DiamondIQ · Paper Fund

A public paper fund vs. the prediction markets

When our calibrated v2 win-probability model diverges from a prediction market's price by at least 8%, this fund takes a simulated position on the model's side — sized by quarter-Kelly, capped at 10% of bankroll, with every venue fee modeled. Positions are logged the moment they open, before the game resolves.

Simulated paper fund. Fills are simulated at the price available at decision time — no real orders, no trading account, no money at risk. This is a transparency exhibit for our model, not investment or betting advice. Methodology is documented in full below.
Bankroll
$50.00
from $50.00 start
Cumulative ROI
0.0%
after fees
Settled record
0-0
no settled positions
Open positions
0
awaiting resolution

Equity curve — from $50

$50 start$49$51

No positions have settled yet — the curve begins at the $50.00 starting bankroll and will extend as games resolve.

Open positions — logged before they resolve

No open positions right now.

Settled positions

Nothing settled yet.

Market coverage — today's 15-game slate

VenueMarkets foundLiquid enough
Kalshi13/15 (87%)13/15 (87%)
Polymarket15/15 (100%)15/15 (100%)

"Liquid enough" applies a modest bar (Kalshi: any resting open interest; Polymarket: ≥ $5k resting liquidity on the moneyline). Both venues list per-game MLB moneyline markets; depth concentrates in the hours around first pitch.

Backtest — the strategy over our graded log (fees included)

Backtest universe is our forward-graded pregame_close log (no free historical Kalshi/Polymarket price archive exists), priced off the de-vigged odds-API line widened to a conservative taker ask, charged Kalshi's real fee schedule. Small samples are not evidence of an edge either way.

Over 30 positions from 235 graded games (≥8% divergence bar): -9.5% ROI after fees, 37% hit rate, max drawdown 19.3%. Pre-fee ROI would be -7.1% — fees cost $1.94.
The backtested edge is negative after fees. On this sample the rule does not beat the market — that finding is reported, not hidden.
Threshold sweep (same rule, different bars)
BarPositionsHit rateROI (post-fee)Max DD
5%6445%0.6%13.6%
8%3037%-9.5%19.3%
10%1040%-1.3%7.4%
15%20%-9.9%9.9%

Methodology & honesty

Entry rule. Buy the side where our calibrated v2 model prices the outcome above the market by at least 8% — the same divergence bar the model's "notable calls" use. No discretion; the rule is a constant in the code.

Sizing. Fractional Kelly. For a binary contract bought at price q with model prob p, full Kelly is (p − q) / (1 − q); we take 25% of that, capped at 10% of bankroll per position, and floor to whole contracts.

Fees, modeled explicitly. Kalshi taker fee ceil(0.07·C·P·(1−P)) per fill (settlement free); Polymarket sports taker fee shares·0.05·P·(1−P). A strategy that's positive pre-fee and negative post-fee is the most common self-deception here, so both numbers are always shown.

Conservative fills. We fill at the price available at decision time — the venue ask, never a better mid — and never assume size a thin book couldn't absorb. In the backtest, where only a de-vigged closing line exists, we widen it toward a taker ask before charging fees.

Settlement. Positions settle on the same game outcomes we grade on /model. Bankroll starts at $50.00.

Phase 1 is paper only. No trading credentials, no order placement. The architecture is built so a future execution module could subscribe to the same signals — but nothing here places an order.

Model probabilities come from our graded win-probability model. Data via the MLB Stats API, Kalshi + Polymarket public market data, and The Odds API. Updated on a schedule.