Automated Trading Strategies: January Portfolio Update: In 2021 Our Top Strategies Made Over $3.3M Based on NT8 Backtest Results

It’s been one year since we started this. With every update, the data grows richer and our insights more insightful.

We all want to believe there’s a way to measure the forward strength of a model, but the best you can do is make predictions about the future based on strategy performance and then track that performance, as well as the rationale for your predictions, over time. We use profit factor as the primary basis for that prediction, but there are a host of attributes we track in our search for the holy grail of automated trade strategy.

This is why we publish updates every two months to track the progress of strategies. We like to look at how our top automated trading strategies are performing and update you on our key findings, highlights, takeaways and what’s in the pipeline. The following post is our January update. You can view past updates here:

• March Update
• May Update
• July Update
• September Update
• November Update

What Are We Looking For In These Updates

We are looking for one thing in particular: consistency. When we say consistency, we mean strategies that perform like the backtest, specifically with regard to profit factor. We are tracking our best strategies to find the best strategy attributes. We can then use these attributes to create a strategy that gets us closer to the holy grail of automated trade strategy.

We define the holy grail of trade strategy as having the following attributes:

• Profit factor greater than 3
• Annual drawdown less than 3%
• Annual return greater than 500%
• Maximum daily low of -$1,000
• Avg Daily profit greater than $1,000
• Less than 5,000 trades annually
• Greater than 253 trades annually

For this update, we have an additional task. We need to cull the field a bit and this post will serve as documentation for that process. In total, we are sharing the performance statistics on 43 of our strategies. At the end of this post, our focus list will fall to 27 strategies. We will continue to update all 43 strategies every two months and you will continue to see some of them updated on a weekly basis in The Mudder Report, but only 27 strategies will make the final cut. The new list will serve as a way to focus our efforts, while the old list will serve as a reminder for how far we’ve come.

With that, let’s dive into the update.

November Vs. January

So where do we stand in comparison with our last update?

The table below provides the backtest results from our last update: 11/01/2020 - 11/01/2021

Click to enlarge

For links to all strategies click here.

The following table provides the backtest results for the current or most recent update: 01/01/2021 - 11/01/2022

Click to enlarge

Strategies with less than 50 trades for the year were not included.

First, let’s review what the trend was coming into the January update.

• In general, we had a deterioration from July to September. Profit per day decreased from $388 to $355 and profit per trade decreased from $145 to $116. Most of the deterioration occurred with optimized strategies. When we removed optimized strategies the metrics improved. What we’ve found over the year is that as much fun as optimized strategies are to ‘hunt’, they are more prone to overfitting and therefore have highly inconsistent results.

• Moving into November, net profit for the total portfolio was basically flat, decreasing slightly from $2,788,075 to $2,746430, but the portfolio profit factor increased from 1.24 to 1.35 on fewer trades. Profit per day decreased from $355 to $304, but profit per trade increased from $116 to $230. Even though net profit decreased, it was a leaner and more efficient portfolio.

• Moving into January, net profit for the total portfolio increased from $2,746430 to $3,314,996, and the portfolio profit factor increased from 1.50 to 1.54. In addition to a higher profit factor, we also have a slightly lower max drawdown percentage. Profit per day continued to decrease from $304 to $294 and profit per trade decreased slightly from $230 to $224.

We’ve been at this for a year, and we’re going to talk about more of our insights in the upcoming annual report, but what I’d like to do now is compare the January 2022 update with the May 2021 update. The May 2021 update is the first update we had where profit factor was used to track performance.

The following chart was from the May 2021 update:

The average profit factor was 1.07. The average profit factor for the same strategies after the January 2022 update was 1.11, which is fairly consistent. Still, only a few of our first 15 strategies offer the kind of performance we’re interested in continuing to research. Going forward, we’re going to focus our attention on Strategies: 3, 4, 7, 8, and 10. So that’s an overview of our first 15 strategies. Only 5 made the cut. Now, let’s look at how Strategies 23 through 36 performed.

Strategy 23 had a drop in profit factor from 1.21 to 1.08. Even though Strategy 23 continues to have one of the lowest draw-downs at 1.97%, we don’t like the inconsistency and we really don’t like the low net income. As a result, we will no longer be studying Strategy 23.

Strategy 24 had a profit factor of 1.62 on the last update, and a profit factor of 1.64 on the January update. This is the kind of consistency we’re looking for in a higher profit factor strategy. Profit per trade went down from $395 to $373 on fewer trades, and drawdown remains fairly high at 7.45%, but we’re going to continue to study it.

Strategy 25’s profit factor jumped from 1.12 in the November update to 1.25 in the January update, which is highly inconsistent. In addition, it has a low net profit and a high drawdown at 11.07%. We will not be studying this strategy going forward.

Strategy 26 is one of our most consistent strategies from a stats perspective, but 1.17 is still a low profit factor and it made only $18K for the year on a 12.41% drawdown. We will not be studying this strategy going forward.

Strategy 28 only made 81 trades from January 1, 2021 to January 2, 2022, but it maintains a consistently high profit factor at 1.58, down from 1.60 in the last update. We will continue to study Strategy 28 due to its profit factor, consistency and high profit per trade at $387, but the return on max drawdown is a concern at only 191%.

Strategy 29 performed poorly and has a high drawdown, so we will not be studying Strategy 29 going forward.

Strategy 30, coming in at only 116 trades for the year, is one of our best performers with a profit factor that increased from 2.09 to 2.33. This may seem inconsistent, but at this level of gross profitability all we wanted to see is a profit factor greater than 2.09, and we got it. Strategy 30 also has a relatively low drawdown at only 3.72%, a return on max drawdown of 717% and a net profit of $81K. We will definitely continue to study this strategy.

