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Vovik, we might be making a logical error with this comparison since the holdings are different (note FDN and TLH). It would be a bit like saying: "The Distilled Bear algo that Leandro posted here made 9,532%, so the "Distilled Bear" is better than the "Dual Momentum with Out Days" and the "In & Out".
In addition, I think that there could be value in moving beyond the comparison of individual in & outs and working on the question of whether it might be beneficial to combine them. Of course, be aware that this is 'rich data modelling' at its finest.

Peter,

Using definitions in your previous post:
 
> [parameter minimalists]  Menno  <----  Vladimir  ---->  Peter  [rich data modelists] 

I have the following questions/comments:

How did you determine that Menno belongs to "parameter minimalist" category?

Appreciate!

Liam Op, no worries. Check out page 2 of this thread, searching (Ctrl + F) for the term "Gedankenexperiment".

Peter,

For comparison with Leandro Maia version Vladimir posted his v1.9

Interim stock taking
It’s been about half a year since the post below on Quantopian. Since then, much work has been done on multiple fronts. Merging this discussion into QuantConnect also has fired things up substantially.

This discussion has brought about multiple threads. An overview can be useful for people who want to explore all or some of the work that has been done. So here is a current list, approximately in chronological order:
- This thread. Focuses on developing in & out algos which then can be used to trade great stock selection strategies. It is an incubator for in & out-related ideas that are then further developed in dedicated threads.
- Amazing returns = superior stock selection strategy + superior in & out strategy (link). Focuses on combining in & outs with stock selection strategies, trying to find optimal combinations. Mostly uses the In & Out and Distilled Bear algos from this thread, combining it with leveraged ETFs (TQQQ and bonds), stock selections based on valuation (‘Valuation Rockets’), and stock selections based on quality fundamentals and momentum (‘Quality Companies in an Uptrend’).
- Dual Momentum with Out Days (link). Focuses on combining different in & outs (In & Out, Intersection of ROC comparison using Out_Day approach) with different equity ETFs (QQQ, FDN, IWF). Also, a focus on reducing the number of out signals (e.g. only using USD or Gold vs Silver and Utilities vs Industrials) as well as using leverage to boost returns.
- Intersection of ROC comparison using OUT_DAY approach (link). Focuses on combining the ‘Intersection of ROC comparison using OUT_DAY approach’ in & out algo with different equity holdings (e.g., combining QQQ, MSFT, and NFLX; FNGS; top 10 tech gainers). Also, discussion of sensitivity, leveraging, adding (trailing) stop losses, portfolio optimization, identifying 2x/3x bear and 2x/3x bull regimes.
- A very profitable version of IN and OUT, and why it is likely to fail in real life trading like its siblings (link). Argues that the in & out algos are overfitted and predicts that they will generate negative alpha in the future. Discusses strategies to test for overfitting and out-of-sampling testing. Also includes results from a parameter optimization run for the Dual Momentum In & Out.
- ROC comparison Utilities and Industrials (link). Focuses on minimizing the number of out signals to only one pair comparison (utilities vs industrials) and tests out-of-sample, compared to the other in & out backtests, by including the period from 1998-2007.

Great stuff, keep it up!

Welcome to the discussion, Strongs. My two cents regarding whether the system will continue to work:
- of course, nobody can predict this with certainty
- one thing to consider: The critical component of the system in my view is not the bond side, but the equity side. You need to make a great selection here for the system to perform. For example, see our discussions in Amazing returns = superior stock selection strategy + superior in & out strategy.
- currently, bonds are going down since inflation and interest expectations are increasing (= economic recovery expectations). Currently, the algo is in equity not in bonds. When the algo does a solid job, then we only go in bonds when there is (substantial) jitter in the equity market. Jitter could occur because of doubts concerning the speed of the economic recovery. When there are these kinds of doubts, then bond prices will increase (= yields will fall) and, if the system switches accurately, we will benefit from this uptick to some extent.