Evaluation of Super-Trend indicator’s parameters for all major FOREX pairs over 12 years

By Sven Schmidt1,*

1Scientific-Trading.Com, Pilotystr. 4, 80538 München, Germany

*Corresponding author, Email: scientific-trading@web.de
All rights reserved, 2011

Abstract

The Super-Trend indicator used in technical chart analysis provides signals whenever a rate change appears that excess an upper or lower border. The borders are defined by the Average-True-Rate of a given past-period-window times a defined multiplier-parameter. The Super-Trend indicator is frequently used in chart analysis like in FOREX trading, but no systematic and large-scale analysis of the parameters influence was available yet.

In this study, we used real daily rate date of all major FOREX pairs of the last 12.5 years and calculated trading performance for 9,200 Super-Trend parameter pairs each. A long trade was open whenever the indicator signals an up-trend and closed if a change to down-trend occurred and vice versa for short trades.

Our analysis revealed that for some currency pairs like EUR/USD a huge parameter range delivers good results, whereas for some markets like GBP/JPY the parameter range is quite limited. In addition, we provide optimal parameters of the Super-Trend indicator for all major markets on daily basis evaluated over a time period of more than 12 years for the first time.

Introduction

Technical chart analysis (TA) tries to predict future movements of financial markets solely based on chart data (Wikipedia, Technical Analysis, 2011). Although highly disputed and in contrast to fundamental analysis, TA is widely used in market analysis and trading (Wikipedia, Fundamental Analysis, 2011). A corner stone of TA is the use of indicators that are supposed to give relevant information of current and future price developments (Wikipedia, Technical Analysis, 2011). A multitude of indicators are available, an overview of basic concepts is given in (Wikipedia, Technical Analysis, 2011).

Here, we analyzed the relatively new Super-Trend indicator as published on mql4.com (Robinson, 2008) and further described for example by Kolier (Kolier, 2010). Briefly, the indicator is a break-out indicator which provides a signal for an up- or down trend whenever the break-out border is crossed by the current price. The borders are calculated by the current price plus the Average-True Range (ATR) (Wilder, 1978) times a multiplier parameter. The ATR is an average of the True Range (Wilder, 1978) and provides a measure of the volatility. Therefore, the Super-Trend indicator gives a signal if sudden price movements exceed the expected market movements.

The Super-Trend indicator experienced an astonishing attraction with more than seven million web pages (Google, 2011/08) only a couple of years post publication already. However, although widely used, no systematic and no large-scale analysis of the Super-Trend indicator is available so far. Here, we simulated trading based on Super-Trend indicators’ signals for all major FOREX pairs of real daily rates of the last 12.5 years. We evaluated about 10,000 Super-Trend parameters for 12 major FOREX pairs and provide novel insights into the applicability of the indicator as well as best parameter setting for the first time.

Methods

Data

Daily rate data was downloaded end of July 2011 using the History-Center of Metatrader 4 (MetaQuotes) for 12 major FOREX pairs and for the maximum available time period as shown in (Table 1). Here, the open, close, high and low prices were used.

Table 1 Data used in this study.

FOREX currency pair Oldest time point Latest time point
AUD/USD 16/06/2003 29/07/2011
EUR/AUD 05/12/2006 29/07/2011
EUR/CHF 04/01/1999 29/07/2011
EUR/GBP 04/01/1999 29/07/2011
EUR/JPY 04/01/1999 29/07/2011
EUR/USD 04/01/1999 15/07/2011
GBP/CHF 05/01/1999 29/07/2011
GBP/JPY 04/01/1999 29/07/2011
GBP/USD 04/01/1999 29/07/2011
USD/CAD 04/01/1999 29/07/2011
USD/CHF 04/01/1999 29/07/2011
USD/JPY 04/01/1999 29/07/2011

Historical daily charts rates (Open, High, Low, Close and Volume) were available for more than 12.5 years for all pairs except AUD/USD and EUR/AUD.

Performance Evaluation

The Super-Trend indicator algorithm as published by (Robinson, 2008) was used to define an up- or down trend based on historical daily rates data. The indicator’s parameter “window size” and “multiplier” were analyzed between [5 to 50] step size 1 and [0.1 to 20] step size 0.1, respectively. Shorter windows than five are hardly informative. Thus, in total 46*200 = 9,200 parameter pairs were tested for each currency pair.

