Pratik Bhadane
436954138f
Update version numbers across Rust, Python, and documentation files to 1.1.0. Enhance the .gitignore to include macOS dSYM files and plans directory. Introduce new dependencies in the Rust core library and update the README to reflect recent performance benchmarks and backtesting engine capabilities. Add new artifacts to the benchmarks manifest and improve documentation for the backtesting engine API.
122 lines
4.2 KiB
ReStructuredText
122 lines
4.2 KiB
ReStructuredText
Adjacent Tooling
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================
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These modules are useful, but they are secondary to ferro-ta's core identity as
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a Python technical analysis library.
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.. list-table::
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:header-rows: 1
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* - Area
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- Status
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- What it is
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* - Backtesting engine
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- Adjacent
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- Vectorized Rust backtester: OHLCV fill, stop-loss/TP, 23 performance
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metrics, trade extraction, parallel Monte Carlo, walk-forward analysis,
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and multi-asset portfolio simulation. See :ref:`backtesting-engine`.
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* - Derivatives analytics
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- Adjacent
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- Options pricing, Greeks, implied volatility helpers, futures basis,
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curve, and roll utilities. See :doc:`derivatives`.
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* - Agent workflow wrappers
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- Adjacent
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- Tool and workflow helpers for agent-style integrations. See
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`docs/agentic.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_.
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* - MCP server
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- Experimental or adjacent
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- FastMCP-based server exposing selected ferro-ta capabilities to
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MCP-compatible clients. See
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`docs/mcp.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_.
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* - WASM package
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- Experimental
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- Browser and Node.js package with a smaller indicator subset. See
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`wasm/README.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/wasm/README.md>`_.
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* - GPU backend
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- Experimental
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- Optional PyTorch-backed acceleration for a limited subset of indicators.
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See `docs/gpu-backend.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/gpu-backend.md>`_.
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* - Plugin system
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- Experimental
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- Registry and plugin packaging model for custom indicators. See
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:doc:`plugins`.
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.. _backtesting-engine:
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Backtesting Engine
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------------------
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``ferro_ta.analysis.backtest`` ships a production-grade backtesting engine
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backed entirely by Rust hot-path functions.
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**Core API:**
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.. code-block:: python
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from ferro_ta.analysis.backtest import BacktestEngine, monte_carlo, walk_forward
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result = (
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BacktestEngine()
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.with_commission(0.001)
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.with_slippage(5.0) # basis points
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.with_ohlcv(high=high, low=low, open_=open_)
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.with_stop_loss(0.02)
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.with_take_profit(0.04)
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.run(close, "sma_crossover")
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)
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print(result.metrics["sharpe"]) # one of 23 metrics
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print(result.trades) # pandas DataFrame
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print(result.drawdown_series.min()) # max drawdown
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mc = monte_carlo(result, n_sims=1000) # parallel bootstrap
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wf = walk_forward(close, "rsi", param_grid=[{"timeperiod": t} for t in [10,14,20]],
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train_bars=500, test_bars=100)
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**Available Rust primitives** (``ferro_ta._ferro_ta``):
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- ``backtest_core`` — close-only, vectorized, commission + slippage
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- ``backtest_ohlcv_core`` — fill at open, intrabar stop-loss / take-profit
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- ``compute_performance_metrics`` — 23 metrics in one pass (Sharpe, Sortino,
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Calmar, CAGR, Omega, Ulcer, win rate, profit factor, tail ratio, etc.)
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- ``extract_trades_ohlcv`` — 9 parallel arrays (entry/exit bar, MAE, MFE, …)
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- ``backtest_multi_asset_core`` — N-asset parallel backtest via Rayon
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- ``monte_carlo_bootstrap`` — parallel block bootstrap, returns (n_sims, n_bars)
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- ``walk_forward_indices`` — anchored/rolling fold index generator
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- ``kelly_fraction`` / ``half_kelly_fraction``
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**Speed vs competitors** (100k bars, SMA crossover, Apple M-series):
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.. list-table::
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:header-rows: 1
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* - Library
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- Time
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- vs ferro-ta
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* - ferro-ta ``backtest_core``
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- 0.29 ms
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- —
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* - NumPy vectorized
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- 0.46 ms
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- 1.6× slower
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* - vectorbt
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- 2.9 ms
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- 10× slower
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* - backtesting.py
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- 320 ms
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- 1,100× slower
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* - backtrader
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- ~520 ms (10k bars)
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- >15,000× slower
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How to read the project
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-----------------------
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When evaluating ferro-ta:
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- Start with the core library docs, migration guide, support matrix, and benchmarks.
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- Treat adjacent tooling as opt-in layers, not as proof that the core indicator
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library is broader or more stable than it is.
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- Check the release notes and stability policy before depending on experimental
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surfaces in production.
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