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The bot, which is a full-time automated trading bot that executed 8,894 trades on short-term crypto prediction contracts and reported making nearly $150,000 without human intervention.
This scheme (see below X in a recent post) exploited the short-lived time when the price of “Yes” and “No” contracts on five-minute bitcoin and ether markets dropped below $1. theoretically, those two outcomes should always be $1. If they don’t, say they trade at a combined $0. If a trader buys both sides and lock in 97, the market settles on its own terms with three-cent profit.
That translates to about $166, or roughly that of . A total of 80 profit per trade thin enough to be invisible on any single execution, but meaningful at scale. If the bot was using around $1,000 per round-trip and clipping a 1-1, what would have been its usage of that bot? It is the kind of return profile that looks boring on a per-trade basis but impressive in aggregate, and has an attractive return rate with 5-to-3% edge each time. No excitement is required for machines to be a . And they need to be repeatable.
That’s like money for free, it sounds like . Such gaps are often fleeting, usually lasting milliseconds in practice and tend to be short-lived if not always the case of . But the episode highlights something more than a single glitch crypto’s prediction markets are increasingly becoming arenas for automated, algorithmic trading strategies and an emerging AI-driven arms race.
The data shows that order-book depth of about $5,000 to $15,000 per side during active sessions, as such, typical five-minute bitcoin prediction contracts on Polymarket carry order–book thickness of around $5,000 or $150,000 per. A BTC perpetual swap book on major exchanges like Binance or Bybit is a number of orders of magnitude thinner than that’s the case with That’.
A desk attempting to deploy $100,000 per trade would blow through liquidity and erase any edge in the spread that existed. For the time, it is a game of traders who are comfortable sizing in the low four figures.
When $1 isn’t $1
Indiction markets such as Polymarket, users can trade contracts tied to real-world outcomes (from election results to bitcoin in the next five minutes) using prediction. The terms for each deal are usually $1 (if it is the case) and $0(if not) – depending on their contract.
In a perfectly effective market, “Yes” and “No” should always be the same price as 1 in if it is not more than $1. A “Yes” should trade at 52 cents if it is 48 cent, and “No” must trade for 52cents.
market, markets are rarely perfect for but. s can temporarily dislocate themselves, resulting in thin liquidity, fast-moving prices in the underlying asset and order-book imbalances. volatility, and market makers may pull quotes from. Aside from one side of the book, retail traders may aggressively hit one. This may be lower than $1, or less for a split second by .
For a sufficiently fast system, that’s enough.
This is not the first time these types of micro-inefficiencies have been introduced by . Derivatives exchange BitMEX in the late 2010s had been able to sell similar short-duration “up/down” contracts, but after traders found ways tosystematically extract small edges, some were pulled by the venue. Tooling What’s the new tooling for ?
Retail traders regarded these BitMEX contracts as early-day punts of directional punting. A small group of quantitative traders soon realized the contracts were systematically undervalued relative to the options market and began taking advantage with automated strategies that the venue’s infrastructure was not built to defend against its competitors.
Many of the products were eventually relisted by BitMEX as well as being delisted in response to an initial bid for . The official logic was a bit of demand, but traders at the time blamed it on the contracts becoming uneconomical for the house once the arb crowd moved in.
Today, much of that activity can be automated and increasingly optimized by AI systems.
Beyond glitches: Extracting probability
The sub-$1 arbitrage is the simplest example. More sophisticated strategies go further, comparing pricing across different markets to identify inconsistencies.
For example, options markets (such as Options markets) effectively encode traders’ collective expectations about where an asset might trade in the future. Call and put options at different strike prices can be used to calculate an implied probability distribution, a market-based estimate of the likelihood of various outcomes.
In simple terms, options markets act as giant probability machines.
In case options pricing implies, say a 62% probability that bitcoin will close above ‘levels’ over – but if the same outcome is tied to only 5% probability (the difference comes from an agreement with predicting market) it means just 55% likelihood. One of the markets may be underpricing risk, .
