Automated Trading Strategy #64
This portfolio has a backtested win rate of 100% on 1,266 trades over a one year period from March 1, 2022 to March 1, 2023. It made $15K and used 6 different micro futures instruments.
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.
As a quick reminder, 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 64
“If you can't describe what you are doing as a process, you don't know what you're doing.”
W. Edwards Deming, father of “quality” management.
My first internship was with GE Medical Systems. The management team at the time (Jeffrey Immelt & Keith Sherin) was subsequently hand chosen to succeed Jack Welch for a reason that was well known throughout the GE biosphere: Six Sigma.
Six Sigma is a quality management program that had been used by GE Medical Systems to not only ‘get everyone on the same page’ in terms of language, but systematically measure, across business lines and segments, process improvements that achieved a ridiculous level of increased profitability. Immelt and Sherin stepped in as CEO and CFO of GE Corporate and Six Sigma spread like a new religion across the company.
This way of thinking was driven by the idea that defects are ‘bad’, and that the best way to improve the product was to reduce the number of defects. It is a deceptively simple philosophy and was a completely different way of thinking about ‘fixing the problem’. Instead of focusing on individual employee contribution to the process, Six Sigma focused on the process itself. The famous red bean experiment is often used to explain how focusing on process improvement is a better investment in time and money than improvements made by individual employee performance. It suggests that individual performance does not matter if the process itself is flawed.
Likewise, what can we do to improve the overall process of finding the holy grail of automated trading strategies?
We already have one great example of this—the shift from range to minute based charts, which greatly improved backtest accuracy. It doesn’t matter if you find the holy grail of automated trade strategy if the backtest is inaccurate. Since the backtest is our primary tool for identifying strategies for the forward test, it became imperative to stop everything until we could develop a better process, which entailed switching to a data series that was easier for the simulation to simulate.
What’s the next breakthrough? We’re going to work backwards to find that answer.
Re-framing The Question
Moving from range to minute based charts was by far the best ‘change in process’ we’ve made so far. It increased the value of all strategies by making all backtests more reliable. While searching for the next best strategy is important, we’re also looking for that next best change in the strategy development process.
Working backwards, we want to end with a way to readily identify and eliminate trade defects. This requires a keen definition for what a defect is. If the result of the process is a portfolio with a 100% win rate, then a defect is any trade that results in a loss.
Over the next quarter, in addition to expanding our forward test to include additional asset classes, we’re also going to focus on placing the same process we’ve used to create Strategy 63 (and now Strategy 64) on our other strategies. By isolating a set of constraints we can focus on creating a large list of strategy variations with one thing in common: a 100% win rate. Then we can focus on the defect (trade loss) or when the trade loss occurred (which should be rare) rather than structural changes in the strategy (or its parameters) that increase profit factor. We can also focus on strategies with a low MAE, and low intraday drawdown; the lower the MAE, the lower we can set the stop loss.
This is a large project that we’ll be working on over the next two quarters. I know the approach is rather surgical, but I believe it will expose some large opportunities to apply across all strategies.