Welcome back! In today’s video, we will be building an intelligent financial assistant in Python. Before we dive into the coding part, let’s take a moment to see what we will end up with.
We will have a chatbot-like financial assistant that is already trained. We can interact with it by entering various commands. For example, we can say “hello” and it will respond with a greeting. We can also ask it to plot a stock for us by providing the ticker symbol and starting date. Additionally, we can ask it to show our portfolio, and it will provide us with the current value.
To create this financial assistant, we will need several external libraries, which we will install using pip. These libraries include:
- neuralintense (a library created to simplify the creation of chatbots and AI assistants)
- matplotlib (for visualizations)
- mpl_finance (for candlestick charts)
- pandas (for working with data frames)
- pandas_datareader (to get data from the Yahoo Finance API)
Now that we have the necessary libraries, we can start coding. The first thing we need to do is define our portfolio, which we will store in a pickle file for serialization. Next, we will implement various functions to handle different user intents, such as adding stocks to the portfolio, removing stocks, showing the portfolio, calculating portfolio worth and gains, plotting stock charts, and buying stocks. We will also implement error handling for cases where the user input is incorrect or the requested data is unavailable.
After defining these functions, we will create an instance of the generic assistant class from the neuralintense library. We will pass the path to our intents.json file and a dictionary that maps intents to their corresponding functions. Finally, we will train and save the assistant model.
Once the model is trained and saved, we can interact with the financial assistant by entering messages and receiving responses. The assistant will recognize the intent of the message and execute the corresponding function. We will use a while loop to continuously prompt the user for input and display the assistant’s responses.
That’s it! We have successfully built an intelligent financial assistant in Python. We can now engage with it, get information about our portfolio, plot stock charts, and perform other financial tasks.
If you want to explore more functionalities, you can add additional intents and functions to handle them. The neuralintense library makes it easy for you to expand the capabilities of your financial assistant.
Thank you for joining us in this tutorial. We hope you enjoyed it and found it informative. If you have any questions or comments, please leave them below. Stay tuned for more exciting content, and don’t forget to subscribe to our channel for regular updates. Visit Techal for more insightful articles.
