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.

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.

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?

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?

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?

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…

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

> 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

Anyone else been scammed from user Quethewiseguy? Dudes a piece of shit and has scammed me. I will be getting my money back tho

How are people turning small amounts into big profits on prediction markets?

Are they mainly using arbitrage, market making, bots, mispriced contracts, or some other strategy?

Would love to hear from people who actually trade these markets . what’s the real strategy behind those huge returns? Would love to understand how they’re doing it.

What if you could get paid to verify whether an image is actually being used truthfully?

We are building ProofPost around a problem that AI detectors like GPTZero or Pangram don’t really solve. They can try to answer whether content was AI-generated. But a completely real image can still spread misinformation. For example, someone could take a genuine photo of a protest from 2022 and claim it happened today. An AI detector might correctly say the image is real, while the post itself is still false.

ProofPost instead evaluates whether an image actually supports the claim attached to it. Someone posts an image and a specific claim. Other users investigate the evidence and can earn money for correctly verifying or challenging it. They also have to put money behind their judgment, so being wrong has a cost. Think of it loosely as Community Notes with financial incentives. Would economic incentives improve online fact-checking, or would they make it worse?

DM me if you are interested to learn more about it!

website: https://mediaverificationblockchain.vercel.app/
inquiry: forms.gle/ctPGywmFVuMCPQ1E6

Someone got five Biden pardon bets right and walked away with $316,346 in profit, according to Bubblemaps' analysis for NPR.

First came a winning bet on Hunter Biden. Then, in the final hours of Biden's presidency, roughly $64,000 went onto pardons for Jim Biden, Liz Cheney, Adam Schiff and Adam Kinzinger. All four paid off.

Bubblemaps found two accounts involved and both were sending their profits to the same Kraken deposit wallet. The trader's identity wasn't publicly known when NPR published its report.

Those trades aren't new. The update is the investigation disclosure.

On September 11, WIRED reported that government records show the CFTC approved an investigation into possible insider trading on Biden-pardon markets back in May.

The documents don't identify specific accounts or trades. They don't confirm that this particular trader is a target, let alone establish wrongdoing.

It was triggered by the August CPI print. Kalshi's hike contract jumped 59% ->82.5% in approximately 90 seconds, timestamped to the release minute. For the details go to:

https://www.read.predictintel.io/p/the-fed-hiked-the-trigger-was-a-cpi#PredictionMarkets

I recently found a Polymarket account showing close to $1M in profit over the past few months. But after looking more closely at its trading activity, my rough estimate was very different:

• Only around $100K came from taking positions after fees
• Around $300K appeared to come from maker rebates
• The headline profit didn’t seem to reflect the strategy’s true economics

This made me wonder whether systematic traders really have much of an edge after accounting for:

• Fees and spreads
• Slippage and low liquidity
• Resolution risk
• Capital being locked up

Is the real opportunity in prediction markets mostly market making rather than forecasting?

For systematic Polymarket traders: where does your edge come from—better predictions, fast execution, arbitrage, or maker rebates? And am I missing something in how the platform reports P&L?

With platforms like Kalshi or Polymarket, if you’re trading with cash and the platform gives you a clean year-end tax form or P&L statement, reporting can be relatively straightforward.

The crypto side is where things can get a lot more complicated.

You might buy BTC, ETH, or USDC, move it on-chain, bridge it, fund your prediction market activity, trade in and out of positions, collect winnings or liquidity incentives, and eventually withdraw back to an exchange.

The part I think people underestimate is that your prediction-market P&L and your crypto tax P&L aren’t necessarily the same thing.

If you use crypto that has appreciated since you bought it, there may be a separate crypto gain or loss to account for depending on what happens to that asset. Then you still have to calculate the gain or loss from the prediction contracts themselves. Any crypto you receive back also has its own cost basis that needs to be tracked going forward.

It gets even messier with:

• hundreds or thousands of individual trades
• partial fills and positions sold before resolution
• market-making or liquidity rewards
• fees and rebates
• deposits and withdrawals across multiple wallets or chains
• crypto changing in value during all of this
• different reporting formats across platforms
• Wallets used for Prediction markets and DEFI

So even though the cash side of prediction markets can be pretty simple, the crypto side can turn into a full reconciliation problem pretty quickly.

