Dual Momentum with Out Days

QuantConnect Community Discussions

QUANTCONNECT COMMUNITY

Join Our Discord Channel

Join QuantConnect's Discord server for real-time support, where a vibrant community of traders and developers awaits to help you with any of your QuantConnect needs.

Draft Discussions

Bookmarked Discussions

Dual Momentum with Out Days

Last comment by - August 2022

Dual Momentum with Out Days

New version of "Dual Momentum IN OUT" strategy doubles net profit while maintaining risk metrics. Compounded annual return of 30.164%.

Share New Research

Start New Discussion Sign up

SEARCH DISCUSSIONS

365,700 Quants.

Become a Quant

VOTE FOR UPCOMING FEATURES

Share your input and vote on our future direction.

LEAN Roadmap

JOIN OUR Community MAILING LIST

Create an account on QuantConnect for the latest community delivered to your inbox.

Dual Momentum with Out Days

Vladimir | December 2020

Inspired by T Smith idea to implement Gary's Antonacci's dual momentum approach to ETF
selection in "IN OUT" strategy.

-The execution code has been completely changed to keep levarage under control and avoid
insufficient buying power warnings.
-To calculate returns I used widely used in industry momentum with excluding period.
-Modified components that are more in line with the strategy.
-The IN OUT part of the strategy has not changed except for some cosmetics
to make it more readable for myself.

"DUAL MOMENTUM IN OUT" nearly doubled "IN OUT" Net Profit while maintaining risk metrics at the same level.

Compounding Annual Return
30.164%

Sharpe Ratio
1.667

PSR
97.773%

Beta
0.057

Drawdown
19.300%

Annual Standard Deviation
0.154

Here is my second version of "DUAL MOMENTUM-IN OUT".

Author

Vladimir

December 2020

Upvote

22 people upvoted this

Joao Antunes

February 2021 |

Accepted Answer

Thank you so much for sharing your algo with us. This is GREAT! I'm learning a ton just by reading through the code.

I'd like to contribute where I can, so this is my first take at it.

I converted your "v2.5 Dual Momentum with Out Days by Vladimir" into QuantConnect's new Algorithm Framework. In particular, I've split your algo into the signal generation (Alpha Model) and placing trades (Portfolio Construction Model).

The converted code is as close as possible to yours (save some optimizations here and there). Note that the insights (buy/sell) are generated for SPY by DualMomentumWithOutDaysAlphaModel. DualMomentumWithOutDaysPortfolioConstructionModel then captures these SPY insights and converts them into buy/sell orders for one of the 4 assets.

Hopefully, this makes it easier to plug in and combine different strategies. I could make it more generic too, eg, pass the list of stocks and bonds are parameters.

Let me know what you (and the community) think!

PS: I'm not sure how familiar with git folks are (eg, GitHub.com, GitLab.com), but I think there's some value in using a collaborative version control system where we could collaborate/share/comment on the code in a more structured way. Just a thought. :)

Carsten INVESTOR

Carsten _ | December 2020

Hi Vladimir

good to see you again here...just some off topic question...i arrived as well from quantopian and try to translate/learn porting an algo from zipline. I did the tutorial and the framework they are using looks far better than quantopian but the warming up and other stuff is a bit nerve ripping...

Do you thnik it would be difficult to move that algo aboue into the quantopian framework concept?

        # Universe Model
        self.SetUniverseSelection(MyUniverseSelectionModel())
        # Alpha Model
        self.SetAlpha(MOMAlphaModel())
        # PortfolioConstruction Model
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
        # RiskManagement Model 
        self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(1))
        # Execution Model 
        self.SetExecution(ImmediateExecutionModel())

I translated already an algo with pre-set ticker, filtering and ranking, if you are intersted i dont mind to post it here. It still has some issus with warm up.

For me alone its quite hard, but I know that you are a very experienced programmer..

Moving to the framework it might a lot easyer to contribute as one only need to improve some modules and don't need to touch the whole code, and one can reuse a lot from other algos.

