Investing in any cryptocurrency involves risk. In this article, we explain how to reduce your risk and the most common risks associated with investing in tokens or yield farms.
Position Sizing In Crypto #
Position sizing is a crucial strategy for reducing and mitigating risk while investing in fast-moving instruments such as yield farms and other DeFi projects.
By using appropriate position sizing when making an investment, you control the level of risk added to your overall portfolio. The “position” in position sizing refers to the number of units invested in a particular farm, token, or vault.
An investor will take into account their level of risk tolerance when deciding on position sizing – a more risk-averse investor will tend to keep their positions smaller relative to the account total.
Example
Investor: Alice
Portfolio Value: $30,000
Risk Tolerance: Moderate
Position Sizing: 2%
Position: $600
“Alice” has $30,000 to invest. She has decided to risk a maximum of 2% of her investment capital on a single trade. Alice has found an interesting token or farm in the DeFi space: high APYs are promised and she expects the token to have enormous growth. To limit the risk to her total portfolio, Alice decides to input 2% of her portfolio capital. This means that she can invest a maximum of $600 (2% * $30,000) in the farm/token.
The advantage of this position sizing strategy is that even if Alice were to be rug pulled five times, she would lose a maximum of 10% ($3000 or $30,000) of her total portfolio.
Stop-Loss orders #
Another method commonly used to manage risk is the stop-loss. Stop-loss orders are mainly used on centralized exchanges. Not all decentralized exchanges support stop-losses: Pancakeswap or Sushiswap do not support this feature (yet). Synthetix and Fulcrum are examples of decentralized exchanges that do support this feature.
Several methods can be used to determine a stop-loss. The first method is the percentage method. This method means that a certain percentage is used to determine the stop-loss. 2-20% is often chosen. Let’s say you bought a token worth $50., the stop-loss for a long position is then set a stop-loss at $40 (80% of $50). The stop-loss will exit your buy and immediately sell your position to ensure you don’t lose more money when the price hits $40.
Trailing Stop-Loss Orders #
Another method is the trailing stop-loss. The difference between a normal and a trailing stop-loss is that the trailing stop-loss follows the asset’s price. Suppose an investor has taken a long position of $60 on a certain token. He then places a trailing stop-loss order of 6% ($3.50 below the token price). If the price of the token rises, the price of the stop-loss order also rises: after all, there must remain a difference of 6% between the token price and the stop-loss price. By doing so, the investor can take advantage of a rising price, while if the price falls, the stop-loss order is triggered.
Finally, there are various methods that are based on trading indicators and technical analysis. These can be based on the moving averages, relative strength indexes (RSI) or support and resistance lines. Suppose a trader has determined a temporary trading range for a token through technical analysis. The investor has drawn both support and resistance lines in his trading plot.
If he wants to open a long position, he can place it just above the support line. If the price moves up to the resistance line, he can sell and take his profit. But what if the price goes down? To reduce the risk, the trader can place a stop-loss below the support line. This order can be based on percentage, on indicators etc. If the price falls out of the trading range, the stop-loss will trigger, selling the token and protecting against further losses.
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Conclusión #
The risk reduction methods mentioned in this article can be helpful when reducing risk, but there are still many risks involved when investing in crypto and DeFi yield farms. It is imperative to do sufficient research before investing in a DeFi project. The farms list at Rugdoc.io provides up to date analysis of hard rug risk in new yield farms.