Python Trading Strategy | Backtesting, Code, List, Examples
The Python code language allows for backtesting and executing Python Trading Strategy Algorithms. Python is an open-source, high-level yet easy-to-learn computer programming language that is used in a wide variety of applications, including algorithmic trading and data analysis. With all of its packages being free for commercial use, Python has become the preferred programming language for creating trading algorithms in recent years.
As a trader, you don’t want to do the hard work of monitoring the screen all the time and analyzing price movements looking for trade setups. This manual way of trading can be extremely hard and even prone to mistakes based on emotional and psychological biases. Moreover, there is a limit to how much data you can process, how fast you can do that, and how long you can watch the market on any given day. In today’s tech-driven financial markets, you need algorithmic trading to stand a chance at success, and that’s where the Python trading strategy comes in. But what is Python and how is it used in trading?
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In this post, we take a look at Python and how it is used in algorithmic trading. However, before we start we would like to remind you about our very extensive library of backtested trading strategies:
What is Python?
Python is an open-source, object-oriented, and high-level programming language with dynamic semantics. With high-level built-in data structures, combined with dynamic typing and dynamic binding, Python is very attractive for rapid application development, as well as for use as a scripting or glue language to connect existing components together.
The language is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. Python is used in a wide variety of applications, including algorithmic trading and data analysis.
With all of its packages being free for commercial use, Python has become the preferred programming language for creating trading algorithms in recent years. The language allows for backtesting and executing trading algorithms.
Python and automatic trading
A trading algorithm (trading algo) is a computer program that analyzes the markets, identifies trading opportunities, executes them, and manages the trades according to its predefined set of instructions. The predefined set of instructions could be based on a mathematical model or KPIs, such as timing, price, and quantity.
The practice of automated trading with algorithms is known as algorithmic trading. This is used by the world’s major banks and Wall Street institutions to trade traditional assets (like stocks) and newer markets (like cryptocurrencies). When properly implemented, good trading algorithms can generate profits at a speed and frequency that cannot be matched by manual traders.
To implement algorithmic trading, a trader needs to write the code that will execute trades once certain conditions are met or pay a programmer to do it. One of the most commonly used programming languages for writing such codes is Python. The language is also widely used in other areas of fintech, such as data analysis, the cryptocurrency markets, risk management, and banking services. The language is used by investors and institutions every day to perform a wide array of functions, including quantitative research, as well as backtesting and executing trading algorithms.
Python is often used by quant traders, as it allows users to build intricate statistical models using scientific libraries, such as Pandas, NumPy, Scikit-learn, and Zipline. Updates to these libraries are regularly released in the developer community, so they’re improving every day.
While there are other programming languages, Python is the most popular in financial trading automation. Since most trading algos are coded with Python, it’s also much easier to collaborate, swap code, and crowdsource for assistance when using the language for your algorithmic trading.
Why do traders use Python? Why use Python?
There are many things traders can do with Python. They can use it to analyze big datasets to gain market insights and improve their returns — analyzing tons of tick data to get the best trade executions. Python can also be used to deal with more unusual types of data, such as text. Analyzing huge data set is possible with Python because traders can use it to build their own data connectors.
Apart from data research and analysis, traders also use Python to create their trade execution mechanisms and implement risk and order management processes. Most of all, they use the language to create the framework for strategy backtesting, as well as walk-forward analysis and optimization testing modules.
Interestingly, the learning curve for Python isn’t as steep as other languages like C++. So, it is very easy for a trader to not only create trading algorithms but also perform the full course of backtesting.
What is backtesting?
Backtesting is a way of assessing the potential performance of a trading strategy by applying it to historical price data. To perform python backtesting in algorithmic trading, the strategy has to be coded into a trading algo, which is then run on the historical price data. A backtest has strict rules for when to buy and when to exit. So, you can find out with certainty how the strategy has performed in the past.
Strategy backtesting is based on the idea that a strategy that performs well on past data will likely perform well in current and future market conditions. If backtesting works, traders and analysts may have the confidence to employ it going forward.
However, note that while backtesting is very helpful in assessing the performance of a strategy, it does not guarantee that a strategy will be successful in the current market, as past results are never a fool-proof indicator of future performance. Backtesting in Python and other languages is only a simulation that tells you about past market conditions, and more importantly, it helps you know whether there is a bug in the trading algo.
It is important to bear in mind that real trades incur fees, which are not usually included in backtests. So, you need to account for these trading costs when performing your testing as they will affect your profit-loss (P/L) margins on a live account.