Archive
Every report, newest first.
The Knight Capital disaster was a state failure. How to model strategy execution and the order lifecycle with finite-state machines in Python: guards, kill switch, cooldown, transition testing, and the bridge to LangGraph.
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 create a solution to use any type of data within a zipline pipeline. We will create an algorithm that queries the datalake, performs the appropriate transformations, and subsequently ingests the data into zipline within the so-called bundle.
In this article, we will explore the creation of an algorithm to manage financial data in our datalake, using ArcticDB as the backend and the End Of Day Historical (EODH) data provider.
In the previous article, we introduced NoSQL databases and their advantages, use cases, and relevant justifications.
This article explains the characteristics and qualities of a datalake, while also explaining an execution plan for its creation based on Python and ArcticDB.
Learn algorithmic trading with Python from scratch. Discover how to create, analyze, and optimize trading strategies step by step in this practical course.
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 calculate the US dollar liquidity index in Python, using data from the Federal Reserve FRED API and OpenBB.
In this article, we explain how to get free market data for stocks, futures, options, forex, and more using OpenBB and Python.
In this new article, we cover how to create a professional environment for algorithmic quant trading. To do so, we use a stack of technologies that make deployment easier, allowing us to focus on pure research rather than infrastructure.
In this second part of the Zipline tutorial, we will perform our first data analysis using bundles and build a basic strategy.
In algorithmic trading there are countless options for carrying out your analyses or backtests, but we are going to use the best quant stack to build a professional research and backtesting pipeline.
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.