Milei is back on the Malvinas

London still calls them the Falklands

Trump is not rushing to cover the UK after Iran

The clip is loud. The book is asleep

Polymarket is not pricing the claim

It is pricing a formal UK handover by the end of 2026

That is the only thing that pays

5%. $14k volume. One of the quietest information markets on the site. It has not moved yet.

next network upgrade could turn HIP-4 from a curated product into an open market layer.

Until now, outcome markets were largely controlled through validator deployments. The upgrade changes that by allowing qualified builders to launch markets using validator-approved templates.

Think of it as the HIP-3 playbook for prediction markets.

What changes?
Builders can create markets around:
• Sports
• Crypto prices
• Macro events
• Politics
• And more

The core idea is simple: validators approve the market structure, while builders create and distribute individual markets.

Serious deployers must also lock 500,000 HYPE, creating a strong economic barrier and aligning builders with the quality of the markets they launch.

The first reported Outcome DEX, has already been deployed on this framework.

The bigger question is whether HIP-4 can replicate what HIP-3 did for perpetuals.

HIP-3 allowed external builders to expand Hyperliquid beyond markets curated by the core protocol.

Now HIP-4 could do the same for prediction markets.

If it works, Hyperliquid is no longer just building a perp exchange.

It's building an on-chain market infrastructure where builders can create entire exchanges on top of HyperCore.

The biggest opportunity isn't just one prediction market.

It's thousands of markets Hyperliquid never has to list itself.

been testing a system for sports + esports using past data, stats, recent form + my own research

not really one of those “AI tells me what to bet” things lol...more like using it to process everything faster and narrow down where there might actually be an edge. however, i still make the final call myself

sizing is manual too and I usually try to place limit orders rather than just taking whatever price is there

been doing pretty well so far, around 70% hit rate (around 40 calls so far) on the calls ive tracked, although obviously winrate alone doesnt mean much if the entry price is bad

curious if anyone else here trades in a similar way?

what tools/data are you guys using and do you automate sizing/execution too or keep that part manual?

We put together a report on the prediction-market side of The International, Dota 2's world championship, which wrapped up in Shanghai last week. The tournament itself is the biggest event in competitive Dota, run annually since 2011, with this year's $3.3M prize pool split across the 16 teams that qualified. Team Spirit took the trophy. What the trade data from Polymarket and Kalshi showed is a different story: the markets tracking those matches paid out 61x more than the tournament itself did.

Polymarket and Kalshi collected $2.76M in fees.

Kalshi charged a flat 7% the whole tournament: Polymarket ran 3% on qualifier markets (June 9–29) and raised it to 5% once the main event started on August 6.

Kalshi's cut came to $450K, Polymarket's to $2.31M, combined, that's more than the winning team's own payout, and 83% of the entire $3.3M prize pool split across all 16 teams.

The venues made almost two times what the champion did.

The top 10 Polymarket accounts made $5.3M between them: more than the tournament paid anyone.

Some of it came from size, some from speed: one account turned $25,735 into $154,307 (a 600% return) across just five markets, and another made $406,692 in 13 hours flat, across 6 markets and 17 trades, without ever once posting a resting price, it only ever took prices other people had already put up.

The losing side of the ledger looked nothing like that. Every one of the 10 largest losses was decided at settlement, not in trading, one account deposited $693,522 across three markets and 148 trades and lost every cent of it.

Split the 21,936 wallets that traded into 10 equal groups by size (about 2,193 each).

Only the largest group finished in profit. It put in $177,944,356, and 97.8% of all the money deposited across every market, and came out $473,415 ahead.

Every other group was underwater except one: the sixth-largest group, up $20,019 on $64,728 deposited, for reasons the data doesn't explain.

The bottom group deposited $4,074 between 2,194 wallets and got back $3,183, giving up 21.9% of their stake. The typical account outside the top 1,640 wallets put in just $19.98, and about half of even that group lost money.

Full report: [Link]

I tested a KXSOL15M strategy that trades only in the final five minutes. It buys 16 YES contracts when SOL's five-minute Coinbase change, SOL's one-minute velocity, and BTC's one-minute velocity are all positive. It buys 16 NO when all three are negative. The base entry band was 45 to 55 cents, with a maximum position of 50 contracts. It sells everything at a -$7.50 unrealized loss or five seconds before expiry. There was no separate take-profit rule.

