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import streamlit as st
import yfinance as yf
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
st.title("Stockiza: Stock Price App")
# Get user input for stock symbol
stock_symbol = st.text_input("Enter a stock symbol:", "AAPL")
# Add a button to fetch data
fetch_button = st.button("Fetch Data")
if fetch_button:
try:
# Fetch stock data using yfinance
stock = yf.Ticker(stock_symbol)
stock_info = stock.info
# Display stock information
st.subheader(f"{stock_info['longName']} ({stock_symbol})")
# Check if 'currentPrice' key exists in stock_info
if 'currentPrice' in stock_info:
st.write(f"Current Price: ${stock_info['currentPrice']:.2f}")
else:
st.write("Current Price: Not available")
# Check if other keys exist before accessing them
if'regularMarketDayRange' in stock_info:
st.write(f"Day's Range: ${stock_info['regularMarketDayRange']}")
if 'fiftyTwoWeekRange' in stock_info:
st.write(f"52-Week Range: ${stock_info['fiftyTwoWeekRange']}")
if'regularMarketVolume' in stock_info:
st.write(f"Volume: {stock_info['regularMarketVolume']:,.0f}")
if'marketCap' in stock_info:
st.write(f"Market Cap: ${stock_info['marketCap']:,.2f}")
# Add a graph
stock_data = stock.history(period="5y")
fig, ax = plt.subplots()
ax.plot(stock_data.index, stock_data["Close"])
ax.set_title(f"{stock_symbol} Stock Price")
ax.set_xlabel("Date")
ax.set_ylabel("Price ($)")
st.pyplot(fig)
# Prepare data for time series model
stock_data['Date'] = pd.to_datetime(stock_data.index)
stock_data['Year'] = stock_data['Date'].dt.year
stock_data['Month'] = stock_data['Date'].dt.month
stock_data['Day'] = stock_data['Date'].dt.day
# Split data into training and testing sets
X = stock_data[['Year', 'Month', 'Day']]
y = stock_data['Close']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train a random forest regressor model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
rmse = mse ** 0.5
st.write(f"Root Mean Squared Error (RMSE): {rmse:.2f}")
# Use the model to predict the stock price 5 years from now
future_date = pd.to_datetime('2027-12-31')
future_data = pd.DataFrame({'Year': [future_date.year], 'Month': [future_date.month], 'Day': [future_date.day]})
future_price = model.predict(future_data)
st.write(f"Predicted Price 5 Years from Now: ${future_price[0]:.2f}")
except Exception as e:
st.error(f"Error: {e}") |