Automated Trading Strategy #62
Strategy 62 made $73K last year. It's the first to test a theory introduced a year ago. This portfolio of equity futures made 99 trades, has a 67% win rate and a profit factor of 7.66.
Important: There is no guarantee that our strategies will have the same performance in the future. We use backtests to compare historical strategy performance. Backtests are based on historical data, not real-time data so the results we share are hypothetical, not real. There are no guarantees that this performance will continue in the future. Trading futures is extremely risky. If you trade futures live, be prepared to lose your entire account. We recommend using our strategies in simulated trading until you/we find the holy grail of trade strategy.
Our goal is to find the holy grail of automated trade strategy as defined below:
Profit factor greater than 3
Annual drawdown less than 3%
Annual return on max drawdown greater than 500%
Maximum daily net loss of -$1,000
Avg Daily profit greater than $1,000
Less than 5,000 trades annually
More than 253 trades annually
We haven’t found the holy grail yet, but we get closer with every strategy. Click here for the most recent performance chart.
Strategy 62
A mudder horse is defined by Merriam-Webster as:
1 : a race horse that runs well on a wet or muddy track.
I’m a big fan of the show Seinfeld. One of my favorite episodes is when Kramer wins a bet made on a horse race. As the story goes in the following clip, not only was the horse a mudder, but “His mother was a mudder; his father was a mudder.”
In the end, Kramer wins the bet because it rained. The race track was “muddy” and the horse he bet on was known to perform well in rainy conditions. If we translate this strategy to trading, the holy grail may be the ability to define a market condition and then run the strategy that does well in those conditions.
I made this case about a year ago in the post:
At the time, we were at a crossroads. In the end, we decided to study both of the following theories concurrently:
The holy grail of trade strategy is a static program
The holy grail of trade strategy is a moving target
Naturally, over the last year I’ve become obsessed with ways to define the various “conditions” of the market. To date, the best metric for defining the condition of the market is with price change and the best way to test for market conditions within the limitations of the simulation is with time, minutes in particular.
This is a fairly obvious attribute of the market that anyone can witness by watching the market for a week. You’ll notice certain hours of the day where the price changes more than others. What makes trading hard is that the market can be trending or choppy within these highly volatile periods. This revelation can send any decent trader down a rabbit hole of algorithms to measure chop or trend strength. I’ve been down that hole many times only to reemerge with a very simple indicator: