Example Model & Results

In our experience, consistent and effective methodologies are anything but complex. Every successful methodology we have ever created and employed has the same basic construct starting with a solid macro thesis rooted in some repeating activity which is not too specific in nature. From there the edge is broken down further to find some realistic and repeatable method for capturing the manifestation of the original thesis. Finally, the construction and application of the risk model which builds a mathematical advantage into event series is critical to make profitability consistently likely to be independent from the win rate. As a way to keep traders focused on the importance of each part of the methodology’s process, we track an independent version of the shared methodology with its own set of fully disclosed entry, exit, trade management and risk management rules in the member’s area. It is quite purposefully different from what the partners and contributing members trade themselves so as to build confidence that there is no one best way to trade any market in any time frame as the canvas is far too complex and ever changing. In addition all the components are relative to each other making seeking answers to many common retail questions futile. Instead, we advocate focusing on seeing the big picture of consistent (not ridiculous) returns as most successful professionals do, which allows you to trust the pieces of the puzzle to work in concert as they were designed to. In short, you trust the edge proven through common sense and diligent ongoing observation to continue to exist, manage your risk, and grind it out. This mechanical model is certainly robust enough to be traded on its own, or you can use additional discretionary components related to order flow, volume and developing market structure to improve upon it even more. But as a confidence builder for anyone who thinks positive returns are impossible without an expert ability to read the market and make decisions,  note the equity curve and statistics for our example model below. As can be easily seen, a simple thesis, small edge, and sound risk management is capable of producing enviable returns with little complexity.

When looking at the statistics for the example model one of the first things you will notice is the win rate. After many years of building many successful models in our algo development company the highest and most consistent equity curves for every methodology created have come from win rates lower than 50% on average. All things being equal, it is far easier and more efficient to derive profitability from how much you win, rather than how often you win. For brand new traders this may come as a surprise, but for most aspiring traders with any experience thus far, it may resonate as an “ahh-hah” moment. Most are aware that the vast majority of retail traders lose money. Ask yourself a rhetorical question: “What is it that nearly all of  those losing traders are trying to do”? Answer: Seek out the elusive set of parameters or criteria which will point the way to winning nearly every trade. The markets are too smart for that, and if fact, the very auction process itself dictates that because of the complexity of the environment there are a nearly infinite numbers of time frames and objectives which dictate near certain relativity between the number of times you win and how much you win. In a nutshell, its quite easy to win more often if you accept smaller wins relative to risk and more difficult to win if you require larger profits when you do. This is probably the single most important thing to understand about any methodology you choose to trade. Once you develop your thesis you must study this relativity between win rate and profit factor historically so you can draw factual conclusions as to what range of values are both practical and robust. Perhaps most importantly, when you eventually discover where the “sweet spot” is between those two opposing objectives, you must accept it and learn to control your own emotions to trade the methodology efficiently.

One of the biggest dangers in developing any methodology is curve fitting. As professional developers we take this very seriously and never use optimization of any kind in the creation of our models and methodologies. All are built on the merits of the original macro thesis – our reason for trading proven to be edged to us through manual chart confirmation over large slices of time.  The only way to really know that data hasn’t been curve fit even if accidentally is to see robustness in the application of multiple trade management and risk management criteria. The graph that follows illustrates the robustness in our example model clearly. A wide range of stop and target schemes applied to the same entry criteria are all profitable, and many to a very similar degree. Larger stops and targets are in the direction of the arrows and any portion of the graph not under water is profitable.

To enhance the learning process we use this model as a basis for all discussions in the members area and all execution and risk modeling rules are fully disclosed. If access to those rules along with direct interaction with the partners, access to the knowledge base archives, and PRE-MARKET delivery of our  daily trade plan worksheets appeals to you, start here for more information about premium membership and how to inquire about current slot availability:

 Premium Membership

The example model and statistics are hypothetical and tracked for ongoing educational purposes. Please read our full disclaimer for more a more complete disclosure about risks in futures trading and the limitations of hypothetical results. For additional information about the levels we publish and trade each day that in turn drive this example model, be sure to see the daily worksheet tutorial and the frequently asked questions.