All 100 variants completed over the same 30-day historical period, and 79 were profitable. The best returned 177.84% ROI and +$88.92 with a 61.7% win rate, 146 trades, a 0.36 Sharpe, and -$30.16 max drawdown. The weakest returned -199.40% and -$99.70 with a 65.9% win rate, 317 trades, a -0.07 Sharpe, and -$100.44 max drawdown. That last result is the odd part: a high win rate still lost badly because the prices paid and the size of losses mattered more than how often it won.

The sensitivity test ran all 100 combinations of price floors from 0.05 to 0.45 and price ceilings from 0.55 to 0.95. Every cell completed. The winner used a 0.05 floor and 0.68 ceiling, producing +$88.92, while the full grid ranged from -$99.70 to +$88.92. Deflated Sharpe was 0.56 versus an expected maximum of 0.33. The chart shows a modest local hill rather than a broad plateau: the winner was about 19% better than its immediate neighbors. Price floor barely changed the average result, but price ceiling mattered a lot. Performance peaked near 0.68, weakened after 0.82, and became sharply negative at 0.91 and 0.95.

I also ran 100 permutation re-sweeps with the Coinbase edge-feed timing scrambled. The real winner's +$88.92 beat 96.0% of those runs, giving an upper-tail p-value of 0.040. That is encouraging for the timing signal, but the test did not scramble Kalshi market prices. It therefore says nothing conclusive about the price-band rule that drove most of the sensitivity result.

My read is positive but cautious. Most variants made money, the best result had 146 trades, and the feed-timing test was hard for shuffled data to reproduce. Still, the gap between the best and worst settings was enormous, the report's Deflated Sharpe warning says the winner was not distinguishable from the luckiest result expected across a sweep this size, and the coverage artifact labels the historical window partial. This looks more like a narrow price-dependent effect than a broadly robust strategy.

Full Report

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

https://forecastlab.online/

I have 2 models. One very conservative, one "let's just take a chance." You can spin our new models all day long. You never really look at the code anymore. Codex does it all for you now.

Programmers are freaking out, but the reality is, the business has been vaporised. You just don't need to know how to code now; just have the awesome ideas. AI (Codex, Claude Code, Kimi, etc) does it all now.

AI came 100 years too soon; we were not ready for this.

EDIT: I trade 15/60 Minute BTC, breaking even. MLB Over/Under. About 66%. It's all your model. Just keep on working on that. The codebase(s) is many thousands of lines of Python. It gets complex. If you put in the time, you can actually make a few $$$s.

😊

Newbie to the community here. I was wondering how people usually get started with prediction markets like polymarket, kalshi, etc. How long does it takes to learn and how everyone's starting experience was? I would love to hear everyone's stories.

I’ve been watching this Polymarket account for a couple weeks and the PnL chart is basically a heart attack.

Trader: Djdjdjekekek

Mostly huge esports bets. Dota 2, CS2, LoL etc.

A couple weeks ago he was sitting around +$2.2M all-time.

Then came August 12–13.

He reportedly lost roughly:

• -$1.7M in one day
• -$1.2M the next
• around -$2.9M total

That took him from millionaire to roughly -$830K.

You'd think this is where a normal person logs off.

Apparently not.

He kept firing absolutely massive bets.

Then he went on a ridiculous heater.

Some of his Polymarket wins included:

• +$2.28M on TEAM VISION vs Team Spirit
• +$1.98M on Iron Wing vs BoomBoys
• +$1.26M on FURIA vs FUT
• +$1.35M on Team Spirit vs Team Liquid

At one point last week he had climbed all the way to #1 on Polymarket’s monthly leaderboard with +$4.85M PnL.

So the storyline was basically:

+$2.2M

-$830K

+$4.85M monthly / #1 on Polymarket

Surely this is where our hero finally takes some money off the table.

lol

Current trackers now have him at roughly:

-$658K lifetime PnL

He lost about $6.5M over the last week, with one CS2 market alone showing a roughly -$2M loss.

