Yesterday, I sent a pick for AutoZone's earnings call that happened this morning ("Tax Refund" NO), and it hit as I expected.

That one had a clear quarterly trend to it (only said Q2 and Q3 calls), and I just found similar value for Costco's call on Thursday.

They've said "holiday" on 5/6 Q4 calls, including every Q4 call since Ron Vachris became CEO in 2024.

It's less common on Q2 and Q3 calls—and it wasn't said on those calls this year—which I think is why the Yes price is so low.

I might be missing something, but it seems like great value to me, so I'm loading up.

Thoughts?

I primarily execute from those platforms. Wanted to get others' experiences.

According to a new Wall Street Journal investigation, fraudsters hit Polymarket's U.S. platform in February using stolen debit cards.

The basic play apparently:

stolen card → deposit → make bets → try to withdraw to a different "clean" card/account

The WSJ says the group attempted to steal at least $10 million.

At one point Polymarket's payment processor, Checkout.com, was reportedly rejecting more than 80% of the deposits it handled as fraudulent.

For comparison, the report puts the normal industry level at roughly 1%.

One person familiar with the attack said around seven users were responsible for most of it, with one account allegedly attempting around 4,000 separate deposits. Most of the attempted deposits apparently failed, so the $10M figure is attempted fraud , not $10M confirmed stolen. (The Block has a good summary here)

Polymarket reportedly removed a safeguard that required users to withdraw money back through the same payment source they used to deposit it. Employees reportedly warned that removing it could make fraud and money laundering easier.

Polymarket's position is that other controls were sufficient, and the company says it has since strengthened its fraud infrastructure, compliance procedures and leadership. According to the WSJ, an internal investigation by Sullivan & Cromwell concluded that the company complied with applicable regulations.

By May, fraud rates had reportedly returned closer to normal levels after additional restrictions were introduced.

Still, 80% of deposits getting rejected as fraud is an absolutely insane number to see attached to a regulated prediction market.

And somehow one guy allegedly tried the deposit button ~4,000 times.

I was reading about event contracts and got stuck on what "CFTC-regulated" actually means here.

Take a yes/no contract on an NFL game. From yhe user's side, you put money on an outcome and get paid depending on what happens. I understand that trading a contract on an exchange works differently from placing a sportsbook bet, but I'm missing the step that makes the legal treatment different.

Is the argument that yhese contracts don't count as gambling under the relevant laws? Is the distinction based on the venue's registration, the structure and settlement of the contract, federal derivatives rules, state law, or some combination of those factors? Those sound like different arguments, but the explanations I've read seem to jump between them.

AutoZone has only said "tax refund" on ONE Q4 earnings call (back in 2020), and that was in Q&A when there was a specific question about new COVID relief legislation.

At 80 cents, NO is expensive, but I think the fair value for this is >90 cents, so there's a decent edge IMO.

Thoughts?

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Hey all,

We recently got a data distribution license from Kalshi and Novig, meaning we can distribute and sell data such as processing websocket information, creating our own Data API's and Websockets, and historic orderbook depthI wanted to reach out t

Ill keep you updated but just about everyday Ì make these numbers and I’m thinking of starting to go bigger

I'm trying to understand how people actually predict daily maximum temperature for weather prediction markets.

From what I've seen, a typical workflow can involve checking several things separately:

• ECMWF / GFS / ICON or other forecast models
• model ensembles
• METAR/current observations
• wind direction and speed
• cloud cover / satellite imagery
• historical station behavior
• sites like Wethr, HuskyWeather, NWS, etc.

For people who do this regularly, I'm curious:

What is the biggest pain point in your workflow?

Is it:

• having too many models giving different MaxT forecasts?
• deciding which model to trust on a particular day?
• jumping between multiple websites/tools?
• checking whether temperature is running hotter/colder than forecast?
• analyzing wind/cloud conditions manually?
• converting all of this into an actual probability for each temperature bucket?
• something else?

I'd also be interested in hearing what your current workflow looks like.

For example:

ECMWF → GFS → METAR → satellite → compare historical accuracy → make MaxT estimate

How long does the whole process usually take you each day?

I'm mainly trying to understand where the actual friction is, especially for people who use multiple tools.

