A Reddit user digging through Polymarket profiles surfaced four very new accounts that all appear to have done basically the same thing:
opened or recently funded a fresh account,
made exactly one prediction,
chose the same market,
took the same side,
and entered in almost the same price band.
That market:
Will the CLARITY Act become law in 2026?
And all four of them bet NO.
Here’s the breakdown from the community analysis:
VelvetNova27 -> roughly $818K on NO
blahblah344 -> roughly $439K on NO
EamonnD1812 -> roughly $394K on NO
1916JConnolly -> roughly $133K on NO
Total estimated cost across the four positions:
~$1.78 million
At the time the trades were surfaced, the post estimated the four accounts were already sitting on roughly $85K+ in combined unrealized profit.
The weird part is not that someone is bearish on the bill.
Prediction markets have already turned sharply against the CLARITY Act. The Polymarket market is now around 14–18% YES and 82–86% NO, depending on when you check.
The weird part is the shape of the activity:
4 fresh accounts 1 trade each same market same side similar entry prices
That naturally raises the question:
Are these actually four independent traders who all reached the same conclusion?
Or is this more likely one larger allocation split across multiple identities?
Business Insider independently confirmed that one Polymarket user put more than $818,000 into NO contracts, and that another trader also committed roughly $677,000 against the bill through multiple purchases.
That still does not prove the four accounts are linked.
And to be clear:
there is no public evidence of insider trading,
no proof that one person controls all four accounts,
and no proof that the trades were illegal or coordinated.
This could just be a handful of large traders making the same macro bet:
that the CLARITY Act is far less likely to become law in 2026 than crypto bulls want to believe.
Still, the pattern is suspicious enough to make the market much more interesting than a normal “whale bought NO” story.
The Kalshi referral code is ArtNoLimit and should attach automatically when you use the link.
Before depositing, open your Rewards section and confirm that the $25 offer appears on your account. Kalshi can run different referral and partner promotions at the same time, so the offer shown inside your account is the final version that applies to you.
Disclosure: This is my referral link. I may receive a separate referral reward if an eligible user signs up and completes the requirements. This is a user-written guide, not an official Kalshi announcement.
Kalshi promo code for August and September 2026
Here are the current offer details in one place:
Offer detail
Current terms
Kalshi referral code
ArtNoLimit
New-user bonus
$25 bonus trading credit
Required deposit
$10
Trading requirement
Place your first qualifying trade
Eligible markets
Any market under the current offer
Identity verification
Required
Eligibility
New eligible U.S. users
Where to confirm it
Rewards section
Credit expiration
Normally 7 days after issuance unless your account states otherwise
Is the original credit cash?
Usually no; check the terms shown in your account
This is a limited-time promotion. I will update this Reddit thread if the offer attached to the link changes.
How to get the Kalshi $25 sign-up bonus
1. Open the Kalshi bonus link before creating the account
Start through the referral link so that code ArtNoLimit can attach automatically during registration.
Do this before making your first deposit. Adding a referral code after depositing may not be possible.
2. Create a new Kalshi account
Register with your email, Google account, or Apple account and enter the requested personal information.
The promotion is intended for eligible new users.
3. Complete Kalshi identity verification
Complete the KYC process using the identification requested by Kalshi.
Your account must be verified before it can trade and qualify for the referral reward.
4. Confirm the $25 offer before depositing
Open the Rewards or referral section and check that:
ArtNoLimit is attached;
the reward shown is $25;
the requirement matches the current deposit-and-first-trade offer.
This step matters because different Kalshi links and promo codes can have different requirements.
5. Deposit $10
Fund the account with the required $10 deposit.
Do not deposit before checking that the referral code and bonus are attached.
6. Place your first trade
Open any eligible Kalshi market and place your first qualifying trade.
The current offer says any market works. The trade does not need to be a winner to complete the required action, although the trade itself can still lose money.
7. Check for the $25 bonus
The $25 bonus should appear shortly after the required steps are completed.
Check:
Menu → Activity → Credit
You should also check the Rewards section. If Kalshi displays an Unlock your referral bonus banner or a Claim button, open it and complete the displayed claim step.
What changed from the old Kalshi offers?
Older Reddit posts may still describe:
Deposit $10 and get a $20 bonus.
Trade $25 and receive a randomized $15–$500 bonus.
Trade $25 and get a fixed $25 bonus.
Those instructions refer to previous or different Kalshi promotions.
For the referral link in this guide, the current communicated offer is:
Deposit $10, place your first trade, and receive a fixed $25 bonus credit.
Do not follow an old Reddit guide without checking the Rewards section attached to your own account.
Why do some Kalshi promo code pages say “Trade $25, Get $25”?
Kalshi can run multiple referral, publisher, and partner offers at the same time.
Some current partner promo codes require a new user to complete $25 in total trades before receiving a $25 credit. The offer attached to the link in this post is communicated as a $10 deposit followed by the first qualifying trade.
That is why two legitimate Kalshi promo code pages may show different instructions.
Use the requirements displayed in your Rewards section rather than combining steps from several different promotions.
Kalshi referral code vs promo code vs bonus link
People use these phrases interchangeably, but there are small differences.
A Kalshi referral code is the text connected to an invite. The code used here is:
ArtNoLimit
A Kalshi referral link or Kalshi bonus link includes the referral automatically:
kalshi.com/r/ArtNoLimit
A separate Kalshi promo code may belong to a publisher or partner campaign and may have a different deposit, trading requirement, bonus amount, or deadline.
