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Developing Bitcoin Algorithmic Trading Strategies
Traders Magazine Online News, July 31, 2018
Developing algorithmic trading models and strategies is no simple task. To make matters worse the current state of crypto is highly volatile and rapidly changing. The market has become war zone due to regulations from the SEC and various governments targeting crypto exchanges. Despite all of the negative news, many traders are making it big in day-trading crypto assets.
In a previous post I wrote about our progress in making short-term predictions. In this post I take it to a next level. I will illustrate our efforts of how we are incorporating the predictions into a successful trading strategy.
Manual modelling
Developing an algorithmic model/strategy usually starts by looking at the raw data but more importantly analysing several indicators such as SMA, MACD, EMA and RSI. The whole point of the process is trying to find some patterns that are pretty obvious to the human eye, but also that these patterns are reoccurring throughout history and hopefully will continue to do so in the future.
In theory
Most strategies are based around making a profit, so people look for ways to detect a valley so they can trigger a “buy” signal, and then let the system wait until there’s a peak to trigger a “sell” signal. This is risky business, since there are no guarantees, i.e.: a system cannot accurately tell (given historical events) whether that market price is at a valley or not?—?it can only make a calculated guess. And that’s the whole point, we try to make a calculated guess, that is the plausibility of being at a valley/peak and triggering a trading signal (either buy or sell).
In reality
The reality is much more tricky than the theory. A system may indicate it’s at a valley, but a few minutes later the price drops even lower.
If you have ever written code for large software projects then you know that error/failure rate grows in proportion to every new line of code added. This means that the more code you write, the more mistakes/bugs/errors you induce?—?humans are not perfect, neither are programmers no matter how brilliant. When we write code we usually have a clear goal in mind, thus we know what the output should be given some clearly defined input. But try doing that with input date of (crypto-) assets (e.g. Bitcoin), you’ll quickly realize that it’s not a trivial thing to do. The input is never the same, and we cannot simply rely on a bunch of “if this?—?do that” code. However, that’s the only language a computer understands, it’s our most reliable tool for the job.
To make our job easier we have to introduce math and statistics to aid us. This is done by creating indicators, as mentioned earlier, such as SMA and MACD. An indicator is a function that takes raw data, transforms it and spits something new out. Indicators make our lives easier in analyzing the raw market data, they allow us to detect/see patterns that we wouldn’t otherwise see. If you know a tad about trading then you’ll know how useful the MACD, long & short SMA plots are.
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