July 21, 2023
If you talk to professional, high-end, serious money managers, you’ll quickly learn an important thing. Something fundamental to investing and trading, but that you hardly ever hear from the talking heads on CNBC or Bloomberg or the pundits on many of the mainstream investment sites.
These money managers you speak to may have their own funds, or they may work for large institutions. They may be long-term value investors or short-term traders. They may trade in commodities, equities, bonds, crypto, spot, or derivatives. But there’s one thing that all of them will tell you is a crucial aspect of their work.
Risk management.
Crypto risk management is what separates the winners from the losers, the estates from the bungalows, and the Lambos from the Chevys. Especially during treacherous times like crypto winter.
There are many different types of crypto risk and many different methods of managing them, but they all begin with live trading data. Let’s look at two of the risks of investing in crypto you face, and what data you’ll want to have at hand to help deal with them.
Risk #1: Volatility
Technically, volatility is the statistical measure of the distribution of returns of a portfolio or the price of a single asset over a specific period of time. More colloquially, crypto volatility is the average of how much a crypto’s price will vary during a 24-hour period. The greater the variance, or price range, the more volatile the crypto is. Also, crypto volatility is a key factor in determining crypto option prices.
Because crypto volatility is so critical to crypto risk management, there are several common, useful, but slightly different measurements of it.
What data is helpful?
The broadest measure of volatility is standard deviation. The greater the standard deviation of a crypto, the more volatile it is.
Another common measure of volatility is Beta, which describes a crypto’s returns against those of a relevant benchmark or other crypto. Beta uses a baseline of 1.0, so if your crypto has a Beta of 1.2 vs Bitcoin, it's 20 percent more volatile than Bitcoin.
Beta is particularly useful for measuring a potential purchase’s effect on your current portfolio. If you want to purchase a crypto, and its Beta is .90 vs your current portfolio, it means that this purchase will bring down the volatility of your portfolio. If it’s 1.10, it will raise the volatility.
Experienced investors use live crypto indicators, metrics, and signals every day, and Amberdata provides them here.
Risk #2: Liquidity
Crypto markets are relatively small compared to traditional financial markets, which can make them more susceptible to large buy or sell orders affecting the price more significantly.
Furthermore, the less liquid a market is, the harder it is to get even a small order executed at the price you specify. And, low market liquidity is always the most dangerous at exactly the worst time—in, say, a rapidly declining market that you need to get out of quickly.
What data is helpful?
The best way to avoid liquidity risk is to trade only in very liquid markets. Luckily, there's quite a bit of live crypto market data that will help you do just that.
- Market capitalization - Total value of all the coins in the protocol. Large market cap implies a greater ability to trade significant volume (although not all coins are actively circulating).
- Circulating supply - How many tokens are actively traded.
Trade volume - High trade volume is a very good definition of a liquid market.
- Order book - The order book displays the list of current buy and sell orders for a cryptocurrency, along with the corresponding price and quantity.
- Market depth - Market depth data is similar to the order book, except that it shows the total cumulative volume in the order book. It provides an overview of the liquidity that exists at each price point, often presented in the form of a depth chart or other type of graphic.
- Bid-ask spread - This is the difference between the price a buyer is willing to pay for a crypto and the price a seller is willing to accept. A small spread usually indicates a more liquid market because the asset can be bought or sold without causing a significant price change. A small spread also implies there will be no execution slippage under normal circumstances.
- On-chain metrics - On-chain metrics that monitor blockchain activity are not direct indicators of trade liquidity, but they can be helpful in determining whether the protocol looks like a possible investment, or if its activity is too small or concentrated. Some of these metrics include number of active addresses, transaction count, the concentration of coins in wallets with extremely large balances (ie, “whales”), and the dollar value of the blockchain's daily transaction fees.
Risk Management Strategies Enabled by Real-Time Data
Stop-loss orders
A crypto stop-loss order is a trade order you place with an exchange that instructs them to automatically execute that order when the crypto’s price reaches a certain price, predetermined by you. A stop-loss order can either be a sell order (for a crypto you are long) or a buy order (for a crypto you are short).
For example, you are long on crypto at $100. You put in a stop-loss order at $90. You have now limited your loss to 10 percent. If the market turns against you, once the crypto hits $90, your sell order hits the market.
It’s important to note that in a very fast and volatile or thin market, your order may not execute exactly at $90. The market may run through your stop and you’ll execute lower. Nonetheless, stop-loss orders are a great risk management tool for two reasons:
- They force you to think in advance about how much risk you’re willing to take on a position or your portfolio. This sort of hard, rational planning in advance of market moves forces you to truly think through what you’re trying to accomplish and how much risk you can handle.
