chore: release v1.0.4

- Deleted the .coverage file to clean up the repository.
- Updated .gitignore to ensure .coverage and .coverage.* files are ignored in future commits.
- Revised README.md to enhance clarity and conciseness regarding the library's capabilities and performance.
- Improved documentation for the MCP server, emphasizing its expanded functionality and integration with clients.

.github/workflows/release.yml

@@ -0,0 +1,60 @@
+name: Release
+
+# 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*.*.*"
+
+permissions:
+  contents: write
+
+jobs:
+  create-release:
+    name: Create GitHub Release
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: Extract version from tag
+        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
+        id: changelog
+        env:
+          TAG_NAME: ${{ github.ref_name }}
+        run: |
+          python3 - <<'PY'
+          import re, os, pathlib
+
+          version = os.environ["TAG_NAME"].lstrip("v")
+          text = pathlib.Path("CHANGELOG.md").read_text(encoding="utf-8")
+
+          pattern = rf"## \[{re.escape(version)}\].*?\n(.*?)(?=\n## |\Z)"
+          match = re.search(pattern, text, re.DOTALL)
+          body = match.group(1).strip() if match else f"Release {version}"
+
+          with open(os.environ["GITHUB_OUTPUT"], "a") as f:
+              f.write(f"body<<EOF\n{body}\nEOF\n")
+          PY
+
+      - name: Create GitHub Release
+        uses: softprops/action-gh-release@v2
+        with:
+          tag_name: ${{ github.ref_name }}
+          name: v${{ steps.version.outputs.version }}
+          body: ${{ steps.changelog.outputs.body }}
+          draft: false
+          prerelease: ${{ contains(github.ref_name, '-') }}

.gitignore

@@ -31,6 +31,8 @@ env/
 # WASM build output
 wasm/pkg/
 wasm/pkg-web/
+.coverage
+.coverage.*
 coverage.xml
 .hypothesis/
 
@@ -39,4 +41,4 @@ coverage.xml
 
 # DS Store in all directories
 .DS_Store
-*.DS_Store
+*.DS_Store

CHANGELOG.md

@@ -9,6 +9,68 @@ and the project uses [Semantic Versioning](https://semver.org/).
 
 ## [Unreleased]
 
+## [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
+
+- Public package metadata helpers: `ferro_ta.__version__`, `ferro_ta.about()`,
+  and `ferro_ta.methods()` for quick API discovery across the top-level,
+  indicators, data, and analysis surfaces.
+- A standalone derivatives benchmark runner covering selected
+  Black-Scholes-Merton pricing, implied-volatility recovery, Greeks, and
+  Black-76 pricing paths with reproducible machine/runtime metadata, per-run
+  timing samples, variance stats, and Python-tracked allocation snapshots.
+- A one-command version bump helper, `scripts/bump_version.py`, plus `make version
+  VERSION=X.Y.Z` for aligned Cargo, Python, WASM, Conda, and docs release
+  surfaces.
+
+### Changed
+
+- Tightened the homepage and docs product narrative so the core Rust-backed
+  Python TA library leads, while adjacent tooling is called out separately.
+- Strengthened benchmark evidence and support documentation with clearer
+  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
@@ -293,7 +355,9 @@ and the project uses [Semantic Versioning](https://semver.org/).
 
 ---
 
-[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.2...HEAD
+[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/v1.0.4...HEAD
+[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
 [1.0.0]: https://github.com/pratikbhadane24/ferro-ta/releases/tag/v1.0.0

Cargo.lock

@@ -207,7 +207,7 @@ checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719"
 
 [[package]]
 name = "ferro_ta"
-version = "1.0.2"
+version = "1.0.4"
 dependencies = [
  "criterion",
  "ferro_ta_core",
@@ -222,7 +222,7 @@ dependencies = [
 
 [[package]]
 name = "ferro_ta_core"
-version = "1.0.2"
+version = "1.0.4"
 dependencies = [
  "criterion",
  "wide",

Cargo.toml

@@ -5,9 +5,16 @@ resolver = "2"
 
 [package]
 name = "ferro_ta"
-version = "1.0.2"
+version = "1.0.4"
 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 = "1.0.2" }
+ferro_ta_core = { path = "crates/ferro_ta_core", version = "1.0.4" }
 
 [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
+.PHONY: help dev build test lint typecheck fmt docs clean bench version
 
 # Default target
 help:
@@ -15,6 +15,7 @@ help:
 	@echo "  make typecheck  Run mypy + pyright type checkers"
 	@echo "  make docs       Build the Sphinx documentation"
 	@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 clean      Remove build artefacts"
 
@@ -48,6 +49,10 @@ docs:
 bench:
 	cargo bench -p ferro_ta_core
 
+version:
+	@test -n "$(VERSION)" || (echo "Usage: make version VERSION=X.Y.Z" && exit 1)
+	python3 scripts/bump_version.py "$(VERSION)"
+
 audit:
 	cargo audit
 	uv run --with pip-audit pip-audit

RELEASE.md

@@ -42,6 +42,8 @@ Before starting a release:
 - [ ] `CHANGELOG.md` has a `## [X.Y.Z]` section (not `[Unreleased]`) with all
       changes since the last release documented under `### Added`, `### Changed`,
       `### Fixed`, `### Removed` headings.
+- [ ] Public docs match the release: `docs/conf.py`, `docs/changelog.rst`, and
+      `docs/support_matrix.rst` reflect the version and current support status.
 
 ---
 
@@ -62,26 +64,39 @@ Example: current version is `0.1.0` and you are adding new indicators → new ve
 
 ## Step 2 — Sync version everywhere
 
-These files must carry **the same version string** (e.g. `0.2.0`). Update all before tagging:
+These files must carry **the same version string** (e.g. `0.2.0`). The easiest
+way to do that is:
+
+```bash
+python3 scripts/bump_version.py 0.2.0
+python3 scripts/bump_version.py --check
+```
+
+That script updates the tracked release-version carriers for you.
+
+Files covered by the bump script:
 
 | File | Location |
 |------|----------|
 | `Cargo.toml` | Root (source of truth) |
 | `crates/ferro_ta_core/Cargo.toml` | Same version for crates.io publish |
+| `crates/ferro_ta_core/README.md` | Installation snippet should show the current crate version |
 | `pyproject.toml` | Root |
-| `wasm/package.json` | `"version": "0.2.0"` |
+| `wasm/package.json` | Package version |
+| `conda/meta.yaml` | Conda recipe version |
+| `docs/conf.py` | Default Sphinx release must resolve to the same version |
 
 **`Cargo.toml`** (root):
 ```toml
 [package]
 name = "ferro_ta"
-version = "0.2.0"   # ← update here
+version = "X.Y.Z"   # ← or use scripts/bump_version.py X.Y.Z
 ```
 
 **`pyproject.toml`**:
 ```toml
 [project]
-version = "0.2.0"   # ← must match Cargo.toml exactly
+version = "X.Y.Z"   # ← must match Cargo.toml exactly
 ```
 
 > **Rule:** `Cargo.toml` is the source of truth. Sync the others to match before tagging.
@@ -91,18 +106,24 @@ version = "0.2.0"   # ← must match Cargo.toml exactly
 ## Step 3 — Update CHANGELOG.md
 
 1. Open `CHANGELOG.md`.
-2. Rename the `[Unreleased]` section to `[0.2.0] — YYYY-MM-DD` (today's date).
+2. Rename the `[Unreleased]` section to `[X.Y.Z] — YYYY-MM-DD` (today's date).
 3. Add a fresh empty `[Unreleased]` section at the top.
 4. Update the comparison links at the bottom:
 
 ```markdown
-[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/v0.2.0...HEAD
+[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/vX.Y.Z...HEAD
-[0.2.0]: https://github.com/pratikbhadane24/ferro-ta/compare/v0.1.0...v0.2.0
+[X.Y.Z]: https://github.com/pratikbhadane24/ferro-ta/compare/vPREVIOUS...vX.Y.Z
 ```
 
 Follow the [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) format:
 `Added`, `Changed`, `Deprecated`, `Removed`, `Fixed`, `Security`.
 
+Also update the docs-facing release surfaces for the same version:
+
+- `docs/changelog.rst` with a concise release-notes entry
+- `docs/support_matrix.rst` if supported versions, tested wheels, or module
+  stability changed
+
 ---
 
 ## Step 4 — Commit the version bump

TA_LIB_COMPATIBILITY.md

@@ -0,0 +1,262 @@
+# 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.
+

VERSIONING.md

@@ -21,16 +21,33 @@ Currently supported: **3.10, 3.11, 3.12, 3.13** (see `pyproject.toml`).
 
 ## Release playbook
 
-1. **Bump the version** in `Cargo.toml` and `pyproject.toml` to the new version
+### Fast path
-   (e.g. `1.0.1`).
+
-2. **Update `CHANGELOG.md`**: move the `[Unreleased]` block to a new dated section
+1. **Bump tracked version files with one command**:
+   ```bash
+   python3 scripts/bump_version.py 1.0.3
+   ```
+   or:
+   ```bash
+   make version VERSION=1.0.3
+   ```
+2. **Verify everything matches**:
+   ```bash
+   python3 scripts/bump_version.py --check
+   ```
+3. **Update `CHANGELOG.md`**: move the `[Unreleased]` block to a new dated section
    `[1.0.1] — YYYY-MM-DD` and open a fresh `[Unreleased]` block.
-3. **Commit** the version bump and changelog update with message
+4. **Commit** the version bump and changelog update with message
    `chore: release v1.0.1`.
-4. **Create a tag**: `git tag v1.0.1 && git push origin v1.0.1`.
+5. **Create a tag**: `git tag v1.0.1 && git push origin v1.0.1`.
-5. **Create a GitHub Release** for tag `v1.0.1` — the CI `build-wheels` and
+6. **Create a GitHub Release** for tag `v1.0.1` — the CI `build-wheels` and
    `publish` jobs trigger automatically on `release: published`.
 
+The bump script updates the tracked release-version carriers that are easy to
+miss manually: root Cargo, Python packaging, the core crate, the core crate
+README install snippet, the WASM package, the Conda recipe, and the docs pages
+that show the current released version.
+
 ## Breaking-change policy
 
 - Removing an indicator or changing its signature is a **MAJOR** change.

api/main.py

@@ -106,7 +106,7 @@ MAX_SERIES_LENGTH = int(os.environ.get("MAX_SERIES_LENGTH", "100000"))
 app = FastAPI(
     title="ferro-ta API",
     description="REST API for ferro-ta technical analysis indicators and backtesting.",
-    version="1.0.0",
+    version=ft.__version__,
     docs_url="/docs",
     redoc_url="/redoc",
 )

benchmarks/README.md

@@ -1,14 +1,21 @@
 # ferro-ta Benchmark Suite
 
-> **62 indicators × 6 libraries** — accuracy and speed verified on **100,000 bars** (LARGE dataset).
+> Reproducible speed and accuracy comparisons across 62 indicators and the
+> libraries available in your environment.
 
 ## Overview
 
-The benchmark suite compares **ferro-ta** against five popular Python technical-analysis libraries on a common dataset and shared wrappers so timings are directly comparable.
+The benchmark suite compares **ferro-ta** against other Python
+technical-analysis libraries on a common dataset and shared wrappers so the
+results are easier to reproduce and critique.
+
+It is not designed to prove that ferro-ta wins everywhere. It is designed to
+show where ferro-ta is faster, where it only ties, and where another library
+still wins.
 
 | Library   | Notes |
 |-----------|-------|
-| **TA-Lib** | C extension; gold standard for accuracy and speed |
+| **TA-Lib** | C extension; widely used comparison baseline |
 | **pandas-ta** | Pure Python; broad indicator set |
 | **ta** | Simple API; some indicators use O(n²) loops and are very slow |
 | **Tulipy** | C extension; truncated output (no leading NaN padding) |
@@ -37,9 +44,23 @@ from benchmarks.data_generator import SMALL, MEDIUM, LARGE
 
 - **Harness:** [pytest-benchmark](https://pytest-benchmark.readthedocs.io/) with `benchmark.pedantic(..., iterations=5, rounds=20, warmup_rounds=2)`.
 - **Reported metric:** **Median time per call** in **microseconds (µs)** — lower is better.
-- **Machine info:** Stored in `benchmarks/results.json` (`machine_info`, `commit_info`) for reproducibility.
+- **TA-Lib head-to-head JSON:** `benchmarks/bench_vs_talib.py` records per-run samples, variance stats, machine/runtime/build metadata, and Python-tracked peak allocation snapshots.
+- **Machine info:** Stored in the generated JSON artifacts for reproducibility.
 - **Libraries:** Only libraries present in the environment are benchmarked; missing ones are skipped.
 
+## Current checked-in TA-Lib artifact
+
+The checked-in `benchmarks/artifacts/latest/benchmark_vs_talib.json` artifact
+uses contiguous `float64` arrays at 10k and 100k bars on an Apple M3 Max,
+CPython 3.13.5, and Rust 1.91.1 with the default release profile
+(`lto = true`, `codegen-units = 1`).
+
+- ferro-ta is ahead outside the tie band on 6 of 12 rows at 10k bars and 6 of 12 rows at 100k bars.
+- TA-Lib still wins in the current artifact on `STOCH` and `ADX`, and remains close on `EMA`, `RSI`, `ATR`, and `OBV` depending on size.
+- The public claim should therefore be read as "often faster on selected indicators," not "faster everywhere."
+- When publishing performance statements, point readers to the raw JSON artifact, not just the summary table.
+- The artifact now includes per-run samples, variance stats, and Python-tracked allocation snapshots for each compared indicator.
+
 ## Reproducible Perf Artifacts
 
 Use the perf-contract runner when you want a compact set of machine-readable
@@ -140,7 +161,7 @@ The speed table includes **all 62 indicators**. **Number** = median µs; **N/A**
 **Takeaways:**
 
 - **`ta`** is 20–350× slower on ATR, CCI, ADX, MFI (O(n²) Python loops).
-- **ferro-ta** is typically 2–4× faster than **pandas-ta** across indicators.
+- **ferro-ta** is often materially faster than **pandas-ta** on the checked-in 100k-bar table.
 - **TA-Lib** and **Tulipy** (C extensions) are strong; ferro-ta is competitive and avoids native dependencies.
 
 ---
@@ -160,9 +181,14 @@ uv run pytest benchmarks/test_speed.py --benchmark-only -k "test_large_dataset"
 # Regenerate the Speed Comparison markdown table from results.json
 uv run python benchmarks/benchmark_table.py
 
-# TA-Lib head-to-head with machine-readable summary + git/runtime metadata
+# TA-Lib head-to-head with machine/runtime/build metadata, per-run samples,
+# variance stats, and Python-tracked allocation snapshots
 uv run python benchmarks/bench_vs_talib.py --sizes 10000 100000 --json benchmark_vs_talib.json
 
+# Selected derivatives analytics comparison (BSM price, IV, Greeks, Black-76)
+# against built-in analytical references plus optional installed libraries
+uv run python benchmarks/bench_derivatives_compare.py --sizes 1000 10000 --json benchmark_derivatives_compare.json
+
 # Optional regression check used in CI
 uv run python benchmarks/check_vs_talib_regression.py --input benchmark_vs_talib.json
 
@@ -184,6 +210,25 @@ uv run python benchmarks/run_perf_contract.py --output-dir benchmarks/artifacts/
 
 Without `uv`: use `pytest` and `python` from the same environment where `ferro_ta` and optional libs (e.g. `talib`, `pandas_ta`, `ta`, `tulipy`, `finta`) are installed.
 
+### Derivatives analytics
+
+`benchmarks/bench_derivatives_compare.py` focuses on selected options-analytics
+paths rather than the full surface area:
+
+- `BSM` call pricing
+- call implied-volatility recovery
+- call Greeks
+- `Black-76` call pricing
+
+The script always includes two analytical baselines:
+
+- `reference_numpy` — pure NumPy formulas with vectorized IV bisection
+- `reference_python_loop` — scalar `math`-based reference for sanity checking
+
+If `py_vollib` is installed, it is added automatically as an extra baseline.
+The output JSON includes runtime/build metadata, per-run timing samples,
+variance stats, and Python-tracked peak allocation snapshots.
+
 ### WASM
 
 From the `wasm/` directory:

benchmarks/bench_vs_talib.py

@@ -1,37 +1,34 @@
 """
 ferro_ta vs TA-Lib speed comparison.
 
-Measures throughput (M bars/s) for both libraries on the same data and parameters,
+Measures throughput (M bars/s) for both libraries on the same synthetic data
-and reports speedup (talib_time / ferro_ta_time; > 1 means ferro_ta is faster).
+and parameters. The output is intentionally evidence-heavy:
 
-Requirements:
+- median timings
-    pip install ta-lib   # or conda install ta-lib
+- per-run timing samples
+- variability stats
+- Python-tracked peak allocation snapshots
+- machine, runtime, and build metadata
 
-Run:
+This is meant to support a narrow claim: ferro-ta is often faster on selected
-    python benchmarks/bench_vs_talib.py
+indicators, not universally faster.
-    python benchmarks/bench_vs_talib.py --json results.json
-    python benchmarks/bench_vs_talib.py --sizes 10000 100000  # default: 10k, 100k, 1M
-
-If ta-lib is not installed, the script still runs and reports ferro_ta timings only (no speedup).
-Methodology: same synthetic data, same parameters, median of 7 runs after warmup.
-Environment: document Python version and OS when publishing results.
 """
 
 from __future__ import annotations
 
 import argparse
-from datetime import datetime, timezone
 import json
-import platform
+import math
-import subprocess
 import sys
 import time
+import tracemalloc
 from typing import Any
 
 import numpy as np
 
 try:
     import talib  # noqa: F401
+
     TALIB_AVAILABLE = True
 except ImportError:
     TALIB_AVAILABLE = False
@@ -39,6 +36,11 @@ except ImportError:
 
 import ferro_ta
 
+try:
+    from benchmarks.metadata import benchmark_metadata, package_versions
+except ModuleNotFoundError:  # pragma: no cover - script execution fallback
+    from metadata import benchmark_metadata, package_versions
+
 # ---------------------------------------------------------------------------
 # Configuration
 # ---------------------------------------------------------------------------
@@ -46,100 +48,138 @@ import ferro_ta
 N_WARMUP = 1
 N_RUNS = 7
 DEFAULT_SIZES = [10_000, 100_000, 1_000_000]
+TIE_EPSILON = 0.05
 
 _rng = np.random.default_rng(42)
 
 
-def _git_info() -> dict[str, Any]:
+def _median(values: list[float]) -> float:
-    """Best-effort git metadata for benchmark reproducibility."""
+    ordered = sorted(values)
-    try:
+    mid = len(ordered) // 2
-        commit = subprocess.check_output(
+    if len(ordered) % 2:
-            ["git", "rev-parse", "HEAD"], text=True, stderr=subprocess.DEVNULL
+        return ordered[mid]
-        ).strip()
+    return (ordered[mid - 1] + ordered[mid]) / 2.0
-    except Exception:
-        commit = None
-
-    try:
-        dirty = bool(
-            subprocess.check_output(
-                ["git", "status", "--porcelain"],
-                text=True,
-                stderr=subprocess.DEVNULL,
-            ).strip()
-        )
-    except Exception:
-        dirty = None
-
-    return {"commit": commit, "dirty": dirty}
 
 
-def _runtime_info() -> dict[str, Any]:
+def _summary_stats(samples_ms: list[float]) -> dict[str, float]:
+    if not samples_ms:
+        return {
+            "median_ms": 0.0,
+            "mean_ms": 0.0,
+            "min_ms": 0.0,
+            "max_ms": 0.0,
+            "stddev_ms": 0.0,
+            "cv_pct": 0.0,
+        }
+
+    mean_ms = sum(samples_ms) / len(samples_ms)
+    variance = (
+        sum((sample - mean_ms) ** 2 for sample in samples_ms) / (len(samples_ms) - 1)
+        if len(samples_ms) > 1
+        else 0.0
+    )
+    stddev_ms = math.sqrt(variance)
+    cv_pct = (stddev_ms / mean_ms * 100.0) if mean_ms else 0.0
     return {
-        "generated_at_utc": datetime.now(timezone.utc).isoformat(),
+        "median_ms": round(_median(samples_ms), 4),
-        "python_version": sys.version.split()[0],
+        "mean_ms": round(mean_ms, 4),
-        "platform": platform.platform(),
+        "min_ms": round(min(samples_ms), 4),
-        "machine": platform.machine(),
+        "max_ms": round(max(samples_ms), 4),
+        "stddev_ms": round(stddev_ms, 4),
+        "cv_pct": round(cv_pct, 3),
     }
 
 
+def _outcome(speedup: float) -> str:
+    if speedup > 1.0 + TIE_EPSILON:
+        return "ferro_ta_win"
+    if speedup < 1.0 - TIE_EPSILON:
+        return "talib_win"
+    return "tie"
+
+
 def _summary_for_size(results: list[dict[str, Any]], size: int) -> dict[str, Any]:
-    rows = [r for r in results if r.get("size") == size and "speedup" in r]
+    rows = [row for row in results if row.get("size") == size and "speedup" in row]
     if not rows:
         return {"size": size, "rows": 0}
 
-    speedups = [float(r["speedup"]) for r in rows]
+    speedups = [float(row["speedup"]) for row in rows]
-    wins = sum(1 for s in speedups if s > 1.0)
+    wins = sum(1 for row in rows if row.get("outcome") == "ferro_ta_win")
-    speedups_sorted = sorted(speedups)
+    ties = sum(1 for row in rows if row.get("outcome") == "tie")
-    mid = len(speedups_sorted) // 2
+    losses = sum(1 for row in rows if row.get("outcome") == "talib_win")
-    if len(speedups_sorted) % 2:
-        median = speedups_sorted[mid]
-    else:
-        median = (speedups_sorted[mid - 1] + speedups_sorted[mid]) / 2.0
-
     return {
         "size": size,
         "rows": len(rows),
         "wins": wins,
-        "win_rate": wins / len(rows),
+        "ties": ties,
-        "median_speedup": round(median, 4),
+        "losses": losses,
+        "win_rate": round(wins / len(rows), 4),
+        "non_loss_rate": round((wins + ties) / len(rows), 4),
+        "median_speedup": round(_median(speedups), 4),
         "min_speedup": round(min(speedups), 4),
         "max_speedup": round(max(speedups), 4),
+        "talib_wins_or_ties": [
+            row["indicator"]
+            for row in rows
+            if row.get("outcome") in {"talib_win", "tie"}
+        ],
     }
 
