from kaggle 2nd i guess

app.py

@@ -1,3 +1,116 @@
-a = 10
-b = 5
-print(a+b)
+!pip install yfinance pandas numpy scikit-learn matplotlib
+import yfinance as yf
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+import time
+
+# Function to fetch historical data
+def get_historical_data(ticker, interval, period):
+    stock_data = yf.download(ticker, interval=interval, period=period)
+    return stock_data
+
+# Function to fetch real-time data
+def get_realtime_data(ticker):
+    stock = yf.Ticker(ticker)
+    data = stock.history(period='7d', interval='1m')
+    return data
+
+# Function to preprocess data
+def preprocess_data(data):
+    data['returns'] = data['Close'].pct_change()
+    data['target'] = np.where(data['returns'] > 0, 1, 0)
+    data.dropna(inplace=True)
+    return data
+
+# Function to train a simple model
+def train_model(data):
+    X = data[['Open', 'High', 'Low', 'Close', 'Volume']]
+    y = data['target']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    model = RandomForestClassifier(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+    
+    predictions = model.predict(X_test)
+    
+    accuracy = accuracy_score(y_test, predictions)
+    print(f'Model Accuracy: {accuracy}')
+    
+    return model
+
+# Function to simulate trading
+def simulate_trading(model, data, initial_balance=1):
+    data['predicted'] = model.predict(data[['Open', 'High', 'Low', 'Close', 'Volume']])
+    
+    balance = initial_balance
+    positions = 0
+    
+    for i in range(len(data)-1):
+        if data['predicted'].iloc[i] == 1:
+            # Buy
+            positions += balance / data['Close'].iloc[i]
+            balance = 0
+        else:
+            # Sell
+            balance += positions * data['Close'].iloc[i]
+            positions = 0
+    
+    # Sell remaining positions at the last data point
+    balance += positions * data['Close'].iloc[-1]
+    
+    return balance
+
+# Function to plot real and predicted data
+def plot_data(data):
+    plt.figure(figsize=(10, 6))
+    
+    plt.plot(data['Close'], label='Real Data', color='blue')
+    plt.scatter(data.index, data['Close'], c=data['predicted'], cmap='coolwarm', marker='o', label='Predicted Data')
+        
+    plt.title('Real vs Predicted Data')
+    plt.xlabel('Date')
+    plt.ylabel('Closing Price')
+    plt.legend()
+    plt.show()
+
+# Main function
+def main():
+    ticker = 'AAPL'  # You can change this to the desired stock symbol
+    
+    # Fetch real-time data every 1 minute for the last 1 day
+    data = get_realtime_data(ticker)
+    
+    # Preprocess data
+    data = preprocess_data(data)
+    
+    model = train_model(data)
+    
+    while True:
+        # Fetch real-time data every 1 minute
+        new_data = get_realtime_data(ticker)
+        
+        # Update the existing data with new data
+        data = pd.concat([data, new_data])
+        
+        # Preprocess data
+        data = preprocess_data(data)
+        
+        # Simulate trading
+        final_balance = simulate_trading(model, data)
+        
+        # Print current balance
+        print(f'Current Balance: {final_balance:.2f} Rupees')
+        
+        # Plot real and predicted data
+        plot_data(data)
+        
+        # Wait for 1 minute before fetching new data
+        time.sleep(60)
+
+if __name__ == "__main__":
+    main()