Learn Dashboard — bpleone / trade

Trades in memory

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— closed · — open

Win rate

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Realized outcomes only

Avg R per trade

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Expectancy

Conditional rules learned

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Per setup × dimension × bucket

🧠 What the brain thinks right now

REAL

Computing…

Base Setup Weights

Multipliers applied to raw signal scores. 1.00 = neutral, >1.0 = boost, <1.0 = fade.

🔝 Top 12 Conditional Insights

Performance by Sector

By Day of Week

By Hold Period

Setup Expectancy Distribution

🧬 How the self-learning works

1. Record every trade

Open in Journal or via Paper Trade → close → expectancy in R is computed (P&L ÷ stop distance). Win/loss flagged. Sector, day-of-week, VIX regime captured.

2. Rebalance setup weights

Once a setup has 3+ closed trades, the base weight gets re-scored from realized expectancy and squashed to [0.5, 1.6]. High-edge setups amplified; low-edge faded.

3. Conditional adjustments

For each setup × dimension (sector / DoW / hold band / regime), conditional adj. factors are computed and applied multiplicatively. Final score = raw × base × Π(conditional adj.). Squashed gentler [0.7, 1.35].