Dual Momentum with Out Days

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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%.

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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

Vladimir INVESTOR

February 2021

Mark hatlan
Symbols are parameters in this strategy.
You can try whatever you want and choose the ones that suit you most.

Upvote

Mark hatlan INVESTOR

February 2021

Yeah I understand that, I was just wondering if you had a specific reason for having those 2 for the stocks as opposed to any other 2, just because those 2 have behaved similarly. Thats all.

Upvote

Shile Wen INVESTOR

UW CFRM | February 2021

Hi Joao,

For personal "version control", each time a backtest is run, the code used in execution is saved in the backtest details.

For collaborative version control, one idea is to use Skylight and and turn the local Skylight folder into a Git repository so that version control can be done.

Best,
Shile Wen

Upvote

1 person upvoted this

Vladimir INVESTOR

February 2021

Joao Antunes,

Thank you for sharing your great Alpha Model v2.5 Dual Momentum with Out Days.

Upvote

Will Berger INVESTOR

March 2021

Guys,

I have been running the Model v2.5 Dual Momentum with out Days for the last month. The draw down so far is 10%+. Not insignificant.

I believe this is being caused by the rise in the 10 year treasury yield and growth stocks are feeling the impact. The 3 trade signals in the algorithm today do not take this into account and was wondering should there be a 4th and what should be the action.

In this case both equity and bonds are suffering. Would it not make sense to sell both equity and bonds when detected?

Any thoughts?

Will

Upvote

1 person upvoted this

Vladimir INVESTOR

March 2021

Will Berger,

Correlation of daily returns between stock index and Treasury bonds has been negative not only
in the last 10 years.
You can find research that proves that over the past 85 years or even over the past 250 years.
But sometimes the correlation can change sign, or both go up, or both go down.
In the latter case, it makes sense to switch from Treasury bonds (TLT, TLH)
to Treasury bills (SHY, SHV).
You can try this.

Upvote

Will Berger INVESTOR

March 2021

Ok. Will try it out.

On my first point, the algorithm in my observations from the past has done a good job of not over reacting when the market declines, but I don't think it is handling the scenario we are in today where the us yield is rising and the growth stocks like qqq are being signifcantly being effected by it. Maybe choosing a value stock as the alternative dual stock will do the trick. Will play with that as well.

Thanks for the suggestion Vladimir.

Upvote

Vladimir INVESTOR

March 2021

Will Berger,

Symbols are parameters in this strategy.
You can try whatever you want and choose the ones that suit you most.

Upvote

PieterStam INVESTOR

March 2021

Neat looking code will definitely play around with this

Upvote

1 person upvoted this

Mohamed Ajmal INVESTOR

July 2021

Just a theoretical suggestion - our signals are almost mostly external - not of our own assets. Can we add a signal where if our asset performance is deviating from a pattern by x margin, we return to cash/bonds too.

Upvote

Alexandre Catarino INVESTOR

QuantConnect | July 2021

Hi Mohamed Ajmal ,

Your theoretical suggestion describes how we use alternative data in QuantConnect/Lean. For example, TiingoNews is an external signal. Could you please open a thread for the second question with mode details (e.g.: a practical example on how it should work, perhaps with a backtest)? I think it's out of the scope of this thread.

Upvote

Alan chen INVESTOR

September 2021

Dear Vladimir,

Many thanks for sharing this strategy and I have spent some time studying it. I made some minor adjustments to the code and have it live on a paper account with IB. However, I come to cross a problem and hope you can share some insights. the backtest version stayed in Bond ETF since June 2021, but the live version got in and out of equity since June 2021 multiple times during the same period. Have your code experienced such behaviors? what could the possible causes?

Many thanks

Upvote

Vladimir INVESTOR

September 2021

Alan chen,

Can you share the code with some minor adjustments?

