Unless you can include the <i>complete irrationality</i> of the users fueling the crypto bubble to get rich into the statistical model, any statistical analysis of those prices will be misleading.<p>Traditional historical trend analyses of stock prices assume the market is <i>somewhat</i> rational. (but not 100% efficient, which is where money can be made)
At our local Chicago Python User group chapter (chipy.org) one of the organizers of the Financial Group did a similar analysis. HODL is extremely hard to beat if not impossible.<p>See <a href="https://github.com/Chipy-Finance/CryptoTechnicals/blob/master/FinSIG%20Dec%202017%20-%20Technical%20Crypto%20Strategy.key" rel="nofollow">https://github.com/Chipy-Finance/CryptoTechnicals/blob/maste...</a><p><a href="https://github.com/Chipy-Finance/CryptoTechnicals/blob/master/Buy%20and%20Hold.ipynb" rel="nofollow">https://github.com/Chipy-Finance/CryptoTechnicals/blob/maste...</a><p>There is more in the github directory also.
I ran some tests of my own with simulated automated trading strategies over the past few years of historical Bitcoin price data. I used a genetic algorithm to encode a range of time parameters for MACD, along with different trading triggers and amounts. After a day or two of running it against a huge number of permutations and generations optimized for highest overall return, I came to a similar conclusion: the fittest configurations could do fairly well in automated trading (the more active ones were highly sensitive to trading fees [which were simulated] and market depth [was not simulated]), but over the span of more than a few months, a simple buy and hold strategy produced better returns in almost any time window.
This may be a bit pedantic, but the phrase the author uses in his disclaimer "Experts agree that cryptocurrencies are a bubble." bothers me because it's kind of misleading.<p>The actual fact at hand is ~96% of economists think that the crypto market has been subject to a massive speculation bubble as of November. While that's sort of close to Roman's disclaimer, there is some nuance that ought to be given, since a) 4% of professionals dissent, and b) the question asked of economists had a temporal element to it, and they might have answered differently if the market hadn't tripled in value in as many months. It's the difference between 'the housing market is in a bubble currently', and 'housing is a bubble'.
Don't work with closing prices for statistical analysis. Work with returns (really log returns). This would have simplified some of the code, and made the plots in the same units rather than requiring separate axes.<p>More detail: <a href="https://quantivity.wordpress.com/2011/02/21/why-log-returns/" rel="nofollow">https://quantivity.wordpress.com/2011/02/21/why-log-returns/</a>
It's an open secret that "buy and hold" is hard to beat.<p>This is the reason why ETFs and other unmanaged asset options have become so popular in recent years.<p>But that insight is not new. The phrase "time in the market beats timing the market" goes way back and also popular stock market participants like Buffet or Kostolany have been saying this for decades.<p>With regards to cryptocurrencies, we are seeing in a condensed time frame the same development that for example the stock market went through. Cryptocurrencies might become a new asset class but that doesn't mean that it works financially differently than the other asset classes. So it should be no surprise that the same trading strategies end up being the most profitable.
My next step is to implement strategy testing. I would like to end this series with a classifier that predicts buy or hold.<p>Follow me on Twitter to get the latest updates: <a href="https://twitter.com/romanorac" rel="nofollow">https://twitter.com/romanorac</a>