:
     streamer = factory()
     last = np.nan
-    for high_value, low_value, close_value, volume_value in zip(high, low, close, volume):
+    for high_value, low_value, close_value, volume_value in zip(
+        high, low, close, volume
+    ):
         last = streamer.update(
             float(high_value),
             float(low_value),
@@ -154,7 +156,9 @@ def run_streaming_benchmark(
 
 
 def main() -> int:
-    parser = argparse.ArgumentParser(description="Benchmark streaming indicator execution.")
+    parser = argparse.ArgumentParser(
+        description="Benchmark streaming indicator execution."
+    )
     parser.add_argument("--bars", type=int, default=100_000)
     parser.add_argument("--seed", type=int, default=2026)
     parser.add_argument("--json", dest="json_path")

benchmarks/bench_vs_talib.py

@@ -309,9 +309,13 @@ def run_comparison(sizes: list[int], json_path: str | None) -> list[dict[str, An
 
     if not TALIB_AVAILABLE:
         print("Note: ta-lib not installed. Reporting ferro_ta timings only.")
-        print("Install with: pip install ta-lib (or conda install ta-lib) for comparison.\n")
+        print(
+            "Install with: pip install ta-lib (or conda install ta-lib) for comparison.\n"
+        )
 
-    print(f"\nferro_ta vs TA-Lib — median of {N_RUNS} measured runs after {N_WARMUP} warmup")
+    print(
+        f"\nferro_ta vs TA-Lib — median of {N_RUNS} measured runs after {N_WARMUP} warmup"
+    )
     print(f"Sizes: {sizes}")
     print()
 
@@ -328,11 +332,15 @@ def run_comparison(sizes: list[int], json_path: str | None) -> list[dict[str, An
             if size == 1_000_000 and name in SKIP_1M_FOR:
                 continue
 
-            ft_samples_ms = _timed_runs_ms(ft_run, open_, high, low, close, volume, size)
+            ft_samples_ms = _timed_runs_ms(
+                ft_run, open_, high, low, close, volume, size
+            )
             ft_stats = _summary_stats(ft_samples_ms)
             ft_median_ms = float(ft_stats["median_ms"])
             ft_m_bars_s = _throughput_m_bars_s(size, ft_median_ms)
-            ft_peak_bytes = _python_peak_bytes(ft_run, open_, high, low, close, volume, size)
+            ft_peak_bytes = _python_peak_bytes(
+                ft_run, open_, high, low, close, volume, size
+            )
 
             row: dict[str, Any] = {
                 "indicator": name,
@@ -351,11 +359,15 @@ def run_comparison(sizes: list[int], json_path: str | None) -> list[dict[str, An
             }
 
             if TALIB_AVAILABLE:
-                ta_samples_ms = _timed_runs_ms(ta_run, open_, high, low, close, volume, size)
+                ta_samples_ms = _timed_runs_ms(
+                    ta_run, open_, high, low, close, volume, size
+                )
                 ta_stats = _summary_stats(ta_samples_ms)
                 ta_median_ms = float(ta_stats["median_ms"])
                 ta_m_bars_s = _throughput_m_bars_s(size, ta_median_ms)
-                speedup = ta_median_ms / ft_median_ms if ft_median_ms > 0 else float("inf")
+                speedup = (
+                    ta_median_ms / ft_median_ms if ft_median_ms > 0 else float("inf")
+                )
                 outcome = _outcome(speedup)
                 ta_peak_bytes = _python_peak_bytes(
                     ta_run, open_, high, low, close, volume, size

benchmarks/benchmark_table.py

@@ -9,7 +9,9 @@ Reads results.json and prints a markdown table: all indicators × all libraries.
 Unsupported (indicator, library) pairs show N/A. Supported pairs missing benchmark
 data show ERR (indicating the benchmark run was incomplete or failed).
 """
+
 from __future__ import annotations
+
 import json
 import sys
 from pathlib import Path
@@ -21,9 +23,11 @@ if _root not in (Path(p).resolve() for p in sys.path):
 
 from benchmarks.wrapper_registry import (
     INDICATOR_CATEGORIES,
-    LIBRARY_NAMES as LIBS,
     is_supported,
 )
+from benchmarks.wrapper_registry import (
+    LIBRARY_NAMES as LIBS,
+)
 
 
 def _all_indicators() -> list[str]:
@@ -34,11 +38,17 @@ def _all_indicators() -> list[str]:
 def main():
     p = Path(__file__).parent / "results.json"
     if not p.exists():
-        print("Run: pytest benchmarks/test_speed.py --benchmark-only --benchmark-json=benchmarks/results.json -v", file=sys.stderr)
+        print(
+            "Run: pytest benchmarks/test_speed.py --benchmark-only --benchmark-json=benchmarks/results.json -v",
+            file=sys.stderr,
+        )
         sys.exit(1)
     raw = p.read_text().strip()
     if not raw:
-        print("results.json is empty. Run the full benchmark suite first.", file=sys.stderr)
+        print(
+            "results.json is empty. Run the full benchmark suite first.",
+            file=sys.stderr,
+        )
         sys.exit(1)
     try:
         data = json.loads(raw)

benchmarks/check_vs_talib_regression.py

@@ -88,7 +88,9 @@ def main() -> int:
 
     data = json.loads(path.read_text(encoding="utf-8"))
     if not data.get("talib_available", False):
-        print("ERROR: TA-Lib was not available; cannot enforce TA-Lib regression policy.")
+        print(
+            "ERROR: TA-Lib was not available; cannot enforce TA-Lib regression policy."
+        )
         return 1
 
     summary_by_size = {
@@ -133,9 +135,7 @@ def main() -> int:
         )
 
         if rows < args.min_rows:
-            failures.append(
-                f"size={size} rows {rows} < min_rows {args.min_rows}"
-            )
+            failures.append(f"size={size} rows {rows} < min_rows {args.min_rows}")
         if med < median_floor.get(size, float("-inf")):
             failures.append(
                 f"size={size} median_speedup {med:.4f} < floor {median_floor[size]:.4f}"

benchmarks/profile_runtime_hotspots.py

@@ -50,11 +50,17 @@ def _naive_beta(x: np.ndarray, y: np.ndarray, window: int) -> np.ndarray:
     for end in range(window, len(x)):
         start = end - window
         rx = np.array(
-            [x[idx + 1] / x[idx] - 1.0 if x[idx] != 0.0 else np.nan for idx in range(start, end)],
+            [
+                x[idx + 1] / x[idx] - 1.0 if x[idx] != 0.0 else np.nan
+                for idx in range(start, end)
+            ],
             dtype=np.float64,
         )
         ry = np.array(
-            [y[idx + 1] / y[idx] - 1.0 if y[idx] != 0.0 else np.nan for idx in range(start, end)],
+            [
+                y[idx + 1] / y[idx] - 1.0 if y[idx] != 0.0 else np.nan
+                for idx in range(start, end)
+            ],
             dtype=np.float64,
         )
         mean_x = float(np.sum(rx)) / window
@@ -120,7 +126,12 @@ def build_hotspot_report(
     high = close + rng.uniform(0.1, 2.0, price_bars)
     low = close - rng.uniform(0.1, 2.0, price_bars)
     iv = rng.uniform(10.0, 40.0, iv_bars).astype(np.float64)
-    ohlcv = {"close": close, "high": high, "low": low, "volume": np.full(price_bars, 1000.0)}
+    ohlcv = {
+        "close": close,
+        "high": high,
+        "low": low,
+        "volume": np.full(price_bars, 1000.0),
+    }
 
     rows = [
         (
@@ -218,7 +229,9 @@ def build_hotspot_report(
     results.sort(key=lambda row: row["fast_ms"], reverse=True)
     total_fast_ms = sum(float(row["fast_ms"]) for row in results) or 1.0
     for row in results:
-        row["share_of_suite_pct"] = round(float(row["fast_ms"]) / total_fast_ms * 100.0, 2)
+        row["share_of_suite_pct"] = round(
+            float(row["fast_ms"]) / total_fast_ms * 100.0, 2
+        )
 
     return {
         "metadata": benchmark_metadata(
@@ -249,7 +262,9 @@ def main() -> int:
         window=args.window,
     )
 
-    print(f"{'Category':<16} {'Case':<18} {'Fast (ms)':>10} {'Ref (ms)':>10} {'Speedup':>10}")
+    print(
+        f"{'Category':<16} {'Case':<18} {'Fast (ms)':>10} {'Ref (ms)':>10} {'Speedup':>10}"
+    )
     print("-" * 70)
     for row in payload["results"]:
         print(

benchmarks/results.json

@@ -16094,4 +16094,4 @@
     ],
     "datetime": "2026-03-23T17:14:05.427766+00:00",
     "version": "5.2.3"
-}
+}

benchmarks/run_perf_contract.py

@@ -52,7 +52,9 @@ def build_indicator_latency_report(*, rounds: int = 5) -> dict[str, Any]:
 
     rows: list[dict[str, Any]] = []
     for entry in INDICATOR_SUITE:
-        elapsed_ms = _time_min(lambda entry=entry: _run_indicator(entry, ohlcv), rounds=rounds)
+        elapsed_ms = _time_min(
+            lambda entry=entry: _run_indicator(entry, ohlcv), rounds=rounds
+        )
         rows.append(
             {
                 "name": entry["name"],
@@ -192,10 +194,7 @@ def main() -> int:
             fixtures=[FIXTURE_PATH],
             extra={"output_dir": str(output_dir)},
         ),
-        "artifacts": {
-            name: file_info(path)
-            for name, path in artifacts.items()
-        },
+        "artifacts": {name: file_info(path) for name, path in artifacts.items()},
     }
     manifest_path = output_dir / "manifest.json"
     _write_json(manifest_path, manifest)

benchmarks/test_accuracy.py

@@ -5,65 +5,66 @@ For each indicator we compare ferro_ta output against every available reference
 Tolerances are based on known algorithmic differences (e.g. Wilder vs SMA seed).
 We only compare the overlapping (valid) suffix of each output array.
 """
+
 from __future__ import annotations
+
 import numpy as np
 import pytest
 
 from benchmarks.data_generator import MEDIUM
 from benchmarks.wrapper_registry import (
-    execute_indicator,
-    INDICATOR_NAMES,
-    INDICATOR_CATEGORIES,
-    CUMULATIVE_INDICATORS,
     BINARY_INDICATORS,
+    CUMULATIVE_INDICATORS,
+    INDICATOR_CATEGORIES,
+    INDICATOR_NAMES,
     available_libraries,
+    execute_indicator,
     is_supported,
 )
 
 # Reference = ferro_ta; compare against each library that has a non-empty result.
 REFERENCE_LIB = "ferro_ta"
-COMPARISON_LIBS = [l for l in available_libraries() if l != REFERENCE_LIB]
+COMPARISON_LIBS = [
+    library for library in available_libraries() if library != REFERENCE_LIB
+]
 
 # Per-indicator tolerances  (rtol, atol)
 _TOLERANCES: dict[str, tuple[float, float]] = {
-    "ATR":    (1e-3, 0.05),   # Wilder's smoothing seed differs
-    "NATR":   (1e-3, 0.10),
-    "BBANDS": (1e-3, 0.20),   # ddof=0 vs ddof=1
+    "ATR": (1e-3, 0.05),  # Wilder's smoothing seed differs
+    "NATR": (1e-3, 0.10),
+    "BBANDS": (1e-3, 0.20),  # ddof=0 vs ddof=1
     "STDDEV": (1e-3, 0.20),
-    "VAR":    (1e-3, 0.50),
-    "MACD":   (1e-3, 1e-3),   # double EMA seed
-    "KAMA":   (1e-3, 1e-3),
-    "STOCH":  (1e-3, 0.10),   # smoothing method differences
-    "SAR":    (1e-3, 0.20),
-    "ADOSC":  (1e-3, 0.20),
-    "ADX":    (1e-3, 0.50),   # Wilder's ADX
-    "PLUS_DI":(1e-3, 0.50),
-    "MINUS_DI":(1e-3, 0.50),
-    "PPO":    (1e-2, 1e-3),
-    "CMO":    (1e-3, 0.10),
-    "TRIX":   (1e-3, 1e-3),
-    "CCI":    (1e-3, 0.10),
+    "VAR": (1e-3, 0.50),
+    "MACD": (1e-3, 1.00),  # seed differences across libraries
+    "KAMA": (1e-3, 1e-3),
+    "STOCH": (1e-3, 0.10),  # smoothing method differences
+    "SAR": (1e-3, 0.20),
+    "ADOSC": (1e-3, 0.20),
+    "ADX": (1e-3, 0.50),  # Wilder's ADX
+    "PLUS_DI": (1e-3, 0.50),
+    "MINUS_DI": (1e-3, 0.50),
+    "PPO": (1e-2, 1e-3),
+    "CMO": (1e-3, 0.10),
+    "TRIX": (1e-3, 0.05),
+    "CCI": (1e-3, 0.10),
     "SUPERTREND": (1e-2, 0.50),
     "KELTNER_CHANNELS": (1e-2, 0.50),
     "DONCHIAN": (1e-4, 1e-4),
-    "HT_DCPERIOD": (1e-2, 1.0),
-    "VWAP":   (1e-3, 0.10),
-    "AROON":  (1e-4, 1e-3),
+    "HT_DCPERIOD": (1e-2, 2.0),
+    "VWAP": (1e-3, 0.10),
+    "AROON": (1e-4, 1e-3),
     "LINEARREG": (1e-4, 1e-4),
     "LINEARREG_SLOPE": (1e-4, 1e-4),
     "CORREL": (1e-4, 1e-3),
-    "BETA":   (1e-3, 1e-3),
-    "TSF":    (1e-4, 1e-4),
-    "EMA":    (1e-3, 0.30),   # ta library uses different EMA seed
-    "DEMA":   (1e-3, 0.50),
-    "TEMA":   (1e-3, 0.50),
-    "T3":     (1e-3, 0.50),
-    "HULL_MA":(1e-3, 0.10),
-    "WMA":    (1e-4, 1e-4),
-    "TRIMA":  (1e-4, 1e-4),
-    "MACD":   (1e-3, 1.00),   # seed differences across libraries
-    "TRIX":   (1e-3, 0.05),
-    "HT_DCPERIOD": (1e-2, 2.0),
+    "BETA": (1e-3, 1e-3),
+    "TSF": (1e-4, 1e-4),
+    "EMA": (1e-3, 0.30),  # ta library uses different EMA seed
+    "DEMA": (1e-3, 0.50),
+    "TEMA": (1e-3, 0.50),
+    "T3": (1e-3, 0.50),
+    "HULL_MA": (1e-3, 0.10),
+    "WMA": (1e-4, 1e-4),
+    "TRIMA": (1e-4, 1e-4),
 }
 
 _DEFAULT_TOL = (1e-4, 1e-5)
@@ -71,33 +72,33 @@ _DEFAULT_TOL = (1e-4, 1e-5)
 # Pairs that use correlation check (>=0.95) due to known algorithmic divergence
 # Format: (indicator, library) or just indicator (applies to all libs)
 _CORRELATION_PAIRS: set[tuple[str, str]] = {
-    ("PPO", "talib"),         # different PPO formula normalization
+    ("PPO", "talib"),  # different PPO formula normalization
     ("PPO", "pandas_ta"),
     ("PPO", "tulipy"),
     ("STOCH", "ta"),
     ("SUPERTREND", "pandas_ta"),
     ("KELTNER_CHANNELS", "pandas_ta"),
     ("KELTNER_CHANNELS", "ta"),
-    ("EMA", "finta"),         # finta EMA uses different initialization
-    ("KAMA", "pandas_ta"),    # pandas_ta KAMA has slightly different seed
-    ("RSI", "ta"),            # ta uses SMA warmup vs Wilder
-    ("RSI", "finta"),         # same
+    ("EMA", "finta"),  # finta EMA uses different initialization
+    ("KAMA", "pandas_ta"),  # pandas_ta KAMA has slightly different seed
+    ("RSI", "ta"),  # ta uses SMA warmup vs Wilder
+    ("RSI", "finta"),  # same
 }
 
 # Pairs that are skipped because they are structurally incompatible
 _SKIP_PAIRS: set[tuple[str, str]] = {
-    ("BBANDS", "finta"),        # finta normalizes band differently
-    ("ATR", "finta"),           # finta ATR uses simple TR not Wilder
-    ("STDDEV", "finta"),        # finta uses population std
-    ("TRIMA", "finta"),         # finta TRIMA uses different formula
-    ("PPO", "finta"),           # finta PPO scaling incompatible
-    ("STOCH", "finta"),         # finta STOCH formula differs
-    ("VWAP", "pandas_ta"),      # pandas_ta VWAP anchors to session start
+    ("BBANDS", "finta"),  # finta normalizes band differently
+    ("ATR", "finta"),  # finta ATR uses simple TR not Wilder
+    ("STDDEV", "finta"),  # finta uses population std
+    ("TRIMA", "finta"),  # finta TRIMA uses different formula
+    ("PPO", "finta"),  # finta PPO scaling incompatible
+    ("STOCH", "finta"),  # finta STOCH formula differs
+    ("VWAP", "pandas_ta"),  # pandas_ta VWAP anchors to session start
     ("HT_TRENDMODE", "talib"),  # binary; Hilbert seed diverges
-    ("CMO", "talib"),           # ferro_ta CMO smoothing variant corr < 0.90
+    ("CMO", "talib"),  # ferro_ta CMO smoothing variant corr < 0.90
     ("CMO", "pandas_ta"),
     ("CMO", "finta"),
-    ("PLUS_DI", "pandas_ta"),   # pandas_ta ADX column naming corr < 0.70
+    ("PLUS_DI", "pandas_ta"),  # pandas_ta ADX column naming corr < 0.70
 }
 
 MIN_OVERLAP = 30  # minimum points to make comparison meaningful
@@ -114,12 +115,14 @@ def _compare(ref: np.ndarray, cmp: np.ndarray, indicator: str, library: str) ->
     c = cmp[-n:]
     if indicator in BINARY_INDICATORS or (indicator, library) in _CORRELATION_PAIRS:
         # Use correlation check for structurally different algorithms
-        corr = np.corrcoef(r, c)[0, 1] if not indicator in BINARY_INDICATORS else None
+        corr = np.corrcoef(r, c)[0, 1] if indicator not in BINARY_INDICATORS else None
         if indicator in BINARY_INDICATORS:
             agree = np.mean(r == c)
             assert agree >= 0.80, f"Binary agreement {agree:.1%} < 80%"
         else:
-            assert corr >= 0.90, f"Correlation {corr:.4f} < 0.90 (structural divergence)"
+            assert corr >= 0.90, (
+                f"Correlation {corr:.4f} < 0.90 (structural divergence)"
+            )
     elif indicator in CUMULATIVE_INDICATORS:
         dr, dc = np.diff(r), np.diff(c)
         if len(dr) < 5 or len(dc) < 5:
@@ -136,6 +139,7 @@ def _compare(ref: np.ndarray, cmp: np.ndarray, indicator: str, library: str) ->
 
 # ── dynamically generate one test per (indicator, library) pair ─────────────
 
+
 def pytest_generate_tests(metafunc):
     if "indicator" in metafunc.fixturenames and "library" in metafunc.fixturenames:
         params = []
@@ -172,6 +176,7 @@ class TestAccuracy:
 
 # ── quick smoke tests that always run (no skip) ──────────────────────────────
 
+
 class TestSmoke:
     """Sanity checks that ferro_ta returns non-empty finite arrays."""
 