Strategies 31 and 32 are both marked by high volatility. Even though the return on max drawdown is only 300% for Strategy 31, we’re going to continue to study due to the high number of trades. Strategy 32 performed poorly. It was one of only two strategies that had a negative net profit in 2021. We will no longer be studying Strategy 32.

Strategy 33 has always been one of our favorite strategies. Strategies 34 and 35 are the same as Strategy 33, but they are use a different data series. The strategy tested well on other contracts like RTY and GC, so we decided to track all three (RTY, GC and NQ) on the performance chart. What does the most recent update tell us about Strategy 33?

• The gold (GC) backtest was a disappointment, so we’re no longer going to study GC.
• The RTY backtest showed a profit factor of 3.02, down from 3.29 in November. The inconsistency is worrisome, but 3.02 is still high. The only issue with this backtest is the low trade count at 76 trades on the year, but those 76 trades made $58K.
• The NQ backtest saw an increased profit factor from 1.45 to 1.73 on 186 trades and $110K in net profit.
• If we move to Strategy 34, we’re looking at the 1,000 tick data series for the same strategy. Both NQ and ES performed well with profit factor’s over 2.00.
• Strategy 35 is the same as Strategy 33 and 34, but we used an odd data series on it — 669 ticks. This was a mistake made on the data pull, but the results were so good that it seemed almost irresponsible not to report. So, we included Strategy 35 as an ‘optimized strategy’ with an implied warning — in other words, we were very concerned with its consistency. Our concerns have been somewhat allayed because the profit factor for Strategy 35 increased from 2.21 on 155 trades to 2.84 on 118 trades. Net income increased from $150K to $162K for the year and return on max drawdown increased from 1177% to 1271%. We continue to be impressed by Strategy 33 and its ability to perform well using various instruments and data series.

Strategy 36 also performed well with a profit factor that increased from 2.11 to 2.66. While it only made 99 trades for the year, it has a low drawdown at 2.65% and a net profit of $77K. We will also continue to monitor Strategy 36.

Strategies 37 through 41a(H) were all published after the November update so we’re going to wait until the March 2022 update to assess.

Our New Target List

Our research list has gotten smaller, but strategy performance is improving over time, which is all we can ask for. From a holy grail perspective, we’re only going to focus on the following strategies:

Doing this allows us to focus our efforts for future ATS Research.

We will continue to provide periodic updates on all strategies. We will also continue to track some of the strategies that have been removed from the list on a weekly level in the Mudder Report.

Update Summary

We started this journey down one road. As expected, that journey sent us in many directions. Today we are focused on three paths. There’s strong evidence to suggest that the holy grail of automated trade strategy might be at the end of one (or perhaps all) of these paths. Each path is focused on a different theory about the nature of the holy grail, which we believe is either all or one of the following:

1. a static strategy
2. a static strategy with a profit factor of 1 that flips based on certain market conditions
3. a moving target made up of several strategies

My favorite theory is still #2, though it is proving to be the most difficult to study.

We’re also working on a theory that the holy grail of automated trade strategy might be a scalping strategy. We’ve already found a strategy with over 400 trades and a profit factor greater than 4, but the net profit, as can be expected, is small. The challenge with scalping strategies is making enough profit per trade to cover expenses, i.e. commissions.

The key to finding the holy grail for all three theories as outlined above is to document, narrow and refine. Our bi-monthly updates—like the one you are reading now—along with The Mudder Report and additional backtest research, are meant to share the process with you.

One thing is certain, we’re closer than we were last year and we couldn’t have done it without your support.

Answers to Some Commonly Asked Questions:

• Perhaps one of the most commonly asked questions we get is, how much do I need to get started? This is hard to say because it depends on many things, but ultimately you want to set your simulated account to an amount that is equal to the max drawdown plus some amount to buffer for negative attributes like a low profit factor, high drawdown, commission, slippage, etc. For more on what we think is the best way to approach this question for yourself, click here.
• We continue to receive questions about funded trader programs. In particular, some of you are on the hunt for a strategy that will pass the funded trader program. On our last update we told you not to subscribe if that’s what you’re looking for. That said, in the last month we did discover Strategy 41a(E). It is the strategy with the lowest drawdown at .89%, but it only made $21K in 2021. Based on the backtest, it will get you there, but it might take some time. For more on what we think about the best way to approach funded trader programs click here.
• In general, one of the most common questions we get pertains to the use of our strategies on other assets like crypto. You can read more about that in the post: Do our strategies work on cryptocurrencies?
• Another commonly asked question is in regards to overfitting. You can read the following posts to learn more about overfitting and what we’ve done to reduce its impact: Overfitting: What is it and what can we do about it and What Are We Doing To Ensure Backtest Accuracy? A few months later, we received a question regarding strategy strength and what optimization tools we’re using to increase robustness. We answered that question in the post: How Can You Tell If An Automated Trading Strategy Will Perform Well Over Time?
• One of our subscribers told us that the strategies were too hard to understand. To this we say, “Ask us anything. We’re here to help.” Practice on Strategies 1 and 5 (both are free) first and then ask us if you have any questions. Also, unlike most websites, we give you a full description of the strategy so you can see and play around with the full mechanics of the strategy.
• At least once a day I receive an email telling me that our free strategies (Strategies 1 and 5) aren’t any good. We agree, that’s why we’re giving them away. The goal is to show you how our process works. Many people think we’re just selling strategies, but that’s not the case, we’re on a hunt. We are all on the same hunt and we’re stepping up to take the lead as guide. We use our strategies as a way to raise money for some of the projects we’re trying to do related to the hunt, but our goal is to find the holy grail of automated trade strategy. When the two goals diverge we always steer toward the latter.
• One of the most commonly asked questions we get is: How do I learn how to create automated strategies for myself? Our advice is to use the resources provided by Ninjatrader (we receive no remuneration for saying this). Just go to their Youtube channel and sign up. It’s free and there are many videos to learn from.
• We recently received a question about rigged markets. In particular, he was worried about something referred to as front-running. I’ll be posting a response to this shortly. Hopefully it will debunk many of the myths around ‘rigged markets’. The truth is, what most people think of as rigged, traders have been working with for years. There is little room for regulation in free-markets. This isn’t health care, it’s literally “the market”. There are no nets, nor should there be. Unfair play is fair play.
• Some of you have requested that we use the payoff ratio rather than profit factor to measure the strength of our strategies. We use profit factor to measure the strength of our strategies. Profit factor is calculated:

