Mastering Stock Trading with AI: Custom Indicators in Reinforcement Learning

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Mastering Stock Trading with AI: Custom Indicators in Reinforcement Learning

Table of Contents

  1. Introduction
  2. Using Stable Baselines Three
  3. Sourcing Intraday Time Series Data
  4. Preparing the Data
  5. Including Custom Indicators with Finta
  6. Merging and Formatting the Data
  7. Creating a Custom Environment
  8. Training the Model
  9. Testing the Model Performance
  10. Improving the Model with Callback Functions
  11. Evaluating Performance with Quantitative Metrics
  12. Conclusion

Introduction

In this article, we will explore how to include custom indicators in a reinforcement learning model for trading stocks using machine learning. We will be using Stable Baselines Three, a popular library for reinforcement learning, and the Alpha Vantage API for sourcing intraday time series data. We will also utilize the Finta Package to incorporate technical indicators such as Bollinger Bands, RSI, and VWAP into our data. By creating a custom environment and training our model, we will aim to improve its performance and generate profitable trading strategies. Let's dive into the details and see how we can enhance our trading model with custom indicators.

Using Stable Baselines Three

To begin, we need to install and import the Stable Baselines Three library. This library provides a convenient framework for training and evaluating reinforcement learning models. We will also import other necessary packages for data manipulation and sourcing the data.

Sourcing Intraday Time Series Data

Next, we will source intraday time series data using the Alpha Vantage API. We will specify the symbol "AAPL" (Apple), retrieve the data, and format it into a dataframe. We will sort the values in ascending order to ensure proper sequencing of the data.

Preparing the Data

In our previous video, we made the mistake of not sorting the data correctly, resulting in reversed sequencing. In this article, we will correct this issue and ensure that the data is Read in the correct order. We will also adjust the output size to include a larger number of data rows for more comprehensive analysis. Additionally, we will rename column names and convert the "volume" column into an integer format for consistency.

Including Custom Indicators with Finta

To incorporate custom indicators into our trading model, we will utilize the Finta package. We will install the package and import the necessary modules for technical indicators. In this article, we will focus on Bollinger Bands, RSI, and VWAP indicators. We will Apply these indicators to our data and merge them into our existing dataframe.

Merging and Formatting the Data

Once we have included our custom indicators, we will merge them with our original data dataframe. We will perform an inner merge using the "timestamp" column to ensure proper alignment of the data. Finally, we will fill any missing values with zeros to maintain data integrity.

Creating a Custom Environment

In order to use our custom indicators in our reinforcement learning model, we need to Create a custom environment. We will define a function called "signals" that takes our environment as input. This function will process our data and replace the default indicators with our custom indicators. We will specify the start and end frames Based on the window size and input our custom indicator dataframe. This function will allow our model to make decisions based on the technical indicators we have incorporated.

Training the Model

Once our custom environment is defined, we will initialize our reinforcement learning model using the A2C algorithm. We will pass our custom environment to the model and specify the total number of time steps for training. We will train our model and observe the explained variance to evaluate its performance.

Testing the Model Performance

After training our model, we will test its performance using the validation or testing data. We will reset the environment and input the validation data to evaluate the model's ability to make profitable trades. We will analyze the results and assess the profitability of our trading strategy.

Improving the Model with Callback Functions

To enhance the performance of our model, we will implement callback functions. These functions will save the best loss function and provide metrics throughout the training process. By incorporating callback functions, we can optimize our model and improve its profitability.

Evaluating Performance with Quantitative Metrics

In addition to the explained variance, we will evaluate the performance of our model using quantitative metrics such as the Sharpe ratio. We will also utilize the QuantStats library to analyze the performance of our trading strategy in more Detail. These metrics will provide a comprehensive assessment of our model's profitability and effectiveness.

Conclusion

In conclusion, this article has demonstrated how to include custom indicators in a reinforcement learning model for trading stocks. By using Stable Baselines Three, the Alpha Vantage API, and the Finta package, we have successfully incorporated technical indicators such as Bollinger Bands, RSI, and VWAP into our trading model. We have trained our model, tested its performance, and explored methods to further improve its effectiveness. By utilizing callback functions and quantitative metrics, we can optimize our model and generate profitable trading strategies. With continued experimentation and refinement, we can enhance our trading model's performance and achieve desired financial outcomes

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