Topic
Quantitative models for trading and algorithmic execution.
4 reports
Discover how to identify intraday seasonal patterns in futures and validate trading strategies through backtesting. Learn to use Python to analyze historical data, optimize parameters, and evaluate the performance of your algorithms before applying them to the real market
In the complex field of economics and monetary policy, it is essential to have the ability to analyze and understand trends and the effects of economic policies. In this context, Python, a versatile programming language, has become indispensable for economists and financial analysts.
In this article we will discuss quantitative models in trading. We will explore different quantitative trading models and how they are executed through algorithms.
In probability theory, a stochastic process is a mathematical concept used to represent random quantities that vary with time or to characterize a sequence of random variables that evolve as a function of another variable, generally time.