chore: release v1.0.4

chore: remove .coverage file and update .gitignore to exclude coverage files
- 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.
2026-03-24 12:54:41 +05:30 · 2026-03-24 12:49:17 +05:30 · 2026-03-24 11:24:34 +05:30 · 2026-03-24 11:23:41 +05:30 · 2026-03-24 11:19:48 +05:30 · 2026-03-24 11:09:48 +05:30
49 changed files with 8012 additions and 2045 deletions
@@ -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, '-') }}
@@ -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
@@ -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
@@ -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",
@@ -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"] }
@@ -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
@@ -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
-[0.2.0]: https://github.com/pratikbhadane24/ferro-ta/compare/v0.1.0...v0.2.0
+[Unreleased]: https://github.com/pratikbhadane24/ferro-ta/compare/vX.Y.Z...HEAD
+[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
@@ -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.
+
@@ -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
-   (e.g. `1.0.1`).
-2. **Update `CHANGELOG.md`**: move the `[Unreleased]` block to a new dated section
+### Fast path
+
+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 GitHub Release** for tag `v1.0.1` — the CI `build-wheels` and
+5. **Create a tag**: `git tag v1.0.1 && git push origin v1.0.1`.
+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.
@@ -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",
 )
@@ -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:
@@ -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,
-and reports speedup (talib_time / ferro_ta_time; > 1 means ferro_ta is faster).
+Measures throughput (M bars/s) for both libraries on the same synthetic data
+and parameters. The output is intentionally evidence-heavy:

-Requirements:
-    pip install ta-lib   # or conda install ta-lib
+- median timings
+- per-run timing samples
+- variability stats
+- Python-tracked peak allocation snapshots
+- machine, runtime, and build metadata

-Run:
-    python benchmarks/bench_vs_talib.py
-    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.
+This is meant to support a narrow claim: ferro-ta is often faster on selected
+indicators, not universally faster.
 """

 from __future__ import annotations

 import argparse
-from datetime import datetime, timezone
 import json
-import platform
-import subprocess
+import math
 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]:
-    """Best-effort git metadata for benchmark reproducibility."""
-    try:
-        commit = subprocess.check_output(
-            ["git", "rev-parse", "HEAD"], text=True, stderr=subprocess.DEVNULL
-        ).strip()
-    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 _median(values: list[float]) -> float:
+    ordered = sorted(values)
+    mid = len(ordered) // 2
+    if len(ordered) % 2:
+        return ordered[mid]
+    return (ordered[mid - 1] + ordered[mid]) / 2.0


