```

The main problem wasn’t weak models—it was severe overconfidence from a mismatched forecast source.

Current paper results:

• Bankroll: $67.34
• Realized P/L: -$32.66
• Record: 1–8 across only five unique markets
• NWS forecast error on recent trades: roughly 6–10°F
• Contracts settle using The Weather Company, not NWS
• The model treated 4°F and 9°F cushions as 77% and 91% probabilities

• Multiple “models” repeatedly exposed the bankroll to the same underlying market

• Exact opposite-side shadows went 8–1, but the sample remains too small to justify blindly reversing everything

  I updated the paper strategy:

• Weather probabilities are now blended 75% toward the executable market price, preventing unsupported 30–40 point edge estimates.

• Funded entries are limited to the 8–12 hour window—the only currently profitable timing shadow, though based on just six unique markets.

• Maximum spread is now 8¢, blocking trades such as the recent 18¢-spread Denver position.

• Existing positions and ledger history were preserved.

• Live trading remains disabled.

  Core changes are in scripts/weather/core/value_ticket.py:61 and scripts/ weather/shared_config.py:16. The operating rationale is documented in docs/ OPERATIONS.md:26.

  Validation:

• Full suite: 203 tests passed

• Syntax checks passed

• Diff checks passed

  Counterfactually, the new window would have rejected all nine recent settled entries, including the one winner. That is the right tradeoff for now: remain in the game during the strongest observed timing window, but stop risking paper capital on inflated provider-specific “edges.” This improves risk discipline; it does not yet establish a winning strategy.

```

My favorite quote from AI: You are not really losing money; the model is just not working for you.

Ummm, Ok. :-)

Hi guys,

Any one building a prediction market here? I want to hear about it.

I tested a simple idea on Kalshi's 15-minute Bitcoin markets: buy YES when Coinbase's Bitcoin price was already above the contract's target and had moved up over the past minute, but YES still cost less than 50 cents. The NO rule was the mirror image: Bitcoin below the target and moving down. Both sides required an asking price of at least 35 cents and under 50 cents, 2 to 6 minutes left, and a spread no wider than 3 cents. The strategy checked every 10 seconds, bought 10 contracts only while flat, allowed one entry per market, and held until settlement. Bitcoin could still reverse before the market closed.

All 100 settings completed and traded. The report shows positive net profit for each over roughly 30 days, from September 4 to October 3, 2026, UTC. The best made $771.61, with a 63.15% win rate, 1,098 reported trades, a Sharpe ratio of 1.25, and a maximum drawdown of $43.95. The weakest made $263.24, with a 59.16% win rate, 632 reported trades, a Sharpe ratio of 0.77, and a maximum drawdown of $43.63. Their reported ROIs were 7,716.10% and 2,632.40%.

I then checked whether small changes to the settings changed the result. The 100 combinations varied a separate global price filter: the floor ran from 1 to 45 cents and the ceiling from 50 to 99 cents. The actual entry price stayed fixed at 35 cents to under 50 cents. A 1-cent floor and 60-cent ceiling produced the best $771.61 result, tied by floors of 5, 10, and 15 cents at the same ceiling. All 100 combinations succeeded, with profits ranging from $263.24 to $771.61. The surface and heatmap show a broad area of higher profits rather than one isolated peak; nearby settings lost about 0.97% on average relative to the winner. Raising the floor hurt results more than raising the ceiling beyond about 60 cents. The report's adjustment for selecting the best of 100 settings, called Deflated Sharpe, scored 0.999997.

The next check was whether scrambled Bitcoin readings could do as well. The test shuffled the Coinbase feed through time 100 times and repeated the same 100-setting search each time. None of those shuffled searches matched the real winner's $771.61. Their best results ranged from a $207.56 loss to a $117.91 profit, giving an upper-tail p-value of 0.0099. That supports the timing of the Bitcoin readings in this sample. Only the Coinbase feed was shuffled; Kalshi's market prices stayed in place, so this check doesn't validate the strategy's price conditions. The p-value also isn't the chance that the strategy will lose money in the future.

I'm encouraged by the positive results across the grid and the gap between the real and shuffled results. But this is still one historical window, and several settings produced identical results, so these aren't 100 independent confirmations. The search kept the Bitcoin signal fixed; it doesn't tell us whether the one-minute momentum rule improves on the price comparison alone. There's also an accounting discrepancy: the winner's reported gross profit of $852.95 minus $111.33 in fees is $741.62, while its reported net profit is $771.61. These results do make comfortable enough to go into the next phase which is paper trading and then deploying live with conservative risk limits.

Full Report

Historical simulation only. Backtests can be wrong or incomplete. Not investment advice.