Pre-launch hardening: KOL module, Telegram, scanners, WS resilience
Big-picture changes since b941223:
KOL pipeline (new) — Substack/podcast/blog RSS → AI ticker extraction →
on-chain wallet diff → talks-vs-trades divergence detection. Daily polls,
19 feeds, divergence emits Post + Telegram fan-out.
Telegram push (new) — walletless free tier + wallet-linked Pro upgrade,
in-bot preference commands (/trump /btc /funding /kol /conf /quiet),
signed-envelope API for dashboard. Disconnect-wallet keeps free
subscription.
BTC funding-rate reversal scanner (new) — hourly cron, 30d cumulative
funding threshold + mean-revert + 7d price confirm, emits via
/api/signals/ingest. BTC bottom-reversal scanner promoted to System 2.
WS broadcast rewrite — per-client send timeout + parallel fan-out
(asyncio.gather). Fixes "Binance WS no close frame" reconnect storms +
APScheduler 11-min job misses, both caused by one slow client stalling
the kline loop.
Error visibility — three silent-error sites (trumpstruth/truth_social
fetchers, funding_reversal scanner) now include exception type name so
httpx ConnectError-style empty-message errors stop logging blank lines.
Telegram bot loop now classifies ReadTimeout vs network vs unknown +
logger.exception for the unknown bucket.
Security hygiene — trumpsignal.db untracked from git (held subscriber
wallets + encrypted HL keys + 22 bot trades); .gitignore now blocks
*.db/.next/backups. CORS only allows FRONTEND_URL in production.
New ops scripts —
- scripts/preflight.py: env/DB/Telegram/AI auth verification gate
- scripts/backup_db.sh: cron-friendly daily DB backup (SQLite + Postgres)
- scripts/seed_kol_wallets.py: idempotent KOL on-chain wallet seeder
15 new Alembic migrations (007-021) covering convex strategy fields,
phase-1 safety, two-system frozen exits, invalidation prices, dynamic
SYS2 leverage, staged de-risk + pyramiding, peak gain tracking, risk
mode, auto-trade + grow flags, KOL module, KOL on-chain, KOL divergence,
Telegram bindings + walletless.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
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"""
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Past-7-day backtest under user-specified params:
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- Margin: $100, leverage 20x → notional $2000 per trade
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- SL: -30% on margin = -1.5% on notional move
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- TP: "exit at the future peak" (look-ahead — see caveat at end)
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- Window: 1 hour after publish
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- Fees: 9 bps round-trip (HL taker × 2)
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We only have the price_impact_m5/m15/m1h fields, which store the MAXIMUM
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FAVORABLE EXCURSION (MFE). We do NOT have the trough / max ADVERSE excursion
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(MAE), so we cannot detect a real intra-hour SL hit. That makes the
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optimistic sim a CEILING, not a real-world outcome.
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We compute three scenarios so you can see the honest spread:
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A) "Perfect-foresight": exit at MFE peak. SL ignored (assume MAE = 0).
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This is what the user asked for. Upper bound only.
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B) "Pessimistic": if MFE < 0, assume SL hit (-1.5% × $2000 = -$30).
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If MFE ≥ 0, exit at MFE peak.
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Closer to reality but still optimistic on the wins.
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C) "Realistic fixed TP": TP at +1.5%, SL at -1.5% (symmetric).
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If MFE ≥ TP → win. Else → unknown end-of-window
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price, conservative model: take MFE × 0.5 as exit.
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This is the closest to a real bot's behavior.
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"""
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import sqlite3
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import statistics
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from pathlib import Path
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DB = Path(__file__).resolve().parents[1] / "trumpsignal.db"
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NOTIONAL = 2000.0
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MARGIN = 100.0
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SL_PCT = 1.5 # 1.5% notional move = -30% margin
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FEE_BPS_RT = 0.0009 # 9 bps × $2000 = $1.80 per trade
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def fetch_week():
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"""Last 7 days of actionable signals, anchored to the LATEST post in the
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DB (not wall-clock now) so the script works regardless of how stale the
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snapshot is."""
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con = sqlite3.connect(DB)
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con.row_factory = sqlite3.Row
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latest = con.execute("SELECT MAX(published_at) FROM posts").fetchone()[0]
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rows = con.execute("""
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SELECT id, signal, ai_confidence, price_at_post,
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price_impact_m5, price_impact_m15, price_impact_m1h,
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published_at, text
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FROM posts
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WHERE signal IN ('buy', 'short') -- exclude sell (semantic bug)
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AND price_at_post IS NOT NULL
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AND price_impact_m1h IS NOT NULL
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AND published_at >= datetime(?, '-7 days')
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ORDER BY published_at
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""", (latest,)).fetchall()
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con.close()
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return [dict(r) for r in rows], latest
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def trade_pnl_usd(gross_pct: float) -> float:
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"""Convert a notional % move into $ PnL on $2000 notional, after fees."""
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return NOTIONAL * (gross_pct / 100.0) - NOTIONAL * FEE_BPS_RT
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def sim_perfect(rows):
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"""A) Exit at peak. No SL. (User's request — look-ahead bias.)"""
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pnls = []
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for r in rows:
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peak = r["price_impact_m1h"] # already side-adjusted, %
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# Even peak < 0 still "exits at peak" per user spec
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pnls.append(trade_pnl_usd(peak))
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return pnls
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def sim_pessimistic(rows):
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"""B) If peak < 0, assume SL hit. Else exit at peak."""
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pnls = []
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for r in rows:
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peak = r["price_impact_m1h"]
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if peak < 0:
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pnls.append(trade_pnl_usd(-SL_PCT)) # SL = -$30 + fees
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else:
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pnls.append(trade_pnl_usd(peak))
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return pnls
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def sim_fixed_tp(rows, tp_pct=1.5):
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"""C) TP=tp_pct, SL=-1.5%. Real-bot behavior."""
