242 lines
8.8 KiB
Python
242 lines
8.8 KiB
Python
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"""
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report.py — Daily P&L report for MoneyMaker dry run.
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Usage:
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python report.py # full P&L report
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python report.py --fees # fee drag analysis only
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python report.py --signals # signal accuracy summary only
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"""
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import sys
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import time
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import json
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import argparse
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from datetime import datetime, timezone
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from pathlib import Path
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import yfinance as yf
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from db import get_conn, DB_TYPE
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C25_PATH = Path(__file__).parent / "c25.json"
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_c25_raw = json.loads(C25_PATH.read_text())
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C25: dict[str, dict] = {k: v for k, v in _c25_raw.items() if not k.startswith("_")}
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CAPITAL = 10_000 # DKK — must match portfolio.py
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def _c25_day_return() -> float | None:
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"""Fetch today's return for OMX C25 CAP index from Yahoo Finance."""
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try:
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info = yf.Ticker("^OMXC25").fast_info
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prev = info.get("previousClose") or info.get("regularMarketPreviousClose")
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last = info.get("lastPrice") or info.get("regularMarketPrice")
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if prev and last and prev > 0:
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return (last - prev) / prev * 100
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except Exception:
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pass
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return None
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def _unrealised_pnl(db) -> tuple[float, list[dict]]:
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"""
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Calculate unrealised P&L for all open positions.
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Returns (total_unrealised_dkk, details_list).
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"""
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positions = db.execute("SELECT * FROM positions").fetchall()
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details = []
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total_unreal = 0.0
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for p in positions:
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ticker = p["ticker"]
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shares = float(p["shares"])
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entry = float(p["entry_price"])
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entry_date = p["entry_date"]
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yf_ticker = C25.get(ticker, {}).get("ticker_yahoo", ticker + ".CO")
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try:
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last = yf.Ticker(yf_ticker).fast_info.get("lastPrice") or entry
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except Exception:
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last = entry
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cost = shares * entry
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value = shares * last
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unreal = value - cost
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pct = (unreal / cost * 100) if cost else 0
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total_unreal += unreal
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details.append({
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"ticker": ticker,
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"shares": shares,
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"entry": entry,
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"last": last,
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"cost": cost,
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"value": value,
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"unreal": unreal,
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"pct": pct,
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"entry_date": entry_date,
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})
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return total_unreal, details
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def _realised_pnl(db) -> float:
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"""Sum all realised P&L from position_events."""
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row = db.execute(
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"SELECT COALESCE(SUM(pnl_dkk),0) AS total FROM position_events WHERE action='sell'"
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).fetchone()
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return float(row["total"] or 0)
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def _total_fees(db) -> float:
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"""Sum all fees from saxo_orders."""
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try:
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row = db.execute(
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"SELECT COALESCE(SUM(fee_dkk),0) AS total FROM saxo_orders"
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).fetchone()
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return float(row["total"] or 0)
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except Exception:
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return 0.0
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def _invested(positions_details: list[dict]) -> float:
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return sum(p["cost"] for p in positions_details)
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def _signal_accuracy(db) -> dict:
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"""
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Calculate signal accuracy from completed trades (position_events).
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A signal is 'correct' if the trade had P&L > 0 (signal_correct=1).
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Also returns overall signal scan stats from article_signals.
