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Jimmy7892
67ab17280b
Python DSL, examples, docs, benchmarks, and tests. Rust engine distributed as pre-compiled wheel via PyPI.
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
"""Statistical Arbitrage -- spread z-score vs ETH anchor.
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Demonstrates:
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- symbol_ref() for cross-asset signals
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- Kalman filter for spread equilibrium
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- Z-score mean-reversion sizing
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Usage:
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python examples/05_stat_arb.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, kalman
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from manifoldbt.helpers import time_range, Slippage, Interval
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# -- Spread construction ------------------------------------------------------
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pair_close = mbt.symbol_ref("ETHUSDT", "close")
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ratio = close / (pair_close + mbt.lit(1e-12))
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# -- Kalman equilibrium -------------------------------------------------------
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equilibrium = kalman(ratio, q=1e-4, r=1e-2)
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spread = ratio - equilibrium
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# -- Z-score signal -----------------------------------------------------------
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spread_z = spread.zscore(28)
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signal = -spread_z # mean-revert: short when z > 0, long when z < 0
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# -- Strategy -----------------------------------------------------------------
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strategy = (
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mbt.Strategy.create("stat_arb")
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.signal("pair_close", pair_close)
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.signal("spread", spread)
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.signal("spread_z", spread_z)
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.signal("signal", signal)
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.size(mbt.col("signal"))
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.describe("Spread z-score mean reversion vs ETH")
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)
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# -- Config -------------------------------------------------------------------
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start, end = time_range("2022-01-01", "2026-01-01")
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config = mbt.BacktestConfig(
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universe=[1, 2, 5], # BTC, ETH, BNB
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.hours(24),
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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=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=30,
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symbol_names={"BTCUSDT": 1, "ETHUSDT": 2, "BNBUSDT": 5},
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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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t0 = time.perf_counter()
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result = mbt.run(strategy, config, store)
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elapsed = time.perf_counter() - t0
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print(result.summary())
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print(f"\nElapsed: {elapsed:.3f}s")
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mbt.plot.summary(result, show=True)
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