If you are curious about the world of crypto trading, you may have encountered expressions such as “bots”, “high-frequency”, “algorithm”, “trend following” or “mean reversion” – some familiar, some new. At HAL (formerly Napbots), we believe learning about crypto should be easy. We hope the differences between trading bots frequencies will no longer be strangers to you once you have read this article.

Low, Medium and High Frequency: what's the difference?

Faster Than You Can Think

Simply put, a bot is an autonomous program which is buying or selling an asset following on a computer based algorithm. When talking about bots, “frequency” refers to the number of transactions a bot makes in a given time period. 

Given that the crypto market is highly volatile, it makes sense to have automated instances perform actions. Prices can rise or fall steeply within minutes, and humans simply don’t think (or act) fast enough. 

Characteristics of Low, Medium and High Trading Frequencies

Source: HAL, CoinShares

High-frequency trading (also spelled HTF) bots are meant to be especially fast. It only takes them a second, or even a microsecond, to buy or sell an asset. HTF bots take advantage of market fluctuations by acting on them before humans can intervene. Their objective is to make as many transactions as possible in a given day and to rake in micro-gains. 

Mid-frequency trading (MFT) bots are usually trading on a 15 minutes to 4 hours time frame. They are designed to capture intraday trends by reacting to market situations within a single day. On HAL, Pulse Hourly, Dynamic and Artificial Intelligence (AI) Pick strategies are all MFT strategies. 

Long-frequency trading (LFT) bots execute transactions over wider time frames: 4 hours, one day, a week… Their investment horizon spans several days or even months. They are mostly used in long or medium-term trend-following strategies. All Wise strategies as well as Pulse: DOT on HAL LFT strategies. 

When Strategy Matters More Than Speed

Bots are helpful in executing orders, but their real value lies in the strategy they follow. Of course, there is no single, “perfect” strategy for bots – just as there isn’t one for trading in general. Bot designers and users have to choose the strategy that fits them best depending on their profile and their understanding of the market. 

Trend-following bots aim to stick to the market. The bot detects intraday trends, and uses its order speed to “beat the market”. If the bot analyzes the market as bullish (with rising prices), it will buy the asset. Conversely, if the market is bearish (with falling prices), it will sell the asset. The logic behind this is that the trend will continue. If so, the bot will have bought the asset at a low price in a bullish market, while selling it before its price keeps falling in a bearish market.

Mean reverting is another strategy that can be applied by trading bots. Mean reverting theory posits that, sooner or later, the price of an asset will return to its long-term average. The bot therefore buys the asset when it is oversold, that is, when the asset has been trading at lower price levels and there is potential for its value to rise. When the asset is overbought, and trading at higher levels, the bot sells it. When the price returns to its range, the price unwinds its positions – it stops operating.

Advantages and Disadvantages

Algorithmic trading relies on robust and rigorous rules. Once the rules have been designed, after back-testing (running the bot on past market data to see how well it would have performed), the bot sticks to them. This means the trader no longer has to be connected to the exchange or make decisions once the bot is up and running. 

However, past performance is not necessarily an indicator of future performance. When the market is very unstable, it can send what analysts refer to as “false signals”. For instance, the bot might spot an upward trend, only for the asset to crash in the following hours (or weeks). 

This advantage/disadvantage pair applies to all bots, regardless of their frequency. But what about each type of bot? 

  • High-frequency bots should be most profitable in a noisy market, as they rebound quickly (even if they are not immune to false signals). However, given their high amount of orders, they come with high trading fees, collected on each transaction by crypto exchanges. It might seem like a small amount at first, sometimes as low as 0.10% per trade for spot trading on Binance, but keep in mind that it can rapidly add up.
  • Medium-frequency bots are also designed to profit from intraday volatility, in wider timeframes than HF Bots. But they can suffer from fast reversals and local liquidity lacks when the asset is not trading at all. Trading costs may also be a concern.
  • Low-frequency bots are meant to be profitable following market trends for a longer time period, and their low frequency comes with lower trading fees. However, they do not react well to volatility in market regimes (if the market goes from rangy to trendy or vice-versa).

As with all types of trading, trading with bots has its pros (research-backing, autonomy) and its cons (risk in a volatile market). It’s up to you to decide how it can fit within your strategy.

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Investing involves risk, including the possible loss of all the money you invest. In particular, crypto-assets are a highly volatile and speculative asset class. HAL is only suitable for traders who are willing to bear the risk of loss and experience sharp drawdowns. Past performance is not necessarily a guide to future performance.

The purpose of this material is to provide objective, educational and interesting commentary and analysis on developments in the crypto-assets sector. Nothing in this material should be interpreted as constituting an offer of (or any solicitation in connection with) any investment products or services by any member of the CoinShares Group where it may be illegal to do so. Access to any investment products or services of the CoinShares Group is in all cases subject to the applicable laws and regulations relating thereto.