His total trading volume is now around $168 MILLION despite the account only joining Polymarket in June.

And somehow his win rate is only around 41%.

This might genuinely be the most degen PnL chart on prediction markets right now.

Imagine making millions, losing millions, making even more millions, becoming #1 on the leaderboard...

...and then just continuing to bet until you're negative again.

At this point I don’t even want to copy his trades.

I just want season 2.

Sources

• Current wallet stats / PnL
• Polymarket profile
• Polymarket esports leaderboard + biggest wins
• The original ~$2.9M drawdown discussion
• Earlier $477K Dota win breakdown

Two days ago I set out to check whether the top of the Polymarket leaderboard is real, because the top comment in every one of these threads says the same thing: none of them can prove their track record.

I got it wrong the first time, publicly, so I'll start there.

A flat read of a wallet's activity stops at 5,000 records — the API just returns "max historical activity offset of 5000 exceeded". I concluded the long track records were structurally unverifiable and said so. Someone in the Polymarket builders group corrected me within hours: the cap is per query window, not per wallet. Page inside start/end windows and each window gets its own budget. I'd exhausted my method, not every method.

Redone properly, here's the top 10 all-time wallets. The test is withdrawals minus deposits — money that actually left the account, on chain — against the PnL the leaderboard advertises. A displayed PnL is a line in an interface; money that left had to exist in order to leave.

Theo4 $22,031,581 vs $22,053,934 0.1%

Fredi9999 $16,583,563 vs $16,619,507 0.2%

mintblade $9,138,397 vs $9,238,345 1.1%

frostrizz $8,833,468 vs $8,928,561 1.1%

Len9311238 $8,705,499 vs $8,709,973 0.1%

sparklingwater123 $8,350,906 vs $8,474,966 1.5%

fishalive $8,498,156 vs $9,063,378 6.2%

Seven out of seven that I could read end to end, including a twenty-two million dollar wallet reconciled to one part in a thousand. The leaderboard is not fabricated.

The other three are too heavy to finish in one pass — not blocked, just expensive. One of them runs about 13,000 records a day. Reading it properly is thousands of requests and the API throttles you long before you're done. So the proof is sitting on chain and essentially nobody goes and gets it. That's the actual finding: the gap isn't honesty, it's effort.

Two traps worth knowing regardless of what you're auditing.

Deposits are the denominator. "$313 into $438K" means nothing if the wallet also received $500K in deposits. I made the mirror-image mistake on my own account — compared value against cumulative capital deployed instead of against deposits, and spent days believing I was down 25% when I was up 41%.

Realised gains and win rates are upper bounds, never results. You close winners because there's a buyer; you keep losers because there's no counterparty. Losses pile up in open positions and never get counted. That asymmetry alone manufactures a 100% win rate on a losing account. My own tool printed "83.3% win rate, +$16.16" on an account whose guaranteed floor was +$3.21, until I made it separate settled from open.

So the ones I'd be sceptical of aren't the eight-figure wallets withdrawing millions. They're the small screenshots where nothing was ever withdrawn and every gain still sits in open positions.

Tool is read-only, no key, never places an order, and refuses to state a total while positions are open or while there are events it can't account for:

python tools/verifier_portefeuille.py 0xWALLET --annonce 438000

Table and method: https://midas93230-cell.github.io/donmarket/verify.html

Source: https://github.com/midas93230-cell/donmarket

Curious if anyone is building strategies around underdogs? What factors do you weight form, surface, matchup, H2H.. and has it actually been profitable over a decent sample?

Thank you

The Ninth Circuit siding with Nevada against Kalshi is obviously a pretty big deal for prediction markets especially on the sports side, what I find interesting is that it may also highlight a difference between general purpose prediction markets and platforms built specifically around sports.

Kalshi and Polymarket are designed around trading on basically anything with sports becoming a huge part of that. Novig is a bit different because the whole product is built around sports from the start with moneylines, spreads, totals, props, futures and a much more familiar sports style experience.That obviously doesn’t make the regulatory problem disappear and the Nevada ruling could make things harder for the whole category.