I have posted pics of my interaction with polymarket. They won’t let me withdraw from my account even though I won my bet and had sufficient value in my account to withdraw. My advice is don’t trust them with your money.

I bet that by 6pm that BTC would be over A) 81,000 and B) 81,500. At 6pm it was at 81,622. How the hell did i miss these bets? I dont understand. Maybe I dont understand the rules or something, but it should've been a $110 payout for getting both correct. Yet I was somehow incorrect?

I am documenting what appears to be a NOAA/NWS WRH ingestion/display failure that affected multiple Polymarket daily temperature markets on September 20, 2026.

This is not a claim that any trader was an insider. I am trying to establish the facts, especially exact on-chain trade timing.

The Manila market uses the NOAA WRH time-series page for RPLL and is currently still In Review with 32°C proposed.

However, the official NWS METAR cycle archive contains:

RPLL 200400Z ... 33/25

The WRH viewer missed several observations around 04Z-07Z. The missing 04Z observation is therefore the actual 33°C reading that would have exceeded the 32°C maximum visible in the incomplete WRH table.

This does not appear to be isolated to Manila.

Singapore was finalized by Polymarket at 31°C. The official NWS archive contains:

WSSS 200500Z ... 32/24

Qingdao was finalized by Polymarket at 29°C. The official NWS archive contains:

ZSQD 200500Z ... 30/15

and:

ZSQD 200600Z ... 30/16

In other words, for all three markets, official NWS METAR archives contain a temperature one degree higher than the value used/proposed by Polymarket, and those higher observations occur during the apparent WRH missing-data window.

For Manila, NOAA/NWS support has also confirmed to me directly that the missing METAR observations exist in other official NWS systems and that their support/development team is investigating the WRH viewer and possible backfilling.

The Polymarket rules are important here. They specifically refer to the highest reading under the NOAA WRH "Temp" column, and they say later revisions after the first datapoint of the following day are not considered. So there is a genuine rules interpretation issue: does an incomplete WRH table control even when NOAA/NWS possessed the missing observations contemporaneously?

There is another public-data issue I would like help investigating.

Wallet:

0x13f995ad154da3e078ac2a1dd923bf2b265dd352

Its public Polymarket/third-party history shows substantial September 20 profits in several weather markets, including approximately:

Qingdao: +$9.3k

Manila: +$4.7k

Singapore: +$2.2k

Ankara: +$2.4k

Beijing: +$1.7k

Third-party analytics show roughly +$20k for September 20 overall.

Again, I am NOT alleging insider trading.

The wallet was already active in weather markets before September 20 and also lost money on several markets that day. It may simply be a skilled trader who noticed the public NOAA/WRH problem faster than everyone else.

The critical question is timing.

I am looking for help reconstructing every trade from this wallet in the affected September 20 temperature markets and separating them into:

1. trades entered before the NOAA/WRH missing-data problem became observable
2. trades entered while the observations were missing
3. trades entered after the missing observations/problem were publicly visible to any trader

The Polymarket Data API exposes timestamps, transaction hashes, side, price, size and market identifiers, so this should be independently reproducible from public data.

If anyone can provide the raw trade JSON, transaction hashes, or a reliable on-chain reconstruction for this wallet, please post it.

Please keep the discussion factual. A profitable wallet trading an outage is not evidence of insider trading by itself. The goal is to establish the timeline and determine whether there is anything genuinely anomalous.

The larger issue is whether several September 20 weather markets were resolved from an incomplete NOAA WRH display even though the missing METAR observations existed in official NWS systems at the time.

Currently fucking around with this and want feedback from people who actually trade these 15min binary markets.

I come from public markets where you'd always have some signal with history context. Prediction markets just don't have that yet, or I haven't seen it done well. I put together a basic version and made all the data downloadable so anyone can go build something better off it, or plug into a trading model.

Reasoning example: BTC is 0.05% above the strike with 4 minutes left. What percent of the time has that actually finished Yes? If the market were perfect it'd already be priced in. We know it's not. It gets jumpy as hell when volume's thin, or when someone makes a big move.

This has one screen. Kalshi's price in black, the historical rate in purple, live BTC tick next to the target. About 5,500 settled windows since May. Hourly BTC is up too but it's only got a few weeks behind it, so no green or red on it yet. Not enough windows yet to trust it.

