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103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
"""Linear Regression Trend -- regression-based trend detection with confidence bands.
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Demonstrates:
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- linreg_slope / linreg_value / linreg_r2 indicators
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- Confidence-weighted sizing (R² as conviction filter)
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- Multi-timeframe: slope on 4h window, trade on 15min bars
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- Trailing stop for trend exits
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- Bracket orders (stop-loss + take-profit)
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The idea: fit a rolling OLS regression on price. When the slope is steep
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and the R² is high (price moves in a straight line), we have a strong trend.
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Size proportionally to slope strength * R² confidence.
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Usage:
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python examples/04_linear_regression.py
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"""
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import os
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import time
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import manifoldbt as mbt
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from manifoldbt.indicators import close, high, low, volume
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from manifoldbt.helpers import time_range, Slippage, Interval
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# -- Regression indicators ----------------------------------------------------
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# Rolling linear regression over 16 bars (16 * 15min = 4h window)
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window = 16
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slope = close.linreg_slope(window) # price change per bar (trend direction)
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fitted = close.linreg_value(window) # regression fitted value
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r2 = close.linreg_r2(window) # goodness of fit (0=noise, 1=perfect line)
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# Normalize slope by price to get a percentage rate
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norm_slope = slope / (close + mbt.lit(1e-12))
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# -- Volatility filter ---------------------------------------------------------
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# ATR-like: average true range normalized by price
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avg_range = (high - low).rolling_mean(window)
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norm_vol = avg_range / (close + mbt.lit(1e-12))
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# -- Signal construction -------------------------------------------------------
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# Conviction = R² (0 to 1). Only trade when R² > 0.6 (strong linear trend)
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has_conviction = r2 > 0.6
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# Direction: positive slope = long, negative = short
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# Magnitude: |normalized slope| / volatility = trend strength vs noise
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trend_strength = norm_slope / (norm_vol + mbt.lit(1e-12))
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# Final signal: direction * conviction, gated by R² threshold
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# Clamp to [-1, 1] range via division by expected max
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raw_signal = mbt.when(
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has_conviction,
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trend_strength * r2 * mbt.lit(0.1), # scale down
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0.0,
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)
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# -- Strategy ------------------------------------------------------------------
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strategy = (
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mbt.Strategy.create("linreg_trend")
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.signal("slope", norm_slope)
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.signal("r2", r2)
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.signal("fitted", fitted)
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.signal("trend_strength", trend_strength)
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.size(raw_signal)
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.trailing_stop(pct=2.0)
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.describe(
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"Rolling OLS regression: trade strong linear trends (high R²), "
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"size by slope strength * confidence, trailing stop exit"
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)
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)
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# -- Config --------------------------------------------------------------------
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start, end = time_range("2022-01-01", "2025-01-01")
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config = mbt.BacktestConfig(
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universe=[1, 2],
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.days(1),
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initial_capital=10_000,
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execution=mbt.ExecutionConfig(
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allow_short=True,
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max_position_pct=0.5,
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),
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fees=mbt.FeeConfig.binance_perps(),
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slippage=Slippage.fixed_bps(2),
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warmup_bars=20,
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)
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# -- Run -----------------------------------------------------------------------
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if __name__ == "__main__":
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root = os.path.join(os.path.dirname(__file__), "..")
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store = mbt.DataStore(
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data_root=os.path.abspath(os.path.join(root, "data")),
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metadata_db=os.path.abspath(os.path.join(root, "metadata", "metadata.sqlite")),
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)
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t0 = time.perf_counter()
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result = mbt.run(strategy, config, store)
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elapsed = time.perf_counter() - t0
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print(result.summary())
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print(f"\nElapsed: {elapsed:.3f}s")
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mbt.plot.summary(result, show=True)
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