Python Trading Strategy (Backtesting, List, And Examples)

Last Updated on November 16, 2022 by Oddmund Groette

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?

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. The language allows for backtesting and executing trading algorithms.

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:

List of trading strategies for Python traders (in plain English)

If you are a Python programmer you might benefit from our very extensive library of trading strategies. If you are looking for the best trading strategy we have hundreds of trading ideas written and explained with bullet points in English. We have assembled description and logic for our free trading strategies on this link:

Likewise, our best trading strategies that require payments come with logic and rules described in English:

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.

The best strategies can be found in our….

Strategy Shop

Backtested trading strategies

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 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 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.

Is Python good for backtesting?

Yes, Python is great for backtesting. Despite the criticisms about its speed, Python is set to remain the programming language of choice for backtesting investment strategies. For a long time now, it has been the world’s most popular systematic trading language and will likely remain so.

One thing that makes Python stand out is its easy-to-read language and the ability to analyze complex financial data, supporting the validation and implementation phases of modeling, and implementation for live use. Together with the associated libraries of Pandas and scikit-learn, provides a platform that can be used to model any strategy.

Python is capable of reading both external data files and streaming data, together with a model development platform capable of working with any of several dozen model types, including those that are the current state of the art, like in extreme boosting and deep learning. Moreover, Python has a huge community that is well served, with at least six open-source backtesting frameworks available.

The Python backtesting framework

These are some common Python backtesting frameworks:

PyAlgoTrade

PyAlgoTrade is a fully documented backtesting framework with paper- and live-trading capabilities. It supports data from Yahoo! Finance, Google Finance, NinjaTrader, and any type of CSV-based time series such as Quandl. The order types supported by PyAlgoTrade include market, limit, stop and stop limit. The platform allows Bitcoin trading via Bitstamp and supports real-time Twitter event handling.

Its merits include:

  • Excellent documentation
  • Comparatively flexible than other platforms
  • Support for event-driven backtesting and TA-lib integration

Its demerits include:

  • Doesn’t support Pandas objects and modules.
  • Fewer strategy analyses are available

Bt – Backtesting for Python

This is a Python backtesting framework for testing quantitative trading methods. It aims to foster the creation of easily testable, reusable, and flexible blocks of strategy logic to facilitate the rapid development of complex trading strategies. The framework is particularly suited to testing portfolio-based STS, with algos for asset weighting and portfolio rebalancing.

Python Trading Strategy Backtesting
Python trading strategy backtesting requires basic coding skills, and thus might not be for everyone.

The merits include:

  • Its tree form makes it easier to build and compose complicated algorithmic trading systems.
  • One can easily generate charts and reports in pdf.
  • It has detailed statistics that can be used to compare strategies.
  • It is coded in Python, so it supports various machine learning and statistical operations.

The demerits include:

  • It is slow in comparison to other platforms.
  • It does not support strategies in languages other than Python.

Backtrader

Backtrader is a Python framework that is exceptionally well documented, with an accompanying blog and an active online community. The platform supports a number of data formats, including CSV files, Pandas DataFrames, blaze iterators, and real-time data feeds from three brokers — Oanda for FX trading and multi-asset class trading via Interactive Brokers and Visual Chart. Not only can the data feeds be accessed simultaneously, but they can even represent different timeframes.

The merits include:

  • It presents a clean code that is easy to use for beginners in Python (with tons of examples).
  • It supports backtesting as well as Live trading.
  • One can easily create custom indicators.
  • There are various Analyzers like TimeReturn, Sharpe Ratio, and SQN.
  • The documentation is very crisp and clear.

The demerits include:

  • It develops issues when handling enormous datasets
  • One may need to use analyzers in the code, even for some basic computations.

Fastquant

This platform makes it easy to backtest trading strategies with as few as three lines of Python code. Fastquant aims to encourage data-driven trading by making quantitative finance analysis accessible to everyone.

The merits include:

  • It requires minimal code, and custom strategies can be made with a minimum amount of code.
  • It is easy to use.
  • It allows simple integration with machine learning and statistical models.

The demerits include:

  • It is relatively slower than other platforms.
  • It doesn’t support languages other than Python.

LEAN (Quantconnect)

This is an open-source algorithmic trading engine built for easy strategy research, backtesting, and live trading. The platform integrates with the standard data providers, and the implementation of the algorithmic trading strategies is quick.

The merits include:

  • It supports strategies developed in various languages and not just Python.
  • It is relatively faster than other platforms.
  • It supports backtesting and Live trading.
  • It has a fantastic community.

The demerits include:

  • It is difficult to use for non-native C# users as the core of the LEAN engine is written in C#.
  • The strategy analysis needs improvement.

Zipline

This is an algorithmic trading simulator that allows paper and live trading. Zipline was developed by Quantopian, so it can be used as a standalone backtesting framework or as part of a complete Quantopian/Zipline STS development, testing, and deployment environment. It can be accessed via the browser-based IPython Notebook interface. Zipline offers an easy-to-use alternative to command line tools and provides 10 years of minute-resolution historical US stock data and a number of data import options.

What is the best backtesting library in Python?

You will have to use a good number of them to know which is best for you. What is best for one trader may not be best for another. However, many traders who have used them attest to the impressive features of these two: QuantConnect and Backtrader. It is not as if they do not have their flaws, as we have pointed out above, but their good features appeal to a lot of traders.