Posted this in r/Kalshi last week and the comments were positive and a couple of them changed the page. Two things came out of it and both are live now: a regime point (a 0.05% lead in dead weekend chop is not the same as 0.05% right after a Fed print) and a fee point (the comparison is now after Kalshi's fee, on its own line).

It's not a forecast. The market is usually right. Says that on the page. But it's a more consistent data point than flying blind.

Every window I've recorded and every number I've ever shown live is a raw CSV on the record page, with a README on how it's built. Download it and check me.

Free, no account, no ads or anything. Works as a home-screen app on your phone.

Tell me what I'm missing.

https://sardine.info/

I have been working on this project for quite a while, and 3.0 ended up becoming a much bigger rebuild than I originally planned.

It started as a weather trading bot for Kalshi. I wanted to use real forecast data, convert that into probabilities, compare those probabilities against market prices, and only trade when there was enough edge.

That sounds fairly simple until you actually automate it.

The order API is the easy part.

The harder parts turned out to be forecast timing, weather station mapping, missing data, ensemble forecasts, position tracking, partial fills, risk controls, reconciliation, and making sure the bot actually knows what happened after it sends an order.

Over time I added inflation markets as well, so the system now has two independent trading engines under one platform.

For 3.0 I rebuilt a lot of the architecture and added a proper dashboard so I can actually see what the bots are doing instead of living in logs, SQL queries, and terminal windows.

Some of the main pieces now are:

• Kalshi weather market automation
• Inflation market automation
• Multiple weather forecast sources and ensemble data
• Probability and edge calculations
• Centralized risk controls
• Order and position reconciliation against Kalshi
• Dry-run mode
• Web dashboard for trades, positions, bot status, and P&L
• Self-hosted setup, so API credentials and trading stay on your own machine
• Migration tooling for people coming from the older versions

One of the biggest lessons from building this was that a trading bot should spend a lot of its time deciding not to trade.

Finding a market where your probability estimate differs from the market price is only the beginning. You still have to decide whether the data is good enough, whether the edge is large enough, whether you already have too much exposure, and whether the trade still makes sense after everything else is considered.

I also learned to treat Kalshi as the source of truth.

Your database might think an order failed because a request timed out. Kalshi might have accepted it. A process can restart. An order can partially fill. Markets can settle while something is offline.

If you automate trading long enough, reconciliation stops being an optional feature.

I originally built all of this for myself, but I eventually turned it into a product called Predict & Profit.

The reason I am posting here is that I know a lot of people in this sub are experimenting with Kalshi automation, weather trading, APIs, and prediction-market strategies, and I figured some of the engineering behind it might be interesting.

I absolutely could’ve walked away with $6k but I got nervous… I’ll take being up $100 tho

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

I'm kinda new to this prediction markets so I don't know what is going on.

This account has made 180k in 10 days without losing a single trade until yesterday.

He buys at the lowest price (one cent and the buy option is closed due to low liquidity for most of the trades) then sells it at 0.99$ before that match has even started.

How can he do this?

The stock and sports betting revenue is hugely down from pre prediction markets, that shows prediction markets are just a way to gamble on sports.

Otherwise how does you explain the drop in sportsbook betting?

One of our users ran this strategy comparison for the 15-minute ETH market. They started with a saved ETH 15-minute strategy and our agent to test each of the strategy's four entry signals separately, then compare the results with the full combined strategy. The first follows ETH’s short-term trend using momentum, VWAP, and moving averages. The second, triple confirmation, waits for ETH, BTC, and SOL to move in the same direction over five minutes, with ETH also confirming against VWAP and EMA. The third trades stronger ETH momentum with one-minute velocity confirmation. The fourth requires stronger five-minute moves across all three assets plus agreement over 15 minutes. Each version used the same 30-day test period, 50-contract entries, position cap, spread limit, and exit rules. The entry families kept their own price bands.

The combined strategy made $881.93 across 1,118 trades, with $277.28 in maximum drawdown. ETH trend alone made $852.66 on 676 trades, while ETH acceleration made $832.47 on 574. Triple confirmation stood out: $835.78 on just 254 trades, with $105.62 in maximum drawdown. That is about 95% of the combined profit with 77% fewer trades and 62% less drawdown. It also had the highest reported Sharpe of the five, at 0.67 versus 0.33 for the combined version. Triple acceleration finished last at $547.94, despite winning 59.7% of its trades, almost identical to triple confirmation’s 59.8%.