@@ -182,7 +187,9 @@ class TestSmoke:
 
         arr = execute_indicator("ferro_ta", indicator, MEDIUM)
         assert len(arr) > 0, f"ferro_ta {indicator} returned empty array"
-        assert np.all(np.isfinite(arr)), f"ferro_ta {indicator} has non-finite values: {arr[~np.isfinite(arr)][:5]}"
+        assert np.all(np.isfinite(arr)), (
+            f"ferro_ta {indicator} has non-finite values: {arr[~np.isfinite(arr)][:5]}"
+        )
 
     @pytest.mark.parametrize("category,indicators", INDICATOR_CATEGORIES.items())
     def test_category_coverage(self, category, indicators):

benchmarks/test_derivatives_speed.py

@@ -10,11 +10,27 @@ Run with:
 from __future__ import annotations
 
 import importlib.util
+import sys
+from pathlib import Path
 
 import numpy as np
 import pytest
 
-from ferro_ta.analysis.options import implied_volatility, option_price
+# Ensure direct benchmark test runs can import local package from `python/`.
+ROOT = Path(__file__).resolve().parents[1]
+PYTHON_SRC = ROOT / "python"
+if str(PYTHON_SRC) not in sys.path:
+    sys.path.insert(0, str(PYTHON_SRC))
+
+HAS_FERRO_EXTENSION = True
+try:
+    from ferro_ta.analysis.options import implied_volatility, option_price
+except ModuleNotFoundError:
+    HAS_FERRO_EXTENSION = False
+
+pytestmark = pytest.mark.skipif(
+    not HAS_FERRO_EXTENSION, reason="ferro_ta extension is not built"
+)
 
 
 def _sample_chain(n: int = 1000) -> tuple[np.ndarray, ...]:

benchmarks/test_speed.py

@@ -6,31 +6,36 @@ Run:  pytest benchmarks/test_speed.py --benchmark-only -v
 
 Streaming benchmarks are in test_streaming_speed.py
 """
+
 from __future__ import annotations
+
 import pytest
 
 from benchmarks.data_generator import LARGE
 from benchmarks.wrapper_registry import (
-    execute_indicator,
     INDICATOR_CATEGORIES,
     available_libraries,
+    execute_indicator,
     is_supported,
 )
 
-BENCH_DATA   = LARGE   # 100k bars for main benchmarks
-BENCH_LIBS   = available_libraries()
+BENCH_DATA = LARGE  # 100k bars for main benchmarks
+BENCH_LIBS = available_libraries()
 
 
 def _make_bench(indicator: str, library: str):
     """Return a benchmark function that runs indicator on library (uses BENCH_DATA)."""
+
     def _fn():
         execute_indicator(library, indicator, BENCH_DATA)
+
     _fn.__name__ = f"{library}_{indicator}"
     return _fn
 
 
 # ── Parametrize over all (indicator, library) combinations ───────────────────
 
+
 def pytest_generate_tests(metafunc):
     if "indicator" in metafunc.fixturenames and "library" in metafunc.fixturenames:
         params = []
@@ -53,20 +58,24 @@ class TestSpeed:
 
 # ── Standalone head-to-head for the most important indicators ─────────────────
 
-@pytest.mark.parametrize("indicator,libs", [
-    ("SMA",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("EMA",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("RSI",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("MACD",  ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("BBANDS",["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("ATR",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("CCI",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("WILLR", ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("OBV",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("ADX",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("MFI",   ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-    ("STOCH", ["ferro_ta","talib","tulipy","pandas_ta","ta","finta"]),
-])
+
+@pytest.mark.parametrize(
+    "indicator,libs",
+    [
+        ("SMA", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("EMA", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("RSI", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("MACD", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("BBANDS", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("ATR", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("CCI", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("WILLR", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("OBV", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("ADX", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("MFI", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+        ("STOCH", ["ferro_ta", "talib", "tulipy", "pandas_ta", "ta", "finta"]),
+    ],
+)
 def test_head_to_head(benchmark, indicator, libs):
     """Benchmark ferro_ta vs all peers — for README table generation."""
     if not is_supported("ferro_ta", indicator):
@@ -77,7 +86,11 @@ def test_head_to_head(benchmark, indicator, libs):
 
 # ── Large dataset benchmarks (100k bars) ─────────────────────────────────────
 
-@pytest.mark.parametrize("indicator", ["SMA","EMA","RSI","MACD","ATR","BBANDS","OBV","CCI","ADX","MFI"])
+
+@pytest.mark.parametrize(
+    "indicator",
+    ["SMA", "EMA", "RSI", "MACD", "ATR", "BBANDS", "OBV", "CCI", "ADX", "MFI"],
+)
 def test_large_dataset(benchmark, indicator):
     """Scaling benchmark at 100k bars for ferro_ta."""
     if not is_supported("ferro_ta", indicator):

conda/meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "ferro-ta" %}
-{% set version = "1.0.3" %}
+{% set version = "1.0.6" %}
 
 package:
   name: {{ name|lower }}

crates/ferro_ta_core/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "ferro_ta_core"
-version = "1.0.3"
+version = "1.0.6"
 edition = "2021"
 description = "Pure Rust core indicator library — no PyO3, no numpy dependency"
 license = "MIT"

crates/ferro_ta_core/README.md

@@ -13,7 +13,7 @@ PyO3, NumPy, or Python runtime dependency, which makes it a good fit for:
 
 ```toml
 [dependencies]
-ferro_ta_core = "1.0.3"
+ferro_ta_core = "1.0.6"
 ```
 
 ## Design

docs/adjacent_tooling.rst

@@ -20,7 +20,8 @@ a Python technical analysis library.
        `docs/agentic.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_.
    * - MCP server
      - Experimental or adjacent
-     - Exposes selected ferro-ta capabilities to MCP-compatible clients. See
+     - FastMCP-based server exposing selected ferro-ta capabilities to
+       MCP-compatible clients. See
        `docs/mcp.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_.
    * - WASM package
      - Experimental

docs/agentic.md

@@ -198,6 +198,6 @@ while True:
 
 - `ferro_ta.tools` — module source.
 - `ferro_ta.workflow` — module source.
-- `docs/mcp.md` — MCP server for Cursor/Claude integration.
+- `docs/mcp.md` — MCP server for MCP-compatible clients.
 - `ferro_ta.backtest` — backtest harness.
 - `ferro_ta.registry` — indicator registry.

docs/changelog.rst

@@ -1,7 +1,31 @@
 Release Notes
 =============
 
-These docs track package version ``1.0.3``.
+These docs track package version ``1.0.6``.
+
+1.0.6 (2026-03-24)
+------------------
+
+- Added a repo-managed pre-push gate so the core Rust, Python, docs, and WASM
+  checks can be run locally before release.
+- Expanded Rust-backed analysis/data helpers, broadened the WASM exports, and
+  added cross-surface API manifest verification plus Node conformance checks.
+- Refreshed benchmark coverage and perf artifacts, aligned Python CI with the
+  local tooling flow, and updated the locked security fixes needed for a clean
+  release pass.
+
+1.0.4 (2026-03-24)
+------------------
+
+- Expanded the optional MCP server from a small hand-written subset to the
+  broader public ferro-ta callable surface, including stateful class support
+  through stored-instance management tools.
+- Split the root documentation so the full TA-Lib compatibility matrix lives in
+  ``TA_LIB_COMPATIBILITY.md`` while the README stays product-first and shorter.
+- Refreshed MCP docs/tests and updated locked low-risk Python dependencies as
+  part of the release cleanup pass.
+- Stopped tracking the stray ``.coverage`` artifact and aligned ignore rules
+  for local coverage outputs.
 
 1.0.3 (2026-03-24)
 ------------------
@@ -12,6 +36,8 @@ These docs track package version ``1.0.3``.
   pricing, IV, Greeks, and Black-76 comparisons.
 - Simplified release version bumps with a single script and updated release
   guidance.
+- Fixed Python CI/type-stub gaps around the new metadata API and corrected the
+  tag-driven GitHub Release workflow trigger used for publish automation.
 
 1.0.2 (2026-03-24)
 ------------------

docs/contributing.rst

@@ -26,6 +26,28 @@ Prerequisites: Rust stable toolchain, Python 3.10+, and ``maturin``.
    pytest tests/
 
 
+Git hooks and pre-push checks
+-----------------------------
+
+Install the repository-managed hooks after setting up the environment:
+
+.. code-block:: bash
+
+   make hooks
+
+Run the same push gate manually with:
+
+.. code-block:: bash
+
+   make prepush
+
+You can scope it to selected checks while iterating:
+
+.. code-block:: bash
+
+   make prepush CHECKS="version changelog python_lint"
+
+
 Adding a new indicator
 -----------------------
 

docs/index.rst

@@ -61,7 +61,7 @@ Adjacent and experimental tooling:
 
 - Derivatives analytics — see :doc:`derivatives`
 - Agentic workflow and LangChain tool wrappers — see `Agentic guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_
-- MCP server for Cursor/Claude integration — see `MCP guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_
+- MCP server for MCP-compatible clients — see `MCP guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_
 - WASM, plugins, and other optional surfaces — see :doc:`adjacent_tooling`
 
 Installation

docs/mcp.md

@@ -1,42 +1,56 @@
-# MCP Server — Connect ferro-ta in Cursor
+# MCP Server
 
-ferro-ta ships an MCP (Model Context Protocol) server that exposes
-indicators and backtest tools to AI agents.  This guide shows how to run
-the server and connect it to Cursor or any MCP-compatible client.
+ferro-ta ships an optional MCP (Model Context Protocol) server built on the
+official Python SDK's FastMCP layer. The server now exposes the broad public
+ferro-ta callable surface instead of a tiny hand-picked subset.
+
+That means MCP clients can use:
+
+- Exact top-level ferro-ta exports such as `SMA`, `RSI`, `MACD`, `about`,
+  `methods`, `info`, `benchmark`, and `traced`
+- Non-top-level public tools such as `compute_indicator`, `run_backtest`,
+  `check_cross`, `aggregate_ticks`, `TickAggregator`, and `AlertManager`
+- Legacy lowercase convenience aliases: `sma`, `ema`, `rsi`, `macd`,
+  and `backtest`
+- Generic instance tools for stateful classes and stored callables:
+  `list_instances`, `describe_instance`, `call_instance_method`,
+  `call_stored_callable`, and `delete_instance`
 
 ---
 
 ## Installation
 
-The MCP server requires no additional dependencies beyond ferro_ta itself.
-For the full MCP SDK integration (recommended), install the optional extra:
+Install the optional MCP extra:
 
 ```bash
 pip install "ferro-ta[mcp]"
 ```
 
-or install the `mcp` package separately:
+If you are working from this repository, you can install the same extra into
+the project environment with:
 
 ```bash
-pip install "mcp>=1.0"
+uv sync --extra mcp
 ```
 
 ---
 
 ## Running the server
 
+Run the server over stdio:
+
 ```bash
 python -m ferro_ta.mcp
 ```
 
-The server listens on stdin/stdout using JSON-RPC 2.0 (the MCP protocol).
+The command exits immediately with an install hint if the optional `mcp`
+dependency is missing.
 
 ---
 
 ## Connect in Cursor
 
-1. Open Cursor settings (Command Palette → "Open User Settings (JSON)").
-2. Find or create the `mcpServers` section:
+Add the server to Cursor's MCP settings:
 
 ```json
 {
@@ -44,82 +58,134 @@ The server listens on stdin/stdout using JSON-RPC 2.0 (the MCP protocol).
     "ferro-ta": {
       "command": "python",
       "args": ["-m", "ferro_ta.mcp"],
-      "description": "ferro_ta — Technical Analysis MCP server"
+      "description": "ferro-ta technical analysis tools"
     }
   }
 }
 ```
 
-3. Reload Cursor (Command Palette → "Developer: Reload Window").
-4. The ferro-ta tools will appear in the Tools panel.
+You can place this in your user settings JSON or in a workspace-level
+`.cursor/mcp.json`.
 
-### Workspace-level config
+---
 
-You can also add the config to your project's `.cursor/mcp.json`:
+## Tool naming
+
+The MCP server prefers the real ferro-ta API names.
+
+- Use exact public names when possible, for example `SMA`, `MACD`,
+  `compute_indicator`, `trade_stats`, `TickAggregator`, or `AlertManager`
+- Use the legacy lowercase aliases only when you want the old MCP-friendly
+  shortcuts and result shapes
+- Use `about`, `methods`, `indicators`, and `info` to discover what is
+  available from inside an MCP client
+
+---
+
+## Stateful classes and object references
+
+Class tools return stored object references instead of plain text placeholders.
+For example, calling `TickAggregator` or `AlertManager` returns a payload like:
 
 ```json
 {
-  "mcpServers": {
-    "ferro-ta": {
-      "command": "python",
-      "args": ["-m", "ferro_ta.mcp"]
-    }
-  }
+  "instance_id": "tickaggregator-0001",
+  "type": "ferro_ta.data.aggregation.TickAggregator",
+  "repr": "TickAggregator(rule='tick:2')"
 }
 ```
 
+Use that `instance_id` with:
+
+- `describe_instance` to inspect the stored object and list public methods
+- `call_instance_method` to call methods like `aggregate`, `update`,
+  `run_backtest`, or `to_dict`
+- `delete_instance` to remove stored objects when you are done
+
+If a tool returns a stored callable, use `call_stored_callable`.
+
+---
+
+## Callable references
+
+Some ferro-ta APIs accept other callables, for example `benchmark`,
+`log_call`, `traced`, or `multi_timeframe(indicator=...)`.
+
+Pass public ferro-ta callables using:
+
+```json
+{"callable": "SMA"}
+```
+
+Pass stored objects using:
+
+```json
+{"instance_id": "function-0001"}
+```
+
 ---
 
 ## Example prompts
 
-Once connected, you can ask Claude (or any MCP-enabled AI) things like:
+Once connected, you can ask an MCP-compatible client things like:
 
-> "Compute RSI(14) on this price series: [100, 102, 101, 105, 108, 104, 107]"
+> "Run `SMA` with `close=[100, 101, 102, 103, 104]` and `timeperiod=3`."
 
-> "Run a backtest with the rsi_30_70 strategy on [100, 101, 99, 103, 106, 102, 108, 105, 109, 112, 108, 111]"
+> "Use `compute_indicator` to calculate `MACD` for this close series."
 
-> "List all available ferro_ta indicators"
+> "Call `about` and summarize the current ferro-ta API surface."
 
-> "What does the SMA indicator do?"
+> "Create a `TickAggregator` with `rule='tick:50'`, aggregate this tick data,
+> then delete the instance."
+
+> "Benchmark `SMA` over this price series using a callable reference."
 
 ---
 
-## Available tools
+## Programmatic use
 
-| Tool | Description |
-|------|-------------|
-| `sma` | Simple Moving Average |
-| `ema` | Exponential Moving Average |
-| `rsi` | Relative Strength Index |
-| `macd` | MACD line, signal, histogram |
-| `backtest` | Vectorized backtest (rsi_30_70, sma_crossover, macd_crossover) |
-| `list_indicators` | List all registered indicators |
-| `describe_indicator` | Describe a named indicator |
-
----
-
-## Programmatic use (Python client)
-
-You can also use the MCP handlers directly in Python without the server:
+Use the server entrypoint:
 
 ```python
-from ferro_ta.mcp import handle_list_tools, handle_call_tool
-import numpy as np
+from ferro_ta.mcp import create_server
+
+server = create_server()
+# server.run(transport="stdio")
+```
+
+Or call the handlers directly without starting the server:
+
+```python
+from ferro_ta.mcp import handle_call_tool, handle_list_tools
+import json
 
-# List tools
 tools = handle_list_tools()
-print([t["name"] for t in tools["tools"]])
+print(len(tools["tools"]))
 
-# Call RSI
-close = list(np.cumprod(1 + np.random.default_rng(0).normal(0, 0.01, 50)) * 100)
-result = handle_call_tool("rsi", {"close": close, "timeperiod": 14})
-print(result)
+close = [100, 101, 102, 103, 104]
+result = handle_call_tool("SMA", {"close": close, "timeperiod": 3})
+print(json.loads(result["content"][0]["text"]))
+
+aggregator = json.loads(
+    handle_call_tool("TickAggregator", {"rule": "tick:2"})["content"][0]["text"]
+)
+bars = handle_call_tool(
+    "call_instance_method",
+    {
+        "instance_id": aggregator["instance_id"],
+        "method": "aggregate",
+        "args": [{"price": [1, 2, 3, 4], "size": [1, 1, 1, 1]}],
+    },
+)
+print(json.loads(bars["content"][0]["text"]))
 ```
 
 ---
 
 ## See also
 
-- `ferro_ta.mcp` — module source.
-- `ferro_ta.tools` — underlying tool functions.
-- `docs/agentic.md` — LangChain and workflow integration.
+- `python -m ferro_ta.mcp` - stdio MCP entrypoint
+- `ferro_ta.mcp.create_server()` - FastMCP server factory
+- `ferro_ta.tools.api_info` - API discovery helpers used by the MCP catalog
+- `ferro_ta.tools` - stable wrappers such as `compute_indicator`
+- `docs/agentic.md` - workflow and agent integration notes

docs/support_matrix.rst

@@ -107,7 +107,7 @@ For source builds, packaging details, and platform notes, see
 Release status
 --------------
 
-These docs track package version ``1.0.3``.
+These docs track package version ``1.0.6``.
 
 - Release notes by version: :doc:`changelog`
 - Canonical project changelog: `CHANGELOG.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/CHANGELOG.md>`_

examples/backtesting.ipynb

@@ -21,13 +21,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "\n",
     "import ferro_ta.config as config\n",
-    "from ferro_ta import BBANDS, EMA, RSI, SMA\n",
+    "import numpy as np\n",
     "from ferro_ta.backtest import backtest\n",
     "from ferro_ta.pipeline import Pipeline\n",
     "\n",
+    "from ferro_ta import BBANDS, EMA, RSI, SMA\n",
+    "\n",
     "# Synthetic data\n",
     "np.random.seed(42)\n",
     "n = 300\n",

examples/quickstart.ipynb

@@ -207,9 +207,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from ferro_ta import FerroTAValueError\n",
     "from ferro_ta.exceptions import check_timeperiod\n",
     "\n",
+    "from ferro_ta import FerroTAValueError\n",
+    "\n",
     "try:\n",
     "    check_timeperiod(0)\n",
     "except FerroTAValueError as e:\n",

examples/streaming.ipynb

@@ -22,7 +22,6 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "\n",
     "from ferro_ta.streaming import (\n",
     "    StreamingATR,\n",
     "    StreamingBBands,\n",

perf-contract/batch.json

@@ -2,16 +2,38 @@
   "metadata": {
     "suite": "batch",
     "runtime": {
-      "generated_at_utc": "2026-03-23T21:08:30.877702+00:00",
-      "python_version": "3.12.11",
-      "platform": "macOS-26.3.1-arm64-arm-64bit",
+      "generated_at_utc": "2026-03-24T09:13:04.010216+00:00",
+      "python_version": "3.13.5",
+      "python_implementation": "CPython",
+      "python_executable": "/Users/pratikbhadane/Work/Projects/ferro-ta/.venv/bin/python3",
+      "platform": "macOS-26.3.1-arm64-arm-64bit-Mach-O",
+      "system": "Darwin",
+      "release": "25.3.0",
       "machine": "arm64",
-      "processor": "arm"
+      "processor": "arm",
+      "cpu_model": "Apple M3 Max",
+      "cpu_count_logical": 14,
+      "total_memory_bytes": 38654705664
     },
     "git": {
-      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "commit": "53566b9d82898fa4f95c5190156c969a0bd8e8e3",
       "dirty": true,
-      "branch": "feat/performace-1.0.2"
+      "branch": "main"
+    },
+    "build": {
+      "rustc": "rustc 1.93.1 (01f6ddf75 2026-02-11)\nbinary: rustc\ncommit-hash: 01f6ddf7588f42ae2d7eb0a2f21d44e8e96674cf\ncommit-date: 2026-02-11\nhost: aarch64-apple-darwin\nrelease: 1.93.1\nLLVM version: 21.1.8",
+      "cargo": "cargo 1.93.1 (083ac5135 2025-12-15)",
+      "cargo_release_profile": {
+        "lto": true,
+        "codegen-units": 1
+      },
+      "rustflags": null,
+      "cargo_build_rustflags": null,
+      "maturin_flags": null
+    },
+    "packages": {
+      "numpy": "2.2.6",
+      "ferro-ta": "1.0.4"
     },
     "dataset": {
       "n_samples": 20000,
@@ -23,49 +45,49 @@
   "results": [
     {
       "indicator": "SMA",
-      "parallel_ms": 1.9615,
-      "sequential_ms": 2.0423,
-      "loop_ms": 0.8865,
-      "parallel_speedup_vs_loop": 0.452,
-      "sequential_speedup_vs_loop": 0.4341
+      "parallel_ms": 7.9306,
+      "sequential_ms": 2.4832,
+      "loop_ms": 1.0238,
+      "parallel_speedup_vs_loop": 0.1291,
+      "sequential_speedup_vs_loop": 0.4123
     },
     {
       "indicator": "RSI",
-      "parallel_ms": 2.1731,
-      "sequential_ms": 4.3225,
-      "loop_ms": 3.2043,
-      "parallel_speedup_vs_loop": 1.4745,
-      "sequential_speedup_vs_loop": 0.7413
+      "parallel_ms": 3.9307,
+      "sequential_ms": 5.0938,
+      "loop_ms": 3.6883,
+      "parallel_speedup_vs_loop": 0.9383,
+      "sequential_speedup_vs_loop": 0.7241
     },
     {
       "indicator": "ATR",
-      "parallel_ms": 4.2249,
-      "sequential_ms": 6.6175,
-      "loop_ms": 4.0382,
-      "parallel_speedup_vs_loop": 0.9558,
-      "sequential_speedup_vs_loop": 0.6102
+      "parallel_ms": 9.2546,
+      "sequential_ms": 7.768,
+      "loop_ms": 5.2669,
+      "parallel_speedup_vs_loop": 0.5691,
+      "sequential_speedup_vs_loop": 0.678
     },
     {
       "indicator": "ADX",
-      "parallel_ms": 4.8674,
-      "sequential_ms": 7.6402,
-      "loop_ms": 5.1861,
-      "parallel_speedup_vs_loop": 1.0655,
-      "sequential_speedup_vs_loop": 0.6788
+      "parallel_ms": 9.5489,
+      "sequential_ms": 9.1403,
+      "loop_ms": 7.1578,
+      "parallel_speedup_vs_loop": 0.7496,
+      "sequential_speedup_vs_loop": 0.7831
     }
   ],
   "grouped_results": [
     {
       "case": "close_bundle_3",
-      "grouped_ms": 0.1878,
-      "separate_ms": 0.1719,
-      "speedup_vs_separate": 0.9155
+      "grouped_ms": 0.466,
+      "separate_ms": 0.2003,
+      "speedup_vs_separate": 0.4298
     },
     {
       "case": "hlc_bundle_3",
-      "grouped_ms": 0.2958,
-      "separate_ms": 0.4633,
-      "speedup_vs_separate": 1.5666
+      "grouped_ms": 1.2538,
+      "separate_ms": 0.6999,
+      "speedup_vs_separate": 0.5582
     }
   ]
-}
+}

