With the rapid advancement of technology, financial trading can now leverage programming languages to help us execute investment strategies and analyze market trends more efficiently. This article will introduce algorithmic trading with Python, the benefits of using the Python programming language for financial trading, and how to apply it to financial transactions, allowing investors to harness the power of technology to build their ideal investment portfolios.
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Python is a simple and easy-to-learn programming language characterized by its ability to use a rich set of libraries to encapsulate complex functions. This allows users to accomplish more tasks with simple commands and less code without writing everything from scratch. As a result, Python is widely used in data analysis, machine learning, and web scraping. Particularly in the financial market, investors can use Python for algorithmic trading, quickly analyze market data, customize conditions to create their ideal investment portfolios, and automatically place orders when those conditions are met.
Python for algorithmic trading is like having a personal digital financial advisor. It can quickly process historical data and perform market analysis, filter out effective indicators from vast amounts of trading data, and assist in formulating trading strategies to seize the best opportunities for achieving reasonable returns. Additionally, it can adjust trading strategies to maximize capital efficiency, even with limited funds. Below, we’ll explain the five key benefits to help you understand why learning Python for algorithmic trading is essential.
Python components can be downloaded for free, and most environments for running and writing Python code are also accessible. Some compilers can even be used online without downloading anything, significantly reducing the learning cost. Additionally, Python’s syntax is intuitive and easy to understand, with syntax similar to basic English grammar. Even beginners who have never encountered programming languages can quickly get started.
Python’s built-in libraries can handle many basic operations, such as file reading and matrix plotting. In addition, over 130,000 standard libraries are available for tasks such as machine learning, data visualization, data analysis, and web scraping. These libraries help us visualize complex data, download historical data for backtesting, connect to market quotes, and automate order placement. Commonly used libraries include the Pandas and Numpy data analysis packages and machine learning frameworks like PyTorch and TensorFlow. Combining the necessary functions allows you to create a customized algorithmic trading system that saves time on market monitoring and order execution.
If the current algorithmic trading software doesn’t meet your needs, Python allows you to combine thousands of packages to customize various trading indicators and strategies. You can also optimize or remove unnecessary functions at any time. Virtually any financial product you can imagine can be traded using Python. Other common algorithmic trading platforms, such as TradingView and MultiCharts, may offer beginners more straightforward and user-friendly interfaces. However, they come with certain limitations in terms of functionality and budget. In the long run, Python provides greater flexibility in expanding functionality and applies to broader areas.
Python has been used in algorithmic trading for many years and is quite prevalent in the financial market. As a result, there is a wealth of diverse and rich online learning resources, including articles in both English and Chinese, video courses, and discussion groups. Whether you are a beginner or looking to advance your skills, you can find relevant resources to study and learn, significantly reducing the cost of learning.
Python is a general-purpose programming language, and many applications and online platforms are built using Python. Its syntax is clean and easy to understand, allowing you to handle large amounts of data with just a few lines of code, saving development time and maintenance costs. Its powerful functionality makes it ideal for a variety of tasks. In an era where artificial intelligence is on the rise, Python offers more possibilities than other programming languages.
Algorithmic trading can simulate manual operations, helping us quickly analyze large amounts of information, make rational decisions without being influenced by emotions, and automatically place orders when stock prices trigger specific conditions. This way, there’s no need to monitor the stock market constantly.
Visualization tools like Matplotlib and Seaborn help investors create visual charts, such as line charts, bar charts, and scatter plots, to display market trends, asset performance, and risk assessment results.
TQuant Lab, with its portfolio-tej package, allows users to generate charts for performance analysis with just one click, compared to other backtesting platforms or custom-built backtesting systems. This saves a significant amount of time spent on coding and offers a comprehensive toolkit of chart and indicator options, instilling confidence in users’ market analysis. It provides more standards for evaluating the effectiveness of strategies and can even analyze stocks with lower liquidity in the portfolio, helping identify potential liquidity risks.
Backtesting packages such as Zipline and Backtrader can be used to build and test trading strategies. These packages provide backtesting functionality that allows investors to simulate strategies’ performance on historical data to verify their effectiveness.
