Support Matrix

The primary product is the Python technical analysis library: TA-Lib-style indicator calls backed by a Rust implementation.

Indicator compatibility

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 Migration from TA-Lib.

Module status

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.
`ferro_ta.analysis.resample`	Supported (v1.1.0)	`resample_ohlcv()`, `align_to_coarse()`, `resample_ohlcv_labels()` — pure-NumPy OHLCV bar aggregation across timeframes.
`ferro_ta.analysis.multitf`	Supported (v1.1.0)	`MultiTimeframeEngine` — multi-timeframe signal generation with automatic alignment.
`ferro_ta.analysis.adjust`	Supported (v1.1.0)	`adjust_ohlcv()`, `adjust_for_splits()`, `adjust_for_dividends()` — backward-adjusted price series for equity/index strategies.
`ferro_ta.analysis.plot`	Supported (v1.1.0)	`plot_backtest()` — interactive Plotly backtest visualization (requires plotly).
`ferro_ta.analysis.regime`	Supported (v1.1.0)	`detect_volatility_regime()`, `detect_trend_regime()`, `detect_combined_regime()`, `RegimeFilter` — pure-NumPy 6-state market regime labeling; no ML dependencies.
`ferro_ta.analysis.optimize`	Supported (v1.1.0)	`PortfolioOptimizer`, `mean_variance_optimize()`, `risk_parity_optimize()`, `max_sharpe_optimize()` — portfolio optimization via SLSQP (requires scipy).
`ferro_ta.analysis.live`	Supported (v1.1.0)	`PaperTrader` — event-driven paper trading bridge matching backtest logic exactly.
MCP, WASM, GPU, plugin, and agent-oriented tooling	Experimental or adjacent	Evaluate these independently from the core indicator library.

Backtesting engine features

Feature	Status	Notes
Flat/proportional commission	Supported	Via `CommissionModel` presets and `BacktestEngine.with_commission_model()`.
Bid-ask spread model (`spread_bps`)	Supported (v1.1.0)	New `CommissionModel.spread_bps` field; half-spread deducted per leg.
Short borrow cost (`short_borrow_rate_annual`)	Supported (v1.1.0)	New `CommissionModel.short_borrow_rate_annual` field; accrued per bar for short positions.
Trailing stop loss	Supported	`BacktestEngine.with_trailing_stop(pct)` — intrabar high-water mark tracking.
Breakeven stop (`breakeven_pct`)	Supported (v1.1.0)	`BacktestEngine.with_breakeven_stop(pct)` — moves stop to entry once profit reaches `pct`.
Bracket order priority	Supported (v1.1.0)	When both SL and TP are breached on the same bar, the level closer to open fires first.
Leverage / margin modeling	Supported (v1.1.0)	`BacktestEngine.with_leverage(margin_ratio, margin_call_pct)` — tracks margin and triggers force-close on margin call.
Loss circuit breakers	Supported (v1.1.0)	`BacktestEngine.with_loss_limits(daily, total)` — halts trading on drawdown breach.
Portfolio constraints	Supported (v1.1.0)	`BacktestEngine.with_portfolio_constraints(max_asset_weight, max_gross_exposure, max_net_exposure)` for multi-asset backtests.
Volatility-target position sizing	Supported	`BacktestEngine.with_position_sizing("volatility_target", ...)`.
Walk-forward / Monte Carlo	Supported	Available via `BacktestEngine` higher-level methods.
Benchmark comparison	Supported	`BacktestEngine.with_benchmark(close_array)` — alpha, beta, information ratio.

Supported Python versions

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

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.

Release status

These docs track package version 1.1.4.

Release notes by version: Release Notes
Canonical project changelog: CHANGELOG.md
Stability policy: docs/stability.md

If the package version, docs version, or support matrix disagree, treat that as a documentation bug.