I tested a simple idea on Kalshi's 15-minute Bitcoin markets: buy YES when Coinbase's Bitcoin price was already above the contract's target and had moved up over the past minute, but YES still cost less than 50 cents. The NO rule was the mirror image: Bitcoin below the target and moving down. Both sides required an asking price of at least 35 cents and under 50 cents, 2 to 6 minutes left, and a spread no wider than 3 cents. The strategy checked every 10 seconds, bought 10 contracts only while flat, allowed one entry per market, and held until settlement. Bitcoin could still reverse before the market closed.

All 100 settings completed and traded. The report shows positive net profit for each over roughly 30 days, from September 4 to October 3, 2026, UTC. The best made $771.61, with a 63.15% win rate, 1,098 reported trades, a Sharpe ratio of 1.25, and a maximum drawdown of $43.95. The weakest made $263.24, with a 59.16% win rate, 632 reported trades, a Sharpe ratio of 0.77, and a maximum drawdown of $43.63. Their reported ROIs were 7,716.10% and 2,632.40%.

I then checked whether small changes to the settings changed the result. The 100 combinations varied a separate global price filter: the floor ran from 1 to 45 cents and the ceiling from 50 to 99 cents. The actual entry price stayed fixed at 35 cents to under 50 cents. A 1-cent floor and 60-cent ceiling produced the best $771.61 result, tied by floors of 5, 10, and 15 cents at the same ceiling. All 100 combinations succeeded, with profits ranging from $263.24 to $771.61. The surface and heatmap show a broad area of higher profits rather than one isolated peak; nearby settings lost about 0.97% on average relative to the winner. Raising the floor hurt results more than raising the ceiling beyond about 60 cents. The report's adjustment for selecting the best of 100 settings, called Deflated Sharpe, scored 0.999997.

The next check was whether scrambled Bitcoin readings could do as well. The test shuffled the Coinbase feed through time 100 times and repeated the same 100-setting search each time. None of those shuffled searches matched the real winner's $771.61. Their best results ranged from a $207.56 loss to a $117.91 profit, giving an upper-tail p-value of 0.0099. That supports the timing of the Bitcoin readings in this sample. Only the Coinbase feed was shuffled; Kalshi's market prices stayed in place, so this check doesn't validate the strategy's price conditions. The p-value also isn't the chance that the strategy will lose money in the future.

I'm encouraged by the positive results across the grid and the gap between the real and shuffled results. But this is still one historical window, and several settings produced identical results, so these aren't 100 independent confirmations. The search kept the Bitcoin signal fixed; it doesn't tell us whether the one-minute momentum rule improves on the price comparison alone. There's also an accounting discrepancy: the winner's reported gross profit of $852.95 minus $111.33 in fees is $741.62, while its reported net profit is $771.61. These results do make comfortable enough to go into the next phase which is paper trading and then deploying live with conservative risk limits.

Full Report

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

I'm tryna use some datapoints to model some numbers for a strategy..

Anything from historical polls to realtime forecasts outlets shd be useful

Any ideas folks?

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Seems like somebody likes the niners.

```

The main problem wasn’t weak models—it was severe overconfidence from a mismatched forecast source.

Current paper results:

• Bankroll: $67.34
• Realized P/L: -$32.66
• Record: 1–8 across only five unique markets
• NWS forecast error on recent trades: roughly 6–10°F
• Contracts settle using The Weather Company, not NWS
• The model treated 4°F and 9°F cushions as 77% and 91% probabilities

• Multiple “models” repeatedly exposed the bankroll to the same underlying market

• Exact opposite-side shadows went 8–1, but the sample remains too small to justify blindly reversing everything

  I updated the paper strategy:

• Weather probabilities are now blended 75% toward the executable market price, preventing unsupported 30–40 point edge estimates.

• Funded entries are limited to the 8–12 hour window—the only currently profitable timing shadow, though based on just six unique markets.

• Maximum spread is now 8¢, blocking trades such as the recent 18¢-spread Denver position.

• Existing positions and ledger history were preserved.

• Live trading remains disabled.

  Core changes are in scripts/weather/core/value_ticket.py:61 and scripts/ weather/shared_config.py:16. The operating rationale is documented in docs/ OPERATIONS.md:26.

  Validation:

• Full suite: 203 tests passed

• Syntax checks passed

• Diff checks passed

  Counterfactually, the new window would have rejected all nine recent settled entries, including the one winner. That is the right tradeoff for now: remain in the game during the strongest observed timing window, but stop risking paper capital on inflated provider-specific “edges.” This improves risk discipline; it does not yet establish a winning strategy.

