Initial commit: manifoldbt public repo

Python DSL, examples, docs, benchmarks, and tests. Rust engine distributed as pre-compiled wheel via PyPI.
2026-08-24 14:38:04 +00:00 · 2026-03-17 16:15:40 +01:00
commit 67ab17280b
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+"""Walk-Forward Optimization -- find robust parameters across time (Pro).
+
+Demonstrates:
+  - run_walk_forward() with anchored method
+  - param() for sweep-able parameters
+  - Walk-forward fold results inspection
+
+Usage:
+    python examples/07_walk_forward.py
+"""
+import os
+import time
+import manifoldbt as mbt
+from manifoldbt.indicators import close, ema
+from manifoldbt.helpers import time_range, Slippage, Interval
+
+# -- Strategy with tunable parameters ----------------------------------------
+# Indicators use concrete defaults; the Rust sweep engine replaces param()
+# references at runtime with each grid value.
+fast = ema(close, 12)
+slow = ema(close, 26)
+
+signal = mbt.when(fast > slow, 1.0, mbt.when(fast < slow, -1.0, 0.0))
+
+strategy = (
+    mbt.Strategy.create("wfo_ema")
+    .signal("fast", fast)
+    .signal("slow", slow)
+    .size(signal * 0.25)
+    .param("fast", default=12, range=(5, 30))
+    .param("slow", default=26, range=(20, 60))
+    .describe("EMA crossover with walk-forward parameter optimization")
+)
+
+# -- 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=True,
+        max_position_pct=0.5,
+    ),
+    fees=mbt.FeeConfig.binance_perps(),
+    slippage=Slippage.fixed_bps(2),
+    warmup_bars=60,
+)
+
+# -- 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")),
+    )
+
+    wf_config = {
+        "method": "Anchored",
+        "n_splits": 5,
+        "train_ratio": 0.7,
+        "optimize_metric": "sharpe",
+        "param_grid": {
+            "fast": [5, 8, 12, 16, 20],
+            "slow": [25, 35, 50],
+        },
+        "max_parallelism": 0,
+    }
+
+    print("Running walk-forward optimization (Pro)...\n")
+    t0 = time.perf_counter()
+    result = mbt.run_walk_forward(strategy, wf_config, config, store)
+    elapsed = time.perf_counter() - t0
+
+    folds = result.get("folds", [])
+    best_params = result.get("best_params_per_fold", [])
+
+    for i, (fold, params) in enumerate(zip(folds, best_params)):
+        train = fold.get("train_metric", 0)
+        test = fold.get("test_metric", 0)
+        print(f"  Fold {i+1}: train={train:+.3f}  test={test:+.3f}  params={params}")
+
+    print(f"\n{len(folds)} folds in {elapsed:.2f}s")
+
+    if folds:
+        mbt.plot.walk_forward({"metric": "sharpe", "folds": folds}, show=True)