Backtesting OVER 500 STOCKS with a Trading Strategy using Object Oriented Programming [OOP]

Summary

In this video, the presenter demonstrates how to apply a backtesting class to analyze 500 S&P 500 stocks. Starting with data sourced from Wikipedia, the process includes filtering current and historical ticker symbols to mitigate survivorship bias. By manually correcting anomalies and appending removed tickers, a comprehensive dataset is created. The presenter then shows how to instantiate the backtest class for each stock, enabling a detailed cumulative profit analysis. This insightful overview highlights the significance of data integrity and thorough backtesting in stock market strategies, setting the stage for further exploration of financial analysis.

Takeaways

• 😀 The video demonstrates how to utilize a backtesting class to analyze 500 S&P 500 stocks.
• 📊 Ticker symbols for current S&P 500 companies are retrieved from a Wikipedia table using 'pd.read_html'.
• 🔄 Survivorship bias is addressed by considering both added and removed ticker symbols from the S&P 500.
• 🗓️ Data is filtered to include only changes since January 1, 2019, by transforming date strings into datetime objects.
• ✂️ Added tickers are removed from the current list, while removed tickers are appended back after necessary adjustments.
• 📈 Instances of the backtest class are created for each ticker, allowing for automatic data processing and analysis.
• 💡 Cumulative profits for each stock are calculated, providing insights into the performance of different assets.
• 📉 The analysis reveals that both the top and bottom performing stocks originate from the removed ticker list, highlighting the value of historical data.
• 📊 A DataFrame is generated to visualize cumulative returns, showcasing average profitability and identifying the best and worst performers.
• 🔍 The video encourages viewers to conduct their own analyses and explore strategies, such as incorporating stop-loss mechanisms.

Q & A

What is the main focus of the video?

The video focuses on using a backtesting class to analyze 500 stocks from the S&P 500, emphasizing the importance of including both current and historical ticker symbols to avoid survivorship bias.

How does the presenter obtain the current S&P 500 ticker symbols?

The presenter uses the `pd.read_html` function to scrape the first table from a Wikipedia page that lists all current S&P 500 companies, storing the ticker symbols in a variable called `tickers`.

What is survivorship bias and why is it important to address it?

Survivorship bias occurs when only the successful or currently active entities are considered, which can lead to misleading conclusions. It is important to include historical ticker symbols that have been added or removed from the S&P 500 to ensure a comprehensive analysis.

What is the significance of filtering ticker symbols from 2019 onwards?

Filtering ticker symbols from 2019 onwards allows the analysis to focus on a specific time period for backtesting, ensuring that the data reflects recent market conditions and changes in the S&P 500.

How does the presenter clean the data frame of ticker symbols?

The presenter drops any `NaN` values and duplicates from the data frame and resets the index to ensure it accurately reflects the number of remaining ticker symbols.

What happens during the backtesting process?

The backtesting process involves creating instances of the backtest class for each filtered ticker symbol, which automates the calculation of signals and indicators for further analysis.

What insights can be gained from the cumulative profits analysis?

Analyzing cumulative profits provides insights into the performance of individual stocks, helping to identify the best and worst performers and assess the overall effectiveness of the trading strategy.

Why does the presenter highlight the importance of historical data?

The presenter emphasizes historical data because the best and worst-performing stocks identified in the analysis came from the list of removed tickers, showing that past performance can significantly impact current strategies.

What future enhancements does the presenter suggest for the analysis?

The presenter suggests adding features such as stop-loss mechanisms and testing long-term trends to enhance the backtesting strategy and improve its robustness.

How does the presenter engage the audience regarding future content?

The presenter invites viewers to provide feedback and express interest in further topics, suggesting that additional analysis and enhancements will be covered in future videos.