Automated traders can monitor both venues simultaneously, compare implied probabilities and buy whichever side appears mispriced.
Such gaps are rarely dramatic, and are rare to be dramatic. Some of them may be a few percentage points (sometimes less, sometimes more) for s. And even for high frequency algorithmic traders, small edges can compound more than thousands of trades.
After building it, human intuition is not required for the process. Systems can continuously digest price feeds, recalculate implied probabilities and adjust positions in real time.
Enter the AI agents
What distinguishes today’s trading environment from prior crypto cycles is the growing accessibility of AI tools.
But traders now have to hand-code every rule or manually refine parameters for each rule. The use of machine learning systems can be used to test variations of strategies, optimize thresholds and adapt to changing volatility regimes. In some systems, multiple agents that monitor various markets are used to monitor the performance of different markets, rebalance exposure and shut down automatically when performance declines.
Suppose that a trader would spend $10,000 on an automated strategy, which allows AI-driven systems to scan exchanges (such as the scanning of predictions market prices with derivatives data), and execute trades when statistical differences are above ‘defined threshold’.
In practice, profitability depends heavily on market conditions and on speed.
Competition intensifies once competition becomes more common for an inefficiency. That edge is followed by more bots that chase the same edge as . Glues tighten spreads strictener for . Latency is decisive, . In the end, however, the opportunity shrinks or disappears.
The bigger question is whether bots can earn money on prediction markets. They can, at least a little bit, clearly until competition takes the edge off of . The point of this is that what happens to the markets themselves, but if s do it?
If a large volume share of volume is generated from systems that do not care about the outcome which are simply arbitrating one venue against another – prediction markets will be mirrors of derivatives market rather than independent signals.
Why big firms aren’t swarming
If prediction markets contain exploitable inefficiencies, why aren’t major trading firms dominating them?
One constraint is liquidity. Large crypto derivatives venues are relatively shallow, with many short-duration prediction contracts. Trying to put big money on the table can help price-payers against the trader, cutting theoretical profits by slippage.
There is also operational complexity of . blockchain is a common infrastructure indiction markets, which often involves transaction costs and settlement mechanisms that differ from those of centralized exchanges. Even small frictions matter for high-frequency strategies.
Thus some of the activity seems to be concentrated among smaller, nimble traders who can use small scale (perhaps $10,000 per trade) trading but not materially move or sell out the market.
It’s a dynamic of s that may not last forever. larger businesses, if liquidity deepens and venues mature.’ Larger firms could be more active in the long run-up to as they become increasingly liquid. Until now, prediction markets are in the middle of sophisticated enough to attract quant-style strategies but thin enough so that large-scale deployment is not possible.
A structural shift
At their core, prediction markets are designed to aggregate beliefs to produce crowd-sourced probabilities about future events.
Nevertheless, as automation increases, an increasing share of trading volume may be driven less by human conviction and more by cross-market arbitrage and statistical models.
But that doesn’t necessarily make s useless. Arbitrageurs can also increase pricing efficiency by closing gaps and coordinating odds across venues. But that doesn’t change the character of the market, as does .
What is a venue for commentators on an election or price move that can become fought ground for latency and microstructure benefits?
Cryptography Such evolution is often rapid in crypto, where such a change can be observed. inefficiencies are found, exploited and competed away. When systems are faster, edges that once yielded consistent returns fade.
A shrewd use of an unfunny exploit on a short-term pricing flaw may be the reason behind the reported $150,000 bot haul. This could also be a sign of something more general prediction markets no longer just are digital betting parlors, it may signal that they’re all about the same thing as online betting Parlor. Now, they’re another frontier in algorithmsical finance.
And in an environment where milliseconds matter, the fastest machine usually wins.
Thanks for reading How AI is helping retail traders exploit prediction market glitches to make easy money
