File size: 2,780 Bytes
e3f8fe1 48e8a9b e3f8fe1 48e8a9b e3f8fe1 48e8a9b e3f8fe1 48e8a9b e3f8fe1 48e8a9b e3f8fe1 48e8a9b e3f8fe1 48e8a9b e3f8fe1 48e8a9b 752d251 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import yfinance as yf
import pandas as pd
import numpy as np
import gradio as gr
from prophet import Prophet
import plotly.graph_objs as go
# Stock tickers for dropdown
tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "META", "NFLX", "NVDA", "JPM", "BAC"]
# Function to fetch and process stock data
def get_stock_data(ticker, start_date, end_date):
data = yf.download(ticker, start=start_date, end=end_date)
data.reset_index(inplace=True)
return data
# Train Prophet model
def train_model(data):
df = data[['Date', 'Close']].rename(columns={"Date": "ds", "Close": "y"})
model = Prophet()
model.fit(df)
return model
# Predict future stock prices
def make_predictions(model, periods=90):
future = model.make_future_dataframe(periods=periods)
forecast = model.predict(future)
return forecast
# Function to generate buy/sell prediction
def predict_stock(ticker, start_date, end_date):
data = get_stock_data(ticker, start_date, end_date)
# Train the model
model = train_model(data)
# Make predictions
forecast = make_predictions(model, periods=90)
# Calculate statistics
highest_value = data['Close'].max()
lowest_value = data['Close'].min()
percentage_change = ((data['Close'].iloc[-1] - data['Close'].iloc[0]) / data['Close'].iloc[0]) * 100
last_close_price = data['Close'].iloc[-1]
future_price = forecast['yhat'].iloc[-1]
# Recommendation logic
recommendation = "Buy" if future_price > last_close_price else "Sell"
# Plotting the historical and predicted prices
fig = go.Figure()
fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], mode='lines', name='Historical Data'))
fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast Data'))
fig.update_layout(title=f'Stock Price Prediction for {ticker}',
xaxis_title='Date', yaxis_title='Stock Price')
return {
"Highest Price": highest_value,
"Lowest Price": lowest_value,
"Percentage Change": f"{percentage_change:.2f}%",
"Buy/Sell Recommendation": recommendation,
"Graph": fig
}
# Gradio UI Components
def app_ui():
stock_ticker = gr.Dropdown(choices=tickers, label="Select Stock Ticker")
start_date = gr.DatePicker(label="Select Start Date")
end_date = gr.DatePicker(label="Select End Date")
output = gr.outputs.JSON(label="Stock Prediction Results")
# Define Gradio Interface
interface = gr.Interface(
fn=predict_stock,
inputs=[stock_ticker, start_date, end_date],
outputs=[output, gr.Plot(label="Stock Performance Graph")],
live=True
)
interface.launch()
if __name__ == "__main__":
app_ui() |