Simulate your Trading Strategy with Python

Simulate your Trading Strategy with Python

Backtesting frequencies of dollar-cost averaging in stock market .Simulate your Trading Strategy with Python

Whether you’re a new or an existing investor, you probably have heard of the term dollar-cost averaging, which refers to an investment strategy that alleviates the emotional stress and investment risk by dividing up the total amount to be invested across periodic intervals.

There are studies by institutions such as Vanguard back in 2012 that compared the historical performance between dollar-cost averaging (DCA) and lump-sum investment (LSI). It concluded that on average, an LSI approached outperformed DCA approximately two-thirds of the time, even after adjusting for higher volatility of a stock/bond portfolio. Investing in a lump sum gives investor exposure to the markets earlier, which is especially beneficial if the markets are upward trending in the long term.

However, psychology plays a crucial part in investing. More often than not, an investment decision is made out of fear or greed. There are numerous stories out there about people making or losing money because of FOMO or YOLO.

I really enjoyed the book The Psychology of Moneyby Morgan Housel. In the book he pointed out that

The trick when dealing with failure is arranging your financial life in a way that a bad investment here and a missed financial goal there won’t wipe you out so you can keep playing till the odds fall in your favor.

So in this article, I would like to continue exploring the idea of dollar-cost averaging, by tweaking the trading frequencies to understand how risk and return of assets may vary accordingly.

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

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