Skip to content

Latest commit

 

History

History
150 lines (118 loc) · 3.92 KB

README.md

File metadata and controls

150 lines (118 loc) · 3.92 KB

Stock Prediction App

This project is a Stock Prediction App that leverages historical stock data, machine learning, and Streamlit to provide interactive visualizations and predictions of future stock prices.

Features

  1. Historical Data Retrieval

    • Fetches stock data starting from January 1, 2010, using the Yahoo Finance API.
    • Displays a summary of the data with descriptive statistics.

  2. Visualizations

    • Closing Price vs. Time Chart.
    • Closing Price vs. Time Chart with 100-day and 200-day Moving Averages.

  3. Future Stock Price Prediction

    • Uses a pre-trained LSTM model (keras_model.h5) to predict future stock prices.
    • Displays future predictions for the next 100 days.

  4. Streamlit Interface

    • Interactive user input for stock ticker symbol.
    • Dynamic visualizations and outputs displayed directly in the app.

Dependencies

The app requires the following Python libraries:

  • numpy
  • pandas
  • matplotlib
  • yfinance
  • datetime
  • keras
  • streamlit
  • scikit-learn

Installation

  1. Clone this repository:

    git clone https://github.com/your_username/stock-prediction-app.git
cd stock-prediction-app

  2. Install the required libraries:

    pip install -r requirements.txt

  3. Place the pre-trained LSTM model (keras_model.h5) in the project directory.

Usage

  1. Run the app:

    streamlit run app.py

  2. Open your browser and navigate to the provided local URL (e.g., http://localhost:8501).

  3. Enter a stock ticker symbol (e.g., AAPL) in the input field to visualize historical data and predict future prices.

Code Explanation

Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
from datetime import datetime, timedelta
from keras.models import load_model
import streamlit as st
from sklearn.preprocessing import MinMaxScaler

Retrieve Historical Data

today = datetime.today()
yesterday = today - timedelta(days=1)
start = '2010-01-01'
end = yesterday.strftime('%Y-%m-%d')

st.title('Stock Prediction App')
userInput = st.text_input('Enter a stock ticker:', 'AAPL')
df = yf.download(userInput, start=start, end=end)

Display Descriptive Statistics

st.subheader('Data from 2010 to yesterday')
st.write(df.describe())

Visualization of Historical Data

st.subheader('Closing Price vs Time chart')
fig = plt.figure(figsize=(12, 6))
plt.plot(df['Close'])
st.pyplot(fig)

Visualization with Moving Averages

ma100 = df.Close.rolling(100).mean()
ma200 = df.Close.rolling(200).mean()
fig = plt.figure(figsize=(12, 6))
plt.plot(ma100)
plt.plot(ma200)
plt.plot(df['Close'])
st.pyplot(fig)

Future Predictions

model = load_model('keras_model.h5')
scaler = MinMaxScaler(feature_range=(0, 1))
data_scaled = scaler.fit_transform(df[['Close']])

future_days = 100
last_sequence = data_scaled[-100:]
future_predictions = []
current_input = last_sequence

for _ in range(future_days):
    prediction = model.predict(current_input[np.newaxis, :, :])
    future_predictions.append(prediction[0, 0])
    current_input = np.append(current_input[1:], prediction, axis=0)

future_predictions = scaler.inverse_transform(np.array(future_predictions).reshape(-1, 1))
historical_dates = df.index
future_dates = pd.date_range(historical_dates[-1] + timedelta(days=1), periods=future_days)

st.subheader('Future Price Prediction')
fig = plt.figure(figsize=(12, 6))
plt.plot(historical_dates, df['Close'], label='Historical Prices')
plt.plot(future_dates, future_predictions, label='Predicted Future Prices', color='red')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
st.pyplot(fig)

License

This project is open-source and available under the MIT License.

Acknowledgments

  • Yahoo Finance for stock data.
  • Streamlit for the interactive web app framework.
  • Keras for the deep learning model.