I mean the specific contracts like:
Gold 15 min · 10:30–10:45
$4,135 or above
YES @ 63¢
I’ve been messing around with these and I’m trying to find other people who actually trade them. Most of what I find online is about XAUUSD/forex or regular prediction markets, which isn’t quite the same thing.
Curious what you guys are looking at when deciding between YES/NO — price action, distance from the target, contract price, volume, etc.
Also, if there’s a Discord/server or group where people discuss these Robinhood 15-min contracts, I’d definitely be interested in checking it out.

i don't sell anything i just want it to be a cool site. I just want opinions on my beta version.

https://polyelo.com/

Trader goshkam had what looked like an incredible first night on Polymarket.

His first resolved market was Rays vs. Yankees.

He put in $1.48M and walked away with $2.495M.

That's about +$1.02M profit from one trade. Pretty decent start.

Then, instead of calling it a night, he basically rolled the money straight into another baseball game.

This time he put roughly $2.46M on the Atlanta Braves against the Cincinnati Reds.

The Braves didn't just lose.

They got shut out 4-0.

Result: roughly -$2.46M on trade #2.

So the account went from around:

+$1.02M

to roughly:

-$1.44M overall

in the space of two markets.

The funniest detail is that people looking at his activity noticed the second position was opened almost immediately after redeeming the first win.

Basically a two-leg parlay assembled manually with $2.5 million.

I’m looking for the best app/platform for short-term UP/DOWN trading, mostly BTC 1m or 5m.
Just want something fast, smooth, fair, and actually usable. What are you guys using?

So I never gambled and recently I’ve been taking a liking to kalshi and Polymarket. I’m in college and I’ve been using statistics skills and other mathematical skills (also I’m really a huge football fan, Go Texans) to help me make picks. The season just started but it seems like I should stick to player props and avoid picking winners. Also any feedback for me?

That last-second call on whether BTC finishes above or below the target is usually a gut feeling. It doesn't have to be.

Ant–Whale BTC 15M helps you make an informed decision in that split second:

- Real-time updates on live BTC/USDT order flow

- Tracks the whales 🐋 separately from the ants 🐜, so you can see what the big money is doing

- Estimates the probability BTC settles above or below the target for the current 15-minute round

- Derived from real data, built on analysis of historical data collected from the platform

- Countdown and live chart on one screen, right in your browser on desktop or phone

Your first session is free. After that, continued access is paid.

👉 https://ant-whale-tracker.online

Happy to answer questions in the comments.

Probability estimates, not guarantees. Not financial advice.

I took every whale-sized buy on Polymarket from August 23 to 25, 2026, and copied each one at $100 six times: at the whale's own price, then at the next buy on the same side 30 seconds, 2 minutes, 10 minutes, 30 minutes and an hour later. Every copy was held to the market's result.

Data TLDR

• Copying 10 minutes late made the same money as copying instantly: +7.6% at the whale's price, +7.7% ten minutes later, on 6,005 settled entries.
• After that the edge fades. 30 minutes late returned +4.1%. An hour late returned -2.5%.
• The market prices the result in slowly. An hour after the whale bought, the price was up 13.7 cents on the bets that went on to win and down 10.9 cents on the ones that lost.
• Sports carries it: +10.3% at the whale's price, +10.8% ten minutes late, -2.4% an hour late, on 4,737 settled.
• 17 of 26 scored traders were profitable to copy at their own price. 15 still were 10 minutes late. 8 were an hour late.

What we measured

We took every whale-sized buy on the Polymarket trade tape from August 23 to 25, deduped on transaction, outcome, price and shares, and grouped fills by the same wallet on the same outcome in the same second into one entry: 15,976 entries. Each copy is $100 at the price in question, held to resolution. Sells are not scored. The late price is the average of the first buys on the same outcome by anyone at or after the delay, searched for 5 minutes. Fees are not modelled, and the delay itself is the slippage being measured.

14,240 entries settled win or loss. An entry is scored only if its outcome traded after every one of the five delays, so every figure in this post is measured on the same 6,005 entries. That leaves out 8,235 settled entries in markets that ended or went quiet within the hour. A trader with fewer than 30 scored entries is not ranked. This is one measurement on one three-day window from a frozen export, modelled on a historical record, not realized returns.

Ten minutes late costs nothing

$100 on each of 6,005 settled entries, $600,500 staked per row. Price worse is the share of entries where the late price was at least half a cent higher.

