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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.
282 lines
10 KiB
Python
282 lines
10 KiB
Python
"""Full Visualization Suite -- Bollinger Bands 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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Demonstrates every plotting function available in manifoldbt.
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Usage:
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python examples/06_full_visualization.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, bollinger_bands, ema
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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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# -- 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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# 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 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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)
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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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.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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)
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)
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# -- Config -------------------------------------------------------------------
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start, end = time_range("2021-01-01", "2026-01-01")
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ALL_SYMBOLS = list(range(1, 23)) # 22 symbols: BTCUSDT to ARBUSDT
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config = mbt.BacktestConfig(
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universe=ALL_SYMBOLS,
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.minutes(120),
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initial_capital=100_000,
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execution=mbt.ExecutionConfig(
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allow_short=True,
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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.zero(),
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slippage=Slippage.fixed_bps(0),
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warmup_bars=25,
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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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os.makedirs(os.path.join(root, "output"), exist_ok=True)
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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. Single backtest --------------------------------------------------
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print("Running backtest...")
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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"Elapsed: {elapsed:.3f}s\n")
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# -- 2. Tearsheet (3 figures: overview, returns, rolling) ---------------
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print("Generating tearsheet...")
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mbt.plot.tearsheet(
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result, show=True,
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save=os.path.join(root, "output", "tearsheet.png"),
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)
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# -- 3. Summary 3-panel ---------------------------------------------------
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mbt.plot.summary(result, show=True)
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# -- 4. Candlestick chart (symbol_id=1 matches universe) ----------------
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mbt.plot.chart(
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result, store, symbol_id=1,
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emas=[10, 25],
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smas=[50],
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n_bars=120,
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interactive=False,
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show=True,
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)
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# -- 5. Individual charts -------------------------------------------------
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mbt.plot.equity(result, show=True)
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mbt.plot.drawdown(result, show=True)
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mbt.plot.monthly_returns(result, show=True)
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mbt.plot.annual_returns(result, show=True)
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mbt.plot.returns_histogram(result, show=True)
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mbt.plot.var_chart(result, show=True)
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mbt.plot.rolling_sharpe(result, show=True)
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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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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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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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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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)
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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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.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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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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"y_param": "period",
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"x_values": stds,
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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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}
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print(f"Sweep done in {time.perf_counter() - t0:.1f}s")
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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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fold_months = [
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("2024-01-01", "2024-07-01", "2024-07-01", "2024-09-01"),
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("2024-01-01", "2024-08-01", "2024-08-01", "2024-10-01"),
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("2024-01-01", "2024-09-01", "2024-09-01", "2024-11-01"),
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("2024-01-01", "2024-10-01", "2024-10-01", "2024-12-01"),
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("2024-01-01", "2024-11-01", "2024-11-01", "2025-01-01"),
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]
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wf_folds = []
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for train_start, train_end, test_start, test_end in fold_months:
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ts, te = time_range(train_start, train_end)
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train_cfg = mbt.BacktestConfig(
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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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)
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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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)
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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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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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})
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wf_result = {
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"metric": "sharpe",
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"folds": wf_folds,
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}
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print(f"Walk-forward done in {time.perf_counter() - t0:.1f}s")
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mbt.plot.walk_forward(wf_result, show=True)
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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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# -- 9. Parameter stability -----------------------------------------------
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print("\nRunning stability analysis (BB 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_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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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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)
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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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.stop_loss(pct=2.0)
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.take_profit(pct=4.0)
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)
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r = mbt.run(s, config, store)
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stability_metrics.append(r.metrics.get("sharpe", 0.0))
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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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stab_result = {
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"param_name": "period",
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"metric": "sharpe",
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"values": stability_periods,
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"metric_values": stability_metrics,
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"mean_metric": mean_m,
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"std_metric": std_m,
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"stability_score": 1.0 - (std_m / abs(mean_m)) if mean_m != 0 else 0.0,
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}
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print(f"Stability done in {time.perf_counter() - t0:.1f}s")
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mbt.plot.stability(stab_result, show=True)
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# -- 10. Research report (composite) --------------------------------------
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print("\nGenerating research report...")
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mbt.plot.research_report(
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sweep_result=sweep_result,
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wf_result=wf_result,
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stability_result=stab_result,
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show=True,
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save=os.path.join(root, "output", "research.png"),
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)
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print("\nDone — all visualizations generated.")
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print(f"PNGs saved to {os.path.join(root, 'output')}")
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