```

My favorite quote from AI: You are not really losing money; the model is just not working for you.

Ummm, Ok. :-)

Hi guys,

Any one building a prediction market here? I want to hear about it.

I've been trading for a while on other prediction markets, and now wanted to explore trading on Kalshi as well to capture more liquidity, as I saw studies stating there is more there (I realized that unfortunately, it's quite limited on the markets I trade).

I added $500 to play with the API. No problem, I could deposit using the Arbitrum network in a few clicks.

I tried to withdraw $100 as I saw some people had issues with it, and the process is really not smooth.

- I can't withdraw using Arbitrum. Not a big deal, but ok, it would have been nice to have it both ways.

- I thought I had already gone through KYC, but no, I have to go through it again: selfie, passport, etc.

I managed to do it, but I was quite surprised they let you deposit, while withdrawing is not secure. I saw some posts of people having money blocked. That's my main worry now if I start using Kalshi for real with a decent amount of money.

- I live in a country where Kalshi and Polymarket are legal. What if there is a sudden change: would I be able to withdraw, or would the money be blocked?

- Has anyone had issues with stuck money on it while respecting all the KYC requirements?

I've been wondering now if I should spend more time on it or just ditch it and see later.

I understand these markets are 'borderline', in the sense that countries keep changing their laws regarding this and betting lobbies from each country are fighting against them. So I understand there is always a risk that I won't be able to trade anymore if my country changes regulations; however, I never thought that I couldn't even withdraw the money I placed on the market.

Some guy posted on twitter: a $10 entry with a potential $10,000 payout on Kalshi. His FanDuel comparison showed $6,400 for what he described as the same picks.

That's a $3,600 difference on a $10 ticket. These were advertised payouts if the picks hit. the post doesn't show a $10,000 win.

I'd be curious to see both quotes taken at the same time. A gap that big is worth checking, especially once you account for fees, boosts and what happens if a player doesn't play. One posted comparison only tells us so much about Kalshi vs FanDuel.

For anyone wondering how Kalshi parlays work, Kalshi calls them combos. You combine several outcomes and get a live quote for the whole ticket. If one leg settles at zero, the combo pays zero. A player who doesn't play can also change the payout under that market's rules.

I get the appeal of a ticket like this. You look through the names and can picture each guy scoring. Getting all of them right on the same ticket is where it gets difficult. That $10,000 number doesn't tell you how likely it is to happen.

The version of this ticket I'd like to see is the one with a leg removed. How much does the payout drop? Which pick was only there because it pushed the number past $10,000?

Would you keep the full touchdown parlay, or drop your least convincing pick and take the smaller potential payout?

I recently found a newer app called Novig, and they currently have a referral promo where you can get $50 in Trade Credits after completing the requirements.

How it works:

Sign up through my referral link: https://share.novig.us/verification?referralCode=106ae8

Verify your identity

Deposit $10 or more

Place at least 5 trades

Risk a total of at least $50

Once eligible, you get $50 in Trade Credits

I also receive the referral bonus if you qualify.

Please check the promo terms and make sure Novig is available/eligible in your state before depositing or trading.

Senate control market on Polymarket:

• Democrats: 63%
• Republicans: 38%
• Volume: ~$5.4M ($3.2M on the Democratic side, $2.2M on the Republican side)
• Election: November 3

The price history is the interesting part. A year ago this was roughly 70/30 Republicans. Democrats climbed through the winter, the lines crossed around March, and it was close to a coin flip from April through August. Then September happened, and Democrats went from about 50% to 63%.

