Algo Trading Strategies, Tips, And Tricks

  • Algo trading methods have the potential to make the entire investing approach much more result-oriented
  • The main crypto trading strategies are: sentiment analysis, mean reversion, momentum strategy, arbitrage, market making, and trend strategy
  • Momentum traders essentially follow market trends and momentum, and trades are appropriately performed
  • Trend trading is an algorithmic trading technique that tries to benefit by studying the movement of an asset in a certain direction

Best Algo Trading Crypto Strategies - Top 6

Algo trading strategies explained: Algorithmic trading uses computer algorithms and analysis to open and exit trading positions based on predetermined parameters. crypto algo trading strategies Algorithmic trading is the most advanced type of trading in today's market. It could make the entire investment strategy much more result-oriented. It is a trading system in which robots are programmed with a predetermined list of instructions known as an algorithm, and the computer performs the transaction based on the algorithm. This technology speeds up the trading process, making it more precise, and eliminates the possibility of human error. Trades might well be completed at the precise price and volume indicated, and the time required to conduct a deal is low. It assists in the reduction of losses caused by the time lag between making a trade and its execution. If you want to know everything about algorithmic trading and its strategies, there are several things that you should definitely take into account. The major crypto trading strategies include sentiment analysis, mean reversion, momentum strategy, arbitrage, market making, and trend strategy. We will analyze each of these strategies for algorithmic cryptocurrency trading.

Momentum Strategy

algorithmic trading tipsMomentum strategy means that there are times when markets open with the gap and continue to see their upside momentum, markets open the gap down and continue their momentum. Momentum strategies try to profit from a continuation of a certain move. The underlying assumption for this strategy is that the price moves can hold their momentum for an extended period of time. This is the most basic and commonly used algorithmic trading technique. They basically follow market patterns and momentum, and transactions are executed appropriately. Technical indicators such as moving average and price level changes are studied, and buy or sell orders are created automatically when a set of criteria according to these technical indicators are met. The momentum-based method also takes into account past and current pricing data to determine whether or not the trend is likely to continue and makes judgments appropriately. There are no complicated forecasts to make. It is basically straightforward trend-following. There are several factors that can have a huge effect on the momentum, such as Fundamental factors, news events, market volatility, and so forth. If the desired occurrence happens, the trade is executed; otherwise, it is not. Setting the algorithm in such a manner that the system is directed to purchase shares of a business when the 30-day moving average exceeds the 180-day moving average and sell shares when the 30-day moving average falls below the 180-day moving average is a basic example. This trading technique may be described as a straightforward interpretation of technical indicators. According to the fact that this is one of the simplest algo based trading strategies, it is usually used by more beginner traders rather than professional ones.

Mean Reversion

Another type of algorithmic trading strategy is mean reversion. Mean reversion theory says that the asset values and economic indicators including interest rates are most likely to revert to previous mean prices. The assumption that the potential price returns of the security will be in the reverse direction of the investment's return over some period is known as mean-reversion. The Relative Strength Index, or RSI, is a prominent indicator that analyzes the speed and variation of price changes on a scale of 0 to 100. A higher RSI number is considered to indicate an overbought asset, while a lower RSI value is considered to suggest an oversold asset when attempting to estimate the possibility of mean-reversion. Trend-following and mean-reversion methods are simple to grasp since they examine a security’s time frames and attempt to forecast its possible return, but there are several ways of interpreting previous behavior. The mean reversion algo trading methods determine a cryptocurrency's upper and lower price limits, and the algorithm works to execute trades whenever the price exceeds the usual range. The algorithms generate an average price according to the security's past data and execute a trade in the expectation that the prices would return to the average price. This indicates that if prices are quite high, they will fall, and if values are really low, they will rise. This sort of algorithmic trading method is beneficial when prices are at extreme and investors can profit from unanticipated price fluctuations. However, this approach may fail if prices do not reverse as quickly as predicted and, by that time, the moving average has caught up with the price, resulting in a lower reward/risk ratio.

Sentiment Analysis

Another important algo based trading strategies that should also be considered here is the sentiment analysis. Basically, the robot analyzes the sentiment at which people talk about an asset. For example, there is this thing called the Twitter sentiment analysis tool, which assigns a number to each tweet talking about a specific asset. If they are positively talking about it, then it gets +1 point, but if it is negative then it gets -1 point. The overall sum of these numbers then shows what the sentiment is and how strong it is. With the advancement of computer language processing and comprehension capacity, we may add and employ more elements, such as the news emotion score, and include them into our prediction model. The sentiment-based algorithm depends largely on the sentiment analysis results, which we either produce ourselves or receive from external sources. In terms of developing our own sentiment analysis model, there are several things to be taken into account and several steps to be followed. First of all, the important thing about sentiment analysis is that it requires looking for the desired articles or news. After that, you will be able to educate the model to distinguish between positive and negative emotion and put the model through its paces with large-scale text datasets. On the other hand, it should also be noted that sentiment analysis is a method of algo trading that has its own disadvantages that tell us that it is not the best and ideal tool for algorithmic trading. The first reason is that it can not detect when somebody is being sarcastic. In those cases, prediction of the sentiment becomes very difficult so the sentiment analysis can not even work at all.


Another algorithmic trading strategy that should be taken into account by every trader is the Arbitrage strategy. When there is a price gap between assets on various exchanges, arbitrage opportunities emerge. Arbitrage strategy is one of the Algorithmic trading techniques that take advantage of arbitrage opportunities by utilizing computers to identify and exploit them as fast as possible. If a certain cryptocurrency is