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ChatGPT now helps you backtest simple trading strategies

ChatGPT now helps you backtest simple trading strategies

ChatGPT, an artificial intelligence (AI) language model developed by OpenAI, has released a new tool designed to simplify the process of backtesting trading strategies. 

In a thread published on their X (formerly Twitter) account, on March 27, Pedma, a library of trading strategies, outlined a 6-step process. They utilized ChatGPT to backtest a simple moving average (MA) crossover strategy for Bitcoin (BTC) rather than a ChatGPT trading bot.

Here’s a breakdown of the key components:

ChatGPT Python 

In the first step, the user requests ChatGPT to provide Python code for importing the required libraries for backtesting a trading strategy, with a focus on data handling from Yahoo Finance and visualization. 

By providing the necessary tools right away, ChatGPT saves time and makes it easier for traders to turn their ideas into reality.

Data retrieval from Yahoo Finance

After setting up the initial libraries, Pedma asked ChatGPT how to get data. They wanted to know how to get historical Bitcoin price data (daily OHLC and volume) in Python, starting from January 1, 2020. 

With ChatGPT’s help, users can easily get important info like daily prices and trading volume. 

This means traders can focus on understanding market trends and improving their strategies without wasting time looking for data themselves.

ChatGPT trading strategy

In the third step of the process, Pedma asked ChatGPT to help define a trading strategy using the available Bitcoin data. Specifically, they requested guidance on creating a MA crossover strategy in Python using the ‘pandas tool.’ 

By offering clear guidance on strategy definition, ChatGPT enables traders to develop structured approaches to market analysis, which can help them make smarter decisions when dealing with the market.

Implement the backtest

Once the strategy was defined, Pedma inquired about implementing the backtest using Python and pandas to assess the strategy’s performance. 

They asked ChatGPT how to execute this step to analyze how well the strategy would perform in real market conditions. 

This step helped Pedma understand the effectiveness of their trading idea and its potential to succeed in the market.

Analyze the results and visualize

After completing the backtest, Pedma wanted to see the results and make a graph to show the returns. They asked ChatGPT how to do this using pandas and matplotlib.

Their goal was to understand the important numbers from the backtest and visualize the equity curve to show how the strategy worked with Bitcoin data.

Python code knowledge will help

In the final step, Pedma noticed a problem with the drawdown chart — it wasn’t showing the right data. So, they had to manually fix it to make sure it displayed the correct information. 

While ChatGPT automates much of the process, basic Python knowledge is still recommended. 

More complex strategies might require manual code adjustments, especially for accurate visualization.

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