Paper — every trading figure here (positions, orders, P&L, equity, returns) is simulated on an Alpaca paper account. No real capital is at risk, and no order is ever placed with your money. Running costs shown under ops, such as LLM spend, are real.
Paper — every trading figure here (positions, orders, P&L, equity, returns) is simulated on an Alpaca paper account. No real capital is at risk, and no order is ever placed with your money. Running costs shown under ops, such as LLM spend, are real.
End-of-day reflection (L5). The engine partitions outcomes by strategy × sector × regime, attributes the congruence of factors, and proposes the next deployment's parameters.
Real-time MongoDB Atlas corpus counts + headline performance for this view. Reflection-insights engine reads these every cycle to compute per-strategy × sector × regime cohort attribution.
Decisions analysed
0
decision corpus · not closed trades
Closed Trades
0
Market Snapshots
0
Win Rate
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Total P&L (R)
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Decision funnel, strategy selectivity, and portfolio value
Evaluated
0
Total ticker evaluations
Skipped
0.0%
0 of 0
Selectivity
0.00%
0 entered/held
Closed
0
0W / 0L
Win Rate
—
—
Portfolio
—
Current equity
Which sectors received the most agent attention (decisions + entries)
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How market conditions influenced trading decisions
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Per-strategy recommendation cards. The reflection engine attributes outcomes by cohort, Gemini narrates the evidence into a Proposal, and the operator applies or rejects it for the next deployment.
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Each stage feeds the next. Click any stage to filter the proposals panel below. The flywheel turns once per parameter change — and the L5 UVP is the moat that turns it.
Counts: Decide / Annotate / Score = today (UTC). Reflect = proposals created today. Propose = currently open proposals. Approve / Apply = revisions accepted today.
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Suggested refinements generated automatically from analysis of past trade outcomes
No proposals yet — the reflection agent will generate proposals after analysing trade outcomes.