Algorithmic Trading Strategies

Algorithmic trading looks to remove the human factor and instead follows a predetermined, based statistical strategies that can be run 24/7 by computers with minimal supervision.

Computers can offer several advantages over human traffickers. For one, they can remain active all day, every day without sleep. They can also analyze the data properly and respond to changes in milliseconds. To top it off, they never emotional factors in their decision. Therefore, many investors have long realized that the machine can make a very good trader, given that they are using the correct strategy.

This is how the field of algorithmic trading has grown. While it began with computers traded in traditional markets, the rise of digital assets and 24/7 exchanges have brought this practice to a new level. It almost seems as if the automated trading and cryptocurrencies made for each other. It is true that users will still have to work out their own strategies, but when implemented properly, this technique can help traders take their hands off the wheel and let the math do the job.

What are the main strategies?

The main philosophy behind most of algorithmic trading revolves around using the software to see profitable opportunities and jump on them faster than humans can. The crypto genius guide most common practice is momentum trading, mean reversion, arbitration and various machine-learning strategies.

Most algorithmic trading strategy centers around identifying opportunities in the market based on the statistics. momentum trading strive to follow the current trend; Average return on statistical divergence seen in the market; arbitration search for differences in the exchange rates of different spots; and machine learning strategy to try to automate more complex philosophy or integrating several at once. Not one of these is a simple guarantee for profits, and traders should understand when and where to apply the correct algorithm, or “bots.”

Generally, bots are tested against historical market data, the so-called backtesting. It allows users to test their strategies in real market they plan to release it, but the movement was founded on the past. Some of the risks in doing this can include “overfitting” – that is when the bot a feint around historical data that does not truly reflect the current state, thus leading to a strategy that failed to really produce. A very simple example would be if you design and test a bot to the data of the bull market but began to run it live in a bear market. Obviously, you will not see the results you expect.

What momentum trading?

momentum trading is based around the logic that if the dominant trend has been seen in the market, the trend makes sense to continue at least until the start signal comes in that it has expired.

The idea with momentum trading is that if a particular asset has been engaged primarily in one direction, for example, a few months, then we can assume this trend will continue, at least until the data started to show otherwise. Therefore, the plan will buy at every dip and key profit on each pump, or vice versa when shorting. Of course, traders should be aware of when the market is showing signs of a trend reversal, or a similar strategy could begin to turn around fast enough.

It should also be noted that the merchant does not have to adopt a strategy that tries to buy and sell in real-time lows and highs, or the so-called “catch the knife,” but lock in profits and buy back at a level that is safe enough. Algorithmic trading is ideal for this, because users can simply set the percentage they feel comfortable with and let the code do the rest. This technique alone, however, can be ineffective if the market is moving sideways or more volatile that a clear trend has yet to appear.

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