perf-contract/indicator_latency.json

@@ -2,16 +2,38 @@
   "metadata": {
     "suite": "indicator_latency",
     "runtime": {
-      "generated_at_utc": "2026-03-23T21:08:30.515962+00:00",
-      "python_version": "3.12.11",
-      "platform": "macOS-26.3.1-arm64-arm-64bit",
+      "generated_at_utc": "2026-03-24T09:13:02.973728+00:00",
+      "python_version": "3.13.5",
+      "python_implementation": "CPython",
+      "python_executable": "/Users/pratikbhadane/Work/Projects/ferro-ta/.venv/bin/python3",
+      "platform": "macOS-26.3.1-arm64-arm-64bit-Mach-O",
+      "system": "Darwin",
+      "release": "25.3.0",
       "machine": "arm64",
-      "processor": "arm"
+      "processor": "arm",
+      "cpu_model": "Apple M3 Max",
+      "cpu_count_logical": 14,
+      "total_memory_bytes": 38654705664
     },
     "git": {
-      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "commit": "53566b9d82898fa4f95c5190156c969a0bd8e8e3",
       "dirty": true,
-      "branch": "feat/performace-1.0.2"
+      "branch": "main"
+    },
+    "build": {
+      "rustc": "rustc 1.93.1 (01f6ddf75 2026-02-11)\nbinary: rustc\ncommit-hash: 01f6ddf7588f42ae2d7eb0a2f21d44e8e96674cf\ncommit-date: 2026-02-11\nhost: aarch64-apple-darwin\nrelease: 1.93.1\nLLVM version: 21.1.8",
+      "cargo": "cargo 1.93.1 (083ac5135 2025-12-15)",
+      "cargo_release_profile": {
+        "lto": true,
+        "codegen-units": 1
+      },
+      "rustflags": null,
+      "cargo_build_rustflags": null,
+      "maturin_flags": null
+    },
+    "packages": {
+      "numpy": "2.2.6",
+      "ferro-ta": "1.0.4"
     },
     "fixtures": [
       {
@@ -33,7 +55,7 @@
       "kwargs": {
         "timeperiod": 20
       },
-      "elapsed_ms": 0.0202
+      "elapsed_ms": 0.0233
     },
     {
       "name": "WILLR_14",
@@ -41,13 +63,13 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0183
+      "elapsed_ms": 0.0207
     },
     {
       "name": "STOCH",
       "inputs": "hlc",
       "kwargs": {},
-      "elapsed_ms": 0.0181
+      "elapsed_ms": 0.0202
     },
     {
       "name": "ADX_14",
@@ -55,7 +77,7 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0147
+      "elapsed_ms": 0.0171
     },
     {
       "name": "CCI_14",
@@ -63,13 +85,13 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0144
+      "elapsed_ms": 0.0164
     },
     {
       "name": "MACD",
       "inputs": "close",
       "kwargs": {},
-      "elapsed_ms": 0.0132
+      "elapsed_ms": 0.015
     },
     {
       "name": "ATR_14",
@@ -77,7 +99,7 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0108
+      "elapsed_ms": 0.0116
     },
     {
       "name": "RSI_14",
@@ -85,7 +107,7 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0102
+      "elapsed_ms": 0.011
     },
     {
       "name": "STDDEV_20",
@@ -93,7 +115,7 @@
       "kwargs": {
         "timeperiod": 20
       },
-      "elapsed_ms": 0.0086
+      "elapsed_ms": 0.0103
     },
     {
       "name": "BETA_5",
@@ -101,7 +123,7 @@
       "kwargs": {
         "timeperiod": 5
       },
-      "elapsed_ms": 0.008
+      "elapsed_ms": 0.0091
     },
     {
       "name": "CORREL_30",
@@ -109,15 +131,7 @@
       "kwargs": {
         "timeperiod": 30
       },
-      "elapsed_ms": 0.0068
-    },
-    {
-      "name": "EMA_20",
-      "inputs": "close",
-      "kwargs": {
-        "timeperiod": 20
-      },
-      "elapsed_ms": 0.0052
+      "elapsed_ms": 0.0078
     },
     {
       "name": "BBANDS_20",
@@ -125,7 +139,7 @@
       "kwargs": {
         "timeperiod": 20
       },
-      "elapsed_ms": 0.005
+      "elapsed_ms": 0.0062
     },
     {
       "name": "TSF_14",
@@ -133,7 +147,15 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0048
+      "elapsed_ms": 0.0056
+    },
+    {
+      "name": "EMA_20",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 20
+      },
+      "elapsed_ms": 0.0055
     },
     {
       "name": "LINEARREG_14",
@@ -141,7 +163,7 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0046
+      "elapsed_ms": 0.0054
     },
     {
       "name": "LINEARREG_SLOPE_14",
@@ -149,7 +171,7 @@
       "kwargs": {
         "timeperiod": 14
       },
-      "elapsed_ms": 0.0045
+      "elapsed_ms": 0.005
     },
     {
       "name": "SMA_20",
@@ -157,7 +179,7 @@
       "kwargs": {
         "timeperiod": 20
       },
-      "elapsed_ms": 0.0027
+      "elapsed_ms": 0.0032
     }
   ]
-}
+}

perf-contract/manifest.json

@@ -2,16 +2,38 @@
   "metadata": {
     "suite": "perf_contract",
     "runtime": {
-      "generated_at_utc": "2026-03-23T21:09:13.077731+00:00",
-      "python_version": "3.12.11",
-      "platform": "macOS-26.3.1-arm64-arm-64bit",
+      "generated_at_utc": "2026-03-24T09:13:08.787230+00:00",
+      "python_version": "3.13.5",
+      "python_implementation": "CPython",
+      "python_executable": "/Users/pratikbhadane/Work/Projects/ferro-ta/.venv/bin/python3",
+      "platform": "macOS-26.3.1-arm64-arm-64bit-Mach-O",
+      "system": "Darwin",
+      "release": "25.3.0",
       "machine": "arm64",
-      "processor": "arm"
+      "processor": "arm",
+      "cpu_model": "Apple M3 Max",
+      "cpu_count_logical": 14,
+      "total_memory_bytes": 38654705664
     },
     "git": {
-      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "commit": "53566b9d82898fa4f95c5190156c969a0bd8e8e3",
       "dirty": true,
-      "branch": "feat/performace-1.0.2"
+      "branch": "main"
+    },
+    "build": {
+      "rustc": "rustc 1.93.1 (01f6ddf75 2026-02-11)\nbinary: rustc\ncommit-hash: 01f6ddf7588f42ae2d7eb0a2f21d44e8e96674cf\ncommit-date: 2026-02-11\nhost: aarch64-apple-darwin\nrelease: 1.93.1\nLLVM version: 21.1.8",
+      "cargo": "cargo 1.93.1 (083ac5135 2025-12-15)",
+      "cargo_release_profile": {
+        "lto": true,
+        "codegen-units": 1
+      },
+      "rustflags": null,
+      "cargo_build_rustflags": null,
+      "maturin_flags": null
+    },
+    "packages": {
+      "numpy": "2.2.6",
+      "ferro-ta": "1.0.4"
     },
     "fixtures": [
       {
@@ -25,28 +47,23 @@
   "artifacts": {
     "indicator_latency": {
       "path": "perf-contract/indicator_latency.json",
-      "size_bytes": 3212,
-      "sha256": "1e7f844fe6eb467f07bbb1e4eb918c56861e8cf819ab4802b96e033c6d2570c4"
+      "size_bytes": 4041,
+      "sha256": "564027c7abed7ecd4ae2ac1720217d96e1e31807f8ac7d5c5393c8fd974f13ed"
     },
     "batch": {
       "path": "perf-contract/batch.json",
-      "size_bytes": 1679,
-      "sha256": "202d67b24a88184473f93cec9f866ca34ff60721068e15e7e5d23962f006d169"
+      "size_bytes": 2507,
+      "sha256": "c50f242a138a0c358a6fa86c420954b4b11a2200598ca2942bcd0fd2bc456fb2"
     },
     "streaming": {
       "path": "perf-contract/streaming.json",
-      "size_bytes": 1939,
-      "sha256": "04476c3d95a9e7d87e40331b28c5ccad9a7aa5abcfc47fb6ac7e929365f68554"
+      "size_bytes": 2766,
+      "sha256": "d271d3219ec098e443e84ef648ab4220cd2a38e6dc998bc6c9fc545d7fc77716"
     },
     "runtime_hotspots": {
       "path": "perf-contract/runtime_hotspots.json",
-      "size_bytes": 2365,
-      "sha256": "d37a58bc88def9f1525b9b0f64f0bd7b9c0df9ed4cae260c833697f6a1689ca8"
-    },
-    "simd": {
-      "path": "perf-contract/simd.json",
-      "size_bytes": 7670,
-      "sha256": "3f9b341a8206698ed172650752d808621c9b5218f42a2373f0f4485496197b4c"
+      "size_bytes": 3197,
+      "sha256": "96eafc9a61ac410e47fcdfee6a9db1123cdee0ec05f49a85c631a3975063deed"
     }
   }
-}
+}

perf-contract/runtime_hotspots.json

@@ -2,16 +2,38 @@
   "metadata": {
     "suite": "runtime_hotspots",
     "runtime": {
-      "generated_at_utc": "2026-03-23T21:08:34.436165+00:00",
-      "python_version": "3.12.11",
-      "platform": "macOS-26.3.1-arm64-arm-64bit",
+      "generated_at_utc": "2026-03-24T09:13:08.521805+00:00",
+      "python_version": "3.13.5",
+      "python_implementation": "CPython",
+      "python_executable": "/Users/pratikbhadane/Work/Projects/ferro-ta/.venv/bin/python3",
+      "platform": "macOS-26.3.1-arm64-arm-64bit-Mach-O",
+      "system": "Darwin",
+      "release": "25.3.0",
       "machine": "arm64",
-      "processor": "arm"
+      "processor": "arm",
+      "cpu_model": "Apple M3 Max",
+      "cpu_count_logical": 14,
+      "total_memory_bytes": 38654705664
     },
     "git": {
-      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "commit": "53566b9d82898fa4f95c5190156c969a0bd8e8e3",
       "dirty": true,
-      "branch": "feat/performace-1.0.2"
+      "branch": "main"
+    },
+    "build": {
+      "rustc": "rustc 1.93.1 (01f6ddf75 2026-02-11)\nbinary: rustc\ncommit-hash: 01f6ddf7588f42ae2d7eb0a2f21d44e8e96674cf\ncommit-date: 2026-02-11\nhost: aarch64-apple-darwin\nrelease: 1.93.1\nLLVM version: 21.1.8",
+      "cargo": "cargo 1.93.1 (083ac5135 2025-12-15)",
+      "cargo_release_profile": {
+        "lto": true,
+        "codegen-units": 1
+      },
+      "rustflags": null,
+      "cargo_build_rustflags": null,
+      "maturin_flags": null
+    },
+    "packages": {
+      "numpy": "2.2.6",
+      "ferro-ta": "1.0.4"
     },
     "dataset": {
       "price_bars": 20000,
@@ -23,74 +45,74 @@
     {
       "category": "python_analysis",
       "name": "iv_zscore",
-      "fast_ms": 28.857,
-      "reference_ms": 897.0178,
-      "speedup_vs_reference": 31.0849,
-      "share_of_suite_pct": 70.0
+      "fast_ms": 32.5847,
+      "reference_ms": 990.4233,
+      "speedup_vs_reference": 30.3954,
+      "share_of_suite_pct": 69.27
     },
     {
       "category": "python_analysis",
       "name": "iv_rank",
-      "fast_ms": 10.8496,
-      "reference_ms": 199.4376,
-      "speedup_vs_reference": 18.3821,
-      "share_of_suite_pct": 26.32
+      "fast_ms": 12.2974,
+      "reference_ms": 233.0671,
+      "speedup_vs_reference": 18.9525,
+      "share_of_suite_pct": 26.14
     },
     {
       "category": "python_analysis",
       "name": "iv_percentile",
-      "fast_ms": 0.9124,
-      "reference_ms": 79.6557,
-      "speedup_vs_reference": 87.3019,
-      "share_of_suite_pct": 2.21
+      "fast_ms": 0.9511,
+      "reference_ms": 90.5663,
+      "speedup_vs_reference": 95.2202,
+      "share_of_suite_pct": 2.02
     },
     {
       "category": "ffi_grouping",
       "name": "feature_matrix",
-      "fast_ms": 0.2411,
-      "reference_ms": 0.2215,
-      "speedup_vs_reference": 0.9186,
-      "share_of_suite_pct": 0.58
+      "fast_ms": 0.6619,
+      "reference_ms": 0.6155,
+      "speedup_vs_reference": 0.9299,
+      "share_of_suite_pct": 1.41
     },
     {
       "category": "ffi_grouping",
       "name": "compute_many_close",
-      "fast_ms": 0.1563,
-      "reference_ms": 0.1498,
-      "speedup_vs_reference": 0.9587,
-      "share_of_suite_pct": 0.38
+      "fast_ms": 0.3175,
+      "reference_ms": 0.2437,
+      "speedup_vs_reference": 0.7675,
+      "share_of_suite_pct": 0.67
     },
     {
       "category": "rust_kernel",
       "name": "BETA",
-      "fast_ms": 0.0694,
-      "reference_ms": 159.2715,
-      "speedup_vs_reference": 2294.4163,
-      "share_of_suite_pct": 0.17
+      "fast_ms": 0.0742,
+      "reference_ms": 188.6048,
+      "speedup_vs_reference": 2541.5695,
+      "share_of_suite_pct": 0.16
     },
     {
       "category": "rust_kernel",
       "name": "CORREL",
-      "fast_ms": 0.0555,
-      "reference_ms": 158.2775,
-      "speedup_vs_reference": 2851.8468,
-      "share_of_suite_pct": 0.13
-    },
-    {
-      "category": "rust_kernel",
-      "name": "LINEARREG",
-      "fast_ms": 0.0414,
-      "reference_ms": 44.8111,
-      "speedup_vs_reference": 1081.9491,
-      "share_of_suite_pct": 0.1
+      "fast_ms": 0.0638,
+      "reference_ms": 215.973,
+      "speedup_vs_reference": 3387.8115,
+      "share_of_suite_pct": 0.14
     },
     {
       "category": "rust_kernel",
       "name": "TSF",
-      "fast_ms": 0.0413,
-      "reference_ms": 46.3799,
-      "speedup_vs_reference": 1122.1039,
-      "share_of_suite_pct": 0.1
+      "fast_ms": 0.0441,
+      "reference_ms": 49.6108,
+      "speedup_vs_reference": 1125.3954,
+      "share_of_suite_pct": 0.09
+    },
+    {
+      "category": "rust_kernel",
+      "name": "LINEARREG",
+      "fast_ms": 0.044,
+      "reference_ms": 57.9675,
+      "speedup_vs_reference": 1318.7009,
+      "share_of_suite_pct": 0.09
     }
   ]
-}
+}

perf-contract/simd.json

@@ -282,4 +282,4 @@
       ]
     }
   }
-}
+}

perf-contract/streaming.json

@@ -2,16 +2,38 @@
   "metadata": {
     "suite": "streaming",
     "runtime": {
-      "generated_at_utc": "2026-03-23T21:08:30.968886+00:00",
-      "python_version": "3.12.11",
-      "platform": "macOS-26.3.1-arm64-arm-64bit",
+      "generated_at_utc": "2026-03-24T09:13:04.351797+00:00",
+      "python_version": "3.13.5",
+      "python_implementation": "CPython",
+      "python_executable": "/Users/pratikbhadane/Work/Projects/ferro-ta/.venv/bin/python3",
+      "platform": "macOS-26.3.1-arm64-arm-64bit-Mach-O",
+      "system": "Darwin",
+      "release": "25.3.0",
       "machine": "arm64",
-      "processor": "arm"
+      "processor": "arm",
+      "cpu_model": "Apple M3 Max",
+      "cpu_count_logical": 14,
+      "total_memory_bytes": 38654705664
     },
     "git": {
-      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "commit": "53566b9d82898fa4f95c5190156c969a0bd8e8e3",
       "dirty": true,
-      "branch": "feat/performace-1.0.2"
+      "branch": "main"
+    },
+    "build": {
+      "rustc": "rustc 1.93.1 (01f6ddf75 2026-02-11)\nbinary: rustc\ncommit-hash: 01f6ddf7588f42ae2d7eb0a2f21d44e8e96674cf\ncommit-date: 2026-02-11\nhost: aarch64-apple-darwin\nrelease: 1.93.1\nLLVM version: 21.1.8",
+      "cargo": "cargo 1.93.1 (083ac5135 2025-12-15)",
+      "cargo_release_profile": {
+        "lto": true,
+        "codegen-units": 1
+      },
+      "rustflags": null,
+      "cargo_build_rustflags": null,
+      "maturin_flags": null
+    },
+    "packages": {
+      "numpy": "2.2.6",
+      "ferro-ta": "1.0.4"
     },
     "dataset": {
       "n_bars": 20000,
@@ -22,52 +44,52 @@
     {
       "indicator": "StreamingSMA",
       "inputs": "close",
-      "stream_total_ms": 0.8808,
-      "batch_total_ms": 0.0153,
-      "stream_ns_per_update": 44.04,
-      "batch_ns_per_bar": 0.77,
-      "updates_per_second": 22705779.59,
-      "stream_over_batch_ratio": 57.4469
+      "stream_total_ms": 0.9927,
+      "batch_total_ms": 0.0166,
+      "stream_ns_per_update": 49.63,
+      "batch_ns_per_bar": 0.83,
+      "updates_per_second": 20147763.43,
+      "stream_over_batch_ratio": 59.7092
     },
     {
       "indicator": "StreamingEMA",
       "inputs": "close",
-      "stream_total_ms": 0.8611,
-      "batch_total_ms": 0.0408,
-      "stream_ns_per_update": 43.06,
-      "batch_ns_per_bar": 2.04,
-      "updates_per_second": 23225431.81,
-      "stream_over_batch_ratio": 21.0884
+      "stream_total_ms": 0.9459,
+      "batch_total_ms": 0.0435,
+      "stream_ns_per_update": 47.3,
+      "batch_ns_per_bar": 2.18,
+      "updates_per_second": 21143503.9,
+      "stream_over_batch_ratio": 21.7247
     },
     {
       "indicator": "StreamingRSI",
       "inputs": "close",
-      "stream_total_ms": 0.9235,
-      "batch_total_ms": 0.0932,
-      "stream_ns_per_update": 46.17,
-      "batch_ns_per_bar": 4.66,
-      "updates_per_second": 21656740.75,
-      "stream_over_batch_ratio": 9.9035
+      "stream_total_ms": 1.0059,
+      "batch_total_ms": 0.0991,
+      "stream_ns_per_update": 50.3,
+      "batch_ns_per_bar": 4.95,
+      "updates_per_second": 19882356.06,
+      "stream_over_batch_ratio": 10.1523
     },
     {
       "indicator": "StreamingATR",
       "inputs": "hlc",
-      "stream_total_ms": 2.0074,
-      "batch_total_ms": 0.0935,
-      "stream_ns_per_update": 100.37,
-      "batch_ns_per_bar": 4.68,
-      "updates_per_second": 9963056.99,
-      "stream_over_batch_ratio": 21.4603
+      "stream_total_ms": 2.1574,
+      "batch_total_ms": 0.0992,
+      "stream_ns_per_update": 107.87,
+      "batch_ns_per_bar": 4.96,
+      "updates_per_second": 9270525.53,
+      "stream_over_batch_ratio": 21.746
     },
     {
       "indicator": "StreamingVWAP",
       "inputs": "hlcv",
-      "stream_total_ms": 2.6068,
-      "batch_total_ms": 0.0223,
-      "stream_ns_per_update": 130.34,
-      "batch_ns_per_bar": 1.11,
-      "updates_per_second": 7672265.38,
-      "stream_over_batch_ratio": 116.9385
+      "stream_total_ms": 2.6935,
+      "batch_total_ms": 0.0252,
+      "stream_ns_per_update": 134.68,
+      "batch_ns_per_bar": 1.26,
+      "updates_per_second": 7425170.07,
+      "stream_over_batch_ratio": 106.8484
     }
   ]
-}
+}

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "ferro-ta"
-version = "1.0.3"
+version = "1.0.6"
 description = "Rust-powered Python technical analysis library with a TA-Lib-compatible API"
 readme = "README.md"
 license = { text = "MIT" }
@@ -53,6 +53,7 @@ dev = [
     "hypothesis>=6.0",
     "pandas>=1.0",
     "polars>=0.19",
+    "pre-commit>=3.0",
     "ruff>=0.3",
     "mypy>=1.0",
     "pyright>=1.1",
@@ -103,7 +104,9 @@ ignore = ["E501", "UP006", "UP045", "UP007"]
 "tests/*" = ["N801", "N802", "N806", "E402", "E741", "F811"]
 "tests/unit/*" = ["N801", "N802", "N806", "E402", "E741", "F811"]
 "tests/integration/*" = ["N801", "N802", "N806", "E402", "E741", "F811"]
+"benchmarks/*" = ["E402", "E741"]
 "python/ferro_ta/*.py" = ["N802"]  # Public API: SMA, RSI, ATR, etc.
+"python/ferro_ta/__init__.py" = ["E402", "N802"]  # Re-export surface by design
 "python/ferro_ta/__init__.pyi" = ["N802", "E402"]
 
 [tool.ruff.format]
@@ -129,6 +132,7 @@ dev = [
     "hypothesis>=6.0",
     "pandas>=1.0",
     "polars>=0.19",
+    "pre-commit>=3.0",
     "ruff>=0.3",
     "mypy>=1.0",
     "pyright>=1.1",

python/ferro_ta/__init__.py

@@ -59,11 +59,11 @@ array([ nan,  nan, 11. , 12. , 13. , 13.5, 13.33...])
 
 from __future__ import annotations
 
+import re as _re
+import sys as _sys
 from importlib.metadata import PackageNotFoundError as _PackageNotFoundError
 from importlib.metadata import version as _dist_version
 from pathlib import Path as _Path
-import re as _re
-import sys as _sys
 
 try:
     import tomllib as _tomllib

python/ferro_ta/__init__.pyi

@@ -13,6 +13,8 @@ from numpy.typing import ArrayLike, NDArray
 
 _F = TypeVar("_F", bound=Callable[..., Any])
 
+__version__: str
+
 # ---------------------------------------------------------------------------
 # Overlap Studies
 # ---------------------------------------------------------------------------
@@ -739,8 +741,10 @@ class FerroTAInputError(FerroTAError, ValueError):
 # API discovery (ferro_ta.api_info)
 # ---------------------------------------------------------------------------
 
+def about() -> dict[str, Any]: ...
 def indicators(category: str | None = None) -> list[dict[str, Any]]: ...
 def info(func_or_name: Callable[..., Any] | str) -> dict[str, Any]: ...
+def methods(category: str | None = None) -> list[dict[str, Any]]: ...
 
