# Trade Psychology Coach > Trading psychology monitoring — tilt detection, emotional state tracking, trade-psychology-coach bias alerts, discipline scoring, and behavioral pattern recognition. Use for "trading psychology", "am I tilted", "revenge trading", "emotional trading", "discipline check", "trading mindset", "fear of missing out", "FOMO", "trading emotions", "overtrading detection", "psychology check", or any trading psychology question. Works with trade-journal-performance and drawdown-playbook. - Skill: `mahmoud20138/trade-psychology-coach` (Agent Skill) - Install (CLI): `npx skillmds@latest add mahmoud20138/trade-psychology-coach` - Raw SKILL.md: https://api.skillmd.com/api/skills/mahmoud20138/trade-psychology-coach/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: mahmoud20138 (https://skillmd.com/u/mahmoud20138) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/mahmoud20138/trade-psychology-coach --- # Trade Psychology Coach ```python import pandas as pd, numpy as np from datetime import datetime, timedelta class TradePsychologyCoach: @staticmethod def tilt_detector(trades: pd.DataFrame) -> dict: """Detect revenge trading and tilt from trade patterns.""" if trades.empty or len(trades) < 5: return {"tilt_level": "INSUFFICIENT DATA"} recent = trades.tail(10) # Signs of tilt signals = [] consecutive_losses = 0 for _, t in recent.iterrows(): if t.get("pnl_pips", 0) < 0: consecutive_losses += 1 else: consecutive_losses = 0 if consecutive_losses >= 3: signals.append(f"3+ consecutive losses ({consecutive_losses})") # Increasing position sizes after losses if len(recent) >= 4: last_lots = recent["lot_size"].tail(3).values if all(last_lots[i] > last_lots[i-1] for i in range(1, len(last_lots))): signals.append("DANGER: Increasing size after losses (revenge pattern)") # Rapid-fire trading if "entry_time" in recent.columns and len(recent) >= 3: times = pd.to_datetime(recent["entry_time"]) gaps = times.diff().dt.total_seconds() / 60 if gaps.tail(3).mean() < 15: signals.append("Rapid-fire entries (<15 min apart) — possible overtrading") # Wider stops (desperation) if "stop_loss" in recent.columns and "entry_price" in recent.columns: sl_distances = abs(recent["entry_price"] - recent["stop_loss"]) if sl_distances.tail(3).mean() > sl_distances.mean() * 1.5: signals.append("Stop losses widening — possible hope trading") tilt_level = "HIGH" if len(signals) >= 3 else "MODERATE" if len(signals) >= 2 else "ELEVATED" if signals else "CALM" return { "tilt_level": tilt_level, "signals": signals, "consecutive_losses": consecutive_losses, "action": { "HIGH": "STOP TRADING IMMEDIATELY. Walk away for minimum 4 hours. Review journal.", "MODERATE": "Take a 30-minute break. Reduce position size by 50%.", "ELEVATED": "Be extra cautious. Only take A+ setups.", "CALM": "Emotional state appears normal. Continue with plan.", }[tilt_level], } @staticmethod def trade-psychology-coach_bias_check(trade_context: dict) -> list[dict]: """Flag common trade-psychology-coach biases that may be affecting decision-making.""" biases = [] if trade_context.get("adding_to_loser"): biases.append({"bias": "Sunk Cost Fallacy", "fix": "Your average price is irrelevant. Would you enter this trade fresh at current price?"}) if trade_context.get("moved_stop_loss_further"): biases.append({"bias": "Loss Aversion", "fix": "A stop loss is a contract with yourself. Moving it breaks trust with your system."}) if trade_context.get("closed_winner_early"): biases.append({"bias": "Disposition Effect", "fix": "You cut winners short and let losers run. Reverse this: trail winners, cut losers fast."}) if trade_context.get("entered_because_others_said"): biases.append({"bias": "Herding", "fix": "Other people's analysis is noise unless you independently verified it."}) if trade_context.get("big_win_then_bigger_risk"): biases.append({"bias": "House Money Effect", "fix": "Profits are real money, not 'house money'. Maintain standard risk."}) if trade_context.get("missed_trade_then_chased"): biases.append({"bias": "FOMO", "fix": "There will always be another setup. Chasing fills at bad prices is -EV."}) return biases @staticmethod def discipline_score(journal: pd.DataFrame) -> dict: """Score trading discipline from journal data.""" if journal.empty: return {"score": 0, "error": "No journal data"} scores = [] if "followed_plan" in journal.columns: scores.append(journal["followed_plan"].mean() * 100) if "setup_type" in journal.columns: has_setup = (journal["setup_type"] != "").mean() * 100 scores.append(has_setup) if "lot_size" in journal.columns and "pnl_pips" in journal.columns: losers = journal[journal["pnl_pips"] < 0] if len(losers) > 0: size_after_loss = journal["lot_size"].shift(-1).loc[losers.index] increased = (size_after_loss > losers["lot_size"]).mean() scores.append((1 - increased) * 100) avg = np.mean(scores) if scores else 0 return { "discipline_score": round(avg, 1), "grade": "A" if avg >= 85 else "B" if avg >= 70 else "C" if avg >= 55 else "D" if avg >= 40 else "F", "components": scores, } @staticmethod def pre_trade_checklist() -> dict: return { "questions": [ "Am I following my trading plan?", "Is this setup in my playbook (not a random idea)?", "Am I trading because the setup is there, or because I WANT to trade?", "Would I take this trade if I'd just had 3 losses in a row?", "Is my position size within my rules?", "Have I defined my exit BEFORE entering?", "Am I emotionally neutral right now?"], "rule": "If ANY answer is NO or uncertain — do NOT take the trade.", } ```