Trades were opened upon any trend change indicated by the indicator and closed at the next trend change. For example, the trend is predicted to change to an up-trend a long order was opened and closed as soon as the trend is signaled to change to down-trend according to the Super-Trend indicator. If a closed order yields a loss of more than 10% of its open price than the parameter pair’s performance is labeled with -1. Likewise, any simulation in which more than 30% drawback (relative to the order open price) occurs during the holding time is denoted with a performance of -1. Parameter pairs that are labeled with -1 are referred as “failed” parameter pair.

To avoid any tampering that the account-currency and its exchange rate to the currency pair and lot size is influencing the performance evaluation of the indicator, only the absolute rate difference between open and closing of the orders is noted. Just as well, swaps as well as further costs like commission were not considered. Therefore, the performance evaluation results need to be multiplied by a usual leverage like 100 and a margin for example like 100. With this example leverage, margin and a 0.1 lot, a performance of 0.70619 for the best parameters in EUR/USD would yield around 70,619 EUR net winnings.

Implementation

The Super-Trend indicator as well as the performance test were implemented in Delphi 2009 (CodeGear). Since the original Super-Trend indicator repaints the last bar, i.e. in MetaTrader (MetaQuotes) bar 1, we used the open price of the following bar as trading open price. In addition our simulation executes the indicator every time a new bar is started. Here, this means that each day the indicator is run upon its open time. In a MetaTrader back test this is reflected by a Open-Price simulation. The rationale here is that a sudden out-break which leads to a Super-Trend indicator signal is frequently followed by a rebound and a immediate order opening is of often found to be less efficiency than waiting until the next bar (here day).

Results

In this study, we analyzed the influence of the parameters “multiplier” and “window size” of the Super-Trend indicator (Robinson, 2008). We tested almost 10,000 parameter pairs on the 12 major FOREX currency pairs for a time frame of about 12.5 years (Table 1). Any parameter settings that yielded a draw-back of more than 30% or a single loss of 10% or more were noted as “failed”. Simulated trading was based on opening an order upon a trend-change and closing it on the next trend change i.e. every time a signal of the Super-Trend indicator was issued. Leverage and margin was assumed to be 1, therefore the resulting performance values need to be multiplied to represent real net winnings. For example a leverage and margin of 100 each would means to multiply the performance value by 10,000.

Best parameters of the Super-Trend indicator for each currency pair

We found that i) for all 12 currency pairs an optimal parameter pair can be found and ii) there are striking differences with regard to the optimal parameters between the currencies. The best parameters and the best performance for the major FOREX pairs are shown in Table 2.

Table 2 Performance of Super-Trend indicator with its best parameters

Currency pair

Multiplier1

Window1

Performance2

Trends3

Failed4

AUD/USD

6.1

37

0.5353

25

5499

EUR/AUD

3

6

0.65311

26

1731

EUR/CHF

6.3

14

0.39043

12

0

EUR/GBP

12

28

0.20115

14

1376

EUR/JPY

12.2

10

100.623

7

4208

EUR/USD

4.4

5

0.70619

46

3680

GBP/CHF

6.3

29

0.92112

22

2042

GBP/JPY

10.6

16

171.995

9

3986

GBP/USD

8.3

10

0.81825

13

1794

USD/CAD

10.2

50

0.64825

24

1301

USD/CHF

3.1

41

0.69481

76

5227

USD/JPY

1.4

6

58.507

196

2535

1 Multiplier and window denotes the best multiplier and window size parameter of the Super-Trend indicator
2 Performance shows a raw net win of the simulated trading given the best parameters. As outlined in the Methods sections this raw value needs to be multiplied according to real life leverage and margin as well as lot size. For a leverage and margin of 100 and a lot size of 0.1 the multiplier would be 10,000. The best Super-Trend indicator parameters for EUR/USD would thus yield around 70,619 EUR net winnings.
3 Trends show the number recognized trend phases i.e. how many up- and down episodes were predicted.
4 Failed shows the number of parameter pairs that yield either more than 30% draw-back or a single loss of more than 10%. A low number indicates that the indicator is very applicable to this currency pair no matter which parameters were tested.

