Jesús Cuesta

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.

El desastre de Knight Capital fue un fallo de estado. Cómo modelar la ejecución de estrategias y el ciclo de vida de las órdenes con máquinas de estado finito en Python: guards, kill switch, cooldown, testing y el puente hacia LangGraph.

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

Ultima actualizacion de la liberia qastats de reporting avanzado de portfolios y estrategias de trading en python

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

En esta entrega,ampliamos el backtester para utilizarlo como un buscador de amplio espectro y encontrar ventajas estacionales. Realizamos un backtest a todos los parametros necesarios para analizar la estacionalidad intradia del activo

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.

En este articulo, analizamos el ratio de sharpe, el value at risk (VaR) el Conditional Value At Risk (CVaR), el System Quality Number (SQN) y otros ratios relevantes para analizar el 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