  Gross profit / Gross loss = Profit Factor

   

  Payoff ratio is calculated:

   

  Average winner / Average loser = Payoff Ratio

   

  We prefer profit factor because unlike the average win/loss ratio, also known as the payoff ratio, it tells us immediately if the strategy was profitable or not. The payoff ratio tells you about the average trade, while the profit factor tells you about the total strategy. In order to understand if the strategy was profitable in aggregate, you would need to multiply the payoff ratio by the % of profitable trades or the win rate. The ideal strategy is one that offers a high win rate and a high payoff ratio.

• For the sake of the hunt, let’s create two scenarios to reinforce the difference between profit factor and payoff ratio:

• Scenario #1: If you have a strategy with a payoff ratio of .92 and a win rate of 63%, it means on average your win/loss ratio is .92 so you’re losing money on every trade, but you win more than you lose, so you’re making a profit.

• Scenario #2: If you have a strategy with a payoff ratio of 11.36 and a win rate of 12%, it means on average you make 11x more than you lose on each trade, but you only win 12% of your trades.

• The holy grail of trade strategy might have a payoff ratio of 11.36 and a win rate of 63%. We have yet to find the holy grail of trade strategy, but the first scenario profiled above belongs to Strategy 40 and the second scenario belongs to Strategy 41.

What’s in the pipeline:

We’ve done a lot since the last update:

• We looked at how well our strategies do when backtested with the Gold (GC) Futures in the post: How Well Do Our Strategies Work On Gold (GC) Futures?
• We looked at which strategies are best for trading micro E-mini futures contracts in the post: Which Automated Trading Strategy Is Best For Trading Micro E-mini Futures Contracts?
• We looked at which strategies are best for trading the ES futures contract in the post: Do Our Strategies Work On The E-Mini S&P 500 Futures Contract (ES)?
• We developed several theories about the best way to select strategies to trade at the end of each week in the post: Mudder Report Analysis:
  How to select the best automated trade strategies for the upcoming week...

When we first started this project a year ago we only published 9 strategies. Last week, we published our 41st strategy and it met 5 out of 7 of criteria for the holy grail of automated trade strategy. So we’re close, but not there yet. Here’s what we have planned for 2022 to get us there:

• Our first annual report will go out at the end of the month. It will include longer-dated backtests as well as a brief discussion of each strategy.
• In the first six months of 2022, we plan on including backtests for:

• additional futures contracts like CL, FDAX, YM and bonds

• FX and cryptocurrency

• single stock picks

• We are looking into other platforms for our strategies as well as a few portfolio analysis options that might help to pull out additional insights for better strategy formulation. We’re also looking at testing other data feeds, which is quite expensive.
• We’ve hired someone to start running our weekly selection for the Mudder Report on Collective2 for live/daily updates starting in March. Hopefully, we’ll have a process down by then. By the way, if you run one of our strategies on Collective2, let us know and we’ll pub it for you here. You can read more about Collective2 in the post:Question: Can I Use Your Strategies To Make Money On Collective2?
• We’re also going to start publishing new strategies only on the 1st and 15th of the month starting in February. Some of you have asked for more consistency here and I think this will help to allay those concerns.
• We are slowly making progress on our weekly update series — The Mudder Report. We’ve had some setbacks, but we’re learning a lot. Our goal is not to predict or recommend what to trade in the following week (it’s always fun to try your luck on a simulated account, but if you use the weekly update to trade live, you will get burned). Our goal in developing the Mudder Report is to aid in the development of a few working theories for the holy grail of automated trade strategy.

Finally, on January 21, we are transitioning to our new subscription format. To read more about the transition, click here.

If you have any questions, feel free to contact us directly at automatedtradingstrategies@substack.com or by responding directly to this post.

To view the original post, click here.

How to select the best automated trade strategies for the upcoming week...

Oil painting (c. 1625) by Peter Paul Rubens of the goddess Thetis dipping her son Achilles in the River Styx. She dipped all of him but his heel. In the myths surrounding the Trojan war, Achilles was said to have died from a wound to his heel, which was his only vulnerability.

 

This is a follow up post for the weekly series dedicated to tracking our automated strategies at the weekly level. You can read the first post here. You can also click here for links to all weekly reports so far. The original post provides an overview of our approach/intent along with an explanation of the reporting structure. Just like our annual backtest updates that happen every two months, we’re looking for consistency here, but at a more granular level.

The goal of the series is two fold:

• to compare against annual statistics

• to develop a rubric for a weekly strategy selection process

The latter is based on the premise that the holy grail of automated trade strategy may not be static, but more fluid like a chameleon or rather made up of multiple strategies.

In this post, we’re going to take some time to document our analysis of the first 8 weeks of the Mudder Report — what went well, what didn’t and why? Most importantly, what is the best approach going forward?

Where Are We Now

We started this series in the week beginning 10/24/2021. In total, we’ve made $11,575 based on our selection process. Here’s a quick overview of where we are so far:

So, we’re not in the hole, but we had some bad weeks. And, this happened after tightening our criteria for selection after a bad hit in Week 7. These things happen, but now we need to hold ourselves accountable by answering a few questions:

• What if we only made a selection based on the number of profitable weeks?

• What if we only made a selection based on profit factor?

• What if we focused on the direction or strength of the trend (increasing or decreasing) instead of just the number of profitable weeks or the profit factor?