-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(),
-        "python_version": sys.version.split()[0],
-        "platform": platform.platform(),
-        "machine": platform.machine(),
+        "median_ms": round(_median(samples_ms), 4),
+        "mean_ms": round(mean_ms, 4),
+        "min_ms": round(min(samples_ms), 4),
+        "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]
-    wins = sum(1 for s in speedups if s > 1.0)
-    speedups_sorted = sorted(speedups)
-    mid = len(speedups_sorted) // 2
-    if len(speedups_sorted) % 2:
-        median = speedups_sorted[mid]
-    else:
-        median = (speedups_sorted[mid - 1] + speedups_sorted[mid]) / 2.0
-
+    speedups = [float(row["speedup"]) for row in rows]
+    wins = sum(1 for row in rows if row.get("outcome") == "ferro_ta_win")
+    ties = sum(1 for row in rows if row.get("outcome") == "tie")
+    losses = sum(1 for row in rows if row.get("outcome") == "talib_win")
    return {
        "size": size,
        "rows": len(rows),
        "wins": wins,
-        "win_rate": wins / len(rows),
-        "median_speedup": round(median, 4),
+        "ties": ties,
+        "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]:
-    # Generate OHLCV so that ta crate DataItem constraints hold: low >= 0, volume >= 0,
-    # and low <= open, close <= high, high >= open (see ta DataItemBuilder::build).
+def _synthetic_ohlcv(
+    n: int,
+) -> 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)
-    times.sort()
-    return times[len(times) // 2]
+        samples_ms.append((time.perf_counter() - t0) * 1000.0)
+    return samples_ms
+
+
+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)
-                speedup = ms_ta / ms_ft if ms_ft > 0 else float("inf")
-                m_bars_ft = (size / 1e6) / (ms_ft / 1000) if ms_ft > 0 else 0
-                m_bars_ta = (size / 1e6) / (ms_ta / 1000) if ms_ta > 0 else 0
+                ta_samples_ms = _timed_runs_ms(ta_run, open_, high, low, close, volume, size)
+                ta_stats = _summary_stats(ta_samples_ms)
+                ta_median_ms = float(ta_stats["median_ms"])
+                ta_m_bars_s = _throughput_m_bars_s(size, ta_median_ms)
+                speedup = ta_median_ms / ft_median_ms if ft_median_ms > 0 else float("inf")
+                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"{speedup:<{col_speedup}.2f}x {m_bars_ft:<{col_ft_m}.1f} {m_bars_ta:<{col_ta_m}.1f}"
+                    f"{ft_median_ms:<{col_ft_ms}.3f} {ta_median_ms:<{col_ta_ms}.3f} "
+                    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,
-                    "size": size,
-                    "ferro_ta_ms": round(ms_ft, 4),
-                    "talib_ms": round(ms_ta, 4),
-                    "speedup": round(speedup, 4),
-                    "ferro_ta_m_bars_s": round(m_bars_ft, 2),
-                    "talib_m_bars_s": round(m_bars_ta, 2),
-                }
+
+                row.update(
+                    {
+                        "talib_ms": round(ta_median_ms, 4),
+                        "talib_m_bars_s": round(ta_m_bars_s, 2),
+                        "talib_runs_ms": [round(sample, 4) for sample in ta_samples_ms],
+                        "talib_stats": ta_stats,
+                        "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"{'N/A':<{col_speedup}} {m_bars_ft:<{col_ft_m}.1f} {'N/A':<{col_ta_m}}"
+                    f"{ft_median_ms:<{col_ft_ms}.3f} {'N/A':<{col_ta_ms}} "
+                    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)
-        total = len(results)
-        print(f"Summary: ferro_ta faster on {wins}/{total} rows (speedup > 1).")
+        wins = sum(1 for row in results if row.get("outcome") == "ferro_ta_win")
+        total = len([row for row in results if "speedup" in row])
+        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,
-            "command": "python benchmarks/bench_vs_talib.py",
+            "schema_version": 2,
+            "command": " ".join(["python", *sys.argv]),
            "n_warmup": N_WARMUP,
            "n_runs": N_RUNS,
            "sizes": sizes,
            "talib_available": TALIB_AVAILABLE,
-            "runtime": _runtime_info(),
-            "git": _git_info(),
+            "runtime": metadata["runtime"],
+            "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"
-        with open(json_path, "w") as f:
-            json.dump(out, f, indent=2)
+            out["note"] = "ferro_ta only; ta-lib not installed"
+        with open(json_path, "w", encoding="utf-8") as handle:
+            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")
-    ap.add_argument("--json", default=None, help="Write results to JSON file")
-    ap.add_argument(
+    parser = argparse.ArgumentParser(description="ferro_ta vs TA-Lib speed comparison")
+    parser.add_argument("--json", default=None, help="Write results to JSON file")
+    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

@@ -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

-
-def git_info() -> dict[str, Any]:
-    """Best-effort git metadata for reproducible benchmark artifacts."""
+try:
+    import tomllib
+except ImportError:  # pragma: no cover
    try:
-        commit = subprocess.check_output(
-            ["git", "rev-parse", "HEAD"], text=True, stderr=subprocess.DEVNULL
-        ).strip()
-    except Exception:
-        commit = None
+        import tomli as tomllib  # type: ignore[no-redef]
+    except ImportError:  # pragma: no cover
+        tomllib = None  # type: ignore[assignment]

-    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(
-            ["git", "rev-parse", "--abbrev-ref", "HEAD"],
+        return subprocess.check_output(
+            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]
@@ -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, ...]:
@@ -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
-    major platforms. It provides 155+ indicators via a Rust core and PyO3
-    bindings, with optional pandas / streaming APIs.
+    ferro-ta is a Rust-powered Python technical analysis library with a
+    TA-Lib-compatible API and pre-compiled wheels for the supported platforms.
+    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

@@ -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"
@@ -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
@@ -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.
@@ -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.
@@ -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
----------------------
-
-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.
+See ``benchmarks/README.md`` for the detailed benchmark playbook and the
+checked-in comparison tables.
@@ -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
- ``{-100, 0, 100}`` convention, consistent with TA-Lib
+- Expanded the optional MCP server from a small hand-written subset to the
+  broader public ferro-ta callable surface, including stateful class support
+  through stored-instance management tools.
+- Split the root documentation so the full TA-Lib compatibility matrix lives in
+  ``TA_LIB_COMPATIBILITY.md`` while the README stays product-first and shorter.
+- Refreshed MCP docs/tests and updated locked low-risk Python dependencies as
+  part of the release cleanup pass.
+- Stopped tracking the stray ``.coverage`` artifact and aligned ignore rules
+  for local coverage outputs.

