Safe Choices

Prediction Market Simulator
Polymarket 2025

Parameters

runs
days
%
%
$
funds
~5-10 seconds
Initializing... 0%
Average Return
--
Avg Return (Survivors)
--
Success Rate
--
Bust Rate
--

Risk Analysis

Volatility --
Max Drawdown --
5th Percentile --
95th Percentile --

Portfolio Stats

Surviving Funds --
Survivorship Rate --
Diversification --

Target Performance

Target Reached --
Avg Time to Target --
vs Never Stop --

Kelly Stats

Avg Bet Size --
Avg Edge --
Bets Skipped (No Edge) --

Return Distribution

Capital Evolution

Fund Survivorship

Safe Choices

Assessing the efficiency of "safe bets" on prediction markets

Imagine 50 hedge funds, each deploying $100 on a "State Earthquake Trade." Each fund bets on their state NOT being hit by an earthquake (10% annual probability), earning 1.11x if correct.

After 10 years across 100,000 simulations: 17.42 funds survive on average (range: 4-32). These survivors produced 11% annual returns for a decade. In an efficient market, luck becomes the differentiating factor between investment legends and bankruptcy.

Research Questions

We explore the efficiency of "safe choices" (markets trading at 90+ cents before resolution):

  • How often will traders making random walks across these markets go bust?
  • How often will they beat traditional benchmarks?
  • Does stopping at a return threshold (e.g., 12%) improve outcomes?
  • Does splitting capital across multiple funds improve survivorship?
Hypothesis: Inefficiencies in "safe" markets provide opportunities for positive expected value trades exceeding traditional benchmarks like the risk-free rate.

Dataset

Data collected via Polymarket's Gamma API and CLOB API, filtering out high-variability categories:

Sports Esports Crypto Prices Weather

For remaining markets, we log probability snapshots from 7 days to 1 day before resolution. The dataset covers 2024-2025 and is available on Kaggle.

Simulation Model

1 Each day, decide to invest with probability alpha (trading frequency)
2 Select uniformly at random from eligible markets meeting thresholds
3 Remain locked until resolution, then reinvest winnings
4 Continue until: bust, target reached, or simulation ends

Strategies

Single Fund

Deploy all capital sequentially across safe markets, reinvesting all winnings. High risk, high reward.

Target Return

Same as Single Fund but stop trading once a target return (e.g., Treasury rate or NASDAQ average) is reached.

Multi Fund

Split capital into N independent funds. Tests whether diversification improves survivorship rates.

Technical Notes

  • Uses actual Polymarket data filtered for 2025+ resolution
  • Vectorized operations for performance (~1000 sims in 10-20s)
  • Binary outcomes: win = 1/probability return, loss = total loss
  • Reproducible results via random seeds

Limitations

At $10,000 scale distributed across funds, liquidity isn't a concern. However, scaling this strategy significantly would face real liquidity constraints and potential market impact.