""" RaptorBT - High-performance Rust backtesting engine. Provides Python bindings for a Rust-based backtesting engine built for production quantitative trading: - Sub-millisecond execution on thousands of bars - Disk footprint: <10MB, startup latency: <10ms - 100% deterministic execution (no JIT cache) - Native parallelism via Rayon + explicit SIMD - Full tick-level simulation (no bar resampling required) - 80+ technical indicators from ferro-ta (P0 batch: PPO, APO, ADOSC, OBV, CMO, ARONOSC, BOP, ULTOSC, and more) """ from raptorbt._raptorbt import ( # Config classes PyBacktestConfig, PyInstrumentConfig, PyStopConfig, PyTargetConfig, # Result classes PyBacktestResult, PyBacktestMetrics, PyTrade, # Backtest functions run_single_backtest, run_basket_backtest, run_options_backtest, run_pairs_backtest, run_multi_backtest, run_spread_backtest, run_tick_backtest, # Batch backtest PyBatchSpreadItem, batch_spread_backtest, # Monte Carlo simulation simulate_portfolio_mc, # Tick signal functions compute_tick_entry_signals, compute_tick_exit_signals, # Tick feature functions tick_spread_pct, buy_sell_imbalance_delta, return_window, realized_vol_rolling, oi_position_pct, tick_velocity, # Indicator functions sma, ema, rsi, macd, stochastic, atr, bollinger_bands, adx, vwap, supertrend, rolling_min, rolling_max, # ferro-ta indicator functions cci, willr, sar, plus_di, minus_di, adx_all, adxr, roc, mfi, wma, dema, tema, kama, stochrsi, aroon, trix, natr, trange, stddev, var, linearreg, linearreg_slope, linearreg_intercept, linearreg_angle, tsf, beta, correl, ad, adosc, obv, mom, ppo, cmo, aroonosc, bop, ultosc, typprice, medprice, avgprice, wclprice, midpoint, midprice, t3, trima, apo, # P0 batch vwma, donchian, choppiness_index, hull_ma, chandelier_exit, ichimoku, pivot_points, # Hilbert Transform (cycle) ht_trendline, ht_dcperiod, ht_dcphase, ht_phasor, ht_sine, ht_trendmode, # Market regime detection regime_adx, regime_combined, detect_breaks_cusum, rolling_variance_break, # Portfolio / cross-series tools rolling_beta, drawdown_series, zscore_series, relative_strength, spread, ratio, ) __version__ = "0.4.1" __all__ = [ # Config classes "PyBacktestConfig", "PyInstrumentConfig", "PyStopConfig", "PyTargetConfig", # Result classes "PyBacktestResult", "PyBacktestMetrics", "PyTrade", # Backtest functions "run_single_backtest", "run_basket_backtest", "run_options_backtest", "run_pairs_backtest", "run_multi_backtest", "run_spread_backtest", "run_tick_backtest", # Batch backtest "PyBatchSpreadItem", "batch_spread_backtest", # Monte Carlo simulation "simulate_portfolio_mc", # Tick signal functions "compute_tick_entry_signals", "compute_tick_exit_signals", # Tick feature functions "tick_spread_pct", "buy_sell_imbalance_delta", "return_window", "realized_vol_rolling", "oi_position_pct", "tick_velocity", # Indicator functions "sma", "ema", "rsi", "macd", "stochastic", "atr", "bollinger_bands", "adx", "vwap", "supertrend", "rolling_min", "rolling_max", # ferro-ta indicator functions "cci", "willr", "sar", "plus_di", "minus_di", "adx_all", "adxr", "roc", "mfi", "wma", "dema", "tema", "kama", "stochrsi", "aroon", "trix", "natr", "trange", "stddev", "var", "linearreg", "linearreg_slope", "linearreg_intercept", "linearreg_angle", "tsf", "beta", "correl", "ad", "adosc", "obv", "mom", "ppo", "cmo", "aroonosc", "bop", "ultosc", "typprice", "medprice", "avgprice", "wclprice", "midpoint", "midprice", "t3", "trima", "apo", # P0 batch "vwma", "donchian", "choppiness_index", "hull_ma", "chandelier_exit", "ichimoku", "pivot_points", # Hilbert Transform (cycle) "ht_trendline", "ht_dcperiod", "ht_dcphase", "ht_phasor", "ht_sine", "ht_trendmode", # Market regime detection "regime_adx", "regime_combined", "detect_breaks_cusum", "rolling_variance_break", # Portfolio / cross-series tools "rolling_beta", "drawdown_series", "zscore_series", "relative_strength", "spread", "ratio", ]