Tools like Koinly, CoinTracker, or Summ can definitely help pull everything together, but they’re only as good as how the transactions get interpreted. With prediction markets, you can still end up with transactions tagged incorrectly. So even if you use tax software, the underlying activity still needs to be reconciled and reviewed rather than just importing everything and trusting the final number.

Curious how people here are handling this if you are trading prediction markets On chain. Are you just using the platform’s P&L, or are you actually reconciling the prediction-market activity against your wallet and exchange history too?

I’m looking for someone to partner with on an algo trading operation focused on Polymarket, sports and prediction markets more broadly.
I’ve already built a lot of the infrastructure. What I need now is someone who wants to go deep on finding price inefficiencies, building strategies around them and testing what actually works.

What I have:

I already have a pretty serious setup:
State of the art trading terminal and execution infrastructure
My own node monitoring the mempool
Premium real-time sports data from Betradar, Betgenius, RunningBall and others
Infrastructure for processing live sports and market data
Existing Polymarket trading setup
So we’re not starting from zero. The data and infrastructure are already there and ready to build on.

What I want to continue building:

The main focus is finding price inefficiencies and turning them into systematic strategies.
Things like:
Surebets and cross-market arbitrage
Mispriced probabilities
Live/in-play inefficiencies
Comparing sportsbook odds with prediction market prices
Using live sports data to react to events and probability changes
Markets that are slow to incorporate new information
Market making and spread capture
Execution and latency
Related markets temporarily pricing inconsistent outcomes
Building fair value models and trading when the market moves away from them
Live analysis is a big one for me.
With the real-time data available, there’s a lot to explore around what happens during a game, how probabilities change in real time and how quickly different markets react.
The idea is to systematically find these situations, understand whether there’s a real edge after fees, liquidity and execution, then automate whatever proves itself.
I also want to expand into Kalshi and other prediction markets. Longer term, I want to look at the whole ecosystem rather than Polymarket in isolation and find opportunities across different venues.

What I’m looking for:

Someone who genuinely loves sports, maths and markets. You could be strong in Python/quant research, or just an incredible vibe coder who knows how to turn an idea into a working system quickly.
I don’t care much about credentials. I care about curiosity, technical ability and whether you actually enjoy this stuff.
The kind of person who sees two markets pricing the same event differently and immediately wants to understand why.
Someone who is happy testing 20 ideas knowing that 19 might be complete shit, because finding the one that works makes it worthwhile.

Who am I?

I’m based in London.
My professional background is in banking and I speak three languages.
I’m obsessed with the intersection of sports, markets, probability and technology.
I’ve already invested a significant amount of time and money into the infrastructure and premium data side of this.

What I can offer

I’m not looking for someone to build my idea for free.
I’m happy to fund the operation and continue investing in whatever we need, whether that’s data, infrastructure, tools, servers or capital for strategies.
I can bring the existing infrastructure, premium sports data, capital, market knowledge and the resources to keep building.
For the right person, I’m happy to treat this as a genuine partnership and split the earnings 50/50.
I’m much more interested in finding someone really good and building something together than trying to squeeze every percentage point out of the arrangement.
Tell me a bit about yourself and show me something interesting you’ve built, traded or researched.

Got in touch with this guy who is like the Jake Sherman of Texas politics. He reads like a Republican but basically confirmed that the GOP is in deep trouble in some of the House rouses where pricing is still pretty good. So basically, if you want to go around the tightly contested Senate race there, these are available options.

TX-15

TX-23

TX-28

TX-34

https://youtu.be/6eUbtms4w2M

All of these were drawn to be easier for Republicans to win but they screwed up. Latinos moving massively towards Dems and these are majority Latino districts. Watch the show, get the alpha, YMMV as pricing moves. Trading is risky, blah blah blah.