 
-def _synthetic_ohlcv(n: int) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+def _synthetic_ohlcv(
-    # Generate OHLCV so that ta crate DataItem constraints hold: low >= 0, volume >= 0,
+    n: int,
-    # and low <= open, close <= high, high >= open (see ta DataItemBuilder::build).
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    # Generate OHLCV so that ta crate DataItem constraints hold: low >= 0,
+    # volume >= 0, and low <= open, close <= high, high >= open.
     close = 100.0 + np.cumsum(_rng.standard_normal(n) * 0.5)
     open_ = close + _rng.standard_normal(n) * 0.2
     high = np.maximum(open_, close) + np.abs(_rng.standard_normal(n) * 0.3)
     low = np.minimum(open_, close) - np.abs(_rng.standard_normal(n) * 0.3)
-    # Enforce high >= low and low >= 0 (ta requires non-negative prices)
     high = np.maximum(high, low)
     low = np.maximum(low, 0.0)
-    high = np.maximum(high, low)  # again after clamping low
+    high = np.maximum(high, low)
     open_ = np.clip(open_, low, high)
     close = np.clip(close, low, high)
     volume = np.abs(_rng.standard_normal(n) * 1_000_000) + 500_000
     return open_, high, low, close, volume
 
 
-def _median_time_ms(fn, *args, **kwargs) -> float:
+def _timed_runs_ms(fn, *args, **kwargs) -> list[float]:
     for _ in range(N_WARMUP):
         fn(*args, **kwargs)
-    times = []
+
+    samples_ms: list[float] = []
     for _ in range(N_RUNS):
         t0 = time.perf_counter()
         fn(*args, **kwargs)
-        times.append((time.perf_counter() - t0) * 1000)
+        samples_ms.append((time.perf_counter() - t0) * 1000.0)
-    times.sort()
+    return samples_ms
-    return times[len(times) // 2]
+
+
+def _python_peak_bytes(fn, *args, **kwargs) -> int | None:
+    try:
+        tracemalloc.start()
+        tracemalloc.reset_peak()
+        fn(*args, **kwargs)
+        _, peak = tracemalloc.get_traced_memory()
+        return int(peak)
+    except Exception:
+        return None
+    finally:
+        tracemalloc.stop()
+
+
+def _throughput_m_bars_s(size: int, median_ms: float) -> float:
+    if median_ms <= 0:
+        return 0.0
+    return (size / 1e6) / (median_ms / 1000.0)
+
+
+# ---------------------------------------------------------------------------
+# Benchmarked callables
+# ---------------------------------------------------------------------------
 
 
-# Each entry: (label, ferro_ta_callable, talib_callable, needs_ohlcv)
-# ferro_ta_callable / talib_callable receive (open_, high, low, close, volume) and size;
-# they return (args, ft_kwargs, ta_kwargs) or we use a simpler convention:
-# we pass (o, h, l, c, v) and size; each runner knows how to slice and call.
 def _run_ft_sma(o, h, l, c, v, n):
     return ferro_ta.SMA(c[:n], timeperiod=14)
 
@@ -236,7 +276,6 @@ def _run_ta_wma(o, h, l, c, v, n):
     return talib.WMA(c[:n], timeperiod=14)
 
 
-# List of (indicator_name, ft_runner, ta_runner); skip 1M for very slow indicators if needed
 COMPARISON_CASES = [
     ("SMA", _run_ft_sma, _run_ta_sma),
     ("EMA", _run_ft_ema, _run_ta_ema),
@@ -252,14 +291,14 @@ COMPARISON_CASES = [
     ("WMA", _run_ft_wma, _run_ta_wma),
 ]
 
-# For STOCH/ADX and other heavier indicators, optionally skip 1M to keep runtime reasonable
 SKIP_1M_FOR = {"STOCH", "ADX"}
 
 
 def run_comparison(sizes: list[int], json_path: str | None) -> list[dict[str, Any]]:
     max_size = max(sizes)
     open_, high, low, close, volume = _synthetic_ohlcv(max_size)
-    results = []
+    results: list[dict[str, Any]] = []
+
     col_label = 10
     col_size = 10
     col_ft_ms = 12
@@ -269,12 +308,13 @@ def run_comparison(sizes: list[int], json_path: str | None) -> list[dict[str, An
     col_ta_m = 12
 
     if not TALIB_AVAILABLE:
-        print("Note: ta-lib not installed — reporting ferro_ta timings only (no speedup).")
+        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(f"\nferro_ta vs TA-Lib — median of {N_RUNS} 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()
+
     header = (
         f"{'Indicator':<{col_label}} {'Size':<{col_size}} "
         f"{'ferro_ta(ms)':<{col_ft_ms}} {'TA-Lib(ms)':<{col_ta_ms}} "
@@ -287,82 +327,140 @@ def run_comparison(sizes: list[int], json_path: str | None) -> list[dict[str, An
         for size in sizes:
             if size == 1_000_000 and name in SKIP_1M_FOR:
                 continue
-            ms_ft = _median_time_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)
+
+            row: dict[str, Any] = {
+                "indicator": name,
+                "size": size,
+                "input_layout": {
+                    "dtype": "float64",
+                    "contiguous": True,
+                },
+                "ferro_ta_ms": round(ft_median_ms, 4),
+                "ferro_ta_m_bars_s": round(ft_m_bars_s, 2),
+                "ferro_ta_runs_ms": [round(sample, 4) for sample in ft_samples_ms],
+                "ferro_ta_stats": ft_stats,
+                "python_peak_allocation_bytes": {
+                    "ferro_ta": ft_peak_bytes,
+                },
+            }
+
             if TALIB_AVAILABLE:
-                ms_ta = _median_time_ms(ta_run, open_, high, low, close, volume, size)
+                ta_samples_ms = _timed_runs_ms(ta_run, open_, high, low, close, volume, size)
-                speedup = ms_ta / ms_ft if ms_ft > 0 else float("inf")
+                ta_stats = _summary_stats(ta_samples_ms)
-                m_bars_ft = (size / 1e6) / (ms_ft / 1000) if ms_ft > 0 else 0
+                ta_median_ms = float(ta_stats["median_ms"])
-                m_bars_ta = (size / 1e6) / (ms_ta / 1000) if ms_ta > 0 else 0
+                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")
+                outcome = _outcome(speedup)
+                ta_peak_bytes = _python_peak_bytes(
+                    ta_run, open_, high, low, close, volume, size
+                )
+
                 print(
                     f"{name:<{col_label}} {size:<{col_size}} "
-                    f"{ms_ft:<{col_ft_ms}.3f} {ms_ta:<{col_ta_ms}.3f} "
+                    f"{ft_median_ms:<{col_ft_ms}.3f} {ta_median_ms:<{col_ta_ms}.3f} "
-                    f"{speedup:<{col_speedup}.2f}x {m_bars_ft:<{col_ft_m}.1f} {m_bars_ta:<{col_ta_m}.1f}"
+                    f"{speedup:<{col_speedup}.2f}x {ft_m_bars_s:<{col_ft_m}.1f} {ta_m_bars_s:<{col_ta_m}.1f}"
                 )
-                row = {
+
-                    "indicator": name,
+                row.update(
-                    "size": size,
+                    {
-                    "ferro_ta_ms": round(ms_ft, 4),
+                        "talib_ms": round(ta_median_ms, 4),
-                    "talib_ms": round(ms_ta, 4),
+                        "talib_m_bars_s": round(ta_m_bars_s, 2),
-                    "speedup": round(speedup, 4),
+                        "talib_runs_ms": [round(sample, 4) for sample in ta_samples_ms],
-                    "ferro_ta_m_bars_s": round(m_bars_ft, 2),
+                        "talib_stats": ta_stats,
-                    "talib_m_bars_s": round(m_bars_ta, 2),
+                        "speedup": round(speedup, 4),
-                }
+                        "outcome": outcome,
+                    }
+                )
+                row["python_peak_allocation_bytes"]["talib"] = ta_peak_bytes
             else:
-                m_bars_ft = (size / 1e6) / (ms_ft / 1000) if ms_ft > 0 else 0
                 print(
                     f"{name:<{col_label}} {size:<{col_size}} "
-                    f"{ms_ft:<{col_ft_ms}.3f} {'N/A':<{col_ta_ms}} "
+                    f"{ft_median_ms:<{col_ft_ms}.3f} {'N/A':<{col_ta_ms}} "
-                    f"{'N/A':<{col_speedup}} {m_bars_ft:<{col_ft_m}.1f} {'N/A':<{col_ta_m}}"
+                    f"{'N/A':<{col_speedup}} {ft_m_bars_s:<{col_ft_m}.1f} {'N/A':<{col_ta_m}}"
                 )
-                row = {
+
-                    "indicator": name,
-                    "size": size,
-                    "ferro_ta_ms": round(ms_ft, 4),
-                    "ferro_ta_m_bars_s": round(m_bars_ft, 2),
-                }
             results.append(row)
 
     print()
     if TALIB_AVAILABLE and results:
-        wins = sum(1 for r in results if r.get("speedup", 0) > 1)
+        wins = sum(1 for row in results if row.get("outcome") == "ferro_ta_win")
-        total = len(results)
+        total = len([row for row in results if "speedup" in row])
-        print(f"Summary: ferro_ta faster on {wins}/{total} rows (speedup > 1).")
+        print(f"Summary: ferro_ta ahead outside the tie band on {wins}/{total} rows.")
     print()
+
     if json_path:
+        metadata = benchmark_metadata(
+            "benchmark_vs_talib",
+            extra={
+                "dataset": {
+                    "generator": "synthetic_ohlcv",
+                    "sizes": sizes,
+                    "dtype": "float64",
+                    "array_layout": "C-contiguous",
+                    "seed": 42,
+                },
+                "methodology": {
+                    "warmup_runs": N_WARMUP,
+                    "measured_runs": N_RUNS,
+                    "reported_metric": "median_ms",
+                    "speedup_definition": "talib_median_ms / ferro_ta_median_ms",
+                    "tie_band": f"{1.0 - TIE_EPSILON:.2f} to {1.0 + TIE_EPSILON:.2f}",
+                    "input_layout_notes": (
+                        "Benchmarks use contiguous float64 arrays. If your workload "
+                        "passes non-contiguous arrays or other dtypes, benchmark that "
+                        "separately because wrapper overhead can dominate."
+                    ),
+                    "allocation_notes": (
+                        "python_peak_allocation_bytes is a tracemalloc snapshot of "
+                        "Python-tracked allocations only; it is not a full native RSS "
+                        "or allocator profile."
+                    ),
+                },
+                "packages": package_versions("numpy", "ferro-ta", "TA-Lib"),
+            },
+        )
         out = {
-            "schema_version": 1,
+            "schema_version": 2,
-            "command": "python benchmarks/bench_vs_talib.py",
+            "command": " ".join(["python", *sys.argv]),
             "n_warmup": N_WARMUP,
             "n_runs": N_RUNS,
             "sizes": sizes,
             "talib_available": TALIB_AVAILABLE,
-            "runtime": _runtime_info(),
+            "runtime": metadata["runtime"],
-            "git": _git_info(),
+            "git": metadata["git"],
+            "metadata": metadata,
             "summary": {
                 "total_rows": len(results),
-                "by_size": [_summary_for_size(results, s) for s in sizes],
+                "by_size": [_summary_for_size(results, size) for size in sizes],
             },
             "results": results,
         }
         if not TALIB_AVAILABLE:
-            out["note"] = "ferro_ta only — ta-lib not installed"
+            out["note"] = "ferro_ta only; ta-lib not installed"
-        with open(json_path, "w") as f:
+        with open(json_path, "w", encoding="utf-8") as handle:
-            json.dump(out, f, indent=2)
+            json.dump(out, handle, indent=2)
         print(f"Results written to {json_path}")
+
     return results
 
 
 def main() -> int:
-    ap = argparse.ArgumentParser(description="ferro_ta vs TA-Lib speed comparison")
+    parser = argparse.ArgumentParser(description="ferro_ta vs TA-Lib speed comparison")
-    ap.add_argument("--json", default=None, help="Write results to JSON file")
+    parser.add_argument("--json", default=None, help="Write results to JSON file")
-    ap.add_argument(
+    parser.add_argument(
         "--sizes",
         type=int,
         nargs="+",
         default=DEFAULT_SIZES,
         help="Bar counts to benchmark (default: 10000 100000 1000000)",
     )
-    args = ap.parse_args()
+    args = parser.parse_args()
     run_comparison(args.sizes, args.json)
     return 0
 

benchmarks/metadata.py

@@ -1,56 +1,167 @@
 from __future__ import annotations
 
 import hashlib
+import os
 import platform
+import re
 import subprocess
 import sys
 from datetime import datetime, timezone
+from importlib import metadata as importlib_metadata
 from pathlib import Path
 from typing import Any
 
-
+try:
-def git_info() -> dict[str, Any]:
+    import tomllib
-    """Best-effort git metadata for reproducible benchmark artifacts."""
+except ImportError:  # pragma: no cover
     try:
-        commit = subprocess.check_output(
+        import tomli as tomllib  # type: ignore[no-redef]
-            ["git", "rev-parse", "HEAD"], text=True, stderr=subprocess.DEVNULL
+    except ImportError:  # pragma: no cover
-        ).strip()
+        tomllib = None  # type: ignore[assignment]
-    except Exception:
-        commit = None
 
-    try:
-        dirty = bool(
-            subprocess.check_output(
-                ["git", "status", "--porcelain"],
-                text=True,
-                stderr=subprocess.DEVNULL,
-            ).strip()
-        )
-    except Exception:
-        dirty = None
 
+_ROOT = Path(__file__).resolve().parent.parent
+
+
+def _run_cmd(command: list[str]) -> str | None:
     try:
-        branch = subprocess.check_output(
+        return subprocess.check_output(
-            ["git", "rev-parse", "--abbrev-ref", "HEAD"],
+            command,
             text=True,
             stderr=subprocess.DEVNULL,
         ).strip()
     except Exception:
-        branch = None
+        return None
 
-    return {"commit": commit, "dirty": dirty, "branch": branch}
+
+def _read_toml(path: Path) -> dict[str, Any] | None:
+    if tomllib is None or not path.exists():
+        return None
+    try:
+        with path.open("rb") as handle:
+            return tomllib.load(handle)
+    except Exception:
+        return None
+
+
+def _cpu_model() -> str | None:
+    if sys.platform == "darwin":
+        return (
+            _run_cmd(["sysctl", "-n", "machdep.cpu.brand_string"])
+            or _run_cmd(["sysctl", "-n", "hw.model"])
+            or platform.processor()
+            or None
+        )
+    if sys.platform.startswith("linux"):
+        cpuinfo = Path("/proc/cpuinfo")
+        if cpuinfo.exists():
+            text = cpuinfo.read_text(encoding="utf-8", errors="ignore")
+            for pattern in (r"model name\s+:\s+(.+)", r"Hardware\s+:\s+(.+)"):
+                match = re.search(pattern, text)
+                if match:
+                    return match.group(1).strip()
+        return platform.processor() or None
+    if sys.platform.startswith("win"):
+        return os.environ.get("PROCESSOR_IDENTIFIER") or platform.processor() or None
+    return platform.processor() or None
+
+
+def _total_memory_bytes() -> int | None:
+    if sys.platform == "darwin":
+        raw = _run_cmd(["sysctl", "-n", "hw.memsize"])
+        return int(raw) if raw and raw.isdigit() else None
+
+    if sys.platform.startswith("linux"):
+        meminfo = Path("/proc/meminfo")
+        if meminfo.exists():
+            text = meminfo.read_text(encoding="utf-8", errors="ignore")
+            match = re.search(r"MemTotal:\s+(\d+)\s+kB", text)
+            if match:
+                return int(match.group(1)) * 1024
+        return None
+
+    if sys.platform.startswith("win"):  # pragma: no cover
+        try:
+            import ctypes
+
+            class MEMORYSTATUSEX(ctypes.Structure):
+                _fields_ = [
+                    ("dwLength", ctypes.c_ulong),
+                    ("dwMemoryLoad", ctypes.c_ulong),
+                    ("ullTotalPhys", ctypes.c_ulonglong),
+                    ("ullAvailPhys", ctypes.c_ulonglong),
+                    ("ullTotalPageFile", ctypes.c_ulonglong),
+                    ("ullAvailPageFile", ctypes.c_ulonglong),
+                    ("ullTotalVirtual", ctypes.c_ulonglong),
+                    ("ullAvailVirtual", ctypes.c_ulonglong),
+                    ("ullAvailExtendedVirtual", ctypes.c_ulonglong),
+                ]
+
+            status = MEMORYSTATUSEX()
+            status.dwLength = ctypes.sizeof(MEMORYSTATUSEX)
+            ctypes.windll.kernel32.GlobalMemoryStatusEx(ctypes.byref(status))
+            return int(status.ullTotalPhys)
+        except Exception:
+            return None
+
+    return None
+
+
+def _cargo_release_profile() -> dict[str, Any] | None:
+    cargo_toml = _read_toml(_ROOT / "Cargo.toml")
+    if not cargo_toml:
+        return None
+    profile = cargo_toml.get("profile", {}).get("release")
+    return profile if isinstance(profile, dict) else None
+
+
+def git_info() -> dict[str, Any]:
+    """Best-effort git metadata for reproducible benchmark artifacts."""
+    return {
+        "commit": _run_cmd(["git", "rev-parse", "HEAD"]),
+        "dirty": bool(_run_cmd(["git", "status", "--porcelain"]) or ""),
+        "branch": _run_cmd(["git", "rev-parse", "--abbrev-ref", "HEAD"]),
+    }
 
 
 def runtime_info() -> dict[str, Any]:
     return {
         "generated_at_utc": datetime.now(timezone.utc).isoformat(),
         "python_version": sys.version.split()[0],
+        "python_implementation": platform.python_implementation(),
+        "python_executable": sys.executable,
         "platform": platform.platform(),
+        "system": platform.system(),
+        "release": platform.release(),
         "machine": platform.machine(),
         "processor": platform.processor() or None,
+        "cpu_model": _cpu_model(),
+        "cpu_count_logical": os.cpu_count(),
+        "total_memory_bytes": _total_memory_bytes(),
     }
 
 
+def build_info() -> dict[str, Any]:
+    return {
+        "rustc": _run_cmd(["rustc", "-Vv"]),
+        "cargo": _run_cmd(["cargo", "-VV"]) or _run_cmd(["cargo", "-V"]),
+        "cargo_release_profile": _cargo_release_profile(),
+        "rustflags": os.environ.get("RUSTFLAGS"),
+        "cargo_build_rustflags": os.environ.get("CARGO_BUILD_RUSTFLAGS"),
+        "maturin_flags": os.environ.get("MATURIN_EXTRA_ARGS"),
+    }
+
+
+def package_versions(*names: str) -> dict[str, str | None]:
+    versions: dict[str, str | None] = {}
+    for name in names:
+        try:
+            versions[name] = importlib_metadata.version(name)
+        except importlib_metadata.PackageNotFoundError:
+            versions[name] = None
+    return versions
+
+
 def file_info(path: str | Path) -> dict[str, Any]:
     file_path = Path(path)
     data = file_path.read_bytes()
@@ -71,6 +182,8 @@ def benchmark_metadata(
         "suite": suite,
         "runtime": runtime_info(),
         "git": git_info(),
+        "build": build_info(),
+        "packages": package_versions("numpy", "ferro-ta"),
     }
     if fixtures:
         metadata["fixtures"] = [file_info(path) for path in fixtures]