Upvote

Kamal G INVESTOR

October 2021

I saw the same behavior on V2.5 of Dual Momentum. I haven't had a chance to troubleshoot it yet… hoping someone already has?Below is a capture of the orders on a paper trading account I ran it with for a while.

I see two issues where it quickly goes back and forth between selected equities (not altogether a problem per say but you wouldn't want it trading so often) and another where algo makes the new order before liquidating existing holdings.

2021-08-02T15:10:00.6621505Z SPY 440.31 -20 Market Filled Liquidated - Interactive Brokers Order Fill Event
2021-08-02T15:10:02.8101982Z QQQ 366.46 23 Market Filled Interactive Brokers Order Fill Event
2021-08-04T15:10:03.8015137Z SPY 0 19 Market Invalid
2021-08-04T15:10:04.8019805Z QQQ 366.93 -23 Market Filled Liquidated - Interactive Brokers Order Fill Event
2021-08-05T15:10:04.8010877Z SPY 440.89 19 Market Filled Interactive Brokers Order Fill Event
2021-08-09T15:10:05.8000722Z SPY 441.8607 -22 Market Filled Liquidated - Interactive Brokers Order Fill Event
2021-08-09T15:10:08.8963402Z QQQ 368.3075 26 Market Filled Interactive Brokers Order Fill Event
2021-08-13T15:10:14.800238Z QQQ 368.455 -26 Market Filled Liquidated - Interactive Brokers Order Fill Event
2021-08-13T15:10:15.8012856Z SHV 110.475 89 Market Filled Interactive Brokers Order Fill Event

I'd say the same problem would be happening on other versions of this algo too.

Upvote

Vladimir INVESTOR

October 2021

Kamal G,

Can you share the code with where it quickly goes back and forth between selected equities?

Upvote

Kamal G INVESTOR

October 2021

Hi Vladimir,

Attaching the version in question with a backtest starting in 2020. Points below.

The backtest closely resembles the live trading experience.
We need to avoid the ‘chop’ between the selected equities or bonds, that happened in August 2021 for example. Ie. to avoid unnecessary brokerage fees.
As per above, in live trading maybe we need to add a delay between liquidating a holding and then buying a holding. Sometimes the liquidate/buy execution happens too closely to each other resulting in an invalid order due to lack of funds.

Upvote

Vladimir INVESTOR

October 2021

Kamal G

I see that you modified slightly my version 2.5

But this should not affect the insufficient buying power.

Here is a slightly modified version 2.51, where I switched to minute data for the traded symbols, using your setting and SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin).

I do not see any insufficient buying power order errors or orders with stale prices.

Upvote

Alan chen INVESTOR

October 2021

Dear Vladimir,

sorry for the late response. Was occupied by other events and unable to log in in the past couple of days.

Here is the code i used for live paper trading. Basically, i changed the code to ONData, instead of Scheule.On and had it on paper trading since Mid July 2021. Now i have two issues with the code:

it generated signal to buy equity ETF while the backtest stayed in bond ETF all the time
the code seems to generate/cancel orders every minute after a trade signal is created (you can refer to the attached Pic)

===============================================

import numpy as np

class MarketTimingRuleStrategy(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2021, 8, 1) # Set Start Date
#self.SetEndDate(20019, 12, 31) #Set End Date
#self.SetEndDate(2021,3,7)#set end date
self.cap = 100000

# Teck Stocks: Largest 100 companies on NASDAQ
self.STK1 = self.AddEquity('SSO', Resolution.Daily).Symbol

# Tech Stock: FDN tracks a market-cap-weighted index of the largest and most liquid U.S. Internet companies.
self.STK2 = self.AddEquity('FDN', Resolution.Daily).Symbol

# US Treasury Long Term ETF: TLT tracks a market-weighted index of debt issued by the US Treasury with remaining maturities of 20 years or more.
self.BND1 = self.AddEquity('TLT', Resolution.Daily).Symbol