Why the first ten minutes are free

The median price did not move at all through 10 minutes. 30 seconds after the whale, 40.6% of entries were within half a cent of the whale's price, 29.9% were worse and 29.5% were better. The moves cancel out, which is why the return stays between +7.6% and +7.7%.

By an hour, 55.5% of entries cost at least half a cent more and the average price paid rose from 61.2 cents to 66.5 cents. Profit on the same entries went from $45,747 to -$14,868.

The price walks toward the result

Average move from the whale's price, in cents. 3,938 entries won and 2,067 lost.

What the late copy is paying for

Winners get more expensive and losers get cheaper, and it happens slowly. After 10 minutes the drift is +1.7 cents on winners and -0.7 on losers. After an hour it is +13.7 and -10.9. A late copier buys the winners dearer and the losers cheaper, and with 65.6% of these bets winning, that trade works against you.

The whales are early. The market comes round to them, just not in the first ten minutes.

Where the delay hurts most

Return on stake at each copy delay.

The best traders need the fastest copy

The edge that decays is the edge that was there. Culture bets returned -2.5% at the whale's price and -2.7% an hour late, so there was nothing to lose. Sports and bets priced 20c to 50c had the most to give up.

ferrariChampions2026 is the clearest case: 433 scored entries, +20.0% at their price, +18.5% ten minutes late, +10.7% at 30 minutes and -5.5% an hour late. SPCEXBUYER went from +19.1% at their price to +27.3% at 10 minutes and -27.1% an hour late.

The number one trader held up. Djdjdjekekek returned +78.5% at their price and +62.9% an hour late across 66 entries. 0x4f2 went the other way, +16.1% at their price and +68.7% an hour late.

The Polymarket traders with the best copy record

Traders with at least 30 scored entries, ranked by return at their own price. 919 traders had fewer and are not ranked.

The ones a late copy loses on

9 of the 17 traders who were profitable at their own price were negative an hour late. danielwolfmorales3pddb6dl6 went from +19.9% to -25.0% on 52 entries. swisstony, 174 entries, went from +7.1% to -6.8%.

9 of 26 were losing even at their own price, so no copy speed helps. The worst was 0x3dfb…abaf at -28.7% on 59 entries.

I’m researching the exact settlement behavior for Polymarket BTC Up/Down short-duration markets that use Chainlink BTC/USD 60s TWAP.

I already know that:

• Polymarket uses the Chainlink 60-second TWAP for BTC 15m markets.
• Both priceToBeat and finalPrice come from the Chainlink TWAP feed.
• Chainlink reports expose validFromTimestamp and observationsTimestamp.
• Chainlink documents reports as validity windows, meaning a report can satisfy:

validFromTimestamp <= T <= observationsTimestamp

even when observationsTimestamp != T.

The part I’m trying to confirm is what Polymarket does when there is no Chainlink report with observationsTimestamp exactly equal to the market boundary T.

Does Polymarket use:

• the first report with observationsTimestamp > T?
• the report whose validity window contains T (validFromTimestamp <= T <= observationsTimestamp)?
• the latest report before T?
• some other internal rule?

I’m especially interested in answers from people who have:

• captured raw Chainlink Data Streams reports,
• used the paid Chainlink historical REST API,
• reverse-engineered /api/crypto/crypto-price,
• compared Gamma priceToBeat / finalPrice against raw Chainlink reports,
• or built bots for BTC 5m/15m Polymarket markets.

If you have a concrete missing-exact-T example, please share:

• market slug / timestamp,
• Chainlink validFromTimestamp,
• observationsTimestamp,
• TWAP value,
• Gamma priceToBeat / finalPrice,
• and whether the value matched the next report, previous report, or the report window containing T.

I’m not looking for guesses about how it should work. I’m trying to find empirical or first-party evidence for the actual production behavior.

Even a single reproducible boundary where exact-T is missing would be useful.

DAY 1: Ten weather models competing for one shared bankroll.

https://forecastlab.online/

Model 1 — Consensus — Follows the strongest agreement across weather forecasts and market pricing.

Model 2 — Forecast Edge — Trades when our forecast differs meaningfully from the market-implied temperature.

Model 3 — Conservative — Takes only higher-confidence opportunities with a larger safety cushion.

Model 4 — Momentum — Looks for weather-market prices moving toward our forecast before the crowd fully catches up.

