Initial commit: manifoldbt public repo

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

benchmarks/bench_backtrader_only.py

@@ -0,0 +1,86 @@
+"""Standalone backtrader benchmark - EMA(12/26) crossover on synthetic 1m data."""
+import argparse
+import time
+import numpy as np
+import pandas as pd
+import backtrader as btdr
+
+
+def generate_ohlcv(rows, seed=42):
+    rng = np.random.default_rng(seed)
+    returns = rng.normal(0.0, 0.0003, size=rows)
+    mid = 100.0 * np.exp(np.cumsum(returns))
+    noise = rng.uniform(0.0001, 0.001, size=rows) * mid
+    timestamps = pd.date_range("2022-01-01", periods=rows, freq="1min", tz="UTC")
+    return pd.DataFrame({
+        "timestamp": timestamps,
+        "open": mid + rng.uniform(-0.5, 0.5, size=rows) * noise,
+        "high": mid + noise,
+        "low": mid - noise,
+        "close": mid + rng.uniform(-0.5, 0.5, size=rows) * noise,
+        "volume": rng.uniform(100, 10_000, size=rows),
+    })
+
+
+class EmaCross(btdr.Strategy):
+    def __init__(self):
+        self.fast = btdr.indicators.EMA(self.data.close, period=12)
+        self.slow = btdr.indicators.EMA(self.data.close, period=26)
+        self.crossover = btdr.indicators.CrossOver(self.fast, self.slow)
+
+    def next(self):
+        if self.crossover > 0:
+            self.order_target_percent(target=0.5)
+        elif self.crossover < 0:
+            self.close()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--rows", type=int, default=500_000)
+    parser.add_argument("--runs", type=int, default=5)
+    args = parser.parse_args()
+
+    print(f"Generating {args.rows:,} synthetic 1-min bars...")
+    df = generate_ohlcv(args.rows)
+
+    bt_df = df[["timestamp", "open", "high", "low", "close", "volume"]].copy()
+    bt_df = bt_df.rename(columns={"timestamp": "datetime"}).set_index("datetime")
+    bt_df.index = bt_df.index.tz_localize(None)
+
+    # Warmup
+    print("Warmup run...")
+    cerebro = btdr.Cerebro()
+    cerebro.addstrategy(EmaCross)
+    cerebro.adddata(btdr.feeds.PandasData(dataname=bt_df))
+    cerebro.broker.set_cash(10_000)
+    cerebro.broker.setcommission(commission=0.0005)
+    cerebro.run()
+
+    # Timed runs
+    print(f"Running {args.runs}x timed...")
+    times = []
+    for i in range(args.runs):
+        cerebro = btdr.Cerebro()
+        cerebro.addstrategy(EmaCross)
+        cerebro.adddata(btdr.feeds.PandasData(dataname=bt_df))
+        cerebro.broker.set_cash(10_000)
+        cerebro.broker.setcommission(commission=0.0005)
+
+        t0 = time.perf_counter()
+        cerebro.run()
+        elapsed = time.perf_counter() - t0
+        times.append(elapsed)
+        print(f"  run {i+1}: {elapsed*1000:.1f} ms")
+
+    med = np.median(times)
+    avg = np.mean(times)
+    print(f"\nbacktrader results ({args.rows:,} bars):")
+    print(f"  median = {med*1000:.1f} ms")
+    print(f"  mean   = {avg*1000:.1f} ms")
+    print(f"  min    = {min(times)*1000:.1f} ms")
+    print(f"  max    = {max(times)*1000:.1f} ms")
+
+
+if __name__ == "__main__":
+    main()