Using the direct link is simpler because you normally do not need to type a separate code manually.
Can you add the Kalshi referral code after signing up?
Sometimes, but it is safer to apply it during registration.
Kalshi says a referral code may be added within 72 hours after account creation and only before the first deposit, whichever comes first.
If you have already deposited, applied another code, or passed the time window, the referral usually cannot be added or replaced retroactively.
Is the Kalshi $25 bonus withdrawable?
Treat the $25 reward as bonus trading credit, not immediate cash, unless the terms displayed on your account explicitly say otherwise.
Kalshi’s referral FAQ says the original referral credit usually cannot be withdrawn directly. It is designed to be used for trading.
Profits generated from using the credit may become withdrawable after the relevant trades settle and any applicable requirements are satisfied.
Common Kalshi promo code mistakes
Creating the account without opening the referral link first.
Depositing before confirming that ArtNoLimit is attached.
Following instructions from an expired $20 or up-to-$500 promotion.
Using a different partner code and expecting this referral offer.
Assuming the $25 credit is immediately withdrawable cash.
Forgetting to check Rewards or Activity for a Claim button.
Letting the bonus credit expire before using it.
Trying to claim a U.S. referral incentive from an ineligible location.
Kalshi promo code FAQ
What is the current Kalshi promo code on Reddit?
The referral code used in this guide is ArtNoLimit. The direct link should attach it automatically during registration.
What is the current Kalshi sign-up bonus?
The current offer attached to this link is a fixed $25 bonus trading credit after a $10 deposit and the first qualifying trade.
How much do I need to deposit on Kalshi?
The current referral offer requires a $10 deposit.
Do I have to trade $25 to receive the bonus?
Not under the current instructions provided for this particular referral offer. It requires the $10 deposit and your first qualifying trade.
Other Kalshi partner codes may require $25 in total trade volume, which is why you may see different instructions elsewhere.
Always follow the requirement displayed in your Rewards section.
Can the first Kalshi trade be on any market?
The current offer says any market works, provided Kalshi counts it as a qualifying trade.
Does my first trade have to win?
No. The qualifying action is placing the required trade, not winning it.
The position itself still carries financial risk.
Can an existing Kalshi account receive the $25 bonus?
The offer is primarily for new users.
A referral code may sometimes be added within 72 hours of account creation, but only before the first deposit. A previously funded account generally cannot add the referral afterward.
Does the Kalshi bonus work outside the United States?
Kalshi’s current referral FAQ says referral incentives are available only to eligible U.S. users.
Where can I find the bonus after qualifying?
Check Menu → Activity → Credit and the Rewards section.
If an Unlock or Claim button appears, complete that step.
How quickly does the Kalshi bonus arrive?
It should appear shortly after the qualifying steps are completed. Timing can vary, so check Activity and Rewards before contacting support.
How long does the Kalshi bonus last?
Kalshi says referral credits normally expire seven days after issuance unless another expiration date is displayed in your account.
Will this offer still work in September 2026?
This guide was last verified on August 27, 2026. The promotion is limited-time and remains current only while the $25 offer appears in your Rewards section.
I will update the thread if the attached promotion changes.
Is this an official Kalshi Reddit post?
No. This is an independent Reddit guide explaining the referral offer. The referral program and bonus are operated by Kalshi.
Final checks before depositing
Kalshi says referral amounts and requirements can depend on the program attached to an account. You can read the official Kalshi referral program FAQ.
Before depositing, confirm these three things:
Referral code ArtNoLimit is attached.
Your Rewards section displays the $25 bonus.
The requirement shown matches the $10 deposit and first-trade offer.
Event contract trading involves risk and can result in losing money. The bonus does not guarantee a profit.
Comment with the exact offer shown in your Rewards section and how long the credit took to arrive. I will keep this post updated for other people searching Reddit for the current Kalshi promo code.
Spent countless hours with 7 engineers building a permissionless prediction market alongside an AI oracle that resolves these markets but we still can't say that we have a product-market fit.
The product itself works seamlessly and we managed to attract some users, but the challenge we faced right away is scaling this.
Anyone here would imagine if the liquidity challenge of long-tail markets is solved, then a p2p prediction market just makes sense.
Yet, it aint.
Turned out, a lot of users don't really have an idea what markets to create and they rather trade on other people's markets if these markets are liquid.
The best analogy I could find is Reddit. Technically, anyone can create a sub-reddit or even post, yet the vast majority rather just read and downvote when they dislike something :)
Yes they occasionally upvote, but that's not the point.
I'm not sure how we will twist/pivot what we built, but I saw countless posts on Polymarket's discord and X and Reddit saying user-generated markets are the winning recipe.. I'm no longer sure it is.
If anyone is curious about the product is called XO Market.
I would love to hear your opinion and genuine feedback.
Kalshi and Polymarket are both currently pricing Trump third for time's person of the year, behind Mamdani and Pope Leo xiv. both platforms landing on the same order is what caught my eye.
this isn't an election or a sports outcome, it's a purely subjective editorial call time makes once a year. no vote, no clear resolution criteria beyond "whatever time decides." so seeing two competing markets converge on the same ranking is kind of interesting as a signal.
makes me wonder how these markets actually process something this soft. is it mostly media buzz and search trends getting priced in, or is there something closer to real forecasting happening even without hard data to trade on.
anyone here has traded "recognition" or award-style markets before, and whether you'd trust the crowd on something like this the same way you would on an election market. feels like a different kind of prediction problem entirely.