Upvote

1 person upvoted this

Vladimir INVESTOR

December 2020

Hi, Carsten,

For zipline, you can skip warm-up and consolidation because they are built-in features.

Upvote

2 people upvoted this

Vladimir INVESTOR

December 2020

Hi Carsten,

if you are intersted i dont mind to post it here

I have created thread Transition from Quantopian to QuantConnect.
You can post it there.

Upvote

1 person upvoted this

T Smith INVESTOR

December 2020

Great work. Adjusting the tradeout scheduling and using hourly rather than daily resolution for our signal instruments, I've managed to reduce drawdown.

Upvote

1 person upvoted this

Vladimir INVESTOR

December 2020

T Smith

Thank for the improvement.
"Many a little makes a mickle".
There was a typo in the code on line 154.
I deleted the second "if".

Upvote

2 people upvoted this

Vovik INVESTOR

December 2020

In this backtest I used the original T Smith setup:

IN and OUT assets determined by momentum (IN: QQQ/IWF, OUT: TLT/IEF), 100 day simple return

Net Profit 2097.037%; Sharpe Ratio 1.743; PSR 97.247%; Drawdown 17.900%; Beta 0.026;

It also sufficiently beat the setup of Peter Guenther.

IN: QQQ:1, OUT: TLT:0.5 + IEF:0.5

Great job Vladimir and T Smith.

Upvote

3 people upvoted this

Nathan Swenson INVESTOR

December 2020

Hello Vovik,

Did you fix the issue with the original T.Smith algo? It has many error in the log with invalid trades due to margin violation. I believe Vladimir's has fixed those issue, but trades different instruments.

Upvote

T Smith INVESTOR

December 2020

Yeah, Nathan Swenson he is using Vlads corrected trade logic to ensure sells are placed before buys and leverage doesn't go beyond 1. He is also using the updated sheduling and resolutions. This gets us out the market quicker, but avoids over trading/rebalancing whilst 'IN'.

Upvote

Vovik INVESTOR

December 2020

Nathan Swenson,

In this backtest I have no margin violation warnings.

Here is the full log.

Algorithm Initializa:   tion: Data for symbol DBB has been limited due to numerical precision issues in the factor file. The starting date has been set to 1/4/2007.
Algorithm Initializa:   tion: Data for symbol UUP has been limited due to numerical precision issues in the factor file. The starting date has been set to 2/19/2007.
Algorithm Initializa:   tion: Data for symbol DBB has been limited due to numerical precision issues in the factor file. The starting date has been set to 1/4/2007.
Algorithm Initializa:   tion: Data for symbol UUP has been limited due to numerical precision issues in the factor file. The starting date has been set to 2/19/2007.
Algorithm Initializa:   tion: Warning: when performing history requests, the start date will be adjusted if it is before the first known date for the symbol.
2008-01-01 00:00:00 :   Launching analysis for b3e5c2897bc5099bb64e69aa0ecedcca with LEAN Engine v2.4.0.0.10016
2007-08-28 10:00:00 :   Algorithm warming up...
2008-01-01 00:00:00 :   Algorithm finished warming up.
2020-12-11 16:00:00 :   Algorithm Id:(b3e5c2897bc5099bb64e69aa0ecedcca) completed in 631.42 seconds at 3k data points per second. Processing total of 2,197,238 data points.

Perhaps the problem is something else.

Upvote

2 people upvoted this

T Smith INVESTOR

December 2020

Vladimir regarding your comment on using more than one indicator to trigger the out of market. I have had a go at implementing it here:

Would appreciate your feedback!

Nathan Swenson this seems to solve the issue of SHY taking us out the market without another signal.

Upvote

1 person upvoted this

Goldie Yalamanchi INVESTOR

December 2020

That's really great T Smith I am not a huge fan of FDN or QQQ but this strat looks great! Quick question...

Are these the only changes to code to change if you don't want 5 signals as the IN/OUT? Say 4 or 3 for example...

pctl = np.nanpercentile(mom, 5, axis=0)

if self.no_signals > 5:

Upvote

EllaHamilton INVESTOR

December 2020

a great algorithm, we will test it on a large scale, and then leave you a review.

