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release: v0.3.0
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@@ -42,13 +42,14 @@ 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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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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output_resolution=Interval.hours(1),
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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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@@ -0,0 +1,145 @@
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"""Stochastic Simulation -- synthetic price paths via SDE expression DSL.
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Demonstrates:
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- Built-in presets (GBM, Heston, Merton, GARCH-JD)
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- Custom SDE model via string expressions
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- Stochastic fan chart visualization
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- CUDA GPU acceleration (device="cuda")
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- All expressions compile to native Rust — full Rayon / CUDA parallelism
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Usage:
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python examples/13_stochastic_simulation.py
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"""
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import time
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import manifoldbt as mbt
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N = 10_000_000 # 10M paths
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DEVICE = "cuda" # "cpu" or "cuda"
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if __name__ == "__main__":
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# ── 1. Geometric Brownian Motion (preset) ───────────────────────────────
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print(f"1. GBM preset ({N:,} paths, 252 steps) [{DEVICE}]")
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t0 = time.perf_counter()
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result = mbt.run_stochastic(
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"gbm",
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s0=100.0,
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n_paths=N,
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n_steps=252,
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dt=1 / 252,
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params={"mu": 0.05, "sigma": 0.20},
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seed=42,
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device=DEVICE,
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)
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elapsed = time.perf_counter() - t0
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print(f" Mean final price: {result['final_price']['mean']:.2f}")
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print(f" Median max DD: {result['max_drawdown']['percentiles'][3][1]:.2%}")
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print(f" Elapsed: {elapsed:.3f}s\n")
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# ── 2. Heston stochastic volatility (preset) ────────────────────────────
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print(f"2. Heston preset ({N:,} paths) [{DEVICE}]")
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t0 = time.perf_counter()
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result = mbt.run_stochastic(
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"heston",
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s0=100.0,
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n_paths=N,
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n_steps=252,
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dt=1 / 252,
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params={"mu": 0.05, "kappa": 2.0, "theta": 0.04, "xi": 0.3},
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seed=42,
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device=DEVICE,
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)
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elapsed = time.perf_counter() - t0
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print(f" Mean final price: {result['final_price']['mean']:.2f}")
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print(f" Ann. vol (mean): {result['annualized_vol']['mean']:.2%}")
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print(f" Elapsed: {elapsed:.3f}s\n")
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# ── 3. Merton Jump Diffusion (preset) ───────────────────────────────────
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print(f"3. Merton Jump Diffusion ({N:,} paths) [{DEVICE}]")
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t0 = time.perf_counter()
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result = mbt.run_stochastic(
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"merton",
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s0=100.0,
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n_paths=N,
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n_steps=252,
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dt=1 / 252,
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params={
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"mu": 0.05,
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"sigma": 0.20,
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"lambda": 1.0, # 1 jump/year on average
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"mu_j": -0.05, # mean jump = -5%
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"sigma_j": 0.08, # jump vol = 8%
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},
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seed=42,
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device=DEVICE,
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)
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elapsed = time.perf_counter() - t0
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print(f" Mean final price: {result['final_price']['mean']:.2f}")
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print(f" Elapsed: {elapsed:.3f}s\n")
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# ── 4. Custom GARCH(1,1) Jump Diffusion ─────────────────────────────────
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print(f"4. Custom GARCH(1,1) Jump Diffusion ({N:,} paths) [{DEVICE}]")
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model = mbt.StochasticModel(
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name="my_garch_jd",
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drift="mu",
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diffusion="sqrt(h)",
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jump_intensity="lambda",
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jump_size="normal(mu_j, sigma_j)",
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state_vars={"h": 1e-4},
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state_update={"h": "omega + alpha * (ret - mu) ** 2 + beta * h"},
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params={
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"mu": 0.08,
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"omega": 1e-6,
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"alpha": 0.10,
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"beta": 0.85,
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"lambda": 5.0,
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"mu_j": -0.02,
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"sigma_j": 0.04,
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},
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)
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t0 = time.perf_counter()
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result = mbt.run_stochastic(
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model,
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s0=100.0,
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n_paths=N,
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n_steps=252,
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dt=1 / 252,
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seed=42,
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device=DEVICE,
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)
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elapsed = time.perf_counter() - t0
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print(f" Mean final price: {result['final_price']['mean']:.2f}")
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print(f" Max DD (P5): {result['max_drawdown']['percentiles'][0][1]:.2%}")
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print(f" Elapsed: {elapsed:.3f}s\n")
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# ── 5. Custom mean-reverting model (CPU — store_paths needs RAM) ────────
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N_PLOT = 10_000
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print(f"5. Custom mean-reverting model ({N_PLOT:,} paths) [cpu, store_paths]")
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mean_rev = mbt.StochasticModel(
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name="mean_reverting",
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# Drift pulls price back toward 100
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drift="kappa * (log(100.0) - log(S))",
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diffusion="sigma",
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params={"kappa": 2.0, "sigma": 0.25},
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)
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t0 = time.perf_counter()
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result = mbt.run_stochastic(
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mean_rev,
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s0=80.0, # start below mean
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n_paths=N_PLOT,
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n_steps=252,
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dt=1 / 252,
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seed=42,
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store_paths=True,
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device="cpu",
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)
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elapsed = time.perf_counter() - t0
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print(f" Mean final price: {result['final_price']['mean']:.2f} (target: 100)")
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print(f" Elapsed: {elapsed:.3f}s\n")
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# ── 6. Fan chart visualization ──────────────────────────────────────────
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print("6. Plotting fan chart...")
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mbt.plot.stochastic_paths(
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result,
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title=f"Mean-reverting model (S0=80, target=100, {N_PLOT:,} paths)",
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show=True,
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)
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@@ -0,0 +1,84 @@
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"""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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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 (forward-filled onto 1h grid)
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trend_fast = ema(h12.close, 20)
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trend_slow = ema(h12.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=[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(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,
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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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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.equity(result, show=True)
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