-**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
- Removed dependency on ``ta`` crate for these indicators
+- Added top-level package metadata helpers such as ``ferro_ta.__version__``,
+  ``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
- ``StreamingSMA``, ``StreamingEMA``, ``StreamingRSI``, ``StreamingATR``, ``StreamingBBands``, ``StreamingMACD``, ``StreamingStoch``, ``StreamingVWAP``, ``StreamingSupertrend``
+- Improved rolling statistical kernels and several Python analysis hotspots.
+- 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
- Multi-output functions return tuples of ``Series``
+- Improved release automation for PyPI, crates.io, and npm.
+- 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
- SUM uses vectorized cumsum (220× faster than a naive loop)
+- First stable release of the Rust-backed Python technical analysis library.
+- 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**
-
- 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
+For the canonical project changelog, including the full per-version details,
+see `CHANGELOG.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/CHANGELOG.md>`_.
@@ -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 ----------------------------------------------------
@@ -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
-powered by Rust and PyO3.
+**ferro-ta** is a Rust-powered Python technical analysis library focused on a
+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, …)
- Batch execution API — run indicators on 2-D arrays of multiple series
+- TA-Lib-style imports such as ``ferro_ta.SMA(close, timeperiod=20)``
+- 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
- Options/IV helpers and derivatives analytics — see :doc:`derivatives`
+- 10 extended indicators not in TA-Lib (VWAP, Supertrend, Ichimoku Cloud, ...)
+
+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>`_
- Sphinx documentation
+- MCP server for MCP-compatible clients — see `MCP guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_
+- WASM, plugins, and other optional surfaces — see :doc:`adjacent_tooling`

 Installation
 ~~~~~~~~~~~~
@@ -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.
- `MCP Server <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_ — run ferro-ta as an MCP server in Cursor/Claude.
+- :doc:`adjacent_tooling` — optional surfaces such as derivatives, MCP, WASM, GPU, plugins, and agent-oriented integrations.

 Indices and tables
 ==================
@@ -1,42 +1,56 @@
-# MCP Server — Connect ferro-ta in Cursor
+# MCP Server

-ferro-ta ships an MCP (Model Context Protocol) server that exposes
-indicators and backtest tools to AI agents.  This guide shows how to run
-the server and connect it to Cursor or any MCP-compatible client.
+ferro-ta ships an optional MCP (Model Context Protocol) server built on the
+official Python SDK's FastMCP layer. The server now exposes the broad public
+ferro-ta callable surface instead of a tiny hand-picked subset.
+
+That means MCP clients can use:
+
+- Exact top-level ferro-ta exports such as `SMA`, `RSI`, `MACD`, `about`,
+  `methods`, `info`, `benchmark`, and `traced`
+- Non-top-level public tools such as `compute_indicator`, `run_backtest`,
+  `check_cross`, `aggregate_ticks`, `TickAggregator`, and `AlertManager`
+- Legacy lowercase convenience aliases: `sma`, `ema`, `rsi`, `macd`,
+  and `backtest`
+- Generic instance tools for stateful classes and stored callables:
+  `list_instances`, `describe_instance`, `call_instance_method`,
+  `call_stored_callable`, and `delete_instance`

 ---

 ## Installation

-The MCP server requires no additional dependencies beyond ferro_ta itself.
-For the full MCP SDK integration (recommended), install the optional extra:
+Install the optional MCP extra:

 ```bash
 pip install "ferro-ta[mcp]"
 ```

-or install the `mcp` package separately:
+If you are working from this repository, you can install the same extra into
+the project environment with:

 ```bash
-pip install "mcp>=1.0"
+uv sync --extra mcp
 ```

 ---

 ## Running the server

+Run the server over stdio:
+
 ```bash
 python -m ferro_ta.mcp
 ```

-The server listens on stdin/stdout using JSON-RPC 2.0 (the MCP protocol).
+The command exits immediately with an install hint if the optional `mcp`
+dependency is missing.

 ---

 ## Connect in Cursor

-1. Open Cursor settings (Command Palette → "Open User Settings (JSON)").
-2. Find or create the `mcpServers` section:
+Add the server to Cursor's MCP settings:

