Update app.py

app.py

@@ -1,5 +1,7 @@
 import streamlit as st
 from thronetrader import StrategicSignals
+from thronetrader.helper.squire import classify  # Import your classification method
+import pandas as pd
 
 def main():
     st.title("Strategic Trading Signals")
@@ -7,8 +9,23 @@ def main():
     # Input for stock symbol
     symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
 
+    # Fetch Alpha Vantage data
+    alpha_vantage_data = fetch_alpha_vantage_data(symbol)
+
+    # Extract relevant data from Alpha Vantage response
+    alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
+    df = pd.DataFrame(alpha_vantage_time_series).T
+    df.index = pd.to_datetime(df.index)
+    df = df.dropna(axis=0)
+
+    # Rename columns
+    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
+
+    # Calculate indicators
+    df = calculate_indicators(df)
+
     # Display strategic trading signals
-    strategic_signals = StrategicSignals(symbol=symbol)
+    strategic_signals = StrategicSignals(df)
 
     st.subheader("Bollinger Bands Signals:")
     bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
@@ -30,6 +47,27 @@ def main():
     rsi_signals = strategic_signals.get_rsi_signals()
     display_signals(rsi_signals)
 
+    # Example of using your classify method
+    classification_result = classify(df, logger)  # Pass your DataFrame and logger
+    st.subheader("Classification Result:")
+    st.write(classification_result)
+
+def fetch_alpha_vantage_data(symbol):
+    api_key = "QR8F9B7T6R2SWTAT"
+    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
+    response = requests.get(url)
+    alpha_vantage_data = response.json()
+    return alpha_vantage_data
+
+def calculate_indicators(data):
+    # Convert all columns to numeric
+    data = data.apply(pd.to_numeric, errors='coerce')
+
+    # Example: Simple condition for doji and inside
+    data['Doji'] = abs(data['Close'] - data['open']) <= 0.01 * (data['high'] - data['low'])
+    data['Inside'] = (data['high'] < data['high'].shift(1)) & (data['low'] > data['low'].shift(1))
+    return data
+
 def display_signals(signals):
     for signal in signals:
         if isinstance(signal, dict):