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Market volatility, illiquidity can be very profitable for Bitcoin traders

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Introduction to "Spreads"

Cryptocurrency markets are often volatile and suffer from periods of limited liquidity. Combined, they increase the risk profile for Blockchain active investors

But with increased risk, the reward potential should also increase. In this article, we explore a method used by professional traders to manage volatility and liquidity issues and explore ways to capitalize on these inefficiencies.

Introduction of medium reversion strategies. Some strategies, however, benefit from volatility and even illiquidity. At the same time, these strategies help mitigate market inefficiencies and should be rewarded for doing so.

The medium reversion is the assumption that the price of an action will tend to approach its average price over time. In other words, the deviations from the average price could be exploited for profit, knowing that the price should tend to return to the average over time.

A simple implementation of this strategy involves and a purchase price, as a market maker. If the market is actually a return to the mean, then the strategy takes advantage of the difference between the purchase price and the selling price. The difference is also called the quoted range.

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Medium reversion strategies negotiate the market as if the market was swinging around a fair price for an asset. Fluctuations are attributable to the uncertainty of other market participants or illiquidity at different levels of the market. Medium Reversion Strategies Fill the Gap Between Buyers and Sellers and Can Seek Profit

This Strategy is More Effective in Markets That Are Both Very Volatile and Average . Volatility measures the size of market fluctuations. With high volatility, market fluctuations are important and the return to mean strategy has a high probability of generating a profit. In low volatility markets, only low spreads are possible and the strategy is less profitable.

The best reversal scenario for averages is a side market characterized by high volatility or market fluctuations. This market scenario is damaging to trend tracking strategies, but profitable for market or average return strategies.

Simple Example

The figure below shows an example of a profitable trading day. reversion strategy. At the beginning of the day, buy and sell orders are placed on the market. The day is growing and volatility is driving a random price

Throughout the day, the strategy buys a Bitcoin at $ 4377.70. At some point later in the day, you sell this Bitcoin at $ 4421.48 and make a profit of 1%. From this profit, fees must be added or deducted. Some exchanges charge as little as 2.5 basis points for providing liquidity using such passive strategies

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Source: Kraken, 02/10/17, Buy it: $ 4377.70, Sale: $ 4421.48

Add to cart Average Reversion Work?

As we have already learned, average return strategies capitalize on the gap between buying and selling prices, usually around of an average price, and all assumptions about the actual price are taken into account.

In general, the purchase and sale orders are determined by

Buy / Sell = average price +/- spread

The difference is determined largely by volatility market and the asymmetry of its returns. undermined by the likelihood of upward or downward market movements. Such movements are not usually equally likely and therefore the gap is adjusted.

Volatility determines the size of the gap. With high volatility, buy and sell orders are executed with a higher probability because the market has larger price fluctuations. On the other hand, if the volatility is low, the buying and selling might not be affected and the strategy retains a long or short position, that is to say either to hold the position. underlying asset or hold cash and be exposed.

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To measure the volatility σ of the cryptocurrency markets, usually an exponential weighted moving average (EWMA) is applied with:

 EWMA "src =" http: // cointelegraph. com / storage / uploads / view / 6f6927f4d0193ef00a54e7c99154319f.png "title =" EWMA "/> </p>
<p> Recursion is usually initiated with the variance over the first 10 days. <strong> λ </strong> determines half of the half-life is the time required for the system to forget half of its previous value. With the daily data, the half-life is generally fixed at 10 days and therefore <strong> λ </strong> at 0.93 </p>
<p> The figure below shows the annualized volatility of Bitcoin prices estimated by the EWMA mod The Bitcoin fluctuates around an annualized volatility of 116% </p>
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<h2 dir= Performance of the Strategy

To test the medium reversion strategy, we explored the prices of buying and selling every day of Since the creation of Bitcoin until the end of September 2017. All data is taken from Cryptocompare. Given an average volatility of 116%, we chose the 1% spread spread between buying and selling prices. If yields were normally distributed, this spread would be reached with a probability of 99.6% over, for example, 10 days.

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Of course, Bitcoin returns do not follow a normal distribution. So what is the probability that an operator can both buy and sell Bitcoin in 10 days and earn 1% each day?

