El-SOUQ Stock Price Forecast Web App

In today's digital age, social media platforms have a significant impact on the financial markets. This project aims to predict stock prices by incorporating the effects of social media data from platforms such as Facebook, LinkedIn, and Twitter. By analyzing the sentiment, trends, and discussions on these platforms, we can gain valuable insights into market behavior and enhance the accuracy of stock price predictions.

So, El-SOUQ Stock Price Predictor is a web application that predicts stock prices based on the effects of social media platforms such as Facebook, LinkedIn, and Twitter. It utilizes historical stock data and employs the Facebook Prophet library to forecast future stock prices. Additionally, the application provides access to trending business news using the News API.

• Backend: Python
• Web Framework: Streamlit
• Data Analysis: Pandas, NumPy
• Data Visualization: Plotly
• Forecasting Library: Facebook Prophet
• Other Libraries: Pystan, Cython, Nltk
• API Integration: Yahoo Finance API, News API
• Web Scraping: Requests
• Deployment: Streamlit

1. Past Trends: Explore the historical trends of a selected stock, including open and close prices. The data is visualized through interactive candlestick charts.

2. Predict Stock Price: Forecast future stock prices using the Facebook Prophet library. Adjust the prediction period and view the forecasted prices along with the trend components.

3. Trending Business News: Stay updated with the latest business news related to the stock market. The application fetches and displays top headlines from the News API.

Stock Price Data Retrieval

Historical stock price data is retrieved from Yahoo Finance using the yfinance library. Here's an example of fetching stock price data for a given symbol:

import yfinance as yf

def get_stock_price_data(symbol):
    # Fetch historical stock price data
    data = yf.download(symbol, start='2015-01-01', end='2023-01-01')
    return data

Sentiment Analysis

To analyze the sentiment of social media posts related to a particular stock, we utilize natural language processing techniques. The code snippet below demonstrates sentiment analysis using the nltk library:

import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

def analyze_sentiment(text):
    # Perform sentiment analysis on text
    sia = SentimentIntensityAnalyzer()
    sentiment_scores = sia.polarity_scores(text)
    return sentiment_scores

Stock Price Prediction with Fbprophet

Fbprophet is a powerful library for time series forecasting. We utilize it to predict future stock prices based on historical data and social media insights. Here's an example of using Fbprophet for stock price prediction:

from fbprophet import Prophet

def predict_stock_prices(data):
    # Prepare data for Fbprophet
    data = data.rename(columns={"Date": "ds", "Close": "y"})

    # Create and fit the Fbprophet model
    model = Prophet()
    model.fit(data)

    # Make future predictions
    future = model.make_future_dataframe(periods=365)  # Predict for one year ahead
    forecast = model.predict(future)

    return forecast

By incorporating the effects of social media data from platforms like Facebook, LinkedIn, and Twitter, this projectenhances the accuracy of stock price predictions. El-SOUQ web application provides an interactive interface where users can input a stock symbol and view the predicted stock prices for the next four years. The application fetches historical stock price data from Yahoo Finance using the yfinance library and collects social media data through APIs provided by Facebook, LinkedIn, and Twitter.

The social media data is then analyzed using natural language processing techniques to determine the sentiment and extract valuable insights. Sentiment analysis is performed using the nltk library, which provides sentiment scores for text data. These sentiment scores help to gauge the overall sentiment and market sentiment towards a particular stock.

The Fbprophet library, developed by Facebook, is employed for time series forecasting and predicting stock prices. The historical stock price data, along with the social media insights, is used to train the Fbprophet model. The model then generates future predictions for the stock prices.

The predicted stock prices are presented graphically using the Plotly library. The interactive charts and graphs provide users with a visual representation of the predicted trends and fluctuations in stock prices over the next four years.

Overall, this project combines the power of machine learning, social media analysis, and data visualization to predict stock prices based on the effects of social media platforms by more than 84% accuracy. By incorporating social media data, investors can gain deeper insights into market behavior and make more informed investment decisions.