https://www.tradingview.com/script/4J5psNDo-A-Physicist-s-Bitcoin-Trading-Strategy/
3. Backtest Results
Backtest results demonstrate significant outperformance over buy-and-hold . The default parameters of the strategy/indicator have been set by the author to achieve maximum (or, close to maximum) outperformance on backtests executed on the BTCUSD ( Bitcoin ) chart. However, significant outperformance over buy-and-hold is still easily achievable using non-default parameters. Basically, as long as the parameters are set to adequately capture the full character of the market, significant outperformance on backtests is achievable and is quite easy. In fact, after some experimentation, it seems as if underperformance hardly achievable and requires deliberately setting the parameters illogically (e.g. setting one parameter of the slow indicator faster than the fast indicator). In the interest of providing a quality product to the user, suggestions and guidelines for parameter settings are provided in section (6). Finally, some metrics of the strategy's outperformance on the BTCUSD chart are listed below, both for the default (optimal) parameters as well as for a random sample of parameter settings that adhere to the guidelines set forth in section (6).
Using the default parameters, relative to buy-and-hold strategy, backtested from August 2011 to August 2020,

• Total cumulative outperformance (total return of strategy minus total return of buy-n-hold): 13,000,000%.
• Rolling 1-year outperformance: mean 318%, median 84%, 1st quartile 55%, 3rd quartile, 430%.
• Rolling 1-month outperformance: mean 2.8% (annualized, 39%), median -2.1%, 1st quartile -7.7%, 3rd quartile 13.2%, 10th percentile -13.9%, 90th percentile 24.5%.

Using the default parameters, relative to buy-and-hold strategy, during specific periods,

• Cumulative outperformance during the past year (August 2019-August 2020): 37%.
• 12/17/2016 - 12/17/2017 (2017 bull market) absolute performance of 2563% vs buy-n-hold absolute performance of 2385%
• 11/29/2012 - 11/29/2013 (2013 bull market) absolute performance of 14033% vs buy-n-hold absolute performance of 9247%

Using a random sample (n=20) of combinations of parameter settings that adhere to the guidelines outlined in section (6), relative to buy-and-hold strategy, backtested from August 2011 to August 2020,

• Average total cumulative outperformance, from August 2011 to August 2020: 2,000,000%.
• Median total cumulative outperformance, from August 2011 to August 2020: 1,000,000%.

EDIT (because apparently not everybody bothers to read the strategy's description):
7. General Remarks About the Indicator
Other than some exponential moving averages, no traditional technical indicators or technical analysis tools are employed in this strategy. No MACD , no RSI , no CMF , no Bollinger bands , parabolic SARs, Ichimoku clouds , hoosawatsits, XYZs, ABCs, whatarethese. No tea leaves can be found in this strategy, only mathematics. It is in the nature of the underlying math formula, from which the indicator is produced, to quickly identify trend changes.
8. Remarks About Expectations of Future Results and About Backtesting
8.1. In General As it's been stated in many prospectuses and marketing literature, "past performance is no guarantee of future results." Backtest results are retrospective, and hindsight is 20/20. Therefore, no guarantee can, nor should, be expressed by me or anybody else who is selling a financial product (unless you have a money printer, like the Federal Reserve does).
8.2. Regarding This Strategy No guarantee of future results using this strategy is expressed by the author, not now nor at any time in the future.
With that written, the author is free to express his own expectations and opinions based on his intimate knowledge of how the indicator works, and the author will take that liberty by writing the following: As described in section (7), this trading strategy does not include any traditional technical indicators or TA tools (other than smoothing EMAs). Instead, this strategy is based on a principle that does not change, it employs a complex indicator that is based on a math formula that does not change, and it places trades based on five simple rules that do not change. And, as described in section (2.1), the indicator is designed to capture the full character of the market, from a macro/global scope down to a micro/local scope. Additionally, as described in section (3), outperformance of the market for which this strategy was intended during backtesting does not depend on luckily setting the parameters "just right." In fact, all random combinations of parameter settings that followed the guidelines outperformed the intended market in backtests. Additionally, no parameters are included within the underlying math formula from which the indicator is produced; it is not as if the formula contains a "5" and future outperformance would depend on that "5" being a "6" instead. And, again as described, it is in the nature of the formula to quickly identify trend changes. Therefore, it is the opinion of the author that the outperformance of this strategy in backtesting is directly attributable to the fundamental nature of the math formula from which the indicator is produced. As such, it is also the opinion of the author that continued outperformance by using this strategy, applied to the crypto ( Bitcoin ) market, is likely, given that the parameter settings are set reasonably and in accordance with the guidelines. The author does not, however, expect future outperformance of this strategy to match or exceed the outperformance observed in backtests using the default parameters, i.e. it probably won't outperform by anything close to 13,000,000% during the next 9 years.
Additionally, based on the rolling 1-month outperformance data listed in section (3), expectations of short-term outperformance should be kept low; the median 1-month outperformance was -2%, so it's basically a 50/50 chance that any significant outperformance is seen in any given month. The true strength of this strategy is to be out of the market during large, sharp declines and capitalizing on the opportunities presented at the bottom of those declines by buying the dip. Given that such price action does not happen every month, outperformance in the initial months of use is approximately as likely as underperformance.

This is a bit different than usual, hopefully you guys don't mind:
You are trading Euros and Bitcoins (E and B for the purposes of this problem). Using your time machine you have found out the forex rates for the next n days. Let the price of 1B in E for day a be pₐ, where 0 < pₐ < 1 and a <= n.
​

Day	Price E/B
1	p₁
2	p₂
3	p₃
...	...
n	pₙ

Every day you can decide if you want to trade all your E to B or all your B to E. If you start with 10E, what is the maximum amount of E you can have after n days?
Note: to convert B->E you multiply by p, and E->B you divide by p.
Because I'm asking for a general method, and there are many of them (some trivial), I encourage everyone to try to post the most computationally efficient solution they can find and to try to beat the current best. I will not consider trying every possible combination (or any solution that is computationally less efficient than it) a valid solution for the sake of the puzzle not being immediately solved. Have fun!

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DOWNLOAD LINK: megafile3.top/file/Technical analysis Professional Bitcoin Trading Strategy

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Author: sahni619