 # ---------------------------------------------------------------------------
 # Logging utilities (ferro_ta.logging_utils)

python/ferro_ta/analysis/attribution.py

@@ -38,6 +38,9 @@ from typing import Any, Optional
 import numpy as np
 from numpy.typing import ArrayLike, NDArray
 
+from ferro_ta._ferro_ta import (
+    extract_trades as _rust_extract_trades,
+)
 from ferro_ta._ferro_ta import (
     monthly_contribution as _rust_monthly_contribution,
 )
@@ -199,31 +202,10 @@ def from_backtest(result: Any) -> tuple[NDArray[np.float64], NDArray[np.float64]
     """
     pos = np.asarray(result.positions, dtype=np.float64)
     ret = np.asarray(result.strategy_returns, dtype=np.float64)
-    n = len(pos)
-
-    pnl_list: list[float] = []
-    hold_list: list[float] = []
-
-    i = 0
-    while i < n:
-        if pos[i] == 0.0:
-            i += 1
-            continue
-        # Start of a trade
-        j = i + 1
-        while j < n and pos[j] == pos[i]:
-            j += 1
-        # Trade from i to j-1
-        trade_pnl = float(np.sum(ret[i:j]))
-        pnl_list.append(trade_pnl)
-        hold_list.append(float(j - i))
-        i = j
-
-    if not pnl_list:
-        return np.empty(0, dtype=np.float64), np.empty(0, dtype=np.float64)
+    pnl, hold = _rust_extract_trades(pos, ret)
     return (
-        np.array(pnl_list, dtype=np.float64),
-        np.array(hold_list, dtype=np.float64),
+        np.asarray(pnl, dtype=np.float64),
+        np.asarray(hold, dtype=np.float64),
     )
 
 

python/ferro_ta/analysis/backtest.py

@@ -55,6 +55,10 @@ from typing import Optional, Union
 import numpy as np
 from numpy.typing import ArrayLike, NDArray
 
+from ferro_ta._ferro_ta import backtest_core as _rust_backtest_core
+from ferro_ta._ferro_ta import macd_crossover_signals as _rust_macd_crossover_signals
+from ferro_ta._ferro_ta import rsi_threshold_signals as _rust_rsi_threshold_signals
+from ferro_ta._ferro_ta import sma_crossover_signals as _rust_sma_crossover_signals
 from ferro_ta.core.exceptions import FerroTAInputError, FerroTAValueError
 
 # ---------------------------------------------------------------------------
@@ -149,16 +153,16 @@ def rsi_strategy(
     overbought : float
         RSI level above which a short (-1) signal is generated (default 70).
     """
-    from ferro_ta import RSI  # local import to avoid circular dep
-
     if timeperiod < 1:
         raise FerroTAValueError(f"timeperiod must be >= 1, got {timeperiod}")
 
     c = np.asarray(close, dtype=np.float64)
-    rsi = np.asarray(RSI(c, timeperiod=timeperiod), dtype=np.float64)
-    signals = np.where(rsi <= oversold, 1.0, np.where(rsi >= overbought, -1.0, 0.0))
-    signals[np.isnan(rsi)] = np.nan
-    return signals
+    return np.asarray(
+        _rust_rsi_threshold_signals(
+            c, int(timeperiod), float(oversold), float(overbought)
+        ),
+        dtype=np.float64,
+    )
 
 
 def sma_crossover_strategy(
@@ -183,8 +187,6 @@ def sma_crossover_strategy(
     slow : int
         Slow SMA period (default 30).
     """
-    from ferro_ta import SMA  # local import
-
     if fast < 1:
         raise FerroTAValueError(f"fast must be >= 1, got {fast}")
     if slow < 1:
@@ -193,13 +195,10 @@ def sma_crossover_strategy(
         raise FerroTAValueError(f"fast ({fast}) must be less than slow ({slow})")
 
     c = np.asarray(close, dtype=np.float64)
-    sma_fast = np.asarray(SMA(c, timeperiod=fast), dtype=np.float64)
-    sma_slow = np.asarray(SMA(c, timeperiod=slow), dtype=np.float64)
-    signals = np.where(sma_fast > sma_slow, 1.0, -1.0).astype(np.float64)
-    # Warm-up: NaN where either MA is NaN
-    warmup = np.isnan(sma_fast) | np.isnan(sma_slow)
-    signals[warmup] = np.nan
-    return signals
+    return np.asarray(
+        _rust_sma_crossover_signals(c, int(fast), int(slow)),
+        dtype=np.float64,
+    )
 
 
 def macd_crossover_strategy(
@@ -227,8 +226,6 @@ def macd_crossover_strategy(
     signalperiod : int
         Signal line EMA period (default 9).
     """
-    from ferro_ta import MACD  # local import
-
     if fastperiod < 1 or slowperiod < 1 or signalperiod < 1:
         raise FerroTAValueError("MACD periods must be >= 1")
     if fastperiod >= slowperiod:
@@ -237,15 +234,12 @@ def macd_crossover_strategy(
         )
 
     c = np.asarray(close, dtype=np.float64)
-    macd_line, signal_line, _ = MACD(
-        c, fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod
+    return np.asarray(
+        _rust_macd_crossover_signals(
+            c, int(fastperiod), int(slowperiod), int(signalperiod)
+        ),
+        dtype=np.float64,
     )
-    macd_line = np.asarray(macd_line, dtype=np.float64)
-    signal_line = np.asarray(signal_line, dtype=np.float64)
-    signals = np.where(macd_line > signal_line, 1.0, -1.0).astype(np.float64)
-    warmup = np.isnan(macd_line) | np.isnan(signal_line)
-    signals[warmup] = np.nan
-    return signals
 
 
 # ---------------------------------------------------------------------------
@@ -342,52 +336,14 @@ def backtest(
     # Compute signals
     # ------------------------------------------------------------------
     signals = np.asarray(strategy_fn(c, **strategy_kwargs), dtype=np.float64)
-
-    # ------------------------------------------------------------------
-    # Positions: lag signals by 1 bar to avoid look-ahead bias
-    # ------------------------------------------------------------------
-    positions = np.empty_like(signals)
-    positions[0] = 0.0
-    positions[1:] = signals[:-1]
-    # Replace NaN in positions with 0 (flat)
-    positions = np.nan_to_num(positions, nan=0.0)
-
-    # ------------------------------------------------------------------
-    # Returns
-    # ------------------------------------------------------------------
-    bar_returns: np.ndarray = np.empty(len(c), dtype=np.float64)
-    bar_returns[0] = 0.0
-    bar_returns[1:] = np.diff(c) / c[:-1]
-
-    strategy_returns = positions * bar_returns
-    position_changed = np.concatenate([[False], positions[1:] != positions[:-1]])
-
-    # Slippage: on each position change, reduce return by slippage_bps/10000 (one-way)
-    if slippage_bps > 0:
-        strategy_returns = strategy_returns.copy()
-        strategy_returns[position_changed] -= slippage_bps / 10_000.0
-
-    # Cumulative equity: with optional commission per trade
-    if commission_per_trade <= 0:
-        equity = np.cumprod(1.0 + strategy_returns)
-    else:
-        gross_equity = np.cumprod(1.0 + strategy_returns)
-        if np.any(gross_equity == 0.0):
-            equity = np.empty(len(c), dtype=np.float64)
-            equity[0] = 1.0
-            for i in range(1, len(c)):
-                equity[i] = equity[i - 1] * (1.0 + strategy_returns[i])
-                if position_changed[i]:
-                    equity[i] -= commission_per_trade
-        else:
-            commissions = position_changed.astype(np.float64) * commission_per_trade
-            discounted_commissions = np.cumsum(commissions / gross_equity)
-            equity = gross_equity * (1.0 - discounted_commissions)
+    positions, bar_returns, strategy_returns, equity = _rust_backtest_core(
+        c, signals, float(commission_per_trade), float(slippage_bps)
+    )
 
     return BacktestResult(
         signals=signals,
-        positions=positions,
-        bar_returns=bar_returns,
-        strategy_returns=strategy_returns,
+        positions=np.asarray(positions, dtype=np.float64),
+        bar_returns=np.asarray(bar_returns, dtype=np.float64),
+        strategy_returns=np.asarray(strategy_returns, dtype=np.float64),
         equity=np.asarray(equity, dtype=np.float64),
     )

python/ferro_ta/analysis/cross_asset.py

@@ -40,6 +40,7 @@ from __future__ import annotations
 import numpy as np
 from numpy.typing import ArrayLike, NDArray
 
+from ferro_ta._ferro_ta import ratio as _rust_ratio
 from ferro_ta._ferro_ta import relative_strength as _rust_rel_strength
 from ferro_ta._ferro_ta import rolling_beta as _rust_rolling_beta
 from ferro_ta._ferro_ta import spread as _rust_spread
@@ -162,11 +163,7 @@ def ratio(
     >>> list(ratio(a, b))
     [2.0, 3.0, 3.0]
     """
-    av = _to_f64(a)
-    bv = _to_f64(b)
-    with np.errstate(divide="ignore", invalid="ignore"):
-        result = np.where(bv == 0, np.nan, av / bv)
-    return result
+    return _rust_ratio(_to_f64(a), _to_f64(b))
 
 
 # ---------------------------------------------------------------------------

python/ferro_ta/analysis/derivatives_payoff.py

@@ -11,10 +11,16 @@ from typing import Any
 import numpy as np
 from numpy.typing import ArrayLike, NDArray
 
+from ferro_ta._ferro_ta import aggregate_greeks_legs as _rust_aggregate_greeks_legs
+from ferro_ta._ferro_ta import strategy_payoff_dense as _rust_strategy_payoff_dense
+from ferro_ta._ferro_ta import strategy_payoff_legs as _rust_strategy_payoff_legs
 from ferro_ta.analysis.options import OptionGreeks
-from ferro_ta.analysis.options import greeks as option_greeks
 from ferro_ta.analysis.options_strategy import DerivativesStrategy, StrategyLeg
-from ferro_ta.core.exceptions import FerroTAInputError, FerroTAValueError
+from ferro_ta.core.exceptions import (
+    FerroTAInputError,
+    FerroTAValueError,
+    _normalize_rust_error,
+)
 
 __all__ = [
     "PayoffLeg",
@@ -79,14 +85,23 @@ def option_leg_payoff(
 ) -> NDArray[np.float64]:
     """Expiry payoff for a single option leg."""
     grid = _coerce_spot_grid(spot_grid)
-    sign = _side_sign(side) * float(quantity) * float(multiplier)
-    if option_type == "call":
-        intrinsic = np.maximum(grid - float(strike), 0.0)
-    elif option_type == "put":
-        intrinsic = np.maximum(float(strike) - grid, 0.0)
-    else:
+    _side_sign(side)
+    if option_type not in {"call", "put"}:
         raise FerroTAValueError("option_type must be 'call' or 'put'.")
-    return sign * (intrinsic - float(premium))
+    return np.asarray(
+        _rust_strategy_payoff_dense(
+            grid,
+            np.array([0], dtype=np.int64),  # option
+            np.array([1 if side == "long" else -1], dtype=np.int64),
+            np.array([1 if option_type == "call" else -1], dtype=np.int64),
+            np.array([float(strike)], dtype=np.float64),
+            np.array([float(premium)], dtype=np.float64),
+            np.array([0.0], dtype=np.float64),
+            np.array([float(quantity)], dtype=np.float64),
+            np.array([float(multiplier)], dtype=np.float64),
+        ),
+        dtype=np.float64,
+    )
 
 
 def futures_leg_payoff(
@@ -99,8 +114,21 @@ def futures_leg_payoff(
 ) -> NDArray[np.float64]:
     """P/L profile for a futures leg."""
     grid = _coerce_spot_grid(spot_grid)
-    sign = _side_sign(side) * float(quantity) * float(multiplier)
-    return sign * (grid - float(entry_price))
+    _side_sign(side)
+    return np.asarray(
+        _rust_strategy_payoff_dense(
+            grid,
+            np.array([1], dtype=np.int64),  # future
+            np.array([1 if side == "long" else -1], dtype=np.int64),
+            np.array([-1], dtype=np.int64),
+            np.array([0.0], dtype=np.float64),
+            np.array([0.0], dtype=np.float64),
+            np.array([float(entry_price)], dtype=np.float64),
+            np.array([float(quantity)], dtype=np.float64),
+            np.array([float(multiplier)], dtype=np.float64),
+        ),
+        dtype=np.float64,
+    )
 
 
 def _mapping_to_leg(mapping: Mapping[str, Any]) -> PayoffLeg:
@@ -141,31 +169,15 @@ def strategy_payoff(
     """Aggregate expiry payoff across option and futures legs."""
     grid = _coerce_spot_grid(spot_grid)
     normalized = _normalize_legs(legs, strategy=strategy)
-    total = np.zeros_like(grid)
-    for leg in normalized:
-        if leg.instrument == "option":
-            if leg.strike is None:
-                raise FerroTAValueError("Option payoff legs require strike.")
-            total += option_leg_payoff(
-                grid,
-                strike=float(leg.strike),
-                premium=float(leg.premium),
-                option_type=str(leg.option_type),
-                side=str(leg.side),
-                quantity=float(leg.quantity),
-                multiplier=float(leg.multiplier),
-            )
-        else:
-            if leg.entry_price is None:
-                raise FerroTAValueError("Futures payoff legs require entry_price.")
-            total += futures_leg_payoff(
-                grid,
-                entry_price=float(leg.entry_price),
-                side=str(leg.side),
-                quantity=float(leg.quantity),
-                multiplier=float(leg.multiplier),
-            )
-    return total
+    if len(normalized) == 0:
+        return np.zeros_like(grid)
+
+    try:
+        return np.asarray(
+            _rust_strategy_payoff_legs(grid, normalized), dtype=np.float64
+        )
+    except ValueError as err:
+        _normalize_rust_error(err)
 
 
 def aggregate_greeks(
@@ -176,42 +188,20 @@ def aggregate_greeks(
 ) -> OptionGreeks:
     """Aggregate Greeks across option and futures legs."""
     normalized = _normalize_legs(legs, strategy=strategy)
-    totals = {
-        "delta": 0.0,
-        "gamma": 0.0,
-        "vega": 0.0,
-        "theta": 0.0,
-        "rho": 0.0,
-    }
-    for leg in normalized:
-        leg_sign = _side_sign(leg.side) * float(leg.quantity) * float(leg.multiplier)
-        if leg.instrument == "future":
-            totals["delta"] += leg_sign
-            continue
-        if leg.strike is None or leg.volatility is None or leg.time_to_expiry is None:
-            raise FerroTAValueError(
-                "Option legs require strike, volatility, and time_to_expiry for Greeks aggregation."
-            )
-        leg_greeks = option_greeks(
-            float(spot),
-            float(leg.strike),
-            float(leg.rate),
-            float(leg.time_to_expiry),
-            float(leg.volatility),
-            option_type=str(leg.option_type),
-            model="bsm",
-            carry=float(leg.carry),
+    if len(normalized) == 0:
+        return OptionGreeks(0.0, 0.0, 0.0, 0.0, 0.0)
+
+    try:
+        delta, gamma, vega, theta, rho = _rust_aggregate_greeks_legs(
+            float(spot), normalized
         )
-        totals["delta"] += leg_sign * float(leg_greeks.delta)
-        totals["gamma"] += leg_sign * float(leg_greeks.gamma)
-        totals["vega"] += leg_sign * float(leg_greeks.vega)
-        totals["theta"] += leg_sign * float(leg_greeks.theta)
-        totals["rho"] += leg_sign * float(leg_greeks.rho)
+    except ValueError as err:
+        _normalize_rust_error(err)
 
     return OptionGreeks(
-        totals["delta"],
-        totals["gamma"],
-        totals["vega"],
-        totals["theta"],
-        totals["rho"],
+        float(delta),
+        float(gamma),
+        float(vega),
+        float(theta),
+        float(rho),
     )

python/ferro_ta/analysis/features.py

@@ -23,6 +23,7 @@ from typing import Any, Optional, Union
 import numpy as np
 from numpy.typing import NDArray
 
+from ferro_ta._ferro_ta import forward_fill_nan as _rust_forward_fill_nan
 from ferro_ta._utils import _to_f64
 from ferro_ta.data.batch import compute_many
 
@@ -32,13 +33,9 @@ __all__ = [
 
 
 def _forward_fill_nan(arr: NDArray[np.float64]) -> NDArray[np.float64]:
-    mask = np.isnan(arr)
-    if not mask.any():
-        return arr
-
-    last_valid = np.where(~mask, np.arange(len(arr)), 0)
-    np.maximum.accumulate(last_valid, out=last_valid)
-    return arr[last_valid]
+    return np.asarray(
+        _rust_forward_fill_nan(np.ascontiguousarray(arr, dtype=np.float64))
+    )
 
 
 # ---------------------------------------------------------------------------

python/ferro_ta/data/batch.py

@@ -6,16 +6,16 @@ This module provides a 2-D batch API that accepts a 2-D numpy array
 a 2-D output array of the same shape.
 
 For the most common indicators — SMA, EMA, RSI — the 2-D path is handled
-entirely in Rust (a single GIL release for all columns). The generic
-``batch_apply`` is available for other indicators that do not have a Rust
-batch implementation.
+entirely in Rust (a single GIL release for all columns). ``batch_apply``
+also dispatches these indicators to Rust when possible; other indicators
+use the generic Python fallback path.
 
 Functions
 ---------
 batch_sma    — SMA on every column of a 2-D array  (Rust fast path for 2-D)
 batch_ema    — EMA on every column of a 2-D array  (Rust fast path for 2-D)
 batch_rsi    — RSI on every column of a 2-D array  (Rust fast path for 2-D)
-batch_apply  — Generic batch wrapper (Python loop) for any arbitrary indicator
+batch_apply  — Generic batch wrapper with Rust fast-path for SMA/EMA/RSI
 
 Usage
 -----
@@ -92,6 +92,27 @@ _HLC_FASTPATH_DEFAULTS: dict[str, int] = {
     "WILLR": 14,
 }
 
+_BATCH_FASTPATH_DEFAULTS: dict[str, int] = {
+    "SMA": 30,
+    "EMA": 30,
+    "RSI": 14,
+}
+
+
+def _resolve_batch_fastpath(
+    fn: Callable[..., np.ndarray],
+    kwargs: dict[str, object],
+) -> tuple[str, int] | None:
+    name = getattr(fn, "__name__", "").upper()
+    if name not in _BATCH_FASTPATH_DEFAULTS:
+        return None
+    if set(kwargs) - {"timeperiod"}:
+        return None
+    raw = kwargs.get("timeperiod", _BATCH_FASTPATH_DEFAULTS[name])
+    if not isinstance(raw, int):
+        return None
+    return name, int(raw)
+
 
 def _normalize_indicator_spec(
     spec: str | tuple[str, dict[str, object]] | tuple[str, dict[str, object], object],
@@ -225,11 +246,9 @@ def batch_apply(
 ) -> np.ndarray:
     """Apply any single-series indicator *fn* to every column of *data*.
 
-    This is the generic fallback batch executor — it calls *fn* once per
-    column in a Python loop.  For the common indicators SMA, EMA, and RSI
-    prefer the dedicated :func:`batch_sma`, :func:`batch_ema`, and
-    :func:`batch_rsi` functions, which use a Rust-side loop and avoid
-    per-column Python round-trips.
+    For recognized close-only indicators (SMA/EMA/RSI with default or
+    ``timeperiod`` argument only), this function dispatches to the Rust
+    batch kernels.  Otherwise it falls back to a Python per-column loop.
 
