# Win Rate Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Win Rate", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

Data: 75% of Traders on Hyperliquid Are Losing Money. What Are the Profitable Ones Using?

On Hyperliquid, approximately 75% of addresses are losing money, indicating that manual traders are increasingly competing against automated systems. Profitable traders primarily employ one of three approaches: 1) Running systematic, high-frequency strategies (e.g., one address executed 261k trades with a 64.75% win rate), 2) Placing high-conviction, asymmetric bets with large positions (e.g., 50 trades yielding $4.48M at a 28% win rate), or 3) Using algorithms for execution while making manual macro judgments. The article argues that traditional methods like chart patterns or reacting to social media news are often already priced in by bots, making such traders the "exit liquidity" for systematic players. Success now depends on unique narrative timing, structural insights, or conviction during market capitulation.

marsbit05/20 07:49

Data: 75% of Traders on Hyperliquid Are Losing Money. What Are the Profitable Ones Using?

marsbit05/20 07:49

Dissecting 112,000 Polymarket Addresses: The Top 1% Who Truly Profit Are All Doing These Five Things

By analyzing 112,000 Polymarket wallets over six months, this study reveals that 87.3% of users lose money. After filtering for statistically significant accounts (8,400 wallets with ≥100 settled positions, >4 months activity, >$10k volume, and participation in ≥2 markets), key patterns among the top 1% of profitable traders emerge: 1. They trade against extreme sentiment, entering when market odds deviate 6-11% from their calculated probabilities, exploiting the "favorite-longshot bias." 2. They use position sizing aligned with a conservative quarter-Kelly Criterion, betting larger only with clear edges. 3. They specialize in 1-2 market categories (e.g., only crypto or weather), avoiding diversification across unrelated fields. 4. They trade price movements, not outcomes, often exiting before settlement after capturing mispricings, with average holding periods of 18-72 hours. 5. They avoid trading during breaking news, waiting for emotional reactions to subside before entering. Common misconceptions include overestimating win rates (top traders average 55-67%, not 80-90%), believing diversification improves results, and overvaluing speed. The top performers focus on process: specializing, recording predictions, sizing positions cautiously, trading only with high edges, and systematically reviewing their trades.

Odaily星球日报03/09 04:05

Dissecting 112,000 Polymarket Addresses: The Top 1% Who Truly Profit Are All Doing These Five Things

Odaily星球日报03/09 04:05

Tracking 'Insider Signals' from Public Data: Screening Strategies for High-Win-Rate Addresses on Polymarket

Title: Tracking "Insider Signals" from Public Data: Strategies for Identifying High-Accuracy Addresses on Polymarket Polymarket, a blockchain-based prediction market platform, offers fully transparent on-chain data, enabling users to analyze trading behaviors and identify addresses with potentially significant informational advantages. Unlike traditional financial markets, all transactions, positions, and timing are publicly accessible, but the key challenge lies in interpreting meaningful signals from vast datasets. High-performing addresses often exhibit distinct patterns: - New addresses making large, concentrated bets in low-liquidity markets. - Specialization in specific sectors (e.g., politics, crypto) rather than diversified trading. - Sudden increases in position sizes, indicating heightened conviction. - Repeatedly accurate timing, entering positions hours before major public news breaks. A systematic approach to identifying such addresses involves: 1. **Screening via Polymarket Analytics**: Filter addresses with consistent 30-day profitability, >55% win rates, and significant net gains in liquid markets. 2. **Analyzing Event-Specific Holdings**: Examine top holders in active markets, focusing on addresses that repeatedly build large positions before full market pricing. 3. **Scrutinizing Trading Behavior**: Assess entry timing (pre-news vs. post-news), concentration of buys, holding duration, and sector focus. Advanced strategies include monitoring exit behaviors (e.g., pre-emptive selling), clustering related addresses via fund flows and gas usage patterns, tracking unusual volume spikes in niche markets, and cross-referencing on-chain activity with external data (e.g., social media, real-world events). By leveraging public blockchain data and behavioral analysis, users can systematically identify and track addresses that may possess informational edges, transforming raw data into actionable insights.