 ```json
 {
@@ -44,82 +58,134 @@ The server listens on stdin/stdout using JSON-RPC 2.0 (the MCP protocol).
    "ferro-ta": {
      "command": "python",
      "args": ["-m", "ferro_ta.mcp"],
-      "description": "ferro_ta — Technical Analysis MCP server"
+      "description": "ferro-ta technical analysis tools"
    }
  }
 }
 ```

-3. Reload Cursor (Command Palette → "Developer: Reload Window").
-4. The ferro-ta tools will appear in the Tools panel.
+You can place this in your user settings JSON or in a workspace-level
+`.cursor/mcp.json`.

-### Workspace-level config
+---

-You can also add the config to your project's `.cursor/mcp.json`:
+## Tool naming
+
+The MCP server prefers the real ferro-ta API names.
+
+- Use exact public names when possible, for example `SMA`, `MACD`,
+  `compute_indicator`, `trade_stats`, `TickAggregator`, or `AlertManager`
+- Use the legacy lowercase aliases only when you want the old MCP-friendly
+  shortcuts and result shapes
+- Use `about`, `methods`, `indicators`, and `info` to discover what is
+  available from inside an MCP client
+
+---
+
+## Stateful classes and object references
+
+Class tools return stored object references instead of plain text placeholders.
+For example, calling `TickAggregator` or `AlertManager` returns a payload like:

 ```json
 {
-  "mcpServers": {
-    "ferro-ta": {
-      "command": "python",
-      "args": ["-m", "ferro_ta.mcp"]
-    }
-  }
+  "instance_id": "tickaggregator-0001",
+  "type": "ferro_ta.data.aggregation.TickAggregator",
+  "repr": "TickAggregator(rule='tick:2')"
 }
 ```

+Use that `instance_id` with:
+
+- `describe_instance` to inspect the stored object and list public methods
+- `call_instance_method` to call methods like `aggregate`, `update`,
+  `run_backtest`, or `to_dict`
+- `delete_instance` to remove stored objects when you are done
+
+If a tool returns a stored callable, use `call_stored_callable`.
+
+---
+
+## Callable references
+
+Some ferro-ta APIs accept other callables, for example `benchmark`,
+`log_call`, `traced`, or `multi_timeframe(indicator=...)`.
+
+Pass public ferro-ta callables using:
+
+```json
+{"callable": "SMA"}
+```
+
+Pass stored objects using:
+
+```json
+{"instance_id": "function-0001"}
+```
+
 ---

 ## Example prompts

-Once connected, you can ask Claude (or any MCP-enabled AI) things like:
+Once connected, you can ask an MCP-compatible client things like:

-> "Compute RSI(14) on this price series: [100, 102, 101, 105, 108, 104, 107]"
+> "Run `SMA` with `close=[100, 101, 102, 103, 104]` and `timeperiod=3`."

-> "Run a backtest with the rsi_30_70 strategy on [100, 101, 99, 103, 106, 102, 108, 105, 109, 112, 108, 111]"
+> "Use `compute_indicator` to calculate `MACD` for this close series."

-> "List all available ferro_ta indicators"
+> "Call `about` and summarize the current ferro-ta API surface."

-> "What does the SMA indicator do?"
+> "Create a `TickAggregator` with `rule='tick:50'`, aggregate this tick data,
+> then delete the instance."
+
+> "Benchmark `SMA` over this price series using a callable reference."

 ---

-## Available tools
+## Programmatic use

-| Tool | Description |
-|------|-------------|
-| `sma` | Simple Moving Average |
-| `ema` | Exponential Moving Average |
-| `rsi` | Relative Strength Index |
-| `macd` | MACD line, signal, histogram |
-| `backtest` | Vectorized backtest (rsi_30_70, sma_crossover, macd_crossover) |
-| `list_indicators` | List all registered indicators |
-| `describe_indicator` | Describe a named indicator |
-
---
-
-## Programmatic use (Python client)
-
-You can also use the MCP handlers directly in Python without the server:
+Use the server entrypoint:

 ```python
-from ferro_ta.mcp import handle_list_tools, handle_call_tool
-import numpy as np
+from ferro_ta.mcp import create_server
+
+server = create_server()
+# server.run(transport="stdio")
+```
+
+Or call the handlers directly without starting the server:
+
+```python
+from ferro_ta.mcp import handle_call_tool, handle_list_tools
+import json

-# List tools
 tools = handle_list_tools()
-print([t["name"] for t in tools["tools"]])
+print(len(tools["tools"]))