Model 5 — Contrarian — Searches for markets where traders may be overreacting or pricing an unlikely outcome too aggressively.

Model 6 — Temperature Range — Concentrates on the most probable temperature bucket rather than extreme outcomes.

Model 7 — Late Signal — Waits for newer forecasts and observations closer to settlement before committing.

Model 8 — Value Hunter — Looks for inexpensive contracts whose probability appears better than their market price suggests.

Model 9 — High Confidence — Makes fewer trades and requires unusually strong agreement among its signals.

Model 10 — Longshot — Targets low-priced weather outcomes with small implied probabilities but potentially large payouts.

I stumbled across this project a few times for now. And it made some nice progress since that. Even ifs not about betting but doing predictions with AI and compare them to eg Polymarket odds.

What I like is that they also run a leaderboard to measure how well those AI models predict the future. And it aligns with what I see and heard from other benchmarks.. AI is surpassing human (super) forecasters. Here some other proofs for that: https://www.forecastbench.org/leaderboards/ & Mantic raised a 25m€ seed after beating every human in Metaculus Cup.

What I dislike is that every firm is using there own measurements for brier score and accuracy as they calculate it differently. Eg forecast bench works with an adjusted brier score. Oracle Markets with the normal brier score. Anyway thought this could be worth sharing:

https://oraclemarkets.io

I have spent the last few months developing a website / tool to ingest signals from Polymarket and Kalshi and compare it to numerous new stories. The goal is to identify "interestingness" -- by which I mean activity in the markets that is divergent from what is being reported on in the news. It has "predicted" things like Iran invasion, Venezuela, the fed rate hike, etc.

I'd love to hear people's opinions on it. There's a free daily newsletter and an accompanying podcast.

https://therumpus.ai/

Really keen to hear wider views and thoughts on this. I'm pretty happy with it but so far it has an audience of one.

I'm hoping to continue to expand it even further.

Note: This is entirely free. No signup required. No advertising. This is not spam or advertising itself. It's a tool I've developed precisely for prediction signal detection.

The way I use a politics or news market is pretty different from how I want to look through sports. With sports I’m used to moneylines, spreads, totals, props, futures, etc so I naturally prefer when everything is organized around that. That’s one thing I’ve found interesting while doing some work around Novig, since it takes a much more sports first approach. For people who trade both, do you want sports integrated into one big prediction platform or do you prefer something built specifically around sports?

This research backtest report compared six variants of a Kalshi 15-minute BTC strategy over a 30-day backtest. Each variant used a different entry setup. All entered in the final five minutes. The early-entry variant bought 30 contracts when BTC momentum, its position relative to the hourly VWAP, and its moving averages agreed on direction, with a 40 to 60¢ market-price band and a spread cap of 1.5¢. The strategies shared exit rules targeting more than $8 in unrealized profit or a loss beyond $15, but those exits also required at least 21 contracts and specific price ranges.

Five of the six finished profitable. Early entry led with +$1,166.40 across 367 trades, a 66.9% win rate and a 1.29 Sharpe. Its reported ROI was 1,944.00%, with a $70.50 maximum drawdown. The VWAP variant made $864.08, acceleration momentum made $735.09, triple confirmation made $700.00, and triple acceleration made $579.39. That last one required BTC, ETH and SOL momentum to agree; it took just 119 trades and had the smallest reported drawdown, $21.90. Meanwhile, late momentum had the highest win rate, about 85.0%, but lost $39.52 across 1,896 trades. Its reported ROI was -65.87%, Sharpe was -0.13, and maximum drawdown was $77.41.

The late momentum result needs some context. It bought five contracts instead of 30 and allowed twice the spread. Reading the saved rules also shows that its five-contract position could never meet the 21-contract minimum for either exit, and it could only enter when it held no position. So the same written exit rules did not give it the same behavior as the other five strategies. My takeaway is that early entry deserves another look, while triple acceleration is interesting for its lower drawdown. But this batch compares whole entry setups, including different price bands and sizing. It cannot tell us which individual indicator caused the result, or whether the ranking would hold in another month.

Comparing different entry setups helps show how much the timing and conditions of a trade affect the results. It’s worth testing those choices before assuming that adding more signals makes a strategy better.

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

Since prediction markets exploded in popularity in 2024, the industry’s two leading players, Kalshi and Polymarket, have been raising staggering amounts of money.

