Automated Risk Management in Cryptocurrency Trading: Strategies and Tools



Contemporary retail traders working in the crypto domain have to rely on a variety of tools to be successful. Things like copy trading, arbitrage bots, DCA buying, and others are not just simple complimentary investment methods. These tools are mandatory for inclusion in any efficient trading system and have to be utilized to their fullest potential if you want to achieve consistency and increase the value of your portfolio!

What Is Automated Risk Management and How Does it Help with Crypto Trading?

When it comes to trading cryptocurrencies and other digital assets, speed decides everything. The market is also quite volatile which is a big problem for some intraday traders. With so much on the line, retail traders have to use various forms of automation to stay ahead of the market.

If you are using bots to execute your technical analysis strategy, you have to use automated risk management tools.

Let’s talk about several most important risk management strategies that can be used in crypto trading:

  1. Using automatically placed take profit and stop loss orders.
  2. Employing bots that execute hedging orders when a certain event is triggered.
  3. Using statistical arbitrage and pair trading to avoid unnecessary risks.
  4. Employing automated strategies like spread trading.

Depending on your risk tolerance, portfolio composition, and preferred asset classes, you may need to choose one method of risk management over others. However, it is important to remember that you need them to avoid losses and prevent total financial ruin if something bad happens to the crypto industry.

While it is unlikely that this industry will fall apart, we still must be prepared for the worst outcomes and protect our assets from unforeseen catastrophes.

How to Choose an Appropriate Automated Risk Management System for Your Crypto Trading Goals

When using bots and other types of automation, you must remember that protection from failure is your responsibility. If a trader that you follow or a financial guru that taught you a new strategy failed as an advisor, it is still your mistake to believe them in the first place. You alone can protect your portfolio from any danger and risk.

Let’s talk about aforementioned automated risk tools that can be used to fine-tune your algorithmic trading system.

Take profit and stop loss

Use these orders strategically to enhance your system and add some nuance to it. You may use the best technical analysis strategy and deploy it to TradingView to generate reliable signals. However, it will not protect you from a sudden price retracement, unforeseen volatility, and other factors capable of turning a good trading signal into a financial disaster.

Here are some tips on how to place these orders:

  • Set realistic take goals to ensure that you do not hold your position for too long.
  • Have strict rules about how much you are willing to lose on a single deal, never break it.
  • Using percentile points instead of raw numbers can be beneficial for cryptocurrency trading strategies.

Many automation vendors provide advanced tools to change any bot settings including “take profit” and “stop loss” mechanics. Do not overestimate the power of these simple tools that can prevent you from losing a massive amount of money!

Hedging bots

Hedging is a method of protecting your portfolio by using financial assets or market positions countering your other positions. It is a good idea to have a Plan B when things go south in the market. Instead of spending a good portion of your screen explaining how it works, let’s take a simple example:

  • You set up a bot that opens a long position on Bitcoin without any leverage.
  • You also set up another bot that opens a leveraged short position in certain scenarios.
  • The second bot must activate if the long position starts getting too close to your stop loss.

In this case, the long positioned is effectively protected by an automated leveraged short position that will be opened if something goes wrong. You should adjust the ratios according to the size of leverage used for the second bot.

Using statistical arbitrage

It is possible to create a complex trading system that works with hundreds and even thousands of different assets. It takes time and usually requires deep understanding of financial markets. The method is focused on several things:

1.   Identifying correlated financial assets with similar price action changes.

2.   Identifying assets that have similar patterns but in the opposite direction.

3.   Launching a bot that can trade both types of assets to create a low-risk system.

While it seems like an ideal ATS for anyone, it has some important aspects that one must fully comprehend and account for before implementing:

  • The turnover rate is very high while margins usually stay low.
  • Due to higher turnover, you need to cut trading costs as much as possible.
  • The volume of analysis is quite high and requires strong dedication.

Automating low-risk and low-reward systems

Spread trading is a good example of a system where retail traders can extract profits consistently, but margins are very low. Obviously, such methods work excellently with automation and can be very efficient. What you need to start a spread trading system:

1.       Find an asset with high enough volatility to have more entry points.

2.       Identify a related asset that also has high volatility.

3.       Launch bots that simultaneously buy one type and sell another.

Opposite market positions on related assets can earn you a spread between two deals. Two financial assets used in the system are called legs. A good example of two legs is Ethereum (ETH) and one of its layer-2 tokens Polygon (MATIC). Usually, these assets are strongly related and can produce a spread.

The Benefits of Automating Your Crypto Trading with an AI-Powered Risk Management System

It is possible to use Artificial Intelligence to enhance various risk management methods or refine ideas that you want to implement. AI-powered risk management systems are still a thing of the future, but this future is closer than one might think.