The differences in the best parameters reflect the underlying characteristics of the markets. We further found that for the currency pairs EUR/CHF, USD/CAD, EUR/GBP only 0% to 15% of the tested parameters were reported to yield a draw back or loss beyond acceptance. In contrast in AUD/USD 59.8% of the parameter pairs failed. This gives an overall impression of the parameter robustness of Super-Trend in each market. A more detailed view of the parameter space and the resulting winnings is shown for EUR/USD in Figure 1. The large violet areas show parameter pairs that lead to loss, or inacceptable draw-backs.

Figure 1 Super-Trend performance on EUR/USD for all tested parameter values.
Positive values indicate a net win and are colored yellow to red, negative values show a loss or inacceptable draw-backs or single losses (violet colors). It can be seen that multipliers between about 1.5 and 4 lead to winnings almost independent of the window size. In contrast larger multiplier values depend strongly on the window size and usually lead to a loss or to huge draw-backs / single negative order results. Performances need to be multiplied, e.g. with 10,000 for a leverage and margin of 100 and lot size of 0.1.

In contrast to the parameter performance of EUR/USD, for the currency pair EUR/CHF almost all larger multiplier values lead to significant net wins (Figure 2). Parameter space evaluation figures for all currency pairs are given in the supplements. Traders should take these characteristics under consideration whenever using the Super-Trend indicator.

Figure 2 Super-Trend performance on EUR/CHF for all tested parameter values.
Positive values indicate a net win and are colored yellow to red, negative values show a loss or inacceptable draw-backs or single losses (violet colors). Performances need to be multiplied, e.g. with 10,000 for a leverage and margin of 100 and lot size of 0.1.

A single best parameter pair for all currency pairs

We further questioned whether a single parameter pair would be applicable to all currency pairs analyzed in this study and over the total time of the last 12.5 years. We defined an overall best performance by the averaged net wins of all currency pairs for a given parameter pair. JPY currencies were excluded here since their absolute value range would bias the average net wins of the other currencies. We found that the multiplier 7.9 and a window size of 9 provided on average the best net wins. Details of the application of this parameter setting are shown in Table 3. It can be seen that the absolute wins are significantly less if a global best parameter is used.

Table 3 Performance for overall optimal Super-Trend parameter

Currency pair

Performance1

AUD/USD

0.11059

EUR/CHF

0.1206

EUR/GBP

0.02121

EUR/USD

0.32266

GBP/CHF

0.57063

GBP/USD

0.44759

USD/CAD

0.3972

USD/CHF

-0.1427

1 Performance shows a raw net win of the simulated trading given the best parameters. As outlined in the Methods sections this raw value needs to be multiplied according to real life leverage and margin as well as lot size. For a leverage and margin of 100 and a lot size of 0.1 the multiplier would be 10,000. The best Super-Trend indicator parameters for EUR/USD would thus yield around 70,619 EUR net winnings.

Discussion

We showed that the Super-Trend indicator can be a powerful indicator given right parameter settings. In our large-scale and systematic analysis we provided the optimal parameters for 12 major FOREX currency pairs for the first time and longest time period. The back test simulations exceeded more than 12.5 years and showed significant differences in the characteristics of the parameter space performance. Our presented results hopefully help to replace gut feeling with rationale in regard to parameter choice. We further give an overall best parameter recommendation and visualizations of the effects of the parameter selection.

References

forex-indicators.net. (n.d.). ATR indicator explained. Retrieved 08 2011, from http://forex-indicators.net/volatility-indicators/atr

Kolier, L. (2010, 11 22). How Super Trend works. Retrieved 08 2011, from http://kolier.li/indicator/how-supertrend-mq4-works-logic-of-supertrend-indicator-created-by-jason-robinson-jnrtradin

MetaQuotes. (n.d.). Forex Trading Platform MetaTrader 4. Retrieved 08 2011, from http://www.metatrader4.com/

Robinson, J. (2008, 07 20). Codebase MQL4, Super Trend source code. Retrieved from http://codebase.mql4.com/3959

Wikipedia. (2011, 08 09). Fundamental Analysis. Retrieved from http://en.wikipedia.org/wiki/Fundamental_analysis

Wikipedia. (2011, 08 11). Technical Analysis. Retrieved from http://en.wikipedia.org/wiki/Technical_analysis

Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.

