Adjacent Tooling

These modules are useful, but they are secondary to ferro-ta’s core identity as a Python technical analysis library.

Area	Status	What it is
Backtesting engine	Adjacent	Vectorized Rust backtester: OHLCV fill, stop-loss/TP, 23 performance metrics, trade extraction, parallel Monte Carlo, walk-forward analysis, and multi-asset portfolio simulation. See Backtesting Engine.
Derivatives analytics	Adjacent	Options pricing, Greeks, implied volatility helpers, futures basis, curve, and roll utilities. See Derivatives Analytics.
Agent workflow wrappers	Adjacent	Tool and workflow helpers for agent-style integrations. See docs/agentic.md.
MCP server	Experimental or adjacent	FastMCP-based server exposing selected ferro-ta capabilities to MCP-compatible clients. See docs/mcp.md.
WASM package	Experimental	Browser and Node.js package with a smaller indicator subset. See wasm/README.md.
GPU backend	Experimental	Optional PyTorch-backed acceleration for a limited subset of indicators. See docs/gpu-backend.md.
Plugin system	Experimental	Registry and plugin packaging model for custom indicators. See Writing a plugin.

Backtesting Engine

ferro_ta.analysis.backtest ships a production-grade backtesting engine backed entirely by Rust hot-path functions.

Core API:

from ferro_ta.analysis.backtest import BacktestEngine, monte_carlo, walk_forward

result = (
    BacktestEngine()
    .with_commission(0.001)
    .with_slippage(5.0)                       # basis points
    .with_ohlcv(high=high, low=low, open_=open_)
    .with_stop_loss(0.02)
    .with_take_profit(0.04)
    .run(close, "sma_crossover")
)

print(result.metrics["sharpe"])               # one of 23 metrics
print(result.trades)                          # pandas DataFrame
print(result.drawdown_series.min())           # max drawdown

mc = monte_carlo(result, n_sims=1000)         # parallel bootstrap
wf = walk_forward(close, "rsi", param_grid=[{"timeperiod": t} for t in [10,14,20]],
                  train_bars=500, test_bars=100)

Available Rust primitives (ferro_ta._ferro_ta):

• backtest_core — close-only, vectorized, commission + slippage

• backtest_ohlcv_core — fill at open, intrabar stop-loss / take-profit

• compute_performance_metrics — 23 metrics in one pass (Sharpe, Sortino, Calmar, CAGR, Omega, Ulcer, win rate, profit factor, tail ratio, etc.)

• extract_trades_ohlcv — 9 parallel arrays (entry/exit bar, MAE, MFE, …)

• backtest_multi_asset_core — N-asset parallel backtest via Rayon

• monte_carlo_bootstrap — parallel block bootstrap, returns (n_sims, n_bars)

• walk_forward_indices — anchored/rolling fold index generator

• kelly_fraction / half_kelly_fraction

Speed vs competitors (100k bars, SMA crossover, Apple M-series):

Library	Time	vs ferro-ta
ferro-ta backtest_core	0.29 ms	—
NumPy vectorized	0.46 ms	1.6× slower
vectorbt	2.9 ms	10× slower
backtesting.py	320 ms	1,100× slower
backtrader	~520 ms (10k bars)	>15,000× slower

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.