# US Treasury Medium Term ETF: TLH tracks a market-weighted index of debt issued by the U.S. Treasury. Remaining maturity must be between 10 and 20 years.
self.BND2 = self.AddEquity('TLH', Resolution.Daily).Symbol


self.ASSETS = [self.STK1, self.STK2, self.BND1, self.BND2]

# Overall Market
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol

# Industrial: XLI tracks the broad US industrial sector. A 7% drop over an approx. 3-month trading period is considered a substantial drop
self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol

# Utlities: XLU tracks a market-cap-weighted index of US utilities stocks drawn exclusively from the S&P 500.
self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol

# iShares Silver Trust (NYSEArca: SLV)
self.SLV = self.AddEquity('SLV', Resolution.Daily).Symbol

# SPDR Gold Trust (NYSEArca: GLD)
self.GLD = self.AddEquity('GLD', Resolution.Daily).Symbol

# Currencies ETF: FXA tracks the changes in value of the Australian dollar relative to the US dollar.
self.FXA = self.AddEquity('FXA', Resolution.Daily).Symbol

# Currencies ETF: FXF tracks the changes in value of the Swiss Franc relative to the US dollar.
self.FXF = self.AddEquity('FXF', Resolution.Daily).Symbol

# Resources: Investco's 3 industrial metals DBB ETF. A 7% drop over an approx. 3-month trading period is considered a substantial drop
self.DBB = self.AddEquity('DBB', Resolution.Daily).Symbol

# Currencies ETF: UUP provides inverse exposure to an index of USDX futures contracts that rises in value as the dollar appreciates relative to a basket of world currencies.
self.UUP = self.AddEquity('UUP', Resolution.Daily).Symbol

# Natural Resources: IGE tracks a market-cap-weighted index of US-listed natural-resource-related companies.
self.IGE = self.AddEquity('IGE', Resolution.Daily).Symbol

# Cost of debt: The SHY ETF (iShares 1-3 Year Treasury Bond) provides the signal. SHY tracks short-term US Treasury debt (1-3 years)
# and changes in this debt’s ‘risk-free’ interest yield should be indicative of changes in firms’ cost of debt which is based on the
# risk-free rate (risk-free rate + risk premium). A 60 basis points increase (i.e., drop in the bond price) over an approx. 3-month
# trading period is considered substantial.
self.SHY = self.AddEquity('SHY', Resolution.Daily).Symbol

self.FORPAIRS = [self.XLI, self.XLU, self.SLV, self.GLD, self.FXA, self.FXF]
self.SIGNALS = [self.XLI, self.DBB, self.IGE, self.SHY, self.UUP]
self.PAIR_LIST = ['S_G', 'I_U', 'A_F'] #Silver - Gold, Industrial - Utilities, Australia - Swiss Franc

self.INI_WAIT_DAYS = 15
self.SHIFT = 55
self.MEAN = 11
self.RET = 126
self.EXCL = 5
self.leveragePercentage = 101
self.selected_bond = self.BND1
self.selected_stock = self.STK1
self.init = 0

self.bull = 1
self.exit = None
self.count = 0
self.outday = 0
self.in_stock = 0
self.spy = []
self.wait_days = self.INI_WAIT_DAYS
self.wt = {}
self.real_wt = {}
#self.SetWarmUp(timedelta(126))

#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 100),
# self.calculate_signal)

#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120),
# self.trade_out)

#self.Schedule.On(self.DateRules.WeekEnd(), self.TimeRules.AfterMarketOpen('SPY', 120),
# self.trade_in)