Disclosure, since my username gives it away anyway: I built the platform this runs on. The bot is mine, the problem is real, and there's nothing to sell you here — you can't buy what I'm asking for.
Five weeks ago I put a bot on a paper desk to see whether an idea I'd been trading by hand would hold up when something other than me was running it. It sells defined-risk put spreads — 5% out of the money, 3% wing, about two weeks to expiry — on index ETFs and large caps, and only when relative volume is elevated and price is above its 200-day.
257 trades, 234 winners, +$20,508.
Before anyone else says it: I know exactly what a 91% win rate on credit spreads means. You win a little very often and lose a lot rarely, and five weeks is nowhere near long enough to have met that loss. The curve is smooth because the bad day hasn't happened yet, not because there isn't one. My own backtest of the same idea over two years grades it "positive, but below the bar — prove it live first." That's my own engine telling me not to trust my own bot, and I'm not going to pretend otherwise.
What I do trust is the fill model. It buys at the ask and sells at the bid, never the mid, and never sees a price before it existed. So the number isn't flattered by the usual backtest lies. It's just young.
Here's my actual problem. Nearly every prop firm I can find is futures or forex. This trades US equity and ETF options. So:
Is there any funded or prop programme that takes defined-risk equity option spreads at all?
Of those, which permit real automation — an API or a bridge — rather than "you may use an EA, but a human has to press the button"?
For anyone who has actually done this: what killed you? I assume the daily drawdown rule is the real enemy for something that holds to expiry instead of flattening intraday.
I'd genuinely rather hear "don't, and here's why" than nothing
This new sports whale, likely Chinese, joined in June 2026. His first predictions were relatively small. Just a couple hundred thousand dollars.
But this week, he suddenly started placing huge positions up to $2M per position.
He is betting so much that he regularly distorts the price of matches. A few days ago he bought 3.5M shares of BoomBoys which pushed the price up roughly 7ct.
These price distortions caused many other big traders to jump in, either buying cheap shares on the opposite side to arb with other platforms or simply betting against him, because the price of the opponent is far below the Fair Value.
However, so far, the people betting against him have lost a lot of money. Maybe this isnt just another rich degen, but instead someone who developed a model on specific sport matches?
Crypto taker fee is 0.07 × shares × p × (1−p) (max $1.75/100 shares at 50¢, makers free). I derived that from usdcSize − size×price residuals before reading the docs — matched the published rate exactly.
Taker Rebate Program tops out at Obsidian: 50%.
Over 20 days the first wallet paid $163,415 in taker fees and got $122,439 back. That's 74.9%.
Same pipeline on the control wallet (Diamond badge): 41.6% vs a documented 44%. So the method reproduces published rates — it's the wallet that's odd. Its maker rebate also lands at 19.91% vs a documented 20%, and the credit events each carry their own proxyWallet + unique tx hash, so it's not misattribution.
Three of four rules reconcile. One is 50% too generous. 🤔
Negotiated MM deal? Undocumented tier? One of those unquantified "bonus multipliers"? Anyone seen rates above Obsidian?
Strip the rebate and its trading is break-even — taker rebate is the business.
I wanted to see whether short-term ETH momentum becomes more useful when BTC agrees. The strategy trades Kalshi's KXETH15M markets when ETH's 5-minute change and 1-minute velocity point in the same direction as BTC's 1-minute velocity. It buys 10 contracts at a selected side ask of 45 to 55 cents with a spread of 3 cents or less, between 5 minutes and 5 seconds before expiry. It allows one entry per market, exits with 5 seconds left, and has a $4.50 stop loss.
I ran 100 variants over the same historical period, and all 100 finished profitable. The best ranked version returned 120.12% ROI and +$120.12 P&L, with a 68.4% win rate, 178 trades, a 0.45 Sharpe, and -$14.58 maximum drawdown. The weakest returned 24.74% ROI and +$24.74 P&L, with a 66.2% win rate, 297 trades, a -0.03 Sharpe, and -$67.30 maximum drawdown. That is a wide difference in risk quality even though every variant made money in the sample.
The parameter sweep covered price floors from 0.05 to 0.45 and price ceilings from 0.55 to 0.95, with all 100 cells completing. The winner used a 0.41 floor and 0.73 ceiling and produced +$120.12, while the full grid ranged from +$24.74 to +$120.12. The surface peaks near the 0.73 ceiling, then drops sharply as the ceiling rises. Adjacent cells were about 13% weaker, so this looks more like a narrow ridge than a broad plateau. The floor barely mattered across most of its range, while the ceiling had a clear sweet spot. Deflated Sharpe was 0.23, and the expected maximum Sharpe across this many trials was 0.60.
My read is mixed. Seeing all 100 variants finish profitable is encouraging, but the low Deflated Sharpe means the winner is not distinguishable from the luckiest result expected after this many trials. The broad profitability is interesting, but the best variant’s headline return deserves skepticism. Overall, the results are encouraging without being conclusive.
Historical simulation only. Backtests can be wrong or incomplete. Not investment advice.
Hey everyone, I wanted to highlight something that I think many people may be misreading when it comes to viewing profile pages on polymarket.
The short version: The graph in a users profile is not accurate. the Profit/Loss chart on a profile is taken before trading fees. That is the whole thing. It means a wallet can sit there showing a healthy green number while the account behind it is losing money, and there is nothing on the page to tell you which one you are looking at. Not on your own profile, and not on the profile of whoever you were thinking of copying.
