Research

We are excited to announce the release of QAStats V3, with new features and improvements for quantitative analysis.

In this installment, we expand the backtester to use it as a broad-spectrum search engine to find seasonal edges. We run a backtest on all the necessary parameters to analyze the intraday seasonality of the asset

In this article, we analyze the Sharpe ratio, Value at Risk (VaR), Conditional Value at Risk (CVaR), the System Quality Number (SQN), and other relevant ratios to analyze the backtest.

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

After a long period completely disconnected, we return to writing some notes for you. On this occasion, we are going to program from scratch a complete research process, and instead of going for a specific asset, we are going to create a complete method, reusable in the future, to be able to run it

A quant strategy is a systematic strategy, driven by data and the model, which is programmed to generate investment decisions. The main pillar within quant research is the scientific method.

In this article, we will analyze the impact of the seasonal factor on US Treasury bond yields using Python.

Over time, some codes can start to see the light of day publicly, and reviewing old repositories, I found a very primitive version of a reporting library that made our lives much easier at the time.

In this article, we explain how to get free market data for stocks, futures, options, forex, and more using OpenBB and Python.