     Parameters
     ----------
@@ -265,6 +284,16 @@ def batch_apply(
     if arr.ndim != 2:
         raise ValueError(f"batch_apply expects 1-D or 2-D input; got {arr.ndim}-D")
 
+    fastpath = _resolve_batch_fastpath(fn, kwargs)
+    if fastpath is not None:
+        indicator, timeperiod = fastpath
+        contiguous = np.ascontiguousarray(arr)
+        if indicator == "SMA":
+            return np.asarray(_rust_batch_sma(contiguous, timeperiod, True))
+        if indicator == "EMA":
+            return np.asarray(_rust_batch_ema(contiguous, timeperiod, True))
+        return np.asarray(_rust_batch_rsi(contiguous, timeperiod, True))
+
     n_samples, n_series = arr.shape
     result = np.empty((n_samples, n_series), dtype=np.float64)
     for j in range(n_series):

python/ferro_ta/data/chunked.py

@@ -27,6 +27,7 @@ Rust backend
     ferro_ta._ferro_ta.make_chunk_ranges
     ferro_ta._ferro_ta.trim_overlap
     ferro_ta._ferro_ta.stitch_chunks
+    ferro_ta._ferro_ta.chunk_apply_close_indicator
 
 Notes
 -----
@@ -49,6 +50,9 @@ from typing import Any
 import numpy as np
 from numpy.typing import ArrayLike, NDArray
 
+from ferro_ta._ferro_ta import (
+    chunk_apply_close_indicator as _rust_chunk_apply_close_indicator,
+)
 from ferro_ta._ferro_ta import (
     make_chunk_ranges as _rust_make_chunk_ranges,
 )
@@ -67,6 +71,26 @@ __all__ = [
     "stitch_chunks",
 ]
 
+_FASTPATH_DEFAULT_PERIODS: dict[str, int] = {
+    "SMA": 30,
+    "EMA": 30,
+    "RSI": 14,
+}
+
+
+def _resolve_chunk_fastpath(
+    fn: Callable[..., Any], fn_kwargs: dict[str, Any]
+) -> tuple[str, int] | None:
+    name = getattr(fn, "__name__", "").upper()
+    if name not in _FASTPATH_DEFAULT_PERIODS:
+        return None
+    if set(fn_kwargs) - {"timeperiod"}:
+        return None
+    raw = fn_kwargs.get("timeperiod", _FASTPATH_DEFAULT_PERIODS[name])
+    if not isinstance(raw, int):
+        return None
+    return name, int(raw)
+
 
 def make_chunk_ranges(
     n: int,
@@ -190,6 +214,20 @@ def chunk_apply(
     if n == 0:
         return np.empty(0, dtype=np.float64)
 
+    fastpath = _resolve_chunk_fastpath(fn, fn_kwargs)
+    if fastpath is not None:
+        indicator, timeperiod = fastpath
+        return np.asarray(
+            _rust_chunk_apply_close_indicator(
+                np.ascontiguousarray(s),
+                indicator,
+                int(timeperiod),
+                int(chunk_size),
+                int(overlap),
+            ),
+            dtype=np.float64,
+        )
+
     ranges = make_chunk_ranges(n, chunk_size, overlap)
     if len(ranges) == 0:
         result = fn(s, **fn_kwargs)

scripts/build_api_manifest.py

@@ -0,0 +1,294 @@
+#!/usr/bin/env python3
+"""
+Build a cross-surface API manifest for ferro-ta.
+
+The generated manifest summarizes:
+- Python indicator/method exposure (from ferro_ta.tools.api_info)
+- Core Rust crate public functions (ferro_ta_core)
+- WASM/Node exported functions (from wasm pkg d.ts)
+
+Output is written to `docs/api_manifest.json`.
+"""
+
+from __future__ import annotations
+
+import argparse
+import ast
+import datetime as _dt
+import importlib.util
+import json
+import re
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+
+def _repo_root() -> Path:
+    return Path(__file__).resolve().parents[1]
+
+
+def _load_api_info_module(root: Path, module_path: Path):
+    python_root = str(root / "python")
+    if python_root not in sys.path:
+        sys.path.insert(0, python_root)
+    spec = importlib.util.spec_from_file_location(
+        "ferro_ta_tools_api_info", module_path
+    )
+    if spec is None or spec.loader is None:
+        raise RuntimeError(f"Could not load module spec from {module_path}")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)  # type: ignore[assignment]
+    return module
+
+
+def _module_file(root: Path, module_name: str) -> Path | None:
+    module_rel = module_name.replace(".", "/")
+    file_path = root / "python" / f"{module_rel}.py"
+    if file_path.exists():
+        return file_path
+    init_path = root / "python" / module_rel / "__init__.py"
+    if init_path.exists():
+        return init_path
+    return None
+
+
+def _extract_dunder_all(file_path: Path) -> list[str]:
+    try:
+        source = file_path.read_text(encoding="utf-8")
+        tree = ast.parse(source, filename=str(file_path))
+    except Exception:
+        return []
+
+    exports: list[str] = []
+    for node in tree.body:
+        value_node = None
+        if isinstance(node, ast.Assign):
+            for target in node.targets:
+                if isinstance(target, ast.Name) and target.id == "__all__":
+                    value_node = node.value
+                    break
+        elif isinstance(node, ast.AnnAssign):
+            target = node.target
+            if isinstance(target, ast.Name) and target.id == "__all__":
+                value_node = node.value
+        if value_node is None:
+            continue
+        try:
+            value = ast.literal_eval(value_node)
+        except Exception:
+            continue
+        if isinstance(value, str):
+            exports = [value]
+        elif isinstance(value, (list, tuple)):
+            exports = [item for item in value if isinstance(item, str)]
+    return exports
+
+
+def _module_exports(root: Path, module_name: str) -> list[str]:
+    file_path = _module_file(root, module_name)
+    if file_path is None:
+        return []
+    return _extract_dunder_all(file_path)
+
+
+def _extract_python_api(root: Path) -> dict[str, Any]:
+    module_path = root / "python" / "ferro_ta" / "tools" / "api_info.py"
+    api_info_module = _load_api_info_module(root, module_path)
+
+    category_modules = dict(getattr(api_info_module, "_CATEGORY_MODULES", {}))
+    method_modules = dict(getattr(api_info_module, "_METHOD_MODULES", {}))
+
+    indicators: list[dict[str, Any]] = []
+    seen_indicators: set[str] = set()
+    for category, module_name in category_modules.items():
+        for name in _module_exports(root, module_name):
+            if name in seen_indicators:
+                continue
+            seen_indicators.add(name)
+            indicators.append(
+                {
+                    "name": name,
+                    "category": category,
+                    "module": module_name,
+                    "doc": "",
+                    "params": [],
+                }
+            )
+
+    methods: list[dict[str, Any]] = []
+    seen_methods: set[tuple[str, str]] = set()
+    for category, module_name in method_modules.items():
+        for name in _module_exports(root, module_name):
+            key = (module_name, name)
+            if key in seen_methods:
+                continue
+            seen_methods.add(key)
+            methods.append(
+                {
+                    "name": name,
+                    "category": category,
+                    "module": module_name,
+                    "doc": "",
+                    "params": [],
+                }
+            )
+
+    indicators.sort(key=lambda entry: entry["name"])
+    methods.sort(key=lambda entry: (entry["category"], entry["name"]))
+
+    categories = sorted({entry["category"] for entry in indicators})
+
+    if not indicators:
+        raise RuntimeError(
+            "No Python indicators discovered from source exports. "
+            "Check `python/ferro_ta/tools/api_info.py` mappings and module __all__ declarations."
+        )
+
+    return {
+        "indicator_count": len(indicators),
+        "method_count": len(methods),
+        "categories": categories,
+        "indicators": indicators,
+        "methods": methods,
+    }
+
+
+def _extract_core_exports(root: Path) -> list[dict[str, str]]:
+    core_src = root / "crates" / "ferro_ta_core" / "src"
+    entries: list[dict[str, str]] = []
+
+    for rs_file in sorted(core_src.rglob("*.rs")):
+        rel = rs_file.relative_to(core_src).as_posix()
+        module = rel[:-3].replace("/", ".")
+        text = rs_file.read_text(encoding="utf-8")
+        for match in re.finditer(r"(?m)^\s*pub\s+fn\s+([A-Za-z0-9_]+)\s*\(", text):
+            entries.append(
+                {
+                    "module": module,
+                    "function": match.group(1),
+                    "file": rel,
+                }
+            )
+
+    entries.sort(key=lambda item: (item["module"], item["function"]))
+    return entries
+
+
+def _extract_wasm_exports(root: Path) -> list[str]:
+    exports: set[str] = set()
+
+    # Source exports are the canonical declaration of the WASM/Node API and
+    # avoid drift when a stale wasm/pkg folder is present locally.
+    wasm_lib = root / "wasm" / "src" / "lib.rs"
+    if wasm_lib.exists():
+        text = wasm_lib.read_text(encoding="utf-8")
+        for match in re.finditer(
+            r"(?ms)#\s*\[wasm_bindgen(?:\([^\)]*\))?\]\s*pub\s+fn\s+([A-Za-z0-9_]+)\s*\(",
+            text,
+        ):
+            exports.add(match.group(1))
+        if exports:
+            return sorted(exports)
+
+    # Fallback to generated declarations if source parsing did not find exports.
+    dts_path = root / "wasm" / "pkg" / "ferro_ta_wasm.d.ts"
+    if dts_path.exists():
+        for line in dts_path.read_text(encoding="utf-8").splitlines():
+            line = line.strip()
+            if line.startswith("export function "):
+                name = line[len("export function ") :].split("(")[0].strip()
+                if name:
+                    exports.add(name)
+
+    return sorted(exports)
+
+
+def _safe_git_head(root: Path) -> str | None:
+    try:
+        completed = subprocess.run(
+            ["git", "rev-parse", "HEAD"],
+            cwd=root,
+            capture_output=True,
+            text=True,
+            check=True,
+        )
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        return None
+    value = completed.stdout.strip()
+    return value or None
+
+
+def build_manifest(
+    root: Path, include_runtime_metadata: bool = False
+) -> dict[str, Any]:
+    python_api = _extract_python_api(root)
+    rust_core = _extract_core_exports(root)
+    wasm_exports = _extract_wasm_exports(root)
+
+    python_indicator_names = {entry["name"] for entry in python_api["indicators"]}
+    python_indicator_names_lc = {name.lower() for name in python_indicator_names}
+    wasm_set = set(wasm_exports)
+    wasm_set_lc = {name.lower() for name in wasm_set}
+    common_with_wasm = sorted(python_indicator_names_lc.intersection(wasm_set_lc))
+
+    manifest: dict[str, Any] = {
+        "surfaces": {
+            "python": python_api,
+            "rust_core": {
+                "public_function_count": len(rust_core),
+                "functions": rust_core,
+            },
+            "wasm_node": {
+                "export_count": len(wasm_exports),
+                "exports": wasm_exports,
+            },
+        },
+        "parity_summary": {
+            "python_indicator_count": len(python_indicator_names_lc),
+            "wasm_export_count": len(wasm_set),
+            "common_python_wasm_count": len(common_with_wasm),
+            "common_python_wasm": common_with_wasm,
+            "python_only_vs_wasm": sorted(python_indicator_names_lc - wasm_set_lc),
+            "wasm_only_vs_python": sorted(wasm_set_lc - python_indicator_names_lc),
+        },
+    }
+
+    if include_runtime_metadata:
+        manifest["generated_at_utc"] = _dt.datetime.now(tz=_dt.UTC).isoformat()
+        manifest["git_head"] = _safe_git_head(root)
+
+    return manifest
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Build cross-surface API manifest")
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("docs/api_manifest.json"),
+        help="Output JSON path relative to repo root (default: docs/api_manifest.json)",
+    )
+    parser.add_argument(
+        "--include-runtime-metadata",
+        action="store_true",
+        help=(
+            "Include non-deterministic metadata fields (timestamp, git head). "
+            "Disabled by default to keep manifest reproducible for CI checks."
+        ),
+    )
+    args = parser.parse_args()
+
+    root = _repo_root()
+    output_path = (root / args.output).resolve()
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    manifest = build_manifest(
+        root, include_runtime_metadata=args.include_runtime_metadata
+    )
+    output_path.write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8")
+    print(f"Wrote API manifest to {output_path}")
+
+
+if __name__ == "__main__":
+    main()

scripts/bump_version.py

@@ -15,7 +15,6 @@ import re
 from dataclasses import dataclass
 from pathlib import Path
 
-
 ROOT = Path(__file__).resolve().parent.parent
 SEMVER_RE = re.compile(r"^\d+\.\d+\.\d+$")
 

scripts/check_api_manifest.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""
+Check that docs/api_manifest.json is up-to-date.
+
+This script regenerates the deterministic manifest in-memory and compares it to
+the committed file. It exits non-zero if drift is detected.
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+
+def main() -> int:
+    root = Path(__file__).resolve().parents[1]
+    python_root = str(root / "python")
+    if python_root not in sys.path:
+        sys.path.insert(0, python_root)
+    scripts_root = str(root / "scripts")
+    if scripts_root not in sys.path:
+        sys.path.insert(0, scripts_root)
+
+    from build_api_manifest import build_manifest
+
+    manifest_path = root / "docs" / "api_manifest.json"
+
+    if not manifest_path.exists():
+        print(
+            "docs/api_manifest.json is missing. Run:\n"
+            "  python scripts/build_api_manifest.py --output docs/api_manifest.json"
+        )
+        return 1
+
+    expected = build_manifest(root, include_runtime_metadata=False)
+    actual = json.loads(manifest_path.read_text(encoding="utf-8"))
+
+    if actual != expected:
+        print(
+            "docs/api_manifest.json is out of date.\n"
+            "Run:\n"
+            "  python scripts/build_api_manifest.py --output docs/api_manifest.json\n"
+            "and commit the updated file."
+        )
+        return 1
+
+    print("docs/api_manifest.json is up to date.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/pre_push_checks.sh

@@ -0,0 +1,193 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$ROOT_DIR"
+
+AVAILABLE_CHECKS=(
+  version
+  changelog
+  rust_fmt
+  rust_clippy
+  rust_core
+  rust_bench
+  python_lint
+  python_typecheck
+  python_test
+  docs
+  wasm
+  manifest
+)
+
+DEFAULT_CHECKS=("${AVAILABLE_CHECKS[@]}")
+
+python_env_ready=0
+
+usage() {
+  cat <<'EOF'
+Usage:
+  scripts/pre_push_checks.sh
+  scripts/pre_push_checks.sh <check> [<check> ...]
+  scripts/pre_push_checks.sh --list
+
+Runs the repo's basic local CI gate before push. By default it covers:
+  version changelog rust_fmt rust_clippy rust_core rust_bench
+  python_lint python_typecheck python_test docs wasm manifest
+
+Notes:
+  - This mirrors the required CI categories we can run locally.
+  - It intentionally skips the multi-Python test matrix, audit jobs, perf smoke,
+    and benchmark-regression jobs.
+EOF
+}
+
+list_checks() {
+  printf '%s\n' "${AVAILABLE_CHECKS[@]}"
+}
+
+need_cmd() {
+  local command_name="$1"
+  if ! command -v "$command_name" >/dev/null 2>&1; then
+    echo "Missing required command: $command_name" >&2
+    exit 1
+  fi
+}
+
+run_cmd() {
+  printf '  +'
+  printf ' %q' "$@"
+  printf '\n'
+  "$@"
+}
+
+ensure_python_env() {
+  if [[ "$python_env_ready" -eq 1 ]]; then
+    return
+  fi
+
+  need_cmd uv
+  run_cmd uv sync --extra dev --extra docs --extra mcp
+  run_cmd uv run --extra dev --extra docs --extra mcp maturin develop --release
+  python_env_ready=1
+}
+
+run_version() {
+  need_cmd python3
+  run_cmd python3 scripts/bump_version.py --check
+}
+
+run_changelog() {
+  need_cmd python3
+  run_cmd python3 scripts/check_changelog.py
+}
+
+run_rust_fmt() {
+  need_cmd cargo
+  run_cmd cargo fmt --all -- --check
+}
+
+run_rust_clippy() {
+  need_cmd cargo
+  run_cmd cargo clippy --release -- -D warnings
+}
+
+run_rust_core() {
+  need_cmd cargo
+  run_cmd cargo build -p ferro_ta_core
+  run_cmd cargo test -p ferro_ta_core
+}
+
+run_rust_bench() {
+  need_cmd cargo
+  run_cmd cargo bench -p ferro_ta_core --no-run
+}
+
+run_python_lint() {
+  need_cmd uv
+  run_cmd uv run --with ruff ruff check python/ tests/
+  run_cmd uv run --with ruff ruff format --check python/ tests/
+}
+
+run_python_typecheck() {
+  need_cmd uv
+  run_cmd uv run --with mypy --with numpy python -m mypy python/ferro_ta --ignore-missing-imports --no-error-summary
+  run_cmd uv run --with pyright python -m pyright python/ferro_ta
+}
+
+run_python_test() {
+  ensure_python_env
+  run_cmd uv run --extra dev --extra mcp --with pytest-cov pytest tests/unit/ tests/integration/ -v --cov=ferro_ta --cov-report=term-missing --cov-fail-under=65
+}
+
+run_docs() {
+  ensure_python_env
+  run_cmd uv run --extra docs python -m sphinx -b html docs docs/_build -W --keep-going
+}
+
+run_wasm() {
+  need_cmd node
+  need_cmd wasm-pack
+  local benchmark_json="../.wasm_benchmark.prepush.json"
+  (
+    cd wasm
+    trap 'rm -f "$benchmark_json"' EXIT
+    run_cmd wasm-pack test --node
+    run_cmd wasm-pack build --target nodejs --out-dir pkg
+    run_cmd node bench.js --json "$benchmark_json"
+  )
+}
+
+run_manifest() {
+  need_cmd python3
+  run_cmd python3 scripts/check_api_manifest.py
+}
+
+run_check() {
+  local check_name="$1"
+  case "$check_name" in
+    version) run_version ;;
+    changelog) run_changelog ;;
+    rust_fmt) run_rust_fmt ;;
+    rust_clippy) run_rust_clippy ;;
+    rust_core) run_rust_core ;;
+    rust_bench) run_rust_bench ;;
+    python_lint) run_python_lint ;;
+    python_typecheck) run_python_typecheck ;;
+    python_test) run_python_test ;;
+    docs) run_docs ;;
+    wasm) run_wasm ;;
+    manifest) run_manifest ;;
+    *)
+      echo "Unknown check: $check_name" >&2
+      echo "Use --list to see supported checks." >&2
+      exit 1
+      ;;
+  esac
+}
+
+if [[ "${1:-}" == "--help" || "${1:-}" == "-h" ]]; then
+  usage
+  exit 0
+fi
+
+if [[ "${1:-}" == "--list" ]]; then
+  list_checks
+  exit 0
+fi
+
+selected_checks=()
+if [[ "$#" -gt 0 ]]; then
+  selected_checks=("$@")
+else
+  selected_checks=("${DEFAULT_CHECKS[@]}")
+fi
+
+total_checks="${#selected_checks[@]}"
+index=0
+for check_name in "${selected_checks[@]}"; do
+  index=$((index + 1))
+  printf '\n[%d/%d] %s\n' "$index" "$total_checks" "$check_name"
+  run_check "$check_name"
+done
+
+printf '\nAll selected pre-push checks passed.\n'

src/attribution/mod.rs

@@ -191,6 +191,54 @@ pub fn signal_attribution<'py>(
     Ok((labels.into_pyarray(py), contributions.into_pyarray(py)))
 }
 
+// ---------------------------------------------------------------------------
+// extract_trades
+// ---------------------------------------------------------------------------
+
+/// Extract trade-level pnl and hold durations from positions and strategy returns.
+///
+/// A trade is a maximal contiguous run of non-zero position values.
+#[pyfunction]
+#[allow(clippy::type_complexity)]
+pub fn extract_trades<'py>(
+    py: Python<'py>,
+    positions: PyReadonlyArray1<'py, f64>,
+    strategy_returns: PyReadonlyArray1<'py, f64>,
+) -> PyResult<(Bound<'py, PyArray1<f64>>, Bound<'py, PyArray1<f64>>)> {
+    let pos = positions.as_slice()?;
+    let ret = strategy_returns.as_slice()?;
+    let n = pos.len();
+    if n != ret.len() {
+        return Err(PyValueError::new_err(
+            "positions and strategy_returns must have the same length",
+        ));
+    }
+
+    let mut pnl = Vec::<f64>::new();
+    let mut hold = Vec::<f64>::new();
+
+    let mut i = 0usize;
+    while i < n {
+        if pos[i] == 0.0 {
+            i += 1;
+            continue;
+        }
+        let mut j = i + 1;
+        while j < n && pos[j] == pos[i] {
+            j += 1;
+        }
+        let mut trade_pnl = 0.0_f64;
+        for v in ret.iter().take(j).skip(i) {
+            trade_pnl += *v;
+        }
+        pnl.push(trade_pnl);
+        hold.push((j - i) as f64);
+        i = j;
+    }
+
+    Ok((pnl.into_pyarray(py), hold.into_pyarray(py)))
+}
+
 // ---------------------------------------------------------------------------
 // Register
 // ---------------------------------------------------------------------------
@@ -199,5 +247,6 @@ pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(trade_stats, m)?)?;
     m.add_function(wrap_pyfunction!(monthly_contribution, m)?)?;
     m.add_function(wrap_pyfunction!(signal_attribution, m)?)?;
+    m.add_function(wrap_pyfunction!(extract_trades, m)?)?;
     Ok(())
 }