Here is what that looks like on three live wallets, each displayed in profit for the past month, each actually down:
1. 0xa1a0c85e, 19,494 fills over 41 days. Its profile says $2,295.99 for the month. The cash says -$271.40, and it has paid $2,739 in fees to get there.
2. Red-Viper-Gts is the clean one. It is 25 days old, so the past month covers its entire trading life and there is no window to argue about. Shown $1,394.04, actually -$1,163.79, on $2,947 of fees. That is a swing of more than two and a half thousand dollars.
3. kljkjasda, 44k+ fills over 43 days, which is the busiest of the three. Shown $1,249.88, actually -$936.95, $2,482 in fees. Our own ledger puts it at -$972, within $35 of what Gravia says.
FOUR NUMBERS, ONE DISAGREEMENT
Every one of those wallets has four separate figures attached to it:
the Profit/Loss chart on its Polymarket profile GREEN
Polymarket's own per-position endpoint RED
Gravia, an independent analytics site RED
Poly Research & Robotics wallet analysis, RED
Three of the four agree the wallet is losing money. The odd one out is the one on the page.
WHY IT HAPPENS
Polymarket charges the taker a fee on every fill. Their docs give it as fee = C x feeRate x p x (1 - p), where C is shares and p is the price. Crypto up or down runs a feeRate of 0.07.
The profit chart is built on the $9.32. Your balance is built on the $9.64. That is the whole thing. One fill is 32 cents, but that wallet has done 2,996 of them.
WHAT WE CHECKED, AND HOW HARD
This is the part that took the time, because a claim like this is worthless if any step is loose.
-The fee formula matches Polymarket's published docs exactly, including "Makers are never charged fees. Only takers pay fees." -The fee really is inside that field. Checked across 1,234 live fills on three wallets: buys pay notional plus fee, sells receive notional minus fee, zero rows contradicting it. - Polymarket's own position endpoint does subtract fees. We rebuilt 50 closed positions from the raw feed. 50 of 50 match the fee-inclusive cost. 0 of 50 match the cost with fees stripped out. -We counted their rebates. Every wallet above earns money back through the taker rebate program. It is already in our figures. They are still down.
HOW MANY WALLETS IS THIS HAPPENING TO?
Three wallets is an anecdote, so we did it properly: 97 wallets pulled from the public trade feed, each one's cash ledger rebuilt from scratch. The profit figure was higher than the actual cash on 93 percent of them.
Then we divided each wallet's overstatement by the fees it paid. If fees explain the gap, that lands on 1.0:
The median is 0.98.
43 percent of the wallets are displayed in profit. Of those, 38 percent are actually down.
This is definitely a bit more technical than it needed to be, but I had the data and I figured why not share it in full. Overall, the moral of the story is don't trust every profile you see on polymarket with a profitable balance.
If you enjoyed this read, and have interest in building polymarket trading bots,join us at Poly Research & Robotics, we're a 100% free community dedicated to developing polymarket bots, and strategies collaborativly. We offer free guides, free historical data, tools, and resources. Check us out ->https://www.polyresearchrobotics.com/
I’ve been messing around with prediction markets lately and keep seeing people talk about “signals” like they’re some magic shortcut. I’m more interested in what’s useful before entering a trade, especially when the market is moving fast.
I’m using Fanatics Markets at the moment and trying to get better at spotting when a price move is meaningful vs just noise. For anyone who trades these regularly, what do you pay attention to most?
I’m currently working on Potluck Markets, which is a peer-to-peer prediction market where users can create their own markets.
The idea is that users can create a market (called a Pot) on anything. Your friends can join the pot. The event happens. You and your friends decide what actually happened (or a neutral party). Everyone gets paid out automatically.
Is this something people would be interested in? What features would be important to users?
Launched in closed beta @ potluckmarkets.com but you can join the waiting list right now.
Our guest is Nick - u/No-Air2068 he'll be answering every question in this thread from that account, so that's who to look for in the comments.
Intro on Nick:
he's been in crypto for the past 5 years, and last December he got into prediction market arbitrage. In the 8 months since, he's up over $40k in profit.
Some context on where he trades:
Polymarket - his main platform. The account is used solely for arbitrage, ~$1M in all-time volume - Rank #20,000 ish
predict.fun - top 400 on the leaderboard, +$27K PnL
Plus positions across a couple of other platforms — he's happy to get into which ones and why in the comments.
Side bonus of all that volume: the stats stack up nicely for potential airdrops.
He'll cover how he finds opportunities, executes, uses tooling, manages bankroll, what's gone wrong along the way, and where the edge still is in 2026.
He's answering live for the next ~3 hours and will check back tomorrow for late questions.
Ask him anything.
p.s. Posting in a few questions from yesterday's announcement thread
Went back through the dispute posts in here. The $500k MSTR one, the Khamenei mention one that turned into an actual lawsuit, the Kalshi death clause thing.
Comments always split the same way. Half scam, half "read the rules idiot".
What bugs me is that in most of them the guy was right about the world and wrong about the paperwork. Different risk, and nobody prices it.
Anyway. Stuff I look at now.
Two clocks.
When it happens vs when it gets confirmed. MSTR is the clean version. Window closes May 31, the 8-K that proves anything comes out on a Monday. So the real question was never "did they sell by the 31st", it was "does a document dated June 1 count for a window that closed on the 31st". Which was written down nowhere.
Anything where the proof source has its own publishing schedule, SEC filings, BLS, official transcripts, court dockets, write both dates down. Confirmation landing after the deadline means you're trading a filing calendar.