• Are certain weeks (i.e., last trading week in December) inherently better or worse for all strategies?

• And, perhaps the most obvious question, what if we just go back to our original selection criteria?

To read the rest of this post on Automated Trading Strategies, click here.

Automated Trading Strategies: November Update

In The Last 12 months Our Strategies Have Made Over $2.7M Based on Backtest Results

We all want to believe there’s a way to measure the forward strength of a model, but as we’ve explained before, market data is not random. It is based on auction mechanics. So, the same tools we use to validate and test the quality of data in a random data set can’t always be applied to the market. This is why we say ‘don’t use the simulated data feed within Ninjatrader’. It’s a random, internally generated market and has no correlation to real market data. So what can you do? The best you can do is make predictions about the future based on strategy performance and then track that performance, as well as the rationale for your predictions, over time.

This is why we publish updates every two months to track the progress of strategies. We like to look at how our top automated trading strategies are performing and update you on our key findings, highlights, takeaways and what’s in the pipeline. The following post is our November update. You can view past updates here:

• March Update

• May Update

• July Update

• September Update

What Are We Looking For In These Updates

We are looking for one thing in particular: consistency. When we say consistency, we mean strategies that perform like the backtest. We are tracking our best strategies to find the best strategy attributes. We can then use these attributes to create a strategy that gets us closer to the holy grail of automated trade strategy?

We define the holy grail of trade strategy as having the following attributes:

• Profit factor greater than 3

• Annual drawdown less than 3%

• Annual return greater than 500%

• Maximum daily low of -$1,000

• Avg Daily profit greater than $1,000

• Less than 5,000 trades annually

• Greater than 253 trades annually

These attributes are not only how we define the holy grail, but some of them help to strengthen the strategy.

We’ve also added two columns to this backtest result: gross loss and gross profit. Why did we do this? We are increasingly focused on transparency. As much as possible, we want you to be able to understand how a strategy works. In our May update we decided to add a column for profit factor because someone asked the following question:

It’s nice that you can make $100K on a strategy, but how much did it take to get there?

It’s a good question for both the novice and professional. Ultimately, it gets at reinvestment risk, which can be hard to understand if you’re not used to trading futures contracts. We tried to address this question in the post: Reinvestment Vs Total Investment.

Now, we’re adding one more level of transparency because not everyone is going to read the article above. Instead of just looking at profit factor, which is calculated by dividing gross profit by gross loss, we’re showing the actual values of gross profit and loss.

We’ve also added RTY for Strategies 17 and 33 to the update. Both backtested well when we compared the NQ futures contract with the RTY contract in the post: Which Equity Index Will Lead To The Holy Grail Of Automated Trade Strategy: NQ Vs. RTY. While both have less than 253 trades in the year, they also have a high profit factor so we’re going to continue to track/test these strategies under the RTY contract as well.

Finally, to make room for these changes, we’ve removed the ‘# of trading days’ column in lieu of a note at the bottom of the chart since the number of trading days for each strategy is the same at 253.

With that, let’s dive into the update.

September Vs. November

So where do we stand in comparison with our last update?

The table below provides the backtest results from our last update: 8/30/2020 - 8/30/2021

[Our apologies for the small font, but you can enlarge by clicking on the picture.]

For links to all strategies click here.

The following table provides the backtest results for the current or most recent update: 11/01/2020 - 11/01/2021

[Our apologies for the small font, but you can enlarge by clicking on the picture.]

First, let’s review what the trend was coming into the September update. In general, we had a deterioration from July to September. This is an excerpt from the September update:

“Average profit factor for the entire portfolio decreased from 1.26 to 1.24 and average net profitability decreased from $98K to $89K. Profit per day decreased from $388 to $355 and profit per trade decreased from $145 to $116. The number of profitable trades also decreased slightly from 44% to 43%.”

Most of the deterioration occurred with optimized strategies (marked in yellow on the chart). When we removed optimized strategies from the portfolio the metrics improved. What we’ve found over the year is that as much fun as optimized strategies are to ‘hunt’, they are more prone to overfitting and therefore have highly inconsistent results.

Now let’s get to what we found in the most recent update:

For the year, net profit for the total portfolio decreased slightly from $2,788,075 to $2,746430 with a total max drawdown of $689K. The portfolio profit factor increased from 1.24 to 1.35 on fewer trades. In addition to a higher profit factor, we also have a lower max drawdown percentage from 8% to 7%. We also saw a decrease in average profit per strategy from $90K to $78K along with a 2% increase in the number of profitable trades. The decrease in net income led to a drop in profit per day from $355 to $310, however, because the number of trades decreased and the profit factor improved, the profit per trade increased from $116 to $188. So even though net profit decreased, this is a leaner portfolio; it’s more efficient. This is often the tradeoff in performance.

The bigger question, or the question you should be asking yourself, is how can I guarantee this will happen again next year? Unfortunately, the answer is “you can’t” and don’t believe anyone that says you can. All we can do is track how these strategies perform over time. Specifically, we are looking for consistency, which is why we conduct an update every two months like the one you’re reading now. We continue to mine and isolate the attributes of our top performing strategies with consistent results. The goal is to take the best attributes from the best performing strategies to create the holy grail of trade strategy.

In September, we published a post titled: Top 5 Strategies By Performance. It gives an overview of the top 5 strategies in each category (based on data from the September update). This is a summary of the post:

• Strategy 3 has the highest net profitability at $331K.

• Strategy 3 has the highest profit per day at $1,311.

• Strategy 3 has the highest daily cumulative profit at $331K.

• Strategy 8 has the highest percent profitability at 58%.

• Strategy 10 has the highest return on max draw at 1958%.

• Strategy 23 is the only strategy in this range with a drawdown of less than 1%

• Strategy 23 has the lowest daily net profit at $1,675.

• Strategy 25 has the highest daily net profit at $19K.

