tradeloop-tilt-checker 1.1.0

Red Day Autopsy CLI: deterministic local statement forensics for first-break trades, leak cost, and next-session seatbelts.

TradeLoop Red Day Autopsy

I blew 11 FundingPips evals.

The pattern was always the same: the first loss hurt, but the trade after the loss broke the account.

TradeLoop Red Day Autopsy takes a real statement and shows:

1. the first trade where your rule stopped being real
2. the cost of the leak in that window
3. the seatbelt rules to carry into the next session

Deterministic. Versioned. Same input, same output. No AI in the score.

This is the open-source reference implementation behind TradeLoop, the hosted Red Day Autopsy and Tomorrow's Seatbelt product. Run it locally, inspect the math, challenge the rules, or add broker formats.

Run It Locally

pip install tradeloop-tilt-checker
tilt-check examples/sample_trades.csv

JSON output for audits, tests, or prop-firm review:

tilt-check examples/sample_trades.csv --format json --currency USD

The CLI never uploads your trades. It reads a local CSV and prints Markdown or JSON.

Required CSV columns are case-insensitive and alias-friendly:

• timestamp
• symbol
• side
• entry_price
• exit_price
• quantity
• pnl
• optional duration_minutes
• optional id

What It Detects

This package implements four open-core signatures:

Signature	What it catches
revenge_size_escalation	Size jump immediately after a losing trade.
post_loss_tilt_session	Cluster of trades inside the post-loss window.
loss_streak_oversize	Three losses followed by a larger recovery attempt.
drawdown_doubling	Adding size while the recent trade window is already net negative.

Thresholds use per-trader baselines where possible. A trader is compared to their own typical size and pace, not a universal lot-size rule.

Example Output

{
  "engine_version": "rda-0.1.0",
  "privacy": "local_only_no_upload",
  "score": 532,
  "band": "Fragile",
  "first_break_trade": {
    "row": 14,
    "symbol": "XAUUSD",
    "pattern": "revenge_size_escalation",
    "size_multiple": 2.4,
    "minutes_after_loss": 6.0
  },
  "leak_cost": {
    "currency": "USD",
    "amount": 320.0,
    "method": "sum_abs_negative_pnl_on_flagged_break_trades"
  },
  "seatbelts": [
    "No size increase for 15 minutes after a losing trade.",
    "If you take 3 trades after a loss inside the window, stop the session."
  ]
}

See examples for synthetic/anonymized statements plus expected outputs.

Methodology

The full public explanation is in METHODOLOGY.md.

Short version:

• Sort trades chronologically.
• Build a robust per-trader baseline from median and IQR.
• Evaluate each trade as if it arrived live.
• Freeze every signature fire with evidence.
• Pick the first material break.
• Sum the negative PnL of flagged break trades as leak cost.
• Generate seatbelts from the fired patterns.

This is for discipline forensics, not financial advice and not signal generation. It does not tell you what to buy or sell.

Hosted App Vs Open Core

Capability	This repo	Hosted TradeLoop
Local CSV analysis	Yes	Yes
Markdown report	Yes	Yes
JSON contract	Yes	Yes
First-break trade	Yes	Yes
Leak cost	Yes	Yes
Seatbelt suggestions	Yes	Yes
Multi-statement history	No	Yes
Live Seatbelt interrupt	No	Yes, Pro
Outcome Receipt	No	Yes
Score Card mint/share	No	Yes
Prop-firm Passport	No	Yes
Broker sync / webhooks	No	Yes

Use this repo when you want to audit the promise. Use the hosted app when you want the 60-second product loop and next-session continuity.

• Hosted app: https://app.tradeloop.trade/score?utm_source=github&utm_medium=oss&utm_campaign=red_day_autopsy
• Methodology: https://app.tradeloop.trade/methodology?utm_source=github&utm_medium=oss&utm_campaign=red_day_autopsy
• Telegram bot: https://t.me/TradeLoopTiltBot

Why This Exists

TradeLoop was built after Varun Teja Chunduri cycled through 11 FundingPips $5K/$10K evaluation accounts between October 2025 and May 2026. The corrected public disclosure is roughly Rs 50,000 to Rs 60,000 in evaluation fees and about $3,000 to $5,000 in cumulative simulated capital wiped.

The point of publishing the core is simple: traders and prop firms should be able to inspect how the promise is kept.

Contributing

Good first contributions:

• add a broker CSV format
• add a synthetic example case
• improve a seatbelt rule
• challenge a methodology assumption
• add a parser regression test

Read CONTRIBUTING.md before opening a PR.

License

Apache License 2.0. See LICENSE.