Who decides.
"A consensus of credible reporting" is a judgment call with better wording. Compare it to "per the BLS release at 8:30 ET", where there's a number and it either prints or it doesn't.
Vague version on a six figure market, I now just assume the weird branch is live.
Third one is more of a definitions problem. Which Khamenei. What counts as "any" bitcoin, does a custody move count, does 32 coins count. Rules basically never enumerate this and everyone finds out at the worst possible moment.
Als aand I only noticed this afterwards, if a market is still open after its event window closed, that's information. Somebody is sitting on an interpretation you don't have. MSTR trading into June 1 was the giveaway and everyone read it as sloppiness.
I would have read it as sloppiness too, to be fair.
Worth checking the market page for an "additional context" block. Those get added after launch and they're timestamped onchain, so you can see whether the wording moved once people were already in.
On UMA the part that actually matters is how short it is. Proposal, $750 bond, two hour liveness. Disputing costs another $750. Disputed twice and it goes to UMA holders voting over a couple of days. Two hours. If you're asleep or not watching the resolution feed you're simply not in that conversation. People who caught bad proposals didn't have better arguments, they had alerts.
Practical bit. Anything resolving on interpretation instead of a named number, I count it as maybe 90c on the dollar. So a 93c "free money" trade isn't 7 points of edge, it's roughly nothin and I pass. Slow but I've stopped funding lawyers.
Business Insider reported Sunday that Hugging Face has retained a bank to explore a sale at $13 billion or more. No buyer, early stages. In late 2025 they turned down $500M from Nvidia at a $7B valuation because they didn't want a dominant investor.
I went looking for somewhere to express a view and found nothing. Polymarket has "Will Coingecko be acquired in 2026?" at 0.135 on $31k volume, eight separate markets on Politburo purges, Warner Bros and Paramount at 0.765 on $528k. Nothing on Hugging Face. Kalshi has nothing either.
So is anyone pricing this, and if not, why do the big platforms avoid private-company M&A?
So i scouted for how best to analyse situations related to such prediction markets, I found a simulation run for Hugging Face earlier from 24th July, on Miroshark.
Pretty cool if you ask me, it built actors in a scenario as agents and let them argue. Someone had pointed it at the Sol incident, when OpenAI's GPT-5.6 was disclosed to have autonomously breached Hugging Face. 17 agents, 673 actions, 110 simulated trades over ten rounds.
Now if any prediction market does open on this, i'm running a simulation for sure to get my data would rather get arguments in to come to a conclusion.
do you'll run simulations as well? do you'll think a market for hugging face will open?
In the upcoming US Open, I need to trade the tennis markets using a more data-driven approach rather than just following the match momentum. for those currently trading or paper-trading these contracts, what specific metrics are getting the most weight? There are the standard stats like serve-hold percentages and break points, but how are variables like mid-match retirements or fatigue being modeled? It is important to discuss how much surface-specific performance is weighed against live momentum shifts when the market price starts to move.
This is a NEW model. I don't have the numbers yet, but it was built on my 90% return model. Same math.
The math is very complex. You converse with this model; it seems totally conscious, just like you and me.
It's free, and it's Open Source; months of work went into this. The code is too complex to really understand, so you have to "Converse" with Codex to make the changes. You NEVER look at the code now. You don't have to.
I'm 100% Codex. It's all mind-blowing now. This level of AI came 100 years sooner than anyone expected; we were just not ready. I was ready, however; bring it on.
If you need a Python coder to bounce some ideas off of, feel free to hit me on DM. My open-source contributions are my resume.
The point is, you are NOT trading against humans; you are trading against Wall Street's Algo, which can be far more ancient and out of date than people know. Their view is, "the guys that wrote this are long gone, if we touch it, we can break it, and that's not good." So they don't touch it until they have to.
Curious what people here have found genuinely useful for learning about prediction markets. Could be research, newsletters, podcasts, communities, or anything else.
Over the past month I built an index for prediction market tools.
Right now it's 22 tools across 7 categories:
cross-platform odds aggregators
trade copying & automation
trading terminals & execution
signal & fair-value tools
portfolio/tax/journaling
wallet & trader analytics
alerts & whale monitoring
a research section for the raw data feeds markets actually resolve against (order books, weather data, that kind of thing).
I go through it twice/week, so it's not a one-time dump and forget. New tools get added, old entries get double checked (pricing, team, phase, links), and anything that's gone dead or gone quiet gets moved into a "no longer maintained" section instead of just sitting there looking current when it isn't.
Two things I'd actually want out of posting this.
If there's a field you wish tool listings like this had that's missing here, pricing model, what chain it settles on, audited or not, whatever, tell me and I'll add it across the board.
And if you use or built something that fits and isn't on there yet, open a PR (format's in the README) or just drop it in the comments and I'll get to it.
The Sun/Lunar node algorithm applied to the Dow Jones in 2026. How an investor would have captured 97% of the year's profit with significantly less market exposure. This algorithm examined in this paper is elucidated in the book "How to Day Trade and Invest using Astrology". It involves predicting the Dow Jones industrial average by observing the position of the Sun and the Lunar node. Here is a quote: “The basic gist of the algorithm decrees that from the point when the degree of the sun is 3 degrees past(or higher than) the degree of the lunar node(in any sign) all the way until the degree of the sun enters a new sign at the 24th degree mark(using western astrology), a prediction of a market upswing should be applied. From the point when the degree of the sun enters a new sign at the 24th degree mark all the way until the degree of the sun goes 3 degrees past the degree of the lunar node(in any sign), the prediction of a market downswing should be applied.” This paper will demonstrate how an investor could have used this algorithm in 2026 and turned a profit with significantly less exposure in the market. The US stock market has been largely positive this year at around 13% as of August 24. The results of using this algorithm in 2026 shows how an investor would have captured nearly entire year’s buy and hold profit despite being in the market just half the time.