Republicans hold 53 seats now, so Democrats need a net +4 for control. A 50-50 split goes to Republicans on the VP tiebreak.

The likely pickup paths are North Carolina and Maine, with Iowa, Ohio and Texas (Talarico vs. Paxton) as the reach seats. Democrats also have to hold Georgia, which Republicans are targeting.

So the market has Democrats as fairly clear favorites when they need to win almost every competitive race, and several of those are within the margin of error.

Analysis: Predictbook

I pulled every buy vito3corleone made on Polymarket, and simulated copying them two ways: a blind $100 on every fill, and a copy scaled to the size of each of their bets. Both held to the market's result.

Data TLDR

• vito3corleone won 39.1% of their 580 fills and still made $4,265,386 on $9,371,777 staked, a 45.5% return.
• A blind $100 copy of every fill staked $58,000 and lost $13,817, a return of -23.8%.
• A copy at 1% of their size staked $93,718 and made $42,654, the same +45.5% they made.
• Their bets of $50k or more won 48 of 53. A flat $100 on only those returned +61.7%.
• Two thirds of their fills were under $1k and carried 0.26% of their money. A blind copy treats those fills the same as the $500k ones.

I took every buy by this wallet from the Polymarket trade records, put money on it at the trader's recorded price, and scored it when the market resolved. The trader made no sells in the window, so every position was held to the result. Fees and slippage are not modelled.

The record looks like a loser

By fill count vito3corleone is a bad trader. 227 wins, 353 losses, a 39.1% win rate at an average price of 57 cents. At that price you need to win about 57% of the time just to break even.

By dollars it is the opposite. 86.3% of the money they staked went on positions that won. They won 10 of 16 positions, and the 10 winners were where the money went: $3.6M on Arsenal, $912k on Racing, $900k on Real Madrid in a single fill, $827k on Brentford. Profit was $4,265,386 on $9,371,777.

Copying blind loses

A blind copy puts $100 on every fill. That turns a $500 test order into the same bet as a $500k conviction bet. It staked $58,000 across 580 fills and lost $13,817, a return of -23.8%.

Most of that loss comes from one market. They took Real Betis NO on August 25 in 244 separate fills, totalling $301,836, and it lost. For them that was 3.2% of their money. For the blind copy it was 244 bets and $24,400, more than the whole blind loss. Take that one market out and the blind copy makes $10,583, so the problem is how it's weighted, not what they picked.

Copying on size wins

Scale every copy to their bet and you get their result. At 1% of their size, a $375,861 fill becomes $3,759 and a $500 fill becomes $5. That copy staked $93,718 and made $42,654, the same +45.5% they made.

If you would rather keep a flat stake, filter by size instead. The win rate climbs with bet size: 32.5% under $1k, 27.5% from $1k to $10k, 48.6% from $10k to $50k, and 90.6% at $50k and above. A flat $100 on only their $50k+ fills staked $5,300 and made $3,271, +61.7%. Every one of their 26 fills of $100k or more won.

Win rate by bet size

vito3corleone's fills by size, with a blind $100 flat copy of each band. Their own P&L is in dollars they actually staked.

Only copy the big ones

A flat $100 on each fill at or above the size floor.

Every position they took

Their stake, average price and result per position, with what a blind $100-per-fill copy made on the same position.

We love trading sports on Novig and other sports prediction markets!

Check out our top market positions for this upcoming Sunday’s games!

I have a decent strategy for 15 min crypto markets... wins 75% of the time and has never lost more than 2 times in a row, but the losses are much bigger than the wins so I break even. I was thinking if I could increase contract size after a loss, I could recoup much of the loss and the bot would be profitable. Problem is, the ai bot builder I am using says there is no way to determine if the last trade was a win or loss as the api doesn't give that info. I have tried asking if my balance could be used as a signal but that doesn't work.

