In this article: how we perceive intuition and talent; sharpening learning curve through try-and-fail process; simulated environments; DT-Box tool;

5 minutes to read

I want to start with a story from the book “Atomic Habits” by James Clear, which involves a woman named Dr. Laura, a radiologist, who was in the audience during a presentation.

During the presentation, she noticed that the man sitting two rows in front of her had a drooping face on one side and a slightly curled hand. From her experience as a radiologist, she quickly recognized these signs as potential symptoms of a stroke. She approached the man and asked if he was feeling okay. He mentioned that he had been feeling a bit off but hadn’t thought much of it. Because of Dr. Laura’s discerning observation and her quick actions, the man was rushed to the hospital and received treatment that likely saved his life.

In the case of Dr. Laura, her years of experience as a radiologist allowed her to immediately recognize the symptoms of a stroke. To the average person, the man’s appearance might have seemed only slightly off, but to her, the signs were clear.

For me that story is a great example of how accumulated knowledge and experience can manifest as intuition. The more time and effort we invest in mastering a particular domain, the more we internalize its patterns and subtleties. Over time, this deep knowledge becomes almost second nature, enabling us to make rapid decisions that appear as intuition to outsiders.

Another memorable example which I like, is from the book “The Talent Code” by Daniel Coyle describes the Spartak Tennis Club in Eastern Europe. On the surface, Spartak seemed like a rundown facility with only one indoor court. Yet, it has produced more top-20 women players than the entire United States. Coyle delves into the training methods at Spartak and finds that they focus intensely on skill development. Young players often practice without a ball, shadowing strokes for hours. This seemingly simple act engrains the correct form and technique deep into their muscle memory. So, when they play in real matches, their strokes and movements have an intuitive, fluid quality, borne from hours of deep practice.

These stories underscore the principle that talent isn’t just something you’re born with—it’s cultivated through specific types of practice, repetition, and refinement. Over time, this deep practice gives rise to what appears as ‘intuitive’ talent or expertise. These people have internalized the skills, movements, and strategies to the point where they can execute them almost reflexively, without conscious thought.

As you may probably guess, the same principles applies to trading skills. Defining a successful trader might seem straightforward at first – it’s someone who consistently profitable. However, the world of trading is ever-evolving – with changes in trading conditions, regulations, new instruments, and more, our expertise can quickly become outdated. Consequently, to maintain consistent profitability, traders must continuously adapt and refine their skills. In other words, you need to sharpen your learning curve to be constantly profitable. And if you are able to learn and apply new knowledge faster than everybody else, that will give you a competitive edge.

That brings us to the next question – how exactly can we improve and speed up our learning process as traders? One may think that it is required to use better learning materials – let’s say, take sophisticated courses or master classes from infamous professionals. Or find and read specific books about profitable trading strategies. And while that can be reasonable thing to do when you want to get a taste of what trading is about, I would argue that it will be as helpful as reading a book about riding a bicycle.

In my experience successful traders most of the time learn through try-and-fail process. Instead of being guided step-by-step or being provided direct answers, they discover solutions through their own mistakes and successes. Their firsthand experiences, even those that result in mistakes, often lead to deeper understanding and longer-lasting retention.

The only problem here is that failure has quite definite limit – it is your trading deposit. This makes each loosing trade seem weightier than any profitable one, leading to a fear of losses that can sometimes overshadow the aspiration for consistent gains. It’s like how after a painful breakup, you might begin to fear romantic relationships altogether.

However, there are professions with comparable risk profiles. For pilots, astronauts, and surgeons, a mistake often signifies the end of a career, if not worse, rather than a valuable learning experience. But within these professions, we can also find a solution for practicing when the cost of failure is high: they utilize simulated environments. Pilots frequently train with aircraft simulators, and in fact, for all professional pilots, simulator training and examinations are mandatory every six to twelve months. Meanwhile, for surgeons, the use of VR simulation environments has become increasingly prevalent in recent times.

So my answer to the main question of the article is the following: if you want to become better at something, you have to use better learning tools. Or to be more precise, if you want to become a better trader you have to use better simulation tools.

Simulation and optimization of models on historical data are commonplace for algo traders. But for manual and grey-box traders, finding a suitable simulation environment can be particularly challenging.

In an ideal world, what would the perfect simulation environment look like? I can think of a tool that can visually replay recorded historical data, constructing charts as they would appear in real-time, building bars tick by tick. Yet, it would also offer the flexibility to adjust the playback speed. It should encompass all the indicator functionalities you’d typically use in real-time. Crucially, it must be capable of sending and executing orders, as well as calculating trade statistics. Ideally, it would also support execution algorithms, such as trailing stops or reverse orders.

The ideal tool, in every sense, would allow me to simulate trading alongside my strategies. This means I’d be able to activate or deactivate one or multiple strategies as I deem fit, or adjust their parameters on-the-fly. Imagine having two distinct strategies: one for trend following and another for mean-reversion. The trend-following strategy typically holds a position longer, tolerating certain drawdowns. On the other hand, the mean-reversion strategy jumps in when prices stray from the historical average, aiming to exit at the earliest profit opportunity. As a trader, I would leverage a broad array of information, from the current market state to trading volumes and indicators, to determine which strategy has the higher winning probability at any given moment. I’d activate the chosen strategy for a specific duration, switching it off once conditions shift. Additionally, each strategy would incorporate safety measures against losses, ensuring that even if I err in my strategy selection, the system mitigates potential losing trades.

Such a simulation tool serves multiple purposes. It facilitates the rapid validation of trading ideas on historical data without the need for any code-writing. It aids in pinpointing and refining any flaws or inconsistencies in your trading logic. More importantly, it catalyzes the generation of new trading ideas. Additionally, you can replay the previous trading day to identify areas of improvement. Unlike algorithmic traders who primarily focus on aggregate statistics from numerous trades, your involvement remains hands-on, overseeing every trade entry and exit. This provides a rich feedback loop. Essentially, the tool empowers you to learn through persistent repetition, culminating in what was referred to as ‘intuitive experience’ in the earlier stories.

While there are existing software which partially cover functionality of my vision of such tool, especially in part of fully manual trading, I have never came across a tool which allows you to backtest grey box trading with several strategies. So some time ago I started experimenting with customization of available solutions to have an instrument which will help me and my clients with sharpening learning curve in the way I describe in this article.

That work led to what I now call DT-Box, tool designed to enhance your manual and grey-box trading skills through visual backtesting. Here you can find my article about how this tool you can help you grow from an idea to a profitable trading strategy. Then here you can find a guide on how you can install DT-Box and here there is a comprehensive user guide to the application. And finally here you can get the source code, which you can customize to fit your requirements.

If you like the article, please let me know your thoughts and ideas – pavel@pavelchigirev.com