Innovative way to effectively use AI for algorithmic trading: HyperQuant philosophy

The use of Artificial Intelligence (AI) for trading on cryptocurrency exchanges is a trendy practice that has quickly spread around the world. But let’s try to analyze how realistic is the creation of a platform for profitable AI-based trading.

AI-enhanced technology development is becoming increasingly common in our daily life. Artificial neural networks are the basis of AI algorithms. The principle of a neural network creation, that all operations are built around, is relatively simple. In the machine learning — artificial neural networks form a family of statistical education models, created akin biological neural networks (central nerve systems of animals, particularly — brain). In their essence these are communication systems that transfer messages to each other and have digital weight. This makes neural networks adaptable to input and capable of learning. Hence the systems based upon neural networks are constantly adapting to the changing conditions in real time.

An example of a neural network model, bases upon the three-layer perceptron.

The main disadvantages of and issues with AI- based trading

  1. A wrong education or re-optimization. The market is a fully open system with the constantly changing quantitative and qualitative membership of its participants. The transpiring changes impact the market growth, its fluctuations, its volatility and rate changes. Developers often try to input a lot of unconnected and unformatted entries into the AI, which is a serious mistake. A neural network trained, for example,to recognize faces on photos, is not suited for exchange trade — and vice versa. The exchange statistics shows that 80% of the accounts tied to the usage of a neural network — are set to zero within the first year after creation.
  2. The lack of or incorrect risk management. Survival on the market is directly connected to the ability to control risks. Only expert finance and risk management allows traders to outlive the so common turbulence periods. AI, capable of forecasting the market changes with 90% accuracy, can create a series of 10 to 100 loss making deals in sequence. Even by correctly forecasting the direction of the change and the future price levels (the important indicators are price levels, not prices) — it is impossible to accurately pinpoint how the price changes from the old one (point A on the graph) to the new one (point B).
AI, trained only to predict the final result, can easily lose all the invested funds during the self-education period.

3. BlackBox. Few understand that by teaching a neural network — a trader receives a black box. The final product is a closed construction with a decision making algorithm incomprehensible even for the trader. With unfavourable environment and the consequent loss of a significant investment — the trader won’t be able to accurately locate the reason for the break down.

HyperQuant philosophy.

Quantitative hedge funds are similar to the private clubs, that require a high financial level to enter. There is a big reason for such exclusivity. Contrary to usual funds — quant ones always try to reach market neutrality with the strategy portfolios, thus lessening the impact from market movement dynamics. This allows the investor not to worry about the black swan and long-term investment risks.

Our philosophy is formed around departure from market risks by creating a correct risk management structure, balancing strategies and using a wide diversification. That is why HyperQuant uses AI not just for predicting the market but for making the best investment choices upon thorough classification. The core of our platform is a neural network — a constantly learning rating mechanism. The non-stopping development comes with the platform growth and the increase in the elements of the latter leads to the increase in information received by the neural network. This, in turn, makes the neural network re-education more efficient. It also helps to quickly adapt a newly introduced element in the system.

How this works.

A complex rating system is a right way for the development of the financial platform. A rating is the instrument’s potential in a certain period of time depending on the combination of quantitative and qualitative characteristics, expressed in a final digital mark. A rating can be calculated with the help of different statistical methods. In the world financial system the ratings are comprised by independent rating agencies — Moody’s, Standard and Poor’s as well as Fitch Ratings. They are used for evaluating the credit solvency of a company. Using such rating a potential investor can understand whether he/she should purchase the obligations of the firm and how reliable this investment is.

AI forms the investment rating based on its own analysis system. It can be shown to the user with any understandable graphic method. The investment success is analyzed using a wide range of criteria, exceeding the simple methods of risk-profitability evaluation.

A diagram detailing the data process required for the construction of risk management system models.

In case of a rating mark going down — the neural network has an in-built alert system. If a user is performing risky actions, the trading result lowers or the portfolio element diversification is not high enough — the system displays a warning to the user. Similarly to a traffic light it varies depending on how critical the situation is. In the worst case scenario the system can block any access to the problematic element, thus avoiding the full investment loss.

HyperQuant is a smart home for AI based prediction systems.

When developing AI systems, aspiring entrepreneurs rarely consider the potential difficulties arising on a real market, especially when their systems need to be scaled. The algorithmic trading vitally requires a correctly built infrastructure for easy access to the exchange systems. This influences both the speed of receiving information online and the routing of the market orders flow to the exchanges.

  • HyperQuant offers a united interface for receiving and unifying the information gathered from all cryptocurrency exchanges.
  • Moreover, the request transfer protocol, developed by HyperQuant (HQ-FOT protocol — the counterpart of FIX/FAST protocols), allows to speed up the transfer of market orders in exchange systems by up to 10 times.
  • HyperQuant platform automatically balances and sorts out the flow of orders on crypto-exchanges thus stabilizing the pressure on them.
  • The utilized quoting strategies allow to boost the exchange positions many time over without causing sudden rate fluctuations.
  • With these operations HyperQuant platform deals with the main activity load, allowing the central AI to focus only on its primary function — transferring the profitable signals to the user.

Cutting-edge AI-based technologies are rapidly evolving and flourishing these days. It is going to be the next “Big Thing”, a new trillion dollar industry. But here at HyperQuant we are not just developing yet another smart algorithm or a neural network, we are building a huge platform, a future home for thousands of AI-based systems.

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