Overall Concept

Incremental trading signals and matching strategy

In trading, a prevalent question is why automated strategies often yield less profit than manual ones. Even though most trades are automated, manual systems are undeniably the most successful. This paradox is further complicated by the fact that manual traders only trade for a few hours a day or a few days a month. It's a common belief that an automated trading strategy, which operates 24/7 and is devoid of emotions, should outperform a manual trader who works 8 hours a day, five days a week, and is subject to emotional influences.

However, the reality is that a manual trader typically executes fewer trades but with larger profits on average, while an automated trading system conducts numerous trades with smaller profits. In essence, the manual trader comes out on top! A closer examination of this behavior reveals the reason: human flexibility. Despite being emotionless and tireless, algorithms are notably inflexible. Other contributing factors include broker fees, such as commissions or adjusted spreads, which tend to increase disproportionately with the number of trades performed.

A manual trader employs a strategy to generate raw signals, each individually evaluated in the current market environment. This evaluation serves as a filtering process, considering factors such as candle patterns, chart patterns, Fibonacci patterns, timing, analysis of higher timeframes, the latest economic news, and more. This multifaceted approach results in a strategy composed of multiple sub-strategies, each employing a different filtering process. Assuming that only 50% of 20 raw signals generated are deemed valid, only 10 remain, each filtered by a different sub-strategy. The more filter factors considered, the fewer actual trades per sub-strategy.

The image below depicts a typical price chart with three viable trading opportunities. A manual trader can discern that each trading window possesses distinct characteristics, necessitating different signal validation approaches. For instance, strategies one and two call for short trades, while strategy three requires a long trade. Moreover, the order configuration must also vary. Some trades necessitate a stop-loss trailing system, while others require a hedging approach, and so on.

A manual trader can adapt to each trading window. Over an extended period of practice, a manual trader tends to implement various subtly different trading strategies rather than adhering to a single static one. This dynamic approach still adheres to a strict system but allows adaptability when necessary. For instance, in the event of a Federal Reserve (FED) release, no trader would mindlessly open a trade five minutes prior just because the Relative Strength Index (RSI) oscillator generates a signal.

When examining the performance of each subtly different trading strategy, it becomes apparent that at specific points in time, one strategy may yield positive results while another may underperform. The overall performance is the cumulative result of all these sub-strategies. This highlights the importance of diversification and adaptability in trading strategies.

As the number of sub-strategies increases, the overall performance tends to stabilize. This can be likened to listening to a person's voice. When you listen to an individual, you can understand each word. However, when you listen to a large group of people, distinguishing individual words becomes challenging, resulting in a monotonous noise.

In the trading context, this implies that the more sub-strategies are employed, the less volatile the final performance line will be. This is because the diversification of strategies tends to smooth out the performance curve, much like how many voices blend into a consistent sound.

While the ideal performance line is positive, it can also be negative or move sideways.

The key to enhancing the performance of an automated trading strategy lies in emulating the actions of a manual trader rather than blindly opening more and more trades in the hope of increasing output. This is precisely what the Expert Advisor Builder and Custom Expert Advisor are designed to do. Instead of focusing on a single automatic strategy that trades frequently, these applications aim to execute numerous smaller strategies that trade less frequently. To achieve this, two requirements must be met:

• The ability to quickly change and test trading strategies
• Access to a large tick data basis for extended backtesting

The necessity for the first requirement stems from the fact that the coding and testing of a trading strategy typically demand a significant investment of time. The complexity of the strategy directly influences the amount of effort required. For instance, a simple indicator-based strategy without stop-loss trailing takes a few hours to develop and test. However, more sophisticated strategies involving pattern analysis could take up to several months to implement and thoroughly test.

To address this, an abstract trading algorithm is implemented, capable of combining signals and filtering through a configuration-driven approach rather than a code-driven one. This means that instead of individually coding each sub-strategy, a single algorithm encompasses all signal and filter blocks. These blocks can be turned on or off as needed, allowing for the strategy to be recombined repeatedly. This approach significantly reduces the time and effort required to develop and test new strategies.

Given that this approach typically reduces the number of trades per sub-strategy, ensuring that the strategy test still yields statistically significant results is crucial. For a day trading strategy, it's recommended to backtest over a period that allows the trading systems to trade at least 100-150 times to assess long-term stability. This recommendation should also be applied to each sub-strategy, necessitating an extension of the test period. Instead of the 6-12 months typically used for a day trading strategy, the strategy should be tested over several years to yield statistically valid results.

This requirement is met by utilizing high-quality tick data in various spread configurations available for the past decade and more. While such data is typically challenging to find, the MT5 Tick Data provides convenient access to a comprehensive tick data database as a subscribable product. This ensures you have the data to conduct extensive and reliable backtesting for your trading strategies.

Practical implementation

While theory provides the foundation, practical implementation brings it to life. This section will guide you on using the Expert Advisor Builder with the Custom Expert Advisor to get to life trading systems with multiple sub-strategies. This allows for realizing dynamic and adaptable trading systems, enhancing their effectiveness and profitability.

However, the overall concept can be summarized in two sentences:

• The Expert Advisor Builder is used to develop a trading module and to generate an output file containing all information about the module.
• The Custom Expert Advisor executes the output files the Expert Advisor Builder produced.

As evident, both applications work in tandem! However, for some use cases, the Expert Advisor Builder suffices. For instance, if you only want to validate a trading idea that you will eventually execute manually, you can test the strategy upfront in the strategy tester. In contrast, to execute a trading strategy completely autonomously, you must subscribe to a Custom Expert Advisor plan and use the output files as input for this application.

This principle might be familiar from other development processes. For example, a designer uses an editor application in CAD development to model and export an output file containing the 3D model. Analogously, the editor application is the Expert Advisor Builder, and the SET files represent the 3D model, respectively, its export file. After the designer has developed the 3D model, he might want to produce it with a 3D printer. The 3D model then serves as input for the 3D printer to create a physical object. In this case, the 3D printer is the Custom Expert Advisor, which can use the output files from the Expert Advisor Builder and execute one or more so-called trading modules. A single SET file defines an entire trading module.

The illustration below visualizes the concept.

Looking at the Custom Expert Advisor in this illustration, you can see that two variants of this application exist - a live variant and a tester variant. Both perform the same task but use different input folders. Therefore, the live variant is exclusively responsible for executing live charts, and the tester variant is accountable for validating trading strategies with the strategy tester. This ensures the input and output data separation to avoid interference.

Just as a 3D printer can print multiple objects simultaneously, the Custom Expert Advisor can execute various input files simultaneously. In addition to that, each trading module can be turned on and off while the Custom Expert Advisor is active. This helps maintain a continuous trading journal and adequately inspect the drawdown and performance.

This unique principle of separation of expert advisor development and execution opens up an almost unlimited range of possibilities. The most interesting by far are self-adapting trading systems that turn trading modules on and off with the help of a feedback loop. Every aspect of the Expert Advisor Builder and Custom Expert Advisor is designed to be fully automated.

Since many trading strategies only work for a limited period, finding an algorithm-based system that operates profitably for an extended period is challenging. This means trading strategies have to be adapted continuously. Depending on the timeframe, it can sometimes be commonplace. Using the Expert Advisor Builder to improve this workflow, many different trading strategies for MetaTrader can be generated and evaluated quickly.

Previously, this approach was exclusively available to large funds and institutional entities. Now, it's accessible to everyone!