Coming up with an algorithmic trading strategy for Bitcoin that will work perfectly takes time and effort but it totally worth it. You have to start by looking at the raw data as well as analyzing several indicators like the RSI, MACD, EMA, etc. The purpose of the examination is to locate a pattern that is not only obvious to the human eye but also one that keeps repeating itself.
In its simplest form, the strategy is all about identifying a drop in price and then triggering a buy and then identifying a peak price to trigger the sell. This is where it gets really interesting and risky at the same time – interesting because if you can predict these lows and highs, you will smile all the way to the bank and risky because it is not possible to be 100% accurate. But you really do not need to be 100% accurate in order to be profitable. The secret is to win big and lose small.
There are four main methods of trading bitcoin with algos:
• High-Frequency Trading – this is where large volumes of transactions are executed at high speeds
• Scalping – this is where you jump in and out of trades quickly with the aim of making profits from small changes in price.
• Transaction Cost Reduction – this is where huge trades are broken down into smaller ones and then fed into the market over time in order to get the best prices.
• Arbitrage - this is where you simultaneously buy or sell the Bitcoin in different markets. The idea is to make a profit from the differences in prices across the different exchanges.
Indicators play a vital role when coming up with relatively accurate algos. An indicator can be said to be a function that takes raw data, transforms it, and gives a certain output. The indicators will, therefore, analyze and transform raw market data thereby giving us a sneak preview into the patterns that we can base our trades on. Some of the patterns that we see through indicators would be totally invisible to the naked human eye. The reason indicators do such a great job in making predictions is they are based on past raw data. If you were to study trading charts, you will soon realize that history has a knack of repeating itself. When creating “if this…then this” conditions in the algos, it is important to factor in other costs in the ROI. For instance, the trading fees of executing the trade must always be factored in.
After studying the market conditions, the charts, and the indicators, you will know exactly where to open and close your trades beforehand. Once you know the criteria for opening and closing a trade, you can tell a computer to execute the trade on your behalf based on those conditions. As long as you setup the instructions correctly, the computer will execute the instructions faster and more accurately than you. An algorithm makes it possible to execute trades based on complex rules. For instance, you can combine several indicators and then scan the market for assets that meet the conditions for entering a trade.