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, ...]:

conda/meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "ferro-ta" %}
-{% set version = "1.0.0" %}
+{% set version = "1.0.4" %}
 
 package:
   name: {{ name|lower }}
@@ -39,11 +39,12 @@ about:
   home: https://github.com/pratikbhadane24/ferro-ta
   license: MIT
   license_family: MIT
-  summary: A fast Technical Analysis library — TA-Lib alternative powered by Rust and PyO3
+  summary: Rust-powered Python technical analysis library with a TA-Lib-compatible API
   description: |
-    ferro-ta is a drop-in TA-Lib alternative with pre-compiled wheels for all
+    ferro-ta is a Rust-powered Python technical analysis library with a
-    major platforms. It provides 155+ indicators via a Rust core and PyO3
+    TA-Lib-compatible API and pre-compiled wheels for the supported platforms.
-    bindings, with optional pandas / streaming APIs.
+    It provides 155+ indicators via a Rust core and PyO3 bindings, with
+    optional pandas and streaming APIs.
   doc_url: https://github.com/pratikbhadane24/ferro-ta
   dev_url: https://github.com/pratikbhadane24/ferro-ta
 

crates/ferro_ta_core/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "ferro_ta_core"
-version = "1.0.2"
+version = "1.0.4"
 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 = "1.0.2"
+ferro_ta_core = "1.0.4"
 ```
 
 ## Design

docs/adjacent_tooling.rst

@@ -0,0 +1,48 @@
+Adjacent Tooling
+================
+
+These modules are useful, but they are secondary to ferro-ta's core identity as
+a Python technical analysis library.
+
+.. list-table::
+   :header-rows: 1
+
+   * - Area
+     - Status
+     - What it is
+   * - Derivatives analytics
+     - Adjacent
+     - Options pricing, Greeks, implied volatility helpers, futures basis,
+       curve, and roll utilities. See :doc:`derivatives`.
+   * - Agent workflow wrappers
+     - Adjacent
+     - Tool and workflow helpers for agent-style integrations. See
+       `docs/agentic.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_.
+   * - MCP server
+     - Experimental or adjacent
+     - 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
+     - Browser and Node.js package with a smaller indicator subset. See
+       `wasm/README.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/wasm/README.md>`_.
+   * - GPU backend
+     - Experimental
+     - Optional PyTorch-backed acceleration for a limited subset of indicators.
+       See `docs/gpu-backend.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/gpu-backend.md>`_.
+   * - Plugin system
+     - Experimental
+     - Registry and plugin packaging model for custom indicators. See
+       :doc:`plugins`.
+
+How to read the project
+-----------------------
+
+When evaluating ferro-ta:
+
+- Start with the core library docs, migration guide, support matrix, and benchmarks.
+- Treat adjacent tooling as opt-in layers, not as proof that the core indicator
+  library is broader or more stable than it is.
+- Check the release notes and stability policy before depending on experimental
+  surfaces in production.

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/benchmarks.rst

@@ -1,20 +1,130 @@
 Benchmarks
 ==========
 
-The authoritative benchmark workflow is in ``benchmarks/``:
+The benchmark suite is meant to support a narrow claim: ferro-ta is often
+faster on selected indicators, and the evidence is published in a reproducible
+form.
+
+What is published
+-----------------
+
+The authoritative benchmark workflow lives in ``benchmarks/``:
 
 - Cross-library speed suite: ``benchmarks/test_speed.py``
 - Cross-library accuracy suite: ``benchmarks/test_accuracy.py``
-- TA-Lib head-to-head speed script: ``benchmarks/bench_vs_talib.py``
+- TA-Lib head-to-head script: ``benchmarks/bench_vs_talib.py``
 - Table generation from benchmark JSON: ``benchmarks/benchmark_table.py``
+- Perf-contract artifact bundle: ``benchmarks/run_perf_contract.py``
 
-Run the cross-library speed suite on 100,000 bars:
+Latest checked-in TA-Lib artifact
+---------------------------------
+
+The current checked-in TA-Lib comparison artifact benchmarks contiguous
+``float64`` arrays at 10k and 100k bars on an ``Apple M3 Max`` with 14 logical
+cores, about 38.7 GB RAM, ``CPython 3.13.5``, and ``Rust 1.91.1`` using the
+default release profile (``lto = true``, ``codegen-units = 1``).
+
+Summary from ``benchmarks/artifacts/latest/benchmark_vs_talib.json``:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Size
+     - Rows
+     - ferro-ta wins
+     - Median speedup
+     - TA-Lib wins or ties
+   * - ``10,000``
+     - 12
+     - 6
+     - ``1.0850x``
+     - ``EMA``, ``RSI``, ``ATR``, ``STOCH``, ``ADX``, ``OBV``
+   * - ``100,000``
+     - 12
+     - 6
+     - ``1.0784x``
+     - ``EMA``, ``RSI``, ``ATR``, ``STOCH``, ``ADX``, ``OBV``
+
+Examples from the 100k-bar run:
+
+.. list-table::
+   :header-rows: 1
+
+   * - Indicator
+     - ferro-ta
+     - TA-Lib
+     - Speedup
+     - Read
+   * - ``SMA``
+     - ``0.0985 ms``
+     - ``0.2241 ms``
+     - ``2.2751x``
+     - clear ferro-ta win
+   * - ``BBANDS``
+     - ``0.2122 ms``
+     - ``0.4966 ms``
+     - ``2.3402x``
+     - clear ferro-ta win
+   * - ``MACD``
+     - ``0.5152 ms``
+     - ``0.7111 ms``
+     - ``1.3801x``
+     - ferro-ta win
+   * - ``STOCH``
+     - ``1.7064 ms``
+     - ``0.7603 ms``
+     - ``0.4455x``
+     - TA-Lib win
+   * - ``ADX``
+     - ``0.7910 ms``
+     - ``0.5769 ms``
+     - ``0.7294x``
+     - TA-Lib win
+   * - ``ATR``
+     - ``0.5087 ms``
+     - ``0.5147 ms``
+     - ``1.0118x``
+     - tie on this machine
+
+Methodology notes
+-----------------
+
+- The head-to-head script uses the same synthetic OHLCV generator, the same
+  parameters, and the same contiguous ``float64`` array layout for both
+  libraries.
+- Reported speedup is ``TA-Lib median time / ferro-ta median time``.
+- The script uses 1 warmup run and 7 measured runs per case, and now records
+  the full per-run timing samples, not just one selected number.
+- Published JSON artifacts include machine/runtime metadata, git metadata, Rust
+  toolchain and build-profile metadata, per-run variance statistics, and
+  Python-tracked peak allocation snapshots.
+- Allocation snapshots are based on ``tracemalloc`` and capture Python-tracked
+  allocations only; they are not full native RSS profiles.
+- If your workload uses non-contiguous arrays, different dtypes, or different
+  batch sizes, benchmark that exact workload. Those factors can materially
+  change the result.
+
+Reproduce the TA-Lib comparison
+-------------------------------
+
+.. code-block:: bash
+
+   pip install ta-lib
+   python benchmarks/bench_vs_talib.py --sizes 10000 100000 --json benchmark_vs_talib.json
+
+The JSON output is the main artifact to review when publishing performance
+claims.
+
+Cross-library suite
+-------------------
+
+Run the broader speed suite on 100,000 bars:
 
 .. code-block:: bash
 
    uv run pytest benchmarks/test_speed.py --benchmark-only --benchmark-json=benchmarks/results.json -v
 
-Selected results on a modern CPU (100,000 bars):
+Selected throughput examples from the checked-in table:
 
 .. list-table::
    :header-rows: 1
@@ -38,25 +148,16 @@ Selected results on a modern CPU (100,000 bars):
    * - ``STOCH``
      - 33 M bars/s
 
-Multi-size and JSON output
+Perf-contract artifacts
---------------------------
+-----------------------
 
-To build the markdown comparison table from the JSON output:
+Use the perf-contract runner when you want a compact, machine-readable artifact
+bundle for single-series latency, batch throughput, streaming throughput, and
+hotspot attribution:
 
 .. code-block:: bash
 
-   uv run python benchmarks/benchmark_table.py
+   uv run python benchmarks/run_perf_contract.py --output-dir benchmarks/artifacts/latest
 
-Comparison with TA-Lib
+See ``benchmarks/README.md`` for the detailed benchmark playbook and the
-----------------------
+checked-in comparison tables.
-
-To measure speedup vs TA-Lib on the same data and parameters, run:
-
-.. code-block:: bash
-
-   pip install ta-lib
-   python benchmarks/bench_vs_talib.py --sizes 10000 100000 --json benchmark_vs_talib.json
-
-See the README “Performance vs TA-Lib” section for methodology and a
-representative comparison table. The script prints a table of median times and
-speedup (TA-Lib time / ferro_ta time); use ``--json out.json`` to save results.

docs/changelog.rst

@@ -1,55 +1,57 @@
-Changelog
+Release Notes
-=========
+=============
 
-1.0.0 (2026)
+These docs track package version ``1.0.4``.
-------------
 
-**Candlestick Pattern Parity (61/61)**
+1.0.4 (2026-03-24)
+------------------
 
-- All 61 TA-Lib candlestick patterns implemented in Rust
+- Expanded the optional MCP server from a small hand-written subset to the
-- ``{-100, 0, 100}`` convention, consistent with TA-Lib
+  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.
 
-**Numerical Parity**
+1.0.3 (2026-03-24)
+------------------
 
-- RSI, ATR/NATR, CCI, BETA, STOCH, STOCHRSI, ADX/DX/DI/DM all rewritten to match TA-Lib seeding
+- Added top-level package metadata helpers such as ``ferro_ta.__version__``,
-- Removed dependency on ``ta`` crate for these indicators
+  ``ferro_ta.about()``, and ``ferro_ta.methods()``.
+- Added a standalone derivatives benchmark artifact for selected options
+  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.
 
-**Streaming / Incremental API**
+1.0.2 (2026-03-24)
+------------------
 
-- New :mod:`ferro_ta.streaming` module with bar-by-bar stateful classes
+- Improved rolling statistical kernels and several Python analysis hotspots.
-- ``StreamingSMA``, ``StreamingEMA``, ``StreamingRSI``, ``StreamingATR``, ``StreamingBBands``, ``StreamingMACD``, ``StreamingStoch``, ``StreamingVWAP``, ``StreamingSupertrend``
+- Added reproducible perf-contract artifacts, TA-Lib regression guards, and
+  updated benchmark tooling.
+- Tightened the public benchmark documentation so claims, caveats, and evidence
+  live closer together.
 
-**Pandas Integration**
+1.0.1 (2026-03-24)
+------------------
 
-- All indicators transparently accept ``pandas.Series`` and return ``Series`` with original index preserved
+- Improved release automation for PyPI, crates.io, and npm.
-- Multi-output functions return tuples of ``Series``
+- Fixed CI workflow issues that caused otherwise healthy release jobs to fail.
+- Ensured the published WASM package includes its built ``pkg/`` artifacts.
 
-**Math Operators / Transforms**
+1.0.0 (2026-03-23)
+------------------
 
-- 24 functions: arithmetic (ADD/SUB/MULT/DIV), rolling (SUM/MAX/MIN/MAXINDEX/MININDEX), element-wise math transforms
+- First stable release of the Rust-backed Python technical analysis library.
-- SUM uses vectorized cumsum (220× faster than a naive loop)
+- Shipped broad TA-Lib coverage, streaming APIs, extended indicators, and the
+  initial Sphinx documentation set.
+- Added the benchmark suite, release playbook, and compatibility/testing
+  scaffolding for stable releases.
 
-**Documentation**
+For the canonical project changelog, including the full per-version details,
-
+see `CHANGELOG.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/CHANGELOG.md>`_.
-- Sphinx documentation setup with API reference, quickstart guide, and benchmarks page
-
-**Benchmarking Suite**
-
-- ``benchmarks/test_speed.py`` for authoritative ``pytest-benchmark`` speed runs
-- ``benchmarks/bench_vs_talib.py`` for TA-Lib head-to-head comparisons
-
-**Extended Indicators**
-
-- ``VWAP`` — cumulative or rolling window
-- ``SUPERTREND`` — ATR-based trend signal
-
-**Additional Extended Indicators**
-
-- ``ICHIMOKU`` — Ichimoku Cloud (Tenkan, Kijun, Senkou A/B, Chikou)
-- ``DONCHIAN`` — Donchian Channels (upper, middle, lower)
-- ``PIVOT_POINTS`` — Classic, Fibonacci, and Camarilla pivot points
-
-**Type Stubs & Packaging**
-
-- ``python/ferro_ta/__init__.pyi`` type stub for IDE auto-completion
-- ``pyproject.toml``: added optional extras (benchmark, pandas, docs, all), project URLs, Python 3.10–3.13 classifiers

docs/conf.py

@@ -4,7 +4,17 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 import os
+import re
 import sys
+from pathlib import Path
+
+try:
+    import tomllib
+except ImportError:  # pragma: no cover
+    try:
+        import tomli as tomllib  # type: ignore[no-redef]
+    except ImportError:  # pragma: no cover
+        tomllib = None  # type: ignore[assignment]
 
 # Add the python source directory so autodoc can import ferro_ta
 # Only add if ferro_ta is not already installed (e.g. from a wheel in CI)
@@ -17,8 +27,28 @@ except ImportError:
 project = "ferro-ta"
 copyright = "2024, pratikbhadane24"
 author = "pratikbhadane24"
-# Version from env (e.g. set in CI from git tag) or default
+
-release = os.environ.get("FERRO_TA_VERSION", "1.0.0")
+
+def _default_release() -> str:
+    if tomllib is None:
+        return "0+unknown"
+    pyproject_toml = Path(__file__).resolve().parents[1] / "pyproject.toml"
+    try:
+        if tomllib is not None:
+            with pyproject_toml.open("rb") as handle:
+                data = tomllib.load(handle)
+            return data.get("project", {}).get("version", "0+unknown")
+        text = pyproject_toml.read_text(encoding="utf-8")
+        match = re.search(r'^version\s*=\s*"([^"]+)"', text, re.MULTILINE)
+        if match:
+            return match.group(1)
+        return "0+unknown"
+    except Exception:
+        return "0+unknown"
+
+
+# Version from env (e.g. set in CI from git tag) or default to pyproject.toml
+release = os.environ.get("FERRO_TA_VERSION", _default_release())
 version = release
 
 # -- General configuration ----------------------------------------------------

docs/index.rst

@@ -3,43 +3,66 @@ ferro-ta Documentation
 
 .. toctree::
    :maxdepth: 2
-   :caption: Contents
+   :caption: Core Library
 
    quickstart
    migration_talib
+   support_matrix
    pandas_api
    error_handling
    api/index
    streaming
-   extended
    batch
-   derivatives
+   extended
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Evidence and Releases
+
    benchmarks
-   plugins
    changelog
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Adjacent and Experimental
+
+   derivatives
+   adjacent_tooling
+   plugins
    contributing
 
 Overview
 --------
 
-**ferro-ta** is a fast Technical Analysis library — a drop-in alternative to TA-Lib
+**ferro-ta** is a Rust-powered Python technical analysis library focused on a
-powered by Rust and PyO3.
+TA-Lib-compatible API for NumPy-centered workloads.
 
-Features:
+.. important::
+
+   Performance varies by indicator, array layout, warmup, build flags, and
+   machine. ferro-ta is often faster on selected indicators, not universally
+   faster. See :doc:`benchmarks` for the reproducible workflow, methodology
+   notes, and the indicators where TA-Lib still wins or ties in the current
+   checked-in artifact.
+
+Core library:
 
 - 160+ indicators covering all TA-Lib categories
-- 10 extended indicators not in TA-Lib (VWAP, Supertrend, Ichimoku Cloud, …)
+- TA-Lib-style imports such as ``ferro_ta.SMA(close, timeperiod=20)``
-- Batch execution API — run indicators on 2-D arrays of multiple series
+- Pre-built wheels for the supported Python/OS matrix
 - Pure Rust core library (``crates/ferro_ta_core``) — no PyO3 / numpy dependency
+- Batch execution API — run indicators on 2-D arrays of multiple series
 - Streaming / bar-by-bar API for live trading
 - Transparent pandas.Series support
-- Math operators and transforms
 - Type stubs (.pyi) for IDE auto-completion
-- WASM binding for browser/Node.js use
+- 10 extended indicators not in TA-Lib (VWAP, Supertrend, Ichimoku Cloud, ...)
-- Options/IV helpers and derivatives analytics — see :doc:`derivatives`
+
+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>`_
-- Sphinx documentation
+- WASM, plugins, and other optional surfaces — see :doc:`adjacent_tooling`
 
 Installation
 ~~~~~~~~~~~~
@@ -70,11 +93,11 @@ Further Reading
 - `Architecture <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/architecture.md>`_ — Rust/Python layout, two-crate design, binding flow.
 - `Performance Guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/performance.md>`_ — when to use raw numpy vs pandas/polars, batch notes, tips.
 - `API Stability <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/stability.md>`_ — stability tiers, versioning, and deprecation policy.
+- :doc:`support_matrix` — parity status, tested wheel targets, supported Python versions, and experimental modules.
 - `Rust-First Policy <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/rust_first.md>`_ — all compute logic belongs in Rust; how to add new indicators.
 - `Out-of-Core Execution <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/out-of-core.md>`_ — chunked processing and Dask integration.
 - :doc:`derivatives` — IV helpers, options pricing/Greeks/IV, futures analytics, strategy schemas, and payoff helpers.
-- `Agentic Workflow <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_ — tools.py, workflow.py, LangChain integration.
+- :doc:`adjacent_tooling` — optional surfaces such as derivatives, MCP, WASM, GPU, plugins, and agent-oriented integrations.
-- `MCP Server <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_ — run ferro-ta as an MCP server in Cursor/Claude.
 
 Indices and tables
 ==================

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
+ferro-ta ships an optional MCP (Model Context Protocol) server built on the
-indicators and backtest tools to AI agents.  This guide shows how to run
+official Python SDK's FastMCP layer. The server now exposes the broad public
-the server and connect it to Cursor or any MCP-compatible client.
+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.
+Install the optional MCP extra:
-For the full MCP SDK integration (recommended), install the optional 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)").
+Add the server to Cursor's MCP settings:
-2. Find or create the `mcpServers` section:
 
 ```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").
+You can place this in your user settings JSON or in a workspace-level
-4. The ferro-ta tools will appear in the Tools panel.
+`.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": {
+  "instance_id": "tickaggregator-0001",
-    "ferro-ta": {
+  "type": "ferro_ta.data.aggregation.TickAggregator",
-      "command": "python",
+  "repr": "TickAggregator(rule='tick:2')"
-      "args": ["-m", "ferro_ta.mcp"]
-    }
-  }
 }
 ```
 