• Strategy 28 has the lowest daily cumulative profit at $9,690.

• Strategy 28 has the highest profit per trade at $525/trade.

• Strategy 32 has the highest profit factor at 1.93.

Subscribers, click here for a link to a discussion on how we used these top strategies to mine for the specific attributes that make these some of our best strategies. We will include an update to this post in the annual report for subscribers in the beginning of January 2022.

Profit Factor

We want to take some time in this update to focus on profit factor (gross profit / gross loss). It is our primary gauge of performance. Specifically, we want to track how consistent the profit factor is from the September update to the November update. Actual net profit might change, but all we really care about is profit factor from a consistency perspective. The following table provides an overview of the profit factor increase from the last period to the current period.

First, a bit on how to read this table:

• The right side is the strategy.

• The left side is the change in profit factor from the last update.

• All strategies marked in yellow have been optimized.

• All strategies marked in blue experienced a considerable change in profit factor (at least .03 points) from the last update.

• All strategies with an “*” have less than 253 trades for the year.

Based on our logic, everything in white would be considered “consistent” from the September update to the November update. So that’s strategies: 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 31 and 35. Everything in blue would not be considered consistent. Some strategies experienced an increase with the September update, like Strategy 4, but are now back to where they were with the July update. This could be a kind of consistency as well. We are in the process of creating a graph to visually track the profit factor over time. This should provide more insight into what ‘longer-term consistency’ looks like.

We also noticed the following:

• Almost all optimized strategies, except for Strategy 13, experienced a considerable change.

• Almost all strategies with less than 253 trades, except for Strategy 31 and 35, had a considerable change.

What does this mean? It means our previous observations are supported here as well: optimized strategies and strategies with a low trade count tend to have inconsistent backtest results. This does not mean that the strategy is not ‘good’, it simply means that we can’t trust the backtest results as much as we can for unoptimized strategies or strategies with more than 253 trades per year. It also means that these strategies will require more time than others to determine consistency. It also suggests that many of our earlier strategies are consistent, while our more recent strategies are not. Again, this does not mean that these strategies are poor performers, but our only means of testing a strategy is with a backtest so we must focus on those strategies that we know are more consistent with backtest results. Going forward, we will not be publishing/recording any optimized strategies or strategies with less than 253 trades for the year.

Some of you may be asking, why not just extend the backtest period until you get 500+ trades? It’s a good question. We did just that and conducted an audit to test the data. The further back you go, the more issues we found with the data and we used several data sources. So for now at least we’re only backtesting on the most recent 12 month period.

Answers to Some Commonly Asked Questions:

• Perhaps one of the most commonly asked questions we get is, how much do I need to get started? This is hard to say because it depends on many things, but ultimately you want to set your simulated account to an amount that is equal to the max drawdown plus some amount to buffer for negative attributes like a low profit factor. For more on what we think is the best way to approach this question for yourself, click here.

• We continue to receive questions about funded trader programs. There is no easy answer to this question, but we can tell you for certain that the answer does not lie with one of our strategies. I passed the test with 5 days to spare, but it wasn’t with one of our strategies. So there’s no need to subscribe if that’s what you’re looking for. That said, we can give you some guidance on what you need to think about in order to pass. For more on what we think about the best way to approach funded trader programs click here.

• In general, one of the most common questions we get pertains to the use of our strategies on other assets like crypto. You can read more about that in the post: Do our strategies work on cryptocurrencies? We’re also looking at other equity indices and asset classes. For a post on what we found when comparing NQ to RTY, click here. We also recently started a series that looks at futures contracts for other assets like commodities, bonds and forex. The first strategy in the series was Strategy 36 because it has the highest profit factor. You can view the results here. Going forward, each backtest will have its own post.

• Another commonly asked question is in regards to overfitting. You can read the following posts to learn more about overfitting and what we’ve done to reduce its impact: Overfitting: What is it and what can we do about it and What Are We Doing To Ensure Backtest Accuracy? A few months later, we received a question regarding strategy strength and what optimization tools we’re using to increase robustness. We answered that question in the post: How Can You Tell If An Automated Trading Strategy Will Perform Well Over Time? After writing these posts, we became interested in creating a strategy that optimizes in real time. That is, how can we can optimize a strategy as close to the event as possible. In this way, the optimization is still based on backtest results, but the data used in the backtest is based on a very short period of time. Theoretically, overfitting should be less of a problem the closer the optimization occurs to the event. You can read more about this here.

• One of our subscribers told us that the strategies were too hard to understand. To this we say, “Ask us anything. We’re here to help.” Practice on Strategies 1 and 5 (both are free) first and then ask us if you have any questions. Also, unlike most websites, we give you a full description of the strategy so you have the mechanics of the strategy, not just a license to use it.

• Another common complaint/question we get is: Your free strategies aren’t that good. Why is that? I agree, that’s why we’re giving them away. The goal is to show you how our process works.

• One of the most commonly asked questions we get is: How do I learn how to create automated strategies for myself? Our advice is to use the resources provided by Ninjatrader. We receive no remuneration for saying this. We are saying this because we like the platform and they have some great classes online. To learn more about their daily training programs, click here. See below for an overview of the kind of training they have and it’s all free.

  

What’s in the pipeline:

• We just started a weekly update series. The goal of the series is to track strategies on a weekly basis to see if we can discover how to select the holy grail of trade strategy. It is based on the premise that the holy grail of automated trade strategy is not static, but fluid or rather made up of multiple strategies. To read more about that, click here. We’re going to call the weekly update the ‘Mudder’ Report. If you don’t understand why, you will after reading the article. Some of you got upset because we used a horse race analogy. This is the ultimate hunt, not a horse race, we get it. But, we can gain insight by looking at the hunt from different angles. Looking at strategies on a weekly basis helps to understand what’s happening between each update.

