OP:


I think what you wanted to do was a good idea (profiting as much as possible from bitcoin's price behaviour but integrating trading into the mix but with the least effort (aka fewer trades possible)), but the actual implementation might not be the best one.

First of all it is based on assumptions that should not be taken for granted:

"(1) Bitcoin can experience massive gains in very short time, so by default, your position should be long.

(2) However, if there is a very clear trend reversal to the downside, sell.

(3) If you sold too early (into a downtrend that didn't manifest), don't hesitate to buy back at a small loss if necessary, because: see (1) above."


A trader should not be biased, for all he knows, bitcoin is a bubble that might continue to crash once it actually bursts. So the "favour a long position" strategy might work as long as bitcoin is in "pump mode", until it doesn't (the "dump mode"). A trader should only focus on the chart, it should tell him all he needs to know.

I think you should just forget about algorithms and complicated indicators and just trade with very basic TA tools like triangles and trend lines (on high time frames only, considering you want to minimise effort as a trader) depending on how much effort you want to put in the "trading part" of the investment strategy (if you want to incorporate short selling or not, how many trades do you want do perform every a month/every few months, etc).


Instead of "LazyWhale algorithm", it would just be the "LazyWhale strategy".

An example using 2013-2014:



Most of these is what I actually did in my trading (while shorting on the downside instead of just holding USD) and posted them here (as others have, it's just to say that it's not just hindsight ), playing smaller waves too tho (it depends how "Lazy" you want your "LazyWhale" to be).


If you use complicated indicators and such, your "buy signal" might be a mistake and you might buy at a top. You should instead use simple trade setups like "buy the wedge breakout/sell the wedge breakdown" because that way you know that when you execute your trade the price will move in your direction massively right away. You're able to time the market (and estimate the magnitude of a trade) and avoid getting chopped off by consolidations and noise that might mess with your indicators and algorithms.

PS: you cannot really see the double top in that high time frame but on lower ones it is pretty flagrant.

Alright. Initially, when people asked how the algorithm works internally, I refused to specify it any further. Now that nobody cares anymore, here's the basic idea ^_^

Note: I'm not going to fill in the exact numerical parameters of the the technical/indicator signals. No need to encourage frontrunning. But in principle, with the details I'm about to give, you could find your own (and probably very similar) parameters by training the individual parts over the market data that is the same for everyone.

The trade signals of this algorithm are a complex condition based on three technical signals.

Signal #1, the "main" signal, is a medium term momentum signal. Think "daily EMA20+10 crossover", but the average I use is instead a Hull type average (and the parameters are obviously not 20/10).

Important to note here: signal #1 is symmetric wrt 'buy' and 'sell'.

If used alone, signal #1 is profitable over the global bitcoin history (GX, BS, BF), and roughly uniformly profitable as well. However, it yields slightly too many trades, and tends to sell too early during major rallies. So, it is not the desired signal yet that only sells when it really has to.

Signal #2 is a short-term momentum signal. It acts as a filter applied to #1. Think of it as an "optimal entry/exit" filter. It cannot initiate additional trades by itself, it can only delay a trade based on signal #1. It is basically capturing the idea: if the medium term is up, but the short term is drastically down, better wait a bit before you buy (similar for delaying a sell trade). Signal #2 is buy/sell symmetric as well, and I don't really think it is problematic (also, leaving it out doesn't change the results from a pure signal #1 strategy much).

Signal #3 is the tricky one. You could call it the "bubble filter". Just like #2, it doesn't do anything other than (possibly) delay signals coming from #1, i.e. it doesn't add to the total number of trade signals but only *reduces* them under certain conditions.

It defines certain market conditions (think: long term momentum) to be so strongly bullish that a mid-term momentum signal from #1 that says 'sell' should be ignored for the moment, until either the mid term is up again, or the market condition becomes less bullish, in which case the 'sell' goes through.

And here's the tricky bit. Signal #3 is not buy/sell symmetric. It only delays 'sell' trades, but not 'buy' trades, in case the long term momentum is flipped around

I know. That buy/sell asymmetry is a big violation of all that is holy in algorithmic trading. Reeks of overfitting.

Still, given the history of the Bitcoin market so far, I can see some justification for it. It works well for 2011, 2012, 2013. In 2014, it still works well enough for the first half, but from then on, it's not really justified anymore.

I guess it all depends now if Bitcoin will ever go through a significant bull market again, and when. Let's say it does ("Bitcoin is dead" users need not reply ). That still doesn't mean that the exact parameters of the "bubble filter" are optimal, but it's probably not going to be the millstone around the algorithms neck that it is right now, delaying sell signals when it really shouldn't.

In a way, I'm making a human choice for my algorithmic method here: the assumption, that in the long run, it is better to err on the side of buying than on the side of selling - a choice that is justified by the global history of the market, but not by the more local one.

Comments welcome.

Now, if I may suggest something is to check, if signal #1 has the long enough observation window? That is whether the medium term signal is enough to filter our major trend change and thus adjust the asymmetry of signal #3 accordingly.

sig-0=: if average-long > average-long-sig, bull. if average-long < average-long-sig, bear.

bull: keep your current code
bear: provide symmetric modification to sig-3

True, but: how do you backtest a discretionary strategy (based on trendline estimations, among others)?

There's no way around the following dilemma:

Using the full range of human intuition, pattern recognition and, well, intelligence, yields "discretionary trading". Quite possible the most profitable way to trade, but also the hardest one to estimate, quantitatively, if and how profitable it is.

Algorithmic trading is a reduction of the above in terms of available algorithms (not everything a human can do can be implemented as a TM), it gives some additional capacity however (crunching big data), and it comes with a boon: running the algorithm on historical data to estimate performance.

I'm dismissing neither. Both are valuable tools. I just reject any easy answer that says "only algo trading works", or "only discretionary trading gives good results". Both answers would be missing the trade-off involved in this decision, imo.