+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 |
+Use the server entrypoint:
-|------|-------------|
-| `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:
 
 ```python
-from ferro_ta.mcp import handle_list_tools, handle_call_tool
+from ferro_ta.mcp import create_server
-import numpy as np
+
+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 = [100, 101, 102, 103, 104]
-close = list(np.cumprod(1 + np.random.default_rng(0).normal(0, 0.01, 50)) * 100)
+result = handle_call_tool("SMA", {"close": close, "timeperiod": 3})
-result = handle_call_tool("rsi", {"close": close, "timeperiod": 14})
+print(json.loads(result["content"][0]["text"]))
-print(result)
+
+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.
+- `python -m ferro_ta.mcp` - stdio MCP entrypoint
-- `ferro_ta.tools` — underlying tool functions.
+- `ferro_ta.mcp.create_server()` - FastMCP server factory
-- `docs/agentic.md` — LangChain and workflow integration.
+- `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

@@ -0,0 +1,117 @@
+Support Matrix
+==============
+
+The primary product is the Python technical analysis library: TA-Lib-style
+indicator calls backed by a Rust implementation.
+
+Indicator compatibility
+-----------------------
+
+.. list-table::
+   :header-rows: 1
+
+   * - Status
+     - Scope
+     - Notes
+   * - Exact parity
+     - Common TA-Lib-compatible indicators such as ``SMA``, ``WMA``,
+       ``BBANDS``, ``RSI``, ``ATR``, ``NATR``, ``CCI``, ``STOCH``,
+       ``STOCHRSI``, and most candlestick patterns
+     - Matches TA-Lib numerically within floating-point tolerance in the
+       current comparison suite.
+   * - Approximate parity
+     - EMA-family indicators (``EMA``, ``DEMA``, ``TEMA``, ``T3``, ``MACD``),
+       ``MAMA`` / ``FAMA``, ``SAR`` / ``SAREXT``, and ``HT_*`` cycle
+       indicators
+     - Same API and intended use, with convergence-window or floating-point
+       differences documented in the migration guide.
+   * - Intentionally different
+     - ferro-ta-only indicators such as ``VWAP``, ``SUPERTREND``,
+       ``ICHIMOKU``, ``DONCHIAN``, ``KELTNER_CHANNELS``, ``HULL_MA``,
+       ``CHANDELIER_EXIT``, ``VWMA``, and ``CHOPPINESS_INDEX``
+     - These extend the library beyond TA-Lib and are not parity claims.
+
+For migration details and known indicator-specific differences, see
+:doc:`migration_talib`.
+
+Module status
+-------------
+
+.. list-table::
+   :header-rows: 1
+
+   * - Surface
+     - Status
+     - Notes
+   * - Top-level indicators and category submodules
+     - Stable core
+     - This is the main supported surface of the project.
+   * - ``ferro_ta.batch``
+     - Supported
+     - Public API is supported; internal dispatch may evolve.
+   * - ``ferro_ta.streaming``
+     - Supported, still evolving
+     - Suitable for live workflows; some API details are still marked
+       experimental in the stability policy.
+   * - ``ferro_ta.extended``
+     - Supported extension
+     - Useful indicators beyond TA-Lib, but not part of drop-in parity claims.
+   * - ``ferro_ta.analysis.*``
+     - Adjacent tooling
+     - Useful analytics helpers, but not the primary product story.
+   * - MCP, WASM, GPU, plugin, and agent-oriented tooling
+     - Experimental or adjacent
+     - Evaluate these independently from the core indicator library.
+
+Supported Python versions
+-------------------------
+
+.. list-table::
+   :header-rows: 1
+
+   * - Python
+     - Status
+   * - 3.13
+     - Supported and tested in CI
+   * - 3.12
+     - Supported and tested in CI
+   * - 3.11
+     - Supported and tested in CI
+   * - 3.10
+     - Supported and tested in CI
+   * - < 3.10
+     - Not supported
+
+Tested wheel targets
+--------------------
+
+.. list-table::
+   :header-rows: 1
+
+   * - OS
+     - Architecture
+     - Wheel status
+   * - Linux
+     - ``x86_64`` (manylinux2014 / ``manylinux_2_17``)
+     - Tested wheel target
+   * - macOS
+     - ``universal2``
+     - Tested wheel target for Intel and Apple Silicon
+   * - Windows
+     - ``x86_64``
+     - Tested wheel target
+
+For source builds, packaging details, and platform notes, see
+`PLATFORMS.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/PLATFORMS.md>`_.
+
+Release status
+--------------
+
+These docs track package version ``1.0.4``.
+
+- Release notes by version: :doc:`changelog`
+- Canonical project changelog: `CHANGELOG.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/CHANGELOG.md>`_
+- Stability policy: `docs/stability.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/stability.md>`_
+
+If the package version, docs version, or support matrix disagree, treat that as
+a documentation bug.

perf-contract/batch.json

@@ -0,0 +1,71 @@
+{
+  "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",
+      "machine": "arm64",
+      "processor": "arm"
+    },
+    "git": {
+      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "dirty": true,
+      "branch": "feat/performace-1.0.2"
+    },
+    "dataset": {
+      "n_samples": 20000,
+      "n_series": 32,
+      "total_bars": 640000,
+      "seed": 42
+    }
+  },
+  "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    }
+  ],
+  "grouped_results": [
+    {
+      "case": "close_bundle_3",
+      "grouped_ms": 0.1878,
+      "separate_ms": 0.1719,
+      "speedup_vs_separate": 0.9155
+    },
+    {
+      "case": "hlc_bundle_3",
+      "grouped_ms": 0.2958,
+      "separate_ms": 0.4633,
+      "speedup_vs_separate": 1.5666
+    }
+  ]
+}

perf-contract/indicator_latency.json

@@ -0,0 +1,163 @@
+{
+  "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",
+      "machine": "arm64",
+      "processor": "arm"
+    },
+    "git": {
+      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "dirty": true,
+      "branch": "feat/performace-1.0.2"
+    },
+    "fixtures": [
+      {
+        "path": "/Users/pratikbhadane/Work/Projects/ferro-ta/benchmarks/fixtures/canonical_ohlcv.npz",
+        "size_bytes": 75586,
+        "sha256": "60192f8349fb06cd59ef7f70fd77aa8280399e819d7cc5eed3ca95cf5ee1a89c"
+      }
+    ],
+    "dataset": {
+      "fixture": "/Users/pratikbhadane/Work/Projects/ferro-ta/benchmarks/fixtures/canonical_ohlcv.npz",
+      "bars": 2000,
+      "rounds": 5
+    }
+  },
+  "results": [
+    {
+      "name": "VAR_20",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 20
+      },
+      "elapsed_ms": 0.0202
+    },
+    {
+      "name": "WILLR_14",
+      "inputs": "hlc",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0183
+    },
+    {
+      "name": "STOCH",
+      "inputs": "hlc",
+      "kwargs": {},
+      "elapsed_ms": 0.0181
+    },
+    {
+      "name": "ADX_14",
+      "inputs": "hlc",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0147
+    },
+    {
+      "name": "CCI_14",
+      "inputs": "hlc",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0144
+    },
+    {
+      "name": "MACD",
+      "inputs": "close",
+      "kwargs": {},
+      "elapsed_ms": 0.0132
+    },
+    {
+      "name": "ATR_14",
+      "inputs": "hlc",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0108
+    },
+    {
+      "name": "RSI_14",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0102
+    },
+    {
+      "name": "STDDEV_20",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 20
+      },
+      "elapsed_ms": 0.0086
+    },
+    {
+      "name": "BETA_5",
+      "inputs": "pair_hl",
+      "kwargs": {
+        "timeperiod": 5
+      },
+      "elapsed_ms": 0.008
+    },
+    {
+      "name": "CORREL_30",
+      "inputs": "pair_hl",
+      "kwargs": {
+        "timeperiod": 30
+      },
+      "elapsed_ms": 0.0068
+    },
+    {
+      "name": "EMA_20",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 20
+      },
+      "elapsed_ms": 0.0052
+    },
+    {
+      "name": "BBANDS_20",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 20
+      },
+      "elapsed_ms": 0.005
+    },
+    {
+      "name": "TSF_14",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0048
+    },
+    {
+      "name": "LINEARREG_14",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0046
+    },
+    {
+      "name": "LINEARREG_SLOPE_14",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 14
+      },
+      "elapsed_ms": 0.0045
+    },
+    {
+      "name": "SMA_20",
+      "inputs": "close",
+      "kwargs": {
+        "timeperiod": 20
+      },
+      "elapsed_ms": 0.0027
+    }
+  ]
+}

perf-contract/manifest.json

@@ -0,0 +1,52 @@
+{
+  "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",
+      "machine": "arm64",
+      "processor": "arm"
+    },
+    "git": {
+      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "dirty": true,
+      "branch": "feat/performace-1.0.2"
+    },
+    "fixtures": [
+      {
+        "path": "/Users/pratikbhadane/Work/Projects/ferro-ta/benchmarks/fixtures/canonical_ohlcv.npz",
+        "size_bytes": 75586,
+        "sha256": "60192f8349fb06cd59ef7f70fd77aa8280399e819d7cc5eed3ca95cf5ee1a89c"
+      }
+    ],
+    "output_dir": "perf-contract"
+  },
+  "artifacts": {
+    "indicator_latency": {
+      "path": "perf-contract/indicator_latency.json",
+      "size_bytes": 3212,
+      "sha256": "1e7f844fe6eb467f07bbb1e4eb918c56861e8cf819ab4802b96e033c6d2570c4"
+    },
+    "batch": {
+      "path": "perf-contract/batch.json",
+      "size_bytes": 1679,
+      "sha256": "202d67b24a88184473f93cec9f866ca34ff60721068e15e7e5d23962f006d169"
+    },
+    "streaming": {
+      "path": "perf-contract/streaming.json",
+      "size_bytes": 1939,
+      "sha256": "04476c3d95a9e7d87e40331b28c5ccad9a7aa5abcfc47fb6ac7e929365f68554"
+    },
+    "runtime_hotspots": {
+      "path": "perf-contract/runtime_hotspots.json",
+      "size_bytes": 2365,
+      "sha256": "d37a58bc88def9f1525b9b0f64f0bd7b9c0df9ed4cae260c833697f6a1689ca8"
+    },
+    "simd": {
+      "path": "perf-contract/simd.json",
+      "size_bytes": 7670,
+      "sha256": "3f9b341a8206698ed172650752d808621c9b5218f42a2373f0f4485496197b4c"
+    }
+  }
+}

perf-contract/runtime_hotspots.json

@@ -0,0 +1,96 @@
+{
+  "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",
+      "machine": "arm64",
+      "processor": "arm"
+    },
+    "git": {
+      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "dirty": true,
+      "branch": "feat/performace-1.0.2"
+    },
+    "dataset": {
+      "price_bars": 20000,
+      "iv_bars": 50000,
+      "window": 252
+    }
+  },
+  "results": [
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "category": "rust_kernel",
+      "name": "BETA",
+      "fast_ms": 0.0694,
+      "reference_ms": 159.2715,
+      "speedup_vs_reference": 2294.4163,
+      "share_of_suite_pct": 0.17
+    },
+    {
+      "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
+    },
+    {
+      "category": "rust_kernel",
+      "name": "TSF",
+      "fast_ms": 0.0413,
+      "reference_ms": 46.3799,
+      "speedup_vs_reference": 1122.1039,
+      "share_of_suite_pct": 0.1
+    }
+  ]
+}

perf-contract/simd.json

@@ -0,0 +1,285 @@
+{
+  "metadata": {
+    "suite": "simd",
+    "runtime": {
+      "generated_at_utc": "2026-03-23T21:09:13.028473+00:00",
+      "python_version": "3.12.11",
+      "platform": "macOS-26.3.1-arm64-arm-64bit",
+      "machine": "arm64",
+      "processor": "arm"
+    },
+    "git": {
+      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "dirty": true,
+      "branch": "feat/performace-1.0.2"
+    },
+    "dataset": {
+      "price_bars": 20000,
+      "iv_bars": 50000,
+      "window": 252
+    },
+    "variants": [
+      "portable_release",
+      "simd_release"
+    ]
+  },
+  "results": [
+    {
+      "name": "compute_many_close",
+      "category": "ffi_grouping",
+      "portable_ms": 0.1711,
+      "simd_ms": 0.1618,
+      "speedup_simd_vs_portable": 1.0575
+    },
+    {
+      "name": "iv_percentile",
+      "category": "python_analysis",
+      "portable_ms": 0.9126,
+      "simd_ms": 0.9096,
+      "speedup_simd_vs_portable": 1.0033
+    },
+    {
+      "name": "iv_zscore",
+      "category": "python_analysis",
+      "portable_ms": 29.0039,
+      "simd_ms": 28.9123,
+      "speedup_simd_vs_portable": 1.0032
+    },
+    {
+      "name": "iv_rank",
+      "category": "python_analysis",
+      "portable_ms": 10.8629,
+      "simd_ms": 10.862,
+      "speedup_simd_vs_portable": 1.0001
+    },
+    {
+      "name": "BETA",
+      "category": "rust_kernel",
+      "portable_ms": 0.0696,
+      "simd_ms": 0.0696,
+      "speedup_simd_vs_portable": 1.0
+    },
+    {
+      "name": "CORREL",
+      "category": "rust_kernel",
+      "portable_ms": 0.0555,
+      "simd_ms": 0.0556,
+      "speedup_simd_vs_portable": 0.9982
+    },
+    {
+      "name": "TSF",
+      "category": "rust_kernel",
+      "portable_ms": 0.0414,
+      "simd_ms": 0.0415,
+      "speedup_simd_vs_portable": 0.9976
+    },
+    {
+      "name": "LINEARREG",
+      "category": "rust_kernel",
+      "portable_ms": 0.0413,
+      "simd_ms": 0.0416,
+      "speedup_simd_vs_portable": 0.9928
+    },
+    {
+      "name": "feature_matrix",
+      "category": "ffi_grouping",
+      "portable_ms": 0.2543,
+      "simd_ms": 0.2634,
+      "speedup_simd_vs_portable": 0.9655
+    }
+  ],
+  "reports": {
+    "portable_release": {
+      "metadata": {
+        "suite": "runtime_hotspots",
+        "runtime": {
+          "generated_at_utc": "2026-03-23T21:08:38.698373+00:00",
+          "python_version": "3.12.11",
+          "platform": "macOS-26.3.1-arm64-arm-64bit",
+          "machine": "arm64",
+          "processor": "arm"
+        },
+        "git": {
+          "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+          "dirty": true,
+          "branch": "feat/performace-1.0.2"
+        },
+        "dataset": {
+          "price_bars": 20000,
+          "iv_bars": 50000,
+          "window": 252
+        }
+      },
+      "results": [
+        {
+          "category": "python_analysis",
+          "name": "iv_zscore",
+          "fast_ms": 29.0039,
+          "reference_ms": 903.5384,
+          "speedup_vs_reference": 31.1523,
+          "share_of_suite_pct": 70.04
+        },
+        {
+          "category": "python_analysis",
+          "name": "iv_rank",
+          "fast_ms": 10.8629,
+          "reference_ms": 201.621,
+          "speedup_vs_reference": 18.5606,
+          "share_of_suite_pct": 26.23
+        },
+        {
+          "category": "python_analysis",
+          "name": "iv_percentile",
+          "fast_ms": 0.9126,
+          "reference_ms": 80.0849,
+          "speedup_vs_reference": 87.7523,
+          "share_of_suite_pct": 2.2
+        },
+        {
+          "category": "ffi_grouping",
+          "name": "feature_matrix",
+          "fast_ms": 0.2543,
+          "reference_ms": 0.2222,
+          "speedup_vs_reference": 0.874,
+          "share_of_suite_pct": 0.61
+        },
+        {
+          "category": "ffi_grouping",
+          "name": "compute_many_close",
+          "fast_ms": 0.1711,
+          "reference_ms": 0.1413,
+          "speedup_vs_reference": 0.8257,
+          "share_of_suite_pct": 0.41
+        },
+        {
+          "category": "rust_kernel",
+          "name": "BETA",
+          "fast_ms": 0.0696,
+          "reference_ms": 162.9168,
+          "speedup_vs_reference": 2341.2971,
+          "share_of_suite_pct": 0.17
+        },
+        {
+          "category": "rust_kernel",
+          "name": "CORREL",
+          "fast_ms": 0.0555,
+          "reference_ms": 163.8589,
+          "speedup_vs_reference": 2950.1793,
+          "share_of_suite_pct": 0.13
+        },
+        {
+          "category": "rust_kernel",
+          "name": "TSF",
+          "fast_ms": 0.0414,
+          "reference_ms": 49.2846,
+          "speedup_vs_reference": 1189.9901,
+          "share_of_suite_pct": 0.1
+        },
+        {
+          "category": "rust_kernel",
+          "name": "LINEARREG",
+          "fast_ms": 0.0413,
+          "reference_ms": 47.5241,
+          "speedup_vs_reference": 1149.7853,
+          "share_of_suite_pct": 0.1
+        }
+      ]
+    },
+    "simd_release": {
+      "metadata": {
+        "suite": "runtime_hotspots",
+        "runtime": {
+          "generated_at_utc": "2026-03-23T21:08:57.755423+00:00",
+          "python_version": "3.12.11",
+          "platform": "macOS-26.3.1-arm64-arm-64bit",
+          "machine": "arm64",
+          "processor": "arm"
+        },
+        "git": {
+          "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+          "dirty": true,
+          "branch": "feat/performace-1.0.2"
+        },
+        "dataset": {
+          "price_bars": 20000,
+          "iv_bars": 50000,
+          "window": 252
+        }
+      },
+      "results": [
+        {
+          "category": "python_analysis",
+          "name": "iv_zscore",
+          "fast_ms": 28.9123,
+          "reference_ms": 909.2586,
+          "speedup_vs_reference": 31.4489,
+          "share_of_suite_pct": 69.98
+        },
+        {
+          "category": "python_analysis",
+          "name": "iv_rank",
+          "fast_ms": 10.862,
+          "reference_ms": 198.2076,
+          "speedup_vs_reference": 18.2478,
+          "share_of_suite_pct": 26.29
+        },
+        {
+          "category": "python_analysis",
+          "name": "iv_percentile",
+          "fast_ms": 0.9096,
+          "reference_ms": 78.1232,
+          "speedup_vs_reference": 85.889,
+          "share_of_suite_pct": 2.2
+        },
+        {
+          "category": "ffi_grouping",
+          "name": "feature_matrix",
+          "fast_ms": 0.2634,
+          "reference_ms": 0.2219,
+          "speedup_vs_reference": 0.8425,
+          "share_of_suite_pct": 0.64
+        },
+        {
+          "category": "ffi_grouping",
+          "name": "compute_many_close",
+          "fast_ms": 0.1618,
+          "reference_ms": 0.155,
+          "speedup_vs_reference": 0.9583,
+          "share_of_suite_pct": 0.39
+        },
+        {
+          "category": "rust_kernel",
+          "name": "BETA",
+          "fast_ms": 0.0696,
+          "reference_ms": 161.4576,
+          "speedup_vs_reference": 2320.3601,
+          "share_of_suite_pct": 0.17
+        },
+        {
+          "category": "rust_kernel",
+          "name": "CORREL",
+          "fast_ms": 0.0556,
+          "reference_ms": 162.6189,
+          "speedup_vs_reference": 2923.4861,
+          "share_of_suite_pct": 0.13
+        },
+        {
+          "category": "rust_kernel",
+          "name": "LINEARREG",
+          "fast_ms": 0.0416,
+          "reference_ms": 48.029,
+          "speedup_vs_reference": 1155.0143,
+          "share_of_suite_pct": 0.1
+        },
+        {
+          "category": "rust_kernel",
+          "name": "TSF",
+          "fast_ms": 0.0415,
+          "reference_ms": 47.55,
+          "speedup_vs_reference": 1145.783,
+          "share_of_suite_pct": 0.1
+        }
+      ]
+    }
+  }
+}

perf-contract/streaming.json

@@ -0,0 +1,73 @@
+{
+  "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",
+      "machine": "arm64",
+      "processor": "arm"
+    },
+    "git": {
+      "commit": "2d5000262f0f1439546bd4872235aae0333880a4",
+      "dirty": true,
+      "branch": "feat/performace-1.0.2"
+    },
+    "dataset": {
+      "n_bars": 20000,
+      "seed": 2026
+    }
+  },
+  "results": [
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "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
+    }
+  ]
+}

pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "maturin"
 
 [project]
 name = "ferro-ta"
-version = "1.0.2"
+version = "1.0.4"
-description = "A fast Technical Analysis library — TA-Lib alternative powered by Rust and PyO3"
+description = "Rust-powered Python technical analysis library with a TA-Lib-compatible API"
 readme = "README.md"
 license = { text = "MIT" }
 requires-python = ">=3.10"
@@ -104,6 +104,7 @@ ignore = ["E501", "UP006", "UP045", "UP007"]
 "tests/unit/*" = ["N801", "N802", "N806", "E402", "E741", "F811"]
 "tests/integration/*" = ["N801", "N802", "N806", "E402", "E741", "F811"]
 "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]

python/ferro_ta/__init__.py

@@ -59,7 +59,51 @@ 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
+
+try:
+    import tomllib as _tomllib
+except ImportError:  # pragma: no cover
+    try:
+        import tomli as _tomllib  # type: ignore[no-redef]
+    except ImportError:  # pragma: no cover
+        _tomllib = None  # type: ignore[assignment]
+
+
+def _detect_version() -> str:
+    try:
+        return _dist_version("ferro-ta")
+    except _PackageNotFoundError:
+        pass
+
+    if _tomllib is not None:
+        pyproject_toml = _Path(__file__).resolve().parents[2] / "pyproject.toml"
+        if pyproject_toml.is_file():
+            try:
+                with pyproject_toml.open("rb") as handle:
+                    data = _tomllib.load(handle)
+                return data.get("project", {}).get("version", "0+unknown")
+            except Exception:
+                pass
+
+    pyproject_toml = _Path(__file__).resolve().parents[2] / "pyproject.toml"
+    if pyproject_toml.is_file():
+        try:
+            text = pyproject_toml.read_text(encoding="utf-8")
+            match = _re.search(r'^version\s*=\s*"([^"]+)"', text, _re.MULTILINE)
+            if match:
+                return match.group(1)
+        except Exception:
+            pass
+
+    return "0+unknown"
+
+
+__version__ = _detect_version()
 