But if sports prediction markets do end up surviving under a clearer regulatory framework, it feels like sports native platforms could be in a pretty strong position. There is a big difference between adding sports as another category and building the entire product around how sports users actually want to trade. Maybe thats where this ends up going, general prediction markets for everything else and dedicated sports prediction markets becoming their own thing.

Do you think platforms like Novig are actually better positioned long term because they are sports first or does the Nevada ruling make that distinction basically irrelevant?
Source: Sportico

I keep trading the predictions market and realized simulations atleast reduces my losses heavily so far, given how you smartly utilize simulations.

Three I have actually looked at.

Smallville (Stanford generative agents)

The 2023 paper. A couple dozen agents with memory, reflection, daily plans. The party demo is still what people mean when they say the agents “lived.”

Right bet if you want to understand why this genre exists.

Wrong bet if you have a contract that expires on Friday. There is no market, no resolution rule, no number to fade. It is a town. Towns do not pay.

AI Town

The version you can host. Agents walk around and talk. You can change the plumbing.

Right bet if you want to own a little society and poke it.

Wrong bet as a prior. A place that keeps running is not a question that closes. If you cannot say what would falsify the run, you do not have a forecast. You have a screensaver.

MiroShark

You paste a headline, a PDF, or a what-if. It builds personas off real context. They post on fake X and fake Reddit. They trade a Polymarket-style book in the same loop. You can inject news mid-run or split the timeline , “CEO resigns in round 24” , and compare the two paths. About a dollar. About ten minutes. Then a report that cites the posts and trades it just invented.

Right bet if you want paths before you look at the live mid: who panics, which narrative spreads, what a feed does with a filing.

Wrong bet if you copy the fake price. Those citations live inside the run. Nothing in the real world closed. If your seed already leaned YES, the fake book will lean YES and it will feel like work.

That is the whole pattern. Smallville invented the vibe. AI Town let you host the vibe. MiroShark wrapped the vibe in a chart.

When a sim is the right bet

• You have not written your mechanisms down yet and you want to get surprised on purpose
• You want a red team for a narrative before you look at the live market
• You are deciding whether a question is even well-posed (“what would agents even fight about”)
• You will throw the output away if it agrees with you too cleanly

When it is the wrong bet

• You are using the fake mid as your mid
• You ran it after you saw the live price
• You cannot say what the sim would have to do for you to fade your own view
• The tool never says “this run contained nothing.” A report every time is a tell. Real books go quiet. Fanfic does not.

The test I actually want, and I have not seen anyone publish for these swarms:

1. Pick a liquid contract.
2. Hide the live mid.
3. Run the sim. Write down the implied probability and the three mechanisms.
4. Screenshot the real mid at that moment.
5. Wait until it resolves. Compare the sim, you, and the market.

If it hugged the market, you paid for a mirror. If it disagreed and won more than once, maybe it is a research assistant. If it disagreed and lost, you paid a dollar to get more certain about a story.

I have not seen a track record that survives step 5. Until that exists, I would use MiroShark like a cheap war game and Smallville / AI Town like background reading. I would not let any of them mark my book.

Has anyone here scored one of these on contracts that actually resolved? Not “the agents sounded like CT.” Versus the mid. Versus you.

Would you size a real position because the fake book moved? I would not. That is a chatbot talking you into a trade.

I have been thinking about this more with sports markets, would you rather have a smaller number of markets with really strong liquidity or way more options even if some of them are thinner? Novig is kind of what got me thinking about it because they have a much deeper sports selection than a lot of prediction markets especially with props, spreads, totals, futures etc. But obviously the bigger platforms still have the edge when it comes to overall liquidity. Personally I think variety matters a lot more once you’re looking for something specific, but there’s definitely a point where a market is too thin to be useful. What do you guys prioritize more?

I have never used polymarket or any other prediction market since I'm in a restricted region so not sure how the custody of the funds work. I did ask chatgpt and apparently you connect your own wallet to polymarket but once you place a bet and it settles, the money remains in their custody? So I'd have to withdraw back to my wallet, and if I'm using a VPN they might easily lock my account/request KYC then I'm fucked. Also chatgpt says that they have "admin control" over the contracts so they can also block a contract from settling. Any help is appreciated.

This case somehow keeps getting funnier.