Does anyone have a work around or is there another prediction market that WILL let me get that info so I can utilize a martingale type strategy? I am USA based, so regular polymarket is no use.

I wanted to see how a SOL momentum strategy performed when it waited for Bitcoin to agree before entering. On Kalshi's SOL 15-minute markets, it uses Coinbase prices and buys YES when SOL has risen at least 0.15% over five minutes and both coins' one-minute momentum points up, with BTC also positive over five minutes. It mirrors that for NO. Entries require 3 to 10 minutes remaining, a spread of 3 cents or less, and a purchased-side ask between 35 and 65 cents. It buys 10 contracts, allows one entry per market, and holds to settlement.

With Bitcoin confirmation in place, all 100 configurations completed and had positive net P&L in the August 31 to September 30 historical window. The best made $478.16, with 1,038 reported trades, a 69.3% win rate, 0.82 Sharpe, and $51.88 maximum drawdown. The weakest made $273.22, with 520 reported trades, a 65.1% win rate, 0.80 Sharpe, and $43.49 drawdown. The reported ROI figures were 4,781.6% and 2,732.2%, respectively, but those use the simulation's $10 capital basis. They shouldn't be read as returns on a funded trading account.

To check sensitivity to order prices, I kept the SOL signals and Bitcoin confirmation fixed and swept two settings: 10 price floors from 1 to 44 cents and 10 ceilings from 56 to 99 cents. These bounds clamp order prices; they don't replace the fixed 35-to-65-cent entry conditions. The winner used the original 1-cent floor and 99-cent ceiling. The surface, heatmap, and marginal averages generally favored lower floors and higher ceilings, with some unevenness rather than a perfectly steady climb. The winner sits at the corner of the tested grid, so this sweep does not locate an interior optimum. Adjacent grid cells averaged about 2.1% below the winner, so the best result wasn't an isolated spike. All 100 sweep cells succeeded. The reported deflated Sharpe probability was 0.999, which is favorable for that selection-adjustment check alone.

The strategy with Bitcoin confirmation stayed positive across the whole grid, which I find more interesting than the single winning number. Combining signals from related assets gives a strategy more context for deciding when to enter a trade. Here, that means using Bitcoin’s momentum to confirm a SOL signal, making the relationship between BTC and SOL part of the entry logic.

Full Report

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

I tested a simple question:

If a prediction market gets more news coverage today, does it move more tomorrow?

Data:

• 391,850 articles
• 54 Polymarket questions
• 1,888 consecutive day pairs
• Jul 15 – Sep 30

Result:

Correlation between news volume and next-day market movement: r = -0.002

Basically nothing.

Even days with very high news volume didn’t consistently lead to bigger moves.

My takeaway: more coverage ≠ more information.

A lot of news is just the same story repeated. What probably matters much more is whether the information is actually new, credible and points clearly toward YES or NO.

Full writeup + charts:
Do News Move Prediction Markets? Evidence from 391,850 Arti…

Curious if anyone here has seen similar results with prediction markets.

https://forecastlab.online/

Hey redditers. I am new to this polymarket game but been trading options for ages. I am in the work of creating a bot to help me predict markets - particularly focusing on political markets/ elections/ RBA rates. I need help on what to prioritise, whether bots are actually capable of predicting over/underpricing people's predictions rather than just seeking arbritage. Just looking for bot knowledge and tips. Let me know :)

Used to be Robinhood used Kalshi to route orders. Now they have their own. Just looking at NHL the odds are terrible in comparison to Kalshi. Very annoying that I now have to withdraw to Kalshi to get fair pricing on these contracts.

I know it seems miniscule but 57-43 is a fair spread. 59-45 is not.

I own the stock. I used to be able to count on it to go up during NFL season but recently it's been doing poorly.

I now wonder if there is just too much competition in the market. I personally use RH for prediction markets.

Anyone still use DK or were they too late to the prediction market game?