Initial commit: manifoldbt public repo

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

examples/10_monte_carlo.py

@@ -0,0 +1,67 @@
+"""Monte Carlo Simulation -- confidence intervals on equity paths (Pro).
+
+Demonstrates:
+  - py_run_monte_carlo() for bootstrapped equity paths
+  - Monte Carlo fan chart visualization
+  - Risk metrics from simulated distributions
+
+Usage:
+    python examples/10_monte_carlo.py
+"""
+import os
+import time
+import manifoldbt as mbt
+from manifoldbt.indicators import close, ema
+from manifoldbt.helpers import time_range, Slippage, Interval
+
+# -- Strategy -----------------------------------------------------------------
+fast = ema(close, 12)
+slow = ema(close, 26)
+
+trend = fast - slow
+
+strategy = (
+    mbt.Strategy.create("mc_ema_cross")
+    .signal("fast", fast)
+    .signal("slow", slow)
+    .signal("trend", trend)
+    .size(mbt.when(trend > 0.0, 0.5, 0.0))
+    .stop_loss(pct=3.0)
+    .describe("EMA crossover for Monte Carlo analysis")
+)
+
+# -- Config -------------------------------------------------------------------
+start, end = time_range("2021-01-01", "2025-01-01")
+
+config = mbt.BacktestConfig(
+    universe=[1],
+    time_range_start=start,
+    time_range_end=end,
+    bar_interval=Interval.hours(12),
+    initial_capital=10_000,
+    execution=mbt.ExecutionConfig(
+        allow_short=False,
+        max_position_pct=0.5,
+    ),
+    fees=mbt.FeeConfig.binance_perps(),
+    slippage=Slippage.fixed_bps(2),
+    warmup_bars=30,
+)
+
+# -- 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")),
+    )
+
+    # 1. Run base backtest
+    print("Running base backtest...")
+    t0 = time.perf_counter()
+    result = mbt.run(strategy, config, store)
+    print(result.summary())
+    print(f"Elapsed: {time.perf_counter() - t0:.3f}s\n")
+
+    # 2. Monte Carlo fan chart
+    mbt.plot.monte_carlo(result, n_simulations=10000, seed=42, show=True)