AI PROJECT CLASS X/XII

Project 5:

Stock Price Predictor

Objective: Build a system that can predict the price of a specific stock using AI.

Creating a stock price predictor using AI and Python involves several steps and theories from both finance and machine learning.

1. Data Collection

• Historical Data: Use APIs (like Yahoo Finance or Alpha Vantage) to gather historical stock prices.
• Additional Data: Consider including other relevant data such as trading volume, market indices, and macroeconomic indicators.

2. Data Preprocessing

• Cleaning Data: Handle missing values, remove outliers, and normalize the data.
• Feature Engineering: Create new features from existing data, such as moving averages, volatility, and RSI (Relative Strength Index).

3. Choosing the Model

• Traditional Machine Learning Models:
  • Linear Regression: For simple price forecasting.
  • Decision Trees / Random Forests: Good for handling non-linear relationships.
  • Support Vector Machines (SVM): Effective for classification problems in stock movement direction.
• Deep Learning Models:
  • Recurrent Neural Networks (RNNs): Especially LSTM (Long Short-Term Memory) networks are suited for time series data.
  • Convolutional Neural Networks (CNNs): Can be applied to analyze stock price data as images (e.g., candlestick charts).

4. Model Training

• Split the dataset into training, validation, and test sets.
• Use techniques like k-fold cross-validation to improve model robustness.
• Optimize hyperparameters using methods like Grid Search or Random Search.

5. Model Evaluation

• Use metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or R-squared to evaluate performance.
• Backtesting: Test the model against historical data to see how well it would have performed.

6. Deployment

• Once the model is trained and evaluated, deploy it using Flask or FastAPI for a web application.
• Implement real-time data fetching and prediction capabilities.

7. Continuous Learning

• Stock market conditions change over time, so models should be updated regularly with new data.

Sample Python Code:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import yfinance as yf

ticker = ‘AAPL’ # Example stock
data = yf.download(ticker, start=’2010-01-01′, end=’2023-01-01′)

data[‘Returns’] = data[‘Close’].pct_change()
data[‘Lag1’] = data[‘Returns’].shift(1)
data.dropna(inplace=True)

X = data[[‘Lag1’]]
y = data[‘Returns’]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f’Mean Squared Error: {mse}’)

last_return = data[‘Returns’].iloc[-1]
future_prediction = model.predict([[last_return]])
print(f’Predicted future return: {future_prediction}’)