Mastering Stock Analysis with Python

Step 1: Data Extraction

Retrieve historical stock data effortlessly using the yfinance library from Yahoo Finance. Access data dating back to January 2010 for in-depth trend analysis.

Step 2: Data Preparation

The collected data is converted into a pandas DataFrame, date columns are formatted, and data is scaled for consistency. Splitting into training (80%) and testing (20%) datasets ensures robust model evaluation. Data is transformed into sequences to uncover intricate patterns.

Step 3: Building the Model

Craft a powerful deep learning model using the LSTM architecture, known for capturing temporal dependencies. This model features two LSTM layers with 50 units, followed by a dense layer with 25 units and a final dense layer for predicting future stock prices. Training spans 100 epochs.

Step 4: Visualization and Analysis

Visualize the model's predictions alongside actual stock prices using matplotlib. Explore trends, oscillations, and potential turning points for deeper insights into the market.

Step 5: Model Accuracy

Evaluate the model's accuracy by calculating the mean squared error (MSE) and presenting it as a percentage. This metric provides a clear measure of the model's performance, boosting confidence in navigating the stock market's complexities.

Using Reinforcement Learning for Stock Trading

Extracting Historical Stock Data

The extract_stock_data() function uses the yfinance library to fetch historical stock data for a specified stock symbol from Yahoo Finance. The function downloads the data from January 1, 2010, to June 28, 2023, and retrieves the closing prices. The extracted data is stored in a pandas DataFrame.

Preparing the Data

The prepare_data() function takes the extracted stock data and performs data preparation steps. It calculates the daily returns by using the pct_change() method on the closing prices column of the DataFrame. The function removes any rows with NaN values caused by the returns calculation. The prepared data, including the date, closing prices, and returns, is stored in the DataFrame.

Implementing the Reinforcement Learning Model

The StockTrader Class

The StockTrader class represents a reinforcement learning model for stock trading. It initializes with the prepared stock data, an initial state of 0, and a profit of 0. The get_action() method randomly selects an action (buy or sell) by choosing between -1 and 1. The step() method takes an action and updates the state and profit based on the action's impact on the stock price. If the action is to buy, the profit decreases by the current price; if the action is to sell, the profit increases by the current price. The method also checks if the episode is done, which occurs when the state reaches the end of the data. If the episode is done, the remaining stock is sold at the last price, and the final profit is calculated. The train() method trains the model by running multiple episodes of the trading process, recording the profit achieved in each episode.

Main Program Execution

The main program begins by specifying the stock symbol of interest (e.g., 'GOOGL'). The extract_stock_data() function is called to fetch the historical stock data for the specified symbol. The prepare_data() function is then used to prepare the extracted stock data. An instance of the StockTrader class, named trader, is created, passing in the prepared data. The number of episodes for training is set to 1000. The train() method of the trader instance is called to train the reinforcement learning model for the specified number of episodes. The profits achieved in each episode are stored in the profits list. Finally, the program visualizes the profits over the episodes using a line plot, showing the trend of the trading profits.