Data Science, Pyspark, Data Analytics, Machine Learning, Artificial Intelligence.
spark.sparkContext.broadcast(data) def simulate_trial(_): data = broadcast_data.value daily_return_percentages = np.random.normal(cagr / number_of_trading_days, std_dev / np.sqrt(number_of_trading_days), number_of_trading_days) + 1 price_series = [data['Adj Close'][-1]] for j in