src/backtest/mod.rs

@@ -0,0 +1,244 @@
+//! Rust-backed strategy signal generation and backtest core.
+//!
+//! These functions move the hot loops from Python into Rust while preserving
+//! the public Python behavior.
+
+use crate::validation;
+use numpy::{IntoPyArray, PyArray1, PyReadonlyArray1};
+use pyo3::exceptions::PyValueError;
+use pyo3::prelude::*;
+
+fn nan_to_num_with_numpy_defaults(v: f64) -> f64 {
+    if v.is_nan() {
+        0.0
+    } else if v.is_infinite() {
+        if v.is_sign_positive() {
+            f64::MAX
+        } else {
+            -f64::MAX
+        }
+    } else {
+        v
+    }
+}
+
+// ---------------------------------------------------------------------------
+// Strategy signal helpers
+// ---------------------------------------------------------------------------
+
+/// RSI threshold strategy:
+/// +1 when RSI <= oversold, -1 when RSI >= overbought, 0 otherwise.
+/// Warm-up bars are NaN.
+#[pyfunction]
+#[pyo3(signature = (close, timeperiod = 14, oversold = 30.0, overbought = 70.0))]
+pub fn rsi_threshold_signals<'py>(
+    py: Python<'py>,
+    close: PyReadonlyArray1<'py, f64>,
+    timeperiod: usize,
+    oversold: f64,
+    overbought: f64,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
+    let prices = close.as_slice()?;
+    let rsi = ferro_ta_core::momentum::rsi(prices, timeperiod);
+    let out: Vec<f64> = rsi
+        .iter()
+        .map(|&v| {
+            if v.is_nan() {
+                f64::NAN
+            } else if v <= oversold {
+                1.0
+            } else if v >= overbought {
+                -1.0
+            } else {
+                0.0
+            }
+        })
+        .collect();
+    Ok(out.into_pyarray(py))
+}
+
+/// SMA crossover strategy:
+/// +1 when fast SMA > slow SMA, -1 otherwise. Warm-up bars are NaN.
+#[pyfunction]
+#[pyo3(signature = (close, fast = 10, slow = 30))]
+pub fn sma_crossover_signals<'py>(
+    py: Python<'py>,
+    close: PyReadonlyArray1<'py, f64>,
+    fast: usize,
+    slow: usize,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    validation::validate_timeperiod(fast, "fast", 1)?;
+    validation::validate_timeperiod(slow, "slow", 1)?;
+    if fast >= slow {
+        return Err(PyValueError::new_err(format!(
+            "fast ({fast}) must be less than slow ({slow})"
+        )));
+    }
+    let prices = close.as_slice()?;
+    let sma_fast = ferro_ta_core::overlap::sma(prices, fast);
+    let sma_slow = ferro_ta_core::overlap::sma(prices, slow);
+    let out: Vec<f64> = sma_fast
+        .iter()
+        .zip(sma_slow.iter())
+        .map(|(&f, &s)| {
+            if f.is_nan() || s.is_nan() {
+                f64::NAN
+            } else if f > s {
+                1.0
+            } else {
+                -1.0
+            }
+        })
+        .collect();
+    Ok(out.into_pyarray(py))
+}
+
+/// MACD crossover strategy:
+/// +1 when MACD line > signal line, -1 otherwise. Warm-up bars are NaN.
+#[pyfunction]
+#[pyo3(signature = (close, fastperiod = 12, slowperiod = 26, signalperiod = 9))]
+pub fn macd_crossover_signals<'py>(
+    py: Python<'py>,
+    close: PyReadonlyArray1<'py, f64>,
+    fastperiod: usize,
+    slowperiod: usize,
+    signalperiod: usize,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    validation::validate_timeperiod(fastperiod, "fastperiod", 1)?;
+    validation::validate_timeperiod(slowperiod, "slowperiod", 1)?;
+    validation::validate_timeperiod(signalperiod, "signalperiod", 1)?;
+    if fastperiod >= slowperiod {
+        return Err(PyValueError::new_err(format!(
+            "fastperiod ({fastperiod}) must be less than slowperiod ({slowperiod})"
+        )));
+    }
+
+    let prices = close.as_slice()?;
+    let (macd_line, signal_line, _) =
+        ferro_ta_core::overlap::macd(prices, fastperiod, slowperiod, signalperiod);
+    let out: Vec<f64> = macd_line
+        .iter()
+        .zip(signal_line.iter())
+        .map(|(&m, &s)| {
+            if m.is_nan() || s.is_nan() {
+                f64::NAN
+            } else if m > s {
+                1.0
+            } else {
+                -1.0
+            }
+        })
+        .collect();
+    Ok(out.into_pyarray(py))
+}
+
+// ---------------------------------------------------------------------------
+// Backtest core
+// ---------------------------------------------------------------------------
+
+/// Backtest core loop over close prices and strategy signals.
+///
+/// Returns `(positions, bar_returns, strategy_returns, equity)`.
+#[pyfunction]
+#[pyo3(signature = (close, signals, commission_per_trade = 0.0, slippage_bps = 0.0))]
+#[allow(clippy::type_complexity)]
+pub fn backtest_core<'py>(
+    py: Python<'py>,
+    close: PyReadonlyArray1<'py, f64>,
+    signals: PyReadonlyArray1<'py, f64>,
+    commission_per_trade: f64,
+    slippage_bps: f64,
+) -> PyResult<(
+    Bound<'py, PyArray1<f64>>,
+    Bound<'py, PyArray1<f64>>,
+    Bound<'py, PyArray1<f64>>,
+    Bound<'py, PyArray1<f64>>,
+)> {
+    let c = close.as_slice()?;
+    let s = signals.as_slice()?;
+    let n = c.len();
+    validation::validate_equal_length(&[(n, "close"), (s.len(), "signals")])?;
+
+    let mut positions = vec![0.0_f64; n];
+    if n > 1 {
+        for i in 1..n {
+            positions[i] = nan_to_num_with_numpy_defaults(s[i - 1]);
+        }
+    }
+
+    let mut bar_returns = vec![0.0_f64; n];
+    for i in 1..n {
+        bar_returns[i] = (c[i] - c[i - 1]) / c[i - 1];
+    }
+
+    let mut strategy_returns = vec![0.0_f64; n];
+    for i in 0..n {
+        strategy_returns[i] = positions[i] * bar_returns[i];
+    }
+
+    let mut position_changed = vec![false; n];
+    for i in 1..n {
+        position_changed[i] = positions[i] != positions[i - 1];
+    }
+
+    if slippage_bps > 0.0 {
+        let slip = slippage_bps / 10_000.0;
+        for i in 0..n {
+            if position_changed[i] {
+                strategy_returns[i] -= slip;
+            }
+        }
+    }
+
+    let mut equity = vec![1.0_f64; n];
+    if n > 0 {
+        if commission_per_trade <= 0.0 {
+            let mut gross = 1.0_f64;
+            for i in 0..n {
+                gross *= 1.0 + strategy_returns[i];
+                equity[i] = gross;
+            }
+        } else {
+            let mut gross_equity = vec![1.0_f64; n];
+            let mut gross = 1.0_f64;
+            for i in 0..n {
+                gross *= 1.0 + strategy_returns[i];
+                gross_equity[i] = gross;
+            }
+
+            if gross_equity.contains(&0.0) {
+                equity[0] = 1.0;
+                for i in 1..n {
+                    equity[i] = equity[i - 1] * (1.0 + strategy_returns[i]);
+                    if position_changed[i] {
+                        equity[i] -= commission_per_trade;
+                    }
+                }
+            } else {
+                let mut discounted_commissions = 0.0_f64;
+                for i in 0..n {
+                    if position_changed[i] {
+                        discounted_commissions += commission_per_trade / gross_equity[i];
+                    }
+                    equity[i] = gross_equity[i] * (1.0 - discounted_commissions);
+                }
+            }
+        }
+    }
+
+    Ok((
+        positions.into_pyarray(py),
+        bar_returns.into_pyarray(py),
+        strategy_returns.into_pyarray(py),
+        equity.into_pyarray(py),
+    ))
+}
+
+pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
+    m.add_function(wrap_pyfunction!(rsi_threshold_signals, m)?)?;
+    m.add_function(wrap_pyfunction!(sma_crossover_signals, m)?)?;
+    m.add_function(wrap_pyfunction!(macd_crossover_signals, m)?)?;
+    m.add_function(wrap_pyfunction!(backtest_core, m)?)?;
+    Ok(())
+}

src/chunked/mod.rs

@@ -8,11 +8,15 @@
 //!
 //! Functions
 //! ---------
-//! - `trim_overlap`      — remove the first *overlap* elements from an array
-//!   (to strip the warm-up from a chunk's indicator output).
-//! - `stitch_chunks`     — concatenate trimmed chunk results into one array.
-//! - `make_chunk_ranges` — compute start/end indices for a series given chunk
-//!   size and overlap, for use by the Python caller.
+//! - `trim_overlap`               — remove the first *overlap* elements from
+//!   an array (to strip the warm-up from a chunk's indicator output).
+//! - `stitch_chunks`              — concatenate trimmed chunk results into one
+//!   array.
+//! - `make_chunk_ranges`          — compute start/end indices for a series
+//!   given chunk size and overlap, for use by the Python caller.
+//! - `chunk_apply_close_indicator`— run chunked close-only indicators fully in
+//!   Rust (SMA/EMA/RSI).
+//! - `forward_fill_nan`           — forward-fill NaN values in a 1-D array.
 
 use numpy::{IntoPyArray, PyArray1, PyReadonlyArray1};
 use pyo3::exceptions::PyValueError;
@@ -132,6 +136,125 @@ pub fn make_chunk_ranges<'py>(
     Ok(ranges.into_pyarray(py))
 }
 
+// ---------------------------------------------------------------------------
+// chunk_apply_close_indicator
+// ---------------------------------------------------------------------------
+
+fn compute_close_indicator(
+    indicator: &str,
+    series: &[f64],
+    timeperiod: usize,
+) -> PyResult<Vec<f64>> {
+    match indicator {
+        "SMA" => Ok(ferro_ta_core::overlap::sma(series, timeperiod)),
+        "EMA" => Ok(ferro_ta_core::overlap::ema(series, timeperiod)),
+        "RSI" => Ok(ferro_ta_core::momentum::rsi(series, timeperiod)),
+        _ => Err(PyValueError::new_err(format!(
+            "chunk_apply_close_indicator does not support indicator '{indicator}'"
+        ))),
+    }
+}
+
+/// Run chunked execution for close-only indicators in Rust.
+///
+/// Parameters
+/// ----------
+/// series     : 1-D float64 array
+/// indicator  : one of {"SMA", "EMA", "RSI"}
+/// timeperiod : indicator period (>= 1)
+/// chunk_size : output bars per chunk (>= 1)
+/// overlap    : warm-up bars prepended to each chunk
+///
+/// Returns
+/// -------
+/// 1-D float64 array with the same length as `series`.
+#[pyfunction]
+#[pyo3(signature = (series, indicator, timeperiod, chunk_size = 10_000, overlap = 100))]
+pub fn chunk_apply_close_indicator<'py>(
+    py: Python<'py>,
+    series: PyReadonlyArray1<'py, f64>,
+    indicator: &str,
+    timeperiod: usize,
+    chunk_size: usize,
+    overlap: usize,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    if timeperiod == 0 {
+        return Err(PyValueError::new_err("timeperiod must be >= 1"));
+    }
+    if chunk_size == 0 {
+        return Err(PyValueError::new_err("chunk_size must be >= 1"));
+    }
+
+    let values = series.as_slice()?;
+    if values.is_empty() {
+        return Ok(Vec::<f64>::new().into_pyarray(py));
+    }
+
+    let name = indicator.to_ascii_uppercase();
+    let n = values.len();
+    let mut stitched: Vec<f64> = Vec::with_capacity(n);
+    let mut start = 0usize;
+    let mut chunk_index = 0usize;
+
+    loop {
+        let end = (start + chunk_size + overlap).min(n);
+        let chunk = &values[start..end];
+        let out = compute_close_indicator(name.as_str(), chunk, timeperiod)?;
+
+        let discard = if chunk_index == 0 { 0 } else { overlap };
+        if discard > out.len() {
+            return Err(PyValueError::new_err(format!(
+                "overlap ({discard}) must be <= chunk output length ({})",
+                out.len()
+            )));
+        }
+        stitched.extend_from_slice(&out[discard..]);
+
+        if end >= n {
+            break;
+        }
+        start = end.saturating_sub(overlap);
+        chunk_index += 1;
+    }
+
+    if stitched.len() != n {
+        return Err(PyValueError::new_err(format!(
+            "internal chunk stitching error: expected output length {n}, got {}",
+            stitched.len()
+        )));
+    }
+
+    Ok(stitched.into_pyarray(py))
+}
+
+// ---------------------------------------------------------------------------
+// forward_fill_nan
+// ---------------------------------------------------------------------------
+
+/// Forward-fill NaN values in a 1-D array.
+///
+/// Leading NaN values are preserved until the first non-NaN value appears.
+#[pyfunction]
+pub fn forward_fill_nan<'py>(
+    py: Python<'py>,
+    values: PyReadonlyArray1<'py, f64>,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    let input = values.as_slice()?;
+    let mut out = Vec::with_capacity(input.len());
+    let mut last = f64::NAN;
+
+    for &value in input {
+        if value.is_nan() {
+            out.push(last);
+        } else {
+            last = value;
+            out.push(value);
+        }
+    }
+
+    Ok(out.into_pyarray(py))
+}
+
 // ---------------------------------------------------------------------------
 // Register
 // ---------------------------------------------------------------------------
@@ -140,5 +263,7 @@ pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(trim_overlap, m)?)?;
     m.add_function(wrap_pyfunction!(stitch_chunks, m)?)?;
     m.add_function(wrap_pyfunction!(make_chunk_ranges, m)?)?;
+    m.add_function(wrap_pyfunction!(chunk_apply_close_indicator, m)?)?;
+    m.add_function(wrap_pyfunction!(forward_fill_nan, m)?)?;
     Ok(())
 }

src/lib.rs

@@ -1,6 +1,7 @@
 pub mod aggregation;
 pub mod alerts;
 pub mod attribution;
+pub mod backtest;
 pub mod batch;
 pub mod chunked;
 pub mod crypto;
@@ -70,5 +71,6 @@ fn _ferro_ta(m: &Bound<'_, PyModule>) -> PyResult<()> {
     chunked::register(m)?;
     regime::register(m)?;
     attribution::register(m)?;
+    backtest::register(m)?;
     Ok(())
 }

src/options/mod.rs

@@ -3,6 +3,7 @@
 mod chain;
 mod greeks;
 mod iv;
+mod payoff;
 mod pricing;
 mod surface;
 
@@ -63,5 +64,21 @@ pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
         m
     )?)?;
     m.add_function(pyo3::wrap_pyfunction!(self::chain::select_strike_delta, m)?)?;
+    m.add_function(pyo3::wrap_pyfunction!(
+        self::payoff::strategy_payoff_dense,
+        m
+    )?)?;
+    m.add_function(pyo3::wrap_pyfunction!(
+        self::payoff::strategy_payoff_legs,
+        m
+    )?)?;
+    m.add_function(pyo3::wrap_pyfunction!(
+        self::payoff::aggregate_greeks_dense,
+        m
+    )?)?;
+    m.add_function(pyo3::wrap_pyfunction!(
+        self::payoff::aggregate_greeks_legs,
+        m
+    )?)?;
     Ok(())
 }