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pnls = []
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for r in rows:
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peak = r["price_impact_m1h"]
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if peak >= tp_pct:
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pnls.append(trade_pnl_usd(tp_pct)) # TP hit
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elif peak < 0:
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pnls.append(trade_pnl_usd(-SL_PCT)) # SL likely
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else:
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# Held the hour, peak was below TP → exit somewhere between
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# 0 and peak. Use peak/2 as a midpoint estimate (conservative).
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pnls.append(trade_pnl_usd(peak / 2.0))
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return pnls
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def stats(pnls, label):
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n = len(pnls)
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if n == 0:
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return None
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wins = sum(1 for p in pnls if p > 0)
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losses = sum(1 for p in pnls if p < 0)
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total = sum(pnls)
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biggest_win = max(pnls)
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biggest_loss = min(pnls)
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win_rate = wins / n * 100
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# Margin return: total $ / total margin used
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total_margin_used = MARGIN * n
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roi_on_margin = (total / total_margin_used) * 100
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return {
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"label": label,
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"n": n,
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"wins": wins,
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"losses": losses,
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"win_rate_pct": round(win_rate, 1),
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"total_usd": round(total, 2),
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"avg_per_trade_usd": round(total / n, 2),
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"biggest_win_usd": round(biggest_win, 2),
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"biggest_loss_usd": round(biggest_loss, 2),
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"roi_on_margin_pct": round(roi_on_margin, 1),
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}
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def print_stats(s):
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if s is None:
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print(" (no trades)")
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return
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print(f" n trades: {s['n']}")
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print(f" win rate: {s['win_rate_pct']}% ({s['wins']}W / {s['losses']}L)")
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print(f" total PnL (USD): ${s['total_usd']:+,.2f}")
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print(f" avg per trade: ${s['avg_per_trade_usd']:+,.2f}")
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print(f" biggest win: ${s['biggest_win_usd']:+,.2f}")
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print(f" biggest loss: ${s['biggest_loss_usd']:+,.2f}")
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print(f" ROI on margin: {s['roi_on_margin_pct']:+.1f}% "
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f"(${s['total_usd']:+.0f} on ${MARGIN * s['n']:.0f} total margin used)")
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def main():
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rows, anchor = fetch_week()
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print("=" * 72)
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print(f"PAST-7-DAY BACKTEST — TrumpSignal AI strategy")
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print(f"(7 days back from latest DB post: {anchor[:19]})")
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print("=" * 72)
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print(f"Position size: ${MARGIN} margin × 20x leverage = ${NOTIONAL:.0f} notional")
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print(f"Stop loss: -30% margin = -{SL_PCT}% notional")
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print(f"Take profit: see scenarios below")
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print(f"Fees: 9 bps round-trip = ${NOTIONAL * FEE_BPS_RT:.2f} per trade")
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print(f"Hold window: 1 hour")
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print(f"Sample: {len(rows)} actionable signals (buy/short, sell excluded)")
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if rows:
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print(f"Date span: {rows[0]['published_at'][:10]} → {rows[-1]['published_at'][:10]}")
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print()
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if not rows:
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print("⚠️ No actionable signals in the last 7 days.")
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print(" Either Trump didn't post anything market-relevant, or all signals")
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print(" were 'hold'. Cannot run backtest.")
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return
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print("=" * 72)
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print("SCENARIO A — 'Perfect foresight' (exit at peak, ignore SL) ← user spec")
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print(" This is the THEORETICAL CEILING. No real bot can hit this.")
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print("=" * 72)
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print_stats(stats(sim_perfect(rows), "perfect"))
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print()
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print("=" * 72)
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print("SCENARIO B — 'Pessimistic' (SL hits if peak<0, else exit at peak)")
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print(" Closer to honest, but wins are still cherry-picked at peak.")
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print("=" * 72)
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print_stats(stats(sim_pessimistic(rows), "pessimistic"))
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print()
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print("=" * 72)
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print("SCENARIO C — 'Realistic fixed TP/SL' (TP=+1.5%, SL=-1.5%)")
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print(" Closest to what a real bot with these params would yield.")
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print(" THIS is the only one defensible for marketing.")
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print("=" * 72)
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print_stats(stats(sim_fixed_tp(rows, tp_pct=1.5), "realistic"))
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print()
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# Also try a couple of TP variations to find sweet spot
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print("=" * 72)
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print("Realistic sim — TP threshold sweep (SL fixed at -1.5%)")
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print("=" * 72)
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print(f" {'TP%':>6} {'n':>4} {'win%':>6} {'total$':>10} {'avg$':>8} {'ROI%margin':>11}")
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for tp in [0.5, 1.0, 1.5, 2.0, 3.0]:
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s = stats(sim_fixed_tp(rows, tp), f"tp={tp}")
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print(f" {tp:>5.1f}% {s['n']:>4} {s['win_rate_pct']:>5.1f}% "
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f"${s['total_usd']:>+8.2f} ${s['avg_per_trade_usd']:>+6.2f} "
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f"{s['roi_on_margin_pct']:>+9.1f}%")
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print()
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print("=" * 72)
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print("BOTTOM LINE")
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print("=" * 72)
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perfect = stats(sim_perfect(rows), "")
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realistic = stats(sim_fixed_tp(rows, 1.5), "")
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print(f" Perfect-foresight ceiling: ${perfect['total_usd']:+,.2f} "
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f"(do NOT put on homepage — look-ahead bias)")
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print(f" Realistic fixed TP=1.5%: ${realistic['total_usd']:+,.2f} "
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f"(this is the marketable number, IF positive)")
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if __name__ == "__main__":
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main()
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