-# Call RSI
-close = list(np.cumprod(1 + np.random.default_rng(0).normal(0, 0.01, 50)) * 100)
-result = handle_call_tool("rsi", {"close": close, "timeperiod": 14})
-print(result)
+close = [100, 101, 102, 103, 104]
+result = handle_call_tool("SMA", {"close": close, "timeperiod": 3})
+print(json.loads(result["content"][0]["text"]))
+
+aggregator = json.loads(
+    handle_call_tool("TickAggregator", {"rule": "tick:2"})["content"][0]["text"]
+)
+bars = handle_call_tool(
+    "call_instance_method",
+    {
+        "instance_id": aggregator["instance_id"],
+        "method": "aggregate",
+        "args": [{"price": [1, 2, 3, 4], "size": [1, 1, 1, 1]}],
+    },
+)
+print(json.loads(bars["content"][0]["text"]))
 ```

 ---

 ## See also

- `ferro_ta.mcp` — module source.
- `ferro_ta.tools` — underlying tool functions.
- `docs/agentic.md` — LangChain and workflow integration.
+- `python -m ferro_ta.mcp` - stdio MCP entrypoint
+- `ferro_ta.mcp.create_server()` - FastMCP server factory
+- `ferro_ta.tools.api_info` - API discovery helpers used by the MCP catalog
+- `ferro_ta.tools` - stable wrappers such as `compute_indicator`
+- `docs/agentic.md` - workflow and agent integration notes
@@ -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.
@@ -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
+    }
+  ]
+}
@@ -0,0 +1,163 @@
+{
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+}
@@ -0,0 +1,285 @@
+{
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+}
@@ -0,0 +1,73 @@
+{
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+}
@@ -4,8 +4,8 @@ build-backend = "maturin"

 [project]
 name = "ferro-ta"
-version = "1.0.2"
-description = "A fast Technical Analysis library — TA-Lib alternative powered by Rust and PyO3"
+version = "1.0.4"
+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]
@@ -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,
@@ -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)
@@ -1,19 +1,23 @@
 """
 ferro_ta.api_info — API discovery helpers.

-Provides :func:`indicators` and :func:`info` for exploring the ferro_ta
-indicator catalogue without reading source code.
+Provides :func:`indicators`, :func:`methods`, :func:`about`, and :func:`info`
+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.

@@ -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())
@@ -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

@@ -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
@@ -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(
@@ -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
@@ -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 = [
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-    { 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" },
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+    { 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" },
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 [[package]]
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 ]

+[[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 = [
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 [[package]]
 name = "ferro-ta"
-version = "1.0.2"
+version = "1.0.3"
 source = { editable = "." }
 dependencies = [
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 [[package]]
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-version = "12.8.4.1"
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 [[package]]
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-version = "12.8.90"
+name = "nvidia-cuda-cupti"
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 source = { registry = "https://pypi.org/simple" }
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 [[package]]
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-version = "12.8.93"
+name = "nvidia-cuda-nvrtc"
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 source = { registry = "https://pypi.org/simple" }
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Author	SHA1	Message	Date
Pratik Bhadane	29c6f5cf84	chore: release v1.0.4	2026-03-24 12:54:41 +05:30
Pratik Bhadane	58a1dc2308	chore: remove .coverage file and update .gitignore to exclude coverage files - 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.	2026-03-24 12:49:17 +05:30
Pratik Bhadane	b66b05682e	docs: finalize 1.0.3 release notes	2026-03-24 11:24:34 +05:30
Pratik Bhadane	e4ac7dd4d3	fix: repair release workflow	2026-03-24 11:23:41 +05:30
Pratik Bhadane	1f053c1001	fix: unblock python ci	2026-03-24 11:19:48 +05:30
Pratik Bhadane	13cb0dc95a	chore: release v1.0.3	2026-03-24 11:09:48 +05:30
Pratik Bhadane	0382c4e302	ci: add release.yml to auto-publish on version tag push Pushing v* tag now triggers: 1. release.yml — creates a GitHub Release (published, not draft), pulling the release notes from CHANGELOG.md automatically. 2. CI.yml (release: published event) — builds wheels for Linux / macOS / Windows × Python 3.10-3.13, sdist, publishes to PyPI via trusted publisher, publishes ferro_ta_core to crates.io, and generates SBOMs. Pre-release tags (e.g. v1.1.0-rc.1) are marked as pre-release on GitHub automatically. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-24 03:00:06 +05:30
Pratik Bhadane	7736effc27	fix(bench): adjust feature_matrix speedup floor to 0.40 for cross-architecture compatibility	2026-03-24 02:49:29 +05:30