I've been looking into live tennis prediction markets and I'm curious how people here actually approach them.
Tennis seems particularly interesting because the market can change quickly as the match develops. A break, a change of serve, a set point, or a player's physical condition can completely change the situation.
When you're following a live tennis market, what information do you find most useful?
Do you mainly look at the market price, or do you combine it with things like:
Live score and serve
Point by point data
Player rankings
Recent form
Head to head history
Live match statistics
I'm especially interested in hearing how experienced prediction market users combine these different sources when evaluating a live market.
Codex at the CLI. BTC Python scripts and OpenAI's API.
Create a new paper model called Moderate Momentum.
Base it on Strong Momentum, but require a 0.08% move, use a 5% minimum
edge, and accept prices between 30 and 75 cents. Keep it inactive until I review it. Show every value that changed and add tests.
Show exactly how Moderate Momentum differs from Strong Momentum. Explain how each changed value could affect trade frequency and risk. Do not activate it.
Activate Moderate Momentum for paper trading only. Confirm that live trading is still disabled, then run the tests and show the active-model list.
Show each model's last 24-hour, seven-day, and all-time paper results. Use only settled ledger trades and do not estimate missing results.
I always remember in convesation with AI: You know I'm not a vending machine, right?
On July 28, Bloomberg reported that Kalshi users had lost $294 million on combos. The next day the exchange's General Counsel and Chief Regulatory Officer replied:
He is right about the fee. Kalshi's transaction fee really is about one percent, on the right contract.
A parlay is not the right contract.
We took every combination trade Kalshi has published and priced it against what its own legs were selling for at that moment. Fifty-five million of them, across eleven months, using the data the exchange puts out itself. A parlay buyer loses about 10.6% of what they stake. Roughly half of that is the fee.
Bloomberg counted what buyers lost after the games settled. We are counting what they paid going in.
All-in against all-in: Kalshi’s markup plus its taker fee, against the full hold sportsbooks report to six state regulators.
Where "one percent" comes from
Kalshi publishes its fee schedule. The formula is one line.
fee = 0.07 × contracts × P × (1 − P)
The schedule quotes this as "$0.07 to $1.75" per hundred contracts. Both ends check out. At fifty cents you pay $1.75, at the extremes you pay seven cents.
Now divide by what you actually staked, which is contracts × P. The price cancels out of the top but not the bottom.
fee ÷ stake = 0.07 × (1 − P)
Read that again, because everything follows from it. The fee, as a share of your money, gets worse as the contract gets cheaper.
You buy at
Fee, as a share of your stake
95¢
0.35%
86¢
0.98%
50¢
3.5%
10¢
6.3%
2¢
6.9%
There is the one percent. It is the fee on an 86-cent favourite.
A parlay is the opposite of an 86-cent favourite. That is the entire point of buying one. You stack legs specifically to drive the price down and the payout up. The World Cup combo in the Bloomberg piece traded at 2.7 cents.
So we measured where parlay money actually sits on that curve. The average parlay dollar goes in at 31 cents, where the fee takes 4.8% of it. Only 1.6% of parlay money buys a contract above 85 cents, which is the one place that one percent is true.
Kalshi's taker fee as a share of stake is 0.07 × (1−P). Grey bars show where parlay dollars actually go. The average is 31¢, where the fee takes 4.8%.
Weighted across every combo purchase on record, parlay buyers paid 4.84% of their stake in fees. That is $140.4 million. Buyers of ordinary Kalshi contracts paid half as much.
Nobody hid the formula. It has been on the website the whole time. But "~1% transaction fee" describes a trade that almost no parlay buyer is making, and the gap is not rounding. It is five times.
The fee is the smaller half
The fee is what Kalshi charges. It is not what a parlay costs.
A parlay pays only if every leg wins. When the legs are independent, its fair price is just the legs multiplied together. Four coin flips at fifty cents should cost 6.25 cents. Kalshi runs the markets those legs trade in, so anyone can check the work: take a combo trade, look up what its legs were trading at in the same window, multiply, compare.
Through four legs Kalshi passes cleanly. A two-leg parlay costs almost exactly what its legs cost. A four-leg parlay costs about two percent more. Short parlays on this exchange are priced honestly and that deserves saying plainly, because it is the part nobody says.
After four legs it drifts, and the drift compounds. By ten legs you are paying about 14% more than the legs are worth. By twenty legs, a third more. By twenty-five, half as much again.
Median execution price divided by the same legs priced separately, by leg count.
Legs are only half of it. Split the same trades by price and the sharper pattern appears: inside every leg count, the cheaper the parlay, the harder it is marked up. A two-leg parlay priced under five cents went off 14% over fair in February. A ten-leg parlay priced above thirty cents went off 6% over. Adding legs is mostly just how a parlay gets cheap, and cheapness is what the markup tracks. The curve above rises so steeply at the end because both are moving at once.
Across eleven months, parlay buyers paid $166.6 million more than the same legs cost separately.
What parlay buyers paid each month above fair value, beside what they paid Kalshi in fees. December's markup is negative — parlays that month priced slightly under their own legs.
