The lifeblood of algorithmic trading is the perpetual seeking of new strategies. After your backtesting and execution systems are built, the majority of time is spent in discovery mode, which is where the fun begins.

I am constantly searching. Experimenting with unique combinations of hypotheses across tens of millions of backtests in pursuit of strategies that will reliably generate profits deep into the forseeable future.

Such strategies are not easy to locate. They are elusive because they sit at the golden intersection of multiple metrics that most traders don’t know to look for. They search half-blind, usually using only pure profit as a guide.

In this article I will explain every single one of the traits I look for when comparing strategy backtest results. This is the set of metrics I used to discover my bitcoin bot’s highly profitable strategy.

1 - High Profit Per Month

The obvious one. It goes without saying that your strategy’s backtest should return a high profit value. However what’s not so obvious is specifically filtering for profit over time.

The time factor is important because many algo traders fall into the trap of filtering for profit per trade, which is fine but suboptimal because it tends to locate strategies that trade infrequently.

You want to look for average profit generated over a specific period of time (day, month, quarter, year - doesn’t matter) because your goal isn’t just to make money, it’s to make money within the shortest period of time as possible. This metric selects for profit and time efficiency in one.

2 - Low Drawdown

A criminally underrated metric. Over the course of your backtest, what was the largest percentage drawdown your strategy experienced from it’s starting capital? A high figure here may indicate a fatal flaw in your strategy waiting to be exposed in a live trading environment. I’ll show you why: