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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>
76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
"""
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Feature-outcome analysis: do the indicator's own entry features separate
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winners from losers? Uses the logged condition values (no recalculation) joined
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to each signal's backtested outcome.
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If a feature is predictive, win rate should trend across its buckets.
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If win rate is flat across every feature, the entries have no edge to tune.
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"""
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import csv
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from datetime import datetime
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from core.connection import signal_file_path
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from core.backtest import run_backtest
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# 1. Outcomes per signal (independent mode = every signal evaluated, baseline exits)
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rep = run_backtest(sequential=False)
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outcome_by_time = {t.time.strftime("%Y-%m-%d %H:%M"): t.outcome for t in rep.trades}
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# 2. Raw logged feature values
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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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rows = []
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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: continue
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if not r[0][:4].isdigit(): continue # skip header
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d = dict(zip(COLS, r))
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try:
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t = datetime.strptime(d["time"], "%Y.%m.%d %H:%M").strftime("%Y-%m-%d %H:%M")
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except ValueError:
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continue
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oc = outcome_by_time.get(t)
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if oc not in ("WIN","LOSS"): continue
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rows.append({
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"win": 1 if oc == "WIN" else 0,
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"direction": d["direction"],
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"adx": float(d["adx"]),
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"mom_fast": abs(float(d["mom_fast"])), # magnitude in signal direction
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"mom_med": abs(float(d["mom_med"])),
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"mom_slow": abs(float(d["mom_slow"])),
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"vol_ratio": float(d["vol_ratio"]),
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"hour": int(d["time"][11:13]),
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})
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n = len(rows)
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base_wr = sum(r["win"] for r in rows) / n * 100
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print(f"signals analysed: {n} baseline win rate: {base_wr:.1f}%\n")
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def buckets(feature, k=5):
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vals = sorted(r[feature] for r in rows)
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cuts = [vals[int(len(vals)*i/k)] for i in range(1, k)]
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def b(v):
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for i, c in enumerate(cuts):
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if v <= c: return i
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return k-1
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groups = [[] for _ in range(k)]
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for r in rows:
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groups[b(r[feature])].append(r["win"])
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print(f"=== {feature} (quintiles, low -> high) ===")
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for i, g in enumerate(groups):
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if not g: continue
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wr = sum(g)/len(g)*100
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lo = "min" if i==0 else f"{cuts[i-1]:.3f}"
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hi = "max" if i==k-1 else f"{cuts[i]:.3f}"
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bar = "#" * int(wr)
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print(f" Q{i+1} [{lo:>7}..{hi:>7}] n={len(g):>4} WR={wr:5.1f}% {bar}")
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print()
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for feat in ["adx","mom_fast","mom_med","mom_slow","vol_ratio","hour"]:
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buckets(feat)
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# direction split
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for dval in ("LONG","SHORT"):
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g = [r["win"] for r in rows if r["direction"]==dval]
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if g:
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print(f"direction {dval}: n={len(g)} WR={sum(g)/len(g)*100:.1f}%")
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