Update app.py

app.py

@@ -1,79 +1,12 @@
-import streamlit as st
-import requests
-from Pandas_Market_Predictor import Pandas_Market_Predictor
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error, r2_score
+from thronetrader import RealTimeSignals
 
-# Hard-coded API key for demonstration purposes
-API_KEY = "QR8F9B7T6R2SWTAT"
+realtime_signals = RealTimeSignals(symbol="AAPL")
 
-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
+print(realtime_signals.get_financial_signals())
+print(realtime_signals.get_insider_signals())
 
-def calculate_indicators(data):
-    data = data.apply(pd.to_numeric, errors='coerce')
-    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 prepare_data(data, target_column='Close'):
-    X = data.drop(target_column, axis=1)
-    y = data[target_column]
-    return X, y
-
-def train_linear_regression(X_train, y_train):
-    model = LinearRegression()
-    model.fit(X_train, y_train)
-    return model
-
-def main():
-    st.title("Stock Price Predictor")
-
-    # Use the hard-coded API key
-    api_key = API_KEY
-
-    # Fetch Alpha Vantage data
-    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)', {})
-    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)
-
-    # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(df)
-
-    # Prepare data for linear regression
-    X, y = prepare_data(df)
-
-    # Split data into training and testing sets
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # Train linear regression model
-    model = train_linear_regression(X_train, y_train)
-
-    # Make predictions on the test set
-    y_pred = model.predict(X_test)
-
-    # Display linear regression results
-    st.subheader("Linear Regression Results:")
-    st.write("Mean Squared Error:", mean_squared_error(y_test, y_pred))
-    st.write("R-squared Score:", r2_score(y_test, y_pred))
-
-    # Delete the DataFrame to release memory
-    del df
-
-if __name__ == "__main__":
-    main()
+series1, series2 = realtime_signals.get_trading_volume()
+print(series1.name)
+print(series1.to_dict())
+print(series2.name)
+print(series2.to_dict())