Update app.py

app.py

@@ -1,27 +1,80 @@
-import yfinance as yf
 import streamlit as st
+from thronetrader import StrategicSignals
+from thronetrader.helper.squire import classify  # Import your classification method
 import pandas as pd
 
-# Function to fetch historical stock data
-def fetch_stock_data(symbol, start_date, end_date):
-    data = yf.download(symbol, start=start_date, end=end_date)
-    return data
-
-# Streamlit UI
 def main():
-    st.title("Stock Data Viewer")
+    st.title("Strategic Trading Signals")
+
+    # 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(df)
 
-    symbol = st.text_input("Enter Stock Symbol (e.g., AAPL):")
-    start_date = st.date_input("Select Start Date:")
-    end_date = st.date_input("Select End Date:")
+    st.subheader("Bollinger Bands Signals:")
+    bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
+    display_signals(bollinger_bands_signals)
 
-    if st.button("Fetch Stock Data"):
-        # Fetch stock data
-        stock_data = fetch_stock_data(symbol, start_date, end_date)
+    st.subheader("Breakout Signals:")
+    breakout_signals = strategic_signals.get_breakout_signals()
+    display_signals(breakout_signals)
+
+    st.subheader("Crossover Signals:")
+    crossover_signals = strategic_signals.get_crossover_signals()
+    display_signals(crossover_signals)
+
+    st.subheader("MACD Signals:")
+    macd_signals = strategic_signals.get_macd_signals()
+    display_signals(macd_signals)
+
+    st.subheader("RSI Signals:")
+    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
 
-        # Display the raw stock data
-        st.subheader("Raw Stock Data:")
-        st.write(stock_data)
+def display_signals(signals):
+    for signal in signals:
+        if isinstance(signal, dict):
+            st.write(f"Date: {signal.get('date', 'N/A')}")
+            st.write(f"Signal: {signal.get('signal', 'N/A')}")
+        else:
+            st.write("Invalid signal format.")
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()