Cryptocurrency Analysis with Python: A Beginner’s Guide to the Simple Moving Average (SMA) Crossover Strategy (part 1)
Welcome back to our tutorial series on Cryptocurrency Analysis with Python! In our previous tutorials, we explored powerful Python libraries like Matplotlib, mplfinance, and yfinance, which enable us to load and visualize cryptocurrency data from popular sources such as Yahoo Finance. Additionally, we delved into understanding key concepts like returns, rewards, and risk in the context of cryptocurrencies. In this tutorial, we will build upon our knowledge and take our analysis to the next level by implementing a Simple Moving Average (SMA) Crossover Strategy using Python. Don’t worry if you’re a complete beginner; we’ll explain everything from scratch without assuming any prior knowledge. Before we delve into the strategy itself, we will cover crucial concepts such as logarithmic returns, Maximum Drawdown (MDD), and simple moving averages. These foundational concepts play a vital role in making informed decisions in the dynamic cryptocurrency market.
Disclaimer: I am not a financial advisor and this article is not financial advice. This is purely introductory knowledge. All investment-related queries should be directed to your financial advisor.
