Technology has become an asset in finance: financial institutions are now evolving to technology companies rather than only staying occupied with just the financial aspect: besides the fact that technology brings about innovation the speeds and can help to gain a competitive advantage, the rate and frequency of financial transactions, together with the large data volumes, makes that financial institutions’ attention for technology has increased over the years and that technology has indeed become the main enabler in finance.
Among the hottest programming languages for finance, you’ll find R and Python, alongside languages such as C++, C#, and Java. In this tutorial, you’ll learn how to get started with Python for finance. The tutorial will cover the following:
- The basics that you need to get started: for those who are new to finance, you’ll first learn more about the stocks and trading strategies, what time series data is and what you need to set up your workspace.
- An introduction to time series data and some of the most common financial analyses, such as moving windows, volatility calculation, … with the Python package Pandas.
- The development of a simple momentum strategy: you’ll first go through the development process step-by-step and start by formulating and coding up a simple algorithmic trading strategy.
- Next, you’ll backtest the formulated trading strategy with Pandas, zipline and Quantopian.
- Afterward, you’ll see how you can do optimizations to your strategy to make it perform better, and you’ll eventually evaluate your strategy’s performance and robustness.