Seasonality in Python III - Statistics on Trading
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.
Topic
Algorithms
Trading algorithms and automated execution in Python.
6 reports
Seasonality in Python II - Backtesting
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
Seasonality in Python. Searching for Intraday Patterns
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
Your first system in Python: moving-average crossover on SPY
Moving-average crossover system on SPY in Python: data with yfinance, look-ahead-free signals, drawdown and optimisation. Practice for course level 2.
Impact of Seasonal Factor on US Bonds
In this article, we will analyze the impact of the seasonal factor on US Treasury bond yields using Python.
Quantitative Models and Algorithmic Execution
In this article we will discuss quantitative models in trading. We will explore different quantitative trading models and how they are executed through algorithms.