Upvote

Guy Fleury INVESTOR

December 2020

This strategy, like its predecessors in the other 2 or 3 “...In Out...” threads, is totally dependent on its up/down trend declarations. And the first question to ask should be: How reliable is this trend definition?

I see the strategy as a variant to a 60-day over a 252-day moving average crossover to bond switcher, but still responding to daily price variations. The advantage goes to being long for longer periods than being in bonds. And this translates - in an upmarket - to taking a higher average percent profit than when it loses a trade (about 2:1). And there, even with a hit rate in the 50s, you are bound to make some overall profits just for playing the game.

The above chart illustrates where I'm at presently. There was some leveraging (1.4x). Not excessive when compared to the 89.86% CAGR. The strategy could more than afford to pay the leveraging fees. 3x-leveraged ETFs were used which made the strategy behave the same as if leveraged by 4.2x. The initial capital was set at 100k as most here. Increasing the initial capital to 1 million did increase performance about 10-fold since the strategy is fully scalable.

To get there, I modified a few things. One of interest might be the following section:

        mom['S_G'] = (mom[self.SLV] - mom[self.GLD])*0.0 
        mom['I_U']  = (mom[self.XLI]   - mom[self.XLU])*0.0
        mom['A_F'] = (mom[self.FXA] - mom[self.FXF])*0.0

which flatlined most of the momentum signals leaving only UUP. Why do this? It resulted in higher profits. It also said that those signal components were not needed or could be considered as irrelevant to the task at hand. There is a lot that could be said about this strategy's strengths and weaknesses.

Upvote

2 people upvoted this

Hannes R INVESTOR

Hannes | December 2020

Hello Vladimir and all,

I see your many contributions and I am still a complete newbie, thank you for sharing all these invaluable insights and your experience online for everyone to learn from and contribute too (me too hopefully, one day).

I will need to spend several hours to understand your code and the logic behind , (would be great if there were comments in the code explaining the logic =D) but have some question around lookback bias in the meantime. This strategy is almost too good to be true - what are some of the items in here that may have lead to over-optimization? I see Guy Fleury's expert critique on the MA crossover periods defined a fixed value, what are some of the other things? Would you say that the asset selection could also be biased?

Thank you for your teachings!

Upvote

Vladimir INVESTOR

December 2020

Guy Fleury,

I am aware of your incredible ability to send any profitable strategy into the sky since we
discussed Andreas Clenow's momentum in 2017.

It's no secret, but simple math, that if you use 3x tools and apply a 1.4 leverage to
a strategy with 30% CAGR you should get much more than 150% CAGR.
You got 89.86% CAGR because these 3x instruments only started trading in 2010.
The decision to use 3x tools and a leverage greater than 1.0 is usually made
at the last stage of development strategies.
Before that, we must use 1x tools and use 1.0 leverage for compatibility of results.

I can also see that you are starting to improve the strategy in the right direction by
cutting half the sources.
Hopefully I can see results where you apply pressure points and other tools from your
toolbox.

Upvote

5 people upvoted this

T Smith INVESTOR

December 2020

Hi Guy Fleury

Great to see you in the discussion,. I have played with using just dollar as our signal, and have decrease sharpe considerably. whilst bring beta to 0.5.

Upvote

T Smith INVESTOR

December 2020

Vladimir totally agree. Best to build an algo with no leverage and just look to improving sharpe ratio. Leveraging later is then an option. Have you had much of a look into the concept of requiing more than 1 signal to exit the market?

Upvote

Vladimir INVESTOR

December 2020

T. Smith,

I don't have a solution right now, but I definitely want to either change the decision logic or reduce the number of sources.
Two months ago, this algorithm had only 4-6 sources and much fewer parameters.

I have played with using just dollar as our signal.

