Automation and machine learning have changed how individuals and investing firms manage stocks and account portfolios. Instead of relying solely on the knowledge and expertise of an individual trader or firm, anyone with access to data science tools can now use algorithms and artificial intelligence to gain deeper insights into economic data. By managing and assessing risk, evaluating options, and tailoring financial strategy, algorithmic trading with Python is one of many ways for beginners in data science to improve their chances of investment success!
What is Algorithmic Trading?
Algorithmic trading uses algorithms and digital tools to make trading decisions. Algorithmic trading uses vary, but there are usually two ways to use algorithms in finance and investing: stock trading/investing and financial portfolio management. Algorithmic trading primarily empowers a machine to make supervised decisions about when to buy and sell stocks and other investments. Using machine learning models and data science libraries, this form of algorithmic trading trains a model to learn enough about a portfolio or industry to make informed decisions on behalf of the trader. For example, algorithmic trading applications and programs monitor a stock over time, with criteria to trigger the machine to buy or sell the stock.
The second use of algorithmic trading is the development of machine learning models that make informed decisions about building a financial portfolio. Collecting and analyzing data on past business and economic trends enables anyone with knowledge of data science tools to make inferences about the future of a particular industry or investment. Informed traders use market trend data to mitigate risk and pick the best options for themselves and their clients. Predictive analytics and artificial intelligence are leveraged to learn more about the prospects of specific industries and companies.
Finance and Investing for Beginner Data Scientists
Before beginning the algorithmic trading process, it is essential to understand how stocks, investments, and economic markets work. In finance and investing, stocks are a type of investment representing a company's share. In addition to stocks, an individual or firm can also invest money in a particular industry or items, such as real estate or the development of new technology. That said, investing comes with a certain amount of risk because the return you make on an investment depends on the number of shares you buy and the investment performance. So, it is crucial to understand the investment and its market value.
Option valuation allows you to calculate the value of your stocks on a given day and in the future. A trader analyzes stock value and market volatility to determine how many stock shares to buy. Investors and firms use this information to craft option strategies and timelines for buying and selling stocks. This data collection for risk management and modeling is beneficial for data scientists in algorithmic trading.
Why Data Scientists Use Python for Algorithmic Trading
Data science professionals in finance and investing most commonly engage in algorithmic trading with Python. The Python programming language has several statistical and machine learning-based data science libraries. Libraries like pandas, NumPy, and sci-py have unique uses in algorithmic trading, from developing the models needed to execute trading to analyzing the risk behind an investment. In addition, financial trading libraries like FinTA and Backtrader aid in selecting trading indicators and testing finance models. These libraries construct programs that monitor stock prices and conduct trades for data scientists using virtual environments, like notebooks or terminals.
The business and finance spheres are known for using the large-scale collection of numerical data. And a lot of economic and financial data is available to data scientists through public data sets and open-access resources. For example, a data scientist can use publicly available data from the government or financial institutions to track the rise and fall of a company’s stock prices over time. Analyzing this historical data using today’s data analytics technologies enables data scientists to predict that stock’s future success and set price and timing targets for buying and selling the stock.
Introduction to Algorithmic Trading Strategies
Traders in banking institutions or investment firms engage in a strategy known as high-frequency trading (HFT), running computer programs and algorithms to make high-speed, high-volume trades. This type of trading allows investment firms to buy and sell at a much higher rate than individual brokers. However, high-frequency trading is not readily available to individuals outside the finance industry. While large HFT firms can raise capital and buy software and data science tools to experiment with high-frequency trading, most individuals do not have those types of resources.
However, a vast amount of public data is available to individuals interested in building algorithmic trading models. Traders use statistical hypothesis testing and formulas to inform their decision-making. So, becoming a successful algorithmic trader requires knowledge of several statistical strategies. For example, exponential moving average (EMA) and moving average convergence divergence (MACD) calculate risk by tracking market trends, such as stock price and volatility.
In algorithmic trading, these formulas form the basis of a machine learning model that knows when to buy or sell a stock and test the safety or success of a market. Like other machine learning models, trading models must be evaluated where backtesting strategies are applied. Backtesting is a process that uses historical data to test how well a trading strategy would do, running an exploratory risk analysis to validate the method for the model. These trading strategies also allow data science professionals and investors to engage with financial data and technology, providing a potentially lucrative earning opportunity.
Interested in Financial Data and Python?
The popularity of automation and machine learning in finance creates new opportunities for data scientists and investors to apply their data analytics and modeling knowledge toward more innovative investments. Noble Desktop offers a range of data science classes and bootcamps that cover financial data analysis. To learn more about using the Python programming language for financial analysis, the Python for Finance Bootcamp offers training in risk management and financial data analysis. In addition, analysts interested in algorithmic trading and financial technology should consider the FinTech Bootcamp and the Python Machine Learning Bootcamp for developing and evaluating machine learning models.