• We’ve had several requests to publish the Mudder Report on Sunday and we’re going to make every effort to do so, but it also dawned on us that some people might be using the Mudder Report to actually trade live. Our goal is not to predict or recommend what to trade in the following week. It’s always fun to try your luck on a simulated account, but if you use the weekly update to trade live, you will get burned. Our goal in developing the Mudder Report is to aid in the development of a few working theories for the holy grail of automated trade strategy, which we believe could be any one or more of the following:

  • static

  • a moving target (uses multiple strategies)

  • made up of multiple contracts using a static strategy

  • made up of multiple contracts using multiple strategies.

• We also have several new strategies coming out in the next two weeks. Stay tuned.

If you have any questions, feel free to contact us directly at automatedtradingstrategies@substack.com.

2022 Could Be One Of The Most Volatile Years In Recent History

Calm Before A Storm, Henry Moore, 1883

Last week we published the results from a backtest of all strategies on the E-Mini Russell 2000 (RTY). We then compared those results with the E-Mini NASDAQ 100 (NQ) backtest. There were some interesting observations that you can read more about here.

In that post we talked about the impact of volatility on trading profits. We also reviewed the definition of volatility:

“A measure of dispersion around the mean.”

…and, discussed what our next steps were in the process: namely to compare our backtest results with other assets. In particular, those assets that may be more volatile than indices, like commodities.

Why are we doing this? Of course we’re curious about how well our strategies perform across multiple asset classes, but we also think 2022 is going to be one of the most volatile years in recent history. In general, higher volatility leads to better trading opportunities. And, we believe that volatility will be driven by inflation.

Why does inflation matter? Higher inflation or inflation volatility drives overall volatility in the markets, especially the commodity and currency markets. Since volatility is a trader’s best friend and we’re in the process of trying to find the holy grail of automated trade strategy (both moving target and static), it’s important to know how inflation will impact the markets. At the very least, we need to make an informed guess.

The good news (at least for traders) is that we believe 2022 will be one of the most volatile years in recent history and we are not prone to hyperbole. If this is the case, we want to make sure we’re looking at all options because there are assets that are even more volatile in price than the NQ and RTY. We are referring to commodities.

Inflation is Here

Unless you’ve been under a rock, you know that inflation is here. Prices for underlying inputs have gone up all over the world and those underlying inputs are commodities, quite literally. What does this mean? It means prices are going up because the value of the currency in which that asset is expressed in is going down. This spells trouble for all major currencies (this is a global issue), but assets that derive their value from something other than the backing of a central government will go up in both price and value. These are the assets you want to own.

Everyone has a different opinion as to why prices are rising. Some say it’s due to supply chain bottlenecks. Others say it’s due to enormous pandemic-related stimulus programs conducted by central banks. Still others say it’s the historically accommodative amounts of liquidity (cash) and historically easy financial conditions (rates near 0%), but we can all agree on the end result because it’s staring us in the face: inflation. Inflation is here and it’s not going anywhere.

As central banks print cash, managers are looking for a place to park it, which is pushing markets up even higher, especially those markets used to hedge risk. In other words, the premium on insurance (futures contracts) is going up.

While there may not be a consensus by the Fed on when it will start raising rates, the market is assuming at least three rate hikes before the end of 2023. And, while Jay Powell, US Fed chair, doesn’t want to link the start of tapering asset/bond buying programs with the onset of higher policy rates, we know they will be overlapping events. There are reports that show the Fed parring off its Treasury bond buys by $10 billion and its mortgage-backed security purchases by $5 billion each month beginning as soon as the middle of November.

The buyback scheme was put in place to address ‘dysfunctional trading conditions’ in the bond market during one of the sharpest contractions in history, but who’s to say that tapering the program won’t create another run on liquidity. If it does, the Fed will only be compelled to hasten its plans to raise rates in the face of increasingly higher inflation. As it stands, investors are challenging the view that the market is ready to stand on its own two feet. When you mix that with continued supply chain bottlenecks, which the Fed has already admitted has caught them off guard, it makes for fun times in the world of trading. This is especially true for commodities, which always increase with inflation and make equity indices have the appearance of treading water.

We are at the beginning of a perfect storm. And, it all makes sense because we are on the heels of what some would say has become a biblical event — the global pandemic.

The chart below provides an overview of the most volatile equity indices.

https://take-profit.org/en/volatility/indices/

Compare this with the chart for commodities:

https://take-profit.org/en/volatility/indices/

We are currently working on backtest results for some of these markets and will share them with subscribers first. This series will take some time to complete and we’re going to drop one backtest at a time based on each strategy. We’ll start with the strategies that have the highest profit factor for the NQ futures contract. The backtest will include the following futures contracts:

6B - British Pound (FX)

6E - Euro (FX)

CL - Crude Oil

ES - E-Mini S&P 500

FDAX - DAX

GC - Gold

NQ - E-Mini NASDAQ 100

RTY - E-Mini Russell 2000

YM - Mini Dow

ZB - 30 year Bond

ZN - 10Year Note

ZS - Soybeans

ZW - Wheat

Here’s the first backtest for Strategy 36:

For links to all strategies click here

This backtest is based on data from November 1, 2020 to November 1, 2021.

As you can see, Strategy 36 continues to perform well with NQ. It has a slightly higher profit factor than it did in the original backtest from August 31, 2020 to August 31, 2021, but it also performs well with CL (2.56 profit factor), ES (2.12 profit factor), ZS (1.82 profit factor) and YM (2.71 profit factor). It did not do well with currencies, gold, or bonds. In fact, it performed so poorly with bonds (.51 profit factor) that the inverse strategy might be worth looking at. We will be investigating this relationship further.

What’s Next

It is our goal to drop at least one backtest per week based on the instruments listed above. These will be short posts and not all posts will be available to the public, but our hope is to develop any trends we find among certain asset classes before the new year. We also plan on doing another backtest on the most relevant crypto crosses, micro contracts like MNQ, and certain stocks in the NQ index.

