Trading

Learn how to calculate expected returns with Capital Asset Pricing Model (CAPM) and how to do it in Python.

Learn how to compute the efficient frontier using Pandas in Python. A step-by-step guide on how to load financial data, calculate expected returns and covariance matrix, minimize portfolio variance. Use this method to analyze and optimize your portfolio's risk and returns.

Learn how to implement an ARIMA model in Python using the statsmodels library by defining model parameters, fitting the model, and making predictions with the fitted model.

Stochastic processes in Python can be modeled using Numpy or Scipy libraries by generating random numbers, defining the process parameters, simulating the process by iterating through the equations, and plotting the results using Matplotlib.

Learn how to predict stock price using Geometric Brownian Motion (GBM) process by updating the stock price over multiple time steps using randomly generated normal variables and the mean return and standard deviation of the stock's returns

Python script for Monte Carlo simulation of stock price prediction using the geometric Brownian motion model to determine the mean, upper, and lower bounds of the simulated prices.

Compute Black-Scholes option pricing model in Python using numpy and scipy.stats.norm, formula calculates price of European call or put option based on inputs of underlying asset price, strike price, time to expiration, risk-free rate, volatility, and option type

Python function calculates order flow by subtracting sell volume from buy volume. Call order_flow(buy_volume, sell_volume) with volume of buy and sell orders.

Python script calculates Bollinger Bands by finding moving average, standard deviation, upper band (MA + 2STD), and lower band (MA - 2STD) of close prices. Default window is 20 and number of standard deviations is 2. Call bollinger_bands(prices) with close price data series.