Note down entery and exit Why we took trade? What were each indicator signal on that specific time when we took trade How many indicators agreed at trade time Always backtest using engine.py and signal_aggregator.py Dont take trades on weekends and Fridays Keep TP small 1.3 Reward ratio At the end of test make table of results and print all values you observed. Trade context Time of day and trading session (Asian/London/NY/overlap) Day of week (track Mon/Tue/Wed/Thu performance separately) Market regime at entry: trending, ranging, or choppy (use ATR or ADX) Volatility level (ATR value or ATR percentile vs recent average) Pair/instrument traded News proximity (high-impact news within 30 min before/after entry) Trade execution details Spread at entry in pips Slippage on entry and exit (planned vs actual fill) Time in trade (minutes/hours from entry to exit) Maximum Adverse Excursion (MAE): how far against you it went Maximum Favorable Excursion (MFE): how far in profit before pullback Risk metrics Max consecutive losses (streak) Max consecutive wins (streak) Expectancy: (Win% x Avg Win) minus (Loss% x Avg Loss) Expectancy per hour in trade Recovery factor: Total R divided by Max DD Indicator/signal analysis Each indicator's standalone win rate when it agreed with the trade Confluence level performance (2 vs 3 vs 4+ indicators agreeing) Which indicator disagreed on losing trades (find the "canary") Decision quality Rate each setup A/B/C at entry, before knowing outcome Verify A-grade setups actually win more often Track system trades vs discretionary deviations separately Process notes Screenshot or chart snapshot at entry and exit Emotional state (calm, FOMO, revenge, tired) What would have happened if you held longer or exited sooner Aggregate analysis at end of backtest Equity curve, not just total R Performance breakdown by session, day, volatility regime Win rate over rolling 20-trade windows (check for edge decay) Critical extra "Do not trade" log: setups flagged but skipped, and what happened after