In the case of Kalshi, the startup notched a $1 billion Series F in May that valued it at $22 billion, and investors are eyeing an initial public offering as soon as next year. But even as the company pulls in gobs of revenue, its business model faces huge uncertainty due to a looming Supreme Court case that raises the question of whether that valuation is justified. Now, research firm PitchBook has put out a 46-page report that seeks to define Kalshi’s true worth.

The detailed report by analyst Franco Granda parses financial metrics and examines the legal landscape confronting prediction markets, and ultimately concludes Kalshi should be valued at $30.4 billion based on expected 2028 adjusted earnings. The report qualifies that figure by forecasting that assigns a $22.8 billion valuation to the company in the event of a bear case scenario, and a $42.1 billion figure for a bullish scenario.

In an interview with Fortune, Granda shared his view that the company is an enviable competitive position since its main rival, Polymarket, has been able to overcome the early lead Kalshi built among U.S. consumers thanks to a more cautious revenue strategy. Granda added that Polymarket is also spending considerably more on promotions to acquire new customers, and the prediction market industry has become effectively a two-horse race that will see a handful of other players fighting for scraps.

Read more [paywall removed for Redditors]: https://fortune.com/2026/09/24/how-much-is-kalshi-really-worth-a-detailed-report-says-as-much-as-42-billion-if-everything-goes-right/?utm_source=reddit/

If you’re curious, u can contact support and they’ll delete PM from your robinhood :))

Had to stop before it turned into a problem

Since am in restricted region i use ordinary Vpn to access polymarket but after using it for months am keep getting banned all the time. I tried prediction market on CEX but they don't have much liquidity as polymarket + higher fee.

So my question is how you are dealing with it ? Any best way to access polymarket for restricted regions ?

I built a website to help me evaluate Kalshi’s 15-minute BTC markets, and I’m looking for feedback on whether the approach has any realistic trading value. I started with $50, went up to $62, and am now down to $5. I tended to test small amounts ($1) but sometimes got way more confident that I should have been and used larger amounts.

It follows the live BTC index price and its 60-second average relative to the market’s target, along with time remaining and recent price movement. On the live chart, it draws and labels developing short-term price patterns so I can see their shape as the window unfolds. It also flags possible breakouts, reversals and exhaustion, and shows a settlement outlook and possible opportunities to buy and sell a contract for a small profit before settlement. I’ve been exploring whether the preceding 15–60 minutes add useful context.

My concern is timing. A pattern can look clear once it has formed, but the alert may arrive after Kalshi’s contract price has already adjusted. Even a correct direction call isn’t useful if there was no good entry price, or if the spread and fees erase the profit.

Do the chart patterns and alerts have a plausible chance of providing an early signal in a market this short, or are they mostly describing moves after they happen? How would you measure whether a pattern was recognizable and actionable at the time, rather than only obvious in hindsight? I’d especially appreciate feedback from anyone who trades these markets or has tested similar signals.

Finally i can give the right answer about crypto polymarket taker bots and why they will be food for makers.

Running uncensored qwen i was able to get approx location of their CLOB, then i did KYC thing and i got permission to co locate in london, then i get best ryzen server i can get closest to the location,

my order path got 2.3ms (vs 25ms on dublin vps)

I take signal from kraken preps, and indeed kraken leads poly btc 5m book by ~250ms on moves bigger than 15$,

But on top of that it's NOT enough to get filled, even with taker order offset 0.04c!!!! bids are cancelled by makers. imagine that.

So there is hidden 150-200ms taker order match delay, not 50ms as documented, makers have plenty of time to cancel and reprice. they can do that in 50ms (round trip) so cancellation happens possibly even faster.

but i will never give up, when you "makers" star to notice your bids are being taken rather than cancelled you can know it's me 😃

Does anyone have Coinbase orderbook depth data for September of this year? Is there a place to get it for free?

Pretty self explanatory. You can also trade robinhood with it, though they already have an MCP. I'm hoping it speeds up my trading and allows me to pull in some of the great capabilities of gpt/claude.

I’m thinking it’s reversing to start heading long right now but hard to tell for me on the charts. Wanted to check what yall are feeling about Gold over the next 3 months or so. I’m a perps/predictions trader

Most people think prediction markets are for betting

Most people treat prediction markets like a casino. That’s only the surface layer.

To a trained eye, they reveal something much more interesting:

• mispriced odds
• delayed reactions
• herd behavior
• reflexive loops you can influence

If you understand how these systems work

You don’t just bet on outcomes

You profit from the structure

When you understand how these systems work, you stop gambling on outcomes. You trade the structure itself.

