Jan. 24, 2021
Here is another post made on a QuantConnect forum. It could be viewed as a follow-up to the articles Stock Portfolio Backtesting and The In & Out Stock Trading Strategy.
Is there something in @Vladimir's In & Out strategy (version 1.5)? What I see is that there is money in there. But you have to determine that for yourself. What follows is not intended to convince you. You have to do your own homework.
Is there an edge that could persist going forward? Is it of any consequence what this strategy did over its simulated past? Is this strategy overfitted or not? In all simplicity: is it worth it? There is so much that could be said about this strategy.
We can gather opinions, anybody can have those, even people that did not even look at the code to see what it really does. But what I see is that little is given to substantiating those opinions. I find it understandable since I do not know what the future will bring either. For instance, just consider: during 2019, who programmed their strategies beforehand to handle the impact of Covid-19 in 2020? It should be remembered as a testament to our own predictive abilities, which might be considered as rather limited when faced with uncertainty.
Notwithstanding, is there really a positive probabilistic edge? To answer that question, we might have to answer another one. Is the signal used predictive in some way of the average market trend? Or expressed differently, is its trend-following declaration good enough to extract profits from stock price gyrations going forward?
An even more elementary question might be: how is this trading strategy making its money? If we know how and why, then we could evaluate if the trade mechanics could prevail going forward. And thereby see that the strategy might, in fact, continue to extract profits going forward.
Trading And Investing Theories
We have theories for everything, even for stock trading and investing. One of the most limiting theories is the MPT (Modern Portfolio Theory) which already dates back to the '60s. It can be summarized that the expected optimal long-term market portfolio resides on the Markowitz efficient frontier. This states that over the long term, for a fully invested portfolio, your expected future outcome will “tend” to the market average. It does not say that the ride will be a straight line. Even for such a portfolio, you should expect a lot of ups and downs.
Yet, most modern portfolio managers have a hard time hitting that mark. Anyone exceeding that mark using their private IP is immediately put in the overfitted trading strategy bin. This is without even a demonstration or proof that the trading strategy is or is not overfitted.
The other guy does better than them; therefore, their strategy is overfitted, while theirs is still better and not overfitted for some nonsensical reason. Period. There is no proof or corroborating evidence, just an opinion, and apparently, it should be sufficient. Well, for me, it is not. I will do my homework and determine what holds and what does not. I will test the thing and see what is under the hood.
We need to know how a trading strategy is making or losing money. That sounds simple, we could tentatively say that the trading rules were the main reason for the generated profits. Whatever, the question remains: is it really so?
Drawdown Protection
We do not need drawdown protection when the market is going up. It is when it is going down that such protection shows its value.
For instance, @Frank was kind enough to put out a version of @Vladimir's 1.5 that has stop-loss procedures in place. For me, it contradicts something I have said in a prior post: “...the strategy does not need or require a stop-loss”. A simple test to see if this holds is to enable the stop-loss and see what happens. Here is what I found: the strategy will slowly degrade til there is not enough money left in the account to even execute a single trade. You simply lose, and the reason is also quite simple.
For those finding weaknesses in this trading strategy, know that there are some. However, once you have identified those areas of vulnerability, it is your responsibility to compensate for them. If you think that bond prices might fall going forward, then you should add procedures in your version of the program that would handle the situation. You identify some weaknesses in the program structure and its trading mechanics, then find ways to correct them. You have a template that is offered on a silver platter, it's debugged, and it provides right out of the box a higher return than market averages. What you need to do is prove to yourself that the strategy has merit and that it can withstand time. Push it to its limits, see it blow up to identify added structural deficiencies, and then correct those potential problems if you can. That is the job.
Walk-forward
This strategy has been in a walk-forward mode (out-of-sample) since at least last October, and it is still going strong. There is something in the trading methods used. It is up to you to determine if it is enough for you or not. All I know is that you can push this strategy to quite a great extent, even with simple administrative procedures, and it can translate into quite a pretty penny. At least, I think I have demonstrated that it can be done over past market data (see prior posts in that forum). It is not just an opinion; it is corroborating evidence of what the strategy could have done over past market data.
I know what makes this strategy tick. Where do the profits come from, and how they are made. There is math underneath that governs this trading strategy, and you can control the math. So, my suggestion is to dismantle the strategy and then reconstruct the parts you want, see why it is profitable, and see how you could improve on its design. I would add that if you do not see how this strategy is making its money, how can you control it? How could you even trust it?
Once you will know what this strategy really does and how it operates, you will be able to control it and then add the protection you think it might need or whatever. It should be a way for you to gain the confidence needed by first showing to yourself, under your own set of rules and constraints, the level at which you might find an executable compromise. It is always up to you.
Jan. 24, 2020, © Guy R. Fleury. All rights reserved.