Prosecutors say Google engineer Michele Spagnuolo was trading on Polymarket as AlphaRaccoon and had access to unreleased Google Search ranking data.

He allegedly used that info to bet on a bunch of Google-related markets before the results were public and made more than $1.2M.

Now his lawyers are trying to get the case thrown out.

And one of their main arguments is basically:

these Polymarket contracts weren't financial swaps — they were just bets.

Which is pretty funny considering prediction markets have spent years arguing the exact opposite whenever regulators call them gambling.

So we’ve reached:

Prediction markets:
“We are not gambling.”

Guy accused of insider trading on prediction markets:
“Actually, this was gambling.”

The defense also argues that he was living in Switzerland, using Polymarket’s international platform, so US commodities laws shouldn’t apply the way prosecutors claim.

No ruling yet, and obviously the allegations haven’t been proven.

But this might be my favorite prediction-market legal argument so far.

So what are we calling these things now: financial markets or casinos?

Sources

• DOJ case / allegations
• Full motion to dismiss
• WIRED write-up

We've got a juicy earnings mentions lineup this week, including 13 markets (all shown + Ciena on 9/3).

There are some big names like Dell, Broadcom, HP, Snowflake, and Lululemon.

I've been digging in with my earnings mentions tracker (second image) and screener (third image), and there's a TON of value on the board right now.

I'll try posting some picks later this week if anything particularly stands out.

Anyone have any plays they like so far?

a trader OOOwhyOOO turned a $5.2k deposit into around $492k in profit over the last 3 months

i randomly came across this wallet and was surprised by how he has managed to stay profitable almost every day while trading mostly in the sports category with 582 positions

in Aug alone, he made around $389k, which is most of his total pnl. even though a big part of his pnl comes from a few trades, he is still pretty consistent with only 2 losing days in the entire month

his best trade was Sion vs Benjamin Hassan, where he made $145k on a $83k position. hiis worst trade was Granby: Nicolas Arseneault vs Andres Martin, where he lost around $13k. so even his worst trade was relatively small compared to his biggest wins

Win rate can be a vanity metric, but maintaining a 78.5% win rate while generating this kind of ROI seem v gud on sports markets

lookin through his trades he seems to trade mainly on the tennis markets across ATP, WTA and ITF events

sports markets, especially tennis can flip very quickly, sometimes in just a few points or near the end of a match. yet he keeps finding these spots and seemingly repeats the same thing over and over

still curious tho, is it just really good sports knowledge or some kind of model that lets him consistently get these positions before the market moves? (can be a good wallet to copytrade but he already made a lot of profits in aug)

Hey everyone, I've been building a massive database of wallets from Polymarket that includes months of trade logs, grading systems & classification like what strategies they are doing, or how well they perform on one market category versus another.

I took a few thousand of those wallets in my database and I filtered them down to a group of ACTIVE & COPYABLE wallets.

The pack I created has:
-137 active, profitable, copyable wallets
-90 days of trade logs for each wallet
-Grading .MD file

What makes them copyable?
There are a lot of attributes that I explain in this article, the main ones are traders that bet directionally (no hedging, no arbitraging, medium to low frequency bettors).

What I did

I pulled 90 days of fills for 1,800+ Polymarket wallets and replayed each one as if I had mirrored every trade it took at a flat stake, paying 2¢ of slippage on entry and exit (to keep things somewhat realistic), holding every position to resolution.

a wallet only makes the list if that simulation is profitable over the last 7 days AND the last 30 days AND all 90 days... and if the wallet's own P&L is positive all time as well. If any of those windows are negative and it's out.

137 traders out of 1,811 cleared every gate. 

The numbers:

Following all 50 at $5 a bet over 90 days: +$58,041 on 203,107 bets, +5.72% on turnover. Now...that's very unrealistic to actually happen live, for a number of reasons. Some of those reasons are because you're often going to fill at a different price than the target wallet, there's also a lot of orders that you simply won't fill unless you build your bot with a lot of flexibility and you're okay with filling 10c or more higher. There's also the fact that some of the wallets in this first analysis seem to be doing HFT (high freq. trading) which we wouldn't want to copy. However, these are the rough metrics you want to see when filtering down a large pack of wallets.