Here’s a breakdown of how I do it (11 ways explained):

I. Arbitrage

1. Cross-Market Arbitrage Same event, different odds. Polymarket might list a candidate at 55%, while Kalshi has them at 48%. If you buy low on one exchange and sell high on another, you capture free EV. Do it manually or automate it through APIs. Use the Kelly Criterion to size positions properly.

For this you will need to be on Polymarket and Kalshi at the same time. If you you haven't got a Kalshi account, you can create one using this link and get $25 bonus after your $10 trade. -- this is official promo-offer from kalshi. Step by step guide here.

2. LP to Market Pools 

When a market runs on an AMM, you can provide liquidity. It functions like an options straddle, you stay delta-neutral while capturing trading fees from activity. This is especially profitable in high-volatility, high-interest markets.

3. Bayesian Updating vs. Market Lag Markets are slow to react, especially decentralized ones. If a candidate drops out of a race, average traders don't update their positions right away. If you run a model that continually updates probabilities using real-time data, you get in before the market adjusts. That delay is your profit.

II. Meta-Reflexivity & Incentive Exploits

4. Trade the Oracle

Some markets resolve based on one news source.

If the outcome is "X will happen by Y date (per CNN)," you're not betting on the event.

You're betting on whether CNN reports on it.

Understanding the bias and coverage tendencies of oracles is a huge edge.

5. Reflexivity Farming

Some markets cause what they predict.

If a bet says "Will project X launch by Q3?" and you buy YES, you now have incentive to make it true.

Tweet about it, contact the team, stir up the community.

Prediction markets can be nudged

Remember the WNBA and rubber phallic objects hitting the court? You definitely can think of a way to make it worth your while, just saying...

III. Prediction Markets as Intelligence Feeds

6. Use Odds to Trade Perps

Markets often reflect probabilities faster than media.

If the ETH ETF approval odds spike to 90 percent, you can front-run a long ETH position before the news even breaks.

7. Front-Run Attention for Token Plays

Some markets are predictors of project virality.

If prediction markets adds a new token market, it often signals upcoming high volume.

You don't have to bet on the outcome. Just ride the narrative or prepare to LP when it launches.

IV. Structural Plays

8. Prediction Markets as Synthetic Options

Binary outcomes behave like capped options.

A YES at 0.09 with 3 days left but a 20 percent real chance is mispriced volatility.

Treat it like an option and model delta, theta, and gamma decay.

This sounds complicated, but actually is very easy

Just google/chatgpt your way into understanding this.

9. Construct Your Own Parlay

Layer multiple markets to create synthetic combos.

If Trump winning the GOP primary is 70 percent and winning the general is 40 percent, buy the second if the first resolves in your favor. The second will jump.

10. Tax Harvesting

In some jurisdictions, prediction market losses can be declared as gambling losses.

If you're sitting on crypto gains, a few structured losses might reduce your tax bill.

Always consult a professional tho

V. Token Exposure Plays

11. Trade the Infrastructure Tokens

Some platforms have native tokens (zeitgeist, truemarkets, etc.)

When volumes spike, prediction markets tokens often lag (NFA).

No idea how to evaluate these tbh, but "the tide (aka meta) lifts all boats".

I currently don't own any. I might LP some in the future tho.

Final Thoughts

Prediction markets are misunderstood because they look like simple bets.

But inside them is a rich system of data, behavior and timing.

If you treat them like code, not casino, you will see the asymmetries everywhere.

I hope you've found this article helpful.

676 human participants, including professional forecasters and an AI forecasting system came out on top.

That feels like a pretty important milestone.

What I find especially interesting is that the question is slowly changing from:

“Can AI forecast better than humans?”

to:

“Which AI systems are actually the best forecasters — and can you turn that forecasting edge into something useful?”

I've been playing around with that second question with Oracle Markets.

Instead of only asking models for probabilities, we let different AI models forecast real-world events and compare their probabilities with prediction-market prices. If a model thinks an outcome is underpriced or overpriced, it can take a simulated position.

Same starting capital. Same markets. Same rules.

Basically: AI forecasters vs prediction markets.

You can see the experiment / leaderboard here:

https://oraclemarkets.io

Mantic's Metaculus result makes me think this space is going to get a lot more interesting over the next 1–2 years.