input: Write a python function to predict stock prices for the next 30 days using a RandomForestRegressor model.
  assert len(predict_next_30_days("company_stock_prices.csv")) == 30
  assert isinstance(predict_next_30_days("company_stock_prices.csv"), list)
output: def predict_next_30_days(csv_file):
      import pandas as pd
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from datetime import timedelta
      
      # Load the data
      data = pd.read_csv(csv_file, parse_dates=['Date'])
      data.sort_values(by='Date', inplace=True)
      
      # Prepare the features and target
      data['Day'] = (data['Date'] - data['Date'].min()).dt.days
      X = data[['Day']]
      y = data['Close']
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
      
      # Standardize the data
      scaler = StandardScaler()
      X_train_scaled = scaler.fit_transform(X_train)
      X_test_scaled = scaler.transform(X_test)
      
      # Train the model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(X_train_scaled, y_train)
      
      # Predict the next 30 days
      last_day = data['Day'].max()
      future_days = pd.DataFrame({'Day': [last_day + i for i in range(1, 31)]})
      future_days_scaled = scaler.transform(future_days)
      predictions = model.predict(future_days_scaled)
      
      return predictions.tolist()