Mother of Dragons trading strategy…

nonetheless · Tue, 21 Jan 2020 10:47:18 +0000

Well, I had to call it something, right? Attached is the first code I’ve put together that ‘seems to work’. Survives all manner of WF tests: 70-30, 50-50, 30-70 – however you want to chop it. Also does well in Vonasi’s robustness tester, Random 10-10. I tried hitting it with a very big hammer but still performs well. Uses a combination of HullMA and Supertrend on the 2h TF to define the primary trend, then another HullMA + trailing stop on a 5m TF to control entry and exit. The 5m TF doesn’t give us much in the backtest but it does well in clear uptrends and downtrends. Just that choppy bit in March 2019 where it couldn’t cope. I hate to see whole month where a strategy doesn’t come good, but one out of 16 is not too shabby. It runs around the clock so I’ve allowed for a spread of 3.3, calculated for IG as: (6.5 x 2.4 + 6.5 x 1.6 + 2 x 9.8 + 9 x 3.8)/24 All backtesting was done with MM disabled but I ran the final optimization with it turned on just to demonstrate the boost it gives. Starts with position size = 1 and by the end of the run when equity has trebled, it’s placing position size of ~3.4, so more or less in keeping with standard practise. Also reduces position size in bad patches. Credit where credit is due: Vonasi wrote the Money Management part and coded the HullMA for me. The trailing stop I believe is Nicolas’ work – big thanks to both! But the really cool thing about this strategy is that the equity graph draws a perfect profile of Daenerys Targaryen! You might have to squint a bit, but if you can’t see Daenerys then you haven’t optimized correctly. All comments, suggestions, mods etc welcome! // DJI 5m DEFPARAM CumulateOrders = False // Cumulating positions deactivated DEFPARAM preloadbars = 5000 Capital = 10000 MinSize = 1 //The minimum position size allowed for the instrument. MM1stType = 0 //Starting type of moneymanagement. Set to 0 for level stakes. Set to 1 for increasing stake size as profits increase and decreasing stake size as profits decrease. Set to 2 for increasing stake size as profits increase with stake size never being decreased. MM2ndType = 1 //Type of money management to switch to after TradesQtyForSwitch number of trades and ProfitNeededForSwitch profit has occurred TradesQtyForSwitch = 15 //Quantity of trades required before switching to second money management choice. ProfitNeededForSwitch = 2 //% profit needed before allowing a money management type change to MM2ndType. DrawdownNeededToSwitch = 8 //% draw down from max equity needed before money management type is changed back to MM1stType. DrawdownNeededToQuit = 25 //% draw down from max equity needed to stop strategy Once MoneyManagement = MM1stType Equity = Capital + StrategyProfit maxequity = max(equity,maxequity) if equity < maxequity * (1 - (DrawdownNeededToSwitch/100)) then enoughtrades = 0 tradecount = 0 moneymanagement = MM1stType endif if equity < maxequity * (1 - (DrawdownNeededToQuit/100)) then quit endif if not EnoughTrades then if abs(countofposition) > abs(countofposition[1]) then tradecount = tradecount + 1 endif if tradecount > TradesQtyForSwitch and maxequity >= Capital * (1 + (ProfitNeededForSwitch/100)) then EnoughTrades = 1 MoneyManagement = MM2ndType endif endif IF MoneyManagement = 1 THEN PositionSize = Max(MinSize, Equity * (MinSize/Capital)) ENDIF IF MoneyManagement = 2 THEN PositionSize = Max(LastSize, Equity * (MinSize/Capital)) LastSize = PositionSize ENDIF IF MoneyManagement <> 1 and MoneyManagement <> 2 THEN PositionSize = MinSize ENDIF PositionSize = Round(PositionSize*100) PositionSize = PositionSize/100 // Size of POSITIONS PositionSizeLong = 1 * positionsize PositionSizeShort = 1 * positionsize TIMEFRAME(120 minutes) Period= 520 inner = 2*weightedaverage[round( Period/2)](typicalprice)-weightedaverage[Period](typicalprice) HULLa = weightedaverage[round(sqrt(Period))](inner) c1 = HULLa > HULLa[1] c2 = HULLa < HULLa[1] indicator1 = SuperTrend[5,21] c3 = (close > indicator1) c4 = (close < indicator1) TIMEFRAME(5 minutes) Periodb= 60 innerb = 2*weightedaverage[round( Periodb/2)](typicalprice)-weightedaverage[Periodb](typicalprice) HULLb = weightedaverage[round(sqrt(Periodb))](innerb) c5 = HULLb > HULLb[1] c6 = HULLb < HULLb[1] // Conditions to enter long positions IF c1 AND C3 AND C5 THEN BUY PositionSizeLong CONTRACT AT MARKET SET STOP %LOSS 2.1 SET TARGET %PROFIT 1 ENDIF // Conditions to enter short positions IF c2 AND C4 AND C6 THEN SELLSHORT PositionSizeShort CONTRACT AT MARKET SET STOP %LOSS 1.2 SET TARGET %PROFIT 1 ENDIF //trailing stop function trailingstart = 84 //trailing will start @trailinstart points profit trailingstep = 3 //trailing step to move the "stoploss" //reset the stoploss value IF NOT ONMARKET THEN newSL=0 ENDIF //manage long positions IF LONGONMARKET THEN //first move (breakeven) IF newSL=0 AND close-tradeprice(1)>=trailingstart*pipsize THEN newSL = tradeprice(1)+trailingstep*pipsize ENDIF //next moves IF newSL>0 AND close-newSL>=trailingstep*pipsize THEN newSL = newSL+trailingstep*pipsize ENDIF ENDIF //manage short positions IF SHORTONMARKET THEN //first move (breakeven) IF newSL=0 AND tradeprice(1)-close>=trailingstart*pipsize THEN newSL = tradeprice(1)-trailingstep*pipsize ENDIF //next moves IF newSL>0 AND newSL-close>=trailingstep*pipsize THEN newSL = newSL-trailingstep*pipsize ENDIF ENDIF //stop order to exit the positions IF newSL>0 THEN SELL AT newSL STOP EXITSHORT AT newSL STOP ENDIF //************************************************************************

