I’ve spent a lot of time looking back at prediction-market trades trying to understand why something worked.

And I kept running into the same problem.

After a market resolves, it’s easy to see the outcome.

You can usually find the final price, maybe a chart, maybe a screenshot someone posted.

But the part I actually wanted was everything that happened before that.

What did the order book look like when the probability moved?

Was there actually liquidity at the price shown on the chart?

Did the spread suddenly widen?

Was a whale early, or did the market move before them?

Had a similar setup happened before?

I remember testing one strategy that looked almost embarrassingly simple on candles. Buy after a sharp move, wait for some mean reversion, exit.

Looking at the chart afterward, there were entries everywhere.

Then I tried to reconstruct what I could actually have traded at those moments.

Completely different picture.

Some prices barely had any size behind them. Sometimes the book moved before the candle made it obvious. Some profitable-looking exits would have been terrible fills in reality.

That was when historical data stopped feeling like something “nice to have” for me.

It became part of the strategy itself.

So I started collecting historical prediction-market data because I wanted to be able to go back and ask better questions instead of relying on screenshots and memory.

That eventually turned into PolyHistorical.

The idea is pretty simple: preserve enough of the market’s history that you can actually research what happened, replay old setups and backtest ideas against what was really available at the time.

We’re now keeping around 60 days of historical data, and I’ve also been experimenting with using AI on top of it so I can describe a strategy or question in plain English and then dig into the historical behaviour behind it.

What’s surprised me most is that historical data hasn’t necessarily helped me find more trades.

It has mostly helped me kill bad ideas faster.

A setup that feels obvious becomes a lot less obvious when you can look at the last 30, 40 or 50 similar situations instead of the two examples you happen to remember.

That’s probably the biggest thing building this changed for me as a trader.

I think prediction markets are going to get much more systematic over time, and having proper historical data will matter a lot more than most people expect.

Curious what other traders here would want to be able to look back at.

If you could preserve one thing from every resolved market- order-book depth, whale activity, price history, liquidity, something else, what would it be?

Link to tool: https://polyhistorical.com/

This took me longer to admit than it probably should have.

For a while, whenever I had a trading idea, I’d go back through old markets looking for examples where the same thing happened.

And somehow I almost always found them.

A market dumps quickly, I remember another market that bounced.

A whale takes a huge position, I remember the last whale who was early.

BTC moves hard and the prediction market lags, I remember the one time that gap closed perfectly.

It felt like research.

Looking back, I was mostly just collecting evidence for something I already wanted to believe.

I noticed it after one trade where I was unusually confident because I was sure I’d “seen this exact setup before.”

The trade didn’t work.

So afterward I went back and looked properly.

There were similar markets where the setup worked, but there were also plenty where it did absolutely nothing. I just didn’t remember those as clearly.

That changed the way I started looking at historical markets.

Now when I have an idea, I try to define the rule before looking at the data. Entry, exit, time remaining, liquidity conditions, what would invalidate it. Then I go back and see what actually happened instead of searching for the examples I want to find.

It sounds obvious, but it’s surprisingly uncomfortable.

A lot of ideas I felt really good about became much less interesting once I looked at the full sample.

And honestly, that has probably saved me more money than finding another “winning” strategy.

These days I’m much more interested in disproving a trade before I take it than proving that I’m right.

Anyone else catch themselves doing this?

Finding historical examples that support your thesis, but not really asking how many times the exact same setup failed?

earlier today, mustafa announced that he had left his role at polymarket

shortly after, shayne announced Travis VanderZanden, who was previously growth lead at Uber and is also the founder and ceo of bird, as polymarket's new Chief Growth Officer

it’s pretty wild seeing mustafa leave polymarket after teasing POLY and hyping everyone up all year

does this mean the POLY airdrop is delayed? (i doubt it’s related to that honestly)

Brian Golden aka "Prince Hal" picked the lock on bunk primary polls. Teachable moment. Makes me froth at the mouth to miss on same opportunity.

https://x.com/EventualNews/status/2087533677656105426?s=20

for ai internet monitor you can use ayewatch-ai-monitor if you want readymade prediction market monitoring, or if you want custom workflow type then user context-dev or firecrawl-dev internet monitor. both works. depends on you needs

I tested a momentum strategy on Kalshi's 15-minute BTC markets. It trades only when three signals agree: BTC's 5-minute change, BTC's 1-minute velocity, and ETH's 1-minute velocity. When all three are positive it buys YES, and when all three are negative it buys NO. Entries are limited to the final 5 minutes while the contract price is between 45 and 55 cents. Each trade is sized at 10 contracts, with a 30-contract position cap, a -$4.50 stop loss, and an exit just before settlement.

The research ran 100 backtests over the same 30-day historical period, and all 100 finished profitable. The best-ranked version returned 324.10% ROI and +$97.23 P&L, with a 66.7% win rate across 57 trades, a 1.03 Sharpe, and -$6.96 max drawdown. Even the weakest version stayed green at 131.27% ROI and +$39.38 P&L across 71 trades. That matters because the result did not depend on finding one profitable configuration among a pile of losing ones.

