Win rates, factor contribution, signal history, weight evolution, AI sentiment, risk-adjusted metrics, P&L distribution, factor IC tracking, and signal aging
Percentage of signals that were correct after position close (7-day default hold period)
Performance breakdown: Buy vs Sell vs Strong Buy vs Strong Sell
Which of the 44 factors contributed most to winning vs losing signals. Green bars = winning factor average. Red bars = losing factor average.
Complete log of all tracked signals. Paginated, 20 per page.
| Date | Ticker | Signal | Score | Entry $ | Exit $ | P&L | Status |
|---|
How the WeightEvolution system has adjusted factor weights over time. Factors that contribute to winning signals get increased weight.
Evolved weights vs default research-backed starting weights
Signal performance with risk metrics: Sharpe, Sortino, and Max Drawdown per signal type.
Calculated from closed signal outcome history
| Signal Type | Count | Win Rate | Avg Return | Sharpe | Sortino | Max DD |
|---|
Histogram of return outcomes for all closed signals. Shows distribution shape and skewness.
Bucketed P&L outcomes — green = profit, red = loss
Information Coefficient (IC) and IC Information Ratio (ICIR) per factor category. Shows which factors are truly predictive.
IC: correlation between factor score and subsequent return. ICIR: IC consistency.
| Factor Category | IC | ICIR | Classification | Trend |
|---|
How fresh are your cached signals? Stale signals may not reflect current market conditions.
Age distribution of all cached AI signals across dashboards