Initial commit: manifoldbt public repo

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

examples/08_sweep_2d_heatmap.py

@@ -0,0 +1,88 @@
+"""2D Parameter Sweep Heatmap -- EMA crossover t-stat(alpha).
+
+Demonstrates:
+  - param() in indicator periods (engine re-compiles per combo)
+  - run_sweep() for Cartesian grid search
+  - Heatmap visualization with mbt.plot.heatmap_2d()
+
+Usage:
+    python examples/08_sweep_2d_heatmap.py
+"""
+import os
+import time
+import manifoldbt as mbt
+from manifoldbt.indicators import close, ema
+from manifoldbt.helpers import time_range, Slippage, Interval
+
+# -- Strategy (single definition, param() in periods) ------------------------
+fast = ema(close, mbt.param("fast"))
+slow = ema(close, mbt.param("slow"))
+
+signal = mbt.when(fast > slow, 0.25, mbt.when(fast < slow, -0.25, 0.0))
+
+strategy = (
+    mbt.Strategy.create("ema_cross")
+    .signal("fast", fast)
+    .signal("slow", slow)
+    .size(signal)
+)
+
+# -- Config -------------------------------------------------------------------
+start, end = time_range("2021-01-01", "2026-01-01")
+
+config = mbt.BacktestConfig(
+    universe=[1],
+    time_range_start=start,
+    time_range_end=end,
+    bar_interval=Interval.hours(1),
+    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=80,
+    output_resolution=Interval.days(1),
+)
+
+# -- 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")),
+    )
+
+    fast_values = list(range(5, 1000, 5))
+    slow_values = list(range(10, 5000, 5))
+
+    print(f"Running 2D sweep ({len(fast_values)*len(slow_values)} combos)...")
+    t0 = time.perf_counter()
+    batch = mbt.run_sweep_lite(
+        strategy,
+        {"fast": fast_values, "slow": slow_values},
+        config,
+        store,
+    )
+    elapsed = time.perf_counter() - t0
+
+    # run_sweep_lite iterates sorted keys: fast (outer) × slow (inner)
+    # Reshape into grid[slow][fast] for heatmap (y=slow, x=fast)
+    metric_grid = [[0.0] * len(fast_values) for _ in slow_values]
+    idx = 0
+    for fi, f_val in enumerate(fast_values):
+        for si, s_val in enumerate(slow_values):
+            metric_grid[si][fi] = batch[idx].metrics.get("tstat_alpha", 0.0)
+            idx += 1
+
+    print(f"\n{len(batch)} combos in {elapsed:.2f}s")
+
+    mbt.plot.heatmap_2d({
+        "x_param": "fast",
+        "y_param": "slow",
+        "x_values": fast_values,
+        "y_values": slow_values,
+        "metric": "t-stat(alpha)",
+        "metric_grid": metric_grid,
+    }, show=True)