This topic has 522 replies, 50 voices, and was last updated 3 years ago by LaurentBZH35.

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11/05/2020 at 9:33 AM #149510

AlexMarlier

I also have it live with the sqme issue as you. can be explained by so many reasons such as: spread in live vs spread in backtest, execution from IG in live. lets see whats PRT says about it

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11/05/2020 at 9:35 AM #149511

AlexMarlier

Trader2323 wrote:

Thanks again to all the contributors on this amazing topic.

Anyone running the NAS 5m MoD v4.1 S here ? I launched it without MM (MM=0 & positionsize=1), and it took a position on Nov 3rd, at 06:00:00 UTC, which hit stoploss at 15:15:27 UTC. However, this doesn’t appear in the backtest.

I reached PRT out, and I am now waiting for their answer.

I read that @BobOgden had a similar issue a few days ago with the DJ version.

I also have it live with the same issue as you. can be explained by so many reasons such as: spread in live vs spread in backtest, execution from IG in live. lets see whats PRT says about it

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11/05/2020 at 9:43 AM #149513

nonetheless

@April O’Neil, that looks promising. How much result is OOS? or is it optimized on 100% of data?

nonetheless

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11/05/2020 at 11:01 AM #149519

swetraders

Hi again @nonetheless,, thank you for answering.
So do you recognize the phenomenon, that after optimizing high tf conditions, adding the m5-tf conditions will actually make the total gain less?
And do you have any thoughts about running this code with only h2 & m15/m30 conditions but executed on tf 5?

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11/05/2020 at 11:18 AM #149521

nonetheless

swetraders wrote:

adding the m5-tf conditions will actually make the total gain less

not sure what you mean. You get a better result running it on 15m TF without the 5m conditions? if so, i’d like to see the backtest.

nonetheless

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11/05/2020 at 1:37 PM #149532

swetraders

Ok @nonetheless, so in the attached image you see two different attempts to build a MoD Long for DAX.
Both of these have only been optimzed for H2 and M30+M15 Timeframes so far.
The first one(the one on top) I tried to optimize for high WF and many trades (85.37% winrate, 369 trades)
The second one(the on the bottom) I tried to optimize for highest possible gain (Only 138 trades).

So I have optimized these two from the top down, so the next step now is to optimize the conditions for the M5 timeframe.
I use the differrent code bits shown below, but whatever values i optimize for variables “l, m, n, o, k, j” the results are not getting any better.
I can’t make the M5 conditions to enhance the performance: For the first example I can make small improvements to the gain/wf/pf but just slightly.

When I’m looking at the different versions that you have made, I see that the M5-conditions is the most significant for the performance of the algo. i.e. when I strip the M5-condititions from your versions the backtests often looks bad (similar to what you see in the second attached image).

I’m thinking I’m doing the process wrong. After optimizing the 4 entry conditions of H2 and the 3 entry conditions for M15/M30 I rarely can make the algo any better with the M5 conditions. Scratching my head here.

M5 conditions used:

lengthRSIa = l //RSI period
lengthStocha = m //Stochastic period
smoothKa = n //Smooth signal of stochastic RSI
smoothDa = o //Smooth signal of smoothed stochastic RSI
myRSIa = RSI[lengthRSIa](close)
MinRSIa = lowest[lengthStocha](myrsia)
MaxRSIa = highest[lengthStocha](myrsia)
StochRSIa = (myRSIa-MinRSIa) / (MaxRSIa-MinRSIa)
Ka = average[smoothKa](stochrsia)*100
Da = average[smoothDa](Ka)
c23 = Ka>Da
c24 = Ka<Da

ma3 = average[k,3](typicalPrice)
c21 = ma3 > ma3[1]
c22 = ma3 < ma3[1]

Periodb= j
innerb = 2*weightedaverage[round( Periodb/2)](typicalprice)-weightedaverage[Periodb](typicalprice)
HULLb = weightedaverage[round(sqrt(Periodb))](innerb)
c5 = HULLb > HULLb[1]and HULLb[1]<HULLb[2]
c6 = HULLb < HULLb[1]and HULLb[1]>HULLb[2]

lengthRSIa = l //RSI period

lengthStocha = m //Stochastic period

smoothKa = n //Smooth signal of stochastic RSI

smoothDa = o //Smooth signal of smoothed stochastic RSI

myRSIa = RSI[lengthRSIa](close)