According to our data, this strategy would have been successful in 2297 of 2615 days, or 87.84% of the time

For this backtest, we calculated the purchase orders and selling on +/- 0.5% of the last closing price and compared this price to the highs and lows over the next 10 days. Of course, this strategy would result in large long or short positions on Bitcoin. In fact, for 189 days, the strategy would have sold Bitcoin, but not bought. And for 129 days, the strategy bought Bitcoin, but did not sell it. The main reason for this is the strong upward trend of Bitcoin prices.

To account for the Bitcoin price trend, we use the average moving yield as an estimate and we arbitrarily chose the 60-day reference period. on a previously published strategy. We also tested other retrospective periods that produced similar results, and the 60-day return is in the range of general trend tracking strategies.

The addition of the moving average to move the gap increases the number of days, average reversion strategies buy Bitcoins and reduce the number of days of Bitcoins sales. Therefore, the strategy ends with a long position on Bitcoin rather than a short position

In total, this strategy would have made 1% profit on 2288 days out of 2615. Over 327 days, the strategy either only sold Bitcoin or only bought Bitcoin. Adjusting the average price for the trend has resulted in more days than the strategy has only bought Bitcoin and therefore accumulated Bitcoin over time. As an additional rule, the strategy could sell additional Bitcoin at the end of each month or even keep it as market position.

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A trader can improve this strategy by considering the estimate of volatility and adjusting the spread. With higher volatility estimates, the strategy would seek to profit from a larger spread and vice versa.

Other models use more sophisticated supervised learning algorithms to predict market movements. While some are looking for trading signals in order books, others use market news as an input to make directional predictions (Li et al., 2014; Kanagal et al., 2017)

the liquidity of the market must be considered. We have not considered any limits on the ability to trade at quoted prices. Obviously, this is not always the case and it depends largely on the capital deployed.

Advantages of the Expanded Market

The trading of a medium reversion strategy also plays a crucial role in establishing effective market mechanisms. While the efficiency has already improved considerably in recent years, cryptocurrency markets are still facing fundamental problems typical of any new market.

Illiquidity, inefficiencies and unstable spreads are common symptoms. Medium reversion strategies provide additional liquidity to the market and therefore resolve these frictions. They fill the liquidity gap for which they are paid.

Market making strategies extend mean-reversion strategies, as they trade several times a day with relatively large volumes on both sides of the trade. Market-making strategies provide the essential liquidity for any well-functioning financial system. Sudden peaks in demand or supply, such as when traders buy or sell large volumes, are absorbed by market makers.

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Any successful Bitcoin purchase order requires a seller willing to sell the volume of coins at the indicated price. The exchange (the broker) matches the two parties for the operation to be done

This frequently leads to delays in orders and variable execution prices, to the detriment of traders. By placing orders on either side of the transaction, market makers have the ability to perform more transactions and thus eliminate processing delays. Bitcoin's markets are still seeing very unstable spreads, which are being absorbed by market makers.

By setting bid and ask prices for other traders, market makers profit from the stability and stability of spreads. The higher the liquidity, the more the spread becomes narrow and stable

Conclusion

Medium reversion strategies are very profitable in high volatility markets and can be adjusted according to trends. This strategy can be easily applied to Bitcoin trading and improve the performance of a buy-and-hold strategy. The medium reversion strategy would display buy and sell quotes around the current level of the market and would gain the gap.

Since the strategy is independent of the level of the market, it can add an independent performance to a buy-and-hold strategy. For example, the strategy could use 1% of the portfolio's assets and then aim to earn an additional 1% per day on this part of the portfolio. Historically, this strategy would have added 24% per year to the performance of the portfolio.

Return-to-the-middle strategies provide liquidity to the market. On the other hand, these strategies can end up long or short, if the market does not return to the average. In this case, they take a position on the market. On the other hand, providing liquidity should yield to these strategies a potential profit that rewards the trader for his risk. Market-making strategies extend average reversion strategies by citing more often the differences between bid and ask prices.

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About the Author

Dr. Philipp Kallerhoff is a founder of Protos Cryptocurrency Asset Management. He is a Senior Portfolio Manager and Quantitative Analyst in the Financial Technology and Hedge Funds industry. Philipp holds a PhD in computational neuroscience and is a former student of Singularity University.

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