Paper Trader
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DATA FEED
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Signal Feed — Digital Intern View All Signals →
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Live portfolio
total value
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cash
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return vs capital
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vs SPY (same period)
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max drawdown
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cash deployed
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🐒 monkey rank
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active rnd
Book loading…
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● Net worth ($ total value)
Capital basis (deposits)
SPY (% from same start)
↑ buy ↓ sell
Drawdown from peak (%)
Returns over time — deposit-adjusted portfolio % change over standard windows —
1d
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5d
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1m
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3m
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ytd
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1y
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3y
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5y
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all
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Daily briefing —
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Urgent overnight news
Since you last looked — material events in the window, ranked (no snapshot scanning) —
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| when | event | detail |
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Trade suggestions — co-pilot, not auto-executed —
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| action | ticker | conv. | price | qty | news | RSI | reasons | headline |
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Risk panel
concentration top1
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top3 weight
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leveraged %
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SPY -3% shock
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median age (d)
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stale positions
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🧨 Position blow-up ladder — each held name shocked alone, idiosyncratic (no beta) —
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| ticker | weight | −10% | −25% | −50% | to zero |
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Earnings radar — scheduled gap risk on holdings & watchlist —
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ML scorer · per-position outlook — predicted 5-day forward return from DecisionScorer MLP —
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| ticker | pred 5d | verdict | RSI | MACD | mom 5d | mom 20d | news |
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Position thesis — per-holding integrated view: news, scorer, technicals, last decision, verdict —
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Drawdown anatomy — current DD from peak with per-position contribution —
current equity
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peak equity
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current drawdown from peak
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max drawdown (trough)
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time in DD
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recovered
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Per-position contribution
| ticker | qty | cost | px | P/L $ | P/L % | drag |
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Confidence calibration & signal attribution — do high-confidence trades actually win? which signal types pay? —
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By Opus confidence
| bucket | n | win % | avg ret | avg conf |
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By signal source
| source | n | win % | avg ret | best / worst |
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Recent realized trades
| buy → sell | ticker | return | conf | source | reasoning |
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Capital deployment & liquidity — is the book pinned with no dry powder? —
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cash
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deployed
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positions
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top weight
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unrealized P/L
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last entry
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Decision pipeline health — is the live Opus trader actually deciding? NO_DECISION = parse failure —
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cycles (24h)
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parse-fail (24h)
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fills (24h)
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avg confidence
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since last fill
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signals / cycle
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cycles (all-time)
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parse-fail (all-time)
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fills (all-time)
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Action mix (all-time)
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Recent cycles
| time | outcome | action | conf | signals |
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Decision failure forensics — WHY a cycle produced no decision, with the raw Opus excerpt —
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failures
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rate (24h)
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retry-exhausted
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dominant mode
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open mkt fail%
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closed mkt fail%
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Failure modes
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Hourly parse-fail (last 24h)
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Decision-loss clock — parse-fail by UTC hour (current regime; folds every day onto one 24h clock so a recurring host-load window shows)
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Recent failures — raw Opus excerpt
| time | mode | mkt | excerpt |
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Decision drought drift — what the bot's inaction cost: portfolio vs S&P while it wasn't trading —
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fills / cycles
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droughts
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paralysis droughts
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involuntary alpha bleed
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Droughts (newest first) — alpha = portfolio% − S&P% over the idle window
| start | hrs | cyc | kind | ND% | port% | spy% | alpha% |
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News edge — does a high ai_score headline actually predict the move? (SPY-abnormal) —
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lookback
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articles
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resolved
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tickers priced
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ref horizon
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Forward return by ai_score band — mean abnormal % (raw in muted)
| ai_score band | n@ref | 1d abn | 3d abn | 5d abn | ref hit% |
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Urgent vs normal — 3d abnormal %
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Scorer reliability — how far DecisionScorer predictions actually land from reality —
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directional accuracy
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mean abs error
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90% residual band
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replay samples
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Held positions — prediction with empirical band
| ticker | pred 5d | likely range | verdict | band hit % | trust |
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Calibration by prediction band
| predicted band | n | mean actual | residual P10/P90 | MAE | dir. acc. |
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Scorer ⇄ Opus disagreement — where the ML safety net and Opus are fighting on held positions —
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high conflict
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medium
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aligned
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positions
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| ticker | scorer verdict | pred 5d | last Opus action | conflict | read |
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Portfolio analytics
today's P/L
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max drawdown (all-time)
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sharpe (ann.)
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win rate
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avg winner
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avg loser
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realized P/L
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profit factor
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sortino (ann.)
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calmar
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S&P β
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S&P corr
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avg hold
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Sector exposure
Behavioural edge — payoff ratio, breakeven win-rate, the disposition effect —
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expectancy / trade
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payoff ratio
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win-rate (actual)
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breakeven win-rate
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realized P/L
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round-trips (W/L)
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avg winner
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avg loser
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winner / loser hold
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disposition gap
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Loser autopsy — why each closed trade lost: verbatim thesis, hold, failure mode —
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total realised loss
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losing round-trips
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avg loss
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median hold
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dominant mode
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| ticker | P/L $ | P/L % | hold d | mode | opening thesis |
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Winner autopsy — why each closed trade won: verbatim thesis, hold, success mode —
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total realised gain
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winning round-trips
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avg gain
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median hold
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dominant mode
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| ticker | P/L $ | P/L % | hold d | mode | opening thesis |
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Track record — per-name closed-trade memory: the verbatim history the trader now sees in its own prompt —
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names traded
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closed round-trips
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worst name (net)
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best name (net)
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| ticker | W-L | net $ | closed | last mode | last opening thesis |
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Concentration honesty — pairwise return ρ & effective independent bets —
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mean pairwise ρ
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effective bets
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naive eff. positions
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top weight
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most-coupled pair
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| pair | ρ |
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Capital paralysis — the trap, its cost, and the single sale that unlocks it —
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cash
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deployed
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can act?
