Financial AI Assistant in Python

Introduction

Introduction

In this article, we will walk through the process of building an intelligent financial assistant in Python. This assistant will mimic a chatbot that can perform various financial tasks, such as plotting stock charts and managing a portfolio. The project utilizes several Python libraries to facilitate its functionalities.

Features of the Financial Assistant

Before diving into the code, let's clarify what the financial assistant will be able to do. The following are some of the functional capabilities:

• Respond to greetings.
• Plot candlestick charts for stock price visualization.
• Manage a user portfolio, which includes adding shares, removing shares, and displaying current holdings.
• Calculate the total worth of the portfolio.
• Provide insights on portfolio gains over a given timeframe.

Libraries Used

To create this financial assistant, several external libraries will need to be installed:

• neural-intense: A library created to simplify the creation of AI chatbots and assistants.
• matplotlib: For visualizations like the candlestick charts.
• mplfinance: Specifically for generating candlestick charts.
• pandas: To handle data manipulation and data frames.
• pandas_datareader: To retrieve financial data from Yahoo Finance.

You can install these libraries using the pip command:

pip install neural-intense matplotlib mplfinance pandas pandas-datareader

Assistant Structure

The assistant will be designed to recognize intents from user input. For this, we will need a JSON file that includes various intents, each containing:

• Tag: A category identifier for the intent (e.g., greetings, add stock).
• Patterns: Sample phrases that represent how a user might express that intent.

Sample JSON Structure

Here’s an example of how the JSON intents file might look:

(
  "intents": [
    {
      "tag": "greeting",
      "patterns": ["Hi", "Hello", "Hey"],
      "responses": []
    ),
    (
      "tag": "plot_chart",
      "patterns": ["Plot a stock", "Show my stock chart"],
      "responses": []
    ),
    (
      "tag": "add_portfolio",
      "patterns": ["Add stock", "Buy shares"],
      "responses": []
    )
  ]
}

Implementation Steps

1. Assistant Initialization: Import the required libraries and create a generic assistant instance using the intents defined in your JSON file. Train this model to recognize user inputs.

2. Define the Portfolio: Create a dictionary to manage the user’s stock portfolio, where each entry consists of a stock ticker and the corresponding number of shares held.

3. Function Implementations:

   • Add Stock: Ask the user for the stock ticker and the number of shares they wish to add. Update the portfolio accordingly.
   • Remove Stock: Similar to adding stocks, but for selling them.
   • Show Portfolio: Display the current status of the portfolio.
   • Calculate Portfolio Worth: Retrieve current stock prices and calculate the total value of all holdings.
   • Calculate Gains: Compare the current value of holdings to their historical worth based on a user-specified date.

4. Handling User Requests: Use a continuous loop to handle user input and route it to the appropriate function based on intent recognition.

Here’s a snippet of how to start using the assistant in Python:

assistant = GenericAssistant('intents.json')
assistant.train_model()

Once the assistant is set up, you can input commands like "Show my portfolio" or "Plot a stock" to exhibit the functionality.

Conclusion

In this tutorial, we have developed a simple financial assistant equipped with various features to handle typical financial inquiries and tasks. This project can be expanded further by adding more intents and functionalities.

Keyword

financial assistant, Python, chatbot, stock prices, portfolio management, candlestick chart, machine learning, user interaction

FAQ

Q: What programming language is used for the financial assistant?
A: The financial assistant is built using Python.

Q: What libraries are required for the assistant?
A: The assistant requires neural-intense, matplotlib, mplfinance, pandas, and pandas_datareader.

Q: How does the assistant recognize user intents?
A: The assistant uses machine learning techniques to recognize user intents based on sample patterns defined in a JSON file.

Q: Can I view my stock portfolio?
A: Yes, you can interact with the assistant to show your current stock portfolio.

Q: Is it possible to add or remove stocks from the portfolio?
A: Yes, the assistant allows you to add or remove stocks from your portfolio through simple commands.