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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
2.9 KiB
Python
82 lines
2.9 KiB
Python
"""
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Higher-timeframe trend-alignment filter on top of ADX>35, honest train/test.
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Rule: take a signal only if its direction agrees with the DAILY trend
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(daily close vs daily SMA50), using only completed daily bars strictly BEFORE
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the signal's day (no look-ahead). SMA period fixed at 50 (not tuned).
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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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ADX_MIN = 35.0
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SMA_PER = 50
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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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d1 = mt5.copy_rates_from_pos(SYMBOL, mt5.TIMEFRAME_D1, 0, 4000)
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mt5.shutdown()
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# daily date -> trend_up (close > SMA50), using that day's own close/SMA
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ddates, dclose = [], []
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for r in d1:
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ddates.append(datetime.fromtimestamp(int(r["time"])).date())
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dclose.append(float(r["close"]))
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trend_up_by_date = {}
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for i in range(len(dclose)):
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if i < SMA_PER: continue
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sma = sum(dclose[i-SMA_PER+1:i+1]) / SMA_PER
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trend_up_by_date[ddates[i]] = dclose[i] > sma
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sorted_dates = sorted(trend_up_by_date)
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def htf_up_before(sig_date):
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"""Daily trend from the last completed day strictly before sig_date."""
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import bisect
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j = bisect.bisect_left(sorted_dates, sig_date) - 1
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if j < 0: return None
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return trend_up_by_date[sorted_dates[j]]
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# adx per signal
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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 = {}
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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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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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def aligned(s):
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up = htf_up_before(s.time.date())
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if up is None: return False
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return (s.direction == "LONG" and up) or (s.direction == "SHORT" and not up)
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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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print(f"Daily SMA{SMA_PER} trend alignment on top of ADX>{ADX_MIN:.0f}\n")
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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_htf = [s for s in adx if aligned(s)]
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show(" ADX>35 (current best)", adx)
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show(" ADX>35 + daily-trend", adx_htf)
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print()
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