How to Backtest a Polymarket Strategy with Resolved Markets' Strategy Tester

Resolved Markets' strategy tester lets you describe a Polymarket strategy in plain English, then returns the generated code, an equity curve, and a trade list built on tick-level historical orderbook snapshots. You can start with a free API key (no account creation or credit card required per the site), and the backtester is listed as free with real historical order book data across prediction, crypto, sports, and weather markets. This walkthrough covers the setup, the backtest loop, and the data-alignment problems that most often break a first run.

What you need before you start

• A Resolved Markets account and API key. The site states you can generate a free API key instantly with no account creation and no credit card.
• A strategy you can state in one or two sentences. The tester works from a plain-English description, so vague inputs produce vague code.
• A target market or series. Coverage includes crypto prediction markets, politics and elections, sports and live events, and economic indicators, with 100+ markets tracked across 7 categories.

Step 1: Get your API key

Generate a free API key from the dashboard. Per the site, there is no account creation and no credit card step — just click and go. Keep the key out of version control; you will pass it as an X-API-Key header on requests.

If you sign up for a new Polymarket account through the referral link, the site says it adds 10,000 free API credits to your Resolved Markets account, once per account.

Step 2: Confirm the data you are backtesting against

Before describing a strategy, check that the market and time window you care about are covered. The dataset is described as 4.2B+ orderbook snapshots across 184 recurring and event series since March 2026, with each snapshot containing full bid/ask depth, mid prices, spreads, and crypto spot price.

Two capabilities matter for strategy design:

If your strategy depends on liquidity or spread behavior, you need the depth fields, not just the mid price. A strategy that looks profitable on mid prices can invert once you fill against the actual book.

Step 3: Describe the strategy in plain English

The tester takes a natural-language description and returns the code, equity curve, and trades. Write the description so it pins down four things:

1. Market and window — which market or series, and the date range.
2. Signal — what triggers an entry. Reference fields the data actually has, such as mid price, spread, or bid/ask depth.
3. Sizing and fills — how much you trade and whether you assume you cross the spread or rest in the book.
4. Exit — time-based, price-based, or event-based.

For example, instead of "buy when it looks cheap," write something like: "On the BTC hourly market, enter when the mid drops more than 3 cents below the 24-hour average mid and the spread is under 2 cents; size at 1% of bankroll; exit at the earlier of a 5-cent gain or 30 minutes." The agent then generates code you can inspect, an equity curve, and the trade list.

Step 4: Run the backtest and read the outputs

The tester returns three artifacts. Check each one:

• Code — read it before trusting the result. Confirm the entry and exit conditions match what you described, and that fills use the orderbook depth rather than an idealized price.
• Equity curve — look for the shape, not just the endpoint. A curve that is flat then jumps once usually means the strategy only traded in one window.
• Trades — check timestamps and fill prices against the snapshots. If a fill price sits outside the bid/ask range recorded at that timestamp, the fill model is wrong.

Step 5: Replay the event window

If your strategy is event-driven, use time-based queries and replay snapshots to isolate the window. The site frames this as replaying real market reactions to viral moments, breaking news, and sentiment shifts, minute by minute. Run the same strategy with and without the event window to see whether the edge comes from the event or from the surrounding baseline.

Common failure points

Time misalignment. Snapshots and your signal timestamps must be on the same clock. If you compute a rolling average from one series and fill from another, you can accidentally use future data. Verify that every input to a decision is timestamped at or before the fill.

Missing data in the window. If a market has gaps, the backtest may silently skip periods or carry forward a stale price. Check snapshot density across your date range before trusting the curve.

Fills that ignore depth. A strategy that assumes it always fills at the mid will overstate returns on thin markets. Use the bid/ask depth fields to cap size at what the book actually held.

Overfitting to one viral moment. A single event window can dominate the equity curve. Test across multiple windows and categories before drawing conclusions.

FAQ

Is the backtester free? The site describes backtesting Polymarket strategies for free with real historical order book data, and lists a free tier. Check the pricing page for current limits.

Do I need a credit card? The site states API key generation requires no account creation and no credit card.

What markets are covered? Crypto prediction markets, politics and elections, sports and live events, and economic indicators, with 100+ markets across 7 categories and 30+ cities tracked.

Can I get the generated code? Yes — the tester shows the code alongside the equity curve and trades.

How far back does data go? The dataset is described as snapshots since March 2026. Confirm coverage for your specific market and window before running.