fat tails Archives - Algorithmic and Mechanical Forex Strategies

I said it here and here and here. The biggest issue with my Dominari is trading costs. Things aren’t going to really take off until I do one of two things.

Reduce the trading costs
Make more money on each trade

I’ve been working on Dominari since around September or October of last year. After racking my brain for months, I more or less wrote off the idea of improving the trade profitability.

That suddenly changed last week on Friday after the market closed. The best reason to trade my own systems live is that the agony of underperforming forces creativity. The feeling reminds me a lot of Daymond John’s (the guy from Shark Tank) new book the Power of Broke. When life isn’t going your way, it’s the resourceful and creative who are best able to get to the top.

Nobody wants to feel broke or under extreme stress. As much as we hate those feelings, they’re often the strongest drivers of performance. That’s how I feel right now with Dominari. I’m so close to getting there and wasn’t sure how to fix that missing ingredient.

If it weren’t for that stress, I would not have had my simple but very powerful insight last Friday.

And please don’t laugh. The change is so dumb and obvious that you’re going to wonder what’s wrong with me. When you’re in the thick of designing a system, the ugly truth is that sometimes you get lost in the weeds. Or to use another botany metaphor, you only see the trees instead of the forest.

My key insight was to slightly modify the exit strategy to use limit orders, whereas previously I only exited based on the close of the bar. I noticed two repeated behaviors that finally beat me over the head enough that the point finally sank in.

The number of occasions where my trade closed in the optimal location seemed to be significantly outweighed by the amount of money left on the table. The key insight for me was realizing where to optimally place that limit order. And for those of you on my newsletter, it happens to be closely related to the Auto Take Profit that I’ve been talking about all week.

Backtest assumptions and results

My operating mantra when doing backtests is to minimize the number of assumptions. Spreads for retail traders have changed dramatically from 2008 to today. I remember working as a broker at FXCM when our typical spread on GBPCHF was something like 8-9 pips. I now routinely pay something like 2 pips. It’s impossible to model what happened in the middle without haphazardly guessing.

I find it far more convincing to analyze the raw signal, both on historical and recent market data, then to interpret whether trading costs are likely to be favorable in today’s markets. “Raw signal” is the ideal signal, one which assumes perfect execution, no slippage, no rollover, no spreads and no commissions. The natural result is that you’re overstating historical performance, but the benefit is that you have a very clear idea whether the core idea is a system capable of predicting the market with reasonable risks.

The total leverage employed in the portfolio is 7:1. If I have a $50,000 trading account and held a position in every currency pair in the portfolio, then the notional value of those trades would equal $350,000 (50k * 7).

Another key point is that I used a fixed position size of $12,500 per trade. The size of the trade never increases or decreases during the backtest, which allows me to isolate the impact of the raw signal without adding the variable of money management.

Here are my trade metrics with version 1 of Dominari. Click the images to view them in full size.

The first version of Dominari had a profit factor of 1.26.

After here’s the change with Dominari version 2.0.

My new version of Dominari increases the profit factor to 1.59 with a significantly lower drawdown.

My best case scenario was to hope that the profit factor would jump another 10 points or thereabouts, maybe stretching the profit factor to 1.35 or thereabouts. It’s incredibly exciting to see the edge over breakeven more than double (going from a $0.26 edge to a $0.59 cent edge).

What I’m most excited about is the skew in the returns. Most mean reversion systems look for an edge but are overwhelmed with the impact of losing trades. That was the case with version 1.

The largest losers outweighed the largest winners in version 1.

This new version of Dominari is the very first mean reversion strategy that I’ve ever developed where the winning tails (ie, the biggest winners) nearly equal the losing tails (the biggest losers). It’s almost always the opposite with mean reversion strategies. Said another way, the risk profile of the extreme outcomes significantly improved with version 2.

The impact of the biggest winners is nearly identical to the biggest losers with version 2.

And the metric that most traders care about the most, drawdown, is wildly improved. Version 1 showed a drawdown of 5.72%. The new version is a fraction of that at 1.77%.

Out of sample backtest for Dominari version 2

The out of sample performance is nearly identical to the in sample performance, despite significantly different market conditions.

When I walked my test out of sample onto recent data, covering 2013-2015, the performance characteristics of version 2 are nearly identical to the in-sample test. The profit factor was identical at 1.59, and the max drawdown was 2.01% for 2013-2015.

