Alpha Workbench

Forecast Edge Auditor & Bankroll Simulator

Auditing prediction market skill vs luck across 26-week public event forecast records ($100 Starting Capital)

Ending Bankroll From $100
$384.22
+284.2% Log Gain
Trader Hit Rate
69.2%
18 Wins / 8 Losses (26 Wks)
User Brier Score (Lower=Better)
0.142
vs Market Mkt: 0.238
Edge Significance (Binomial p-value)
p = 0.034
Alpha Proven (p < 0.05)
Max Drawdown
-18.4%
Sharpe: 1.84 | Sortino: 2.61
Dual Analysis: Fractional Kelly Equity Trajectory & Brier Calibration Curve Real-time reactive engine
BANKROLL GROWTH TRAJECTORY ($)
Trader Compounding
$100 Initial Baseline
PROBABILITY CALIBRATION (RELIABILITY)
Trader Calibration
Perfect Calibration
Weekly Public Event Forecast Ledger (Directly Editable)
Wk Contract Bracket Target Market Implied User Prob Edge (EV) Outcome Stake $ Bankroll Brier
Source Grounding (r/PredictionMarkets): “I decided to spend half a year on a single question: How many posts will Elon Musk make next week? In 6 months, the simulated hit rate was around 70%. If someone who made money can slowly lose his edge, what was the real reason: a real read on Musk, or a stretch of volatility? Feel makes you think you understand the game when you are winning... if the judgment is not written down in a way you can check, you cannot even say what you were betting on.”
Execution & Sizing Model
Kelly Criterion Multiplier 0.50x (Half Kelly)
0.10x Conservative 0.50x Growth Optimal 1.0x Full Kelly
Starting Capital ($) $100.00
Market Slippage / Taker Fee 1.0%
Optimal Growth Formula:
f* = (b · p - q) / b
Where p = user forecast, b = market decimal payout odds (1 - price)/price, and q = 1 - p.
Monte Carlo Luck Permutation Test
Testing null hypothesis: "Trader hit rate is indistinguishable from 50/50 fair coin coin-flip luck."
Simulated Iterations: 10,000 runs
Chance ≥ Current Wins: 3.4% (p = 0.034)
95% Confidence Band: 18 to 26 Weeks needed
Brier Skill Score (BSS): +40.3% vs Mkt
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