mirror of
https://github.com/manifoldbt/manifoldbt.git
synced 2026-08-24 14:38:04 +00:00
release: v0.2.0
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github-actions[bot]
@@ -74,7 +74,7 @@ config = mbt.BacktestConfig(
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universe=[1, 2],
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.minutes(15),
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bar_interval=Interval.days(1),
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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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@@ -1,10 +1,9 @@
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"""Full Visualization Suite -- Bollinger Bands mean-reversion + all plots.
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"""Full Visualization Suite -- RSI mean-reversion + all plots.
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Strategy:
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- Long when price touches lower band (oversold)
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- Short when price touches upper band (overbought)
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- Size proportional to distance from middle band
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- Stop-loss 2%, take-profit 4%
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- Long when RSI < 30 (oversold)
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- Short when RSI > 70 (overbought)
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- Exit long when RSI > 50, exit short when RSI < 50
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Demonstrates every plotting function available in manifoldbt.
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@@ -14,48 +13,30 @@ Usage:
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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, bollinger_bands, ema
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from manifoldbt.indicators import close, rsi
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from manifoldbt.helpers import time_range, Slippage, Interval
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upper, middle, lower = bollinger_bands(close, period=20, num_std=2.0)
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trend_ema = ema(close, 100)
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# Z-score: how far price is from the mean, normalized by band width
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band_width = upper - lower
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zscore = (close - middle) / (band_width + mbt.lit(1e-12))
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# Trend filter: EMA(100) above close = downtrend (no longs), below = uptrend (no shorts)
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is_uptrend = close > trend_ema
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is_downtrend = close < trend_ema
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rsi_14 = rsi(close, 14)
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# -- Strategy -----------------------------------------------------------------
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# Entry: touch lower band → long (only in uptrend), touch upper band → short (only in downtrend)
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# Exit: long exits at upper band, short exits at lower band
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# Size flips to 0 at opposite band = exit
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# Entry: RSI < 30 → long, RSI > 70 → short
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# Exit: RSI crosses 50
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# Long signal: price near lower band + uptrend
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long_entry = (zscore < -0.5) & is_uptrend
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# Short signal: price near upper band + downtrend
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short_entry = (zscore > 0.5) & is_downtrend
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long_entry = rsi_14 < mbt.lit(30.0)
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short_entry = rsi_14 > mbt.lit(70.0)
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# Long exits at upper band (zscore > 0.5), short exits at lower band (zscore < -0.5)
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# When neither entry nor in opposite-band exit zone → flat (0)
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signal = mbt.when(
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long_entry, 1.0, # long
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mbt.when(short_entry, -1.0, 0.0), # short / flat
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long_entry, 1.0,
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mbt.when(short_entry, -1.0, 0.0),
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)
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strategy = (
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mbt.Strategy.create("Reversion_strategy")
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.signal("upper", upper)
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.signal("lower", lower)
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.signal("ema100", trend_ema)
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.signal("zscore", zscore)
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mbt.Strategy.create("RSI_strategy")
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.signal("rsi14", rsi_14)
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.size(signal * 0.25)
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.describe(
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"Bollinger Bands mean-reversion: long at lower band, short at upper band, "
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"exit at opposite band. EMA(100) trend filter — no shorts in uptrend, "
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"no longs in downtrend."
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"RSI(14) mean-reversion: long when RSI<30, short when RSI>70, "
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"exit when RSI crosses 50."
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)
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)
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@@ -77,7 +58,7 @@ config = mbt.BacktestConfig(
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),
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fees=mbt.FeeConfig.zero(),
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slippage=Slippage.fixed_bps(0),
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warmup_bars=25,
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warmup_bars=20,
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)
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# -- Run ----------------------------------------------------------------------
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@@ -128,49 +109,46 @@ if __name__ == "__main__":
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mbt.plot.rolling_volatility(result, show=True)
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# -- 6. Sweep heatmap 2D -------------------------------------------------
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# Sweep over BB period and num_std by rebuilding strategies
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print("\nRunning 2D sweep (BB period × num_std)...")
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# Sweep over RSI period and oversold threshold
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print("\nRunning 2D sweep (RSI period × oversold threshold)...")
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t0 = time.perf_counter()
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periods = [10, 15, 20, 30]
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stds = [1.5, 2.0, 2.5, 3.0]
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periods = [7, 10, 14, 21]
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thresholds = [20, 25, 30, 35] # oversold level (overbought = 100 - threshold)
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sweep_strategies = []
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for p in periods:
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for ns in stds:
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u, m, l = bollinger_bands(close, period=p, num_std=ns)
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bw = u - l
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zs = (close - m) / (bw + mbt.lit(1e-12))
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up = close > trend_ema
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dn = close < trend_ema
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for thr in thresholds:
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r14 = rsi(close, p)
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ob = mbt.lit(float(100 - thr))
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os_ = mbt.lit(float(thr))
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sig = mbt.when(
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(zs < -0.5) & up, 1.0,
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mbt.when((zs > 0.5) & dn, -1.0, 0.0),
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r14 < os_, 1.0,
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mbt.when(r14 > ob, -1.0, 0.0),
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)
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s = (
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mbt.Strategy.create(f"bb_p{p}_s{ns}")
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.signal("zscore", zs)
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.size(sig * 0.25)
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mbt.Strategy.create(f"rsi_p{p}_t{thr}")
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.signal("rsi", r14)
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.size(sig * 0.05)
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.stop_loss(pct=2.0)
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.take_profit(pct=4.0)
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)
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sweep_strategies.append(s)
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batch_results = mbt.run_batch_lite(sweep_strategies, config, store)
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# Build a sweep_result dict compatible with heatmap_2d
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metric_grid = []
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idx = 0
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for _ in periods:
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row = []
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for _ in stds:
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for _ in thresholds:
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r = batch_results[idx]
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row.append(r.metrics.get("sharpe", 0.0))
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idx += 1
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metric_grid.append(row)
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sweep_result = {
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"x_param": "num_std",
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"x_param": "oversold_thr",
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"y_param": "period",
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"x_values": stds,
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"x_values": thresholds,
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"y_values": periods,
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"metric": "sharpe",
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"metric_grid": metric_grid,
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@@ -179,7 +157,6 @@ if __name__ == "__main__":
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mbt.plot.heatmap_2d(sweep_result, show=True)
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# -- 7. Walk-forward validation -------------------------------------------
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# Manual walk-forward: split 2024 into 5 folds
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print("\nRunning walk-forward (manual folds)...")
