NASDAQ Price Prediction- Python

In this project, I aim to predict the price movements of NASDAQ stocks using machine learning techniques. The NASDAQ stock market is known for its technology-heavy listings and is a key indicator of the performance of technology and growth stocks. By leveraging historical data and machine learning algorithms, we seek to develop a model that can forecast whether the price of a NASDAQ-listed stock will increase or decrease in the near future.

Project Steps:

1. Installation and Data Acquisition:
   • The first step involves installing the yfinance library, which allows us to obtain historical stock data from Yahoo Finance.
   • We retrieve the necessary data, including open, high, low, close prices, and trading volume.
2. Data Wrangling:
   • Drop irrelevant columns such as "Dividends" and "Stock Splits".
   • Create additional columns:
     • Tomorrow: representing the price of the following day.
     • Target: a binary column indicating whether the price will increase (1) or decrease (0) compared to the current day's closing price.
   • Filter the data to start from January 1, 1990.
3. Model Training:
   • Import necessary libraries for machine learning tasks, such as RandomForestClassifier from scikit-learn.
   • Define the model parameters (e.g., number of estimators, minimum samples split) and initialize the model.
   • Split the data into training and testing sets.
   • Fit the model using the training data.
4. Feature Engineering:
   • Create new predictors to enhance the model's predictive power.
   • Calculate rolling averages over different time horizons.
   • Generate ratio columns and trend columns based on the rolling averages.
   • Incorporate these new predictors into the model.
5. Function for Prediction:
   • Define a function to streamline the prediction process.
   • Input parameters include training data, testing data, predictors, and the trained model.
   • The function fits the model to the training data, predicts probabilities for the testing data, and converts probabilities into binary predictions.
   • It then combines the actual target values with the predicted values for evaluation.
6. Model Evaluation:
   • Test the model's performance using evaluation metrics such as precision score.
   • Assess how well the model predicts the direction of price movements.
   • Fine-tune the model and feature engineering techniques if necessary to improve performance.

Conclusion: By following these steps, I aim to develop a robust machine learning model capable of predicting NASDAQ stock price movements with reasonable accuracy. This project not only demonstrates the application of machine learning in financial forecasting but also provides insights into feature engineering techniques to enhance predictive capabilities.