USD in this strategy has a negative sign.

mom[self.UUP] = mom[self.UUP] * (-1)

Upvote

2 people upvoted this

Guy Fleury INVESTOR

December 2020

@Vladimir, your version of this trading strategy was already tested with 1.0x leverage. My task is to find out if it can scale up and do more, including to see if it can support leveraging in order to find the strategy's operational limits. Something I need to know before the end of the testing process. Otherwise, I will be wasting my time if those limits are found nearby, later, or much worse, after going live.

The way we test trading strategies, they have to generate all their profits from within, no matter what trading methods we use. There are no external funds added during a simulation. The tools we have are part of better stock selection, better timing, better gaming, better process amplification, better trade modulation, better protective measures, etc...

Leverage can be used over selected time periods where you anticipate for whatever reason that it will increase the potential return on your trade. It is like saying: you do not use it all the time, but can use it where you think it might count.

Nonetheless, it is a CAGR game, and basic math will matter. You can apply limited leveraging when your added alpha exceeds leveraging costs (here, the alpha being the excess return over the market average).

You want to know the strategy's limits before your “feasibility” simulation study ends. I have looked at many strategies that do not pass these basic tests. I usually lose interest quite rapidly and expedite their journey to file 13.

Here is a fun observation. After flatlining 3 of the signal components, we are left with a minus UUP. The thing is that if you reverse this signal to +UUP you should see the strategy lose a lot of money. But, if you do this, you get about the same profits, within 0.5% of each other on my 1 million initial capital scenario. Putting into question the “real” value of that signal? Note that you still get a little more with the negative signal (-UUP).

What kind of signal, that it be negative or its reverse, can produce about the same results? I would venture one that does not matter which side it is on.

How about flatlining UUP making the strategy having no switching signal, no trend definition? What would be the result? Such a move reduced overall profits by almost half (-49%). But still managed a +91.25% CAGR compared to the 98.73% and the 98.49% CAGR in -UUP and +UUP respectively. Therefore, UUP's presence was worthwhile, while its predictive powers were nil since you made about as much that UUP declared the trend up or down.

Upvote

Vladimir INVESTOR

December 2020

Guy Fleury,

This strategy yields good results, but I don't like just one word in the code in line 124,
and that word is any()

if (extreme[self.SIGNALS + self.PAIR_LIST]).any():

Just imagine that you are the president of the United States.
You invited the top twelve generals of the country to help you make decisions whether to start
a war with country x or not.
Eleven generals said no, but one said yes.
What will be your decisions?

Upvote

3 people upvoted this

Nathan Swenson INVESTOR

December 2020

Getting out of the Market early is more of a conservative move rather than aggressive such as going to war. I think the point of it is to take the least risk possible. The outer 1% of std deviation if a very high standard to meet, so I can understand why Peter made it that way. If you want multiple confirming signals, then you likely can't use 1% outliers.

The In and Out does very well with 3x leveraged funds because it is overly cautious, generally exiting the market too early, but safetly for the most part. So while you don't get all of the move, you could perhaps take greater risk for the shorter period you are in. The "jitter" from only 1 signal appears valid as the Out holding have done well, at least in the In sample data we've tested. That being said, it's difficult to watch this market zoom higher while sitting in Out holding since 10/6. In reality this is our first real "Out of Sample" data and it's not looking good so far but who knows what happens in the coming weeks. Everyone is predicting all time highs. We shall see.

Upvote

Goldie Yalamanchi INVESTOR

December 2020

Removing SHY from 2020 makes 2020 and possibly 2020+ trade normally and perform normally -- until such time as we can comment back in the SHY indicator.

If we are trying to "ace" the backtest then keeping SHY in always looks good until of course late 2020 when the algo stops trading in October.

But credit where credit is due... T Smith multiple signals (5 of 8) approach still did well with the Qual-Up universe approach from 2014-2018 as well. During those years by commenting out SHY in the original IN/OUT algo, the Qual-Up stocks didn't do well i.e. QQQ would have done well regardless during 2013-2018 because tech has been on a tear the whole past decard.

