What you'll build

A Python script that pulls historical Polymarket probability data from Kwery, computes simple entry/exit signals based on probability thresholds, and evaluates strategy performance over a historical window.

Prerequisites

• A Kwery API key (get one here)
• Python 3.10+
• requests, pandas, and matplotlib installed

Outline

1. Fetch market snapshots over time
2. Build a probability time-series
3. Define a threshold-based signal
4. Simulate trades and compute PnL
5. Visualize results

Step 1 — Fetch historical snapshots

Use the /v1/snapshots/{identifier} endpoint to pull a time-series of probability values for a given Polymarket market.

curl "https://kwery-api.com/v1/snapshots/{identifier}?api-key=YOUR_KEY"

Section coming soon — will include full Python code to paginate and normalize snapshot data.

Step 2 — Build a probability time-series

Parse the snapshot response into a pandas.DataFrame indexed by timestamp, with columns for each outcome's probability.

Section coming soon.

Step 3 — Define entry and exit signals

Apply a simple threshold strategy: buy when probability drops below a configurable floor, sell when it rises above a ceiling.

Section coming soon.

Step 4 — Simulate trades

Walk forward through the time-series, tracking position, entry price, and realized PnL per trade.

Section coming soon.

Step 5 — Visualize results

Plot probability over time with entry/exit markers and a cumulative PnL curve.

Section coming soon.

Next steps

• Extend the strategy with Kelly criterion position sizing
• Compare across multiple markets using /v1/markets
• Add Kalshi data via /v1/kalshi for cross-venue analysis