Update app.py

app.py

@@ -1,62 +1,34 @@
 import streamlit as st
-import requests
-from Pandas_Market_Predictor import Pandas_Market_Predictor
-import pandas as pd
-
-# Hard-coded API key for demonstration purposes
-API_KEY = "QR8F9B7T6R2SWTAT"
-
-def fetch_alpha_vantage_data(api_key):
-    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&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
+from thronetrader import StrategicSignals
 
 def main():
-    st.title("Stock Trend Predictor")
-
-    # Use the hard-coded API key
-    api_key = API_KEY
-
-    # Fetch Alpha Vantage data
-    alpha_vantage_data = fetch_alpha_vantage_data(api_key)
+    st.title("Strategic Trading Signals")
 
-    # 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)
+    # Input for stock symbol
+    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
 
-    # Rename columns
-    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'Low', '4. close': 'Close', '5. volume': 'volume'})
+    # Display strategic trading signals
+    strategic_signals = StrategicSignals(symbol=symbol)
 
-    # Calculate indicators
-    df = calculate_indicators(df)
+    st.subheader("Bollinger Bands Signals:")
+    bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
+    st.write(bollinger_bands_signals)
 
-    # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(df)
+    st.subheader("Breakout Signals:")
+    breakout_signals = strategic_signals.get_breakout_signals()
+    st.write(breakout_signals)
 
-    # Predict Trend
-    indicators = ["Doji", "Inside"]
-    trend = my_market_predictor.Trend_Detection(indicators, 10)
+    st.subheader("Crossover Signals:")
+    crossover_signals = strategic_signals.get_crossover_signals()
+    st.write(crossover_signals)
 
-    # Display results
-    st.subheader("Predicted Trend:")
-    st.write("Buy Trend :", trend['BUY'])
-    st.write("Sell Trend :", trend['SELL'])
-    st.write(f"Standard Deviation Percentage: {my_market_predictor.PERCENT_STD}%")
+    st.subheader("MACD Signals:")
+    macd_signals = strategic_signals.get_macd_signals()
+    st.write(macd_signals)
 
-    # Delete the DataFrame to release memory
-    del df
+    st.subheader("RSI Signals:")
+    rsi_signals = strategic_signals.get_rsi_signals()
+    st.write(rsi_signals)
 
 if __name__ == "__main__":
     main()