Analyst: 6 Secret Hedge Fund Formulas in Crypto Market Prediction

Jakarta, Pintu News – The rapid surge of market prediction in the crypto and cryptocurrency sector is attracting the attention of large institutions to global hedge funds. The monthly volume in March 2026 even exceeded $13.7 billion or around Rp231.94 trillion, jumping 599% compared to the previous year.

Platforms like Polymarket and Kalshi are no longer just places for speculation, but have evolved into competitive arenas based on quantitative strategies. This change marks a major transformation in the way market participants read probabilities and profit opportunities.

Prediction Market turns into a “Quant Battlefield” arena

One analyst said the prediction market has now evolved into a “quant battlefield”, where mathematical strategies dominate. Unlike before, which was dominated by retail traders, institutions are now starting to utilize systematic approaches like in the derivatives market.

Hedge funds use quantitative models to quickly identify mispricing and arbitrage opportunities. This makes the market much more efficient, as well as more competitive.

Significant volume growth indicates increased interest from many quarters, including Wall Street participants. With deeper liquidity, prediction markets are becoming more attractive for both short- and long-term strategies.

In addition, real-time data integration makes decision-making faster and more accurate. In this context, excellence is no longer determined by intuition, but rather by data analysis capabilities.

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6 Quantitative Formulas that are Key

One of the key foundations in market prediction is the Logarithmic Market Scoring Rule (LMSR), which is used to understand price mechanisms. This model helps traders predict the impact of transactions on market movements before they happen. By understanding the LMSR, participants can anticipate price changes faster than other participants. This gives them a significant advantage in fast-moving markets.

In addition, the Kelly Criterion is used to determine the optimal bet size based on probability and risk. Expected Value (EV) gap scanning allows traders to find the difference between market probabilities and personal estimates. KL-Divergence is used to detect statistical inconsistencies between related markets.

Meanwhile, Bregman Projection helps identify complex probabilities in multi-outcome events, and Bayesian Updating ensures that probability estimates are always dynamically updated.

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How the System Works and Implementation Challenges

According to BeInCrypto, to replicate this strategy, analysts suggest using real-time data through APIs such as Polygon. Programming environments such as Python with numpy, scipy, and cvxpy libraries were used to run the mathematical model.

Before being used directly, the system must go through a backtesting process using historical data to avoid overfitting. This approach ensures the strategy remains relevant under real market conditions.

However, implementing a quantitative strategy is not always easy. Factors such as data quality, execution speed and transaction costs can affect the final outcome. In addition, increasingly efficient markets make arbitrage opportunities close faster.

Therefore, risk management such as the use of fractional Kelly and drawdown limits are very important. Without proper risk control, potential losses can increase significantly.

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*Disclaimer

This content aims to enrich readers’ information. Pintu collects this information from various relevant sources and is not influenced by outside parties. Note that an asset’s past performance does not determine its projected future performance. Crypto trading activities are subject to high risk and volatility, always do your own research and use cold hard cash before investing. All activities of buying and selling Bitcoin and other crypto asset investments are the responsibility of the reader.

Reference

• BeInCrypto. Analyst Outlines 6 Formulas Quant Funds Use in Prediction Markets. Accessed March 20, 2026
• Featured Image: Generated by AI