- 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.
After here’s the change with Dominari version 2.0.
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.
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.
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%.
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%.
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