 # ---------------------------------------------------------------------------
 # Exceptions — exported at the top level for convenient catching
@@ -281,6 +325,7 @@ from ferro_ta.indicators.volume import (  # noqa: F401
 )
 
 __all__ = [
+    "__version__",
     # Overlap Studies
     "SMA",
     "EMA",
@@ -459,7 +504,9 @@ __all__ = [
     "VWMA",
     "CHOPPINESS_INDEX",
     # API discovery
+    "about",
     "indicators",
+    "methods",
     "info",
     # Logging utilities
     "enable_debug",
@@ -585,9 +632,10 @@ from ferro_ta.tools.alerts import (  # noqa: F401, E402
 )
 
 # ---------------------------------------------------------------------------
-# API discovery helpers — ferro_ta.indicators() and ferro_ta.info()
+# API discovery helpers — ferro_ta.about(), ferro_ta.methods(),
+# ferro_ta.indicators(), and ferro_ta.info()
 # ---------------------------------------------------------------------------
-from ferro_ta.tools.api_info import indicators, info  # noqa: F401, E402
+from ferro_ta.tools.api_info import about, indicators, info, methods  # noqa: F401, E402
 
 _ALIASED_SUBMODULES = {
     "batch": batch,

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/tools/api_info.py

@@ -1,19 +1,23 @@
 """
 ferro_ta.api_info — API discovery helpers.
 
-Provides :func:`indicators` and :func:`info` for exploring the ferro_ta
+Provides :func:`indicators`, :func:`methods`, :func:`about`, and :func:`info`
-indicator catalogue without reading source code.
+for exploring the ferro_ta public API without reading source code.
 
 Usage
 -----
 >>> import ferro_ta
 >>> ferro_ta.indicators()                      # all indicators, sorted
 >>> ferro_ta.indicators(category="momentum")   # filter by category
+>>> ferro_ta.methods()                         # public callables across modules
+>>> ferro_ta.about()["version"]               # package metadata summary
 >>> ferro_ta.info(ferro_ta.SMA)                 # parameter docs for SMA
 
 API
 ---
 indicators(category=None)   — Return list of dicts describing every indicator.
+methods(category=None)      — Return list of public callables across modules.
+about()                     — Return package/version/module summary metadata.
 info(func_or_name)          — Return a dict with full signature/docstring info.
 """
 
@@ -23,7 +27,7 @@ import importlib
 import inspect
 from typing import Any
 
-__all__ = ["indicators", "info"]
+__all__ = ["indicators", "methods", "about", "info"]
 
 # ---------------------------------------------------------------------------
 # Category → module mapping used by indicators()
@@ -52,6 +56,20 @@ _CATEGORY_MODULES: dict[str, str] = {
     "regime": "ferro_ta.analysis.regime",
 }
 
+_METHOD_MODULES: dict[str, str] = {
+    "top_level": "ferro_ta",
+    **_CATEGORY_MODULES,
+    "options": "ferro_ta.analysis.options",
+    "futures": "ferro_ta.analysis.futures",
+    "backtest": "ferro_ta.analysis.backtest",
+    "options_strategy": "ferro_ta.analysis.options_strategy",
+    "derivatives_payoff": "ferro_ta.analysis.derivatives_payoff",
+    "attribution": "ferro_ta.analysis.attribution",
+    "cross_asset": "ferro_ta.analysis.cross_asset",
+    "tools": "ferro_ta.tools.tools",
+    "viz": "ferro_ta.tools.viz",
+}
+
 
 def _iter_module_callables(
     module_name: str,
@@ -137,6 +155,66 @@ def indicators(category: str | None = None) -> list[dict[str, Any]]:
     return result
 
 
+def methods(category: str | None = None) -> list[dict[str, Any]]:
+    """Return public callables across ferro_ta modules.
+
+    Parameters
+    ----------
+    category : str | None
+        Optional key from :data:`_METHOD_MODULES`, such as ``"top_level"``,
+        ``"options"``, ``"futures"``, or ``"batch"``.
+    """
+    cats: dict[str, str] = (
+        {category: _METHOD_MODULES[category]}
+        if category is not None
+        else _METHOD_MODULES
+    )
+
+    result: list[dict[str, Any]] = []
+    seen: set[tuple[str, str]] = set()
+    for cat, mod_name in cats.items():
+        for name, func in _iter_module_callables(mod_name):
+            key = (mod_name, name)
+            if key in seen:
+                continue
+            seen.add(key)
+            doc = inspect.getdoc(func) or ""
+            first_line = doc.splitlines()[0] if doc else ""
+            try:
+                sig = inspect.signature(func)
+                params = list(sig.parameters.keys())
+            except (ValueError, TypeError):
+                params = []
+            result.append(
+                {
+                    "name": name,
+                    "category": cat,
+                    "module": mod_name,
+                    "doc": first_line,
+                    "params": params,
+                }
+            )
+
+    result.sort(key=lambda d: (d["category"], d["name"]))
+    return result
+
+
+def about() -> dict[str, Any]:
+    """Return a small metadata summary for the installed ferro_ta package."""
+    import ferro_ta  # noqa: PLC0415
+
+    top_level_exports = sorted(getattr(ferro_ta, "__all__", []))
+    return {
+        "name": "ferro-ta",
+        "version": getattr(ferro_ta, "__version__", "0+unknown"),
+        "top_level_export_count": len(top_level_exports),
+        "indicator_count": len(indicators()),
+        "method_count": len(methods()),
+        "categories": sorted(_METHOD_MODULES.keys()),
+        "top_level_exports": top_level_exports,
+    }
+
+
 def info(func_or_name: Any) -> dict[str, Any]:
     """Return detailed information about an indicator function.
 

scripts/bump_version.py

@@ -0,0 +1,187 @@
+#!/usr/bin/env python3
+"""Update or verify ferro-ta version strings across release files.
+
+Usage
+-----
+python3 scripts/bump_version.py 1.0.3
+python3 scripts/bump_version.py --check
+python3 scripts/bump_version.py --show
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+from dataclasses import dataclass
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parent.parent
+SEMVER_RE = re.compile(r"^\d+\.\d+\.\d+$")
+
+
+@dataclass(frozen=True)
+class VersionCarrier:
+    label: str
+    path: Path
+    pattern: str
+    replacement: str
+
+    def read(self) -> str:
+        text = self.path.read_text(encoding="utf-8")
+        match = re.search(self.pattern, text, flags=re.MULTILINE)
+        if not match:
+            raise ValueError(f"Could not find version for {self.label} in {self.path}")
+        return match.group(2)
+
+    def write(self, version: str) -> bool:
+        text = self.path.read_text(encoding="utf-8")
+        updated, count = re.subn(
+            self.pattern,
+            rf"\g<1>{version}\g<3>",
+            text,
+            count=1,
+            flags=re.MULTILINE,
+        )
+        if count != 1:
+            raise ValueError(f"Could not update {self.label} in {self.path}")
+        changed = updated != text
+        if changed:
+            self.path.write_text(updated, encoding="utf-8")
+        return changed
+
+
+CARRIERS = [
+    VersionCarrier(
+        "cargo_root",
+        ROOT / "Cargo.toml",
+        r'(?m)^(version = ")([^"]+)(")$',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "cargo_core_dep",
+        ROOT / "Cargo.toml",
+        r'(ferro_ta_core = \{ path = "crates/ferro_ta_core", version = ")([^"]+)(" \})',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "cargo_core_crate",
+        ROOT / "crates" / "ferro_ta_core" / "Cargo.toml",
+        r'(?m)^(version = ")([^"]+)(")$',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "cargo_core_readme",
+        ROOT / "crates" / "ferro_ta_core" / "README.md",
+        r'(ferro_ta_core = ")([^"]+)(")',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "pyproject",
+        ROOT / "pyproject.toml",
+        r'(?m)^(version = ")([^"]+)(")$',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "wasm_cargo",
+        ROOT / "wasm" / "Cargo.toml",
+        r'(?m)^(version = ")([^"]+)(")$',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "wasm_package",
+        ROOT / "wasm" / "package.json",
+        r'("version": ")([^"]+)(")',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "conda",
+        ROOT / "conda" / "meta.yaml",
+        r'({% set version = ")([^"]+)(" %})',
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "docs_changelog",
+        ROOT / "docs" / "changelog.rst",
+        r"(These docs track package version ``)([^`]+)(``\.)",
+        r"\g<1>{version}\g<3>",
+    ),
+    VersionCarrier(
+        "docs_support_matrix",
+        ROOT / "docs" / "support_matrix.rst",
+        r"(These docs track package version ``)([^`]+)(``\.)",
+        r"\g<1>{version}\g<3>",
+    ),
+]
+
+
+def _read_versions() -> dict[str, str]:
+    return {carrier.label: carrier.read() for carrier in CARRIERS}
+
+
+def _print_versions(versions: dict[str, str]) -> None:
+    for label, version in versions.items():
+        print(f"{label:20} {version}")
+
+
+def _check_versions() -> int:
+    versions = _read_versions()
+    unique = sorted(set(versions.values()))
+    _print_versions(versions)
+    if len(unique) != 1:
+        print()
+        print(f"ERROR: version mismatch detected: {', '.join(unique)}")
+        return 1
+    print()
+    print(f"OK: all tracked versions match {unique[0]}")
+    return 0
+
+
+def _set_version(version: str) -> int:
+    if not SEMVER_RE.match(version):
+        print(f"ERROR: expected MAJOR.MINOR.PATCH, got {version!r}")
+        return 1
+
+    changed_paths: list[Path] = []
+    for carrier in CARRIERS:
+        if carrier.write(version):
+            changed_paths.append(carrier.path)
+
+    if changed_paths:
+        print(f"Updated version to {version}:")
+        for path in sorted(set(changed_paths)):
+            print(f" - {path.relative_to(ROOT)}")
+    else:
+        print(f"No changes needed. All tracked files already use {version}.")
+    return 0
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("version", nargs="?", help="New version to write")
+    parser.add_argument(
+        "--check",
+        action="store_true",
+        help="Fail if tracked version strings do not match",
+    )
+    parser.add_argument(
+        "--show",
+        action="store_true",
+        help="Print tracked version strings without modifying files",
+    )
+    args = parser.parse_args()
+
+    if args.check:
+        return _check_versions()
+    if args.show:
+        _print_versions(_read_versions())
+        return 0
+    if args.version:
+        return _set_version(args.version)
+
+    parser.print_help()
+    return 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

tests/integration/test_integration.py

@@ -227,6 +227,27 @@ def test_indicators_returns_list():
     assert "ATR" in names
 
 
+def test_methods_returns_public_callables():
+    import ferro_ta
+
+    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
+    )
+
+
+def test_about_reports_version_and_counts():
+    import ferro_ta
+
+    meta = ferro_ta.about()
+    assert meta["version"] == ferro_ta.__version__
+    assert meta["indicator_count"] > 20
+    assert meta["method_count"] >= meta["indicator_count"]
+    assert "__version__" in meta["top_level_exports"]
+
+
 def test_indicators_filter_by_category():
     import ferro_ta
 

tests/unit/test_derivatives.py

@@ -1,3 +1,7 @@
+import subprocess
+import sys
+from pathlib import Path
+
 import numpy as np
 import pytest
 
@@ -216,3 +220,34 @@ class TestStrategyAndPayoff:
         assert payoff[1] == pytest.approx(-3.0)
         assert greeks.delta > 0.0
         assert greeks.gamma > 0.0
+
+
+class TestDerivativesBenchmarking:
+    def test_derivatives_benchmark_smoke(self, tmp_path):
+        root = Path(__file__).resolve().parents[2]
+        script = root / "benchmarks" / "bench_derivatives_compare.py"
+        output_path = tmp_path / "derivatives_benchmark.json"
+
+        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()
+        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

@@ -641,6 +641,9 @@ class TestBatchShapeValidation:
 # ---------------------------------------------------------------------------
 
 import os
+import re
+import runpy
+import subprocess
 
 try:
     import tomllib  # Python 3.11+
@@ -673,8 +676,41 @@ def _read_pyproject_version() -> str:
     return data["project"]["version"]
 
 
+def _read_conda_version() -> str:
+    root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    conda_meta = os.path.join(root, "conda", "meta.yaml")
+    text = open(conda_meta).read()
+    match = re.search(r'{% set version = "([^"]+)" %}', text)
+    if not match:
+        raise ValueError("Could not find conda version")
+    return match.group(1)
+
+
+def _read_docs_release() -> str:
+    root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    conf_py = os.path.join(root, "docs", "conf.py")
+    old_env = os.environ.pop("FERRO_TA_VERSION", None)
+    try:
+        data = runpy.run_path(conf_py)
+        return data["release"]
+    finally:
+        if old_env is not None:
+            os.environ["FERRO_TA_VERSION"] = old_env
+
+
+def _run_bump_version_check() -> subprocess.CompletedProcess[str]:
+    root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    return subprocess.run(
+        ["python3", "scripts/bump_version.py", "--check"],
+        cwd=root,
+        text=True,
+        capture_output=True,
+        check=False,
+    )
+
+
 class TestVersionConsistency:
-    """Cargo.toml and pyproject.toml must have the same version string."""
+    """Public version strings should stay aligned with the package version."""
 
     def test_versions_match(self):
         try:
@@ -687,6 +723,41 @@ class TestVersionConsistency:
             f"pyproject.toml={pyproject_ver!r}"
         )
 
+    def test_package_version_matches_project_version(self):
+        cargo_ver = _read_cargo_version()
+        assert ferro_ta.__version__ == cargo_ver
+
+    def test_conda_version_matches_project_version(self):
+        cargo_ver = _read_cargo_version()
+        conda_ver = _read_conda_version()
+        assert conda_ver == cargo_ver
+
+    def test_docs_release_matches_project_version(self):
+        cargo_ver = _read_cargo_version()
+        docs_release = _read_docs_release()
+        assert docs_release == cargo_ver
+
+    def test_docs_changelog_mentions_current_version(self):
+        cargo_ver = _read_cargo_version()
+        root = os.path.dirname(
+            os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        )
+        changelog_rst = os.path.join(root, "docs", "changelog.rst")
+        text = open(changelog_rst).read()
+        assert cargo_ver in text
+
+    def test_api_version_matches_project_version(self):
+        cargo_ver = _read_cargo_version()
+        try:
+            from api.main import app
+        except Exception:
+            pytest.skip("api/main.py not importable")
+        assert app.version == cargo_ver
+
+    def test_bump_version_check_passes(self):
+        result = _run_bump_version_check()
+        assert result.returncode == 0, result.stdout + result.stderr
+
     def test_release_md_exists(self):
         """RELEASE.md must exist in the repository root."""
         root = os.path.dirname(

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

@@ -515,19 +515,24 @@ 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 = [
-    { url = "https://files.pythonhosted.org/packages/7a/d8/b546104b8da3f562c1ff8ab36d130c8fe1dd6a045ced80b4f6ad74f7d4e1/cuda_bindings-12.9.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d3c842c2a4303b2a580fe955018e31aea30278be19795ae05226235268032e5", size = 12148218, upload-time = "2025-10-21T14:51:28.855Z" },
+    { 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" },
-    { url = "https://files.pythonhosted.org/packages/45/e7/b47792cc2d01c7e1d37c32402182524774dadd2d26339bd224e0e913832e/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c912a3d9e6b6651853eed8eed96d6800d69c08e94052c292fec3f282c5a817c9", size = 12210593, upload-time = "2025-10-21T14:51:36.574Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/ef/184aa775e970fc089942cd9ec6302e6e44679d4c14549c6a7ea45bf7f798/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6f3682ec3c4769326aafc67c2ba669d97d688d0b7e63e659d36d2f8b72f32d6", size = 6329075, upload-time = "2026-03-11T00:12:32.319Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/c1/dabe88f52c3e3760d861401bb994df08f672ec893b8f7592dc91626adcf3/cuda_bindings-12.9.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fda147a344e8eaeca0c6ff113d2851ffca8f7dfc0a6c932374ee5c47caa649c8", size = 12151019, upload-time = "2025-10-21T14:51:43.167Z" },
+    { url = "https://files.pythonhosted.org/packages/e0/a9/3a8241c6e19483ac1f1dcf5c10238205dcb8a6e9d0d4d4709240dff28ff4/cuda_bindings-13.2.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:721104c603f059780d287969be3d194a18d0cc3b713ed9049065a1107706759d", size = 5730273, upload-time = "2026-03-11T00:12:37.18Z" },
-    { url = "https://files.pythonhosted.org/packages/63/56/e465c31dc9111be3441a9ba7df1941fe98f4aa6e71e8788a3fb4534ce24d/cuda_bindings-12.9.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:32bdc5a76906be4c61eb98f546a6786c5773a881f3b166486449b5d141e4a39f", size = 11906628, upload-time = "2025-10-21T14:51:49.905Z" },
+    { url = "https://files.pythonhosted.org/packages/e9/94/2748597f47bb1600cd466b20cab4159f1530a3a33fe7f70fee199b3abb9e/cuda_bindings-13.2.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1eba9504ac70667dd48313395fe05157518fd6371b532790e96fbb31bbb5a5e1", size = 6313924, upload-time = "2026-03-11T00:12:39.462Z" },
-    { url = "https://files.pythonhosted.org/packages/a3/84/1e6be415e37478070aeeee5884c2022713c1ecc735e6d82d744de0252eee/cuda_bindings-12.9.4-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:56e0043c457a99ac473ddc926fe0dc4046694d99caef633e92601ab52cbe17eb", size = 11925991, upload-time = "2025-10-21T14:51:56.535Z" },
+    { url = "https://files.pythonhosted.org/packages/52/c8/b2589d68acf7e3d63e2be330b84bc25712e97ed799affbca7edd7eae25d6/cuda_bindings-13.2.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e865447abfb83d6a98ad5130ed3c70b1fc295ae3eeee39fd07b4ddb0671b6788", size = 5722404, upload-time = "2026-03-11T00:12:44.041Z" },
-    { url = "https://files.pythonhosted.org/packages/d1/af/6dfd8f2ed90b1d4719bc053ff8940e494640fe4212dc3dd72f383e4992da/cuda_bindings-12.9.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b72ee72a9cc1b531db31eebaaee5c69a8ec3500e32c6933f2d3b15297b53686", size = 11922703, upload-time = "2025-10-21T14:52:03.585Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/92/f899f7bbb5617bb65ec52a6eac1e9a1447a86b916c4194f8a5001b8cde0c/cuda_bindings-13.2.0-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:46d8776a55d6d5da9dd6e9858fba2efcda2abe6743871dee47dd06eb8cb6d955", size = 6320619, upload-time = "2026-03-11T00:12:45.939Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/19/90ac264acc00f6df8a49378eedec9fd2db3061bf9263bf9f39fd3d8377c3/cuda_bindings-12.9.4-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d80bffc357df9988dca279734bc9674c3934a654cab10cadeed27ce17d8635ee", size = 11924658, upload-time = "2025-10-21T14:52:10.411Z" },
+    { url = "https://files.pythonhosted.org/packages/df/93/eef988860a3ca985f82c4f3174fc0cdd94e07331ba9a92e8e064c260337f/cuda_bindings-13.2.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6629ca2df6f795b784752409bcaedbd22a7a651b74b56a165ebc0c9dcbd504d0", size = 5614610, upload-time = "2026-03-11T00:12:50.337Z" },
+    { url = "https://files.pythonhosted.org/packages/18/23/6db3aba46864aee357ab2415135b3fe3da7e9f1fa0221fa2a86a5968099c/cuda_bindings-13.2.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7dca0da053d3b4cc4869eff49c61c03f3c5dbaa0bcd712317a358d5b8f3f385d", size = 6149914, upload-time = "2026-03-11T00:12:52.374Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/87/87a014f045b77c6de5c8527b0757fe644417b184e5367db977236a141602/cuda_bindings-13.2.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a6464b30f46692d6c7f65d4a0e0450d81dd29de3afc1bb515653973d01c2cd6e", size = 5685673, upload-time = "2026-03-11T00:12:56.371Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/5e/c0fe77a73aaefd3fff25ffaccaac69c5a63eafdf8b9a4c476626ef0ac703/cuda_bindings-13.2.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4af9f3e1be603fa12d5ad6cfca7844c9d230befa9792b5abdf7dd79979c3626", size = 6191386, upload-time = "2026-03-11T00:12:58.965Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/58/ed2c3b39c8dd5f96aa7a4abef0d47a73932c7a988e30f5fa428f00ed0da1/cuda_bindings-13.2.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:df850a1ff8ce1b3385257b08e47b70e959932f5f432d0a4e46a355962b4e4771", size = 5507469, upload-time = "2026-03-11T00:13:04.063Z" },
+    { url = "https://files.pythonhosted.org/packages/1f/01/0c941b112ceeb21439b05895eace78ca1aa2eaaf695c8521a068fd9b4c00/cuda_bindings-13.2.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e8a16384c6494e5485f39314b0b4afb04bee48d49edb16d5d8593fd35bbd231b", size = 6059693, upload-time = "2026-03-11T00:13:06.003Z" },
 ]
 
 [[package]]
@@ -538,6 +543,49 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/07/02/59a5bc738a09def0b49aea0e460bdf97f65206d0d041246147cf6207e69c/cuda_pathfinder-1.4.1-py3-none-any.whl", hash = "sha256:40793006082de88e0950753655e55558a446bed9a7d9d0bcb48b2506d50ed82a", size = 43903, upload-time = "2026-03-06T21:05:24.372Z" },
 ]
 
+[[package]]
+name = "cuda-toolkit"