Upvote

Vladimir INVESTOR

December 2020

Here is updated DUAL MOMENTUM IN OUT v2.1

I have changed line 97 to:

prices = self.History(symbol, period + excl, Resolution.Daily).close

Upvote

2 people upvoted this

EllaHamilton INVESTOR

December 2020

Thx, nice one.

Upvote

Guy Fleury INVESTOR

December 2020

@Vladimir, I go with Nathan's explanation. The strategy goes to the sideline at the first sign of trouble. You do not want to wait for a consensus since you are already dealing with ETFs.

Trading QQQ is like trading a market average surrogate. It holds the same shares as the NASDAQ 100 index as everyone knows. The top ten holdings (AAPL, MSFT, AMZN, TSLA, FB, GOOGL, GOOG, NVDA, PYPL, ADBE) account for 55% of QQQ. It should be view as one of the easiest stock selection you can make. Playing QQQ tends to dampen overall volatility. While using TQQQ puts volatility back into play and at a higher level with an expected beta of 3.0x. Therefore, you are playing QQQ on steroids which evidently brings in higher risk. The reason for “extreme” caution even if there is a cost to it.

This is not a game where we will fix things after we lose. So, we should first play safe whatever the performance level we are at. We might need to compromise like playing this strategy at a higher level but with other strategies in order to reduce overall volatility and drawdowns, or only use part of the available capital (say 10 to 20% as if on riskier assets).

In my previous post, the point was made that you could drop some of the signal components (3 out of 4) and it would increase overall performance. Well, here is another point of interest: self.INI_WAIT_DAYS. I see its use as a way to reduce whipsaws around the moving average crossovers. The original code has it at 15 trading days. No one questioned this as it was a reasonable assumption since there are indeed a lot of whipsaws near those crossovers. Removing it, for instance, making self.INI_WAIT_DAYS = 0, dropped performance considerably and thereby justified its use.

In my version of the program, if you set it to zero, you get a 62.68% CAGR. If you keep it at 15, you have a 97.84% CAGR. If you set it to 10, you get about the same result (97.82%). However, if you set it below 5, something like 2 or 1, you improve the picture considerably. The economic reasoning is simple. The wait days operate on a high decay function: e^(-0,5t). It might also suggest that whipsaws fade away rather fast near the crossovers. Also, by reducing the wait days, you are increasing the number of days the strategy is fully invested.

The table below shows the evolution of the strategy where only the wait days are changed from 15 to 0. I think that the chart speaks for itself. Changing a single number in the program can have a tremendous long-term impact. Note that this is close to a 6000% improvement going from zero wait days to one. Nothing else in the program was changed for these tests.

Upvote

Vladimir INVESTOR

December 2020

Guy Fleury,

Looks like I saw a spreadsheet like above two months ago.
The only difference was Quantopian instead of Quantconnect.
But that not optimized strategy had a completely different decision-making structure:

-Consensus of individual signals.
-Far less degree of freedom.
-Three times fewer sources of information.
-Three times fewer variables.
-Static parameters.

Something like the one below.
In terms of total return, it exceeds the latest In_out_flex_v5 2020-12-16

BTW: What will be your decisions?

Upvote

2 people upvoted this

Frank Schikarski INVESTOR

December 2020

Hi there,

some comments regarding the trigger for in or out:

if (extreme[self.SIGNALS + self.PAIR_LIST]).any():

There are several "regime scouts" that watch out if they observe danger. The beauty of this algo is, that danger is beeing memorized for some days.
They indicate danger, if they observe something extremely unusual, which is less likely than "np.nanpercentile(mom, 1, axis=0)" = lower 1% percentile of the data.
If there are "self.lookback = 252" observations, this roughly translates into less than 2.52 observations, or: the current signal observation is the worst or the second worst within the time period. This is a bit digital = not smooth.
This leads to the algo beeing very sensitive to not only the 1% but also the 252 observations which should not be the case. So we need more observations and then we can improve both the 1% treshold and the number of "regime scouts" theat we want to use in our algo.
As these are only "regime scouts", which only trigger our real algo logic e.g. "long 100 largest companies on Nasdaq" or anything else, I don't consider tuning these more or less independent scouts as overfitting.

