Update app.py

app.py

@@ -1,18 +1,13 @@
 import streamlit as st
-import yfinance as yf
-import pandas as pd
 import requests
-from thronetrader import StrategicSignals
 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, symbol):
-    
-    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
+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
@@ -22,22 +17,18 @@ def calculate_indicators(data):
     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))
+    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(signal_type, signals):
-    st.subheader(f"{signal_type} Signals:")
-    st.write(signals)
-
 def main():
     st.title("Stock Trend Predictor")
 
-    # Input for stock symbol
-    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
+    # Use the hard-coded API key
+    api_key = API_KEY
 
     # Fetch Alpha Vantage data
-    alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol)
+    alpha_vantage_data = fetch_alpha_vantage_data(api_key)
 
     # Extract relevant data from Alpha Vantage response
     alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
@@ -46,39 +37,23 @@ def main():
     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'})
+    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 stock trading signals
-    strategic_signals = StrategicSignals(symbol=symbol)
-
-    # Display loading message during processing
-    with st.spinner("Predicting signals using Strategic Indicators..."):
-        # Display signals
-        st.subheader(":orange[Strategic Indicators Trend Prediction]")
-        display_signals("Bollinger Bands", strategic_signals.get_bollinger_bands_signals())
-        display_signals("Breakout", strategic_signals.get_breakout_signals())
-        display_signals("Crossover", strategic_signals.get_crossover_signals())
-        display_signals("MACD", strategic_signals.get_macd_signals())
-        display_signals("RSI", strategic_signals.get_rsi_signals())
-
     # Create predictor
     my_market_predictor = Pandas_Market_Predictor(df)
 
     # Predict Trend
     indicators = ["Doji", "Inside"]
-
-    # Display loading message during prediction
-    with st.spinner("Predicting trend using AI ...."):
-        # Predict trend
-        trend = my_market_predictor.Trend_Detection(indicators, 10)
+    trend = my_market_predictor.Trend_Detection(indicators, 10)
 
     # Display results
-    st.subheader(":orange[AI Trend Prediction]")
+    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}%")
 
     # Delete the DataFrame to release memory
     del df