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92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
"""Multi-Timeframe Strategy -- trend on 12h, entry on 1h.
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Demonstrates:
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- bt.tf() for referencing higher-timeframe columns
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- extra_timeframes config to inject resampled OHLCV
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- Combining slow trend filter (12h EMA) with faster entry (1h RSI)
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Logic:
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- 12h trend: EMA(20) > EMA(50) → bullish regime
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- 1h entry: RSI(14) < 35 during bullish regime → buy the dip
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- Size: 50% of initial capital when conditions met, else flat
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Data: shared store — real market data from `data/` (see examples/README.md)
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Usage:
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python examples/14_multi_timeframe.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 ema, rsi, close
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from manifoldbt.helpers import time_range, Slippage, Interval
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# -- Higher timeframe references ---------------------------------------------
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h12 = mbt.tf("12h") # references columns like "12h.close"
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# -- Indicators ---------------------------------------------------------------
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# Trend filter on 12-hour bars. `apply()` evaluates the EMA on the 12h grid, so
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# 20 and 50 count 12-HOUR candles. Written `ema(h12.close, 20)` they would count
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# 20 rows of the 1h simulation grid over a step-held column -- under two 12h
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# candles, not twenty. See `bt.tf`.
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trend_fast = h12.apply(ema(close, 20))
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trend_slow = h12.apply(ema(close, 50))
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bullish = trend_fast > trend_slow
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# Entry signal on 1-hour bars (native resolution)
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entry_rsi = rsi(close, 14)
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dip = entry_rsi < 35.0
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# -- Strategy -----------------------------------------------------------------
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strategy = (
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mbt.Strategy.create("multi_tf_trend_dip")
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.signal("bullish", bullish)
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.signal("entry_rsi", entry_rsi)
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.signal("dip", dip)
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.size(mbt.when(mbt.col("bullish") & mbt.col("dip"), 0.5, 0.0))
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.stop_loss(pct=3.0)
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.describe("12h EMA trend + 1h RSI dip-buy, 3% stop-loss")
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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={"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(1),
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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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position_sizing_mode="FractionOfInitialCapital",
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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=50 * 12, # 50 twelve-hour candles, counted in 1h simulation bars
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extra_timeframes={
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"12h": Interval.hours(12),
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},
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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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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.equity(result)
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