Changelog

All notable changes to this project will be documented in this file.

[0.5.2] - 2026-05-31

Discovery API: quantwave.indicators() and quantwave.is_indicator(name).
Rich Metadata: quantwave.metadata(name) returning IndicatorMeta with params, data inputs, outputs, warmup_bars, category, etc.
Streaming lookup: quantwave.streaming_class(name).
Parity testing: quantwave.assert_parity() helper for verifying batch vs streaming bit-identical behavior.
warmup_bars(name, params) helper.
Namespace improvements: New quantwave.results, quantwave.options, and quantwave.talib submodules. Old top-level access now emits deprecation warnings.
Public exception base: quantwave.QuantwaveError.
__version__ properly exposed.
Linux arm64 (aarch64) wheels are now built and published.

Release workflow no longer hard-gates on docs build (docs issues can be fixed independently).

Publishing completeness: Fixed workspace dependency configuration so that cargo publish succeeds for all internal crates (quantwave-core, quantwave-polars, quantwave-plugins, quantwave-backtest, quantwave). Internal crates now correctly declare version.workspace = true in [workspace.dependencies].
Release reliability: Added required build-docs job (export + mkdocs build --strict) to the release workflow. Release publishing now hard-gates on successful docs build. Removed all continue-on-error: true from publish steps — any failure is now fatal.
Docs build: Fixed filename collision (*.py.md landing pages conflicting with *.py notebooks) that was breaking main deploys. Renamed affected landing pages and cleaned up references + committed __pycache__.
Modernized cargo publish steps to use CARGO_REGISTRY_TOKEN environment variable (no more deprecated --token flag).

0.5.1 is the first complete, trustworthy release of the Backtest Engine v0.2 features (including quantwave-backtest crate on crates.io) plus the full Polars + Python package set.

Backtest Engine v0.2 (major milestone under epic quantwave-n1yc):
Rich-Metadata Position Sizing: New PositionSizer trait + InitialRiskPositionSizer that directly consumes rich PA detector metadata (fraction_at_risk, pole_height_atr, strength, etc.) for dynamic, risk-aware sizing. Inspired by QF-Lib patterns. Includes SizingAdapter for seamless streaming Next<T> generators.
Pluggable Realistic Execution Models: Proper CommissionModel and SlippageModel traits with high-quality implementations, including SquareRootMarketImpactSlippage (volatility × √(volume/ADV)) and max_volume_share_limit support.
High-Fidelity Execution Simulator Mode: New execution path that applies the full sophisticated models while being driven by the exact same rich StrategySignal / PA struct stream as the fast vectorized path. Perfect for "pre-live" validation.
Professional Tearsheet & Reporting Layer: New BacktestTearsheet with PerformanceSummary, RiskMetrics, EnrichedTrade (carries full PA metadata for attribution), AttributionReport, to_markdown(), and Polars DataFrame export for Excel. Institutional-quality output.
Full batch + streaming Next<T> parity maintained across all new features.
Updated canonical examples and documentation demonstrating PA detector + rich metadata workflows.

Backtester is now production-grade ready for complex PA + ML strategies (Flags, H&S, Market Structure, etc.).

Options India Analytics: Comprehensive suite for NSE options including Black-Scholes Greeks (Price, Delta, Gamma, Theta, Vega, Rho), Implied Volatility, and Chain Analytics (Max Pain, PCR, GEX, OI Zones, ATM Straddle, Synthetic Futures).
Polars Integration for Options: Full support for options_india as native Polars expressions with robust handling of column-or-value parameters.
NSE Utilities: Added nse_lot_size and moneyness helpers for the Indian market.

Release Build: Resolved a critical 'maturin' conflict where tracked __init__.py files were being overwritten during the wheel build process.
Code Hygiene: Cleaned up all compiler warnings and unused imports across the entire workspace.

Multi-Asset Regime Detection: Enhanced MultiAssetClusterer with rolling correlation structures and dispersion analysis to identify joint market states.
Advanced Conditioned Risk Metrics: Expanded regimes_conditioned_metrics in Polars to include Skewness, Kurtosis, and Sortino Ratio.
Polars Enhancements: Enabled moment and cum_agg features for vectorized higher-order statistics.

Release Stability: Fixed workspace dependency alignment issues that caused CI failures in previous releases.
Compilation: Resolved method resolution errors for skew and kurtosis in Polars pipelines.

Regime Detection Suite (quantwave::regimes):
- Volatility Clustering (Prakash et al. 2021) with online K-Means.
- Hidden Markov Models (Hamilton 1989) with Viterbi decoding.
- Gaussian Mixture Models (Two Sigma 2021) foundations.
- Changepoint Detection (PELT - Killick et al. 2012) for exact segmentation.
Polars integration for all regime detection tools.
Comprehensive documentation and guides for market state tools.