Initial commit: Oil Trading Bot (MT5, WTI)
Headless FastAPI-Backend (server.py + core/engine.py) mit Mobile-PWA (web/), Strategie-/Backtest-Suite und Doku. Secrets, DB, Logs und Laufzeit-State sind via .gitignore ausgeschlossen; Config-Vorlage: oil_widget_config.ini.example. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Axel Hocks
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#!/usr/bin/env python3
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"""B4-Wochenreport (Strategie-Lebenszyklus, s. CLAUDE.md Track B).
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Hält die LIVE-Zahlen (trades-DB) gegen die BACKTEST-Erwartung und flaggt die bekannten
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Leckagen (Sizing-Asymmetrie, Gegen-Signal-Trades, Winner-Kappen, Regime-Verfall).
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Aufruf: python weekly_review.py (letzte 6 Wochen + diese Woche im Detail)
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Zeitspalten der DB sind LOKALE Epoch (Broker-Offset bereits korrigiert).
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"""
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import sqlite3, datetime as dt
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DB = "oil_widget_history.db"
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# ── Backtest-Erwartung (R-basiert, exit-sim; s. CLAUDE.md / backtest_*.py) ──
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EXP_WR = 0.68 # Treffer ~68–71 % (Reversal/Trend, SL2+Trail)
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EXP_PF = 1.30 # Profit-Faktor in R
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EXP_VERH = 0.55 # Ø-Gewinn/Ø-Verlust in R (WR hoch → Wins kleiner, trotzdem PF>1)
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def net(r): return (r["pnl"] or 0.0) + (r["commission"] or 0.0)
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def d_of(r): return "LONG" if r["direction"] in ("BUY","LONG","buy") else "SHORT"
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def align(r):
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rs = (r["rec_signal"] or "WARTEN").upper()
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if rs in ("LONG","SHORT"): return "mit" if rs == d_of(r) else "gegen"
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return "ohne"
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def agg(rows):
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v = [net(r) for r in rows]
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if not v: return None
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n = len(v); wins = [x for x in v if x > 0]; loss = [x for x in v if x < 0]
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w = len(wins); g = sum(wins); ls = -sum(loss)
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aw = g / w if w else 0.0; al = ls / len(loss) if loss else 0.0
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return dict(n=n, wr=w/n, pf=(g/ls if ls > 0 else 99.9), sum=sum(v),
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aw=aw, al=al, verh=(aw/al if al > 0 else 99.9), worst=min(v))
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def line(lbl, s):
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if not s: return f" {lbl:<14} —"
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return (f" {lbl:<14} n={s['n']:>3} WR={100*s['wr']:>3.0f}% PF={s['pf']:>4.2f} "
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f"Verh={s['verh']:>4.2f} Øgew={s['aw']:>+5.2f} Øverl={s['al']:>+6.2f} "
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f"Σ={s['sum']:>+8.2f} Worst={s['worst']:>+7.2f}")
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def _squeeze_section(m5) -> list:
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"""B4-Monitor für den Volatilitäts-Squeeze-Breakout: rechnet die Regel mit den
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LIVE-Params auf den letzten ~30 Tagen M5 NEU (der Setup ist mechanisch → seine
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Live-Performance = Neuberechnung auf frischen Bars, keine Trade-Attribution nötig)
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und hält ØR/PF gegen die Backtest-Erwartung (+0,14…+0,23). m5 = {H,L,C,SP} oder None."""
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if not m5 or len(m5.get("C") or []) < 300:
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return ["", "SQUEEZE-BREAKOUT (B4): keine M5-Daten — übersprungen."]
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try:
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import backtest_breakout_squeeze as BQ
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from core.wave_rec import _SQ_N, _SQ_MULT, _SQ_K
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BQ._N = _SQ_N; BQ._K = _SQ_K # live-Params in den Scanner
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H, L, C, SP = m5["H"], m5["L"], m5["C"], m5["SP"]
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A = BQ._atr_series(H, L, C)
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sq, _ = BQ.scan(H, L, C, A, SP, 1.0, 0, len(C), _SQ_MULT) # SP schon in Preis
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out = ["", "-" * 92,
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f"SQUEEZE-BREAKOUT (B4-Monitor · ~30 Tage M5 neu gerechnet · live-Params "
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f"Box={_SQ_N}/k={_SQ_K}/≤{_SQ_MULT}×ATR):"]
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n = len(sq)
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if n == 0:
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out.append(" keine Squeeze-Ausbrüche im Fenster — weiter beobachten.")
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return out
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w = sum(1 for x in sq if x > 0); s = sum(sq)
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up = sum(x for x in sq if x > 0); dn = -sum(x for x in sq if x < 0)
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pf = up / dn if dn > 0 else 99.9; oR = s / n
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out.append(f" n={n} Treffer={100*w/n:.0f}% ØR={oR:+.3f} PF={pf:.2f} ΣR={s:+.0f}"
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f" (Erwartung ØR +0,14…+0,23 · PF>1)")
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if oR <= 0 or pf < 1.0:
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out.append(" (!) ØR≤0/PF<1 im Fenster → Edge driftet? B5 prüfen (Regimewechsel). "
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"⚠ kleine Stichprobe, nicht überreagieren.")
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elif n < 6:
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out.append(" ⚠ noch wenige Trades — nicht aussagekräftig, weiter beobachten.")
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else:
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out.append(" [OK] im Rahmen der Erwartung — Setup trägt live.")
