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Changelog

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

[Unreleased]

Fixed

  • 68 indicators silently resolved to a streaming class instead of their batch function (quantwave-84cu). The generated TA registry introduced in 0.7.0 derived native batch symbol names with pascal_to_snake() (SuperTrend → super_trend), but the export_*! macros emit pub fn [<$name:lower>] (SuperTrend → supertrend). Every multi-word name missed; the 44 single-word ones (rsi, sma, atr) passed only because pascal_to_snake("Rsi") == "rsi". _resolve_ta_binding treated the miss as a fallback and returned native_streaming, so qw.supertrend was a class while qw.rsi was a function — with no error or warning. qw.supertrend(period=10, multiplier=3.0, high=…, low=…, close=…) again returns list[SuperTrendResult] as it did in 0.6; callers need no changes.
  • _resolve_ta_binding now raises ImportError when an entry declares a native_batch symbol the build does not export, rather than silently substituting the streaming class (whose calling convention differs). A native_batch of None still falls through to streaming/polars as before.
  • Corrected stale hand-written aliases in scripts/api_slug_aliases.json: fm_demodulator, fourier_series_model, my_rsi, precision_trend_analysis named non-existent snake_cased symbols; linreg, oc2, true_range declared batch exports that do not exist and now fall through to their polars methods; sr_monitor declared SrInteractionMonitor, a class never exported to Python.

Added

  • test_registry_native_symbols_resolve_against_build — asserts every declared native symbol exists in the compiled module. The prior test only checked a name was present, never that it resolved, so native_batch: "super_trend" passed cleanly. Plus a regression test that multi-word slugs bind as batch functions, not classes.

[0.7.0] - 2026-07-13

Added

  • Complete classic TA-Lib surface (quantwave.talib): 161 functions, up from 8 — RSI, MACD, SMA, EMA, ATR, ADX, BBANDS, STOCH, OBV, all 61 candlestick patterns, and the math/price transforms. The classic array-in/array-out API (talib.RSI(close, timeperiod=14), multi-output tuples, OHLC/candlestick inputs) delegates to the Polars .ta plugins, so values are the talib-rs-parity-tested Rust results. (quantwave-yp9a)
  • Top-level native symbol access restored: qw.FracDiff, qw.fracdiff, qw.rsi, qw.SuperTrend, … bind alongside the slug-based qw.ta namespace.

Changed

  • Unified the Python FFI on PyO3 (abi3), retiring uniffi. The indicator bindings, the Polars expression plugins, and the backtest engine are now a single PyO3 abi3-py39 extension in one crate (quantwave-py) producing one cdylib — a single maturin build yields one cp39-abi3 wheel with no wheel-merge step. (quantwave-5ipk.10, quantwave-6dgg)
  • Collapsed the three PyO3 crates (quantwave-python, quantwave-plugins, quantwave-backtest-py) into quantwave-py; deleted scripts/build_unified_wheel.py and consolidated to one pyproject.toml.

Fixed

  • Wheel tag / install correctness: the published wheel is now cp39-abi3 and installs correctly on CPython 3.9–3.13. 0.6.1 shipped a py3-none wheel bundling CPython-3.12-only extensions, which broke pip install on 3.9/3.10/3.11/3.13. (quantwave-9gek.1)

[0.6.0] - 2026-06-28

Added

  • Fractional differencing (FracDiff) (quantwave-wnd9): Prado-style stationary features; Rust Next<f64>, Polars lf.ta.frac_diff(), Python fracdiff()
  • HTML tear sheets (quantwave-0gi1): BacktestReport.to_html() / save_html() with equity, drawdown, and trade tables
  • Research loop (Tier 2): qw.build_feature_matrix(), lf.ta().features().recommended_matrix(), lf.bt.monte_carlo(), Rust .bt WFO-optimize + MC bootstrap
  • Product guardrails (Tier 1): scripts/quantwave_verify.sh, metadata drift gate, streamlined CI (verify → plugins → deploy-docs)
  • 55 custom Polars expression plugins and 98 auto-generated pyo3-polars bindings for standard indicators
  • PA foundation: S/R Polars, confluence, geometric patterns with H&S neckline breakout
  • Streaming readiness (quantwave-h6xe) and Rust metadata codegen (quantwave-iqq7)
  • Plugin vs .ta guide, expanded regime user guide, comparison one-pager
  • Indicator doc SOA complete (quantwave-frq0): 220+ native pages under DOCUMENTATION_STANDARDS.md with PNG previews, doc drift script in verify
  • Full visual depth layer (p1k6): docs/generate_all_previews.py + standards lint rejecting placeholders