MinRSIa = lowest[lengthStocha](myrsia)

MaxRSIa = highest[lengthStocha](myrsia)

StochRSIa = (myRSIa-MinRSIa) / (MaxRSIa-MinRSIa)

Ka = average[smoothKa](stochrsia)*100

Da = average[smoothDa](Ka)

c23 = Ka>Da

c24 = Ka<Da

ma3 = average[k,3](typicalPrice)

c21 = ma3 > ma3[1]

c22 = ma3 < ma3[1]

Periodb= j

innerb = 2*weightedaverage[round( Periodb/2)](typicalprice)-weightedaverage[Periodb](typicalprice)

HULLb = weightedaverage[round(sqrt(Periodb))](innerb)

c5 = HULLb > HULLb[1]and HULLb[1]<HULLb[2]

c6 = HULLb < HULLb[1]and HULLb[1]>HULLb[2]

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11/05/2020 at 3:38 PM #149549

nonetheless

When you optimize the longer TFs, what values do you have in the 5m TF?

Also, are you using WF or 100% of data?

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josef1604

nonetheless

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11/05/2020 at 4:45 PM #149552

swetraders

nonetheless wrote:

When you optimize the longer TFs, what values do you have in the 5m TF?

Also, are you using WF or 100% of data?

When I optimize the longer TFs I have removed the M5 timeframe completely. Should one have some kind of default / unoptimized starting values on M5 (and on M15) when optimizing H2. If yes, what should that starting values be?
In this example I use 80%/20% like you.

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josef1604

swetraders

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11/05/2020 at 5:04 PM #149553

nonetheless

Yes, you have to have something entered for all the indicators as they act as a whole, each one reacting to other changes. For DAX long you can use NAS long as a template and start with those values. Or you can use ballpark figures like sl =1, tp =2

the default for StochasticRSI is 14, 14, 10, 3 (although it never ends up that way)

You’ll have to go through them all at least 3 times as by the time you get to the bottom, the ones at the top will need changing – it takes days!

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josef1604

nonetheless

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11/05/2020 at 5:59 PM #149568

swetraders

nonetheless wrote:

Yes, you have to have something entered for all the indicators as they act as a whole, each one reacting to other changes. For DAX long you can use NAS long as a template and start with those values. Or you can use ballpark figures like sl =1, tp =2

the default for StochasticRSI is 14, 14, 10, 3 (although it never ends up that way)

You’ll have to go through them all at least 3 times as by the time you get to the bottom, the ones at the top will need changing – it takes days!

Thank you @nontheless. That kind of explains it, I will use those start values and let you know my results. Cheers!

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11/06/2020 at 2:54 PM #149681

April O’Neil

nonetheless wrote:

@April O’Neil, that looks promising. How much result is OOS? or is it optimized on 100% of data?

I optimised with 100% of data. Basically, for a known stratgy with variables previously validated , the more data you got the better it is. The purpose was mainly to validate the process of construction. Wich is sucessfull, i obtened sometheing very similar with the v2.2 and i fell on very similar variables. I put Dax v3 in demo for the next month.

Swetraders, i think the problem is your start, did you start with natural exits conditions ? At the begin you should have c1 to c7. This is your core strategy. Use a large TP, small SL (like 1.5) and a basic TS (0.3).

By the way nonetheless , do you use this Money management in live with Margin = (close*.005 ) and margin2 = (close*.01) ? Very agressive.

April O’Neil

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11/06/2020 at 3:43 PM #149690

nonetheless

My IG account is in Switzerland and that is the leverage they offer. This varies from country to country and you need to alter those decimals to reflect whatever leverage you get. In the UK it would be .05 for tier 1 and tier 2, possibly also in France.

If you want less aggressive MM just increase the Factor – whatever you feel comfortable with!

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11/06/2020 at 9:44 PM #149730

swetraders

@April O’Neil Yes you are right. @nonetheless What would you say is the minimum number of trades you want to see to start the strategy in demo? 250+ or even more?

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11/07/2020 at 1:46 PM #149767

nonetheless

swetraders wrote:

minimum number of trades

Not something I’ve ever thought about. I’d be more concerned with the win% – number of trades just is what it is.

nonetheless

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11/09/2020 at 4:57 AM #149868

StingRe

Hi @nonetheless,

I’ve noticed in all the strategies that there are about 5-10 trades that have an MFE of 0$. Meaning the trade went red as soon as it was opened.

I’m wondering if there was a way to potentially mitigate these by placing limit / stop orders a few points out from where the daily high / low entry criteria activate.

I understand you would likely miss a few points on trades that ended up being winners but it might keep you out of the MFE 0’s.

I’ve tried to come up with a way to test this in the conditions for long / shot but haven’t had any luck.

Interested in your thoughts or if you’ve tried something similar.

Cheers,

StingRe

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