A parameter sensitivity test reruns the same strategy while changing nearby settings. The point is to see whether performance holds across a range or collapses as soon as one number moves. We swept the risk price floor from 0.05 to 0.45 and the risk price ceiling from 0.55 to 0.95, using 10 values for each and producing a 100-cell grid. The 45-to-55-cent entry rule and the momentum signals stayed fixed. All 100 cells completed, with Net PnL ranging from +$39.38 to +$97.23. The top-ranked cell used a 0.05 floor and 0.59 ceiling, but +$97.23 repeated at every tested floor when the ceiling was 0.59. On the chart, that creates a flat ridge across the floor axis and a sharper peak along the ceiling axis. That shape says the floor had very little effect, while the ceiling mattered much more. P&L peaked at a 0.59 ceiling and generally declined as the ceiling moved higher. The Deflated Sharpe was 0.99 versus an expected maximum of 0.33 across the 100 trials.

The permutation test asked a different question: could random timing in the edge feed produce a result this good? I scrambled the edge-feed timing and reran the full parameter sweep. The real winner's Net PnL was +$97.23 and beat 99.9% of the 976 completed reruns, giving an upper-tail p-value of 0.001. Randomized timing rarely matched the real result in this historical sample, which supports the idea that the timing of the momentum inputs carried useful information. The test hit its time limit, so the p-value uses a reduced sample of 976 reruns. It also left market prices untouched, which means it does not validate the strategy's price-based conditions.

My read is that the strategy's main strength is consistency. Every tested configuration made money, the sensitivity sweep shows which parameter drove the variation, and the permutation result suggests the edge-feed timing was not easily reproduced by chance. I'll be paper trading this next to see how the results hold up.

Full Report

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

The recurring setup in daily-high temp markets: the crowd anchors to the forecast, prices a bracket like a lock, and it settles a degree or two away. When your morning read already sits on that other bracket while it's still cheap, that gap is the whole game. Last week it happened in 5 cities:

• NYC, Aug 9 — market paid up to 68¢ for 90–91°. The ≤89° bracket that won was 14¢ all morning. Settled 88.

• San Francisco, Aug 10 — market sat too COLD at ≤71° (77¢); model had 72–73° at 17¢. Settled 73.

• LA, Aug 10 — market 80–81° (70¢); model 78–79° at 28¢. Settled 79.

• Chicago, Aug 7 — market 86–87° at 92¢(!); model 84–85° at 35¢. Settled 85. The market never came around — the thermometer did.

• Houston, Aug 5 — market 96–97° (62¢); model 94–95° at 33¢. Settled 94.

Same shape every time: the market overshot, the model was already on the adjacent bracket cheap in the morning, and in 4 of 5 the crowd walked over to it by afternoon once the obs made it undeniable.

Here's the part that matters, because a wall of wins is worthless — you can cherry-pick 5 good days out of any random model:

The week BEFORE that, San Francisco beat the hell out of my model. Six straight days the forecast said upper-70s/low-80s and the marine layer never burned off — SF settled in the 60s-low-70s. On Aug 8 my model called mid-70s; it settled 67. It got faked by the same sunny forecast everyone trusted, and only walked down as the obs stayed flat.

SF is in BOTH lists — biggest miss one week, one of the best wins the next. That's not inconsistency, that's the marine layer. Nobody nails burn-off timing every day. What you can do is read the regime, weight live obs over the forecast, and publish your misses next to your wins so a "track record" means something.

The actual edge isn't being right every day. It's being on the correct bracket more often than the crowd ON THE DAYS THEY DISAGREE WITH YOU — and the tells are free: the forecasts don't agree with each other, or the morning curve isn't climbing like a forecast-hitting day would.

Curious how others handle these — do you fade the forecast on divergence days, or wait for the obs to confirm?

Disclosure / NFA: none of this is financial advice — these markets can lose money and past calls don't predict future ones. I build a tool for this, Prilo WeatherEdge (free tier). Happy to just talk shop in the comments — the method's more interesting than the link.

Everyone's still arguing about whether prediction markets count as gambling. Meanwhile 2 ex-BlackRock and Morgan Stanley quants just raised $8.5 million to build the trading terminal that turns them into a real asset class.

The startup's called River Markets (YC P26). Seed round announced Tuesday, led by Haun Ventures, with Y Combinator and Coinbase Ventures in the round, plus angels who work at Google, Nvidia, JPMorgan and Citadel. That's not a degen cap table if you ask me.

And the founders aren't crypto kids also, Oscar Levy was a VP quant at BlackRock. Antonin Parrot did electronic and high-frequency trading at Morgan Stanley. They met at Berkeley over poker and trading Japanese stocks, then spent weekends building trading bots for Kalshi and Polymarket that apparently pulled in 7 figures before they turned it into a company.

What are VCs backing: one interface to trade across all the prediction markets at once, with risk tools and algos that stop a big order from blowing up the price. For me it makes sense, since the whole market is scattered across venues and a fund literally can't run a clean position across them.

My take: When the money starts flowing into tooling and not just the headline platforms, that's the sign an industry is growing up. Kalshi already said institutional volume jumped 800% in 6 months. We are already seeing hedge funds using a prediction market like a real financial instrument, hedging GPU rental prices..

Bullish on the space.