Changed

  • GitHub Actions consolidated from four workflows to CI + Release (v* → crates.io + PyPI)
  • Platform planning docs split into INDICATORS_SOA.md and BACKTEST_SOA.md

Fixed

  • CI: cargo-nextest, maturin venv, uniffi-bindgen==0.31.0 for verify job
  • Doc lint for fractional_differentiation.md (preview PNG + description depth)
  • Empty Python API Reference page (quantwave-rbz4)
  • Broken imports for quantwave >=0.4.1 on fresh installs without polars

[0.5.2] - 2026-05-31

Added (Python DX improvements)

  • 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.

Changed

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

Documentation

  • Official Standards Published: Created docs/DOCUMENTATION_STANDARDS.md (v1.0, 2026-05-31 IST) under task quantwave-d2hk / epic p1k6. Defines mandatory enforceable template for all 223+ indicator pages: required sections (Visual Example, full batch+streaming+Polars Usage Examples, Edge Cases & Limitations, Sources), type-specific guidance (classic scalar / patterns / rich struct / Ehlers), good-vs-bad examples, tone/visual/cross-link rules, and 4-phase rollout.
  • Updated contributing.md (new indicator docs step) and appended full decision record + rationale (diagnosis of thin stubs vs. PA notebook quality) to DOCUMENTATION_DECISIONS.md.
  • Minor alignments in gallery.md.
  • This is the foundation for all future indicator documentation work and the planned xtask generator. See DOCUMENTATION_STANDARDS.md for the complete template and checklist.
  • Candle Standards Proof batch (p1k6 child, 2026-05-31 IST): 8 worst-duplication candlestick pages (doji.md + gravestone/dragonfly variants, harami.md + harami_cross, three_black_crows.md + three_white_soldiers.md, abandoned_baby.md) + engulfing.md enhancement fully rewritten to DOCUMENTATION_STANDARDS.md (mandatory visuals, 3-surface code, edges, authoritative TA-Lib+core sources, no Nison boilerplate). docs/gen_candle_previews.py extended (portable + 8+ generators); 11 professional PNGs produced in assets/candlestick-previews/. Cross-refs + full decision record in DOCUMENTATION_DECISIONS.md. Proves template + gens scale for Phase 1 rollout. See decisions file for files touched and bd tracking attempt details.
  • Ehlers DSP Phase 1 batch 2 (p1k6, 2026-05-31 IST): 5 high-value thin Ehlers DSP pages (ehlers_filter.md, reflex.md, ehlers_stochastic.md, ehlers_loops.md, ultimatesmoother.md) rewritten to full Ehlers/scalar STANDARDS conformance. Extended gen_indicator_previews.py (portable, pure-numpy core ports for the 5, CLI, professional DSP styling); 5 new PNG visuals generated with 2026-05-31 IST captions mapping directly to core .rs Next logic. 3-surface examples, Edge Cases, authoritative sources (exact core paths + Ehlers papers). Cross-refs + detailed decision record appended. Worktree clean for merge. See DOCUMENTATION_DECISIONS.md for complete list of files + checklist confirmation.

[0.5.1] - 2026-05-31

Fixed

  • 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).

Changed

  • 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.

[0.5.0] - 2026-05-30

Added

  • Backtest Engine v0.2 (major milestone):
  • 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.

Changed

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

[0.4.0] - 2026-05-19

Added

  • 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.

Fixed

  • 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.

[0.3.0] - 2026-05-18

Added

  • 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.

Fixed

  • 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.

[0.2.0] - 2026-05-18

Added

  • 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.