- Putting stop-loss orders in place lessens the possibility of human error—that you’ll change your mind when the market is moving against you, not sell, and subject yourself to even larger losses down the road. This happens all the time. Traders ride positions down to $0. It’s just human psychology, and it’s been happening since the first cavemen traded rocks with each other. It's almost always better to consider your risk in advance, set an automatic order and, if the market moves against you, live to trade another day.
What data is helpful?
For the setting of stop-loss orders, the two most important data sets you need are live crypto trading data and historical crypto trading data. The live data, of course, helps you by showing you the current price and activity. Live trading data can help you determine where to set your stop-loss orders in the event that you want to place them not by percentage loss, but just beyond trading price resistance points.
Portfolio Rebalancing
One of the most basic risk management techniques, one that you’ll hear advised over and over, is diversification. Buy into a number of positions, not just a single one, and even better, buy positions that don’t move in lockstep with each other.
The simplest form of diversification is when you divide your portfolio into equal dollar amounts, and buy equal dollar amounts of various cryptos. Example: you have $1 million in cash and want to buy ten cryptos. For simple diversification, you would split your cash into ten equal tranches of $100,000 and use each $100,000 to buy a single crypto.
So, if Bitcoin is trading at $25,000 and Ether at $2,000, that would mean you’d buy four Bitcoin, 50 Ether, and have eight tranches of $100,000 each to buy eight other cryptos. Each position would equal 10 percent of your portfolio.
So far, so good.
However, over time, as the different coins appreciate or depreciate differently, sooner or later your balanced portfolio will become unbalanced. The Bitcoin position might equal 25% of your portfolio and Ether, 12 percent, and the other eight positions, a total of 63 percent rather than 80 percent.
What do you do? Rebalance. You buy and sell the ten cryptos such that each holding will again total 10% of your portfolio. This helps maintain your desired level of risk and keeps the portfolio aligned with your long-term goals. Rebalancing is usually performed at regular intervals, but it can also be triggered by significant market events or changes in your financial situation or investment goals. To see some more complex rebalancing strategies, please see here.
VaR
Value at Risk (VaR) is a statistical technique used to measure and quantify the risk within an investment portfolio over a specific time frame. VaR modeling determines the probability of a certain loss given normal market conditions, making several key assumptions.
It calculates the potential decline of an asset or portfolio over a defined period for a given confidence interval. For example, if a crypto portfolio has a one-day VaR of $1 million at a 95 percent confidence level, there is only a 5 percent probability that the portfolio will lose more than $1 million in a one-day period.
There are several methods to calculate VaR, including the historical method, variance-covariance method, and Monte Carlo simulation. Regardless of the method, VaR is useful because it looks at potential losses in normal market conditions, helping you plan their risk management strategy accordingly.
Stress Testing
Most people are familiar with the term “Stress Test” because, as part of the fallout from the subprime-induced market meltdown of 2008, the U.S. government ordered large financial institutions to undergo stress testing. But stress testing is an important form of risk management for individual investment portfolios also.
Stress testing is the use of a computer simulation to measure the robustness of a portfolio during all market conditions. Such a test usually uses historical data as the underpinning of its simulation, which oftentimes for portfolios is what’s called a Monte Carlo simulation. A Monte Carlo simulation uses repeated random sampling, often on the order of 10,000 samples, to measure the probability of outcomes (particularly extreme outcomes.)
In the context of portfolio testing, you may be running a very aggressive portfolio and are looking for 30 percent annual returns. You can run a Monte Carlo simulation using long-term historical data for all of your portfolio’s components. If the simulation shows that the high probability of a 30 percent return also comes with a high probability of losing everything, you'll probably reconsider. A prudent trader would use this simulation as a starting point to adjust the portfolio to try to maintain a decent probability of a high return while lessening the downside risk.
Other forms of stress tests can also introduce specific values to some market variables, such as what happens during a rapid increase in interest rates, a foreign currency failure, or a spike in the price of oil.
A single comprehensive view
Because the crypto markets are so diverse, your various data needs when managing risk can be confusing and complicated. Your best approach is to find a single vendor who can provide you with a comprehensive view of the entire crypto economy.
At Amberdata, unified API and data services, provide datasets that are indexed, searchable, and normalized to time series, enabling a comprehensive view across the entire crypto-economy through a single integration point, allowing for an accelerated time to market for your digital asset offerings.
Click here to request a demo or more information on how we can help you.
For high-quality, granular data feeds reach out to hello@amberdata.io to inquire about our API solution.