#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose('SPY', 0),
# self.record_vars)

symbols = self.SIGNALS + [self.MKT] + self.FORPAIRS
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(days = 1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)

self.lookback = 252
self.history = self.History(symbols, self.lookback, Resolution.Daily)
if self.history.empty or 'close' not in self.history.columns:
return
self.history = self.history['close'].unstack(level=0).dropna()
self.update_history_shift()


def consolidation_handler(self, sender, consolidated):
self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
self.history = self.history.iloc[-self.lookback:]
self.update_history_shift()


def update_history_shift(self):
self.history_shift_mean = self.history.shift(self.SHIFT).rolling(self.MEAN).mean()


def returns(self, symbol, period, excl):
prices = self.History(symbol, TimeSpan.FromDays(period + excl), Resolution.Daily).close
return prices[-excl] / prices[0]

# edit: AK 1
def OnData(self, data):

mom = (self.history / self.history_shift_mean - 1)

mom[self.UUP] = mom[self.UUP] * (-1) # measure of USD (what is the relationship?)
mom['S_G'] = mom[self.SLV] - mom[self.GLD] # momentum of silver - gold. silver has fallen more than gold in bear markets, but risen more than gold in bull markets.
mom['I_U'] = mom[self.XLI] - mom[self.XLU] # momentum of industrial minus utilities (increase of risk taking appetite as cyclical stocks outperform defensive stocks)
mom['A_F'] = mom[self.FXA] - mom[self.FXF] # momentum of australia minus swiss franc (increase of risk taking appetite as australian is commodities currency vs franc which is a saft haven currency)

# numpy.nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=<no value>)[source]
# Compute the qth percentile of the data along the specified axis, while ignoring nan values.
# Returns the qth percentile(s) of the array elements.
pctl = np.nanpercentile(mom, 1, axis=0)


# Get out (i.e. a short time out of 15 days) once there's extreme price movements in any (or some or all) of the signals, as defined by pct1,
# If this short-time out is accompanied by a clear signal from any of the 3 signal pairs, which signifies that the fundamentals have changed,
# then stay out for much longer, as the wait.days become 15*15 days.

extreme = mom.iloc[-1] < pctl

self.wait_days = int(
max(0.50 * self.wait_days,
self.INI_WAIT_DAYS * max(1,
np.where((mom[self.GLD].iloc[-1]>0) & (mom[self.SLV].iloc[-1]<0) & (mom[self.SLV].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((mom[self.XLU].iloc[-1]>0) & (mom[self.XLI].iloc[-1]<0) & (mom[self.XLI].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((mom[self.FXF].iloc[-1]>0) & (mom[self.FXA].iloc[-1]<0) & (mom[self.FXA].iloc[-2]>0), self.INI_WAIT_DAYS, 1)
)))

adjwaitdays = min(60, self.wait_days)

# self.Debug('{}'.format(self.wait_days))

if (extreme[self.SIGNALS + self.PAIR_LIST]).any():
#if (extreme[self.PAIR_LIST]).any():
self.exit = True
self.bull = False
self.outday = self.count

if self.count >= self.outday + adjwaitdays:
self.bull = True

self.count += 1

self.Plot("In Out", "in_market", int(self.bull))
self.Plot("In Out", "num_out_signals", extreme[self.SIGNALS + self.PAIR_LIST].sum())
self.Plot("Wait Days", "waitdays", adjwaitdays)

if self.returns(self.BND1, self.RET, self.EXCL) < self.returns(self.BND2, self.RET, self.EXCL):
self.selected_bond = self.BND2

#if self.returns(self.BND1, self.RET, self.EXCL) < self.returns(self.BND2, self.RET, self.EXCL):
# self.selected_bond = self.BND1

elif self.returns(self.BND1, self.RET, self.EXCL) > self.returns(self.BND2, self.RET, self.EXCL):
self.selected_bond = self.BND1

if self.returns(self.STK1, self.RET, self.EXCL) < self.returns(self.STK2, self.RET, self.EXCL):
self.selected_stock = self.STK2

