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**Mean Reversion**

The mean reversion strategy is based on the assumption that if the price of a digital asset deviates from its average price, then it is destined to get back to its average at some point. This is not such a bad assumption to make because, in most markets, this assumption often holds true. For instance, suppose the value of a coin falls from its average price of $10.00 to a new low of $9:00 traders will buy it in bulk anticipating it to get back to the $10:00 mark. Similarly, if the prices rise to $11:00 most will sell anticipating it to fall back to the average price. The mean reversion strategy is therefore simply buying low and selling high. An algorithm can be set to calculate the median price of the asset and then programmed to make buy, sell and hold depending on how the price fluctuates from the mean.

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**Momentum Trading**

When the market moment oscillates like an ocean wave, traders can take advantage of the movements and sell at the peak of the waves before it oscillates back to a low. The assumption on which momentum trading is based is that the price of an asset will continue rising above the expected average and then eventually (and inevitably) fall back to a low. For one to be successful in momentum trading, the entry and exit of the trades must be timed well. Usually, the more volume the asset gets the higher the prices rise and as soon as the volume starts to fade, the prices start dipping too.

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**Arbitrage**

Since the cryptocurrency market is fragmented and unregulated, it is very common for the same digital asset to have a different price across different exchanges. For instance, the price of a coin might be $1.00 on Kucoin and 1.11 on Binance. Arbitrage takes advantage of this by buying and selling simultaneously in order to make a profit from the spread. This is arguably a low-risk strategy because it is not subject to market performance like most of the other strategies. However, it requires speedy execution because you have to take advantage of the price differences before they even go out.

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**Naïve Bayes**

Machine learning and AI can be paired to form a powerful trading system. Not knowing the best place to enter or exit a trade costs traders a lot of money. A computer can use ML and AI to optimize these decisions. A Naïve Bayes strategy is one that establishes the probability of an event happening based on some variables. For instance, if the price of Bitcoin fell for the last three days, what is the probability that it will keep falling today? Ideally, you tell the algorithm what to do when a certain probability is attained. For instance, if the probability is >59, you may choose to sell your assets.

As pointed out at the onset of the article, there are many more strategies that can be implemented but these are some of the most notable.