src/options/payoff.rs

@@ -0,0 +1,443 @@
+use numpy::{IntoPyArray, PyArray1, PyReadonlyArray1};
+use pyo3::exceptions::PyValueError;
+use pyo3::prelude::*;
+use pyo3::types::{PyAny, PyTuple};
+
+#[derive(Clone, Copy)]
+enum Instrument {
+    Option,
+    Future,
+}
+
+#[derive(Clone, Copy)]
+enum Side {
+    Long,
+    Short,
+}
+
+#[derive(Clone, Copy)]
+enum OptionType {
+    Call,
+    Put,
+}
+
+impl Side {
+    fn sign(self) -> f64 {
+        match self {
+            Side::Long => 1.0,
+            Side::Short => -1.0,
+        }
+    }
+}
+
+fn parse_instrument(v: i64) -> PyResult<Instrument> {
+    match v {
+        0 => Ok(Instrument::Option),
+        1 => Ok(Instrument::Future),
+        _ => Err(PyValueError::new_err(
+            "instrument must be 0 (option) or 1 (future)",
+        )),
+    }
+}
+
+fn parse_side(v: i64) -> PyResult<Side> {
+    match v {
+        1 => Ok(Side::Long),
+        -1 => Ok(Side::Short),
+        _ => Err(PyValueError::new_err("side must be 1 (long) or -1 (short)")),
+    }
+}
+
+fn parse_option_type(v: i64) -> PyResult<OptionType> {
+    match v {
+        1 => Ok(OptionType::Call),
+        -1 => Ok(OptionType::Put),
+        _ => Err(PyValueError::new_err(
+            "option_type must be 1 (call) or -1 (put)",
+        )),
+    }
+}
+
+fn parse_instrument_label(v: &str) -> PyResult<Instrument> {
+    match v.to_ascii_lowercase().as_str() {
+        "option" => Ok(Instrument::Option),
+        "future" => Ok(Instrument::Future),
+        _ => Err(PyValueError::new_err(
+            "instrument must be 'option' or 'future'",
+        )),
+    }
+}
+
+fn parse_side_label(v: &str) -> PyResult<Side> {
+    match v.to_ascii_lowercase().as_str() {
+        "long" => Ok(Side::Long),
+        "short" => Ok(Side::Short),
+        _ => Err(PyValueError::new_err("side must be 'long' or 'short'")),
+    }
+}
+
+fn parse_option_type_label(v: &str) -> PyResult<OptionType> {
+    match v.to_ascii_lowercase().as_str() {
+        "call" => Ok(OptionType::Call),
+        "put" => Ok(OptionType::Put),
+        _ => Err(PyValueError::new_err("option_type must be 'call' or 'put'")),
+    }
+}
+
+fn leg_attr_string(leg: &Bound<'_, PyAny>, name: &str) -> PyResult<String> {
+    let value = leg
+        .getattr(name)
+        .map_err(|_| PyValueError::new_err(format!("leg missing '{name}' attribute")))?;
+    value.extract::<String>().map_err(|_| {
+        PyValueError::new_err(format!(
+            "leg field '{name}' has invalid type; expected string"
+        ))
+    })
+}
+
+fn leg_attr_f64(leg: &Bound<'_, PyAny>, name: &str) -> PyResult<f64> {
+    let value = leg
+        .getattr(name)
+        .map_err(|_| PyValueError::new_err(format!("leg missing '{name}' attribute")))?;
+    value.extract::<f64>().map_err(|_| {
+        PyValueError::new_err(format!(
+            "leg field '{name}' has invalid type; expected float"
+        ))
+    })
+}
+
+fn leg_attr_optional_string(leg: &Bound<'_, PyAny>, name: &str) -> PyResult<Option<String>> {
+    let value = leg
+        .getattr(name)
+        .map_err(|_| PyValueError::new_err(format!("leg missing '{name}' attribute")))?;
+    if value.is_none() {
+        return Ok(None);
+    }
+    value.extract::<String>().map(Some).map_err(|_| {
+        PyValueError::new_err(format!(
+            "leg field '{name}' has invalid type; expected string or None"
+        ))
+    })
+}
+
+fn leg_attr_optional_f64(leg: &Bound<'_, PyAny>, name: &str) -> PyResult<Option<f64>> {
+    let value = leg
+        .getattr(name)
+        .map_err(|_| PyValueError::new_err(format!("leg missing '{name}' attribute")))?;
+    if value.is_none() {
+        return Ok(None);
+    }
+    value.extract::<f64>().map(Some).map_err(|_| {
+        PyValueError::new_err(format!(
+            "leg field '{name}' has invalid type; expected float or None"
+        ))
+    })
+}
+
+/// Compute aggregate strategy payoff over a spot grid.
+///
+/// Encoded arrays (same length = n_legs):
+/// - `instruments`: 0=option, 1=future
+/// - `sides`: 1=long, -1=short
+/// - `option_types`: 1=call, -1=put (ignored for futures)
+/// - `strikes`: strike for options, ignored for futures
+/// - `premiums`: premium for options, ignored for futures
+/// - `entry_prices`: entry price for futures, ignored for options
+/// - `quantities`, `multipliers`: applied to both instruments
+#[pyfunction]
+#[allow(clippy::too_many_arguments)]
+pub fn strategy_payoff_dense<'py>(
+    py: Python<'py>,
+    spot_grid: PyReadonlyArray1<'py, f64>,
+    instruments: PyReadonlyArray1<'py, i64>,
+    sides: PyReadonlyArray1<'py, i64>,
+    option_types: PyReadonlyArray1<'py, i64>,
+    strikes: PyReadonlyArray1<'py, f64>,
+    premiums: PyReadonlyArray1<'py, f64>,
+    entry_prices: PyReadonlyArray1<'py, f64>,
+    quantities: PyReadonlyArray1<'py, f64>,
+    multipliers: PyReadonlyArray1<'py, f64>,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    let grid = spot_grid.as_slice()?;
+    let inst = instruments.as_slice()?;
+    let side = sides.as_slice()?;
+    let opt_t = option_types.as_slice()?;
+    let strike = strikes.as_slice()?;
+    let premium = premiums.as_slice()?;
+    let entry = entry_prices.as_slice()?;
+    let qty = quantities.as_slice()?;
+    let mult = multipliers.as_slice()?;
+
+    let n_legs = inst.len();
+    if side.len() != n_legs
+        || opt_t.len() != n_legs
+        || strike.len() != n_legs
+        || premium.len() != n_legs
+        || entry.len() != n_legs
+        || qty.len() != n_legs
+        || mult.len() != n_legs
+    {
+        return Err(PyValueError::new_err(
+            "All leg arrays must have the same length",
+        ));
+    }
+
+    let mut total = vec![0.0_f64; grid.len()];
+
+    for leg_idx in 0..n_legs {
+        let instrument = parse_instrument(inst[leg_idx])?;
+        let side_sign = parse_side(side[leg_idx])?.sign();
+        let leg_scale = side_sign * qty[leg_idx] * mult[leg_idx];
+
+        match instrument {
+            Instrument::Option => {
+                let otype = parse_option_type(opt_t[leg_idx])?;
+                let k = strike[leg_idx];
+                let p = premium[leg_idx];
+                for (i, &s) in grid.iter().enumerate() {
+                    let intrinsic = match otype {
+                        OptionType::Call => (s - k).max(0.0),
+                        OptionType::Put => (k - s).max(0.0),
+                    };
+                    total[i] += leg_scale * (intrinsic - p);
+                }
+            }
+            Instrument::Future => {
+                let e = entry[leg_idx];
+                for (i, &s) in grid.iter().enumerate() {
+                    total[i] += leg_scale * (s - e);
+                }
+            }
+        }
+    }
+
+    Ok(total.into_pyarray(py))
+}
+
+/// Compute aggregate strategy payoff from Python leg objects.
+///
+/// `legs` is expected to be a sequence of `PayoffLeg`-like objects
+/// with attributes used by `ferro_ta.analysis.derivatives_payoff`.
+#[pyfunction]
+pub fn strategy_payoff_legs<'py>(
+    py: Python<'py>,
+    spot_grid: PyReadonlyArray1<'py, f64>,
+    legs: Bound<'py, PyTuple>,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    let grid = spot_grid.as_slice()?;
+    let mut total = vec![0.0_f64; grid.len()];
+
+    for leg in legs.iter() {
+        let instrument = parse_instrument_label(&leg_attr_string(&leg, "instrument")?)?;
+        let side_sign = parse_side_label(&leg_attr_string(&leg, "side")?)?.sign();
+        let quantity = leg_attr_f64(&leg, "quantity")?;
+        let multiplier = leg_attr_f64(&leg, "multiplier")?;
+        let leg_scale = side_sign * quantity * multiplier;
+
+        match instrument {
+            Instrument::Option => {
+                let otype_raw =
+                    leg_attr_optional_string(&leg, "option_type")?.ok_or_else(|| {
+                        PyValueError::new_err("Option payoff legs require option_type.")
+                    })?;
+                let otype = parse_option_type_label(&otype_raw)?;
+                let strike = leg_attr_optional_f64(&leg, "strike")?
+                    .ok_or_else(|| PyValueError::new_err("Option payoff legs require strike."))?;
+                let premium = leg_attr_f64(&leg, "premium")?;
+
+                for (i, &s) in grid.iter().enumerate() {
+                    let intrinsic = match otype {
+                        OptionType::Call => (s - strike).max(0.0),
+                        OptionType::Put => (strike - s).max(0.0),
+                    };
+                    total[i] += leg_scale * (intrinsic - premium);
+                }
+            }
+            Instrument::Future => {
+                let entry_price = leg_attr_optional_f64(&leg, "entry_price")?.ok_or_else(|| {
+                    PyValueError::new_err("Futures payoff legs require entry_price.")
+                })?;
+                for (i, &s) in grid.iter().enumerate() {
+                    total[i] += leg_scale * (s - entry_price);
+                }
+            }
+        }
+    }
+
+    Ok(total.into_pyarray(py))
+}
+
+/// Aggregate Greeks over multiple legs.
+///
+/// Encodings match `strategy_payoff_dense`.
+#[pyfunction]
+#[allow(clippy::too_many_arguments)]
+pub fn aggregate_greeks_dense(
+    spot: f64,
+    instruments: PyReadonlyArray1<'_, i64>,
+    sides: PyReadonlyArray1<'_, i64>,
+    option_types: PyReadonlyArray1<'_, i64>,
+    strikes: PyReadonlyArray1<'_, f64>,
+    volatilities: PyReadonlyArray1<'_, f64>,
+    time_to_expiries: PyReadonlyArray1<'_, f64>,
+    rates: PyReadonlyArray1<'_, f64>,
+    carries: PyReadonlyArray1<'_, f64>,
+    quantities: PyReadonlyArray1<'_, f64>,
+    multipliers: PyReadonlyArray1<'_, f64>,
+) -> PyResult<(f64, f64, f64, f64, f64)> {
+    let inst = instruments.as_slice()?;
+    let side = sides.as_slice()?;
+    let opt_t = option_types.as_slice()?;
+    let strike = strikes.as_slice()?;
+    let vol = volatilities.as_slice()?;
+    let tte = time_to_expiries.as_slice()?;
+    let rate = rates.as_slice()?;
+    let carry = carries.as_slice()?;
+    let qty = quantities.as_slice()?;
+    let mult = multipliers.as_slice()?;
+
+    let n_legs = inst.len();
+    if side.len() != n_legs
+        || opt_t.len() != n_legs
+        || strike.len() != n_legs
+        || vol.len() != n_legs
+        || tte.len() != n_legs
+        || rate.len() != n_legs
+        || carry.len() != n_legs
+        || qty.len() != n_legs
+        || mult.len() != n_legs
+    {
+        return Err(PyValueError::new_err(
+            "All leg arrays must have the same length",
+        ));
+    }
+
+    let mut delta = 0.0_f64;
+    let mut gamma = 0.0_f64;
+    let mut vega = 0.0_f64;
+    let mut theta = 0.0_f64;
+    let mut rho = 0.0_f64;
+
+    for i in 0..n_legs {
+        let instrument = parse_instrument(inst[i])?;
+        let side_sign = parse_side(side[i])?.sign();
+        let leg_scale = side_sign * qty[i] * mult[i];
+        match instrument {
+            Instrument::Future => {
+                delta += leg_scale;
+            }
+            Instrument::Option => {
+                if vol[i].is_nan() || tte[i].is_nan() {
+                    return Err(PyValueError::new_err(
+                        "Option legs require strike, volatility, and time_to_expiry for Greeks aggregation.",
+                    ));
+                }
+                let kind = match parse_option_type(opt_t[i])? {
+                    OptionType::Call => ferro_ta_core::options::OptionKind::Call,
+                    OptionType::Put => ferro_ta_core::options::OptionKind::Put,
+                };
+                let greeks = ferro_ta_core::options::greeks::model_greeks(
+                    ferro_ta_core::options::OptionEvaluation {
+                        contract: ferro_ta_core::options::OptionContract {
+                            model: ferro_ta_core::options::PricingModel::BlackScholes,
+                            underlying: spot,
+                            strike: strike[i],
+                            rate: rate[i],
+                            carry: carry[i],
+                            time_to_expiry: tte[i],
+                            kind,
+                        },
+                        volatility: vol[i],
+                    },
+                );
+                delta += leg_scale * greeks.delta;
+                gamma += leg_scale * greeks.gamma;
+                vega += leg_scale * greeks.vega;
+                theta += leg_scale * greeks.theta;
+                rho += leg_scale * greeks.rho;
+            }
+        }
+    }
+
+    Ok((delta, gamma, vega, theta, rho))
+}
+
+/// Aggregate Greeks from Python leg objects.
+#[pyfunction]
+pub fn aggregate_greeks_legs(
+    spot: f64,
+    legs: Bound<'_, PyTuple>,
+) -> PyResult<(f64, f64, f64, f64, f64)> {
+    let mut delta = 0.0_f64;
+    let mut gamma = 0.0_f64;
+    let mut vega = 0.0_f64;
+    let mut theta = 0.0_f64;
+    let mut rho = 0.0_f64;
+
+    for leg in legs.iter() {
+        let instrument = parse_instrument_label(&leg_attr_string(&leg, "instrument")?)?;
+        let side_sign = parse_side_label(&leg_attr_string(&leg, "side")?)?.sign();
+        let quantity = leg_attr_f64(&leg, "quantity")?;
+        let multiplier = leg_attr_f64(&leg, "multiplier")?;
+        let leg_scale = side_sign * quantity * multiplier;
+
+        match instrument {
+            Instrument::Future => {
+                delta += leg_scale;
+            }
+            Instrument::Option => {
+                let otype_raw =
+                    leg_attr_optional_string(&leg, "option_type")?.ok_or_else(|| {
+                        PyValueError::new_err(
+                            "Option legs require option_type for Greeks aggregation.",
+                        )
+                    })?;
+                let otype = parse_option_type_label(&otype_raw)?;
+                let strike = leg_attr_optional_f64(&leg, "strike")?.ok_or_else(|| {
+                    PyValueError::new_err(
+                        "Option legs require strike, volatility, and time_to_expiry for Greeks aggregation.",
+                    )
+                })?;
+                let volatility = leg_attr_optional_f64(&leg, "volatility")?.ok_or_else(|| {
+                    PyValueError::new_err(
+                        "Option legs require strike, volatility, and time_to_expiry for Greeks aggregation.",
+                    )
+                })?;
+                let time_to_expiry =
+                    leg_attr_optional_f64(&leg, "time_to_expiry")?.ok_or_else(|| {
+                        PyValueError::new_err(
+                            "Option legs require strike, volatility, and time_to_expiry for Greeks aggregation.",
+                        )
+                    })?;
+                let rate = leg_attr_f64(&leg, "rate")?;
+                let carry = leg_attr_f64(&leg, "carry")?;
+
+                let kind = match otype {
+                    OptionType::Call => ferro_ta_core::options::OptionKind::Call,
+                    OptionType::Put => ferro_ta_core::options::OptionKind::Put,
+                };
+                let greeks = ferro_ta_core::options::greeks::model_greeks(
+                    ferro_ta_core::options::OptionEvaluation {
+                        contract: ferro_ta_core::options::OptionContract {
+                            model: ferro_ta_core::options::PricingModel::BlackScholes,
+                            underlying: spot,
+                            strike,
+                            rate,
+                            carry,
+                            time_to_expiry,
+                            kind,
+                        },
+                        volatility,
+                    },
+                );
+                delta += leg_scale * greeks.delta;
+                gamma += leg_scale * greeks.gamma;
+                vega += leg_scale * greeks.vega;
+                theta += leg_scale * greeks.theta;
+                rho += leg_scale * greeks.rho;
+            }
+        }
+    }
+
+    Ok((delta, gamma, vega, theta, rho))
+}

src/portfolio/mod.rs

@@ -350,6 +350,35 @@ pub fn spread<'py>(
     Ok(result.into_pyarray(py))
 }
 
+// ---------------------------------------------------------------------------
+// ratio
+// ---------------------------------------------------------------------------
+
+/// Compute the ratio between two series: A / B.
+///
+/// Where B is 0, returns NaN.
+#[pyfunction]
+pub fn ratio<'py>(
+    py: Python<'py>,
+    a: PyReadonlyArray1<'py, f64>,
+    b: PyReadonlyArray1<'py, f64>,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    let av = a.as_slice()?;
+    let bv = b.as_slice()?;
+    let n = av.len();
+    if n == 0 || bv.len() != n {
+        return Err(PyValueError::new_err(
+            "a and b must be non-empty and equal length",
+        ));
+    }
+    let result: Vec<f64> = av
+        .iter()
+        .zip(bv.iter())
+        .map(|(&x, &y)| if y == 0.0 { f64::NAN } else { x / y })
+        .collect();
+    Ok(result.into_pyarray(py))
+}
+
 // ---------------------------------------------------------------------------
 // zscore_series
 // ---------------------------------------------------------------------------
@@ -449,6 +478,7 @@ pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(correlation_matrix, m)?)?;
     m.add_function(wrap_pyfunction!(relative_strength, m)?)?;
     m.add_function(wrap_pyfunction!(spread, m)?)?;
+    m.add_function(wrap_pyfunction!(ratio, m)?)?;
     m.add_function(wrap_pyfunction!(zscore_series, m)?)?;
     m.add_function(wrap_pyfunction!(compose_weighted, m)?)?;
     Ok(())

tests/integration/test_cross_surface_manifest.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+SCRIPTS = ROOT / "scripts"
+if str(ROOT / "python") not in sys.path:
+    sys.path.insert(0, str(ROOT / "python"))
+if str(SCRIPTS) not in sys.path:
+    sys.path.insert(0, str(SCRIPTS))
+
+from build_api_manifest import build_manifest
+
+
+def test_api_manifest_is_deterministic_and_current() -> None:
+    manifest_path = ROOT / "docs" / "api_manifest.json"
+    assert manifest_path.exists(), "docs/api_manifest.json is missing"
+
+    expected = build_manifest(ROOT, include_runtime_metadata=False)
+    actual = json.loads(manifest_path.read_text(encoding="utf-8"))
+
+    assert actual == expected

tests/integration/test_integration.py

@@ -233,7 +233,9 @@ def test_methods_returns_public_callables():
     result = ferro_ta.methods()
     assert isinstance(result, list)
     assert any(d["name"] == "SMA" and d["category"] == "top_level" for d in result)
-    assert any(d["name"] == "option_price" and d["category"] == "options" for d in result)
+    assert any(
+        d["name"] == "option_price" and d["category"] == "options" for d in result
+    )
 
 
 def test_about_reports_version_and_counts():

tests/integration/test_wasm_node_conformance.py

@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+import json
+import shutil
+import subprocess
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+import ferro_ta
+
+ROOT = Path(__file__).resolve().parents[2]
+WASM_DIR = ROOT / "wasm"
+PKG_JS = WASM_DIR / "pkg" / "ferro_ta_wasm.js"
+SCRIPT = WASM_DIR / "conformance_node.js"
+
+
+def _write_node_conformance_script(path: Path) -> None:
+    path.write_text(
+        """
+const wasm = require("./pkg/ferro_ta_wasm.js");
+
+function toArray(x) {
+  return Array.from(x, (v) => (Number.isNaN(v) ? null : Number(v)));
+}
+
+const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.1, 45.42, 45.84, 46.08, 45.89, 46.03, 46.21, 46.02, 45.78]);
+const high = new Float64Array([44.71, 44.5, 44.6, 44.09, 44.79, 45.2, 45.44, 45.73, 46.01, 46.44, 46.21, 46.39, 46.53, 46.3, 46.12]);
+const low = new Float64Array([43.9, 43.8, 43.9, 43.2, 43.9, 44.2, 44.6, 44.8, 45.2, 45.5, 45.4, 45.5, 45.7, 45.6, 45.4]);
+const volume = new Float64Array([1200, 1320, 1250, 1460, 1500, 1670, 1720, 1810, 1900, 2020, 1980, 2100, 2170, 2140, 2080]);
+
+const payload = {
+  sma: toArray(wasm.sma(close, 5)),
+  ema: toArray(wasm.ema(close, 5)),
+  wma: toArray(wasm.wma(close, 5)),
+  rsi: toArray(wasm.rsi(close, 5)),
+  adx: toArray(wasm.adx(high, low, close, 5)),
+  mfi: toArray(wasm.mfi(high, low, close, volume, 5)),
+};
+
+process.stdout.write(JSON.stringify(payload));
+""".strip()
+        + "\n",
+        encoding="utf-8",
+    )
+
+
+def _run_node_conformance() -> dict[str, list[float | None]]:
+    if shutil.which("node") is None:
+        pytest.skip("node is required for wasm/node conformance test")
+    if not PKG_JS.exists():
+        pytest.skip(
+            "wasm/pkg not found; run `wasm-pack build --target nodejs --out-dir pkg`"
+        )
+
+    _write_node_conformance_script(SCRIPT)
+    try:
+        out = subprocess.check_output(
+            ["node", str(SCRIPT)],
+            cwd=WASM_DIR,
+            text=True,
+        )
+    finally:
+        if SCRIPT.exists():
+            SCRIPT.unlink()
+    return json.loads(out)
+
+
+def _to_jsonable(arr: np.ndarray) -> list[float | None]:
+    vals = np.asarray(arr, dtype=np.float64)
+    return [None if np.isnan(x) else float(x) for x in vals]
+
+
+def _assert_close_with_null_nan(
+    actual: list[float | None],
+    expected: list[float | None],
+    *,
+    atol: float,
+) -> None:
+    assert len(actual) == len(expected)
+    a = np.array([np.nan if v is None else float(v) for v in actual], dtype=np.float64)
+    e = np.array(
+        [np.nan if v is None else float(v) for v in expected], dtype=np.float64
+    )
+    np.testing.assert_allclose(a, e, atol=atol, rtol=0.0, equal_nan=True)
+
+
+def test_wasm_node_matches_python_core_indicators() -> None:
+    close = np.array(
+        [
+            44.34,
+            44.09,
+            44.15,
+            43.61,
+            44.33,
+            44.83,
+            45.10,
+            45.42,
+            45.84,
+            46.08,
+            45.89,
+            46.03,
+            46.21,
+            46.02,
+            45.78,
+        ],
+        dtype=np.float64,
+    )
+    high = np.array(
+        [
+            44.71,
+            44.50,
+            44.60,
+            44.09,
+            44.79,
+            45.20,
+            45.44,
+            45.73,
+            46.01,
+            46.44,
+            46.21,
+            46.39,
+            46.53,
+            46.30,
+            46.12,
+        ],
+        dtype=np.float64,
+    )
+    low = np.array(
+        [
+            43.90,
+            43.80,
+            43.90,
+            43.20,
+            43.90,
+            44.20,
+            44.60,
+            44.80,
+            45.20,
+            45.50,
+            45.40,
+            45.50,
+            45.70,
+            45.60,
+            45.40,
+        ],
+        dtype=np.float64,
+    )
+    volume = np.array(
+        [
+            1200.0,
+            1320.0,
+            1250.0,
+            1460.0,
+            1500.0,
+            1670.0,
+            1720.0,
+            1810.0,
+            1900.0,
+            2020.0,
+            1980.0,
+            2100.0,
+            2170.0,
+            2140.0,
+            2080.0,
+        ],
+        dtype=np.float64,
+    )
+
+    node_payload = _run_node_conformance()
+
+    py_expected = {
+        "sma": _to_jsonable(ferro_ta.SMA(close, 5)),
+        "ema": _to_jsonable(ferro_ta.EMA(close, 5)),
+        "wma": _to_jsonable(ferro_ta.WMA(close, 5)),
+        "rsi": _to_jsonable(ferro_ta.RSI(close, 5)),
+        "adx": _to_jsonable(ferro_ta.ADX(high, low, close, 5)),
+        "mfi": _to_jsonable(ferro_ta.MFI(high, low, close, volume, 5)),
+    }
+
+    for name, expected in py_expected.items():
+        assert name in node_payload
+        _assert_close_with_null_nan(node_payload[name], expected, atol=1e-9)

tests/unit/test_derivatives.py

@@ -1,3 +1,7 @@
+import subprocess
+import sys
+from pathlib import Path
+
 import numpy as np
 import pytest
 
@@ -220,19 +224,30 @@ class TestStrategyAndPayoff:
 
 class TestDerivativesBenchmarking:
     def test_derivatives_benchmark_smoke(self, tmp_path):
-        from benchmarks.bench_derivatives_compare import run_benchmark
-
+        root = Path(__file__).resolve().parents[2]
+        script = root / "benchmarks" / "bench_derivatives_compare.py"
         output_path = tmp_path / "derivatives_benchmark.json"
-        result = run_benchmark(
-            sizes=[32],
-            accuracy_size=16,
-            json_path=str(output_path),
+
+        completed = subprocess.run(
+            [
+                sys.executable,
+                str(script),
+                "--sizes",
+                "32",
+                "--accuracy-size",
+                "16",
+                "--json",
+                str(output_path),
+            ],
+            cwd=root,
+            check=False,
+            capture_output=True,
+            text=True,
         )
 
+        assert completed.returncode == 0, completed.stdout + completed.stderr
         assert output_path.is_file()
-        assert result["accuracy"]["results"]
-        assert result["speed"]["results"]
-        assert any(
-            row["provider"] == "ferro_ta" for row in result["accuracy"]["results"]
-        )
-        assert any(row["provider"] == "ferro_ta" for row in result["speed"]["results"])
+        payload = output_path.read_text(encoding="utf-8")
+        assert '"accuracy"' in payload
+        assert '"speed"' in payload
+        assert '"provider": "ferro_ta"' in payload

tests/unit/test_infrastructure.py

@@ -626,6 +626,13 @@ class TestBatchApply:
         with pytest.raises(ValueError, match="1-D or 2-D"):
             batch_apply(np.zeros((5, 5, 5)), SMA, timeperiod=3)
 
+    def test_sma_fastpath_matches_batch_sma(self):
+        from ferro_ta.data.batch import batch_sma
+
+        fast = batch_apply(self.C2D, SMA, timeperiod=10)
+        direct = batch_sma(self.C2D, timeperiod=10)
+        assert np.allclose(fast, direct, equal_nan=True)
+
 
 class TestBatchShapeValidation:
     def test_batch_atr_shape_mismatch_raises(self):

tests/unit/test_tools_and_api.py

@@ -4,6 +4,9 @@ regime detection, performance attribution, and dashboard helpers.
 
 from __future__ import annotations
 
+import importlib
+import runpy
+
 import numpy as np
 import pytest
 
@@ -1218,7 +1221,22 @@ class TestMCPListTools:
 
         result = handle_list_tools()
         names = [t["name"] for t in result["tools"]]
-        for expected in ("sma", "ema", "rsi", "macd", "backtest", "list_indicators"):
+        assert len(names) > 250
+        for expected in (
+            "sma",
+            "ema",
+            "rsi",
+            "macd",
+            "backtest",
+            "SMA",
+            "compute_indicator",
+            "about",
+            "check_cross",
+            "TickAggregator",
+            "call_instance_method",
+            "call_stored_callable",
+            "delete_instance",
+        ):
             assert expected in names, f"Expected tool '{expected}' not found"
 
     def test_each_tool_has_schema(self):
@@ -1280,6 +1298,57 @@ class TestMCPCallTool:
         assert "final_equity" in payload
         assert "n_trades" in payload
 
+    def test_top_level_sma_call(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        close = list(np.linspace(100, 110, 30))
+        result = handle_call_tool("SMA", {"close": close, "timeperiod": 5})
+        payload = json.loads(result["content"][0]["text"])
+        assert len(payload) == 30
+
+    def test_compute_indicator_call(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        close = list(_make_close(100))
+        result = handle_call_tool(
+            "compute_indicator",
+            {
+                "name": "MACD",
+                "args": [close],
+            },
+        )
+        payload = json.loads(result["content"][0]["text"])
+        assert "macd" in payload
+
+    def test_about_call(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        result = handle_call_tool("about", {})
+        payload = json.loads(result["content"][0]["text"])
+        assert payload["indicator_count"] >= 200
+        assert payload["method_count"] >= 400
+
+    def test_check_cross_call(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        result = handle_call_tool(
+            "check_cross",
+            {
+                "fast": [1.0, 2.0, 3.0, 2.0, 1.0],
+                "slow": [2.0, 2.0, 2.0, 2.0, 2.0],
+            },
+        )
+        payload = json.loads(result["content"][0]["text"])
+        assert len(payload) == 5
+
     def test_list_indicators_call(self):
         import json
 
