Portfolio equity
My portfolio — what I own now
| Symbol | Shares | Avg cost | Price | Market value | Unreal. P&L |
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Trade ledger
| Symbol | Status | Entry | Exit / Mark | Shares | P&L $ | P&L % | Opened |
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Meta controls — set these two, the knobs follow
Hard knobs — enforced in code, per run
Investing strategies — educational overview & selection. The bot trades whatever mix you set; every trade still passes the risk engine.
Regime fit reflects how each approach has historically behaved in markets like the
current one. It is generic market-state information, not personalized investment
advice, and past behavior does not guarantee future results.
Staged trades — waiting on their trigger; every armed plan still passes the risk engine
Watchlist & research history — every stock the bot has looked at
Discuss & evolve the bot's strategy
BOT
This is the strategy channel. Tell me how you want to invest — stay on news/sentiment, shift toward swing trading, screen for impact criteria, hold longer. When we land on a change, I'll propose a DIRECTIVE and you commit it to my memory. I read that memory before every trading run.
Closed
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Hit rate
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Realized P&L
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Cumulative realized P&L by trade
Today's research brief
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Prediction calibration — measured hit-rates of the bot's own calls
Playbook — what the bot has learned
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Owner directives — what you've taught it
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Edge verification — pre-registered hypotheses
(docs/experiments/), scored by arithmetic against real prices. Verdicts are
code-assigned; kills and scale-ups still need your sign-off.
| ID | Hypothesis | n | Key stat | Verdict |
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Forecasts scored
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Hit rate (all)
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Daily IC
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Daily NAV — bot vs its no-LLM twin vs SPY vs the forecast shadow (all start at 1.00)
Paired daily excess — differencing removes the shared market move
| Pair | Days | Mean / day | 95% CI |
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Forecast accuracy by driver & liquidity tier — no rate is quoted below n=30
Recent forecasts
| Date | Sym | H | p↑ | Driver | Source | Result |
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