import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf

st.set_page_config(
    page_title="netflypsb",
    page_icon="logo.png",
    menu_items=None
)

st.write("# Stocks Forecast")

st.markdown(
    """
## 🚀📝 Forecast Future Stock Price Movement 


🌟 This app predicts 2 things: future price trajectory and future price
## How to use it? 🎉
- **1. Choose a ticker**: Write the ticker symbol for the stock you want to trade/invest. Refer yahoo finance for the ticker 📝
- **2. Choose analysis length**: Pick the start date and the end date for the data you want to use in the prediction. More is not necessarily better! 🏥
- **3. Choose horizon**: How far into the future do you want the app to predict. Shorter is mathematically better!
- **4. Choose prediction day**: Choose ONE (1) day within the horizon that you picked in step 3. to see the predicted price for that day. 🔍
- **5. Choose forecast models**: More is not necessarily better but usually takes longer.
- **6. Press Analyze** 🚀🎉
- **7. Wait a bit**: It may take some time. Watch the running guy on the top right 🤗
- **8. Do your due diligence**: Don't believe everything. Do your own research. This app may be one of your tools but YOU decide.   



## Let's Connect! 🌐
- If you liked this app, see my other projects at:
  - [☕ My Buy Me a Coffee Page](https://www.buymeacoffee.com/magister)
  - [🤗 My Huggingface Page](https://huggingface.co/netflypsb)
  - [🐦 My X Account](https://twitter.com/VeloVates)

"""
)

# Importing forecasting algorithms from the algo directory
from algo.sarima import sarima_forecast
from algo.linear_regression import linear_regression_forecast
from algo.tbats import tbats_forecast
from algo.random_forest import random_forest_forecast

# Function to fetch stock data
def fetch_stock_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date)
    return data['Close']

# Function to plot forecasts
def plot_forecasts(data, forecasts, title='Stock Price Forecast'):
    plt.figure(figsize=(10, 6))
    plt.plot(data.index, data, label='Historical Prices', color='black', alpha=0.75)
    
    for name, forecast in forecasts.items():
        plt.plot(forecast.index, forecast, label=name)
    
    if len(forecasts) > 1:
        combined_forecast = pd.concat(forecasts.values()).groupby(level=0).mean()
        plt.plot(combined_forecast.index, combined_forecast, label='Combined Forecast', color='red', linestyle='--')
    
    plt.title(title)
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.legend()
    st.pyplot(plt)

# Streamlit UI in Sidebar
st.sidebar.title("Input Parameters")
ticker = st.sidebar.text_input('Enter Ticker Symbol', 'AAPL')
start_date = st.sidebar.date_input('Select Start Date', value=pd.to_datetime('2020-01-01'))
end_date = st.sidebar.date_input('Select End Date', value=pd.to_datetime('2023-01-01'))
forecast_horizon = st.sidebar.number_input('Forecast Horizon (days)', min_value=1, value=180)
forecast_date = st.sidebar.date_input('Forecast Date', min_value=end_date, value=end_date + pd.Timedelta(days=180))

# User selects which forecasting models to use in Sidebar
options = st.sidebar.multiselect('Select forecasting models to use',
                                 ['SARIMA', 'Linear Regression', 'TBATS', 'Random Forest'],
                                 ['SARIMA', 'Linear Regression'])

if st.sidebar.button('Analyze'):
    data = fetch_stock_data(ticker, start_date, end_date)
    forecasts = {}
    
    if 'SARIMA' in options:
        forecasts['SARIMA'] = sarima_forecast(data, forecast_horizon)
    if 'Linear Regression' in options:
        forecasts['Linear Regression'] = linear_regression_forecast(data, forecast_horizon)
    if 'TBATS' in options:
        forecasts['TBATS'] = tbats_forecast(data, forecast_horizon)
    if 'Random Forest' in options:
        forecasts['Random Forest'] = random_forest_forecast(data, forecast_horizon)
    
    plot_forecasts(data, forecasts, f"Forecasted Stock Prices for {ticker}")
    
    # Output the forecasted price for the selected date, if available
    forecast_date_str = forecast_date.strftime('%Y-%m-%d')
    for model_name, forecast in forecasts.items():
        if forecast_date_str in forecast.index:
            st.write(f"Forecasted price by {model_name} on {forecast_date_str}: {forecast.loc[forecast_date_str]:.2f}")