QuantConnect is relatively faster than other platforms and supports backtesting and Live trading. The platform supports strategies developed in various languages and not just Python. However, it may be difficult to use for non-native C# users as the core of the LEAN engine is written in C#.

Backtrader, on the other hand, presents a clean code that is easy to use for beginners in Python (with tons of examples), supports both backtesting and Live trading, and has various Analyzers like TimeReturn, Sharpe Ratio, and SQN. However, it may likely develop issues when handling a huge dataset.

Backtesting library in Python

We have an extensive library of backtested trading strategies on this website where all of them are listed in bullet points (in English). Please click here to read more about or backtesting library in Python.

Pros and cons of using Python

As with everything in life, Python has its merits and demerits. The good thing is that its merits are far more than the demerits.

The pros include:

  • Scalability: Parallelization and the enormous computational power of Python give scalability to the trading portfolio. It is much easier for traders to share functionality between different programs.
  • Ease of use: Python makes it easier to write and evaluate algo trading structures because of its functional programming approach. Python code can be easily extended to dynamic algorithms for trading.
  • Less time consuming: Python can be used to develop some great trading platforms whereas using C or C++ is a hassle and time-consuming job.
  • Dynamic algos: Trading using Python is an ideal choice for people who want to become pioneers with dynamic algo trading platforms.
  • Readability: For those new to algorithmic trading, the Python code is easily readable and accessible.
  • Easy to fix: It is comparatively easier to fix new modules to Python language and make it expansive in trading.
  • Functionality sharing: The existing modules also make it easier for algo traders to share functionality amongst different programs by decomposing them into individual modules which can be applied to various trading architectures.
  • Huge library support: When using Python for trading it requires fewer lines of code due to the availability of extensive Python libraries. Python makes coding comparatively easier in trading. Quant traders can skip various steps which other languages like C or C++ might require.
  • Cheaper to maintain: The overall cost of maintaining the trading system is a lot cheaper, as the trader can easily tweak the code at will.
  • Easy to analyze data: With a wide range of scientific libraries in Python, algorithmic traders can perform any kind of data analysis at an execution speed that is comparable to compiled languages like C++.
  • Hassle-free bugging: Debugging in Python is thorough, as live changes to code and data are allowed. This expedites the debugging process since single errors, rather than multiple ones, appear and can be resolved.

While there are many benefits of using Python, there are also some drawbacks. The cons include:

  • Variables store unnecessary data: In Python, each variable is considered an object. This means that each variable will store unnecessary information like size, value, and reference pointer. This could lead to serious performance bottlenecks and memory leaks if the memory management of different variables isn’t done efficiently.
  • Mobile computing is less efficient: Python may be great for desktop and server applications, but its mobile computing is less efficient. It is considered a weak language for mobile computing, which is why very few mobile applications are built with it.
  • Need to start from scratch: you need to write the code yourself or copy from the free resources laying around on the internet.

After considering its pros and cons, the pros of using Python for trading exceed the drawbacks, which might make it a supreme choice of programming language for algorithmic trading platforms.

Backtesting Python example

To backtest a strategy, you must have written your script. The first step is to import the necessary libraries (Backtrader, QuantConnect, and so on) and download your OHLCV data. You will have to separate your data into two parts — in-sample data and out-of-sample data. Use the in-sample data to run your backtesting while reserving the out-of-sample data for testing after strategy optimization.

Enter your codes in the platform and run your backtesting. Then, optimize by changing certain variables of the strategy, including the trading timeframe. The next is to analyze your results to see how well the strategy actually performed.

Python strategies in plain English

If you are looking for a profitable and robust trading strategy, you might be interested in some of our products. All of our backtested trading strategies, both free and paid, are provided with trading rules and settings in plain English. If you are a Python coder you have literally hundreds of ideas to choose from and code in Python.

For example, we have compiled hundreds of our trading ideas from our articles into an inexpensive product:

That said, our best trading strategies we charge a higher fee for (please also see our shop):

Python vs other trading software (backtesting)

If you are new to trading and looking to start automated/mechanical trading, which direction should you take? Start with Python? Or use one of the existing stand-alone trading software like Amibroker, Tradestation, etc.?

Many are tempted to go for Python. However, we argue this might not be wise. You have to consider this:

  • Trading is about finding the best trading strategies – it’s not about coding.
  • Coding is a tool – nothing else.
  • Using an existing platform might give you a “flying” start.
  • Existing platforms have already in-built functions – you just press buttons.
  • Trading is much about being effective. Trading platforms make you effective.

Trading is about “alpha skills” not “trading skills”. We argue you don’t need any coding capabilities at all to be reasonably successful in trading (or investing, for that matter). Coders and programmers tend to look at the market as science. But the market is non-binary and certainly not stationary and is evolving all the time. Please read our take on this from last year:

During our trading career as full-time traders, we have been in contact with plenty of traders who spend an enormous time building their own trading platforms or making some fancy charts, screens, etc. But at the end of the day, it won’t help you in making money if the strategies are flawed. You should spend your time interpreting the market, backtesting, and understanding yourself, and less time on coding.

Trading software comes with practically all the functions you need. All you need is to type a few trading rules and set some of the settings, and you are good to go.

A software package costs a few dollars but as the saying goes: time is money. For example, Amibroker pricing starts at 300 USD for a lifetime license and Tradestation is more or less free if you use them in your trading. We believe this is well-spent money.

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