Update app.py

import streamlit as st
from thronetrader import StrategicSignals

def main():
st.title("Strategic Trading Signals")

# Input for stock symbol
symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")

# Display strategic trading signals
strategic_signals = StrategicSignals(symbol=symbol)

st.subheader("Bollinger Bands Signals:")
bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
display_signals(bollinger_bands_signals)

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)

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__":
main()

app.py

@@ -1,81 +0,0 @@
-import streamlit as st
-import requests
-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")
-
-    # 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)
-
-    st.subheader("Bollinger Bands Signals:")
-    bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
-    display_signals(bollinger_bands_signals)
-
-    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
-
-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__":
-    main()