Initial commit: manifoldbt public repo

Python DSL, examples, docs, benchmarks, and tests.
Rust engine distributed as pre-compiled wheel via PyPI.

examples/04_linear_regression.py

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