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# Import necessary libraries
import pandas as pd
import yfinance as yf
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
import streamlit as st
from datetime import datetime, timedelta, timezone
# Function to fetch historical stock data
def fetch_stock_data(symbol, start_date, end_date):
data = yf.download(symbol, start=start_date, end=end_date)
return data
# Function to create features for the model
def create_features(data):
data['Date'] = data.index
data['Year'] = data['Date'].dt.year
data['Month'] = data['Date'].dt.month
data['Day'] = data['Date'].dt.day
data['Hour'] = data['Date'].dt.hour
data['Minute'] = data['Date'].dt.minute
return data
# Function to train a machine learning model
def train_model(data):
features = ['Year', 'Month', 'Day', 'Hour', 'Minute']
target = 'Close'
X = data[features]
y = data[target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Evaluate the model
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
st.write(f"Mean Squared Error: {mse}")
return model
# Streamlit UI
def main():
st.title("Stock Price Prediction Tool")
symbol = st.text_input("Enter Stock Symbol (e.g., AAPL):")
start_date = st.date_input("Select Start Date:")
end_date = st.date_input("Select End Date:")
if st.button("Predict Stock Price"):
# Fetch stock data
stock_data = fetch_stock_data(symbol, start_date, end_date)
# Create features
stock_data = create_features(stock_data)
# Train the model
model = train_model(stock_data)
# Predict the stock price for a specific date (e.g., the last date in the dataset)
prediction_date = stock_data['Date'].iloc[-1]
prediction_features = [[
prediction_date.year,
prediction_date.month,
prediction_date.day,
prediction_date.hour,
prediction_date.minute
]]
predicted_price = model.predict(prediction_features)[0]
st.subheader(f"Predicted Stock Price on {prediction_date} (UTC):")
st.write(f"${predicted_price:.2f}")
# Run the Streamlit app
if __name__ == '__main__':
main() |