learn-from-losses

name	learn-from-losses
description	Post-trade review — pull losing trades from the strategy tester, screenshot each entry in context, and cluster common failure patterns. Use when the user asks "why am I losing?", "review my losses", or "what's wrong with my strategy?".

Learn From Losses

You are running a post-trade forensic review to identify patterns in losing trades.

Step 1: Pull Trade History

1. data_get_trades — fetch trade log from the active strategy (capped 20 per request; paginate if needed)
2. Filter to losses only: pnl < 0
3. Sort by abs(pnl) descending — biggest losses first, these teach the most

If the strategy tester isn't open: ui_open_panel with panel: "strategy-tester" first.

Step 2: Set Up Review Chart

1. chart_get_state — confirm symbol/timeframe match the strategy run
2. Optionally state_snapshot current chart state with tag pre-review so you can restore cleanly at the end
3. Add context indicators if missing (e.g., EMA200 for trend, ATR for volatility):
   • chart_manage_indicator add "Moving Average Exponential" (length 200)
   • chart_manage_indicator add "Average True Range"

Step 3: Walk Each Loss

For each losing trade (limit to top 10 by size to stay focused):

1. chart_scroll_to_date — jump to the entry bar (ISO date from the trade record)
2. chart_set_visible_range — zoom to ~50 bars around the entry for context
3. draw_shape with horizontal_line at entry price (label "E")
4. draw_shape with horizontal_line at stop price (label "SL")
5. draw_shape with horizontal_line at exit price (label "X")
6. capture_screenshot — save the annotated context

Example — reviewing a $ES1!` long that stopped out:

chart_scroll_to_date({ date: "2025-03-14T09:45:00" })
chart_set_visible_range({ ... ~50 bars ... })
draw_shape({ type: "horizontal_line", point: { price: 5812.50 }, text: "E" })
draw_shape({ type: "horizontal_line", point: { price: 5805.00 }, text: "SL" })
capture_screenshot()

If CHART_LOADING errors fire between scrolls, wait 1s and retry.

Step 4: Read Each Context

For each loss, pull the state at entry:

1. data_get_study_values — what were RSI, MACD, EMA readings saying at entry?
2. data_get_pine_labels with study_filter — any "Bias" or "Regime" label on that bar?
3. data_get_ohlcv with summary: true, count: 20 — recent price action leading in

Collect into a per-trade record: {date, side, entry_regime, rsi, trend_vs_ema200, atr_multiple_of_sl, outcome}.

Step 5: Cluster Patterns

Look for recurring failure modes across the loss set:

• Counter-trend entries — longs taken below EMA200 (or vice versa)
• Overbought/oversold shorts/longs — RSI > 70 on longs, < 30 on shorts
• Tight stops in high vol — SL < 1 ATR when ATR is elevated
• Chop zone trades — entries inside a recently flat range
• News/session edge — clustering around open, close, or pre-economic releases

Count frequencies. A pattern that appears in 6 of 10 losses is a real leak.

Step 6: Report

Produce a structured writeup:

• Top 3 failure patterns with counts (e.g., "Counter-trend longs: 5/10, avg -$180")
• Specific rule violations (e.g., "40% of losses taken below EMA200 — rule says longs only above")
• Screenshot references for the 2-3 most illustrative examples
• Concrete filter proposals — e.g., "Add close > ema200 gate; would have skipped 4 losses totaling -$620"

Cleanup

• draw_clear — remove all the E/SL/X markers
• state_restore with the pre-review snapshot id to return to the prior layout
• Offer to codify the proposed filter into the Pine strategy (hand off to the pine-develop skill)