Coding

Coding and Financial Analysis¶

In this example, we will set up a workflow for retrieving and plotting stock prices over a specified period. The workflow includes agents for data retrieval, data plotting, and message handling.

Overview¶

The flow includes:

• Code Executor Agent: Executes the code for retrieving and plotting stock prices.
• Code Writer Agent: Writes the code for retrieving and plotting stock prices.
• Get and plot stock data Tool: Fetches stock prices using yfinance and plots them using matplotlib.

Agents and tools¶

Tools¶

1. Get and plot stock data

   • Description: Get and plot the stock prices for the given stock symbols between the start and end dates.
   • Inputs: stock_symbols (str or list), start_date (str in YYYY-MM-DD), end_date (str in YYYY-MM-DD).

   Content:

    def get_and_plot_stock_data(
        stock_symbols: list[str],
        start_date: str,
        end_date: str,
        filename: str,
    ) -> str:
        # pylint: disable=import-outside-toplevel
        import pandas as pd
        import matplotlib.pyplot as plt
        import yfinance as yf
   
        data = yf.download(stock_symbols, start=start_date, end=end_date)
   
        # Get the closing prices
        closing_prices = data['Close']
   
        # Normalize the prices to start at 100 for easier comparison
        normalized_prices = closing_prices.div(closing_prices.iloc[0]) * 100
   
        # Create the plot
        plt.figure(figsize=(12, 6))
        for symbol in stock_symbols:
            plt.plot(normalized_prices.index, normalized_prices[symbol], label=symbol)
   
        plt.title('Stock Prices')
        plt.xlabel('Date')
        plt.ylabel('Normalized Price (Base 100)')
        plt.legend()
        plt.grid(True)
   
        # Save the figure
        plt.savefig(filename)
        plt.close()
        return "ok"
   

   • At the advanced tab, add the extra librabries that we use in our tool
   • Save the tool.

Agents¶

Code Writer Agent¶

1. Models Link a model of your choice to the Code Writer Agent. In our example, we use the gpt-4-turbo model.
2. Tools In the tools tab, add the get_and_plot_stock_data tool to the Code Writer agent. As executor, select the Code Executor agent.

Code Executor Agent¶

In this step, we'll configure a Code Executor Agent to handle the execution of the functions required for retrieving and plotting stock data.

1. Basic configuration

   • Max consecutive auto replies: Let's limit the number of auto-replies to 10 to avoid unnecessary repetition.
   • Agent Default auto-reply: We can set the default auto-reply to `Please continue. If everything is done, reply 'TERMINATE', to avoid repeating the same message when asked.

2. Code Execution

   • At the Code Execution tab, check the box for Use Code Execution.
   • Set the Working Directory to coding (or your designated project folder).
   • Set the Timeout slider to 60 seconds to allow enough time for the code to fetch and plot data without interruption.

   • Under Functions, add the get_and_plot_stock_data functions to allow the Code Executor Agent to access and execute these methods.

     

Flow chats and requirements¶

1. Edit Flow: Set up the flow order to start with the "Code Executor Agent => Code Writer" connection.

Run the flow¶

Press the Run button to execute the flow. When asked, you can press Enter to use the Agents auto-reply message. When you get a message about having the plot generated, you can enter TERMINATE (or exit) to end the flow.

You can view the generated code and plot in the specified code execution folder.

Files used in this example:

• Flow: Coding.waldiez
• Generated notebook: Coding.ipynb
• Tool:
  • get_and_plot_stock_data.waldiezTool