122 lines
4.2 KiB
ReStructuredText
122 lines
4.2 KiB
ReStructuredText
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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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