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https://github.com/manifoldbt/manifoldbt.git
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Jimmy7892
67ab17280b
Python DSL, examples, docs, benchmarks, and tests. Rust engine distributed as pre-compiled wheel via PyPI.
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
"""Diagnostics -- look-ahead bias detection and exposure stability checks.
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Demonstrates:
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- detect_lookahead(): split-test for look-ahead bias
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- check_exposure_stability(): verify positions are consistent across time windows
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- risk_check(): post-run risk metrics validation
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Usage:
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python examples/12_diagnostics.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 -----------------------------------------------------------------
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fast = ema(close, 12)
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slow = ema(close, 50)
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signal = mbt.when(fast > slow, 0.5, 0.0)
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strategy = (
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mbt.Strategy.create("ema_trend")
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.signal("fast", fast)
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.signal("slow", slow)
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.size(signal)
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.stop_loss(pct=3.0)
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)
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# -- Config -------------------------------------------------------------------
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start, end = time_range("2022-01-01", "2025-01-01")
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config = mbt.BacktestConfig(
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universe=[1],
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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=False,
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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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store = mbt.DataStore(
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data_root=os.path.abspath(os.path.join(root, "data")),
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metadata_db=os.path.abspath(os.path.join(root, "metadata", "metadata.sqlite")),
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)
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# -- 1. Look-ahead bias detection -----------------------------------------
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# Splits the time range and compares trades from shorter runs against
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# the full run. If trades differ, the strategy uses future data.
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print("1. Look-ahead bias detection")
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print("-" * 40)
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t0 = time.perf_counter()
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lookahead = mbt.diagnostics.detect_lookahead(strategy, config, store)
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print(lookahead)
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print(f" Elapsed: {time.perf_counter() - t0:.2f}s\n")
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# -- 2. Exposure stability -------------------------------------------------
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# Verifies that utilization and per-symbol exposure are identical
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# across different time windows. Catches position sizing that leaks
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# future data (e.g. z-score over the entire series).
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print("2. Exposure stability")
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print("-" * 40)
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t0 = time.perf_counter()
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stability = mbt.diagnostics.check_exposure_stability(strategy, config, store)
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print(stability)
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print(f" Elapsed: {time.perf_counter() - t0:.2f}s\n")
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# -- 3. Backtest + risk check ----------------------------------------------
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# Run the strategy, then validate risk metrics against thresholds.
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print("3. Backtest + risk check")
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print("-" * 40)
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t0 = time.perf_counter()
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result = mbt.run(strategy, config, store)
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print(result.summary())
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print(f" Elapsed: {time.perf_counter() - t0:.2f}s\n")
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risk = mbt.diagnostics.risk_check(result)
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print("Risk check:")
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print(risk)
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