While the holy grail of trade strategy should work regardless of the market, our hope is to use the fundamental knowledge of what we can expect from the market in 2022 to make some predictions about the best markets and strategies to use to take advantage of what may become a once in a lifetime event. It will also help to understand/gauge the strength of each strategy and what it is mechanically that makes those strategies more robust than others. To learn more about what makes a strategy robust, click here.

Our Collective Freedom, The Ideal of America, Is Not For Sale

The Goldfinch – Carel Fabritius

If forced to label myself, I would say that I'm an egalitarian. I believe that each person has the right to live life in any way they choose as long as it doesn't hurt or infringe upon the rights of others. 

Do unto others as you would have them do unto you.

It's a deceptively simple doctrine to live by, because it takes strength to see yourself in the next man when he can't see himself in you. 

While I believe in local government, I also believe that like Gollum's precious ring, power corrupts. It turns God's best into greedy little trolls and makes it easier for the next man to dismiss you as being beneath him. And, while a federal government may seem like a progressive next step, it is actually antithetical to the movement started by the libertarians and egalitarians that created the ideal of America. None of us, including them, ever lived in its ideal -- a place where all men are created equal, endowed by their creators with certain inalienable rights, among them, life, liberty and the pursuit of happiness. 

Can you imagine thinking that our leaders were taking steps to secure our happiness? It is unimaginable to most, but the founders of this country took the first step. They stepped on the ideal of others to create it for themselves, but it was created nonetheless and we are, for better or worse, who we are because of it. Either way we have a debt to pay and it cannot be paid in dollars. It must be paid by your own pursuit of liberty and justice for all.

Money and freedom are not the same

It is wrong to believe that money and freedom are the same. The brainwashing begins early on in life, so it's hard to decouple the two, but you don't need money to gain your freedom. In fact, the pursuit of money is often at odds with your freedom. You don't need capital for liberty. Liberty is an unalienable right endowed upon us by our creator(s). And, if our creator(s) bestowed it upon us, why let a man, or even a nation led by a man, take that unalienable right away. 

So I fight for liberty for all. These aren't mere words -- they mean everything. They can seem lofty and ephemeral, but there are ways to anchor them to reality. The best way to do this is by asking the right question with every action. Is your goal to be rich or free? Do you strive for acceptance or freedom? Do you worship the dollar or liberty?

Many good people are conflicted by these questions and with good reason. What good is liberty if your child is hungry? It is this fear that causes delusional thinking. Instead of logic and reason, we turn to whatever works, which usually doesn't.

The beautiful thing about the system we live in is that there are many options. It may feel as though your path is pre-defined, but this is yet another delusion. In reality, your options, from minute to minute, are unlimited. And, you don't have to quit your day job to choose a different path.

The right path begins with the right question

José Clemente Orozco's painting, The Demagogue

 

I’m a big fan of Elon Musk. In an interview about how he and his team tackled challenges when creating Tesla, he lamented, "A lot of times the question is harder than the answer. If you can properly phrase the question, then the answer is the easy part." I like this quote because it exposes a truth that we often get wrong—finding your holy grail in life (whatever that may be) is about asking the right questions. If you ask the right questions, the answer will come. 

According to INC magazine,

Musk recalls that his first questions as a founder were pretty mundane, like, how can I make enough money to live and still have time for games and computers? His first few companies, like Zip2 and X.com, were designed to make money. As he got more money, he got frustrated with how money was transferred, and that led to PayPal. Today, the market cap of Tesla is around $40 billion and SpaceX is estimated at $15 billion. As Musk's founder questions have gotten better, his companies have gotten bigger. "To the degree we better understand the universe, then we know which questions to ask," he says.

Elon’s first question was “how do I make money”, but he didn’t start making real money until he asked the question “how can I make transferring money easier?” The answer to that question was Paypal and the rest is history.

So, the right question is not:

How can I make more money? (wrong)

It is:

How can I get more freedom? (right)

Another question I used to ask myself was:

Which political party should I donate to? (wrong)

But the real question to ask is: 

How can I create real change in my neighborhood and beyond? (right)

I haven't voted in an election for many years. I believe in mutual aid, not electoral politics. I donate to the People's Party and Jimmy Dore, but that's it. Any other donation goes straight to the people in need and the people most in contact with those people. 

Take the time to learn more about where you buy groceries. Is it a corporation or employee owned? Before supporting your favorite brands, find out if their employees are on strike and why. If you have the luxury, as much as possible, make your purchase decisions based on liberty rather than the lowest price and our collective freedom will come.

Automated Trading Strategies: September 2021 Update (In The Last 12 months Our Strategies Have Made Over $2.8M Based on Backtest Results)

Every two months we like to look at how our top automated trading strategies are performing and update you on our key findings, highlights, takeaways and what’s in the pipeline. Externally, it tells you where we are and allows subscribers to give their input. Much of our research has been a derivative of subscriber inquiry.

Internally, we are looking at two things in particular — alpha decay/consistency of published strategies — what can this tell us about the best strategy attributes and how can we use these attributes together to create a strategy that gets us closer to the holy grail of trade strategy. We define the holy grail of trade strategy as having the following attributes:

• Profit factor greater than 3

• Annual drawdown less than 3%

• Annual return greater than 500%

• Minimum daily net profit of -$1,000

• Avg Daily profit greater than $1,000

• Less than 5,000 trades annually

• Greater than 500 trades annually

Notice that we’ve added another bullet — the strategy has to produce more than 500 trades per year. We’ve noticed that strategies that produce less tend to have inconsistent backtest results.

We’ve also added two columns to the end of the backtest table - cumulative low and cumulative high (see below). Instead of just looking at the lowest and highest daily net profit, we wanted to look at the cumulative high and low over the year. While this is highly dependent on when you start the strategy, when combined with profit factor and max drawdown, it can provide a compelling risk/reward profile.

