mirror of
https://github.com/manifoldbt/manifoldbt.git
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Jimmy7892
6ba4691a02
- Cross-exchange backtesting (Pro) - Dict universe format (provider-based symbol resolution) - Exogenous data support (register_exo + exo() expressions) - Provider-based data layout (binance/1h/TICKER.arrow) - Preload fix for provider layout - Exo column resampling for multi-resolution - Pro gate for cross-exchange (clean exit) - ATR/ADX rolling SMA fix - Precise mode hybrid fills
92 lines
2.9 KiB
Python
92 lines
2.9 KiB
Python
"""Walk-Forward Optimization -- find robust parameters across time (Pro).
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Demonstrates:
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- run_walk_forward() with anchored method
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- param() for sweep-able parameters
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- Walk-forward fold results inspection
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Usage:
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python examples/07_walk_forward.py
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"""
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import os
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import time
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import manifoldbt as mbt
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from manifoldbt.indicators import close, ema
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from manifoldbt.helpers import time_range, Slippage, Interval
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# -- Strategy with tunable parameters ----------------------------------------
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# Indicators use concrete defaults; the Rust sweep engine replaces param()
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# references at runtime with each grid value.
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fast = ema(close, 12)
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slow = ema(close, 26)
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signal = mbt.when(fast > slow, 1.0, mbt.when(fast < slow, -1.0, 0.0))
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strategy = (
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mbt.Strategy.create("wfo_ema")
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.signal("fast", fast)
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.signal("slow", slow)
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.size(signal * 0.25)
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.param("fast", default=12, range=(5, 30))
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.param("slow", default=26, range=(20, 60))
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.describe("EMA crossover with walk-forward parameter optimization")
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)
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# -- Config -------------------------------------------------------------------
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start, end = time_range("2021-01-01", "2025-01-01")
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config = mbt.BacktestConfig(
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universe={"binance": ["BTC-USDT:perp"]},
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.hours(12),
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initial_capital=10_000,
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execution=mbt.ExecutionConfig(
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allow_short=True,
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max_position_pct=0.5,
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),
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fees=mbt.FeeConfig.binance_perps(),
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slippage=Slippage.fixed_bps(2),
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warmup_bars=60,
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)
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# -- Run ----------------------------------------------------------------------
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if __name__ == "__main__":
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root = os.path.join(os.path.dirname(__file__), "..")
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data_root = os.path.abspath(os.path.join(root, "data"))
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store = mbt.DataStore(
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data_root=data_root,
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metadata_db=os.path.abspath(os.path.join(root, "metadata", "metadata.sqlite")),
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arrow_dir=os.path.join(data_root, "mega"),
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)
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wf_config = {
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"method": "Anchored",
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"n_splits": 5,
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"train_ratio": 0.7,
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"optimize_metric": "sharpe",
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"param_grid": {
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"fast": [5, 8, 12, 16, 20],
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"slow": [25, 35, 50],
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},
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"max_parallelism": 0,
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}
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print("Running walk-forward optimization (Pro)...\n")
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t0 = time.perf_counter()
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result = mbt.run_walk_forward(strategy, wf_config, config, store)
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elapsed = time.perf_counter() - t0
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folds = result.get("folds", [])
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best_params = result.get("best_params_per_fold", [])
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for i, (fold, params) in enumerate(zip(folds, best_params)):
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train = fold.get("train_metric", 0)
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test = fold.get("test_metric", 0)
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print(f" Fold {i+1}: train={train:+.3f} test={test:+.3f} params={params}")
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print(f"\n{len(folds)} folds in {elapsed:.2f}s")
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if folds:
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mbt.plot.walk_forward({"metric": "sharpe", "folds": folds}, show=True)
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