Right now, we have multiple potential developments for the future where AI controls risks and keeps them under certain thresholds:

  • Using programs like ChatGPT to build advanced risk management systems. While some believe that ChatGPT is nothing but an text generator, it collects data from all sorts of sources making it very educated on many topics, including risk management. Upcoming versions of the program may be quite good at creating something that can be efficient in the world of finance.
  • Special services offered by automation vendors like WunderTrading. In the nearest future, many automation vendors will start offering unique services to allow the inclusion of AI-generated scripts and systems that manage risks better than an average human. For many users, such automated crypto trading systems will be incredibly useful!
  • Training AI learning models on the market data and various types of strategies. We have the data on markets that can be accessed easily. With companies offering cloud computing services that allow for quick machine learning using powerful GPUs, we will be able to implement new exciting risk management models into existing algorithmic trading software.

It is too early to fully rely on anything produced by expert AI systems, but we are getting closer to the moment where these systems easily outperform even the best analysts. Keep an eye open for such developments in the fintech industry. They will be game changing!

Best Practices in Implementing Cryptocurrency Risk Management Strategies & Tools

The future of risk management is undeniably in the potential that some interesting software tools and AI systems show in regard to analyzing financial assets. The current approach to risk-reduction revolves mainly around hedging to protect from unforeseen issues, but powerful AIs may be able to predict any risks and build highly efficient strategies that will make many forecasters obsolete.

Before we talk about efficient ways to incorporate AI risk management in your trading system, we need to talk about some important developments happening right now in the fintech industry.

  • Deloitte recently published a study conducted in the banking domain. The main topic of the study was the effect of using AI in risk management systems employed by financial institutions. Banks have to assess people who ask for loans and Deloitte’s BEAT can analyze voice interactions to identify people who display qualities of a reliable person.
  • The Financial Times recently talked about the potential of AI and ML models in the financial sector, but many financial institutions are still opposing the idea of a widespread adoption of these models. The biggest issue, according to experts, is the quality of data fed to machine learning algorithms. Refining the data used for learning is the biggest priority before we can tackle the issue of fully automated risk management.
  • The Frontiers published an article written by a group of scientists and financial experts talking in detail about the importance of creating ground rules and employing trustworthy and responsible AI systems to build automated crypto tools, credit assessment algorithms, and more. The concern these educated people have is that we are still not ready to use these powerful tools effectively without causing any harm to users.

These examples indicate the complex predicament of the financial industry that wants to use the power of AI and ML models but struggles to identify good ways to implement them without putting themselves and their customers at risk. When it comes to cryptocurrency risk management strategies devised with the help from artificial intelligence, the data about their performance is insufficient and unreliable.

To implement these risk management systems and integrate them with existing crypto portfolio management software, we need to have more data on how these automated risk management tools works and perform over longer periods. Right now, we do not know much about average results, potential downsides, and even the degree to which they affect overall portfolio performance.

To put it shortly, the best AI automated risk tools are still understudied. We simply do not know how to implement these AI systems appropriately. However, it does not mean that you should stop researching them!

5 Must-Have Features in an Automated Crypto Risk Management Tool

While we are looking for something that can be effectively used to manage financial risks and build autonomous automated risk assessment models, it is a good idea to talk about what features must be present in any algorithmic trade execution platform that has the necessary toolkit to keep risks under the danger line.

1.   Adjustable settings. Risk tolerance is a personal metric that each retail trader understands differently. It means that every single trader must be able to change various settings to their liking.

2.   Reliability and consistency. Backtesting and regression analysis must be performed to ensure that these risk management strategies actually work and won’t negatively affect users.

3.   Adaptability. One of the most important features for automated crypto tools is flexibility. It must be able to work with different types of assets, markets, and platforms.

4.   Affordability. When it comes to any form of risk-reduction systems, turnover becomes a huge issue. We need tools that are cheap and can be scaled up without cutting holes in our budgets.

5.   User protection. AI systems can “hallucinate”, produce weird results, and otherwise perform unexpectedly. These occurrences should not impact the experience of users.

A very serious issue for this particular topic is that we do not have the data to assess how currently deployed systems work. Past results do not guarantee future performance. It is true with human traders. It is also true with artificial intelligence systems. Many companies are rushing to implement expert AI systems without any concerns for their users!

It is imperative for many service providers to focus on building systems that work as intended and do not produce results that can be harmful to financial wellbeing of users who pay to use them. Risk management is a very complex skill that requires experience, knowledge, and intuition. Contemporary AI systems are still lacking in the latter.

Forbes estimates that the AI industry will grow by at least 37% annually from 2023 through 2030. The retail industry alone is expected to spend over $20 billion on AI products by 2026. Over 90% of financial institutions are already using or plan to start using AI systems in the nearest future. It is inevitable that we will see more AI and ML tools in the financial sector too.

The main takeaway

The cryptocurrency industry is growing in both size and complexity. Very soon, human analysts won’t be able to include everything in their analysis. However, we can expect many AI automated risk management systems to take everything related to the crypto industry in consideration. Whether you like it or not, the future of finance is tightly connected to the development rate of the AI industry. Hopefully, we will be able to start meaningfully experimenting with many AI risk assessment tool!


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