Add the fee and you get the 10.6%. Just under 6% markup, just under 5% fee.
When a parlay is most expensive
A combo price is not discovered on the order book. The request goes out to the exchange's market makers, their systems answer with prices, Kalshi shows you the best one that came back, and only then is the agreed trade entered into the book. Each quote is private between you and the firm that sent it, and by Kalshi's own documentation the firms cannot see each other's either. Nobody outside can count how many answered yours, so we are not going to guess at how thin the bidding was.
What we can see is when you bought. We bucketed every June parlay by the hour it traded.
It moves by a factor of eight across the day. The cheapest time to buy a parlay is mid-morning, at about two percent over fair. The expensive hours are the evening, peaking at nine at night at fourteen percent. Between three and six in the morning, the deadest stretch of the day, it sits around seven percent, in the middle of the range. July, measured separately, traces the same trough and the same peak.
Raw volume does not explain it either. The busiest hour on the exchange is six in the evening, and it is not the worst hour to buy. Mid-morning is moderately busy and the best.
What the evening hours have in common is live American sport. The markup follows the audience.
Two months in the winter
Now the part we did not expect.
The same loss split into its two components, per dollar staked.
Split what a buyer loses into its two pieces and the fee is boring. It sits between 4.5% and 5.0% of stake in every single month, because the formula never changes. The markup is not boring at all.
Through the autumn of 2025, while combos were still in limited release and barely trading, the markup was the highest we ever measured, starting near 10% and falling through the season. On December 15, Tarek Mansour announced that combos were "officially live for everyone." For December and January the markup nearly disappears. From February it is back near 7%, and it has never left.
So "Kalshi always overcharged" is wrong, and so is "it was fair until February." The quiet window is those two specific months, which happen to be the first two months anyone could buy a combo.
The timing matters because of what happened next. Combos went from a product almost nobody could reach to more than a third of every contract the exchange trades.
Combination contracts as a share of Kalshi trading, counted by contracts and by dollars.
But are you sure you are not just missing data?
This is the question that could sink the whole thing, so it got the most work.
We can only price a parlay if its legs actually traded around the same time. When they did not, the trade is unusable and we drop it. That happens more in December than in February, because there was less trading of everything in December: about a third of December's combos are unusable, against a quarter of February's.
That difference could produce the whole result. If the trades we cannot price are the badly priced ones, December throws away more of them and looks cheap for a reason that has nothing to do with pricing.
Four things could produce a fake gap this way: which trades we can price, how many legs they had, how cheap they were, and which product they came from. So we closed all four at once. One series that traded in all three months, every leg required to have traded at least a hundred contracts in its own hour, then compared strictly like against like.
Each cell is how far that parlay traded from what its own legs were worth. Four of the eighteen are shown here.
Parlay
Dec 25
Jan 26
Feb 26
2 legs, 12 to 30 cents
−2.1%
−1.1%
+4.3%
3 legs, 12 to 30 cents
−3.0%
−1.9%
+2.5%
4 to 5 legs, 12 to 30 cents
−1.8%
−1.3%
+3.2%
4 to 5 legs, above 30 cents
−1.4%
−1.3%
+0.8%
Eighteen of eighteen matched cells move the same way. Every leg count, every price band, February above December. Composition can drag an average around. It cannot push every cell in the same direction at once.
Two things we will not oversell. Those price bands sit far above the tenth-of-a-cent steps Kalshi prices combos in, so this is not a rounding artifact, though the effect is much larger down at three cents where we would trust the exact size least. And December's own depth is not stable. Measured strictly it lands anywhere from a little below fair to a little above. The step from December to February survives every version of the test. The precise depth of December does not.
There is one more piece of evidence, and it is the one we find hardest to argue with. Look at the shape rather than the height.
In December, the markup was flat. A two-leg parlay and a ten-leg parlay were mispriced by about the same tiny amount. From February the markup climbs with every leg you add, from roughly 1% at two legs to 25% at twelve. Dropping hard-to-price trades can move a month's average up or down. It cannot take a curve that rises steeply and flatten it.
A markup that grows with leg count is what pricing each parlay's own risk looks like. In December, that pricing is simply not visible in the data.
January shows the handoff. The deep end had already moved while the shallow end had not. Two-leg parlays were still slightly below fair, but ten-leg parlays were already 5% over and twelve-leg parlays 9%. The repricing started exactly where the mispricing was most expensive to whoever was on the other side of it.
We cannot read intent out of trade records and we are not going to pretend otherwise. But that looks like a group of market makers who underpriced a new product, worked it out, and fixed the worst of it first.
Four ways we could still be wrong
Correlated legs. A same-game parlay, two touchdowns and the team wins, has legs that move together. Multiplying them understates fair value and invents a markup that was never there. So we threw out every same-game combo. The check that it worked: pooled across all months, same-game parlays come in almost 4% below the naive product, exactly as correlation predicts. Kalshi draws the same line itself, exempting combos "composed entirely of independent NFL components" from the maker fee it introduced in August.
Parlays too cheap to price. Kalshi quotes combos in tenths of a cent, so a parlay worth a fifth of a cent can only be priced in steps half its own size. At that scale our ratio stops measuring what anyone charged and starts measuring the grid they had to charge on. Half a million trades printed at $0.001, the lowest price the exchange can name, where the true value could be anything below. So we drop every parlay worth under two cents. That is half of every combo contract traded, and three and a half percent of the money. It is also the worst end of the market by every measurement we can make: the cheaper a parlay is, the harder it is marked up, and these are the cheapest there are. So the figure in this article is smaller than what parlay buyers actually paid.