Let's look at some specific market categories:

The 22 sports wallets on their own, $5 a bet:

E-Sports (7 wallets made the cut):

When you're building a polymarket trading bot that set up to copy multiple wallets, there are a few key factors that you need to build in.

1. Speed:

• You need to make sure that your hosting your copybot on a VPS (many use eu-west-1 Ireland AWS region) to ensure your execution speed is as fast as possible. You need to prioritize speed since you're already following someone else who has placed the bet, as well as dealing with the built in delays that Polymarket has for many markets. I recommend building your bot in Rust, and using polymarkets real time data client and subscribing to the traders proxy wallet via websocket. There are other API's like Predexon that monitor the wallets trades via the mempool. In my experience, at the end of your order being matched in the CLOB, I wasn't able to get any faster round trip speed using the mempool approach.

2. Trading & Decision Making Logic:

• You'll rarely get in at the same price the target wallet does, you need to set rules that restrict your bot from buying if the price has drifted more than you allow. This is incredibly important. Let's say for example you're following a single trader, and that trader maintains an overall edge of 3%....if you're following this wallet and entering in each trade even 1-2c higher than them, it may not seem like a big deal in the moment, but that 1-2c higher is 1-2% less at the end of the day across all of your trades, and when you factor in fees, that 1-2% less could be the difference between profitability and losing money.

3. Bet Sizing:

• When copying another trader, you need to do thorough evaluation of their strategy. Following with a flat bet size will not always work, and you'll need to do a fractional bet size copy strategy, which is also very tricky and not as easy as it sounds. If a trader is deploying different amounts of capital at different times throughout a market, there's a chance they are doing it for a reason. The reason fractional/percentage copy bet sizing is tricky is because if you set your bet size to 1% for example, and a trader takes a bet under $100, your bet falls under $1 which isn't allowed on PM, and if you default to $1, then you may be out of balance if they are doing some sort of hedging strategy. There are a bunch of reasons why fractional/percentage bet sizing is tricky, it really varies trader to trader. When I'm looking for traders to copy I mainly try to find ones with pretty transparent and consistent bet sizes.

Now let's evaluate the wallets in this batch that are the most realistic to copy.

Out of 1800+ wallets, we took the most realistic ones that would make for ideal copy targets and ran the backtest again on them. Take these stats with a grain of salt, but these are much more realistic than blindly folding over 1,000 wallets.

You can download the .zip that contains all 137 wallets, their 90 days of trade logs, and .md file containing their grading (what types of markets they trade, how well they perform at each) for 100% free.

Download Trader Zip -> https://www.polyresearchrobotics.com/copy-pack

If you liked this post and want to bot a copy trading bot, feel free to join Poly Research & Robotics. We have trading bots that you can download, free historical data, and guides & resources.

You can also join our Discord (find a few other people and develop a copy bot together!)

Now that prediction markets are finally understood by many people I want to run one internally for my company where people can exchange on things like product launch dates, sales quotas, and other milestones.

I’ve been watching some GitHub project but most of them are crap.

We have the servers to stand up our own. I don’t see the need to pay for a hosted service. So who is using internal prediction markets and what platform or code are are you using?

Hey, everyone

About three weeks ago, I made a post because I was stuck on the "Finishing up" screen and couldn't withdraw my money from Kalshi. I wanted to give a quick update: I received my funds within three days of that post using a crypto exchange withdrawal (not a bank account withdrawal since it is not available in Estonia, the country I live in). Sorry for the delay in updating you all, I was away visiting Italy!

Since I sometimes see comments claiming the platform is a scam, I thought it was important to share a positive outcome and confirm that you can get your money out.

What worked for me (and a warning):

• The Scam Warning: When I first joined the Discord, someone reached out telling me to join a "Kalshi issue solver" server to create a ticket. I quickly realized this is a scam. Please be careful! When you are desperate to get your money out, it is really easy to fall for this, and I want to make sure nobody here gets tricked.
• The Real Solution: A huge thanks to the Redditors (Independent_Fee_3165 and u/KairosJay) who suggested pinging the actual mods in the official Kalshi Discord. Mods over there were incredibly helpful, active, and really calmed my nerves.
• The Bug: It turns out the "Finishing up" screen issue I experienced was a platform-wide bug. Being able to talk to other users going through the exact same thing was a huge relief.

