Automated Trading Strategy #61
Strategy 61 is our most structured yet. It made 222 trades with a profit factor of 3.49 and a net profit of $189K. What’s remarkable about this portfolio is that over 70% of the trades are profitable.
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 61
“As above, so below.”
-Hermes Trismegistus, The Emerald Tablet of Hermes
Buddhist, Jain and Hindu religions describe the universe as a series of cycles. The Mayans and Greeks had elaborate calendars based on the procession of the equinoxes. I’m currently reading the text The Secret Doctrine of the Rosicrucians, by Magus Incognito, [1918]. It teaches that “…that there are Seven Cosmic Principles present and operating throughout the Cosmos, and extending even to its smallest activities.” The concept of cycles and degrees is a common theme in the ancient world. This isn’t hard to believe when you consider the universe is billions of years old, the Earth is four and a half billion years old, and life on Earth is over 3.5 billion years old. All of those years had months, weeks, days and hours leading up to the second that just passed.
Everything happens in cycles of time. Thousands of cycles going through the same cycle. The market is the same way. It moves in fractals. What you observe at higher time frames (1-day chart) will also play out at lower time frames (1-minute chart). You can think of these movements as cycles or processes. Sometimes, depending on the volume, the cycles can become distorted, but they are always present. Automated trading allows us to identify recurring processes and then act on those processes in a calculated and granular way.
Strategy 61 is the most structured automated strategy we’ve ever published to date. It uses the hourly cycles of a day, 23 market hours in total, to identify specific recurring processes. We use the recurring trades found in these time periods to find ways to increase our percentage of profitable trades, a critical factor of success in automated trading strategies due to the propensity for skipped/missed trades when making the transition from historical to live trading.
“…one of the really tough things is figuring out what questions to ask. Once you figure out the question, then the answer is relatively easy.”
-Elon Musk
I love this quote because it’s an eloquent way of putting the power of cyclical behavior to practical use. Over 400 years ago William Shakespeare used the words, “What’s past is prologue” in the play, The Tempest. In the play, the characters suggest that everything that has happened before has set the stage for what will happen in the future. With this in mind, what question can we ask about tomorrow with the knowledge of today? Within the physics of the market’s cyclical behavior, what questions can we ask to get us closer to the holy grail?
Today’s question is: How do we use the 23, 1-hour time periods in a trading session to predict the performance of a strategy?
Note: the goal is to use the strategy as a kind of substrate to define the behavior of the market on it. We’re trying to predict the performance of a strategy in a particular market, not price performance. It’s a subtle difference, but it makes our task easier.
The methodology I’m going to describe to you today may seem like cherry picking (overfitting) at first, and in truth, we won’t know the answer to whether or not it is until we forward-test this strategy for at least 3 months. What we do know is that the “percentage of profitable trades” or “win rate” continues to be the primary success factor for various portfolio strategies we’ve forward-tested over the last five months and that this methodology is a way to directly improve these metrics.
Pockets of Time
Timing is a critical dimension in trading. We aren’t talking about optimizing hyper-parameters within a particular indicator set, we’re talking about something much stronger in the world of market physics—we’re talking about that which gives the market any value (mass) at all. Time is the god particle of market physics.
In the parlance of financial heavyweights Myron Gordon and John Lintner, the only thing that gives your ‘bird in hand’ any value is the fact that it could be worth ‘two in the bush’ tomorrow. Without tomorrow, there is no value. This concept is the foundation for every present and future value equation used to price assets, and underlies much of financial decision making.
Strategy 61 takes advantage of the 23 hour daily trading cycle to find reliable pockets or points of entry for each strategy. In this way, Strategy 61 is a process that can be used to transform any strategy. These are the results when we used this process on three of our best strategies from 12/1/2021 to 12/1/2022:
We’ve got 222 trades with a profit factor of 3.49 and a net profit of $189K in one year. What’s truly remarkable about this portfolio is the high win rate. Over 70% of the trades are profitable. Readers of the Mudder Report know how important this metric is.
To date, we’ve only used this process on a handful of strategies so we’re looking forward to seeing how high we can boost the win rate as we add additional strategies to the list. The hope is to find pockets with 100% win rates. Naturally, some strategies are better at finding these pockets than others and we’re starting to develop a sense for what those strategies are as well.
Now let’s get back to the original question…
How do we use the 23, 1-hour time periods in a daily session to predict the performance of a strategy?
It makes sense that certain time periods or ‘pockets of time’ may be more profitable than others for any given strategy and I’ll show you exactly how to find these pockets in a moment. The obvious next question if you’ve been on the hunt with us is: how do we know these pockets of time will be reliable in the future?
The best way to do this is by analyzing the 1) win rate, the 2) number of trades in the pocket of time and the 3) total net profit made. You can also use 4) the performance of the strategy across instruments in similar markets, i.e., equity futures, to gauge the ‘strength’ of the pocket. In other words, if the pattern or ‘pocket of time’ holds across instruments in the same asset class, it helps to confirm the reliability of the cycle.
Now that we know which metrics can help to increase the reliability of the ‘pocket of time’ in the future, we need to make sure we’re looking at the right data: garbage in, garbage out. This might send you down a rabbit hole, but the question that really matters is: do we find these pockets of time using entry or exit metrics?
I’ve scoured the Internet for help with this question. Intuitively the answer feels like “entry”, but like the chicken and egg debate, there are good arguments to be made on either side.
One of our subscribers best summed up the debate by saying, “Entry is best to start with, but exit is where the biggest improvement is usually available.” Another subscriber noted that entry time appears to be more relevant than exit time because the market moves at certain times of day. I would argue that this observation becomes even more relevant the longer the trade stays in the market, and even more relevant when the bulk of the trades in your strategy exit at the close of the daily session — the last hour of the 23 hour market. Thank you Ben and Kevin for your input. I truly appreciate the feedback.
For those that prefer mathematics and probabilities, entries give you your edge. Your exit gives you the ability to maximize entry or capitalize on the edge you’ve found, but your mathematical expectancy is primarily driven by entry. It is entry that determines the maximum favorable excursion (MFE) and the maximum adverse excursion (MAE). A good entry increases the probability that your MAE will be lower and your MFE higher.
What’s the best way to sum this all up? In a nutshell, your probability for making a profitable trade increases by having a keener focus on both entry and exit stats. But (and this is a huge but), short term or day traders often have a forced exit at the end of the trading session. This means entry will always have more impact on overall performance. Also, like any option with an implied end of day expiration, the closer you move to expiration, the less value theta or time decay has. Since entry always comes before exit, this means entry will always have more impact on overall performance. From this perspective, it makes sense to use backtest data to determine the best time to enter a strategy. Day of week is also important.
For the past two years, we’ve been using backtest data to determine the best day of week and time of day to enter a trade, however, we’ve based this decision on trade exit time. So as much as we can pat ourselves on the back for knowing how to determine the most optimal time for entry, we were asking the wrong questions about how to find the best time to start the strategy.
Given what we know today, the only income metrics that make sense for answering this question are based on time of entry, not exit.
Once you answer this question, other questions come to mind. In particular: can we pinpoint the ‘pocket of time’ throughout the day when we have the highest win rate based on trade entry?
To answer this question, let’s look at a few charts.