+version = "13.0.2"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/57/b2/453099f5f3b698d7d0eab38916aac44c7f76229f451709e2eb9db6615dcd/cuda_toolkit-13.0.2-py2.py3-none-any.whl", hash = "sha256:b198824cf2f54003f50d64ada3a0f184b42ca0846c1c94192fa269ecd97a66eb", size = 2364, upload-time = "2025-12-19T23:24:07.328Z" },
+]
+
+[package.optional-dependencies]
+cublas = [
+    { name = "nvidia-cublas", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+cudart = [
+    { name = "nvidia-cuda-runtime", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+cufft = [
+    { name = "nvidia-cufft", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+cufile = [
+    { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
+]
+cupti = [
+    { name = "nvidia-cuda-cupti", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+curand = [
+    { name = "nvidia-curand", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+cusolver = [
+    { name = "nvidia-cusolver", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+cusparse = [
+    { name = "nvidia-cusparse", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+nvjitlink = [
+    { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+nvrtc = [
+    { name = "nvidia-cuda-nvrtc", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+nvtx = [
+    { name = "nvidia-nvtx", marker = "(python_full_version < '3.11' and sys_platform == 'win32') or sys_platform == 'linux'" },
+]
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -593,7 +641,7 @@ wheels = [
 
 [[package]]
 name = "fastapi"
-version = "0.135.1"
+version = "0.135.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-doc" },
@@ -602,14 +650,14 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "typing-inspection" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/7b/f8e0211e9380f7195ba3f3d40c292594fd81ba8ec4629e3854c353aaca45/fastapi-0.135.1.tar.gz", hash = "sha256:d04115b508d936d254cea545b7312ecaa58a7b3a0f84952535b4c9afae7668cd", size = 394962, upload-time = "2026-03-01T18:18:29.369Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c4/73/5903c4b13beae98618d64eb9870c3fac4f605523dd0312ca5c80dadbd5b9/fastapi-0.135.2.tar.gz", hash = "sha256:88a832095359755527b7f63bb4c6bc9edb8329a026189eed83d6c1afcf419d56", size = 395833, upload-time = "2026-03-23T14:12:41.697Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e4/72/42e900510195b23a56bde950d26a51f8b723846bfcaa0286e90287f0422b/fastapi-0.135.1-py3-none-any.whl", hash = "sha256:46e2fc5745924b7c840f71ddd277382af29ce1cdb7d5eab5bf697e3fb9999c9e", size = 116999, upload-time = "2026-03-01T18:18:30.831Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/ea/18f6d0457f9efb2fc6fa594857f92810cadb03024975726db6546b3d6fcf/fastapi-0.135.2-py3-none-any.whl", hash = "sha256:0af0447d541867e8db2a6a25c23a8c4bd80e2394ac5529bd87501bbb9e240ca5", size = 117407, upload-time = "2026-03-23T14:12:43.284Z" },
 ]
 
 [[package]]
 name = "ferro-ta"
-version = "1.0.2"
+version = "1.0.3"
 source = { editable = "." }
 dependencies = [
     { name = "numpy" },
@@ -1583,137 +1631,152 @@ wheels = [
 ]
 
 [[package]]
-name = "nvidia-cublas-cu12"
+name = "nvidia-cublas"
-version = "12.8.4.1"
+version = "13.1.0.3"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921, upload-time = "2025-03-07T01:44:31.254Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/a5/fce49e2ae977e0ccc084e5adafceb4f0ac0c8333cb6863501618a7277f67/nvidia_cublas-13.1.0.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c86fc7f7ae36d7528288c5d88098edcb7b02c633d262e7ddbb86b0ad91be5df2", size = 542851226, upload-time = "2025-10-09T08:59:04.818Z" },
+    { url = "https://files.pythonhosted.org/packages/e7/44/423ac00af4dd95a5aeb27207e2c0d9b7118702149bf4704c3ddb55bb7429/nvidia_cublas-13.1.0.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:ee8722c1f0145ab246bccb9e452153b5e0515fd094c3678df50b2a0888b8b171", size = 423133236, upload-time = "2025-10-09T08:59:32.536Z" },
 ]
 
 [[package]]
-name = "nvidia-cuda-cupti-cu12"
+name = "nvidia-cuda-cupti"
-version = "12.8.90"
+version = "13.0.85"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182", size = 10248621, upload-time = "2025-03-07T01:40:21.213Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/2a/80353b103fc20ce05ef51e928daed4b6015db4aaa9162ed0997090fe2250/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_aarch64.whl", hash = "sha256:796bd679890ee55fb14a94629b698b6db54bcfd833d391d5e94017dd9d7d3151", size = 10310827, upload-time = "2025-09-04T08:26:42.012Z" },
+    { url = "https://files.pythonhosted.org/packages/33/6d/737d164b4837a9bbd202f5ae3078975f0525a55730fe871d8ed4e3b952b0/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_x86_64.whl", hash = "sha256:4eb01c08e859bf924d222250d2e8f8b8ff6d3db4721288cf35d14252a4d933c8", size = 10715597, upload-time = "2025-09-04T08:26:51.312Z" },
 ]
 
 [[package]]
-name = "nvidia-cuda-nvrtc-cu12"
+name = "nvidia-cuda-nvrtc"
-version = "12.8.93"
+version = "13.0.88"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029, upload-time = "2025-03-07T01:42:13.562Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/68/483a78f5e8f31b08fb1bb671559968c0ca3a065ac7acabfc7cee55214fd6/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:ad9b6d2ead2435f11cbb6868809d2adeeee302e9bb94bcf0539c7a40d80e8575", size = 90215200, upload-time = "2025-09-04T08:28:44.204Z" },
+    { url = "https://files.pythonhosted.org/packages/b7/dc/6bb80850e0b7edd6588d560758f17e0550893a1feaf436807d64d2da040f/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d27f20a0ca67a4bb34268a5e951033496c5b74870b868bacd046b1b8e0c3267b", size = 43015449, upload-time = "2025-09-04T08:28:20.239Z" },
 ]
 
 [[package]]
-name = "nvidia-cuda-runtime-cu12"
+name = "nvidia-cuda-runtime"
-version = "12.8.90"
+version = "13.0.96"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90", size = 954765, upload-time = "2025-03-07T01:40:01.615Z" },
+    { url = "https://files.pythonhosted.org/packages/87/4f/17d7b9b8e285199c58ce28e31b5c5bbaa4d8271af06a89b6405258245de2/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ef9bcbe90493a2b9d810e43d249adb3d02e98dd30200d86607d8d02687c43f55", size = 2261060, upload-time = "2025-10-09T08:55:15.78Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/24/d1558f3b68b1d26e706813b1d10aa1d785e4698c425af8db8edc3dced472/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f82250d7782aa23b6cfe765ecc7db554bd3c2870c43f3d1821f1d18aebf0548", size = 2243632, upload-time = "2025-10-09T08:55:36.117Z" },
 ]
 
 [[package]]
-name = "nvidia-cudnn-cu12"
+name = "nvidia-cudnn-cu13"
-version = "9.10.2.21"
+version = "9.19.0.56"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", 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')" },
+    { name = "nvidia-cublas", 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 = [
-    { url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467, upload-time = "2025-06-06T21:54:08.597Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/84/26025437c1e6b61a707442184fa0c03d083b661adf3a3eecfd6d21677740/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:6ed29ffaee1176c612daf442e4dd6cfeb6a0caa43ddcbeb59da94953030b1be4", size = 433781201, upload-time = "2026-02-03T20:40:53.805Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/22/0b4b932655d17a6da1b92fa92ab12844b053bb2ac2475e179ba6f043da1e/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:d20e1734305e9d68889a96e3f35094d733ff1f83932ebe462753973e53a572bf", size = 366066321, upload-time = "2026-02-03T20:44:52.837Z" },
 ]
 
 [[package]]
-name = "nvidia-cufft-cu12"
+name = "nvidia-cufft"
-version = "11.3.3.83"
+version = "12.0.0.61"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", 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')" },
+    { name = "nvidia-nvjitlink", 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 = [
-    { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695, upload-time = "2025-03-07T01:45:27.821Z" },
+    { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/2f/7b57e29836ea8714f81e9898409196f47d772d5ddedddf1592eadb8ab743/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6c44f692dce8fd5ffd3e3df134b6cdb9c2f72d99cf40b62c32dde45eea9ddad3", size = 214085489, upload-time = "2025-09-04T08:31:56.044Z" },
 ]
 
 [[package]]
-name = "nvidia-cufile-cu12"
+name = "nvidia-cufile"
-version = "1.13.1.3"
+version = "1.15.1.6"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc", size = 1197834, upload-time = "2025-03-07T01:45:50.723Z" },
+    { url = "https://files.pythonhosted.org/packages/3f/70/4f193de89a48b71714e74602ee14d04e4019ad36a5a9f20c425776e72cd6/nvidia_cufile-1.15.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08a3ecefae5a01c7f5117351c64f17c7c62efa5fffdbe24fc7d298da19cd0b44", size = 1223672, upload-time = "2025-09-04T08:32:22.779Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/73/cc4a14c9813a8a0d509417cf5f4bdaba76e924d58beb9864f5a7baceefbf/nvidia_cufile-1.15.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:bdc0deedc61f548bddf7733bdc216456c2fdb101d020e1ab4b88d232d5e2f6d1", size = 1136992, upload-time = "2025-09-04T08:32:14.119Z" },
 ]
 
 [[package]]
-name = "nvidia-curand-cu12"
+name = "nvidia-curand"
-version = "10.3.9.90"
+version = "10.4.0.35"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/aa/6584b56dc84ebe9cf93226a5cde4d99080c8e90ab40f0c27bda7a0f29aa1/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9", size = 63619976, upload-time = "2025-03-07T01:46:23.323Z" },
+    { url = "https://files.pythonhosted.org/packages/1e/72/7c2ae24fb6b63a32e6ae5d241cc65263ea18d08802aaae087d9f013335a2/nvidia_curand-10.4.0.35-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:133df5a7509c3e292aaa2b477afd0194f06ce4ea24d714d616ff36439cee349a", size = 61962106, upload-time = "2025-08-04T10:21:41.128Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/9f/be0a41ca4a4917abf5cb9ae0daff1a6060cc5de950aec0396de9f3b52bc5/nvidia_curand-10.4.0.35-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:1aee33a5da6e1db083fe2b90082def8915f30f3248d5896bcec36a579d941bfc", size = 59544258, upload-time = "2025-08-04T10:22:03.992Z" },
 ]
 
 [[package]]
-name = "nvidia-cusolver-cu12"
+name = "nvidia-cusolver"
-version = "11.7.3.90"
+version = "12.0.4.66"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", 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')" },
+    { name = "nvidia-cublas", 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')" },
-    { name = "nvidia-cusparse-cu12", 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')" },
+    { name = "nvidia-cusparse", 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')" },
-    { name = "nvidia-nvjitlink-cu12", 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')" },
+    { name = "nvidia-nvjitlink", 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 = [
-    { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905, upload-time = "2025-03-07T01:47:16.273Z" },
+    { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" },
+    { url = "https://files.pythonhosted.org/packages/5f/67/cba3777620cdacb99102da4042883709c41c709f4b6323c10781a9c3aa34/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0a759da5dea5c0ea10fd307de75cdeb59e7ea4fcb8add0924859b944babf1112", size = 200941980, upload-time = "2025-09-04T08:33:22.767Z" },
 ]
 
 [[package]]
-name = "nvidia-cusparse-cu12"
+name = "nvidia-cusparse"
-version = "12.5.8.93"
+version = "12.6.3.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", 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')" },
+    { name = "nvidia-nvjitlink", 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 = [
-    { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466, upload-time = "2025-03-07T01:48:13.779Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" },
+    { url = "https://files.pythonhosted.org/packages/fa/18/623c77619c31d62efd55302939756966f3ecc8d724a14dab2b75f1508850/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2b3c89c88d01ee0e477cb7f82ef60a11a4bcd57b6b87c33f789350b59759360b", size = 145942937, upload-time = "2025-09-04T08:33:58.029Z" },
 ]
 
 [[package]]
-name = "nvidia-cusparselt-cu12"
+name = "nvidia-cusparselt-cu13"
-version = "0.7.1"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/56/79/12978b96bd44274fe38b5dde5cfb660b1d114f70a65ef962bcbbed99b549/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623", size = 287193691, upload-time = "2025-02-26T00:15:44.104Z" },
+    { url = "https://files.pythonhosted.org/packages/46/10/8dcd1175260706a2fc92a16a52e306b71d4c1ea0b0cc4a9484183399818a/nvidia_cusparselt_cu13-0.8.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:400c6ed1cf6780fc6efedd64ec9f1345871767e6a1a0a552a1ea0578117ea77c", size = 220791277, upload-time = "2025-08-13T19:22:40.982Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/53/43b0d71f4e702fa9733f8b4571fdca50a8813f1e450b656c239beff12315/nvidia_cusparselt_cu13-0.8.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:25e30a8a7323935d4ad0340b95a0b69926eee755767e8e0b1cf8dd85b197d3fd", size = 169884119, upload-time = "2025-08-13T19:23:41.967Z" },
 ]
 
 [[package]]
-name = "nvidia-nccl-cu12"
+name = "nvidia-nccl-cu13"
-version = "2.27.5"
+version = "2.28.9"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6e/89/f7a07dc961b60645dbbf42e80f2bc85ade7feb9a491b11a1e973aa00071f/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457", size = 322348229, upload-time = "2025-06-26T04:11:28.385Z" },
+    { url = "https://files.pythonhosted.org/packages/39/55/1920646a2e43ffd4fc958536b276197ed740e9e0c54105b4bb3521591fc7/nvidia_nccl_cu13-2.28.9-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:01c873ba1626b54caa12272ed228dc5b2781545e0ae8ba3f432a8ef1c6d78643", size = 196561677, upload-time = "2025-11-18T05:49:03.45Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/b4/878fefaad5b2bcc6fcf8d474a25e3e3774bc5133e4b58adff4d0bca238bc/nvidia_nccl_cu13-2.28.9-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:e4553a30f34195f3fa1da02a6da3d6337d28f2003943aa0a3d247bbc25fefc42", size = 196493177, upload-time = "2025-11-18T05:49:17.677Z" },
 ]
 
 [[package]]
-name = "nvidia-nvjitlink-cu12"
+name = "nvidia-nvjitlink"
-version = "12.8.93"
+version = "13.0.88"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88", size = 39254836, upload-time = "2025-03-07T01:49:55.661Z" },
+    { url = "https://files.pythonhosted.org/packages/56/7a/123e033aaff487c77107195fa5a2b8686795ca537935a24efae476c41f05/nvidia_nvjitlink-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:13a74f429e23b921c1109976abefacc69835f2f433ebd323d3946e11d804e47b", size = 40713933, upload-time = "2025-09-04T08:35:43.553Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/2c/93c5250e64df4f894f1cbb397c6fd71f79813f9fd79d7cd61de3f97b3c2d/nvidia_nvjitlink-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e931536ccc7d467a98ba1d8b89ff7fa7f1fa3b13f2b0069118cd7f47bff07d0c", size = 38768748, upload-time = "2025-09-04T08:35:20.008Z" },
 ]
 
 [[package]]
-name = "nvidia-nvshmem-cu12"
+name = "nvidia-nvshmem-cu13"
 version = "3.4.5"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b5/09/6ea3ea725f82e1e76684f0708bbedd871fc96da89945adeba65c3835a64c/nvidia_nvshmem_cu12-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:042f2500f24c021db8a06c5eec2539027d57460e1c1a762055a6554f72c369bd", size = 139103095, upload-time = "2025-09-06T00:32:31.266Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/0f/05cc9c720236dcd2db9c1ab97fff629e96821be2e63103569da0c9b72f19/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6dc2a197f38e5d0376ad52cd1a2a3617d3cdc150fd5966f4aee9bcebb1d68fe9", size = 60215947, upload-time = "2025-09-06T00:32:20.022Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/35/a9bf80a609e74e3b000fef598933235c908fcefcef9026042b8e6dfde2a9/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:290f0a2ee94c9f3687a02502f3b9299a9f9fe826e6d0287ee18482e78d495b80", size = 60412546, upload-time = "2025-09-06T00:32:41.564Z" },
 ]
 
 [[package]]
-name = "nvidia-nvtx-cu12"
+name = "nvidia-nvtx"
-version = "12.8.90"
+version = "13.0.85"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954, upload-time = "2025-03-07T01:42:44.131Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/f3/d86c845465a2723ad7e1e5c36dcd75ddb82898b3f53be47ebd429fb2fa5d/nvidia_nvtx-13.0.85-py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4936d1d6780fbe68db454f5e72a42ff64d1fd6397df9f363ae786930fd5c1cd4", size = 148047, upload-time = "2025-09-04T08:29:01.761Z" },
+    { url = "https://files.pythonhosted.org/packages/a8/64/3708a90d1ebe202ffdeb7185f878a3c84d15c2b2c31858da2ce0583e2def/nvidia_nvtx-13.0.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cb7780edb6b14107373c835bf8b72e7a178bac7367e23da7acb108f973f157a6", size = 148878, upload-time = "2025-09-04T08:28:53.627Z" },
 ]
 
 [[package]]
@@ -1994,30 +2057,30 @@ wheels = [
 
 [[package]]
 name = "polars"
-version = "1.38.1"
+version = "1.39.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "polars-runtime-32" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c6/5e/208a24471a433bcd0e9a6889ac49025fd4daad2815c8220c5bd2576e5f1b/polars-1.38.1.tar.gz", hash = "sha256:803a2be5344ef880ad625addfb8f641995cfd777413b08a10de0897345778239", size = 717667, upload-time = "2026-02-06T18:13:23.013Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/93/ab/f19e592fce9e000da49c96bf35e77cef67f9cb4b040bfa538a2764c0263e/polars-1.39.3.tar.gz", hash = "sha256:2e016c7f3e8d14fa777ef86fe0477cec6c67023a20ba4c94d6e8431eefe4a63c", size = 728987, upload-time = "2026-03-20T11:16:24.836Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0a/49/737c1a6273c585719858261753da0b688454d1b634438ccba8a9c4eb5aab/polars-1.38.1-py3-none-any.whl", hash = "sha256:a29479c48fed4984d88b656486d221f638cba45d3e961631a50ee5fdde38cb2c", size = 810368, upload-time = "2026-02-06T18:11:55.819Z" },
+    { url = "https://files.pythonhosted.org/packages/b4/db/08f4ca10c5018813e7e0b59e4472302328b3d2ab1512f5a2157a814540e0/polars-1.39.3-py3-none-any.whl", hash = "sha256:c2b955ccc0a08a2bc9259785decf3d5c007b489b523bf2390cf21cec2bb82a56", size = 823985, upload-time = "2026-03-20T11:14:23.619Z" },
 ]
 
 [[package]]
 name = "polars-runtime-32"
-version = "1.38.1"
+version = "1.39.3"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/07/4b/04d6b3fb7cf336fbe12fbc4b43f36d1783e11bb0f2b1e3980ec44878df06/polars_runtime_32-1.38.1.tar.gz", hash = "sha256:04f20ed1f5c58771f34296a27029dc755a9e4b1390caeaef8f317e06fdfce2ec", size = 2812631, upload-time = "2026-02-06T18:13:25.206Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/17/39/c8688696bc22b6c501e3b82ef3be10e543c07a785af5660f30997cd22dd2/polars_runtime_32-1.39.3.tar.gz", hash = "sha256:c728e4f469cafab501947585f36311b8fb222d3e934c6209e83791e0df20b29d", size = 2872335, upload-time = "2026-03-20T11:16:26.581Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ae/a2/a00defbddadd8cf1042f52380dcba6b6592b03bac8e3b34c436b62d12d3b/polars_runtime_32-1.38.1-cp310-abi3-macosx_10_12_x86_64.whl", hash = "sha256:18154e96044724a0ac38ce155cf63aa03c02dd70500efbbf1a61b08cadd269ef", size = 44108001, upload-time = "2026-02-06T18:11:58.127Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/74/1b41205f7368c9375ab1dea91178eaa20435fe3eff036390a53a7660b416/polars_runtime_32-1.39.3-cp310-abi3-macosx_10_12_x86_64.whl", hash = "sha256:425c0b220b573fa097b4042edff73114cc6d23432a21dfd2dc41adf329d7d2e9", size = 45273243, upload-time = "2026-03-20T11:14:26.691Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/fb/599ff3709e6a303024efd7edfd08cf8de55c6ac39527d8f41cbc4399385f/polars_runtime_32-1.38.1-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:c49acac34cc4049ed188f1eb67d6ff3971a39b4af7f7b734b367119970f313ac", size = 40230140, upload-time = "2026-02-06T18:12:01.181Z" },
+    { url = "https://files.pythonhosted.org/packages/90/bf/297716b3095fe719be20fcf7af1d2b6ab069c38199bbace2469608a69b3a/polars_runtime_32-1.39.3-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:ef5884711e3c617d7dc93519a7d038e242f5741cfe5fe9afd32d58845d86c562", size = 40842924, upload-time = "2026-03-20T11:14:31.154Z" },
-    { url = "https://files.pythonhosted.org/packages/dc/8c/3ac18d6f89dc05fe2c7c0ee1dc5b81f77a5c85ad59898232c2500fe2ebbf/polars_runtime_32-1.38.1-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fef2ef2626a954e010e006cc8e4de467ecf32d08008f130cea1c78911f545323", size = 41994039, upload-time = "2026-02-06T18:12:04.332Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/3e/e65236d9d0d9babfa0ecba593413c06530fca60a8feb8f66243aa5dba92e/polars_runtime_32-1.39.3-cp310-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:06b47f535eb1f97a9a1e5b0053ef50db3a4276e241178e37bbb1a38b1fa53b14", size = 43220650, upload-time = "2026-03-20T11:14:35.458Z" },
-    { url = "https://files.pythonhosted.org/packages/f2/5a/61d60ec5cc0ab37cbd5a699edb2f9af2875b7fdfdfb2a4608ca3cc5f0448/polars_runtime_32-1.38.1-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8a5f7a8125e2d50e2e060296551c929aec09be23a9edcb2b12ca923f555a5ba", size = 45755804, upload-time = "2026-02-06T18:12:07.846Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/15/fc3e43f3fdf3f20b7dfb5abe871ab6162cf8fb4aeabf4cfad822d5dc4c79/polars_runtime_32-1.39.3-cp310-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8bc9e13dc1d2e828331f2fe8ccbc9757554dc4933a8d3e85e906b988178f95ed", size = 46877498, upload-time = "2026-03-20T11:14:40.14Z" },