Translating the theoretical into expected performance parameters

Again, those metrics above are in the ideal world of perfect execution and no trading costs. The real world performance will have lower returns and higher drawdowns. The advantage to having live trade data is that I can now make some kind of intelligent estimate of my expected trade accuracy and profit factor. Just how overstated are the idealized returns likely to be?

The process that I went through to calculate the expected profit factor in the real world is a 5 step process. I don’t think it’s going to make any sense if I try to write out the steps in conversational English. Instead, I’ve chosen to share a spreadsheet where you can view the step by step process for how extrapolating live trading data into expected performance with the new strategy. Click here to view the spreadsheet.

The expected profit factor for my live trading is expected to be between 1.29 to 1.39. The expected percent accuracy for live trades should jump from 62.55% to 70.8%.

The traders who will get first crack at the Total Access Apprenticeship are those are subscribed to the free newsletter. If you’re not signed up, make sure to fill in your email address in the orange box at the top right of this page.

If you were walking and randomly it started to rain, would you consider carrying an umbrella tomorrow? Of course you would.

The reason I ask a rhetorical question like that is when people observe a behavior, they respond accordingly. If they expect that something might happen again, they change their behavior to accommodate the change in outcomes.

When you think about forex robots, everybody has the dream of developing a strategy that works forever. It requires no changes. The initial settings always work. Turn it on and move to the beach.

Reality, of course, is more complicated than that.

Walk forward optimization continually optimizes throughout time instead of looking for one set of static settings

That leads to expectations of what you need to do when your strategy inevitably goes awry. It’s very possible that you come up with a strategy that works and does amazingly well on the current market. However, a past genius doesn’t mean future genius. There’s always the chance that your strategy will no longer work in the future.

Why is that? It’s the same reason that you might carry an umbrella tomorrow if it rains today. People observe the market performing in a consistent manner. As more and more people make the observation, people start trading on it. The market responds to those changes, and eventually the opportunity completely washes out as too many people have eared about it.

Walk forward testing is the process of determining whether or not your strategy has washed out. By testing on one set of data, and then testing it on a blind set, you can give yourself an indication of whether your strategy is bad or not. The goal of walk forward isn’t to prove that your strategy is good. It’s to prove that your strategy is not known to be bad.

The process of walk forward testing is very simple. You identify a set of information that you want to use for your testing and optimization. Using a real example, right now it’s the beginning of 2014. So maybe you want to look and test data from 2011 through 2012. That would be your in sample data, and then your out of sample data might be all of 2013.

In order to conduct a walk forward test, you would test and analyze your strategy 2011-2012. Then, to determine if it’s “not known to be bad”, you then walk forward to 2103 to see review the performance.

What you’ve done is a blind test. You didn’t know what how the strategy would perform in 2013 when you tested it in 2011-2012. By putting it on a blind sample, you give it the opportunity to fail.

The reason so many traders put their faith in walk forward testing is because it’s the absolute best tool to identify weaknesses in your optimization. When you’re testing a strategy, it is very likely that you’ve overfit to past opportunities.

Self feedback loops in the current market

Let me give you an example. In the current markets, a lot of traders have been banging gold on the market open where every day at market open., they sell as much gold as they possibly can. Sometimes it’s several multiples of the annual production in a span of a few minutes. What you see is an absolute freefall for five or ten minutes. That state persists for days at a time. But that doesn’t last forever. When enough traders start seeing that people bang gold on the open, they start doing the same thing.

Effectively, whoever wants gold to falloff on the market open has taught other traders to do that trade for them. As people expect gold to fall in the first five minutes of the open, they then change their behavior. Some try to jump on banging the open and go short.

Others start modifying their behavior. They notice that gold free falls for five minutes. Then, all of a sudden it stops, and more than like it reverts to the mean. They’ll start changing their tack and buying after so many minutes have elapsed from the open. They expect that the heavy volume that preceded the selling will eventually return to normal. As people change their behavior, other people respond in kind.

If enough people start selling on the open and then buying on the open five minutes later, you can see that a pattern is forming where one person responds to the actions of another. It’s a self feedback loop where the state that was working for the first couple of days no longer works in the future.

If you can identify a strategy that is able to survive those conditions, and is able to survive conditions where you didn’t do any testing and optimization, you give yourself better odds of succeeding in the future. It means that not very many traders have clued into this trading opportunity that you’ve discovered.

The approach to to walk forward testing is the antidote to the problem known as curve fitting. Curve fitting is the ultimate woulda coulda shoulda strategy. It’s akin to opening a chart from yesterday and saying I would’ve bought here and I would’ve sold here, already knowing what transpired.