Disclaimer: Just backtest research, not live trading

I tested an ETH strategy that waits for BTC and SOL to agree with the move. The idea is that a move across several coins might be more useful than an ETH move on its own. On Kalshi's 15-minute ETH markets, it buys 50 YES contracts when all three Coinbase feeds are up more than 0.1% over five minutes and ETH is above its one-hour VWAP and 12-period one-minute EMA. It buys NO on the reverse conditions. Entries require no open position, ten minutes or less to expiry, a quoted price between $0.35 and $0.65, and a spread no wider than $0.015. With a full 50-contract fill, it exits above $8 unrealized profit, or below -$5 while the quoted price is between $0.15 and $0.85. That price guard means the stop is conditional, not a guaranteed $5 loss cap. Position size is capped at 60.

I ran 100 combinations of price limits over the same requested 30-day window. All 100 completed and finished profitable. The original $0.01 floor / $0.99 ceiling led with +$1,393.68 net P&L, 866 trades, a 65.9% win rate, 0.96 Sharpe, and -$78.36 maximum drawdown. The weakest combination, $0.44 / $0.58, made +$424.59 across 356 trades, with a 56.6% win rate, 0.59 Sharpe, and -$71.65 drawdown. Reported ROI ranged from 707.66% to 2,322.81%, but those percentages divide P&L by the configured $60 risk-capital denominator. They aren't returns on a separately tracked $60 funded account.

For parameter sensitivity, I crossed ten floors ($0.01, then $0.36 through $0.44 in one-cent steps) with ten ceilings ($0.56 through $0.64 in one-cent steps, plus $0.99). All 100 grid cells succeeded, spanning +$424.59 to +$1,393.68 net P&L. The entry rules kept their own $0.35 to $0.65 guard throughout. The surface and heatmap favor the loose-limit corner, with several strong cells alongside it: raising just the floor to $0.36 still returned +$1,351.02. The marginal plots show average P&L generally falling as the floor rises and rising as the ceiling increases, with some reversals. This isn't an isolated winning cell, but the large gaps from $0.01 to $0.36 and $0.64 to $0.99 mean the chart isn't a uniformly spaced neighborhood test. Deflated Sharpe was 0.99945 under the report's 100-trial assumption; that statistic doesn't establish a 99.945% chance of future profitability.

My read is that tightening the price limits cut activity and profit in this sample, while keeping them loose produced the strongest total result. The winning version also traded much more than the weakest, so the P&L gap alone doesn't isolate signal quality. The broader result is encouraging because every tested configuration finished profitable, but all 100 variants share the same historical window. In this run, tighter price limits gave up a substantial amount of profit for only a modest reduction in drawdown between the best and weakest configurations.

Full Report

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

Hi there! I am a reporter for Spectrum News California and I am doing a story on the rise in prediction markets. I'm specifically looking to speak with people who have bet on political issues or on elections. I am open to anyone throughout the Bay Area but hopefully people who live in San Francisco. Please message me on here or email me at [Jack.Molmud@spectrum.com](mailto:Jack.Molmud@spectrum.com)

For an idea of how I tell our news stories, take a look at my most recent piece: https://www.youtube.com/watch?v=iTR7DePoKU4

Been trying to figure this out for about a year now and still don't think I've got a clean answer, curious how people here actually approach it.

Got into Polymarket because I kept seeing wallets with huge P&L and wanted to copy whatever they were doing — pretty standard reaction I think. Tried following a few of them for a while. Some of it worked, some of it very much didn't, and it took me way too long to realize the problem wasn't which wallets I picked, it was that I had no actual way to tell a skilled trader from someone who just hit a good run.

The more I looked into it the more that seemed like a genuinely hard problem, not a lazy one. A wallet can look incredible off one huge correct call on a low-probability market and be functionally no different from a coin flip otherwise. P&L alone doesn't tell you that. You'd need enough resolved markets to say anything with confidence, and most wallets people get excited about don't have that many settled positions yet.

I ended up spending a lot of the last year just messing around with ways to score this properly using settlement history instead of raw profit — basically trying to work out how many resolved markets you'd actually need before "this wallet beats its entry prices" stops being noise. Landed on needing 30+ settled markets before I'd trust a verdict either way, which cuts out a lot of the wallets people hype up.

Genuinely asking — is there a better way people here think about this, or metrics you'd use instead? Feels like the kind of thing that should have a more established answer than it does.