Hodl vs. Mayer-Multiple: What is better Bitcoin news?

Implementation of the Mayer Multiple Strategy
We enter the following code into the “Pine Editor”:
strategy(“Mayer Multiple total”, overlay=true, pyramiding=1000, default_qty_type=strategy.cash, default_qty_value=100000)
psma_length = input(200, title=”Price SMA Length”)
threshold = input(2.4, title=”Threshold”)
startyear = input(2016,title=”Start year”)
fromYear = year >= startyear
toYear = year <= year(timenow)
firstofmonth = dayofmonth == 1
ma = sma(close, psma_length)
multiple = close / ma
strategy.entry(“long”,true, when = firstofmonth and fromYear and toYear and multiple<threshold)
strategy.close(“long”,when=crossover(multiple,threshold) or (dayofmonth(time)+1==dayofmonth(timenow) and month(time)==month(timenow) and year(time)==year(timenow)) )

The Bitcoin news script above defines a small trading strategy:

As long as the Mayer multiple (the quotient of price and moving average) is less than 2.4, Bitcoin news is bought on every first of the month for a fixed amount. The amount taken is an absurdly high 100,000 US dollars – since Bitcoin news can only simulate the buying and selling of entire Bitcoin, such a high amount was chosen as an interim solution. However, tests have shown that the results are transferable to smaller investments.

If either the Mayer multiple is above 2.4 or the current date has been reached, the positions are sold. The whole thing then looks as follows on the Bitfinex exchange for the value pair Bitcoin/US dollar:

We have added our new strategy as an indicator here. This can be achieved by right-clicking on the chart, selecting “Insert Indicator” and selecting the saved strategy under “My Scripts”. We see that almost every month a long position is opened – only at the end of 2017 and beginning of 2018 the positions are closed.

So every month Bitcoin is bought and sold at certain times. In order to be able to assign these sales moments, we add the Mayer multiple indicator described above to the plot:

So we see that if the Mayer multiple on a first of the month was below 2.4, Bitcoin was purchased for a fixed amount. If the Mayer multiple was above 2.4, as it is today, it was cashed out.

Hodl or a Cost-Average Approach as Trading Strategy
This view is a nice visualization of an investment behavior, but what can you learn from it? We would like to compare this strategy with the classic Hodln. Therefore, we define a long-term strategy in which money is invested in Bitcoin every month (a cost-average approach is chosen accordingly). No attention is paid to the price or other indicators:

strategy(“HODL”, overlay=true, pyramiding=1000, default_qty_type=strategy.cash, default_qty_value=100000)
startyear = input(2016,title=”Start year”)
fromYear = year >= startyear
toYear = year < year(timenow)
firstofmonth = dayofmonth == 1
//mma = sma(multiple, msma_length)
strategy.entry(“long”,true, when = firstofmonth)
strategy.close(“long”,when=(dayofmonth(time)+1===dayofmonth(timenow) and month(time)==month(timenow) and year(time)==year(timenow)))

After this script has been saved under the name “HODL”, this strategy can also be added to the chart. The path is the same as described above, but the strategy is “HODL”. The resulting chart looks almost like the ones above, which is why it should not be shown here.

The “Strategy Tester” can be used to compare these two strategies

The “Strategy Tester” shows in the lower windows “Overview”, “Performance Summary” and “List of Trades” how one of the strategies has developed. The Overview shows a graphical overview of the portfolio development, while the List of Trades lists all investments made. The “Performance Summary” is of particular interest to us:

The “Performance Summary” is interesting because it shows, using various parameters, how profit has developed by building up long positions. The profit (net profit), the turnover (gross profit) and the losses (gross loss) are presented. In addition, various variables are listed, such as the max drawdown (the maximum cumulative loss), the buy and hold return (the return that would have been achieved if all the money had been invested right at the start). For the case under consideration, it is sufficient to limit oneself to these variables. We also focus on the relative sizes, as these are also correct in the case of a monthly investment of 100 euros.

To sum up, those who would have invested the same amount in Bitcoin every month since the beginning of 2016 would have achieved a cumulative profit of around 31,000 % today. However, it is noticeable that a relative loss of 12% can be absorbed.