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"""
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# Accuracy from closed trades
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try:
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row = db.execute("""
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SELECT
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COUNT(*) AS total_trades,
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SUM(CASE WHEN signal_correct=1 THEN 1 ELSE 0 END) AS correct,
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SUM(CASE WHEN signal_correct=0 THEN 1 ELSE 0 END) AS wrong,
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COALESCE(AVG(CASE WHEN signal_correct IS NOT NULL
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THEN signal_correct END), -1) AS accuracy
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FROM position_events
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WHERE action = 'sell' AND signal_correct IS NOT NULL
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""").fetchone()
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total_trades = int(row["total_trades"] or 0)
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correct = int(row["correct"] or 0)
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wrong = int(row["wrong"] or 0)
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accuracy_pct = (float(row["accuracy"]) * 100) if total_trades > 0 else None
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except Exception:
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total_trades = correct = wrong = 0
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accuracy_pct = None
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# NLP scan stats
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try:
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srow = db.execute("""
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SELECT
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COUNT(*) AS total_signals,
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SUM(CASE WHEN alert=1 THEN 1 ELSE 0 END) AS alerts,
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AVG(signal_score) AS avg_score
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FROM article_signals
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WHERE signal_score IS NOT NULL
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""").fetchone()
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return {
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"total": int(srow["total_signals"] or 0),
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"alerts": int(srow["alerts"] or 0),
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"avg_score": round(float(srow["avg_score"] or 0), 3),
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"total_trades": total_trades,
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"correct": correct,
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"wrong": wrong,
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"accuracy_pct": accuracy_pct,
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}
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except Exception:
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return {
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"total": 0, "alerts": 0, "avg_score": 0,
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"total_trades": total_trades, "correct": correct,
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"wrong": wrong, "accuracy_pct": accuracy_pct,
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}
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def print_report() -> None:
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db = get_conn()
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now = datetime.now(timezone.utc).strftime("%d %b %Y %H:%M UTC")
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unreal_total, positions = _unrealised_pnl(db)
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realised = _realised_pnl(db)
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fees = _total_fees(db)
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invested = _invested(positions)
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cash = CAPITAL - invested
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net_pnl = realised + unreal_total - fees
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net_pct = (net_pnl / CAPITAL * 100)
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c25_ret = _c25_day_return()
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vs_bench = (net_pct - c25_ret) if c25_ret is not None else None
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sig_stats = _signal_accuracy(db)
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# ── Header ──────────────────────────────────────────────────────────────
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print()
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print("═" * 48)
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print(f" MONEYMAKER P&L · {now}")
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print(f" DB: {DB_TYPE}")
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print("═" * 48)
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print(f" Kapital: {CAPITAL:>10,.0f} DKK")
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print(f" Investeret: {invested:>10,.0f} DKK ({len(positions)} pos.)")
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print(f" Kontant: {cash:>10,.0f} DKK")
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print()
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print(f" Urealiseret: {unreal_total:>+10,.0f} DKK")
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print(f" Realiseret: {realised:>+10,.0f} DKK")
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print(f" Total gebyrer: {-fees:>+8,.0f} DKK")
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print(f" Net P&L: {net_pnl:>+10,.0f} DKK ({net_pct:+.2f}%)")
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print()
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# ── Benchmark ────────────────────────────────────────────────────────────
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if c25_ret is not None:
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bench_icon = "✅" if vs_bench and vs_bench >= 0 else "❌"
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print(f" C25 index: {c25_ret:+.2f}% (i dag)")
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print(f" vs benchmark: {vs_bench:+.2f}% {bench_icon}")
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else:
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print(" C25 index: N/A (marked lukket?)")
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print()
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# ── Open positions ────────────────────────────────────────────────────────
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if positions:
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print(" ┌─ Åbne positioner ─────────────────────────────┐")
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for p in positions:
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icon = "📈" if p["unreal"] >= 0 else "📉"
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print(
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f" │ {icon} {p['ticker']:<8} {p['shares']:.0f} stk "
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f"købt {p['entry']:.0f} nu {p['last']:.0f} "
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f"P&L {p['unreal']:+.0f} ({p['pct']:+.1f}%)"
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)
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print(" └────────────────────────────────────────────────┘")
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print()
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# ── Signal stats ─────────────────────────────────────────────────────────
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print(f" Signaler: {sig_stats['total']:>5} analyseret")
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print(f" Alerts: {sig_stats['alerts']:>5} ⚡ triggers")
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print(f" Gns score: {sig_stats['avg_score']:>5.3f}")
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if sig_stats["total_trades"] > 0:
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acc = sig_stats["accuracy_pct"]
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icon = "✅" if acc and acc >= 50 else "❌"
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print(f" Lukkede handler: {sig_stats['total_trades']} "
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f"({sig_stats['correct']} korrekte / {sig_stats['wrong']} forkerte) "
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f"→ {acc:.0f}% {icon}")
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else:
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print(" Lukkede handler: 0 (ingen salg endnu)")
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if fees > 0:
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drag_pct = (fees / max(abs(net_pnl + fees), 1)) * 100
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print(f" Gebyr-drag: {drag_pct:.1f}% af brutto P&L")
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print("═" * 48)
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print()
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db.close()
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def main():
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parser = argparse.ArgumentParser(description="MoneyMaker P&L rapport")
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parser.add_argument("--fees", action="store_true")
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parser.add_argument("--signals", action="store_true")
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args = parser.parse_args()
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print_report()
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if __name__ == "__main__":
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main()
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