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cycles since fill
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alpha bled
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Unlock ladder — desk cut-priority (biggest loser first)
| ticker | weight% | P/L% | frees $ | cash if sold alone | unlocks? |
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Open-book alpha — how much of the open P&L is selection vs just SPY —
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book alpha vs SPY
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net excess $
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unrealized $
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SPY-equiv $
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anchored names
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| ticker | pos % | SPY % | alpha % | excess $ |
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Overtrading & churn — how often it re-buys a name it just closed, and how fast —
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fast re-entries
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round-trips / day
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median hold
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sub-day trips
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loss in <1d trips
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Fastest same-name re-entries
| ticker | gap (d) | prior P/L $ | closed → re-bought |
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Thesis drift — is the reason each position was opened for still true? —
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| ticker | health | P/L % | held (d) | entry rationale → current drift |
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Signal-feed health — is the live trader receiving any news, or flying blind? —
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blind streak (0-signal cycles)
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newest live article age
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live articles (2h / 24h)
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split-brain DB
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Runner heartbeat — is the decision loop still cycling, or has it wedged/died? —
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since last decision
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intervals elapsed
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expected cadence
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market
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Decision reliability — headline NO_DECISION % vs the true post-restart rate —
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current-regime fail
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headline fail (legacy-incl)
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current sample / total
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legacy dead rows
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dead cycles / day
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ML gate edge — does the DecisionScorer earn its complexity OUT OF SAMPLE, or would a one-line rule do? —
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MLP rank-IC (OOS)
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best one-liner
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IC gap (MLP − best)
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OOS pairs / scorer n_train
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a read-only honesty diagnostic — the gate stays live at n_train ≥ 500 regardless (invariant #5); the value is knowing whether it is modulating real position sizing on signal or on noise.
Funded suggestions — which BUY/ADD idea is fundable, and the sale that unlocks it —
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actionable ideas
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funded now
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partial (cash + sale)
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unlockable via sale
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unfundable
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pairing
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| idea | conv | notional $ | fundability | sell to fund | frees $ |
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Signal follow-through — did it act on the news it saw, and did acting pay (vs SPY)? —
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follow-through
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acted / ignored
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selection edge (ref)
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acted abn% @ref
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ignored abn% @ref
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resolved / signals
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News source edge — which of ~17 collectors' scored headlines actually precede the move (vs SPY)? —
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| collector | abn% @ref | hit | resolved | verdict |
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Behavioural scorecard — do the independent behavioural checks agree on a problem? (no grade, just concordance) —
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| check | verdict | what it says |
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Sector pulse — semis & optical —
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DRAM / semis heatmap — 5d momentum & news pulse —
SOXX baseline: —
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Deduped signals — syndication collapsed, urgency decayed (halflife 4h) —
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Open positions
| ticker | type | qty | avg | now | SL | TP | total $ | % port | P/L | P/L % | held |
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Recent trades
| time | action | ticker | qty | price | reason |
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MACD Signals — per-ticker momentum scan · ⚡ = high-conviction cross-up below zero
| ticker | price | RSI | MACD hist | EMA200 | signal | headline |
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Trade outcomes — closed round-trip statistics · win rate · expectancy · hard-exit attribution —
round trips
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win rate
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expectancy
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profit factor
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avg win: —
avg loss: —
last 20: —
hard SL exits: —
hard TP exits: —
Decision log
| time | open? | signals | action | equity | reasoning |
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Model leaderboard — ranked by avg yearly vs-SPY % · 🐒 random baseline anchored at the bottom —
| # | model | n runs | vs SPY / yr | best % | worst % | vs SPY total | beats 🐒 |
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Backtest equity curves
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last update: —
avg annualized
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total % (mean / median)
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best
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worst
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beat SPY
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win rate
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filtered runs
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Window:
Model:
Median
P25–P75
P5–P95
Actual SPY median
Max drawdown distribution (% below peak, by day from start)
Multi-dimensional analysis
Duration × era × return — three ways to read the same 500+ runs simultaneously.
Duration vs annualized return
each dot = one run · click to drill in · color = market era
Era × duration performance heatmap
avg annualized return % per cell · (n = run count)
Model progress — return by cycle
Best / avg / worst return per cycle of 5 runs. Upward trend = model improving.
Signal Integrity
Permutation test + label contamination audit. Runs every 10 backtest cycles in the background.
SIGNIFICANT (p<0.05) means signal time-ordering carries real predictive value, not random noise.
SIGNIFICANT (p<0.05) means signal time-ordering carries real predictive value, not random noise.
Permutation Test
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p=— · z=—
Label Contamination
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High = Claude labels carry hindsight
(retroactively-collected articles)
(retroactively-collected articles)
Last Validation
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Runs table — click a row to highlight
| # | status | total % | ann. %/yr | vs SPY | window | dur. | trades | signals |
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