Algorithmic or quantitative trading could be defined as the process of designing and developing statistical and mathematical trading strategies. It’s a very challenging field of finance.
Therefore, here in this blog, we try to introduce you to some important things related Automated Trading Python.
Learn Python programming
Successful careers in data science in general require a solid foundation. Regardless of which language you choose, you should have a thorough understanding of the specific topic in that language.
What you can do in the data science Python ecosystem:
Environment setup – This includes creating virtual environments, installing required packages, and working with Jupyter notebooks or Google Colabs.
Data Structures — the most important Python data structures are lists, dictionaries, NumPy arrays, tuples, and sets. I’ve gathered some examples in the linked article.
Object-Oriented Programming — As a Quantitative Analyst, you need to make sure you can write well-structured code using well-defined classes. You will need to learn how to use objects and their methods while using external packages like Pandas, NumPy, and SciPy.
How to write basic trading algorithms
The scope of quantitative finance requires a solid understanding of statistical hypothesis testing and mathematics. A good understanding of concepts such as multivariate calculus, linear algebra, and probability theory will help you lay a good foundation for designing and writing algorithms.
You can start by calculating moving averages for stock data, creating simple algorithmic strategies such as moving average crossing and mean reverting strategies, and learning about relative strength trading.
After taking a small but important leap to practice and understand how basic statistical algorithms work, you are ready to move on to more advanced areas of machine learning techniques. . These require a deep understanding of statistics and mathematics.
Key Performance Indicators — How to evaluate your trading strategy
It is important to be able to concisely describe your strategy. Without an understanding of the strategy, it may behave erratically in the event of external regulatory or regime changes.
Once you have a solid understanding of your strategy, you can use the following performance metrics to determine how good or bad that strategy really is. Quantify the return you get with the volatility of the stock price curve.
Volatility — quantifies the “risk” associated with a strategy. The Sharpe ratio also embodies this property. Higher volatility in the underlying asset often increases the risk of the price curve, which leads to a lower Sharpe ratio.
Maximum Drawdown — maximum peak-to-trough percentage drawdown in the strategy’s stock curve. Maximum drawdown is often studied in combination with the momentum strategy. Learn how to do calculations using the lumpy library.
Capacity/Liquidity — determines the scalability of the strategy for additional capital. Many funds and investment managers are plagued by these capacity issues as strategies increase capital allocation.
CAGR — Measures the average growth rate of a strategy over time. Calculated using the following formula: (Cumulative Strategy Return) ^ (252/Trading Days) — 1
Automated Trading Python is not that very easy to create with a proper plan and preparation anyone can do it. The key to success is learning by mistakes.