比推03/02 15:10

Tracking 'Insider Signals' from Public Data: Screening Strategies for High-Win-Rate Addresses on Polymarket

比推03/02 15:10

The 15-Minute Win-Lose Game: A Million Transaction Records Unveil the 'Folded World' of Bitcoin Prediction Markets

A data analysis of Bitcoin's 15-minute price prediction markets reveals a stark reality dominated by algorithmic trading bots. Over a three-day period encompassing 291 markets, 1.05 million transactions totaling $17 million were recorded. While 17,254 unique addresses participated, the vast majority were retail users treating it like a "lottery," with an almost even split between winners and losers. The key finding is the market's domination by a tiny minority: just 247 algorithm-driven addresses (3.6% of users) executed over 60% of all trades. These bots generated a collective profit of approximately $284,000, while human traders, overall, lost $154,000. Bots also boasted a significantly higher win rate of 65.5% compared to 51.5% for humans. The analysis further debunked the assumption that pure speed guarantees success. The most profitable bot, which earned $54,531, had a high win rate of 72% but was selective, participating in 61% of markets. In contrast, hyper-frequency bots trading over 50 times per hour often had negative returns due to gas fees and intense competition. For human traders, the data suggests a path to success lies in low-frequency, high-conviction trading, where the win rate can reach 55%. However, humans consistently fail at risk management, often holding onto losing positions too long and exiting winners too early, leading to a poor risk-reward ratio. The market is ultimately a hierarchy: top algorithms harvest inferior bots, which in turn harvest undisciplined human traders.

marsbit02/05 06:38

The 15-Minute Win-Lose Game: A Million Transaction Records Unveil the 'Folded World' of Bitcoin Prediction Markets

marsbit02/05 06:38

AI Prediction Record: Want to Make Money in Prediction Markets with AI? But It Might Not Even Have Read the Question Clearly

An experiment tested whether AI (Gemini 2.5 Pro and Grok 4 Fast) could profitably predict outcomes on crypto prediction markets, pitting it against successful human traders. The AIs were given event titles, descriptions, and answer choices (Yes/No), with instructions to use web searches for news and expert analysis while ignoring market data. They were to reason logically and output only a final answer. After 21 settled predictions, Grok achieved the highest win rate at 75%, outperforming the humans (66.7%) and Gemini (52.4%). However, analysis revealed significant flaws in the AI's reasoning: * **Temporal Confusion:** Gemini sometimes misjudged the current date, leading to erroneous conclusions. * **Lack of Depth:** Grok often relied on immediate, surface-level information instead of identifying deeper patterns or historical context. * **Over-reliance on Assumption:** Gemini occasionally based predictions on subjective common-sense assumptions rather than the specific evidence found. * **Misinterpreting Conditions:** Both AIs sometimes failed to correctly parse the precise settlement criteria for a market, leading to logical errors despite having the correct information. The conclusion is that while Grok's win rate was impressive, its underlying reasoning process is often flawed and requires significant refinement to be reliably profitable.

Odaily星球日报01/04 10:54

AI Prediction Record: Want to Make Money in Prediction Markets with AI? But It Might Not Even Have Read the Question Clearly

Odaily星球日报01/04 10:54

AI Prediction Record: Want to Make Money in Prediction Markets with AI? But It Might Not Even Have Read the Question Clearly

Based on an experiment comparing AI predictions against human "smart money" on Polymarket, this article investigates whether AI can reliably profit in prediction markets. The author tested Google's Gemini 2.5 Pro and xAI's Grok (via OpenRouter), both equipped with web search, on 21 resolved non-crypto market questions. The core finding is a divergence in performance: Grok achieved the highest win rate at 75%, outperforming humans (66.7%) and significantly beating Gemini (52.4%). However, a detailed analysis of the AI's reasoning revealed critical flaws. Gemini frequently misjudged the current date, leading to erroneous conclusions. Both models sometimes relied on superficial or commonsense assumptions instead of deep, evidence-based logic. A major failure mode was misinterpreting the specific settlement conditions of a market, such as confusing "any files" with "all files" being released. While Grok's results are promising, the experiment concludes that AI often fails to fully comprehend the question's nuances, highlighting a significant gap between raw information retrieval and true contextual understanding needed for reliable prediction.

marsbit01/04 08:59

AI Prediction Record: Want to Make Money in Prediction Markets with AI? But It Might Not Even Have Read the Question Clearly

marsbit01/04 08:59

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