-    { url = "https://files.pythonhosted.org/packages/91/54/02cd4074c98c361ccd3fec3bcb0bd68dbc639c0550c42a4436b0ff0f3ccf/polars_runtime_32-1.38.1-cp310-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:10d19cd9863e129273b18b7fcaab625b5c8143c2d22b3e549067b78efa32e4fa", size = 42159605, upload-time = "2026-02-06T18:12:10.919Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/81/bd5f895919e32c6ab0a7786cd0c0ca961cb03152c47c3645808b54383f31/polars_runtime_32-1.39.3-cp310-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:363d49e3a3e638fc943e2b9887940300a7d06789930855a178a4727949259dc2", size = 43380176, upload-time = "2026-03-20T11:14:45.566Z" },
-    { url = "https://files.pythonhosted.org/packages/8e/f3/b2a5e720cc56eaa38b4518e63aa577b4bbd60e8b05a00fe43ca051be5879/polars_runtime_32-1.38.1-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:61e8d73c614b46a00d2f853625a7569a2e4a0999333e876354ac81d1bf1bb5e2", size = 45336615, upload-time = "2026-02-06T18:12:14.074Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/3e/c86433c3b5ec0315bdfc7640d0c15d41f1216c0103a0eab9a9b5147d6c4c/polars_runtime_32-1.39.3-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:7c206bdcc7bc62ea038d6adea8e44b02f0e675e0191a54c810703b4895208ea4", size = 46485933, upload-time = "2026-03-20T11:14:51.155Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/8d/ee2e4b7de948090cfb3df37d401c521233daf97bfc54ddec5d61d1d31618/polars_runtime_32-1.38.1-cp310-abi3-win_amd64.whl", hash = "sha256:08c2b3b93509c1141ac97891294ff5c5b0c548a373f583eaaea873a4bf506437", size = 45680732, upload-time = "2026-02-06T18:12:19.097Z" },
+    { url = "https://files.pythonhosted.org/packages/54/ce/200b310cf91f98e652eb6ea09fdb3a9718aa0293ebf113dce325797c8572/polars_runtime_32-1.39.3-cp310-abi3-win_amd64.whl", hash = "sha256:d66ca522517554a883446957539c40dc7b75eb0c2220357fb28bc8940d305339", size = 46995458, upload-time = "2026-03-20T11:14:56.074Z" },
-    { url = "https://files.pythonhosted.org/packages/bf/18/72c216f4ab0c82b907009668f79183ae029116ff0dd245d56ef58aac48e7/polars_runtime_32-1.38.1-cp310-abi3-win_arm64.whl", hash = "sha256:6d07d0cc832bfe4fb54b6e04218c2c27afcfa6b9498f9f6bbf262a00d58cc7c4", size = 41639413, upload-time = "2026-02-06T18:12:22.044Z" },
+    { url = "https://files.pythonhosted.org/packages/da/76/2d48927e0aa2abbdde08cbf4a2536883b73277d47fbeca95e952de86df34/polars_runtime_32-1.39.3-cp310-abi3-win_arm64.whl", hash = "sha256:f49f51461de63f13e5dd4eb080421c8f23f856945f3f8bd5b2b1f59da52c2860", size = 41857648, upload-time = "2026-03-20T11:15:01.142Z" },
 ]
 
 [[package]]
@@ -2557,36 +2620,36 @@ wheels = [
 
 [[package]]
 name = "ruff"
-version = "0.15.5"
+version = "0.15.7"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/77/9b/840e0039e65fcf12758adf684d2289024d6140cde9268cc59887dc55189c/ruff-0.15.5.tar.gz", hash = "sha256:7c3601d3b6d76dce18c5c824fc8d06f4eef33d6df0c21ec7799510cde0f159a2", size = 4574214, upload-time = "2026-03-05T20:06:34.946Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a1/22/9e4f66ee588588dc6c9af6a994e12d26e19efbe874d1a909d09a6dac7a59/ruff-0.15.7.tar.gz", hash = "sha256:04f1ae61fc20fe0b148617c324d9d009b5f63412c0b16474f3d5f1a1a665f7ac", size = 4601277, upload-time = "2026-03-19T16:26:22.605Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/47/20/5369c3ce21588c708bcbe517a8fbe1a8dfdb5dfd5137e14790b1da71612c/ruff-0.15.5-py3-none-linux_armv6l.whl", hash = "sha256:4ae44c42281f42e3b06b988e442d344a5b9b72450ff3c892e30d11b29a96a57c", size = 10478185, upload-time = "2026-03-05T20:06:29.093Z" },
+    { url = "https://files.pythonhosted.org/packages/41/2f/0b08ced94412af091807b6119ca03755d651d3d93a242682bf020189db94/ruff-0.15.7-py3-none-linux_armv6l.whl", hash = "sha256:a81cc5b6910fb7dfc7c32d20652e50fa05963f6e13ead3c5915c41ac5d16668e", size = 10489037, upload-time = "2026-03-19T16:26:32.47Z" },
-    { url = "https://files.pythonhosted.org/packages/44/ed/e81dd668547da281e5dce710cf0bc60193f8d3d43833e8241d006720e42b/ruff-0.15.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6edd3792d408ebcf61adabc01822da687579a1a023f297618ac27a5b51ef0080", size = 10859201, upload-time = "2026-03-05T20:06:32.632Z" },
+    { url = "https://files.pythonhosted.org/packages/91/4a/82e0fa632e5c8b1eba5ee86ecd929e8ff327bbdbfb3c6ac5d81631bef605/ruff-0.15.7-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:722d165bd52403f3bdabc0ce9e41fc47070ac56d7a91b4e0d097b516a53a3477", size = 10955433, upload-time = "2026-03-19T16:27:00.205Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/8f/533075f00aaf19b07c5cd6aa6e5d89424b06b3b3f4583bfa9c640a079059/ruff-0.15.5-py3-none-macosx_11_0_arm64.whl", hash = "sha256:89f463f7c8205a9f8dea9d658d59eff49db05f88f89cc3047fb1a02d9f344010", size = 10184752, upload-time = "2026-03-05T20:06:40.312Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/10/12586735d0ff42526ad78c049bf51d7428618c8b5c467e72508c694119df/ruff-0.15.7-py3-none-macosx_11_0_arm64.whl", hash = "sha256:7fbc2448094262552146cbe1b9643a92f66559d3761f1ad0656d4991491af49e", size = 10269302, upload-time = "2026-03-19T16:26:26.183Z" },
-    { url = "https://files.pythonhosted.org/packages/66/0e/ba49e2c3fa0395b3152bad634c7432f7edfc509c133b8f4529053ff024fb/ruff-0.15.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ba786a8295c6574c1116704cf0b9e6563de3432ac888d8f83685654fe528fd65", size = 10534857, upload-time = "2026-03-05T20:06:19.581Z" },
+    { url = "https://files.pythonhosted.org/packages/eb/5d/32b5c44ccf149a26623671df49cbfbd0a0ae511ff3df9d9d2426966a8d57/ruff-0.15.7-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b39329b60eba44156d138275323cc726bbfbddcec3063da57caa8a8b1d50adf", size = 10607625, upload-time = "2026-03-19T16:27:03.263Z" },
-    { url = "https://files.pythonhosted.org/packages/59/71/39234440f27a226475a0659561adb0d784b4d247dfe7f43ffc12dd02e288/ruff-0.15.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fd4b801e57955fe9f02b31d20375ab3a5c4415f2e5105b79fb94cf2642c91440", size = 10309120, upload-time = "2026-03-05T20:06:00.435Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/f1/f0001cabe86173aaacb6eb9bb734aa0605f9a6aa6fa7d43cb49cbc4af9c9/ruff-0.15.7-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:87768c151808505f2bfc93ae44e5f9e7c8518943e5074f76ac21558ef5627c85", size = 10324743, upload-time = "2026-03-19T16:27:09.791Z" },
-    { url = "https://files.pythonhosted.org/packages/f5/87/4140aa86a93df032156982b726f4952aaec4a883bb98cb6ef73c347da253/ruff-0.15.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:391f7c73388f3d8c11b794dbbc2959a5b5afe66642c142a6effa90b45f6f5204", size = 11047428, upload-time = "2026-03-05T20:05:51.867Z" },
+    { url = "https://files.pythonhosted.org/packages/7a/87/b8a8f3d56b8d848008559e7c9d8bf367934d5367f6d932ba779456e2f73b/ruff-0.15.7-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fb0511670002c6c529ec66c0e30641c976c8963de26a113f3a30456b702468b0", size = 11138536, upload-time = "2026-03-19T16:27:06.101Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/f7/4953e7e3287676f78fbe85e3a0ca414c5ca81237b7575bdadc00229ac240/ruff-0.15.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8dc18f30302e379fe1e998548b0f5e9f4dff907f52f73ad6da419ea9c19d66c8", size = 11914251, upload-time = "2026-03-05T20:06:22.887Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/f2/4fd0d05aab0c5934b2e1464784f85ba2eab9d54bffc53fb5430d1ed8b829/ruff-0.15.7-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e0d19644f801849229db8345180a71bee5407b429dd217f853ec515e968a6912", size = 11994292, upload-time = "2026-03-19T16:26:48.718Z" },
-    { url = "https://files.pythonhosted.org/packages/77/46/0f7c865c10cf896ccf5a939c3e84e1cfaeed608ff5249584799a74d33835/ruff-0.15.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1cc6e7f90087e2d27f98dc34ed1b3ab7c8f0d273cc5431415454e22c0bd2a681", size = 11333801, upload-time = "2026-03-05T20:05:57.168Z" },
+    { url = "https://files.pythonhosted.org/packages/64/22/fc4483871e767e5e95d1622ad83dad5ebb830f762ed0420fde7dfa9d9b08/ruff-0.15.7-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4806d8e09ef5e84eb19ba833d0442f7e300b23fe3f0981cae159a248a10f0036", size = 11398981, upload-time = "2026-03-19T16:26:54.513Z" },
-    { url = "https://files.pythonhosted.org/packages/d3/01/a10fe54b653061585e655f5286c2662ebddb68831ed3eaebfb0eb08c0a16/ruff-0.15.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c1cb7169f53c1ddb06e71a9aebd7e98fc0fea936b39afb36d8e86d36ecc2636a", size = 11206821, upload-time = "2026-03-05T20:06:03.441Z" },
+    { url = "https://files.pythonhosted.org/packages/b0/99/66f0343176d5eab02c3f7fcd2de7a8e0dd7a41f0d982bee56cd1c24db62b/ruff-0.15.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dce0896488562f09a27b9c91b1f58a097457143931f3c4d519690dea54e624c5", size = 11242422, upload-time = "2026-03-19T16:26:29.277Z" },
-    { url = "https://files.pythonhosted.org/packages/7a/0d/2132ceaf20c5e8699aa83da2706ecb5c5dcdf78b453f77edca7fb70f8a93/ruff-0.15.5-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:9b037924500a31ee17389b5c8c4d88874cc6ea8e42f12e9c61a3d754ff72f1ca", size = 11133326, upload-time = "2026-03-05T20:06:25.655Z" },
+    { url = "https://files.pythonhosted.org/packages/5d/3a/a7060f145bfdcce4c987ea27788b30c60e2c81d6e9a65157ca8afe646328/ruff-0.15.7-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:1852ce241d2bc89e5dc823e03cff4ce73d816b5c6cdadd27dbfe7b03217d2a12", size = 11232158, upload-time = "2026-03-19T16:26:42.321Z" },
-    { url = "https://files.pythonhosted.org/packages/72/cb/2e5259a7eb2a0f87c08c0fe5bf5825a1e4b90883a52685524596bfc93072/ruff-0.15.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:65bb414e5b4eadd95a8c1e4804f6772bbe8995889f203a01f77ddf2d790929dd", size = 10510820, upload-time = "2026-03-05T20:06:37.79Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/53/90fbb9e08b29c048c403558d3cdd0adf2668b02ce9d50602452e187cd4af/ruff-0.15.7-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:5f3e4b221fb4bd293f79912fc5e93a9063ebd6d0dcbd528f91b89172a9b8436c", size = 10577861, upload-time = "2026-03-19T16:26:57.459Z" },
-    { url = "https://files.pythonhosted.org/packages/ff/20/b67ce78f9e6c59ffbdb5b4503d0090e749b5f2d31b599b554698a80d861c/ruff-0.15.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:d20aa469ae3b57033519c559e9bc9cd9e782842e39be05b50e852c7c981fa01d", size = 10302395, upload-time = "2026-03-05T20:05:54.504Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/aa/5f486226538fe4d0f0439e2da1716e1acf895e2a232b26f2459c55f8ddad/ruff-0.15.7-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:b15e48602c9c1d9bdc504b472e90b90c97dc7d46c7028011ae67f3861ceba7b4", size = 10327310, upload-time = "2026-03-19T16:26:35.909Z" },
-    { url = "https://files.pythonhosted.org/packages/5f/e5/719f1acccd31b720d477751558ed74e9c88134adcc377e5e886af89d3072/ruff-0.15.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:15388dd28c9161cdb8eda68993533acc870aa4e646a0a277aa166de9ad5a8752", size = 10754069, upload-time = "2026-03-05T20:06:06.422Z" },
+    { url = "https://files.pythonhosted.org/packages/99/9e/271afdffb81fe7bfc8c43ba079e9d96238f674380099457a74ccb3863857/ruff-0.15.7-py3-none-musllinux_1_2_i686.whl", hash = "sha256:1b4705e0e85cedc74b0a23cf6a179dbb3df184cb227761979cc76c0440b5ab0d", size = 10840752, upload-time = "2026-03-19T16:26:45.723Z" },
-    { url = "https://files.pythonhosted.org/packages/c3/9c/d1db14469e32d98f3ca27079dbd30b7b44dbb5317d06ab36718dee3baf03/ruff-0.15.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:b30da330cbd03bed0c21420b6b953158f60c74c54c5f4c1dabbdf3a57bf355d2", size = 11304315, upload-time = "2026-03-05T20:06:10.867Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/29/a4ae78394f76c7759953c47884eb44de271b03a66634148d9f7d11e721bd/ruff-0.15.7-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:112c1fa316a558bb34319282c1200a8bf0495f1b735aeb78bfcb2991e6087580", size = 11336961, upload-time = "2026-03-19T16:26:39.076Z" },
-    { url = "https://files.pythonhosted.org/packages/28/3a/950367aee7c69027f4f422059227b290ed780366b6aecee5de5039d50fa8/ruff-0.15.5-py3-none-win32.whl", hash = "sha256:732e5ee1f98ba5b3679029989a06ca39a950cced52143a0ea82a2102cb592b74", size = 10551676, upload-time = "2026-03-05T20:06:13.705Z" },
+    { url = "https://files.pythonhosted.org/packages/26/6b/8786ba5736562220d588a2f6653e6c17e90c59ced34a2d7b512ef8956103/ruff-0.15.7-py3-none-win32.whl", hash = "sha256:6d39e2d3505b082323352f733599f28169d12e891f7dd407f2d4f54b4c2886de", size = 10582538, upload-time = "2026-03-19T16:26:15.992Z" },
-    { url = "https://files.pythonhosted.org/packages/b8/00/bf077a505b4e649bdd3c47ff8ec967735ce2544c8e4a43aba42ee9bf935d/ruff-0.15.5-py3-none-win_amd64.whl", hash = "sha256:821d41c5fa9e19117616c35eaa3f4b75046ec76c65e7ae20a333e9a8696bc7fe", size = 11678972, upload-time = "2026-03-05T20:06:45.379Z" },
+    { url = "https://files.pythonhosted.org/packages/2b/e9/346d4d3fffc6871125e877dae8d9a1966b254fbd92a50f8561078b88b099/ruff-0.15.7-py3-none-win_amd64.whl", hash = "sha256:4d53d712ddebcd7dace1bc395367aec12c057aacfe9adbb6d832302575f4d3a1", size = 11755839, upload-time = "2026-03-19T16:26:19.897Z" },
-    { url = "https://files.pythonhosted.org/packages/fe/4e/cd76eca6db6115604b7626668e891c9dd03330384082e33662fb0f113614/ruff-0.15.5-py3-none-win_arm64.whl", hash = "sha256:b498d1c60d2fe5c10c45ec3f698901065772730b411f164ae270bb6bfcc4740b", size = 10965572, upload-time = "2026-03-05T20:06:16.984Z" },
+    { url = "https://files.pythonhosted.org/packages/8f/e8/726643a3ea68c727da31570bde48c7a10f1aa60eddd628d94078fec586ff/ruff-0.15.7-py3-none-win_arm64.whl", hash = "sha256:18e8d73f1c3fdf27931497972250340f92e8c861722161a9caeb89a58ead6ed2", size = 11023304, upload-time = "2026-03-19T16:26:51.669Z" },
 ]
 
 [[package]]
 name = "setuptools"
-version = "82.0.0"
+version = "81.0.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/82/f3/748f4d6f65d1756b9ae577f329c951cda23fb900e4de9f70900ced962085/setuptools-82.0.0.tar.gz", hash = "sha256:22e0a2d69474c6ae4feb01951cb69d515ed23728cf96d05513d36e42b62b37cb", size = 1144893, upload-time = "2026-02-08T15:08:40.206Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/0d/1c/73e719955c59b8e424d015ab450f51c0af856ae46ea2da83eba51cc88de1/setuptools-81.0.0.tar.gz", hash = "sha256:487b53915f52501f0a79ccfd0c02c165ffe06631443a886740b91af4b7a5845a", size = 1198299, upload-time = "2026-02-06T21:10:39.601Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e1/c6/76dc613121b793286a3f91621d7b75a2b493e0390ddca50f11993eadf192/setuptools-82.0.0-py3-none-any.whl", hash = "sha256:70b18734b607bd1da571d097d236cfcfacaf01de45717d59e6e04b96877532e0", size = 1003468, upload-time = "2026-02-08T15:08:38.723Z" },
+    { url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" },
 ]
 
 [[package]]
@@ -2963,75 +3026,54 @@ wheels = [
 
 [[package]]
 name = "torch"
-version = "2.10.0"
+version = "2.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-bindings", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "cuda-bindings", marker = "sys_platform == 'linux'" },
+    { name = "cuda-toolkit", extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux'" },
     { name = "filelock" },
     { name = "fsspec" },
     { name = "jinja2" },
     { name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "networkx", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cudnn-cu13", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cusparselt-cu13", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nvshmem-cu13", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "setuptools" },
-    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cufile-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nvshmem-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "setuptools", marker = "python_full_version >= '3.12'" },
     { name = "sympy" },
-    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "triton", marker = "sys_platform == 'linux'" },
     { name = "typing-extensions" },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5b/30/bfebdd8ec77db9a79775121789992d6b3b75ee5494971294d7b4b7c999bc/torch-2.10.0-2-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:2b980edd8d7c0a68c4e951ee1856334a43193f98730d97408fbd148c1a933313", size = 79411457, upload-time = "2026-02-10T21:44:59.189Z" },
+    { url = "https://files.pythonhosted.org/packages/ac/f2/c1690994afe461aae2d0cac62251e6802a703dec0a6c549c02ecd0de92a9/torch-2.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2c0d7fcfbc0c4e8bb5ebc3907cbc0c6a0da1b8f82b1fc6e14e914fa0b9baf74e", size = 80526521, upload-time = "2026-03-23T18:12:06.86Z" },
-    { url = "https://files.pythonhosted.org/packages/0f/8b/4b61d6e13f7108f36910df9ab4b58fd389cc2520d54d81b88660804aad99/torch-2.10.0-2-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:418997cb02d0a0f1497cf6a09f63166f9f5df9f3e16c8a716ab76a72127c714f", size = 79423467, upload-time = "2026-02-10T21:44:48.711Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/f0/98ae802fa8c09d3149b0c8690741f3f5753c90e779bd28c9613257295945/torch-2.11.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:4cf8687f4aec3900f748d553483ef40e0ac38411c3c48d0a86a438f6d7a99b18", size = 419723025, upload-time = "2026-03-23T18:11:43.774Z" },
-    { url = "https://files.pythonhosted.org/packages/d3/54/a2ba279afcca44bbd320d4e73675b282fcee3d81400ea1b53934efca6462/torch-2.10.0-2-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:13ec4add8c3faaed8d13e0574f5cd4a323c11655546f91fbe6afa77b57423574", size = 79498202, upload-time = "2026-02-10T21:44:52.603Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/1e/18a9b10b4bd34f12d4e561c52b0ae7158707b8193c6cfc0aad2b48167090/torch-2.11.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:1b32ceda909818a03b112006709b02be1877240c31750a8d9c6b7bf5f2d8a6e5", size = 530589207, upload-time = "2026-03-23T18:11:23.756Z" },
-    { url = "https://files.pythonhosted.org/packages/ec/23/2c9fe0c9c27f7f6cb865abcea8a4568f29f00acaeadfc6a37f6801f84cb4/torch-2.10.0-2-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:e521c9f030a3774ed770a9c011751fb47c4d12029a3d6522116e48431f2ff89e", size = 79498254, upload-time = "2026-02-10T21:44:44.095Z" },
+    { url = "https://files.pythonhosted.org/packages/35/40/2d532e8c0e23705be9d1debce5bc37b68d59a39bda7584c26fe9668076fe/torch-2.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:b3c712ae6fb8e7a949051a953fc412fe0a6940337336c3b6f905e905dac5157f", size = 114518313, upload-time = "2026-03-23T18:11:58.281Z" },
-    { url = "https://files.pythonhosted.org/packages/16/ee/efbd56687be60ef9af0c9c0ebe106964c07400eade5b0af8902a1d8cd58c/torch-2.10.0-3-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:a1ff626b884f8c4e897c4c33782bdacdff842a165fee79817b1dd549fdda1321", size = 915510070, upload-time = "2026-03-11T14:16:39.386Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/0d/98b410492609e34a155fa8b121b55c7dca229f39636851c3a9ec20edea21/torch-2.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7b6a60d48062809f58595509c524b88e6ddec3ebe25833d6462eeab81e5f2ce4", size = 80529712, upload-time = "2026-03-23T18:12:02.608Z" },
-    { url = "https://files.pythonhosted.org/packages/36/ab/7b562f1808d3f65414cd80a4f7d4bb00979d9355616c034c171249e1a303/torch-2.10.0-3-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:ac5bdcbb074384c66fa160c15b1ead77839e3fe7ed117d667249afce0acabfac", size = 915518691, upload-time = "2026-03-11T14:15:43.147Z" },
+    { url = "https://files.pythonhosted.org/packages/84/03/acea680005f098f79fd70c1d9d5ccc0cb4296ec2af539a0450108232fc0c/torch-2.11.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:d91aac77f24082809d2c5a93f52a5f085032740a1ebc9252a7b052ef5a4fddc6", size = 419718178, upload-time = "2026-03-23T18:10:46.675Z" },
-    { url = "https://files.pythonhosted.org/packages/b3/7a/abada41517ce0011775f0f4eacc79659bc9bc6c361e6bfe6f7052a6b9363/torch-2.10.0-3-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:98c01b8bb5e3240426dcde1446eed6f40c778091c8544767ef1168fc663a05a6", size = 915622781, upload-time = "2026-03-11T14:17:11.354Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/8b/d7be22fbec9ffee6cff31a39f8750d4b3a65d349a286cf4aec74c2375662/torch-2.11.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:7aa2f9bbc6d4595ba72138026b2074be1233186150e9292865e04b7a63b8c67a", size = 530604548, upload-time = "2026-03-23T18:10:03.569Z" },
-    { url = "https://files.pythonhosted.org/packages/ab/c6/4dfe238342ffdcec5aef1c96c457548762d33c40b45a1ab7033bb26d2ff2/torch-2.10.0-3-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:80b1b5bfe38eb0e9f5ff09f206dcac0a87aadd084230d4a36eea5ec5232c115b", size = 915627275, upload-time = "2026-03-11T14:16:11.325Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/bd/9912d30b68845256aabbb4a40aeefeef3c3b20db5211ccda653544ada4b6/torch-2.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:73e24aaf8f36ab90d95cd1761208b2eb70841c2a9ca1a3f9061b39fc5331b708", size = 114519675, upload-time = "2026-03-23T18:11:52.995Z" },
-    { url = "https://files.pythonhosted.org/packages/d8/f0/72bf18847f58f877a6a8acf60614b14935e2f156d942483af1ffc081aea0/torch-2.10.0-3-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:46b3574d93a2a8134b3f5475cfb98e2eb46771794c57015f6ad1fb795ec25e49", size = 915523474, upload-time = "2026-03-11T14:17:44.422Z" },
+    { url = "https://files.pythonhosted.org/packages/6f/8b/69e3008d78e5cee2b30183340cc425081b78afc5eff3d080daab0adda9aa/torch-2.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4b5866312ee6e52ea625cd211dcb97d6a2cdc1131a5f15cc0d87eec948f6dd34", size = 80606338, upload-time = "2026-03-23T18:11:34.781Z" },
-    { url = "https://files.pythonhosted.org/packages/f4/39/590742415c3030551944edc2ddc273ea1fdfe8ffb2780992e824f1ebee98/torch-2.10.0-3-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:b1d5e2aba4eb7f8e87fbe04f86442887f9167a35f092afe4c237dfcaaef6e328", size = 915632474, upload-time = "2026-03-11T14:15:13.666Z" },
