Guide on Algorithmic Trading Using Python for Beginners
Discover the world of algorithmic trading with Python. Learn how to leverage data, libraries, and machine learning techniques, tools and open sources to develop high-performance trading strategies. Gain insights into market trends and make informed decisions.
Tables of Content: Guide for Algorithmic Trading Using Python
- What is Algorithmic trading
- Benefits of Algorithmic trading using Python
- Dataset Prepration on Algorithmic trading
- Tools Recommendation for Algorithmic trading
- Opensource Platform for Algorithmic trading
- Wrap up
What is Algorithmic trading
Algorithmic trading, which is also known as black-box trading, or automated trading, or simply algo-trading, a highly data-driven approach to trade, involves using computers to execute orders based on complex mathematical models. And it refers to the process of using computer programmes that follow an algorithm for placing a trade, in order to generate profits at speeds and frequencies that are impossible to achieve manually.
Benefits of Algorithmic trading using Python
Extension
Python's versatility extends to the realm of algorithmic trading, providing traders with numerous advantages. It has extensive libraries including Numpy, Pandas, Scikit-Learn and so on and so forth. It can empower them with powerful data analysis and manipulation capabilities. Also, some pre-built financial indicators from libraries like TA-Lib simplify the identification of trading opportunities. And Python's object-oriented design enables the creation of sophisticated models using techniques like machine learning and deep learning.
Simplicity
Not yet. Its high-level syntax simplifies the development of complex algorithms, which allows traders to focus on core trading logic rather than low-level coding. The language's support for both supervised and unsupervised learning allows for the creation of models that can predict market trends and identify profitable strategies. Additionally, Python's integration with various data sources, including databases, CSV files, and web scraping tools, ensures seamless access to historical and real-time market data.
Diversified Resources
The open-source nature of Python and its vast community support provide access to free resources, tutorials, and code libraries, which make it an economical choice for traders. Its modular design and reusable components allow for the rapid development and deployment of algorithmic trading systems. Last but not least, the integration capability with cloud services allows traders to scale their operations and manage their risk efficiently.
Dataset Preparation on Algorithmic trading
Regarding algorithmic trading, data preparation is a critical part upon whether successful trading models and strategies can be built or not.
Basically, there are two types of data:
Historical Financial data
Financial data forms the core of each and every algorithmic trading project. Python offers a number of packages that do a great job in handling and working with structured financial data of any kind, such as intraday, end-of-day, high-frequency, and so on and so forth.
Real-Time Data
Algorithmic trading requires dealing with fast in-coming real-time data. This involves a little bit of socket programming, such as preferably with ZeroMQ. Python comes in extremely handy while visualizing this real-time data to derive actionable insights. Here are some visualization libraries that help in analyzing this real-time data:
Matplotlib
It is the O.G. of Python data visualization libraries. Although it is over a decade old, it is still most extensively used for plotting. It was designed to closely resemble MATLAB. Being the first visualization library, matplotlib supports various other packages and libraries that are built on top of it or are designed to work in tandem with it.
Seaborn
It harnesses the power of matplotlib to create beautiful and aesthetically pleasing charts in a few lines of code. Seaborn offers various default styles and color pallets, but since it is built on top of matplotlib, it is recommended to have a fair idea of the working of matplotlib to tweak seaborn’s defaults.
For more details regarding financial data fetching, please check out this API as follows:
Listed Company Financials, Stock Price, News and TTM API
Libraries like NumPy and Pandas enable seamless handling of numerical arrays and tabular data, facilitating complex computations and data transformations. TALIB, a dedicated library for technical indicators, offers a comprehensive range of functions to identify patterns and trends in financial data. Neural network models, such as those built using Keras, can be employed to predict future prices based on historical data.
The Python ecosystem also includes libraries like Scikit-Learn that simplify data preprocessing and feature extraction. By automating the identification of statistically significant indicators, Scikit-Learn streamlines the process of creating predictive models.
PyAlgoTrade, an open-source algorithmic trading platform, provides a comprehensive suite of tools for implementing trading algorithms. It supports a wide range of order types, backtesting capabilities, and live trading execution.
To enhance data preparation efficiency, consider utilizing data preprocessing functions such as normalization and scaling. This ensures that all features have a similar range of values, improving model performance. Additionally, techniques like feature selection and dimensionality reduction can reduce the complexity and computational requirements of the model.
By leveraging the capabilities of Python and its libraries, algorithmic traders can effectively prepare data for building robust trading models that maximize their chances of success.
Tools Recommendation for Algorithmic trading
Python also has its limitations. To overcome these challenges, some tools like PyAlgoTrade with Cython to enhance performance.
Trading isn’t possible without a trading platform. If you’re looking to get going with algorithmic trading using Python, you’re expected to have an idea of the various trading platforms, to choose which one is the best for you. The most popular trading platforms are:
- Oanda (CFD trading)
- FXCM Forex Capital Markets (FX and CFD trading)
- Interactive Brokers (stock and options trading)
- Gemini (cryptocurrency trading)
Opensource Platform for Algorithmic trading
Quantiacs
It is an open-sourced Python platform which provides a toolbox for you to develop and backtest your trading ideas. It also offers free and clean financial data and allows you to develop as many strategies as you want, and the most profitable ones can be submitted in the Quantiacs algorithmic trading competitions.
Quantopian
It is another popular open source python platform for testing and developing trading ideas and strategies. It allocates capital for selected trading algorithms and you get a share of your algorithm’s net profit. It is also supported by an extremely active community where in trading ideas and problems get discussed among the members.
Wrap-up
Algorithm trading makes it possible to monetise the profitable micro moment using automatic buy and sell determination. It can scale up the trading and help you hands-off on many trading decision making. And Python definitely is one of the most popular and useful language that can alleviate the workload and enhance trading experience
