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https://github.com/FXDavid-OffbeatForex/MBT.git
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FXDavidandClaude Sonnet 4.6
3490441649
- Add MIT LICENSE - Anonymize broker path in config.example.yaml; add slippage_points + risk_per_trade - Fix encoding ansi→cp1252 in signals.py (cross-platform safe) - Fix install.py silently ignoring pip errors - Cap get_ohlcv at 2000 bars; add ping tool; clarify HTML report UX in descriptions - Escape symbol/timeframe in HTML report to prevent malformed output - Move run_*.py to scripts/; move example CSVs to examples/ - Update .gitignore to block accidental CSV commits from root - README: Python 3.9+ requirement, Chart.js internet note, new sections for examples/ and scripts/ Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
82 lines
3.3 KiB
Python
82 lines
3.3 KiB
Python
"""
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Test the Kaufman Efficiency Ratio filter on top of ADX>35, honest train/test.
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ER over N bars = |close[t] - close[t-N]| / sum(|close[i]-close[i-1]|).
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~1 = clean trend, ~0 = chop. Cutoff derived from TRAIN only, applied to TEST.
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ER is computed from price (signals unchanged), matched to each signal's bar by
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price (robust to the server/UTC time offset).
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"""
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import csv
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from datetime import datetime
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import MetaTrader5 as mt5
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from core.connection import load_config, signal_file_path
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from core.signals import load_signals
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from core.backtest import run_backtest, report_to_dict
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ER_PERIOD = 20
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ADX_MIN = 35.0
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SYMBOL = "GOLD"
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cfg = load_config()
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mt5.initialize(path=cfg["mt5_path"])
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mt5.symbol_select(SYMBOL, True)
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rates = mt5.copy_rates_from_pos(SYMBOL, mt5.TIMEFRAME_H1, 0, 60000)
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closes = [float(r["close"]) for r in rates]
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mt5.shutdown()
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# map close (rounded to cent) -> list of indices, to locate each signal's bar
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from collections import defaultdict
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idx_by_close = defaultdict(list)
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for i, c in enumerate(closes):
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idx_by_close[round(c, 2)].append(i)
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def efficiency_at(entry_price):
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"""ER over the ER_PERIOD bars ending at the bar whose close == entry."""
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cands = idx_by_close.get(round(entry_price, 2), [])
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if not cands:
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return None
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idx = cands[len(cands)//2] # any matching bar; ER is local, exact bar ~equivalent
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if idx < ER_PERIOD:
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return None
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net = abs(closes[idx] - closes[idx - ER_PERIOD])
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path = sum(abs(closes[j] - closes[j-1]) for j in range(idx - ER_PERIOD + 1, idx + 1))
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return (net / path) if path > 0 else 0.0
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# adx + er per signal time
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COLS = ["time","direction","entry","sl","tp","atr_pips","regime",
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"adx","mom_fast","mom_med","mom_slow","vol_ratio","vpt_dir","near_level"]
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adx_by_time, er_by_time = {}, {}
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with open(signal_file_path(), encoding="ansi") as f:
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for r in csv.reader(f):
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if not r or len(r) < 12 or not r[0][:4].isdigit(): continue
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d = dict(zip(COLS, r))
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try: dt = datetime.strptime(d["time"], "%Y.%m.%d %H:%M")
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except ValueError: continue
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adx_by_time[dt] = float(d["adx"])
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er_by_time[dt] = efficiency_at(float(d["entry"]))
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signals = sorted(load_signals(), key=lambda s: s.time)
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split = int(len(signals) * 0.70)
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train, test = signals[:split], signals[split:]
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# ER cutoff from TRAIN signals that pass ADX>35 (median = above-typical efficiency)
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train_ers = sorted(er_by_time[s.time] for s in train
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if adx_by_time.get(s.time,0) > ADX_MIN and er_by_time.get(s.time) is not None)
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cutoff = train_ers[len(train_ers)//2]
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print(f"ER period={ER_PERIOD}, ADX>{ADX_MIN:.0f}; ER cutoff (train median) = {cutoff:.3f}\n")
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def show(label, sigs):
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if not sigs:
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print(f"{label:<34} (none)"); return
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d = report_to_dict(run_backtest(signals=sigs))
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print(f"{label:<34} trades={d['trades_taken']:>4} WR={d['win_rate_pct']:>5}% "
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f"PF={d['profit_factor']:>5} net={d['net_r']:>7}R maxDD={d['max_drawdown_r']:>6}R")
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for label, subset in [("TRAIN", train), ("TEST", test)]:
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print(f"=== {label} ===")
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adx = [s for s in subset if adx_by_time.get(s.time,0) > ADX_MIN]
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adx_er = [s for s in adx if er_by_time.get(s.time) is not None and er_by_time[s.time] > cutoff]
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show(" ADX>35 (current best)", adx)
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show(" ADX>35 + ER>cutoff", adx_er)
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print()
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