+    { url = "https://files.pythonhosted.org/packages/13/16/42e5915ebe4868caa6bac83a8ed59db57f12e9a61b7d749d584776ed53d5/torch-2.11.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:f99924682ef0aa6a4ab3b1b76f40dc6e273fca09f367d15a524266db100a723f", size = 419731115, upload-time = "2026-03-23T18:11:06.944Z" },
-    { url = "https://files.pythonhosted.org/packages/b6/8e/34949484f764dde5b222b7fe3fede43e4a6f0da9d7f8c370bb617d629ee2/torch-2.10.0-3-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:0228d20b06701c05a8f978357f657817a4a63984b0c90745def81c18aedfa591", size = 915523882, upload-time = "2026-03-11T14:14:46.311Z" },
+    { url = "https://files.pythonhosted.org/packages/1a/c9/82638ef24d7877510f83baf821f5619a61b45568ce21c0a87a91576510aa/torch-2.11.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:0f68f4ac6d95d12e896c3b7a912b5871619542ec54d3649cf48cc1edd4dd2756", size = 530712279, upload-time = "2026-03-23T18:10:31.481Z" },
-    { url = "https://files.pythonhosted.org/packages/0c/1a/c61f36cfd446170ec27b3a4984f072fd06dab6b5d7ce27e11adb35d6c838/torch-2.10.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:5276fa790a666ee8becaffff8acb711922252521b28fbce5db7db5cf9cb2026d", size = 145992962, upload-time = "2026-01-21T16:24:14.04Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/ff/6756f1c7ee302f6d202120e0f4f05b432b839908f9071157302cedfc5232/torch-2.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:fbf39280699d1b869f55eac536deceaa1b60bd6788ba74f399cc67e60a5fab10", size = 114556047, upload-time = "2026-03-23T18:10:55.931Z" },
-    { url = "https://files.pythonhosted.org/packages/b5/60/6662535354191e2d1555296045b63e4279e5a9dbad49acf55a5d38655a39/torch-2.10.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:aaf663927bcd490ae971469a624c322202a2a1e68936eb952535ca4cd3b90444", size = 915599237, upload-time = "2026-01-21T16:23:25.497Z" },
+    { url = "https://files.pythonhosted.org/packages/87/89/5ea6722763acee56b045435fb84258db7375c48165ec8be7880ab2b281c5/torch-2.11.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1e6debd97ccd3205bbb37eb806a9d8219e1139d15419982c09e23ef7d4369d18", size = 80606801, upload-time = "2026-03-23T18:10:18.649Z" },
-    { url = "https://files.pythonhosted.org/packages/40/b8/66bbe96f0d79be2b5c697b2e0b187ed792a15c6c4b8904613454651db848/torch-2.10.0-cp310-cp310-win_amd64.whl", hash = "sha256:a4be6a2a190b32ff5c8002a0977a25ea60e64f7ba46b1be37093c141d9c49aeb", size = 113720931, upload-time = "2026-01-21T16:24:23.743Z" },
+    { url = "https://files.pythonhosted.org/packages/32/d1/8ed2173589cbfe744ed54e5a73efc107c0085ba5777ee93a5f4c1ab90553/torch-2.11.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:63a68fa59de8f87acc7e85a5478bb2dddbb3392b7593ec3e78827c793c4b73fd", size = 419732382, upload-time = "2026-03-23T18:08:30.835Z" },
-    { url = "https://files.pythonhosted.org/packages/76/bb/d820f90e69cda6c8169b32a0c6a3ab7b17bf7990b8f2c680077c24a3c14c/torch-2.10.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:35e407430795c8d3edb07a1d711c41cc1f9eaddc8b2f1cc0a165a6767a8fb73d", size = 79411450, upload-time = "2026-01-21T16:25:30.692Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/e1/b73f7c575a4b8f87a5928f50a1e35416b5e27295d8be9397d5293e7e8d4c/torch-2.11.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:cc89b9b173d9adfab59fd227f0ab5e5516d9a52b658ae41d64e59d2e55a418db", size = 530711509, upload-time = "2026-03-23T18:08:47.213Z" },
-    { url = "https://files.pythonhosted.org/packages/78/89/f5554b13ebd71e05c0b002f95148033e730d3f7067f67423026cc9c69410/torch-2.10.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:3282d9febd1e4e476630a099692b44fdc214ee9bf8ee5377732d9d9dfe5712e4", size = 145992610, upload-time = "2026-01-21T16:25:26.327Z" },
+    { url = "https://files.pythonhosted.org/packages/66/82/3e3fcdd388fbe54e29fd3f991f36846ff4ac90b0d0181e9c8f7236565f82/torch-2.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:4dda3b3f52d121063a731ddb835f010dc137b920d7fec2778e52f60d8e4bf0cd", size = 114555842, upload-time = "2026-03-23T18:09:52.111Z" },
-    { url = "https://files.pythonhosted.org/packages/ae/30/a3a2120621bf9c17779b169fc17e3dc29b230c29d0f8222f499f5e159aa8/torch-2.10.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a2f9edd8dbc99f62bc4dfb78af7bf89499bca3d753423ac1b4e06592e467b763", size = 915607863, upload-time = "2026-01-21T16:25:06.696Z" },
+    { url = "https://files.pythonhosted.org/packages/db/38/8ac78069621b8c2b4979c2f96dc8409ef5e9c4189f6aac629189a78677ca/torch-2.11.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8b394322f49af4362d4f80e424bcaca7efcd049619af03a4cf4501520bdf0fb4", size = 80959574, upload-time = "2026-03-23T18:10:14.214Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/3d/c87b33c5f260a2a8ad68da7147e105f05868c281c63d65ed85aa4da98c66/torch-2.10.0-cp311-cp311-win_amd64.whl", hash = "sha256:29b7009dba4b7a1c960260fc8ac85022c784250af43af9fb0ebafc9883782ebd", size = 113723116, upload-time = "2026-01-21T16:25:21.916Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/6c/56bfb37073e7136e6dd86bfc6af7339946dd684e0ecf2155ac0eee687ae1/torch-2.11.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2658f34ce7e2dabf4ec73b45e2ca68aedad7a5be87ea756ad656eaf32bf1e1ea", size = 419732324, upload-time = "2026-03-23T18:09:36.604Z" },
-    { url = "https://files.pythonhosted.org/packages/61/d8/15b9d9d3a6b0c01b883787bd056acbe5cc321090d4b216d3ea89a8fcfdf3/torch-2.10.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:b7bd80f3477b830dd166c707c5b0b82a898e7b16f59a7d9d42778dd058272e8b", size = 79423461, upload-time = "2026-01-21T16:24:50.266Z" },
+    { url = "https://files.pythonhosted.org/packages/07/f4/1b666b6d61d3394cca306ea543ed03a64aad0a201b6cd159f1d41010aeb1/torch-2.11.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:98bb213c3084cfe176302949bdc360074b18a9da7ab59ef2edc9d9f742504778", size = 530596026, upload-time = "2026-03-23T18:09:20.842Z" },
-    { url = "https://files.pythonhosted.org/packages/cc/af/758e242e9102e9988969b5e621d41f36b8f258bb4a099109b7a4b4b50ea4/torch-2.10.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:5fd4117d89ffd47e3dcc71e71a22efac24828ad781c7e46aaaf56bf7f2796acf", size = 145996088, upload-time = "2026-01-21T16:24:44.171Z" },
+    { url = "https://files.pythonhosted.org/packages/48/6b/30d1459fa7e4b67e9e3fe1685ca1d8bb4ce7c62ef436c3a615963c6c866c/torch-2.11.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a97b94bbf62992949b4730c6cd2cc9aee7b335921ee8dc207d930f2ed09ae2db", size = 114793702, upload-time = "2026-03-23T18:09:47.304Z" },
-    { url = "https://files.pythonhosted.org/packages/23/8e/3c74db5e53bff7ed9e34c8123e6a8bfef718b2450c35eefab85bb4a7e270/torch-2.10.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:787124e7db3b379d4f1ed54dd12ae7c741c16a4d29b49c0226a89bea50923ffb", size = 915711952, upload-time = "2026-01-21T16:23:53.503Z" },
+    { url = "https://files.pythonhosted.org/packages/26/0d/8603382f61abd0db35841148ddc1ffd607bf3100b11c6e1dab6d2fc44e72/torch-2.11.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:01018087326984a33b64e04c8cb5c2795f9120e0d775ada1f6638840227b04d7", size = 80573442, upload-time = "2026-03-23T18:09:10.117Z" },
-    { url = "https://files.pythonhosted.org/packages/6e/01/624c4324ca01f66ae4c7cd1b74eb16fb52596dce66dbe51eff95ef9e7a4c/torch-2.10.0-cp312-cp312-win_amd64.whl", hash = "sha256:2c66c61f44c5f903046cc696d088e21062644cbe541c7f1c4eaae88b2ad23547", size = 113757972, upload-time = "2026-01-21T16:24:39.516Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/86/7cd7c66cb9cec6be330fff36db5bd0eef386d80c031b581ec81be1d4b26c/torch-2.11.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:2bb3cc54bd0dea126b0060bb1ec9de0f9c7f7342d93d436646516b0330cd5be7", size = 419749385, upload-time = "2026-03-23T18:07:33.77Z" },
-    { url = "https://files.pythonhosted.org/packages/c9/5c/dee910b87c4d5c0fcb41b50839ae04df87c1cfc663cf1b5fca7ea565eeaa/torch-2.10.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:6d3707a61863d1c4d6ebba7be4ca320f42b869ee657e9b2c21c736bf17000294", size = 79498198, upload-time = "2026-01-21T16:24:34.704Z" },
+    { url = "https://files.pythonhosted.org/packages/47/e8/b98ca2d39b2e0e4730c0ee52537e488e7008025bc77ca89552ff91021f7c/torch-2.11.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:4dc8b3809469b6c30b411bb8c4cad3828efd26236153d9beb6a3ec500f211a60", size = 530716756, upload-time = "2026-03-23T18:07:50.02Z" },
-    { url = "https://files.pythonhosted.org/packages/c9/6f/f2e91e34e3fcba2e3fc8d8f74e7d6c22e74e480bbd1db7bc8900fdf3e95c/torch-2.10.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:5c4d217b14741e40776dd7074d9006fd28b8a97ef5654db959d8635b2fe5f29b", size = 146004247, upload-time = "2026-01-21T16:24:29.335Z" },
+    { url = "https://files.pythonhosted.org/packages/78/88/d4a4cda8362f8a30d1ed428564878c3cafb0d87971fbd3947d4c84552095/torch-2.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:2b4e811728bd0cc58fb2b0948fe939a1ee2bf1422f6025be2fca4c7bd9d79718", size = 114552300, upload-time = "2026-03-23T18:09:05.617Z" },
-    { url = "https://files.pythonhosted.org/packages/98/fb/5160261aeb5e1ee12ee95fe599d0541f7c976c3701d607d8fc29e623229f/torch-2.10.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6b71486353fce0f9714ca0c9ef1c850a2ae766b409808acd58e9678a3edb7738", size = 915716445, upload-time = "2026-01-21T16:22:45.353Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/46/4419098ed6d801750f26567b478fc185c3432e11e2cad712bc6b4c2ab0d0/torch-2.11.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:8245477871c3700d4370352ffec94b103cfcb737229445cf9946cddb7b2ca7cd", size = 80959460, upload-time = "2026-03-23T18:09:00.818Z" },
-    { url = "https://files.pythonhosted.org/packages/6a/16/502fb1b41e6d868e8deb5b0e3ae926bbb36dab8ceb0d1b769b266ad7b0c3/torch-2.10.0-cp313-cp313-win_amd64.whl", hash = "sha256:c2ee399c644dc92ef7bc0d4f7e74b5360c37cdbe7c5ba11318dda49ffac2bc57", size = 113757050, upload-time = "2026-01-21T16:24:19.204Z" },
+    { url = "https://files.pythonhosted.org/packages/fd/66/54a56a4a6ceaffb567231994a9745821d3af922a854ed33b0b3a278e0a99/torch-2.11.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:ab9a8482f475f9ba20e12db84b0e55e2f58784bdca43a854a6ccd3fd4b9f75e6", size = 419735835, upload-time = "2026-03-23T18:07:18.974Z" },
-    { url = "https://files.pythonhosted.org/packages/1a/0b/39929b148f4824bc3ad6f9f72a29d4ad865bcf7ebfc2fa67584773e083d2/torch-2.10.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:3202429f58309b9fa96a614885eace4b7995729f44beb54d3e4a47773649d382", size = 79851305, upload-time = "2026-01-21T16:24:09.209Z" },
+    { url = "https://files.pythonhosted.org/packages/b1/e7/0b6665f533aa9e337662dc190425abc0af1fe3234088f4454c52393ded61/torch-2.11.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:563ed3d25542d7e7bbc5b235ccfacfeb97fb470c7fee257eae599adb8005c8a2", size = 530613405, upload-time = "2026-03-23T18:08:07.014Z" },
-    { url = "https://files.pythonhosted.org/packages/d8/14/21fbce63bc452381ba5f74a2c0a959fdf5ad5803ccc0c654e752e0dbe91a/torch-2.10.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:aae1b29cd68e50a9397f5ee897b9c24742e9e306f88a807a27d617f07adb3bd8", size = 146005472, upload-time = "2026-01-21T16:22:29.022Z" },
+    { url = "https://files.pythonhosted.org/packages/cf/bf/c8d12a2c86dbfd7f40fb2f56fbf5a505ccf2d9ce131eb559dfc7c51e1a04/torch-2.11.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b2a43985ff5ef6ddd923bbcf99943e5f58059805787c5c9a2622bf05ca2965b0", size = 114792991, upload-time = "2026-03-23T18:08:19.216Z" },
-    { url = "https://files.pythonhosted.org/packages/54/fd/b207d1c525cb570ef47f3e9f836b154685011fce11a2f444ba8a4084d042/torch-2.10.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:6021db85958db2f07ec94e1bc77212721ba4920c12a18dc552d2ae36a3eb163f", size = 915612644, upload-time = "2026-01-21T16:21:47.019Z" },
-    { url = "https://files.pythonhosted.org/packages/36/53/0197f868c75f1050b199fe58f9bf3bf3aecac9b4e85cc9c964383d745403/torch-2.10.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ff43db38af76fda183156153983c9a096fc4c78d0cd1e07b14a2314c7f01c2c8", size = 113997015, upload-time = "2026-01-21T16:23:00.767Z" },
-    { url = "https://files.pythonhosted.org/packages/0e/13/e76b4d9c160e89fff48bf16b449ea324bda84745d2ab30294c37c2434c0d/torch-2.10.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:cdf2a523d699b70d613243211ecaac14fe9c5df8a0b0a9c02add60fb2a413e0f", size = 79498248, upload-time = "2026-01-21T16:23:09.315Z" },
-    { url = "https://files.pythonhosted.org/packages/4f/93/716b5ac0155f1be70ed81bacc21269c3ece8dba0c249b9994094110bfc51/torch-2.10.0-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:bf0d9ff448b0218e0433aeb198805192346c4fd659c852370d5cc245f602a06a", size = 79464992, upload-time = "2026-01-21T16:23:05.162Z" },
-    { url = "https://files.pythonhosted.org/packages/69/2b/51e663ff190c9d16d4a8271203b71bc73a16aa7619b9f271a69b9d4a936b/torch-2.10.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:233aed0659a2503b831d8a67e9da66a62c996204c0bba4f4c442ccc0c68a3f60", size = 146018567, upload-time = "2026-01-21T16:22:23.393Z" },
-    { url = "https://files.pythonhosted.org/packages/5e/cd/4b95ef7f293b927c283db0b136c42be91c8ec6845c44de0238c8c23bdc80/torch-2.10.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:682497e16bdfa6efeec8cde66531bc8d1fbbbb4d8788ec6173c089ed3cc2bfe5", size = 915721646, upload-time = "2026-01-21T16:21:16.983Z" },
-    { url = "https://files.pythonhosted.org/packages/56/97/078a007208f8056d88ae43198833469e61a0a355abc0b070edd2c085eb9a/torch-2.10.0-cp314-cp314-win_amd64.whl", hash = "sha256:6528f13d2a8593a1a412ea07a99812495bec07e9224c28b2a25c0a30c7da025c", size = 113752373, upload-time = "2026-01-21T16:22:13.471Z" },
-    { url = "https://files.pythonhosted.org/packages/d8/94/71994e7d0d5238393df9732fdab607e37e2b56d26a746cb59fdb415f8966/torch-2.10.0-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:f5ab4ba32383061be0fb74bda772d470140a12c1c3b58a0cfbf3dae94d164c28", size = 79850324, upload-time = "2026-01-21T16:22:09.494Z" },
-    { url = "https://files.pythonhosted.org/packages/e2/65/1a05346b418ea8ccd10360eef4b3e0ce688fba544e76edec26913a8d0ee0/torch-2.10.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:716b01a176c2a5659c98f6b01bf868244abdd896526f1c692712ab36dbaf9b63", size = 146006482, upload-time = "2026-01-21T16:22:18.42Z" },
-    { url = "https://files.pythonhosted.org/packages/1d/b9/5f6f9d9e859fc3235f60578fa64f52c9c6e9b4327f0fe0defb6de5c0de31/torch-2.10.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:d8f5912ba938233f86361e891789595ff35ca4b4e2ac8fe3670895e5976731d6", size = 915613050, upload-time = "2026-01-21T16:20:49.035Z" },
-    { url = "https://files.pythonhosted.org/packages/66/4d/35352043ee0eaffdeff154fad67cd4a31dbed7ff8e3be1cc4549717d6d51/torch-2.10.0-cp314-cp314t-win_amd64.whl", hash = "sha256:71283a373f0ee2c89e0f0d5f446039bdabe8dbc3c9ccf35f0f784908b0acd185", size = 113995816, upload-time = "2026-01-21T16:22:05.312Z" },
 ]
 
 [[package]]
@@ -3051,12 +3093,19 @@ name = "triton"
 version = "3.6.0"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/44/ba/b1b04f4b291a3205d95ebd24465de0e5bf010a2df27a4e58a9b5f039d8f2/triton-3.6.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6c723cfb12f6842a0ae94ac307dba7e7a44741d720a40cf0e270ed4a4e3be781", size = 175972180, upload-time = "2026-01-20T16:15:53.664Z" },
     { url = "https://files.pythonhosted.org/packages/8c/f7/f1c9d3424ab199ac53c2da567b859bcddbb9c9e7154805119f8bd95ec36f/triton-3.6.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a6550fae429e0667e397e5de64b332d1e5695b73650ee75a6146e2e902770bea", size = 188105201, upload-time = "2026-01-20T16:00:29.272Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/2c/96f92f3c60387e14cc45aed49487f3486f89ea27106c1b1376913c62abe4/triton-3.6.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49df5ef37379c0c2b5c0012286f80174fcf0e073e5ade1ca9a86c36814553651", size = 176081190, upload-time = "2026-01-20T16:16:00.523Z" },
     { url = "https://files.pythonhosted.org/packages/e0/12/b05ba554d2c623bffa59922b94b0775673de251f468a9609bc9e45de95e9/triton-3.6.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e8e323d608e3a9bfcc2d9efcc90ceefb764a82b99dea12a86d643c72539ad5d3", size = 188214640, upload-time = "2026-01-20T16:00:35.869Z" },
+    { url = "https://files.pythonhosted.org/packages/17/5d/08201db32823bdf77a0e2b9039540080b2e5c23a20706ddba942924ebcd6/triton-3.6.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:374f52c11a711fd062b4bfbb201fd9ac0a5febd28a96fb41b4a0f51dde3157f4", size = 176128243, upload-time = "2026-01-20T16:16:07.857Z" },
     { url = "https://files.pythonhosted.org/packages/ab/a8/cdf8b3e4c98132f965f88c2313a4b493266832ad47fb52f23d14d4f86bb5/triton-3.6.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74caf5e34b66d9f3a429af689c1c7128daba1d8208df60e81106b115c00d6fca", size = 188266850, upload-time = "2026-01-20T16:00:43.041Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/12/34d71b350e89a204c2c7777a9bba0dcf2f19a5bfdd70b57c4dbc5ffd7154/triton-3.6.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:448e02fe6dc898e9e5aa89cf0ee5c371e99df5aa5e8ad976a80b93334f3494fd", size = 176133521, upload-time = "2026-01-20T16:16:13.321Z" },
     { url = "https://files.pythonhosted.org/packages/f9/0b/37d991d8c130ce81a8728ae3c25b6e60935838e9be1b58791f5997b24a54/triton-3.6.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10c7f76c6e72d2ef08df639e3d0d30729112f47a56b0c81672edc05ee5116ac9", size = 188289450, upload-time = "2026-01-20T16:00:49.136Z" },
+    { url = "https://files.pythonhosted.org/packages/ce/4e/41b0c8033b503fd3cfcd12392cdd256945026a91ff02452bef40ec34bee7/triton-3.6.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1722e172d34e32abc3eb7711d0025bb69d7959ebea84e3b7f7a341cd7ed694d6", size = 176276087, upload-time = "2026-01-20T16:16:18.989Z" },
     { url = "https://files.pythonhosted.org/packages/35/f8/9c66bfc55361ec6d0e4040a0337fb5924ceb23de4648b8a81ae9d33b2b38/triton-3.6.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d002e07d7180fd65e622134fbd980c9a3d4211fb85224b56a0a0efbd422ab72f", size = 188400296, upload-time = "2026-01-20T16:00:56.042Z" },
+    { url = "https://files.pythonhosted.org/packages/49/55/5ecf0dcaa0f2fbbd4420f7ef227ee3cb172e91e5fede9d0ecaddc43363b4/triton-3.6.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef5523241e7d1abca00f1d240949eebdd7c673b005edbbce0aca95b8191f1d43", size = 176138577, upload-time = "2026-01-20T16:16:25.426Z" },
     { url = "https://files.pythonhosted.org/packages/df/3d/9e7eee57b37c80cec63322c0231bb6da3cfe535a91d7a4d64896fcb89357/triton-3.6.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a17a5d5985f0ac494ed8a8e54568f092f7057ef60e1b0fa09d3fd1512064e803", size = 188273063, upload-time = "2026-01-20T16:01:07.278Z" },
+    { url = "https://files.pythonhosted.org/packages/48/db/56ee649cab5eaff4757541325aca81f52d02d4a7cd3506776cad2451e060/triton-3.6.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b3a97e8ed304dfa9bd23bb41ca04cdf6b2e617d5e782a8653d616037a5d537d", size = 176274804, upload-time = "2026-01-20T16:16:31.528Z" },
     { url = "https://files.pythonhosted.org/packages/f6/56/6113c23ff46c00aae423333eb58b3e60bdfe9179d542781955a5e1514cb3/triton-3.6.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:46bd1c1af4b6704e554cad2eeb3b0a6513a980d470ccfa63189737340c7746a7", size = 188397994, upload-time = "2026-01-20T16:01:14.236Z" },
 ]
 
@@ -3101,16 +3150,16 @@ wheels = [
 
 [[package]]
 name = "uvicorn"
-version = "0.41.0"
+version = "0.42.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click", marker = "python_full_version < '3.11' or sys_platform != 'emscripten'" },
     { name = "h11", marker = "python_full_version < '3.11' or sys_platform != 'emscripten'" },
     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/32/ce/eeb58ae4ac36fe09e3842eb02e0eb676bf2c53ae062b98f1b2531673efdd/uvicorn-0.41.0.tar.gz", hash = "sha256:09d11cf7008da33113824ee5a1c6422d89fbc2ff476540d69a34c87fab8b571a", size = 82633, upload-time = "2026-02-16T23:07:24.1Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/ad/4a96c425be6fb67e0621e62d86c402b4a17ab2be7f7c055d9bd2f638b9e2/uvicorn-0.42.0.tar.gz", hash = "sha256:9b1f190ce15a2dd22e7758651d9b6d12df09a13d51ba5bf4fc33c383a48e1775", size = 85393, upload-time = "2026-03-16T06:19:50.077Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/83/e4/d04a086285c20886c0daad0e026f250869201013d18f81d9ff5eada73a88/uvicorn-0.41.0-py3-none-any.whl", hash = "sha256:29e35b1d2c36a04b9e180d4007ede3bcb32a85fbdfd6c6aeb3f26839de088187", size = 68783, upload-time = "2026-02-16T23:07:22.357Z" },
+    { url = "https://files.pythonhosted.org/packages/0a/89/f8827ccff89c1586027a105e5630ff6139a64da2515e24dafe860bd9ae4d/uvicorn-0.42.0-py3-none-any.whl", hash = "sha256:96c30f5c7abe6f74ae8900a70e92b85ad6613b745d4879eb9b16ccad15645359", size = 68830, upload-time = "2026-03-16T06:19:48.325Z" },
 ]
 
 [[package]]

wasm/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "ferro_ta_wasm"
-version = "1.0.2"
+version = "1.0.4"
 edition = "2021"
 description = "WebAssembly bindings for ferro-ta technical analysis indicators"
 license = "MIT"

wasm/package.json

@@ -1,6 +1,6 @@
 {
   "name": "ferro-ta-wasm",
-  "version": "1.0.2",
+  "version": "1.0.4",
   "description": "WebAssembly bindings for ferro-ta technical analysis indicators",
   "main": "pkg/ferro_ta_wasm.js",
   "types": "pkg/ferro_ta_wasm.d.ts",