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return out
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except Exception as e:
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return ["", f"SQUEEZE-BREAKOUT (B4): Fehler ({e})"]
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def _fetch_m5(days=30):
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"""M5-Bars für den Squeeze-Monitor (nur STANDALONE — schließt MT5 nur, wenn WIR
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es initialisiert haben; die Server-Instanz reicht die Bars direkt in build_report)."""
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try:
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import MetaTrader5 as mt5
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pre = mt5.terminal_info() is not None # schon vom Server initialisiert?
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if not pre and not mt5.initialize():
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return None
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try:
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sym = next((c for c in ("SpotCrude", "USOIL", "WTI", "XTIUSD")
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if mt5.symbol_info(c)), None)
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if not sym:
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return None
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bars = mt5.copy_rates_from_pos(sym, mt5.TIMEFRAME_M5, 0, days * 288 + 30)
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point = mt5.symbol_info(sym).point
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finally:
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if not pre:
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mt5.shutdown()
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if bars is None:
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return None
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return {"H": [float(b["high"]) for b in bars], "L": [float(b["low"]) for b in bars],
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"C": [float(b["close"]) for b in bars],
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"SP": [float(b["spread"]) * point for b in bars]}
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except Exception:
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return None
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def build_report(db=DB, m5=None) -> str:
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"""Baut den B4-Wochenreport als reinen Text (für Konsole, Mail-<pre>, Telegram).
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m5 = vorab geholte M5-Bars für den Squeeze-Monitor (Server reicht sie unter
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mt5_lock durch); None → Squeeze-Abschnitt versucht selbst zu laden (standalone)."""
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if m5 is None:
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m5 = _fetch_m5()
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out = []; P = out.append
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c = sqlite3.connect(db); c.row_factory = sqlite3.Row
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rows = [r for r in c.execute(
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"SELECT * FROM trades WHERE exit_time IS NOT NULL ORDER BY entry_time")]
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now = dt.datetime.now()
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def wkey(ts): return dt.datetime.fromtimestamp(ts).isocalendar()[:2] # (jahr,kw)
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thisk = now.isocalendar()[:2]
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P("=" * 92)
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P(f" B4-WOCHENREPORT · {now:%d.%m.%Y %H:%M} · Live (EUR netto) vs Backtest-Erwartung")
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P(f" Erwartung: WR~{100*EXP_WR:.0f}% · PF~{EXP_PF:.2f} · Verhältnis~{EXP_VERH:.2f} (R-basiert)")
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P("=" * 92)
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from collections import OrderedDict
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byw = OrderedDict()
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for r in rows: byw.setdefault(wkey(r["entry_time"]), []).append(r)
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P("\nWochenverlauf (Verfall-Check):")
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for (yr, kw), rs in list(byw.items())[-6:]:
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tag = " <- diese Woche" if (yr, kw) == thisk else ""
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P(line(f"KW{kw}/{yr%100}", agg(rs)) + tag)
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tw = byw.get(thisk, [])
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P(f"\nDiese Woche — nach Empfehlung (n={len(tw)}):")
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for k in ("mit", "gegen", "ohne"):
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P(line(k, agg([r for r in tw if align(r) == k])))
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P("\nGesamt (alle Trades):")
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P(line("alle", agg(rows)))
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recent = [r for (yr, kw), rs in list(byw.items())[-4:] for r in rs]
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s = agg(recent)
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P("\n" + "-" * 92)
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P(f"BEWERTUNG (letzte 4 Wochen, n={s['n'] if s else 0}):")
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if s:
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def mark(ok): return "[OK]" if ok else "[!]"
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P(f" {mark(s['wr']>=EXP_WR-0.08)} Trefferquote {100*s['wr']:.0f}% (erwartet ~{100*EXP_WR:.0f}%)")
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P(f" {mark(s['pf']>=1.0)} Profit-Faktor {s['pf']:.2f} (erwartet ~{EXP_PF:.2f}; <1 = verlustbringend)")
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P(f" {mark(s['verh']>=EXP_VERH-0.10)} Verhältnis {s['verh']:.2f} (erwartet ~{EXP_VERH:.2f})")
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flags = []
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gg = agg([r for r in recent if align(r) == "gegen"])
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share_gg = (gg["n"]/s["n"]) if gg else 0
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if share_gg > 0.12:
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flags.append(f"(!) {100*share_gg:.0f}% GEGEN-Signal-Trades (dort WR ~14 % — meiden!)")
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if s["verh"] < EXP_VERH - 0.10:
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flags.append("(!) Verhältnis unter Erwartung → Gewinner zu klein (Kappen) oder Verlierer zu groß (Sizing 90 %)")
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if s["wr"] < EXP_WR - 0.10:
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flags.append("(!) Treffer unter Erwartung → Regime-Verfall ODER zu viele Nicht-Setup-Trades")
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if s["pf"] < 1.0:
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flags.append("(!) PF<1 = Verlustphase → B5 prüfen: Regimewechsel? Sizing? Disziplin?")
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if s["worst"] < -25:
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flags.append(f"(!) Einzelverlust {s['worst']:+.0f} € — 90 %-Margin-Sizing (Memory: risk_pct=1.5)")
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P("\n " + ("\n ".join(flags) if flags else "[OK] keine Leckage-Flags — im Rahmen der Erwartung."))
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P("-" * 92)
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P("Nächster Schritt bei [!]: B5 (behalten/verwerfen) — driftet der Live-Edge weg,")
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P("erst Ursache trennen (Regime vs. Disziplin vs. Sizing), nicht am Signal drehen.")
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for ln in _squeeze_section(m5):
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P(ln)
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return "\n".join(out)
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def main():
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print(build_report())
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if __name__ == "__main__":
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main()
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