Pratik Bhadane
71b6343e92
Refresh the benchmark and performance surface across the repo. This updates the benchmark wrappers and helper scripts, regenerates the checked-in benchmark and perf-contract artifacts, and folds in the related roadmap, compatibility, and example notebook changes that belong with this performance-focused pass. Harden the Python CI and local pre-push flow so the same checks pass reliably in both places. The workflow and pre-push script now use module-safe uv typecheck invocations, the Python test environment installs the optional MCP dependency needed by the MCP server tests, and one-off root benchmark outputs are ignored to keep the repo clean. Align local tooling with the current project configuration by updating the Ruff pre-commit hook, tightening the API typing and MCP server helpers, and refreshing the lockfile to pick up the audited PyJWT fix while preserving the rest of the staged source changes.
ferro-ta Examples
Jupyter notebooks demonstrating key ferro-ta features.
Notebooks
| Notebook | Description |
|---|---|
quickstart.ipynb |
Core API: moving averages, RSI, MACD, Bollinger Bands, batch API, pipeline, pandas integration |
streaming.ipynb |
Streaming bar-by-bar API: StreamingSMA, StreamingRSI, StreamingBBands, StreamingMACD, StreamingATR |
backtesting.ipynb |
Backtesting harness, indicator pipeline for feature engineering, config defaults |
features_21_30.ipynb |
Multi-timeframe, resampling, portfolio analytics, strategy DSL, feature matrix, viz, adapters |
Running the Notebooks
# Install dependencies
pip install ferro-ta jupyter numpy
# Optional: pandas and polars integration
pip install "ferro-ta[pandas]" "ferro_ta[polars]"
# Start Jupyter
jupyter notebook examples/