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t0 = time.perf_counter()
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@@ -197,22 +174,20 @@ if __name__ == "__main__":
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universe=ALL_SYMBOLS, time_range_start=ts, time_range_end=te,
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bar_interval=Interval.minutes(60), initial_capital=100_000,
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execution=config.execution, fees=config.fees,
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slippage=config.slippage, warmup_bars=25,
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slippage=config.slippage, warmup_bars=20,
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)
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ts2, te2 = time_range(test_start, test_end)
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test_cfg = mbt.BacktestConfig(
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universe=ALL_SYMBOLS, time_range_start=ts2, time_range_end=te2,
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bar_interval=Interval.minutes(60), initial_capital=100_000,
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execution=config.execution, fees=config.fees,
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slippage=config.slippage, warmup_bars=25,
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slippage=config.slippage, warmup_bars=20,
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)
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train_r = mbt.run(strategy, train_cfg, store)
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test_r = mbt.run(strategy, test_cfg, store)
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train_m = train_r.metrics
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test_m = test_r.metrics
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test_r = mbt.run(strategy, test_cfg, store)
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wf_folds.append({
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"train_metric": train_m.get("sharpe", 0.0),
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"test_metric": test_m.get("sharpe", 0.0),
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"train_metric": train_r.metrics.get("sharpe", 0.0),
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"test_metric": test_r.metrics.get("sharpe", 0.0),
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})
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wf_result = {
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@@ -224,28 +199,23 @@ if __name__ == "__main__":
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# -- 8. Monte Carlo -------------------------------------------------------
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print("\nRunning Monte Carlo (1000 paths)...")
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mc_result = mbt.py_run_monte_carlo(result.raw, 1000, 42)
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mbt.plot.monte_carlo(mc_result, show=True)
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mbt.plot.monte_carlo(result, n_simulations=1000, seed=42, show=True)
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# -- 9. Parameter stability -----------------------------------------------
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print("\nRunning stability analysis (BB period)...")
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print("\nRunning stability analysis (RSI period)...")
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t0 = time.perf_counter()
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stability_periods = [10, 12, 15, 18, 20, 25, 30, 40]
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stability_periods = [5, 7, 9, 11, 14, 18, 21, 28]
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stability_metrics = []
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for p in stability_periods:
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u, m, l = bollinger_bands(close, period=p, num_std=2.0)
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bw = u - l
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zs = (close - m) / (bw + mbt.lit(1e-12))
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up = close > trend_ema
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dn = close < trend_ema
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r14 = rsi(close, p)
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sig = mbt.when(
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(zs < -0.5) & up, 1.0,
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mbt.when((zs > 0.5) & dn, -1.0, 0.0),
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r14 < mbt.lit(30.0), 1.0,
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mbt.when(r14 > mbt.lit(70.0), -1.0, 0.0),
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)
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s = (
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mbt.Strategy.create(f"bb_stab_{p}")
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.signal("zscore", zs)
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.size(sig * 0.25)
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mbt.Strategy.create(f"rsi_stab_{p}")
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.signal("rsi", r14)
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.size(sig * 0.05)
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.stop_loss(pct=2.0)
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.take_profit(pct=4.0)
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)
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@@ -254,7 +224,7 @@ if __name__ == "__main__":
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import numpy as np
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mean_m = float(np.mean(stability_metrics))
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std_m = float(np.std(stability_metrics))
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std_m = float(np.std(stability_metrics))
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stab_result = {
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"param_name": "period",
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"metric": "sharpe",
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+1
-1
@@ -1,6 +1,6 @@
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[project]
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name = "manifoldbt"
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version = "0.1.3"
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version = "0.2.0"
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description = "Rust-powered backtesting engine for quantitative research"
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requires-python = ">=3.9"
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license = "MIT"
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@@ -275,12 +275,18 @@ def _resolve_store(config: BacktestConfig, store: DataStore) -> DataStore:
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if target == current:
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return store
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# Try the target dataset; if it doesn't exist (no active version),
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# fall back to bars_1m — the engine will resample automatically.
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try:
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return DataStore(
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candidate = DataStore(
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data_root=store.data_root(),
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metadata_db=store.metadata_db(),
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dataset=target,
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)
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# Verify the dataset actually has an active version
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if candidate.active_version(target) is None:
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return store
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return candidate
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except Exception:
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return store
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