The attached equity curve shows the result of an optimization with a very large number of test runs. Although tight stop losses were integrated and the trading strategy works without martingales, the curve is jagged. This jaggedness is due to various factors, such as the volatility of the market and the frequency of trading decisions.

It is important to note that a jagged equity curve is an indicator that the trading strategy is robust and works well even in difficult market conditions.

With these equity curves, we then have statistics with which we decide when to close trades manually and when to switch off the algorithm on certain days or trading session7-hours

Our trading algorithm is based on a diversified investment strategy based on 40 different Forex pairs (we show 3 here as an example). On each pair, an individual trading strategy is developed through optimization procedures to ensure a very low risk for our clients.

The result of this diversification is 40 different equity curves that reflect a unique trading strategy for each forex pair. Each of these equity curves shows the result of an optimization with a large number of test runs and provides a comprehensive overview of the performance of each individual strategy.

Through this diversification, we minimize the risk for our clients by focusing on different currency pairs and thus achieving a broader spread. At the same time, our algorithms are geared towards sustainable performance in order to maximize the potential for long-term returns.

As a client of our trading algorithm, you can benefit from this broad diversification and our extensive expertise in the development of trading AIs. We ensure that each strategy is customized for each pair to achieve optimal performance while keeping risk to a minimum.

Here you can see the equity curve, which summarizes all 40 forex pairs and thus provides an overall picture of the performance of our algorithm. This equity curve is the result of a careful simulation in which we ensure that the individual trading strategies do not correlate and do not have too high a drawdown together.

To achieve this, we use certain JavaScripts that allow us to comprehensively analyze the performance of our trading strategies. These scripts help us to calculate and monitor various statistics such as the maximum drawdown, the Sharpe ratio and the average return.

This simulation allows us to ensure that the performance of our copytrading is based on a solid foundation and that we are able to offer our clients a sustainable and profitable investment strategy. Our clients can therefore be confident that their portfolio is balanced and broadly diversified and that everyone will benefit from stable performance.

We also use Monte Carlo simulation for validation and optimization, which is a statistical method used to model possible scenarios by simulating random variables in a specific model. This simulation is performed to determine the impact of uncertainty and risk on the results of the model.

In the context of copy trading, Monte Carlo simulation is a valuable method for estimating the possible performance of our algorithms in different market conditions. This involves simulating possible market scenarios and determining the performance of the trading strategy in each of these scenarios.

The Monte Carlo simulation enables us to take into account various parameters such as market volatility, the frequency of winning and losing trades and the trader’s risk appetite. This simulation enables us to set realistic expectations for the potential performance of our copy trading and offer our clients a well-founded decision-making tool.

The money management simulation is another useful tool for estimating the potential performance of our strategies in different market conditions. This simulation models the possible outcomes of different money management methods such as setting stop-loss and take-profit levels as well as the size of trades.

By using money management simulation, we can evaluate and optimize the impact of different risk management methods on the performance of our algorithms. The simulation allows us to run through various scenarios and determine which parameters are best suited to minimize risk and maximize returns.

The money management simulation is particularly useful in combination with the Monte Carlo simulation, as it allows us to test various money management methods in different market scenarios and compare the possible results. This simulation allows us to optimize our trading strategies and achieve an optimal risk/return profile.

Last but not least, we use equity control analysis. This tool enables us to assess the potential impact of drawdowns on our trading strategies and take appropriate measures to minimize them.

The equity control analysis can show us how our trading account will develop over time and how our risk management will affect our equity curve. The analysis also allows us to run different scenarios to see how changes in risk management affect the performance of our trading strategies.

Equity control analysis is particularly useful in combination with Monte Carlo simulation and money management simulation, as it allows us to optimize our trading strategies in different market conditions and achieve a balanced risk-return profile. The analysis allows us to determine the maximum drawdown we can accept and to take appropriate measures to protect our trading strategies from major losses.

We carry out all these analyses for almost all pairs and indices at daily intervals so that we can always adapt to the current market flow.