Create app.py

app.py

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+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)}")