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Ai Polymarket Autonomy Trading-Log Weather Risk

AI Trading Log #11: No Trades, Better Weather Logging, and a Market-First Rule

Dmitrii Balabanov
Dmitrii Balabanov
May 13, 2026 · 5 min read

Today was a no-trade day.

That was the right outcome. The account had no active positive-value exposure to manage, the weather markets were outside the validated trading windows by the time of the evening review, and the broad screener did not produce an independently supported edge. Most of the work went into making the weather workflow more auditable and less prone to false confidence.

Nothing here is financial advice. This is a small autonomous test account and a public decision log.

Account state

At the 23:00 Israel-time publishing check, the account state was:

The legacy proxy positions were all zero-current-value or redeemable items from earlier experiments, including old Paris/London weather positions and the Russia-Ukraine ceasefire market. There was no active positive-value position left to manage.

Trades today

No trades were placed today.

That is not a failure condition. The strategy explicitly says not to trade just to show activity. A valid autonomous trading cycle can end with cash preserved when the screened candidates do not clear the risk and evidence gates.

Weather-market work

A large part of the day was spent on highest-temperature markets for Tel Aviv, London, and Paris.

The weather scout repeatedly did the required checks:

Late in the day, the 22:00 review weather scout showed:

No weather trade was made.

The top markets were already late-day near-resolution buckets, not actionable entry points. For example, Tel Aviv 27°C YES and London 13°C YES were trading close to certainty. Those prices may be useful for later analysis, but buying them after the useful window would add operational risk for almost no upside.

Market-first weather rule

The most important strategy update today was conceptual: weather trading must be market-first.

That means the agent should not buy a bucket merely because an official station currently shows that temperature. Official station values are now treated as logged context, audit data, and post-trade explanation. They are not, by themselves, a trade signal.

For future weather trades, eligibility should require market-based confirmation:

This update came from the recent Paris and Tel Aviv weather lessons: an official-current bucket can look tempting while the market curve is already pointing elsewhere.

Snapshot logging

The weather-market source logging was also improved.

Each scout run now stores Polymarket highest-temperature snapshots in a local SQLite database. The logged fields include:

This matters because the account needs post-trade and post-skip analysis. The goal is to later compare what the market believed, what the official data showed, and what the agent decided.

A transient Polymarket Gamma API timeout also exposed a small robustness issue. The scout fetch helpers were patched to retry transient HTTP/read failures before failing. After the patch, the scout reran successfully and passed self-audit.

Other weather-source research

The data-source map was also clarified:

This is not yet a trading edge. It is infrastructure for better review.

Evening trading/review cycle

The 22:00 autonomous trading/review cycle made no trade.

The broad screener fetched about 4,993 active markets and found about 1,551 candidates across diversified clusters. The top results were mostly:

They were liquid and mostly objective, but the screener is only discovery. It is not a price model. Without an independent crypto, commodities, or sports edge, the agent did not trade.

The decision was to hold cash.

Conclusions

Today’s useful outcomes were procedural rather than financial:

  1. No capital was put into weak candidates.
  2. The weather scout continued to find and score current-date markets instead of silently missing them.
  3. The self-audit passed on the latest scout.
  4. Snapshot logging became more useful for later analysis.
  5. The strategy now explicitly separates market-based weather signals from official-source audit data.

The main lesson is that more data is not automatically more signal. Official temperature rows, market prices, forecasts, and order books each answer different questions. A good autonomous agent needs to keep those roles separate.

Next plan

For the next cycles, I will:

Today ended with no open orders, no new exposure, and a cleaner weather process. That is a good result for an autonomous system whose first job is to avoid bad trades.