Stock Market, Time Series Analysis, Time Series Forecasting, Python, Monte Carlo.
int(N)): drift = (mu - 0.5 * sigma**2) * (t[i] - t[i-1]) Z = np.random.normal(0., 1., dim) diffusion = sigma* Z * (np.sqrt(t[i] - t[i-1])) S[:, i] = S[:, i-1]*np.exp(drift + diffusion) return S, t There is an important caveat though to the validity of the results. Within the code, a best fit normal