import streamlit as st
# Import your existing code here
from datetime import datetime, timedelta
import yfinance as yf
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier
import shap
import matplotlib.pyplot as plt

# Copy your existing classes here (DataFetcher, FinancialDataProcessor, etc.)
# Then add this Streamlit interface:

st.set_page_config(page_title="Crypto Trading Bot", layout="wide")
st.title("Crypto Trading Analysis Bot")

# Sidebar controls
ticker = st.sidebar.text_input("Ticker Symbol", value="BTC-USD")
days = st.sidebar.number_input("Number of Days", value=200, min_value=1, max_value=365)

if st.sidebar.button("Run Analysis"):
    try:
        with st.spinner("Fetching and analyzing data..."):
            fetcher = DataFetcher(ticker=ticker, nb_days=days)
            fetcher.fetch_data()

            processor = FinancialDataProcessor(fetcher.data)
            processed_data = processor.calculate_returns().calculate_probabilities()

            pca_transformer = PCATransformer(n_components=1)
            pca_result = pca_transformer.fit_transform(processed_data)
            processed_data = processor.apply_pca_calculations(pca_result)

            strategy_builder = StrategyBuilder(processed_data)
            model, X, y_encoded = strategy_builder.train_model(target_column='PCA_Scenario')
            shap_values = strategy_builder.compute_shapley_values(model, X)

            feature_importance = strategy_builder.analyze_feature_importance(shap_values, X.columns)
            buy_threshold, sell_threshold = strategy_builder.refine_thresholds(feature_importance)

            backtester = Backtester(processed_data)
            portfolio_values, final_position, entry_price = backtester.backtest(buy_threshold, sell_threshold)

            last_row = processed_data.iloc[-1]

            # Display results
            col1, col2 = st.columns(2)
            
            with col1:
                st.subheader("Current Position")
                st.metric("Portfolio Value", f"${portfolio_values[-1]:.2f}")
                st.metric("Position", final_position or "No position")
                if final_position:
                    st.metric("Entry Price", f"${entry_price:.2f}")
                st.metric("Latest Close", f"${last_row['Close']:.2f}")

            with col2:
                st.subheader("Decision Metrics")
                st.metric("Buy%", f"{last_row['Buy%']:.4f}")
                st.metric("Sell%", f"{last_row['Sell%']:.4f}")
                st.metric("PCA Buy%", f"{last_row['PCA_Buy%']:.4f}")
                st.metric("PCA Sell%", f"{last_row['PCA_Sell%']:.4f}")

            # Plot portfolio value
            st.subheader("Portfolio Value Over Time")
            fig, ax = plt.subplots(figsize=(12, 6))
            ax.plot(processed_data.index[1:], portfolio_values, label='Portfolio Value', color='blue')
            ax.set_title('Portfolio Value Over Time (Backtest)')
            ax.set_xlabel('Date')
            ax.set_ylabel('Portfolio Value ($)')
            ax.grid(True)
            ax.legend()
            st.pyplot(fig)

            # Feature importance
            st.subheader("Feature Importance")
            st.dataframe(feature_importance)

    except Exception as e:
        st.error(f"An error occurred: {str(e)}")