Dollar Cost Averaging Index Funds & ETFs
Here we will be using BackTrading as a back testing framework to evaluate the difference between dollar cost averaging strategy and lump-sum investing.
Here we will be using BackTrading as a back testing framework to evaluate the difference between dollar cost averaging strategy and lump-sum investing.
The Efficient Frontier is a common phrase in Modern Finance since the inception of Modern Portfolio Theory in 1952 by Harry Markowitz. Here we learn how to complete portfolio optimisation using the Markowitz’ approach to find both minimum variance and max sharpe ratio portfolio.
In this tutorial we aim to use the key indicators that Warren Buffett uses to determine the strength of an underlying business, so that we can find excellent stocks that are worth more time investigating. However the ASX alone currently has 2061 listed stocks, how can we possibly reduce that number? With our Quant hat on, we can expedite this process using some key assumptions and our skills in python.
Beta weighting is a tool that allows us to approximate our positions in terms of the same benchmark. Today we learn how to beta weight your portfolio in python.
We will use three equivalent methods to estimate the beta coefficients of each security and then progress onto how you can beta weight your portfolio delta’s (the change in value given a unit change of the underlying) to get an approximation for how your portfolio will change with respect to a movement in your benchmark (whether it be a market index or specific security).
In this tutorial we compute and track historical volatility over time. We also explore how to calculate trailing historical finanical metrics like Sharpe, Sortino, Modigliani and Calmer ratio. Along with the calculate of overall max drawdown.
Learn how to use pandas dataframe and plotly to create a historical price and volume stock chart.
In this tutorial we try to understand the difference between simple returns and log returns.
We also talk about normality of financial data, and how to perform statistical tests to test for normality.
Here we learn how to price an American option using the binomial tree model. Within the binomial tree model we compute the maximum of the exercise value or continuation/hold value at each node, and then discount the expected payoff based.
In this tutorial we use the binomial tree model to price a path dependant option, namely a up-and-out barrier put option.
How does one choose the ‘correct’ parameters for the Binomial Asset Pricing Model. In this tutorial we implement Cox, Ross and Rubinstein (CRR), Jarrow and Rudd (JR), Equal probabilities (EQP) and the Trigeorgis (TRG) method!