Of course you’re going to “make money” in that situation. You know with perfect information what the market did. In the future, you don’t know the perfect information. The goal of a strategy is to deal with that ambiguity.

Curve fitting means that you’ve fit everything so perfectly to past market conditions that when new situations inevitably arise, sort of akin to the phrase, “history doesn’t repeat itself, but it rhymes,” your strategy does the same thing.

You want a strategy that does well on past performance, but you’re not coming up with a strategy to make money on historical markets. The purpose of developing a strategy is to make money in future markets. When you’re backtesting, you’re trying to strike the balance between solid historical performance and, most importantly, making sure that that historical knowledge extrapolates to future performance. Your goal is to make money.

Rolling Walk Forward Optimization

Rolling walk forward optimization takes the walk forward idea and continuously improves the strategy by exposing it to new data. So let’s say that you have a twenty four month sample period. One way to go about it would be to optimize your strategy for a period of two months, then to walk it forward to the third month. You observe the behavior and you reoptimize for the second and third month, then walk it forward to the fourth month.

By doing so continuously, you eliminate the decay time of the strategy and give it a chance to adapt to ongoing market conditions. It is sort of the redheaded stepchild to machine learning. Experience and losses give the strategy the opportunity to improve and adjust to the market changes through walk forward optimization.

…you eliminate the decay time of the strategy and give it a chance to adapt to ongoing market conditions

Another important consideration for walk forward analysis is the degrees of freedom within a system. For example, let’s say that you are analyzing a moving averaage cross. You’re using two moving averages and use a fixed stoploss and take profit. That would give you four degrees freedom. The fast moving average is the first degree. The slow moving average is the second degree. The third is the stoploss and the fourth is the take profit.

The more degrees of freedom that you allow in a system vastly increases the chances 0f curve fitting your systems to historical data. The absolute best systems maintain twelve degrees of freedom or less. You want to find trading opportunities that have large numbers of trades and that offer performance that you find satisfactory.

Another element to consider in your optimization is what are you optimizing for. Most people focus on the absolute return. Returns are great, but most traders care much more about how they make their money instead of how much. Let me give you an example. If I had a system that made $25,000 last year, would you want it? Almost everybody says yes.

If I have a system that made $25,000 last year, but you had to lose to $15,000 before you made any money. Most people don’t want that system. What this means is that you care a lot more about the performance on a day-to-day basis rather than end result. The problem with optimization and even walk forward optimization is that you’re not necessarily focused on what you care about in the real world: the way that you’re making your money.

Most charting packages focused on the net outcome and that can cause some weaknesses in your system. If you’re range trading, what you’ve really done is cherry pick the results that are the least affected by substantial news. In effect, you’ve chosen the settings that have not yet been affected by fat tails.

If you’re trend trading, you’ve done the exact opposite. You intentionally pick the settings that maximize the fat tailes that have happened in the past. With trend trading strategies, you probably aren’t going to find consistent performance. Instead, what you’ll find is that the optimization frequently causes long, ongoing droughts of incessant drawdown. Then suddenly, almost out of nowhere, it finds a mega monster winner that returns several multiples of the drawdown that you experienced. This is fine for a hypothetical backtests, but in the real world where you’re suffering losses on a near daily basis, most traders can’t take the pain. The weakness I find with most optimizations is that they don’t look at the consistency of performance. A potential substitute for optimizing a strategy would be looking at the linear regression of the equity curve over time. The best equity curve has the strongest linear regression slope.

Popular charting packages that implement rolling walk forward optimization are Amibroker, TradeStation, Multicharts and NinjaTrader.

Walk forward optimization in NinjaTrader

Open the Strategy Analyzer from the Control Center. Click File / New / Strategy Analyzer.

Open the strategy analyzer in NinjaTrader

Left mouse click on an instrument or instrument list and right mouse click to bring up the right mouse click menu. Select the menu item Walk Forward. You can also click on the “w” icon in the Strategy Analyzer toolbar. If you prefer hot keys, you can also use CTRL + W. Lastly, you can also push the “W” icon at the top left of the Strategy Analyzer.
Select a strategy from the Strategy slide out menu
Set the Walk Forward properties (See the “Understanding Walk Forward properties” section below for property definitions) and press the OK button.

There are many ways to select walk forward optimization in NinjaTrader

The Walk Forward progress will be shown in the Status Bar of the Control Center.