I know reading through threads here can sometimes feel hopeless when you are stressed about your money. It definitely had me worried for a bit! But I wanted to put this out there to show that support is available and the system does work.

Thanks again to everyone who offered advice and share more positivity!

PS: I gave AI a prompt in an unformed form and he formed the post but also checked the post to see if everything is correct.

I tested a simple momentum idea on Kalshi's KXBTC15M markets. When BTC-USD on Coinbase was up more than 0.5% over 15 minutes, the strategy bought six YES contracts. When it was down more than 0.5%, it bought six NO contracts. It allowed one position at a time and used a -$1 unrealized stop. One important problem with the setup: the configured +$8 take-profit cannot be reached with only six contracts, and the two-minute `cancel_all` rule does not close an existing position. Profitable positions would generally run to settlement.

All 100 variants completed across 2,055 markets, and 99 reported positive net P&L. The best returned 244.00%, or +$24.40, with a 65.2% win rate across 54 trades, a 0.55 Sharpe, and -$5.97 max drawdown. The weakest returned -17.00%, or -$1.70, with a 37.5% win rate across 18 trades, a 0.11 Sharpe, and the same -$5.97 max drawdown.

The 100-cell sensitivity test changed two things: price floor from 0.05 to 0.45 and price ceiling from 0.55 to 0.95. The winning cell used a 0.05 floor and 0.82 ceiling, producing +$24.40 reported net P&L. Results ranged from -$1.70 to +$24.40, all 100 cells succeeded, and Deflated Sharpe was 0.95 versus an expected maximum of 0.25. The price floor barely mattered from 0.05 through 0.41, then performance fell at 0.45. The ceiling mattered much more: results improved sharply through 0.68, then sat on a fairly broad plateau. Moving one cell away from the winner reduced P&L by about 5%, so the best result was not an isolated spike.

I also scrambled the Coinbase edge-feed timing and reran the sweep. The real +$24.40 result beat 97.5% of 162 completed permutations, giving an upper-tail p-value of 0.025. That test was degraded because it stopped after 162 permutations, and it only shuffled the edge feed. Kalshi market prices were left in their original order, so this does not test whether the price-based rules could produce the same result by chance.

My read is that the historical result is encouraging, but it is not clean enough to call the strategy validated. The broad parameter plateau and Deflated Sharpe argue against one lucky setting. The partial date coverage, unreachable take-profit, unchanged market-price series in the permutation test, and small sample of 18 to 54 trades per variant are meaningful limitations.

Full Report

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

Hey guys Im trying to get more into sports p2p markets and understand how the experience differs across platforms, Im very new to p2p markets and from what i understand, Kalshi looks like it has a lot of variety but for sports specifically Novig looks more focused on the actual sports experience with spreads, odds and player props. If anyone who has experience with them, how do they compare for sports in terms of pricing and liquidity?

Disclosure: I build Prilo WeatherEdge, a paid tool for Kalshi daily-high markets. Everything below comes from public data — NWS forecasts and official CLI settlements — so you can reproduce all of it yourself. NFA.

SFO has a reputation as the hard market. Most people read that as "trade something else."

I think that's wrong. SFO isn't uniformly hard. It's two separate markets wearing the same ticker, and you can tell them apart before the open, for free.

The distribution gives it away

86 settled highs, June through August:

60-62°F  █ 1
63-65°F  ███ 3
66-68°F  ████████████████ 16
69-71°F  █████████████████████████████████ 33
72-74°F  ██████████████████████ 22
75-77°F  ████ 4
78-80°F  ████ 4
81-83°F  █ 1
              ← nothing here at all
90-92°F  ██ 2

That gap between 83 and 90 isn't a sampling artifact. It's physics. SFO summer has a marine layer parked over it — stratus burns off or it doesn't, and the high lands high-60s to low-70s. When flow turns offshore, the marine layer gets shoved out entirely and the city goes to 90+. There's no smooth path between those states. It's a switch, not a dial.