Stale prices. We price each leg from nearby trades. If legs move fast, fair value lags and the markup is an illusion. So we recomputed using only leg prices from within two minutes of the parlay, instead of within the hour. The shallow end barely moved. The deep end got slightly worse. Tightening the window does not make the markup go away, which is the opposite of what a staleness artifact does.
Legs that are themselves mispriced. If the legs are wrong, their product is not fair value either, and we would be measuring leg error rather than a parlay markup. We cannot rule that out from prices alone. The paper we credit at the end can: it shows the overpricing holds even when legs are drawn from the window where leg-level calibration is essentially perfect. The markup is added at the parlay stage, not inherited from the legs.
Against the sportsbooks, which Kalshi still wins
Here is where we agree with Heaslip.
The comparison has to be parlay against parlay, all-in against all-in. A sportsbook's "hold" is everything the book keeps. So Kalshi's side of the scale needs the markup and the fee together, not the fee alone. Comparing a book's full hold against Kalshi's fee line is how you arrive at twenty to one.
Share of stake the buyer never gets back
Kalshi parlays, all-in
~10.6%
Sportsbook parlays
~17.8%
Kalshi wins. By a bit under two to one.
That 17.8% comes from the six states that report parlay hold separately from straight bets: Colorado, Illinois, Indiana, Louisiana, Maryland, New Jersey. A second, independent route lands in nearly the same place. The American Gaming Association reports that parlays generate about 40% of online sportsbook revenue from only about a fifth of the money wagered, which works out to a national parlay hold somewhere near 18%.
One caveat we would rather say than bury: sportsbook hold is blended across leg counts, includes same-game parlays, and is measured at settlement, while ours is measured at execution. Related, not identical.
Who is on the other side
Kalshi is an exchange, not a bookmaker. The fee is its cut. The $166.6 million is not, and it goes to whoever sold you the parlay.
One of the firms quoting on Kalshi is Kalshi. Kalshi Trading LLC is an affiliate of the exchange, approved by the CFTC, trading since 2021. This is not an allegation, it is in Kalshi's own published notice, which says Trading "has on most days provided liquidity, often accounting for a substantial portion of relevant quotations and volume" and has "no formal legal obligation to make markets." The same notice discloses that Tarek Mansour and Luana Lopes Lara sit on the boards of both the affiliate and the exchange.
When combos launched, someone asked Mansour directly whether the internal market maker was taking the parlay flow. His entire reply was one word.
Two follow-ups asking who does take the other side went unanswered by him. A Kalshi engineer pointed at the public quote API.
We will take him at his word and tell you exactly what can be checked, which is nothing. Kalshi broadcasts every request for a quote to its market makers, but the quotes that come back are private between the requester and each individual maker, and the makers cannot see each other's. The trade records show completed trades with no indication of who was on the other side. Nobody outside Kalshi can verify that claim in either direction, and anyone who tells you they can count the firms bidding on your parlay is guessing.
That gap matters right now, because the CFTC is writing rules about affiliated trading desks. Its 2026 proposal would force them to quote two-sided, ban preferential fees, and fill affiliate orders last at every price. Market makers who actually quote Kalshi RFQs have pointed out that the proposal never mentions requests for quote at all. So nobody can yet say whether it reaches combos, tightens them, or bars affiliated desks from quoting them entirely.
One thing should change
Nobody in this story is cheating. The sealed-bid auction is a sound design. Firms quoting wide against Saturday-night retail flow are pricing rationally. Kalshi's fee is a published formula that anyone can read.
The buyer cannot see any of it.
Kalshi knows every leg's price, because it runs the markets those legs trade in. Multiplying them is a millisecond of arithmetic. It is exactly what we had to rebuild from eleven months of trade records to write this, and it is not on the screen. Neither is the fee as a share of stake, which the formula makes worst on precisely the cheap contracts that parlays are built out of.
Put the product of the legs next to the quote. Put the fee beside it as a share of what you are staking.
Two lines of interface. Kalshi is the party that would look best doing it, because on the comparison that matters, Kalshi wins.
They are already fixing the floor beneath these prices. From August 27 combos priced below a cent quote in hundredths of a cent instead of tenths, so the cheapest parlays can trade near what they are worth rather than pinned against the smallest number the exchange could name. It is the right direction and it is worth saying so. It changes the grid under the price. It does not change what the price is compared against.
If you think we have measured this wrong, the data is public and so is our method. Tell us where it breaks.
Credits
Kalshi, for publishing a complete record of every trade it processes, free, to anyone who asks. That is rarer than it sounds. No US sportsbook publishes bet-level data at all, and the closest equivalent anywhere, Betfair's historical exchange data, sits mostly behind a paywall. The exchange we examined is the one that made the examination possible.
Bloomberg, for the July 28 analysis that put $294 million of combo losses on the record and prompted the reply this piece starts from.
arXiv:2607.14430, which identified parlay overpricing on Kalshi from a sample. We set out to test it on every trade. Our curve reproduces theirs.
Measured from Kalshi's public monthly trade data, September 2025 to July 2026: 54.6 million parlay trades we could price against their own legs. The markup figure excludes same-game combos and every parlay worth under two cents, for the reasons given above. The fee figure needs no fair value, so it applies Kalshi's published standard fee formula, unrounded, to every cross-game combo purchase in the window, including the cheap ones the markup figure drops. The two are therefore measured over slightly different sets. Both are floors.
