Trading

TL;DR * Consumer Shifts: The trend of 'trading up' highlights consumers' preference for premium products, driven by perceived quality and status. * Key Drivers: Economic factors like rising incomes and technological influences such as digital marketing have boosted consumer willingness to invest more. * Market Impact: This trend reshapes spending patterns, dividing markets into luxury and value segments, encouraging innovation and strategic brand positioning. * Opportunities and Risks: Whil

TL;DR * Discover the benefits of creating a TradingView account for enhanced market analysis and community insights. * Follow simple steps for secure TradingView login, whether using email or social media platforms. * Learn to troubleshoot common login issues and implement best security practices, including strong passwords and two-factor authentication. * Optimize your TradingView experience by integrating tools and leveraging educational resources. Introduction to TradingView TradingVi

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