TQuant Lab uses Zipline as its primary backtesting engine, which has been modified to align with the trading practices of Taiwan’s financial markets. After years of development, this backtesting engine has become a foundational backtesting framework commonly used in international quantitative platforms. Maintained by TEJ’s professional quantitative analysis team, it regularly releases new features, enabling backtesting of various assets, including stocks and ETFs. During the backtesting process, logs automatically display records of daily holdings in the portfolio, including information such as cash dividends and stock dividends, reflecting real market scenarios. It also offers various dynamic and static slippage models, such as fixed-point slippage costs and volume-driven dynamic slippage costs. Additionally, it includes unique cost models and trade order delay mechanisms specific to the Taiwanese market. Incorporating trading day annotations provided by the TEJ database avoids backtesting look-ahead bias, ensuring more accurate and reliable results.
Python can integrate with various financial data APIs, such as TEJ API, Alpha Vantage, IEX Cloud, and Yahoo Finance. These APIs provide a wealth of market data, including stock prices, trading volumes, financial statements, etc. Investors can instantly access the latest market data through APIs for real-time analysis and decision-making. TEJ has a comprehensive database, TEJ API, covering operational, financial, and trading aspects. It includes data from various countries, allowing users to find the information they need through the API quickly.
TEJ API is a standout choice, offering API connections in various programming languages, including REST, Python, R, and .NET. This flexibility allows users to work with the language they are most comfortable with, enhancing their productivity and efficiency in deeper data analysis, modeling, and development. The comprehensive database of TEJ is a key advantage, ensuring increased robustness and accuracy in backtesting. This is crucial as it helps users avoid making incorrect decisions based on erroneous backtest results, thereby mitigating unnecessary risks.
【TEJ API】Save Data Processing Time
High-Quality and Reliable Data Source
This time, we’ll introduce a strategy using the example of unusual trading volume. First, let’s explain what unusual trading volume is. It typically refers to a situation where the trading volume of a particular asset is significantly higher than usual within a specific period. Unusual trading volume often reflects heightened interest from market participants in that asset, which may indicate impending price volatility or significant events. Below, we’ll demonstrate how to capture the data and conduct a backtest.
Use get_universe to fetch the required stock pool, which in this case is the top 10 stocks by market capitalization in the M2300 Electronic Industry from 2018-01-01 to 2023-12-29. After fetching the data, ingest it into the tquant dataset for easier use in subsequent steps. Below is the code example.
For a detailed introduction to get_universe, you can refer to the following URL.
TQuant-Lab/lecture/get_universe Documentation
Use the Custom Factor function in the Pipeline to create custom factors for unusual trading volume and price changes. Then, incorporate these factors into the Pipeline, as they will be used in the subsequent backtesting. Below is the code example.
After completing the data preprocessing, the necessary data for backtesting is ready. Now, we will move on to the main focus of this discussion: an in-depth introduction to the Zipline backtesting engine.
The initialize() function is used to define the daily trading environment before the start of trading. In this example, we set the following:
context.long variable to record the assets purchased during the backtestcontext.leverage variable, with the leverage ratio set to 1
The rebalance() function is a key part of constructing the strategy and is called daily after the backtest begins. Its main tasks are to set the trading strategy, place orders, and record trading information.
The analyze() function is primarily used to visualize the strategy’s performance and risk after the backtest. Here, we use Matplotlib to plot the portfolio value and the benchmark cumulative return.
Use run_algorithm() to execute the strategy with the settings mentioned above. Set the trading period from start (2018-01-01) to end (2023-12-29), using the tquant dataset, with an initial capital of 10 million NT dollars. The output results will be a detailed table of daily performance and transaction records.
Want to refine your investment strategy with Python? Besides using Python to connect with TEJ API to access vast financial data, the TQuant Lab backtesting system offers a comprehensive quantitative financial solution, helping investors solve three major pain points:
The TQuant Lab backtesting system considers various risks and conducts cross-validation, including slippage costs, to create a realistic backtesting environment.
The TQuant Lab backtesting system can correct real-time market data errors and update and process large amounts of data in real time, ensuring that the final backtesting results are not compromised.
Sample selection, risk management, and implementation constraints can all lead to biases in backtesting results. The TQuant Lab backtesting system uses historical data that aligns with future market conditions and includes various risk management strategies and market constraints, aiming to produce results that closely reflect actual market outcomes.
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