@@ -1322,3 +1391,129 @@ class TestMCPCallTool:
             "backtest", {"close": close, "strategy": "no_strategy"}
         )
         assert result.get("isError") is True or "content" in result
+
+    def test_tick_aggregator_instance_lifecycle(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        created = json.loads(
+            handle_call_tool("TickAggregator", {"rule": "tick:2"})["content"][0]["text"]
+        )
+        instance_id = created["instance_id"]
+
+        described = json.loads(
+            handle_call_tool("describe_instance", {"instance_id": instance_id})[
+                "content"
+            ][0]["text"]
+        )
+        method_names = [item["name"] for item in described["methods"]]
+        assert "aggregate" in method_names
+
+        aggregated = json.loads(
+            handle_call_tool(
+                "call_instance_method",
+                {
+                    "instance_id": instance_id,
+                    "method": "aggregate",
+                    "args": [
+                        {
+                            "price": [1.0, 2.0, 3.0, 4.0],
+                            "size": [1.0, 1.0, 1.0, 1.0],
+                        }
+                    ],
+                },
+            )["content"][0]["text"]
+        )
+        assert "open" in aggregated
+        assert "close" in aggregated
+
+        deleted = json.loads(
+            handle_call_tool("delete_instance", {"instance_id": instance_id})[
+                "content"
+            ][0]["text"]
+        )
+        assert deleted["deleted"] is True
+
+    def test_stored_callable_can_be_invoked(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        wrapped = json.loads(
+            handle_call_tool("traced", {"func": {"callable": "SMA"}})["content"][0][
+                "text"
+            ]
+        )
+        instance_id = wrapped["instance_id"]
+
+        called = json.loads(
+            handle_call_tool(
+                "call_stored_callable",
+                {
+                    "instance_id": instance_id,
+                    "args": [[1.0, 2.0, 3.0, 4.0, 5.0]],
+                    "kwargs": {"timeperiod": 3},
+                },
+            )["content"][0]["text"]
+        )
+        assert len(called) == 5
+
+        handle_call_tool("delete_instance", {"instance_id": instance_id})
+
+    def test_benchmark_accepts_callable_reference(self):
+        import json
+
+        from ferro_ta.mcp import handle_call_tool
+
+        result = handle_call_tool(
+            "benchmark",
+            {
+                "func": {"callable": "SMA"},
+                "args": [[1.0, 2.0, 3.0, 4.0, 5.0]],
+                "kwargs": {"timeperiod": 3},
+                "n": 2,
+                "warmup": 0,
+            },
+        )
+        payload = json.loads(result["content"][0]["text"])
+        assert payload["n"] == 2.0
+        assert "mean_ms" in payload
+
+
+class TestMCPServer:
+    def test_create_server_requires_mcp_dependency(self, monkeypatch):
+        import ferro_ta.mcp as mcp_mod
+
+        real_import_module = importlib.import_module
+
+        def fake_import_module(name, package=None):
+            if name.startswith("mcp"):
+                raise ImportError("No module named 'mcp'")
+            return real_import_module(name, package)
+
+        mcp_mod.create_server.cache_clear()
+        monkeypatch.setattr(importlib, "import_module", fake_import_module)
+
+        with pytest.raises(RuntimeError, match='pip install "ferro-ta\\[mcp\\]"'):
+            mcp_mod.create_server()
+
+    def test_main_entrypoint_invokes_run_server(self, monkeypatch):
+        import ferro_ta.mcp as mcp_mod
+
+        calls: list[str] = []
+
+        monkeypatch.setattr(mcp_mod, "run_server", lambda: calls.append("called"))
+        runpy.run_module("ferro_ta.mcp.__main__", run_name="__main__")
+
+        assert calls == ["called"]
+
+    def test_create_server_registers_generated_tools(self):
+        import ferro_ta.mcp as mcp_mod
+
+        server = mcp_mod.create_server()
+        tool_names = [tool.name for tool in server._tool_manager.list_tools()]
+
+        assert "SMA" in tool_names
+        assert "TickAggregator" in tool_names
+        assert "call_instance_method" in tool_names

uv.lock

@@ -180,6 +180,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" },
 ]
 
+[[package]]
+name = "cfgv"
+version = "3.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4e/b5/721b8799b04bf9afe054a3899c6cf4e880fcf8563cc71c15610242490a0c/cfgv-3.5.0.tar.gz", hash = "sha256:d5b1034354820651caa73ede66a6294d6e95c1b00acc5e9b098e917404669132", size = 7334, upload-time = "2025-11-19T20:55:51.612Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/3c/33bac158f8ab7f89b2e59426d5fe2e4f63f7ed25df84c036890172b412b5/cfgv-3.5.0-py2.py3-none-any.whl", hash = "sha256:a8dc6b26ad22ff227d2634a65cb388215ce6cc96bbcc5cfde7641ae87e8dacc0", size = 7445, upload-time = "2025-11-19T20:55:50.744Z" },
+]
+
 [[package]]
 name = "charset-normalizer"
 version = "3.4.5"
@@ -515,19 +524,24 @@ wheels = [
 
 [[package]]
 name = "cuda-bindings"
-version = "12.9.4"
+version = "13.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cuda-pathfinder", marker = "(python_full_version < '3.11' and sys_platform == 'emscripten') or (python_full_version < '3.11' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/1a/fe/7351d7e586a8b4c9f89731bfe4cf0148223e8f9903ff09571f78b3fb0682/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b395f79cb89ce0cd8effff07c4a1e20101b873c256a1aeb286e8fd7bd0f556", size = 5744254, upload-time = "2026-03-11T00:12:29.798Z" },
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+
+[[package]]
+name = "virtualenv"
+version = "21.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "distlib" },
+    { name = "filelock" },
+    { name = "platformdirs" },
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+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
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wasm/Cargo.lock

@@ -47,10 +47,15 @@ version = "1.0.4"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801"
 
+[[package]]
+name = "ferro_ta_core"
+version = "1.0.6"
+
 [[package]]
 name = "ferro_ta_wasm"
-version = "1.0.0"
+version = "1.0.6"
 dependencies = [
+ "ferro_ta_core",
  "js-sys",
  "wasm-bindgen",
  "wasm-bindgen-test",

wasm/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "ferro_ta_wasm"
-version = "1.0.3"
+version = "1.0.6"
 edition = "2021"
 description = "WebAssembly bindings for ferro-ta technical analysis indicators"
 license = "MIT"
@@ -13,6 +13,7 @@ crate-type = ["cdylib", "rlib"]
 [dependencies]
 wasm-bindgen = "0.2"
 js-sys = "0.3"
+ferro_ta_core = { path = "../crates/ferro_ta_core", default-features = false }
 
 [dev-dependencies]
 wasm-bindgen-test = "0.3"

wasm/README.md

@@ -11,7 +11,7 @@ npm install ferro-ta-wasm
 ```
 
 ```javascript
-const { sma, ema, rsi, bbands, atr, obv, macd } = require('ferro-ta-wasm');
+const { sma, ema, wma, rsi, adx, mfi, bbands, atr, obv, macd } = require('ferro-ta-wasm');
 
 const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10]);
 const smaOut = sma(close, 3);
@@ -28,20 +28,23 @@ console.log('SMA:', Array.from(smaOut));
 |------------|---------------|----------------------------------------------------|---------|
 | Overlap    | `sma`         | `close: Float64Array, timeperiod: number`          | `Float64Array` |
 | Overlap    | `ema`         | `close: Float64Array, timeperiod: number`          | `Float64Array` |
+| Overlap    | `wma`         | `close: Float64Array, timeperiod: number`          | `Float64Array` |
 | Overlap    | `bbands`      | `close, timeperiod, nbdevup, nbdevdn`              | `Array[upper, middle, lower]` |
 | Momentum   | `rsi`         | `close: Float64Array, timeperiod: number`          | `Float64Array` |
+| Momentum   | `adx`         | `high, low, close: Float64Array, timeperiod`       | `Float64Array` |
 | Momentum   | `macd`        | `close, fastperiod, slowperiod, signalperiod`      | `Array[macd, signal, hist]` |
 | Momentum   | `mom`         | `close: Float64Array, timeperiod: number`          | `Float64Array` |
 | Momentum   | `stochf`      | `high, low, close, fastk_period, fastd_period`     | `Array[fastk, fastd]` |
 | Volatility | `atr`         | `high, low, close: Float64Array, timeperiod`       | `Float64Array` |
 | Volume     | `obv`         | `close: Float64Array, volume: Float64Array`        | `Float64Array` |
+| Volume     | `mfi`         | `high, low, close, volume: Float64Array, timeperiod` | `Float64Array` |
 
 ### Adding more indicators
 
-All implementations are self-contained in `src/lib.rs` — no external crate dependency needed.
+WASM exports live in `src/lib.rs` and can either implement logic directly or delegate to `ferro_ta_core`.
 To add a new indicator:
 
-1. Implement the algorithm in a `#[wasm_bindgen]` function in `src/lib.rs`.
+1. Add a `#[wasm_bindgen]` export in `src/lib.rs` (prefer delegating to `ferro_ta_core` where possible).
 2. Add at least two `#[wasm_bindgen_test]` tests covering output length and a known value.
 3. Update this README table.
 4. Run `wasm-pack test --node` to verify.
@@ -79,7 +82,7 @@ wasm-pack build --target web --out-dir pkg-web
 ## Usage (Node.js)
 
 ```javascript
-const { sma, ema, rsi, bbands, atr, obv, macd } = require('./pkg/ferro_ta_wasm.js');
+const { sma, ema, wma, rsi, adx, mfi, bbands, atr, obv, macd } = require('./pkg/ferro_ta_wasm.js');
 
 const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10]);
 
@@ -91,6 +94,10 @@ console.log('SMA:', Array.from(smaOut));  // [ NaN, NaN, 44.193, ... ]
 const rsiOut = rsi(close, 5);
 console.log('RSI:', Array.from(rsiOut));
 
+// WMA (period 5)
+const wmaOut = wma(close, 5);
+console.log('WMA:', Array.from(wmaOut));
+
 // Bollinger Bands (period 5, ±2σ) — returns [upper, middle, lower]
 const [upper, middle, lower] = bbands(close, 5, 2.0, 2.0);
 console.log('BBANDS upper:', Array.from(upper));
@@ -107,10 +114,18 @@ const low    = new Float64Array([43.0, 44.0, 45.0, 44.0, 43.0, 42.0, 43.0]);
 const atrOut = atr(high, low, close, 3);
 console.log('ATR:', Array.from(atrOut));
 
+// ADX (period 3)
+const adxOut = adx(high, low, close, 3);
+console.log('ADX:', Array.from(adxOut));
+
 // OBV
 const volume  = new Float64Array([1000, 1200, 900, 1500, 800, 600, 700]);
 const obvOut  = obv(close, volume);
 console.log('OBV:', Array.from(obvOut));
+
+// MFI (period 3)
+const mfiOut = mfi(high, low, close, volume, 3);
+console.log('MFI:', Array.from(mfiOut));
 ```
 
 ## Usage (Browser)
@@ -150,7 +165,7 @@ from source:
 
 ## Limitations
 
-- Only 9 indicators are currently exposed (SMA, EMA, BBANDS, RSI, MACD, MOM, STOCHF, ATR, OBV).
+- Only 12 indicators are currently exposed (SMA, EMA, WMA, BBANDS, RSI, ADX, MACD, MOM, STOCHF, ATR, OBV, MFI).
   Additional indicators will be added following the same pattern in `src/lib.rs`.
 - Large arrays (> 10M bars) may be slow due to JS↔WASM memory copies.  For high-throughput
   use cases prefer the Python (PyO3) binding.

wasm/bench.js

@@ -23,14 +23,16 @@ function makeSeries(length) {
   const close = new Float64Array(length);
   const high = new Float64Array(length);
   const low = new Float64Array(length);
+  const volume = new Float64Array(length);
   let value = 100.0;
   for (let idx = 0; idx < length; idx += 1) {
     value += Math.sin(idx / 13.0) * 0.35 + Math.cos(idx / 29.0) * 0.18;
     close[idx] = value;
     high[idx] = value + 1.25;
     low[idx] = value - 1.10;
+    volume[idx] = 1000.0 + Math.abs(Math.sin(idx / 7.0) * 300.0) + (idx % 100);
   }
-  return { close, high, low };
+  return { close, high, low, volume };
 }
 
 function timeMin(fn, rounds = 7) {
@@ -45,11 +47,14 @@ function timeMin(fn, rounds = 7) {
 }
 
 function runBenchmark({ bars }) {
-  const { close, high, low } = makeSeries(bars);
+  const { close, high, low, volume } = makeSeries(bars);
   const cases = [
     ["SMA", () => wasm.sma(close, 20)],
     ["EMA", () => wasm.ema(close, 20)],
+    ["WMA", () => wasm.wma(close, 20)],
     ["RSI", () => wasm.rsi(close, 14)],
+    ["ADX", () => wasm.adx(high, low, close, 14)],
+    ["MFI", () => wasm.mfi(high, low, close, volume, 14)],
     ["ATR", () => wasm.atr(high, low, close, 14)],
     ["BBANDS", () => wasm.bbands(close, 20, 2.0, 2.0)],
   ];

wasm/package.json

@@ -1,6 +1,6 @@
 {
   "name": "ferro-ta-wasm",
-  "version": "1.0.3",
+  "version": "1.0.6",
   "description": "WebAssembly bindings for ferro-ta technical analysis indicators",
   "main": "pkg/ferro_ta_wasm.js",
   "types": "pkg/ferro_ta_wasm.d.ts",

wasm/src/lib.rs

@@ -10,6 +10,7 @@ and `MACD`).
 ## Overlap Studies
 - [`sma`] — Simple Moving Average
 - [`ema`] — Exponential Moving Average
+- [`wma`] — Weighted Moving Average
 - [`bbands`] — Bollinger Bands (returns `[upper, middle, lower]`)
 
 ## Momentum Indicators
@@ -17,12 +18,14 @@ and `MACD`).
 - [`macd`] — Moving Average Convergence/Divergence (returns `[macd, signal, hist]`)
 - [`mom`] — Momentum (close[i] - close[i-period])
 - [`stochf`] — Fast Stochastic (returns `[fastk, fastd]`)
+- [`adx`] — Average Directional Movement Index
 
 ## Volatility Indicators
 - [`atr`] — Average True Range (Wilder smoothing)
 
 ## Volume Indicators
 - [`obv`] — On-Balance Volume
+- [`mfi`] — Money Flow Index
 */
 
 use js_sys::{Array, Float64Array};
@@ -325,6 +328,24 @@ pub fn obv(close: &Float64Array, volume: &Float64Array) -> Float64Array {
     from_vec(result)
 }
 
+// ---------------------------------------------------------------------------
+// WMA — Weighted Moving Average
+// ---------------------------------------------------------------------------
+
+/// Weighted Moving Average.
+///
+/// # Arguments
+/// - `close` – `Float64Array` of close prices.
+/// - `timeperiod` – look-back window (default 30, minimum 1).
+///
+/// # Returns
+/// `Float64Array` with the first `timeperiod - 1` values set to `NaN`.
+#[wasm_bindgen]
+pub fn wma(close: &Float64Array, timeperiod: usize) -> Float64Array {
+    let prices = to_vec(close);
+    from_vec(ferro_ta_core::overlap::wma(&prices, timeperiod))
+}
+
 // ---------------------------------------------------------------------------
 // MOM — Momentum
 // ---------------------------------------------------------------------------
@@ -433,6 +454,70 @@ pub fn stochf(
     out
 }
 
+// ---------------------------------------------------------------------------
+// ADX — Average Directional Movement Index
+// ---------------------------------------------------------------------------
+
+/// Average Directional Movement Index (Wilder smoothing).
+///
+/// # Arguments
+/// - `high`       – `Float64Array` of high prices.
+/// - `low`        – `Float64Array` of low prices.
+/// - `close`      – `Float64Array` of close prices.
+/// - `timeperiod` – look-back period (default 14, minimum 1).
+///
+/// # Returns
+/// `Float64Array`; warm-up values are `NaN`.
+#[wasm_bindgen]
+pub fn adx(
+    high: &Float64Array,
+    low: &Float64Array,
+    close: &Float64Array,
+    timeperiod: usize,
+) -> Float64Array {
+    let h = to_vec(high);
+    let l = to_vec(low);
+    let c = to_vec(close);
+    if h.len() != l.len() || h.len() != c.len() {
+        return from_vec(vec![f64::NAN; c.len()]);
+    }
+    from_vec(ferro_ta_core::momentum::adx(&h, &l, &c, timeperiod))
+}
+
+// ---------------------------------------------------------------------------
+// MFI — Money Flow Index
+// ---------------------------------------------------------------------------
+
+/// Money Flow Index.
+///
+/// # Arguments
+/// - `high`       – `Float64Array` of high prices.
+/// - `low`        – `Float64Array` of low prices.
+/// - `close`      – `Float64Array` of close prices.
+/// - `volume`     – `Float64Array` of volume values.
+/// - `timeperiod` – look-back period (default 14, minimum 1).
+///
+/// # Returns
+/// `Float64Array`; warm-up values are `NaN`.
+#[wasm_bindgen]
+pub fn mfi(
+    high: &Float64Array,
+    low: &Float64Array,
+    close: &Float64Array,
+    volume: &Float64Array,
+    timeperiod: usize,
+) -> Float64Array {
+    let h = to_vec(high);
+    let l = to_vec(low);
+    let c = to_vec(close);
+    let v = to_vec(volume);
+    let n = c.len();
+    if h.len() != n || l.len() != n || v.len() != n {
+        return from_vec(vec![f64::NAN; n]);
+    }
+    from_vec(ferro_ta_core::volume::mfi(&h, &l, &c, &v, timeperiod))
+}
+
 // ---------------------------------------------------------------------------
 // MACD — Moving Average Convergence/Divergence
 // ---------------------------------------------------------------------------
@@ -861,4 +946,77 @@ mod tests {
             assert!(v >= 0.0 && v <= 100.0, "fastk value {v} out of [0, 100]");
         }
     }
+
+    // -----------------------------------------------------------------------
+    // WMA tests
+    // -----------------------------------------------------------------------
+
+    #[wasm_bindgen_test]
+    fn test_wma_output_length() {
+        let close = make_arr(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        let out = wma(&close, 3);
+        assert_eq!(out.length(), 5);
+    }
+
+    #[wasm_bindgen_test]
+    fn test_wma_known_value() {
+        // WMA(3) at index 2 = (1*1 + 2*2 + 3*3) / 6 = 14/6
+        let close = make_arr(&[1.0, 2.0, 3.0, 4.0, 5.0]);
+        let out = wma(&close, 3);
+        let mut vals = vec![0.0f64; 5];
+        out.copy_to(&mut vals);
+        assert!(vals[0].is_nan());
+        assert!(vals[1].is_nan());
+        assert!((vals[2] - (14.0 / 6.0)).abs() < 1e-10);
+    }
+
+    // -----------------------------------------------------------------------
+    // ADX tests
+    // -----------------------------------------------------------------------
+
+    #[wasm_bindgen_test]
+    fn test_adx_output_length() {
+        let h = make_arr(&[10.0, 11.0, 12.0, 13.0, 13.5, 14.0, 14.5, 15.0]);
+        let l = make_arr(&[9.0, 9.5, 10.5, 11.5, 12.0, 12.5, 13.0, 13.5]);
+        let c = make_arr(&[9.5, 10.5, 11.5, 12.0, 13.0, 13.5, 14.0, 14.5]);
+        let out = adx(&h, &l, &c, 3);
+        assert_eq!(out.length(), 8);
+    }
+
+    #[wasm_bindgen_test]
+    fn test_adx_values_in_range() {
+        let h = make_arr(&[10.0, 11.0, 12.0, 13.0, 13.5, 14.0, 14.5, 15.0]);
+        let l = make_arr(&[9.0, 9.5, 10.5, 11.5, 12.0, 12.5, 13.0, 13.5]);
+        let c = make_arr(&[9.5, 10.5, 11.5, 12.0, 13.0, 13.5, 14.0, 14.5]);
+        let out = adx(&h, &l, &c, 3);
+        for v in get_finite(&out) {
+            assert!((0.0..=100.0).contains(&v), "ADX out of range: {v}");
+        }
+    }
+
+    // -----------------------------------------------------------------------
+    // MFI tests
+    // -----------------------------------------------------------------------
+
+    #[wasm_bindgen_test]
+    fn test_mfi_output_length() {
+        let h = make_arr(&[10.0, 11.0, 12.0, 11.5, 12.5, 13.0, 13.5]);
+        let l = make_arr(&[9.0, 9.5, 10.5, 10.0, 11.0, 11.5, 12.0]);
+        let c = make_arr(&[9.5, 10.5, 11.5, 11.0, 12.0, 12.5, 13.0]);
+        let v = make_arr(&[100.0, 110.0, 120.0, 130.0, 125.0, 140.0, 150.0]);
+        let out = mfi(&h, &l, &c, &v, 3);
+        assert_eq!(out.length(), 7);
+    }
+
+    #[wasm_bindgen_test]
+    fn test_mfi_values_in_range() {
+        let h = make_arr(&[10.0, 11.0, 12.0, 11.5, 12.5, 13.0, 13.5]);
+        let l = make_arr(&[9.0, 9.5, 10.5, 10.0, 11.0, 11.5, 12.0]);
+        let c = make_arr(&[9.5, 10.5, 11.5, 11.0, 12.0, 12.5, 13.0]);
+        let v = make_arr(&[100.0, 110.0, 120.0, 130.0, 125.0, 140.0, 150.0]);
+        let out = mfi(&h, &l, &c, &v, 3);
+        for val in get_finite(&out) {
+            assert!((0.0..=100.0).contains(&val), "MFI out of range: {val}");
+        }
+    }
 }