Update app.py

app.py

@@ -7,7 +7,32 @@ from sklearn.decomposition import PCA
 from sklearn.preprocessing import StandardScaler, LabelEncoder
 from sklearn.ensemble import RandomForestClassifier
 import shap
-import matplotlib.pyplot as plt
+import requests
+
+# Google Apps Script Web App URL
+WEB_APP_URL = "https://script.google.com/macros/s/AKfycbzCLDKHozNB6257wxjPzAMBCqXIpzGUuOPbrzkAGrhaS2ujj7aQaXC0mJeVqPbUlzug/exec"
+
+# Utility functions for persistent position storage
+def save_position(position, trade_info, buy_percentage=None, sell_percentage=None):
+    payload = {
+        "action": "save",
+        "position": position,
+        "timestamp": datetime.now().isoformat(),
+        "trade_info": trade_info,
+        "buy_percentage": buy_percentage,
+        "sell_percentage": sell_percentage
+    }
+    response = requests.post(WEB_APP_URL, json=payload)
+    if response.status_code != 200:
+        raise Exception(f"Error saving position: {response.text}")
+
+def load_position():
+    payload = {"action": "load"}
+    response = requests.post(WEB_APP_URL, json=payload)
+    if response.status_code == 200:
+        return response.json().get("position", None), response.json().get("entry_price", None), response.json().get("buy_percentage", None), response.json().get("sell_percentage", None)
+    else:
+        raise Exception(f"Error loading position: {response.text}")
 
 class DataFetcher:
     """Fetches historical financial data using yfinance."""
@@ -147,6 +172,8 @@ class Backtester:
         position = None
         entry_price = None
         portfolio_values = []
+        last_buy_percentage = None
+        last_sell_percentage = None
 
         for i in range(1, len(self.data)):
             last_row = self.data.iloc[i]
@@ -155,11 +182,13 @@ class Backtester:
                 if position != 'Buy':
                     position = 'Buy'
                     entry_price = last_row['Close']
+                    last_buy_percentage = last_row['PCA_Buy%'] if 'PCA_Buy%' in last_row else last_row['Buy%']
             elif (last_row['PCA_Scenario'] == 'Sell' and last_row['PCA_Sell%'] > sell_threshold) or \
                  (last_row['Scenario'] == 'Sell' and last_row['Sell%'] > sell_threshold):
                 if position != 'Sell':
                     position = 'Sell'
                     entry_price = last_row['Close']
+                    last_sell_percentage = last_row['PCA_Sell%'] if 'PCA_Sell%' in last_row else last_row['Sell%']
 
             if position == 'Buy':
                 portfolio_value *= (last_row['Close'] / entry_price)
@@ -168,11 +197,14 @@ class Backtester:
 
             portfolio_values.append(portfolio_value)
 
-        return portfolio_values, position, entry_price
+        return portfolio_values, position, entry_price, last_buy_percentage, last_sell_percentage
 
 def run_analysis():
     """Runs the complete trading analysis."""
     try:
+        # Load previous position
+        current_position, entry_price, current_buy_percentage, current_sell_percentage = load_position()
+
         fetcher = DataFetcher(ticker="^DJI", nb_days=50)
         data = fetcher.fetch_data()
 
@@ -191,40 +223,44 @@ def run_analysis():
         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)
+        portfolio_values, new_position, new_entry_price, new_buy_percentage, new_sell_percentage = 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("Position", final_position or "No position")
-            if final_position:
-                st.metric("Entry Price", f"${entry_price:.2f}")
-
+            st.metric("Position", new_position or "No position")
+            if new_position:
+                st.metric("Entry Price", f"${new_entry_price:.2f}")
 
         with col2:
             st.subheader("Decision Metrics")
             st.metric("Buy%", f"{last_row['PCA_Buy%']:.4f}")
             st.metric("Sell%", f"{last_row['PCA_Sell%']:.4f}")
 
+
+        # Save new position if changed
+        if new_position != current_position:
+            save_position(new_position, {"entry_price": new_entry_price, "timestamp": datetime.now().isoformat()},
+                          buy_percentage=last_row['PCA_Buy%'], sell_percentage=last_row['PCA_Sell%'])
+
+
+        
         return True
 
     except Exception as e:
         st.error(f"An error occurred: {str(e)}")
         return False
 
-# Page configuration
-st.set_page_config(page_title="DOW JONES Trading Bot", layout="wide")
-st.title("DOW JONES Trading Bot")
-
-
 # Run analysis automatically on page load
 run_analysis()
 
 
+
+
 # Educational Slides Section
 st.header("Educational Slides")