Discussing the article: "Statistical Arbitrage with predictions"
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Check out the new article: Statistical Arbitrage with predictions.
We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
Statistical arbitrage is a sophisticated financial strategy that leverages mathematical models to capitalize on price inefficiencies between related financial instruments. Typically applied to stocks, bonds, or derivatives, this approach requires a deep understanding of correlation, cointegration, and the Pearson coefficient, essential tools for identifying and exploiting market opportunities.
Correlation in finance measures how closely two securities move in relation to each other, quantifying the degree to which they are related. Positive correlation indicates that securities typically move in the same direction, while negative correlation means they move in opposite directions. Traders analyze these relationships to predict future price movements.
Cointegration, a more nuanced statistical property, goes beyond correlation by examining whether a linear combination of two or more time series variables remains stable over time. In simpler terms, while the individual securities might follow different paths, their relative movements are tied together by some equilibrium, which they tend to revert to. This concept is crucial in pairs trading, where the goal is to identify pairs of stocks whose prices move together historically and are expected to continue doing so.
Pearson Coefficient is a statistical measure that calculates the strength and direction of the linear relationship between two variables. Values of the Pearson coefficient range from -1 to 1, where 1 signifies a perfect positive linear relationship, -1 a perfect negative linear relationship, and 0 no linear relationship. In statistical arbitrage, a high absolute value of the Pearson coefficient between two assets might suggest a potential trading opportunity, assuming they will revert to a long-term average relationship.
Traders implementing statistical arbitrage strategies rely on algorithms and high-frequency trading systems to monitor and execute trades. These systems are capable of processing vast amounts of data to detect anomalies in asset price relationships quickly. The strategy assumes that the prices of the correlated assets will converge to their historical mean, allowing the trader to make a profit on the price adjustments.
However, the success of statistical arbitrage depends not only on sophisticated mathematical models but also on a trader’s ability to interpret data and adjust strategies based on shifting market conditions. Factors such as sudden economic changes, market sentiment, or political events can disrupt even the most stable relationships, introducing higher levels of risk.
Author: Javier Santiago Gaston De Iriarte Cabrera