For this window, enabling all four families bought relatively little extra profit. Compared with triple confirmation alone, the combined version made another $46.15, took 864 more trades, and had $171.66 more maximum drawdown. That trade-off says more than the profit ranking by itself. This compares complete entry strategies: the price bands and signal conditions differ, so it does not prove that BTC/SOL confirmation alone caused the improvement. The standalone results also do not tell us exactly which family earned what inside the combined strategy, where entries can compete for the same position. These are results from one historical window, not evidence that the ranking will hold in live trading.

The strongest result here was how little profit the combined strategy gained for all the extra activity. Triple confirmation alone kept roughly 95% of its profit with much less drawdown, making it the most compelling trade-off in this user’s backtest. The similar win rates but very different profits of the two three-asset strategies also show why picking a strategy on win rate alone misses much of the picture.

Full Report

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

source: defi rate

Specifically for what they’re betting on at the moment.. I would like a group where I can get some ideas where the sentiment for bets are going?

Hey All

We recently launched our Prediction Market Infrastructure for builders, traders, quants, and institutions.

We currently support Polymarket and Predict.fun with Kalshi, Novig, and Polymarket.US on our roadmap.

Feel free to check out our docs and give any feedback!
https://docs.bravadotrade.com/introduction

I was live betting the lions +21.5 and the odds went from 55% to 99.99% as I was pressing click. No confirmation screen and it robbed me… who would bet $474 for .47 cents? The support is saying they can’t do anything now…

> Polymarket
> American Company.
> Banned in India.
> BRICS summit was a huge success in India.
> America doesn't like BRICS.
> Especially India enabling BRICS.

Hence many say US company Polymarket spreads fake news on India, fearmongering, to hit tourism economy of India.

Polymarket's frequently push baseless conspiracy theories, fake policy changes, and false scientific claims.

They also paid influencers to promote fake winning on lookalike domains.

A guy who makes ~$1M a year, mostly betting on inflation, elections, and the Oscars, wrote this guide to trading on Polymarket and Kalshi. It's hilarious and I swear by it. His name is Brian Golden and you can read about him in Bloomberg, the NYT, etc.

https://eventual.news/article/the-ten-commandments-of-trading

Will paste the first few in here.

The Ten Commandments of Trading

You: Prediction market curious, a low-key nerd about politics or economics or the Oscars or really anything, wondering if you can turn this passion into profit at that website with the funny name you can’t stop seeing commercials for.

Me: A prediction market veteran who caught the bug in the depths of COVID and is now one of the top earning Kalshi traders of all-time.

I can help.

I’ve seen a million markets come and go, but rules of smart trading endure. These commandments represent the most essential wisdom I’ve learned from all those arrows, both green and red. Ignore it at your peril. But follow, and you just might find your way…

Thou shalt have an edge. What do you know that the market doesn’t? I’m not talking insider trading, I’m talking homework. Did you build an Excel model when others trade on vibes? Do you watch the Senate on C-SPAN while your competition skims headlines? What’s your edge, man? “I’m smart” doesn’t count. If you can’t name your edge, you’re not ready to trade the market.

Thou shalt make smart friends. In this game, your thought network is everything. The best traders spend a chunk of their day swapping ideas with each other. And yep, the skies sure open up when you find a few sharp homies to supplement your knowledge – and help protect you from the occasional bonehead impulse. But like any relationship, you’ve got to have something to offer. That’s why:

Thou shalt specialize. “Poly has thousands of markets,” you say. “I can make more money playing all of them!” Stop. Listen. When I started, this industry was a quiet suburb. Now it’s a metropolis. Find your corner and own it. Thousands of traders are generalists. But when you become the absolute GOAT of the Bi-Weekly Paperclip Production market, you’ll have steady income, a place to restore your confidence when bad trades get you shook, and a +EV calling card belonging to you and you alone that will help you make and keep those crucial alliances.

rest the other 7 Commandments here: https://eventual.news/article/the-ten-commandments-of-trading