What if we would (a) keep calculating daily returns for our signals, but (b) do this for every hour with a rolling 24-hours window? This should result in 24 times more observations = increase our resolution, allowing to optimize the 1%, the lookback period and increase the "any" until we get some redundancy from our scouts. Keep exploring ;)...

Upvote

1 person upvoted this

Vladimir INVESTOR

December 2020

Here is the updated DUAL MOMENTUM IN OUT v2.2
-Based on In_out_flex_v5_try.
-Used exponential like smoothing on line 116-121
-Line 120 is commented out.

Upvote

2 people upvoted this

Guy Fleury INVESTOR

December 2020

@Vladimir, yes, as you say: "...the strategy had a completely different decision-making structure". You improved on that strategy design since... thanks.

Changing the number of wait days (self.INI_WAIT_DAYS) in the program is more like an administrative decision. The idea is not bad since we know there will be some whipsaws at crossover times. However, there was no need to wait more than one day or maybe two at the most.

It is not that surprising an observation. We want security, be decisive, and not be clobbered by added trading costs due to whipsaw after whipsaw for days after an exit, and yet, this says do wait but at most one day and probably no more.

Such a small decision with such an impact. You change a single number in the program from 0 to 1 and it increases performance by 5910%!

Upvote

BukavuTrader INVESTOR

Ntale Lukama | December 2020

@Vladimir, Do you have one like that for FOREX?

Upvote

Vladimir INVESTOR

December 2020

BukavuTrader,

Do you have one like that for FOREX?

Not yet, but you can try yourself.

Upvote

Guy Fleury INVESTOR

December 2020

Some added notes. This trading strategy has shown that it could go quite far depending on some of its parameter settings, ETF selection, trading logic, and initial capital. Using 10k, 100k or 1 million is an administrative decision. The program will do its job either way since it is scalable (but up to a limit). It is a simple bond switcher based on QQQ, but it has interesting properties.

The max drawdown and overall volatility will be the same with either capital options. All you will be changing is the ongoing bet size. This will barely change the price at which a trade is executed. But will change the traded quantity. Increasing the capital ten-fold will increase the bet size 10-fold, and in turn, increase profits (losses) 10-fold. However, going for 10 million as initial capital will tend to make the strategy unfeasible since you might end up trading 175,000,000 shares of TQQQ on practically a weekly basis which is more than the average daily volume. So, there are practical limits to the strategy which will need to be addressed.

With 100k you can push the strategy beyond 1 B and with 1 M you can pass the 10 B mark. Almost incredible. However, this is achieved by taking on more risk, using 3x-leveraged ETFs which are also leveraged at 1.4x. Thereby pushing on the machine way beyond what the original design was. Of note, changing the wait days (self.INI_WAIT_DAYS) to 1 had a tremendous impact on overall performance, a real game-changer, and yet, just another administrative decision.

I have not touched risk reduction procedures yet. This comes at a later stage in my testing process. It is expected that by installing protective measures the overall performance will be reduced to some extent. But, I will know that after those measures are added. Meanwhile, I have other tests to make.

My version of this program is dealing with a 3.x leveraged QQQ surrogate (TQQQ). It is playing an index tracker but with 3.x the average market beta saying it swings more than QQQ which is itself an average market consensus equivalent.

In pushing further, the strategy reaches a performance plateau from which it starts breaking down. It does not blow up mind you. It simply trades less and less and thereby generates less and less suggesting not to go that far. But that should be expected. Knowing that the strategy has seen its own built-in structural limits, it is almost time to apply protective measures and scale it down to a more acceptable risk/reward level.

