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import streamlit as st
import pandas as pd
import numpy as np
import yfinance as yf
from datetime import date, timedelta
import matplotlib.pyplot as plt
from pmdarima.arima import auto_arima
# Custom CSS for professional look
st.markdown("""
<style>
.main {
background-color: #f5f5f5;
padding: 20px;
}
.sidebar .sidebar-content {
background-color: #fafafa;
}
.stButton>button {
background-color: #2e7d32;
color: white;
border-radius: 5px;
font-weight: bold;
}
.stButton>button:hover {
background-color: #388e3c;
}
h1, h2, h3 {
color: #333;
}
.stDataFrame {
background-color: #ffffff;
}
.stTable {
color: #333;
}
.stPlotlyChart {
background-color: #ffffff;
}
</style>
""", unsafe_allow_html=True)
# App Title
st.title("📈 Stock Market Forecasting")
# Sidebar Inputs
st.sidebar.header("User Input")
# Inputs for stock ticker, date range, and forecast days
ticker = st.sidebar.text_input("Enter stock ticker:", value="GOOGL")
end_date = st.sidebar.date_input("End Date", value=date.today())
start_date = st.sidebar.date_input("Start Date", value=date.today() - timedelta(days=365))
forecast_days = st.sidebar.slider("Forecast days", min_value=10, max_value=30, value=15)
# Ensure start_date is not later than end_date
if start_date > end_date:
st.sidebar.error("❌ Start date cannot be later than end date.")
st.stop()
# Feature selection for stock attributes (e.g., Close, High, Low, Open)
feature_select = st.sidebar.multiselect(
label="Select Stock Features to Forecast",
options=["Close", "High", "Low", "Open"],
default=["Close"]
)
# Feature descriptions (explanation for each stock feature)
feature_descriptions = {
"Close": "The closing price of the stock at the end of the trading day.",
"High": "The highest price at which the stock traded during the day.",
"Low": "The lowest price at which the stock traded during the day.",
"Open": "The price at which the stock opened at the beginning of the trading day."
}
# Fetch Stock Data based on user input
if st.sidebar.button("Fetch Data"):
st.subheader(f"Stock Data for {ticker.upper()} ({start_date} to {end_date})")
try:
# Download stock data from Yahoo Finance
df = yf.download(ticker, start=start_date, end=end_date, progress=False)
# Check if data is available
if df.empty:
st.error("❌ No data found for the selected ticker or date range.")
st.stop()
st.success("✅ Data fetched successfully!")
# Reset index and insert 'Date' column for better readability
df.insert(0, "Date", df.index)
df.reset_index(drop=True, inplace=True)
st.dataframe(df.head()) # Display first few rows of the stock data
# Initialize an empty DataFrame to store forecasted data for all features
combined_forecast_df = pd.DataFrame()
# Iterate over each selected feature and generate forecasts
for feature in feature_select:
st.write(f"### 🔍 Forecasting: {feature}")
st.write(feature_descriptions[feature])
# Prepare the time series for forecasting (drop NaN values)
series = df[feature].dropna()
# Fit the ARIMA model using the selected feature's time series
model = auto_arima(series,
start_p=1, start_q=1,
max_p=2, max_q=2,
m=12, # Monthly seasonal cycle
start_P=0,
seasonal=True,
d=1, D=1,
trace=True, # Display fitting progress
error_action='ignore',
suppress_warnings=True)
# Forecast future values for the specified number of days
forecast = model.predict(n_periods=forecast_days)
future_dates = pd.date_range(start=df["Date"].iloc[-1] + timedelta(days=1), periods=forecast_days)
# Create a DataFrame to hold the forecasted values
forecast_df = pd.DataFrame({
"Date": future_dates,
feature: forecast
})
# Combine the forecasted data for each feature into a single DataFrame
combined_forecast_df = pd.merge(combined_forecast_df, forecast_df, on="Date", how="outer") if not combined_forecast_df.empty else forecast_df
# Plot historical data and forecasted data
fig, ax = plt.subplots(figsize=(10, 4))
ax.plot(df["Date"], series, label="Historical", color='blue') # Historical data
ax.plot(forecast_df["Date"], forecast_df[feature], label="Forecast", color='orange') # Forecasted data
ax.set_title(f"{feature} Forecast for {ticker.upper()}")
ax.set_xlabel("Date")
ax.set_ylabel(feature)
ax.legend()
# Display the plot
st.pyplot(fig)
# Display the combined forecasted data for all selected features
st.subheader("Combined Forecasted Data")
st.dataframe(combined_forecast_df.head())
except Exception as e:
st.error(f"❌ An error occurred: {str(e)}")
st.stop()
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