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Products that do what Resolved Markets does

Backtest your Polymarket strategy with real historical data

  1. 1HF
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    Quantle28

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    We think backtesting is too difficult, this is our attempt to make it much easier and more fun - let us know what you think!

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  6. 6IA

    Hey HN, I aggregated a bunch of GTM advice, including their rankings. Thought it could be useful for some founders here. Let me know if you have any feedback. Thanks!

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  7. 7

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  8. 8BT
  9. 9CY

    Hi HN! Excited to show off the project we've been working on for the last couple months. We started with an idea for a crazy twist on prediction markets: You come up with a question for traders to predict, and then decide the outcome yourself. For example, you could create a market on “Will my date with [X] go well?” Anyone can bet on it, and the bets create a forecast on the chance your date goes well. After the date is over, you get to judge the result and reward the traders who picked the correct side. There are so many ways for this mechanism to go wrong: the creator of the market can be…

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    Plain English in. Backtest out.

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  16. 16IB

    I built a web page that aggregates data about data center buildup, sovereign fund investments into AI and bottlenecks. The objective is to predict AI race cooldown by looking at a potential decrease of activity involving these elements. The website looks at the quarterly forms from the 5 biggest hyperscalers and adds their CapEx into the mix, calculating a composite index in the end showing how likely it is for the AI race to slow down. Enjoy!

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  17. 17

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  18. 18PC

    Hey HN - my friend and I built Prediction Hunt, a site that aggregates data from prediction markets like Kalshi, Polymarket, and PredictIt into one simple dashboard. It updates every few minutes, shows probabilities for each event, and even highlights arbitrage opportunities when markets disagree. I made it after getting tired of flipping between tabs just to see how each market was pricing the same question. Would love feedback on the product, data accuracy, and any ideas for what you'd want to see next.

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  22. 22BA

    I’ve been experimenting with a graph-based approach to a classic trading problem: why most dip-buying strategies can’t tell the difference between a temporary overreaction and a genuine structural collapse. Most systems treat a −5% move the same regardless of context. My hypothesis was that where a company sits in the market’s structure matters more than the price move itself. The engineering idea I built a knowledge graph of the U.S. public markets with ~207k edges across ~21 relationship types, organized into four layers: Operational: supply-chain relationships (SUPPLIES_TO, PRODUCES)…

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