Finance

We explore what is carry trading and how to apply it in Python. Why today's economic landscape it is a profitable strategy and what risks are associated with it.

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 use yfinance, a Python library to fetch financial market data such as stock prices, dividends, financial statements, options, and sustainability data for a company or index. With yfinance, you can easily retrieve and analyze financial data for informed decision-making.

Learn how to calculate implied volatility in Python using the Black-Scholes model

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.

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.