Update app.py

app.py

@@ -1,81 +1,38 @@
 import streamlit as st
-import requests
-from thronetrader import StrategicSignals
-from thronetrader.helper.squire import classify  # Import your classification method
-import pandas as pd
+import yfinance as yf
+from cuansignal import signals as cs
 
-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)
+def fetch_yfinance_data(symbol, start, end):
+    try:
+        data = yf.download(symbol, start=start, end=end)
+        return data
+    except Exception as e:
+        st.error(f"Error fetching data: {e}")
+        return None
 
-    # 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)
+def main():
+    st.title("Streamlit App with cuansignal and yfinance")
 
-    st.subheader("RSI Signals:")
-    rsi_signals = strategic_signals.get_rsi_signals()
-    display_signals(rsi_signals)
+    # Input parameters
+    symbol = "AAPL"
+    start_date = "2024-02-04T14:20:30Z"
+    end_date = "2024-02-04T14:30:30Z"
 
-    # Example of using your classify method
-    classification_result = classify(df, logger)  # Pass your DataFrame and logger
-    st.subheader("Classification Result:")
-    st.write(classification_result)
+    # Fetch data
+    data = fetch_yfinance_data(symbol, start_date, end_date)
 
-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
+    if data is not None:
+        st.subheader("Original Data:")
+        st.write(data.head())
 
-def calculate_indicators(data):
-    # Convert all columns to numeric
-    data = data.apply(pd.to_numeric, errors='coerce')
+        # Calculate dEMA
+        result = cs.dEMA(data, base='Close', short=10, long=100)
 
-    # 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
+        st.subheader("dEMA Result:")
+        st.write(result.head())
 
-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.")
+    else:
+        st.warning("Failed to fetch data. Check your input parameters.")
 
 if __name__ == "__main__":
     main()