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(This article was first published on R – Pivot Billions, and kindly contributed to R-bloggers)

 

Developing a post-commission profitable currency trading model using Pivot Billions and R.

Needle, meet haystack. Searching for the right combination of features to make a consistent trading model can be quite difficult and takes many, many iterations. By incorporating Pivot Billions and R into my research process, I was able to dramatically improve the efficiency of each iteration making finding that needle in a haystack actually possible. Pivot Billions provided the raw power and scalability, while R provided the higher level manipulations and processes that allowed my to dive deep into my financial data and start to understand the underlying trends.

Utilizing Pivot Billions’ accurate financial backtesting simulator I was able to quickly test each version of my model as I developed it and see how it would perform in the real market. From testing initial general trading strategies to exploring individual and grouped features to see their distribution in my data and their effect on the trading strategies, my research process made great use of both tools. Adding features easily across all 143 Million rows of my data in Pivot Billions and being able to access, test, and simulate the effect of trading using these features from within my R code led to a very promising model ready for live trading.

After implementing this model in my real live trading account, I was able to achieve over 150%  net profit in just four months! While there are still some small drawdowns the overall profit is very consistent and achieves great profitability in a very small amount of time.

I am continuing to trade this model and follow its performance. In the meantime I am working on minimizing its drawdowns and maximizing my profit by incorporating AI. Check out my Pivot Billions and Deep Learning post to see some of my preliminary results.

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