Here is my take. You PLAY the game for its long-term CAGR potential. Which trading methods will give you the highest return within your own trading constraints? Not somebody else's, but your own. We need to answer the question: will we accept 5% more on a temporary max drawdown for 5% more in CAGR? The decision has value and is based on the initial stake:

10k ∙ (1+0.30)^20 - 10k ∙ (1+0.25)^20 = 1,033,135

100k ∙ (1+0.30)^20 - 100k ∙ (1+0.25)^20 = 10,331,346

1M ∙ (1+0.30)^20 - 1M ∙ (1+0.25)^20 = 103,313,464

This should weigh in the evaluation of your acceptable risk/reward scenario. It can be a costly decision.

Upvote

3 people upvoted this

Vladimir INVESTOR

December 2020

Here is the updated DUAL MOMENTUM IN OUT v2.3

Based on In_out_flex_v5_disambiguate_v2.

Upvote

1 person upvoted this

Simone Pantaleoni INVESTOR

LesFlex Quantitative Nerd | December 2020

Great idea Vladimir! I was working on a similar update on the INOUT algo, but you anticipated me! :P

Have you also tried to decrease further the "decay" value for the SELF.WAIT_DAYS variable, reducing the waiting to increase sharpe and return? (guess probably yes, isn't it?)

Upvote

Carsten INVESTOR

Carsten _ | December 2020

Vladimir as you requested.. :) was a bit trick, just happy to get it as a multi AlphaModel running. Its a super simplified version, but you can easily upgrade it. At the end it has much more lines than the normal version. It was quite trick to get the signal into the two AlphaModels. At the end I used ObjectStore. If someone has a simpler solution, with a global variable? please comment.

Upvote

Guy Fleury INVESTOR

December 2020

@Vladimir, I like the behavior and equity line of version 2.3. Remarkable, and great numbers. I will try to find some time to look at it since I think there are things I will learn in the process. Thanks for sharing.

Upvote

Damiano Bolzoni INVESTOR

December 2020

Guys, I really fell in love with this strategy (I actually started following the thread on Quantopian) and so ran some additional backtests taking into consideration several 5-year periods.

The strategy really shines during the 2008-2012 timeframe and then again in 2020. That's how it delivers 30% annual return. Take any other period of time and it will barely matches the returns of holding QQQ: I literally just finished a backtest between 1-1-2013 and 12-31-2019 and it's underperforming by nearly 10% overall.

If one substites QQQ and FDN with SP500 equivalents the same behavior can be observed, actually returns are even worse.

Am I the only one experiecing this?

Upvote

Simone Pantaleoni INVESTOR

LesFlex Quantitative Nerd | December 2020

Just tweeking the Waiting variable using a bigger decay, as suggested above to get slightly better returns and sharpe :)

Upvote

Carsten INVESTOR

Carsten _ | December 2020

Vladimir could you plese check again, should work now, the objektstore object was not created in the initialize, but it was yesterday on my disk as i was finding out how to impement it....

Upvote

Jack Pizza INVESTOR

Sum Sum | December 2020

FYI to make this more robust these same arguments were brought up in the old QT thread.

Not sure if this is implemented in this or not.

There should be a 3rd option or ultimate out where it just goes into cash or adding gold as a 3rd / 4th asset to rotate into.

Given at some point in time stocks and bonds might breakdown together.

Upvote

Platform

Radically Open-Source Algorithmic Trading Engine

Join Our Discord Channel

Quarterly Open-Source Trading Competition

Draft Discussions

Bookmarked Discussions

SEARCH DISCUSSIONS

365,700 Quants.

VOTE FOR UPCOMING FEATURES

JOIN OUR Community MAILING LIST

IN THIS RESEARCH

PARTICIPANTS

Discussion Awards

Actions

Join QuantConnect for Free

Platform

SIGN IN

Radically Open-Source Algorithmic Trading Engine

Join Our Discord Channel

Quarterly Open-Source Trading Competition

Draft Discussions

Bookmarked Discussions

SEARCH DISCUSSIONS

365,700 Quants.

VOTE FOR UPCOMING FEATURES

JOIN OUR Community MAILING LIST

IN THIS RESEARCH

PARTICIPANTS

Discussion Awards

SHARE RESEARCH

SHARE DISCUSSION

SHARE ARTICLE

SHARE

Actions

Join QuantConnect for Free