For example, Strategy 28 has the lowest daily cumulative profit at $9,690, which means that even though the strategy had a max drawdown of $16,365, the total account value never dropped below $9,690 on a rolling or cumulative basis. Yes, this means that on day 1, the strategy made $9,690. Again, this has more to do with when the strategy is started than profitability, but it can make all the difference in the world, especially with a highly volatile strategy. This is the main reason we include profitability per day of week and hour of day in our strategy results. We’ll discuss this more later.

7/2/2020 - 7/2/2021 Vs. 8/30/2020 - 8/30/2021

So where do we stand in comparison with our last update? The table below provides the backtest results from our last update: 7/2/2020 - 7/2/2021

The following table provides the backtest results for the current or most recent update: 8/30/2020 - 8/30/2021

[Our apologies for the small font, but you can enlarge by clicking on the picture.]

In general, when comparing the performance of both time periods, we had the same drawdown as the last update and a slight decrease in the return on max drawdown due to a decline in profitability. Average profit factor for the entire portfolio decreased from 1.26 to 1.24 and average net profitability decreased from $98K to $89K. Profit per day decreased from $388 to $355 and profit per trade decreased from $145 to $116. The number of profitable trades also decreased slightly from 44% to 43%.

Most of the deterioration occurred with optimized strategies (marked in yellow on the chart). Optimized strategies are more prone to overfitting and therefore have highly inconsistent results.

So what happens if we remove all optimized strategies from the portfolio? The chart below provides a comparison with and without optimized strategies.

Profitability increased from $89K to $110K on average. Likewise, average profit factor increased from 1.24 to 1.30, profit per day increased from $355 to $436, and; profit per trade increased from $116 to $155.

What does this tell us? Optimized strategies do not work, at least using the methodology we’ve used in the past — almost all optimized strategies performed worse except for Strategy 22. It also tells us that our unoptimized strategies are performing well against the previous update. Strategies 4, 7, 8, 10, 11, 26 and 31 all performed better. Strategies 4 and 26 had the highest increase in profit factor.

For the year, net profit for the total portfolio increased from $2,223,095 to $2,788,075 with a total max drawdown of $643,640. Specifically, the model is telling us that a $643K investment could have made $2.8 million between 8/30/2020 - 8/30/2021. This amount does not include commissions. We advise a fixed price commission plan for strategies with more than 1 trade per day.

The biggest question, or the question you should be asking yourself, is how can I guarantee this will happen again next year? Unfortunately, the answer is you can’t and don’t believe anyone that says that you can. All we can do is track how these strategies perform over time. Specifically, we are looking for consistency over time, which is why we conduct an update every two months like the one you’re reading now. We continue to mine and isolate the attributes of our top performing strategies with consistent results. The goal is to take the best attributes from the best performing strategies to create the holy grail of trade strategy.

We recently published a post titled: Top 5 Strategies By Performance. It gives an overview of the top 5 strategies in each category. This is a summary of the post:

• Strategy 3 has the highest net profitability at $331K.

• Strategy 3 has the highest profit per day at $1,311.

• Strategy 8 has the highest percent profitability at 58%.

• Strategy 10 has the highest return on max draw at 1958%.

• Strategy 23 is the only strategy with a drawdown of less than 1%

• Strategy 23 has the lowest daily net profit at $1,675.

• Strategy 25 has the highest daily net profit at $19K.

• Strategy 25 has the highest daily cumulative profit at $331K.

• Strategy 28 has the lowest daily cumulative profit at $9,690.

• Strategy 28 has the highest profit per trade at $525/trade.

• Strategy 32 has the highest profit factor at 1.93.

For links to all strategies click here.

Subscribers, click here for a link to a discussion on how we used these top strategies to mine for the specific attributes that make these our best strategies.

What’s in the pipeline:

Much is in the pipeline.

• We recently published a post titled: Do our strategies work on cryptocurrencies? In general, one of the most common questions we get pertains to the use of our strategies on other assets like crypto, so we’re also going to publish backtest results for other asset classes, including stocks.

• We’re also looking at various data series types outside of ‘range’ like minute, tick, volume and Renko. Subscribers can click here for the full post on our experience with Renko backtest results.

• Another commonly asked question is in regards to overfitting. You can read the following posts to learn more about overfitting and what we’ve done to reduce its impact: Overfitting: What is it and what can we do about it and What Are We Doing To Ensure Backtest Accuracy? After writing these posts, we became interested in creating a strategy that optimizes in real time. That is, instead of optimizing a strategy in 1 year backtest results, and then using those results as a way to place parameters on the future, we can optimize a strategy as close to the event as possible. In this way, the optimization is still based on backtest results, but the data used in the backtest is based on a very short period of time. Theoretically, overfitting should be less of a problem the closer the optimization occurs to the event. We’ll be publishing information about this indicator shortly.

• We’ll also be publishing a post looking at strategies that use divergence theory. We started this research with the post: Can We Maximize The Profit Factor By Focusing On Timing?

• We also plan on publishing a follow up post on market structure. We received some great feedback on this post and we’re hoping to incorporate it into our regular analysis.

• We continue to receive questions about the difference between Total Investment & Reinvestment: This is our answer: Reinvestment Vs Total Investment. In addition, we’ve just added cumulative net profit to our table as a key performance indicator. When used in conjunction with max drawdown and return on max drawdown, you can get a better understanding for how much you’ll need to invest/reinvest in a strategy. Some of our strategies have positive cumulative net profit, meaning that the account value never drops below 0. We take a deeper look at these strategies in the post: Top 5 Strategies By Performance Indicator.

• There are several other posts in the works, but it would take too long to discuss them here. Some research has been delayed and some has been pushed up. If you have any questions about anything in particular, please reach out to us directly.