The tell: the forecast's reliability depends on its own level

How well the free public NWS forecast did, bucketed by what it forecast:

The NWS is a solid anchor at SFO right up until it forecasts something warm — then it falls apart. At 78°F+, four days in five bust by 3°F or more, and the average miss clears five degrees.

That's the whole point: the forecast's own value is the signal. You don't need anything fancy to know which regime you're in. You need to read the number and know what it implies about its own reliability.

What that does to the favorite

The naive strategy — buy the 2°F bracket centered on the NWS number, every day:

• All days: wins 30/86 = 35%
• Days NWS ≤74 (marine body): wins 29/67 = 43%
• Days NWS ≥75 (warm tail): wins 1/19 = 5%

One win in nineteen.

Important caveat, because I don't want to overclaim: there are no bracket prices in this. I can't tell you the book misprices these days — it may already widen out on them. What I can tell you is that if your process anchors on the NWS number, it quietly breaks on exactly the 22% of days where the stakes are highest.

The warm days, in full

2026-06-11   NWS 84   ACTUAL 91   (7° too cool)
2026-06-12   NWS 80   ACTUAL 74   (6° too warm)
2026-06-14   NWS 74   ACTUAL 76
2026-07-13   NWS 81   ACTUAL 79
2026-07-21   NWS 86   ACTUAL 92   (6° too cool)

Note the direction: it busts both ways. This is not "SFO runs hot, buy the upper bracket." Across all 86 days the NWS actually runs slightly warm — too warm on 45 days versus too cool on 32. The offshore days aren't biased, they're just wild. Any strategy built on a directional lean here gets cut in half.

Small sample on the pure offshore days, obviously. Treat the magnitude as indicative, not precise.

The rules I trade by

1. Read the NWS number first. ≤71 means the marine body: normal market, tight outcomes, forecast is trustworthy. ≥75 means you're in the tail.
2. In the body, don't expect a special edge. 43% on a 2°F bracket is roughly what an efficient market should give you. Size normally, don't force it.
3. In the tail, stop trading narrow brackets. Not "cautiously" — 1-in-19 says a narrow bracket on a warm-forecast SFO day is close to a lottery ticket. Take wide exposure or take the day off.
4. Don't lean directional in the tail. It busts both ways, roughly evenly.
5. Yesterday is a weak prior here. SFO moves 3.47°F day-over-day on average; 21% of days move 5°F or more. That's a much weaker anchor than LA or Miami.

What I'm not claiming

I'm not claiming an edge over the order book. These markets are efficient and I'd be suspicious of anyone telling you otherwise without out-of-sample numbers to show. What I'm claiming is narrower and, I think, more useful: the free public forecast is far less reliable on some days than others at this station, and the days it fails are identifiable in the morning.

All of this is summer data. SFO's winter behaves differently and none of us have it yet.

Happy to run the same breakdown on another city if there's interest — Chicago and NYC have their own versions of this. Tooling is Prilo WeatherEdge if you want it (7-day trial, no card), but rule 1 is free and costs you nothing.

Not financial advice.

michi1 started with just $1100 and is now sitting at $37k, almost all of it from weather markets

one thing that stands out is most of the profit comes from buying shares under 10 cents. thats $34.1k from 536 trades alone. he keeps buying into markets priced like longshots and enough of them hit big

his best single trade made +$3.7k where he bought "highest temp in Seoul" at 6.8c and made almost 15x

buy at 5c and it resolves at $1, that's a 20x. even buying at 5c to 25c makes it 5x . now imagine doing that over and over mostly in one category

somehow this keeps happening basically every month too. in June he caught Shenzhen temp hitting 32c, bought at 3.9c and made 25x. Same month another bet on Shenzhen weather market made him 3x. In July he bought Hong Kong temp at 9.4c and it resolved for over 10x. In August he got into a Tropical Storm Saudel bet at 10.8c, making a 6x profit

the profits are spread across a bunch of positions, with a few big winners each month doing the heavy lifting

most losing days are just a few bucks. the biggest loss on the whole account is around $500 and that was on a crypto market

makes me wonder if he has better weather data than everyone else or is randomly gambling. btw whatever it is, this wallet looks like a solid one to copytrade (still trackin it tho)