#if self.returns(self.STK1, self.RET, self.EXCL) < self.returns(self.STK2, self.RET, self.EXCL):
# self.selected_stock = self.STK1

elif self.returns(self.STK1, self.RET, self.EXCL) > self.returns(self.STK2, self.RET, self.EXCL):
self.selected_stock = self.STK1

if not self.bull:
for sec in self.ASSETS:
self.wt[sec] = 0.99 if sec is self.selected_bond else 0
self.Debug("Exiting the market; trading BONDS")
self.Notify.Email("alan.chenhd@outlook.com", "Live Trade Executed", "Bond bought")
self.trade()

if self.bull:
for sec in self.ASSETS:
self.wt[sec] = 0.99 if sec is self.selected_stock else 0
self.Debug("Entering the market; trading STOCKS")
self.Notify.Email("alan.chenhd@outlook.com", "Live Trade Executed", "Equity bought")
self.trade()


def trade(self):

for sec, weight in self.wt.items():
if weight == 0 and self.Portfolio[sec].IsLong:
self.Liquidate(sec)

cond1 = weight == 0 and self.Portfolio[sec].IsLong
cond2 = weight > 0 and not self.Portfolio[sec].Invested
if cond1 or cond2:
self.SetHoldings(sec, weight)
if cond1:
self.Debug("Invested; selling")
elif cond2:
self.Debug("Not invested; buying")

===================

Many thanks for your help and Best wishes

Alan

Upvote

Vladimir INVESTOR

October 2021

Alan chen

You substantially changed the original code.

That may be the reason why it behaves differently.

Try the version I published above.

Upvote

Spacetime INVESTOR

October 2021

Error:

ValueError : cannot convert float NaN to integer
at Update
wait_days = int(vola * self.BASE_RET)
===
at Python.Runtime.PyObject.Invoke(PyTuple args in dual_momentum_with_out_days_alpha.py: line 69

Fix:

    def Update(self, algorithm, _data):
        if algorithm.IsWarmingUp or not self.set_flag:
            return []
        self.set_flag = False
        insights = []
		
		#add
        history = self.history[self.MKT]
        if history is None:return
        vola = history.pct_change().std() * np.sqrt(252)
		
		#remove	
        #vola = self.history[self.MKT].pct_change().std() * np.sqrt(252)
        
        wait_days = int(vola * self.BASE_RET)
        
        .
        .
        .
+ Expand
- Collapse

Upvote

Vladimir INVESTOR

October 2021

Spacetime,

I was unable to reproduce the error you reported.

Have you had an error in the latest version 2.51?

Upvote

Spacetime INVESTOR

October 2021

apologies… should have been more clearer…

It is the framework algorithm shared above by Joao Antunes.

Upvote

Kamal G INVESTOR

October 2021

Hi Spacetime,

The cannot convert float to NAN integer occurs due to the multiple time resolutions being used for the same ticker. (Example below that will generate occasional errors). If you change all references to a given ticker to minute, hour, daily only, then the error will stop. (I got a similar error with other algorithms I have been putting together).

After you change a resolution, make sure the backtest is still the same or similar at the very least. Changing the time resolution can change an algorithm a lot!

self.STK2 = self.AddEquity('SPY', Resolution.Minute).Symbol

self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol

Upvote

Carsten INVESTOR

Carsten _ | December 2021

Vladimir

I was trying more pairs for the IN/Out and then giving each pair the same credit. If 80% of the total of 100% credits are reached, I would trigger the out signal. (It would be more like a voting approach)

This would be the opposite of this version were we have few pairs, but to trigger the signal all have to be satisfied. (It would be more like a veto approach)

What is you option on that, did you tried?

Second I would like to add some VIX pairs, like VIX > VIX 3M to trigger or a comparison of VIX momentum.

Did you tried something like that?

Upvote

Michael Rand INVESTOR

August 2022

Joao Antunes was there a reason why the AUD (FXA) - CHF (FXF) was dropped as an out signal from the original algorithm (in favor, I assume, of UUP and DBB)? I understand that UUP/FXA and FXF/DBB probably have similar properties from a market signal perspective.

Upvote

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