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via Udemy |
Go to Course: https://www.udemy.com/course/algorithmic-trading-with-python/
Certainly! Here's a comprehensive review and recommendation for the Coursera course: --- **Course Review and Recommendation: Building a Backtesting System with Python and Plotly** If you're interested in developing a solid understanding of how to design, test, and optimize trading strategies, this Coursera course offers a highly practical and comprehensive approach to backtesting financial models from scratch. **What You'll Learn:** This course takes a hands-on approach, guiding you through the process of building your own backtesting system using Python. You will learn to implement common technical indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Bollinger Bands, combined Bollinger Bands + ADX, and 3 EMA channels. The course emphasizes working with large time-series datasets, leveraging the powerful pandas library and DataFrames to manipulate and analyze market data effectively. Visualization is a key focus, and you'll learn to depict your backtesting results using Plotly, a beautiful and easy-to-understand visualization library that makes results more accessible and insightful. **Performance Metrics and Optimization:** Beyond just running backtests, the course delves into evaluating strategy performance through various metrics such as win rate, Compound Annual Growth Rate (CAGR), expected returns, and maximum drawdown. You'll also explore parameter optimization, allowing you to fine-tune your trading strategies and compare their performance across different settings. **Strategy Simulation and Customization:** The course enables you to simulate strategies on NASDAQ100 stocks, with options to incorporate additional factors like position sizing, stock ranking, stop-loss/take-profit mechanisms, transaction costs, and other trading conditions. This flexibility empowers you to tailor your trading plans according to your hypotheses and risk appetite. **Stock Screening Systems:** A unique highlight of this course is the development of stock screening systems. You'll learn to create systems that identify stocks with compelling buy signals daily, focusing on oversold stocks and stocks exhibiting strong uptrends. These screening tools can be adapted to various trading ideas, enabling more targeted and systematic stock selection. **Who Is This Course For?** This course is suitable for beginners with no prior coding experience, as it starts from scratch with Python programming. It also offers opportunities to enhance and expand your skills independently after completion. **Final Thoughts:** I highly recommend this course for traders, quants, or anyone interested in algorithmic trading and quantitative analysis. The combination of practical coding lessons, comprehensive backtesting techniques, and visualizations makes it an excellent resource for developing and refining trading strategies. Whether you're a novice eager to learn Python or a seasoned trader looking to automate and optimize your strategies, this course provides valuable insights and tools to take your trading to the next level. --- If you'd like, I can help you craft an online review or summary for sharing on social media or a blog!
In this course you will learn to build your own backtesting system from scratch and illustrating with plotly library. We mainly use technical indicator for backtesting such as Exponential Moving Average (EMA), Moving average convergence divergence (MACD), Bollinger Band, Bollinger Band + ADX and 3 EMA channels.We mainly use functions relating to pandas and DataFrame being able to deal with large time-series data. For illustrate result, we use plotly library which is beautiful and easy to understand.During learning backtesting, you will learn many performance measurement. For example, win rate, compound annual growth rate (CAGR) , expected returns and maximum drawdown.Next, you will learn to do parameter optimization and compare many performance measurement in each parameter.You will learn to simulate your strategies with stocks in NASDAQ100 ,also you can add any factors in your trading plan such as position sizing, ranking stocks, cutting loss, taking profit ,transaction cost and other conditions. Lastly, you can make screening system which you can find stocks having interesting buy signals everyday. In this course, I introduce you 2 screening system which are oversold stock and strong uptrend stock. However, you can adapt your trading idea to build your own screening system.Learning python coding in this course is starting from scratch and you can have further developing by yourselves.