Python is one of the most preferred programming languages among quantitative developers because it has extensive packages and libraries for data analysis and algorithmic trading. It also has a relatively simple English-like syntax that makes it easier for both beginners and experienced programmers to write fewer and readable code when expressing quantitative concepts.
How to automate trading using python?
When it comes to automating your trading strategies in Python, you have three options. You could utilize an existing library, build your own automated trading code, or utilize a cloud trading platform. Option 1 (use an existing library) is often preferred because it gets the job done quickly and cost-efficiently. Moreover, the code you write is safely stored in your computer (improving security). Backtrader is one of the most commonly used Python libraries for algorithmic trading.
What is backtrader?
Backtrader is a python library that allows traders and quantitative developers to create reusable trading strategies, indicators, and analyzers without having to build their own infrastructure. Backtrader was originally introduced in 2015 and has been constantly improved since then. It is a simple, flexible, and open-source Python trading and backtesting library with a wide user base.
Below are some of the things that you can do with Backtrader:
- Backtesting – this is a method for assessing the viability of a trading strategy by evaluating its performance based on historical data. If a strategy you backtest seems to work, then you may feel more confident applying it in the future. Backtrader Python library comes with features that allow you to test and optimize your strategies, create visual plots, and more.
- Optimizing strategies – once you create a basic strategy, the next step should be to optimize it. This is important because markets are constantly evolving (we have bear markets, bull markets, periods of inflation and deflation, volatility, and stability). Adjusting a couple of parameters can make the difference between a strategy that is profitable and one that is not. Backtrader comes with the features you need to make sure that each strategy you backtest is optimized to the maximum potential.
- Plotting charts – plotting data feeds, operations, indicators, cash and portfolio value evolution can help you make better sense out of your trading. Backtrader uses some of the facilities provided by the matplotlib python plotting library to create built-in charts. A single line of code is (in many cases) all you need to achieve a visual depiction of your trading.
- Test indicators – Backtrader comes equipped with the most popular indicators. This can be very useful if you want to plan how each one of these can be without having to figure out the complicated math behind them, or code from scratch. You almost always only need a single line of code to test whatever indicator you are interested in.
- Perform live trading – once you are comfortable with the results of your backtests, Backtrader makes it easy for you to move on to live trading. This can be particularly handy if you plan to apply the range of in-built indicators in your trades.
- Support complex strategies – Backtrader allows traders to create complex strategies in line with their trading and investment goals. You may execute multiple traders together, integrate a variety of timeframes, resample data, and more. One of the best things about this Python algorithmic trading library is that it has extensive support for diverse trading scenarios.
Explore our Python/Backtrader FAQs
At Quant Nomad, one of our main goals is to create high-quality guides and help more people learn how to utilize the latest tools and programming languages to improve trading efficiency. This page features a combination of python and backtrader FAQs that we have already created and published. You are welcome to check them out and feel free to get in touch if there’s something specific you’d like us to cover.