R Tutorial : Pitfalls of Trading System Development

Want to learn more? Take the full course at https://learn.datacamp.com/courses/financial-trading-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Designing a trading system is not easy. Financial market data is the amalgamation of fear, greed, noise, and generally, a whole salad of millions of people across the country if not the world taking bets practically simultaneously on the price of financial instruments. Given this fact, it is plain to see why so many financial advisers say "past performance is not indicative of future results". While there is a temptation to try and create trading strategies that have wonderful performance on past data (also known as "in-sample data"), there have been numerous studies showing that when too much effort is placed into this activity, that the system fails going forward on unseen data (or what is more formally referred to as "out-of-sample data"). This is more commonly known as overfitting, which is the act of trying to tailor a backtest to look as good as possible (smoothest line, highest profit, whatever the criteria may be), rather than seeing a backtest as an evaluation of a trading system. In order to avoid such phenomena, trading systems should strive to have as few parameters--essentially the knobs you can turn to change settings--as possible. Why? Because the number of system configurations increases exponentially with parameters. That is, consider a grid search that tests five separate settings for each parameter. A one-parameter system would need only five runs. A two parameter system would need twenty five. A three parameter system would already need one hundred twenty five, and beyond that, the numbers start to get very high very fast. Furthermore, a trading strategy should not perform much differently when the parameters are slightly altered. For instance, a trend-following system should have similar results for similar settings of its trend