Created
January 16, 2020 22:55
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This gist is for MC simulation, at least the basics of it. Usage: Medium article
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| import numpy as np | |
| import pandas as pd | |
| from pandas_datareader import data as wb | |
| import matplotlib.pyplot as plt | |
| from scipy.stats import norm | |
| ticker = 'BA' | |
| data = pd.DataFrame() | |
| data[ticker] = wb.DataReader(ticker, data_source='yahoo', start='2015-1-1')['Adj Close'] | |
| plt.figure(figsize=(10,6)) | |
| plt.plot(data[ticker]) | |
| plt.show() | |
| log_returns = np.log(1 + data.pct_change()) | |
| u = log_returns.mean() | |
| var = log_returns.var() | |
| drift = u - (0.5 * var) | |
| stdev = log_returns.std() | |
| t_intervals = 250 | |
| scenarios = 2 | |
| daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, scenarios))) | |
| price_list = np.zeros_like(daily_returns) | |
| S0 = data.iloc[-1] | |
| price_list[0] = S0 | |
| for t in range(1, t_intervals): | |
| price_list[t] = price_list[t - 1] * daily_returns[t] | |
| plt.figure(figsize=(10,6)) | |
| plt.plot(price_list) | |
| plt.show() |
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