Supplementary Material

http://www.scientific-trading.com/research/params_ST_daily1/EURAUD_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/EURCHF_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/EURGBP_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/EURJPY_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/EURUSD_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/GBPCHF_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/GBPJPY_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/GBPUSD_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/USDCAD_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/USDCHF_D1_eval_ST_params.jpg

http://www.scientific-trading.com/research/params_ST_daily1/USDJPY_D1_eval_ST_params.jpg

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About Scientific-Trading.com / TradingResearch
Researcher in technical analysis of FOREX markets

15 Responses to Evaluation of Super-Trend indicator’s parameters for all major FOREX pairs over 12 years

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  4. bob says:

    what do you mean when you say window size?

  5. peppe says:

    hi and thank you for posting this research. Some time ago i made, by myself, similar studies on technical indicators (not only on parameters, but also on “robustness” of them, because i found most indicators and trading systems are overoptimized and curve fitted). I ended with results similar than yours. The big problem is: are parameters stable in time? Is there a correlation between profitable combinations of parameters from period t-2 to t-1 and profitable parameters of period t-1 to t? This is what really counts (and, till now, i couldn’t find any significant correlation). I would be pleased to show you some of my images (some are in three dimensions :) ) and, if you like, collaborate with you in these studies.

    • ivpfinance says:

      Hi, I have spent the last 6 years trying to get the answer to the same question as you post. My impression is that even if there is a magical set of indicators and their setting that would give good results for a very very long time, a better solution is to constantly adapt the feature selection. I am deeply into machine learning so maybe it’s just my craze…

      • peppe says:

        of course, i also come to that conclusion (in theory, nothing would be better than an auto adapting system, with some feedback cycle with parameters and results). BTW i didn’t come to any useful results in this way (some time ago i read an article, on mql4 o mql5, i don’t remember and i can’t find it out anymore. It was about self optimizing expert advisor. Very interesting, but not really useful). Did you find some more interesting results using self optimization?

      • ivpfinance says:

        hi I do neural networks and svm with some positive results. More I dig into it my computing power needs grow exponentaly and my programs get more and more complicated and all sorts of related problems impede further progress but I have an impression it is the right path.

      • peppe says:

        interesting. I can understand what you’re facing. Optimizing (or similar) it’s a really computation-intensive task. Especially if you have to do continuously!

  6. Jay says:

    Sven – this is a really good piece of research. Nice!

  7. Anonymous says:

    It is in point of fact a great and helpful piece of information. I’m glad that you shared this helpful info with us. Please keep us informed like this. Thank you for sharing.

  8. Anonymous says:

    This is exactly why I joined for this website: I
    never find postings like this anywhere else !

  9. Anonymous UK says:

    I read your Blog / Evaluation last year and have successfully used ST ever since. After playing for far too many hours I found the following by accident and now use 2 ST signals together. The following has worked this year on GBP/USD

    4hr:(10-2.8) & (200-1.5).
    Use the (10-2.8) for trend and (200-1.5) as an oversold / overbought signal.

    Wait every 4hrs for the (10-2.8) to signal UPTREND:BUY. Place 33% of your trade on this signal in the direction of the trend. Close the position in profit or if the (10-2.8) changes trend.

    Then check again every 4hrs for the (200-1.5) to signal sell. If the (10-2.8) does not change trend then this sell signal should be considered oversold. BUY the market with the remaining 66% of your trade.Close the position in profit or if the (10-2.8) changes trend

    If the (10-2.8) signals downtrend. Wait for the (200-1.5) to signal overbought.

    ONLY BUY on the 4hr signal. Don’t jump the gun.
    SELL anytime. Preferably when you are in profit.

    I don’t have the resources to back test 12.5 years. But it has worked this year (2013).

  10. Andrew says:

    I read this site 12 months ago. I found the follow strategy by accident (trial & lots of errors)

    This may be of interest – It works for me!

    “Super Trend” (S.T) trading strategy for GBP/USD

    SET UP
    • Only 1 screen required.
    • 4hr time frame
    • S.T (10 – 2:8) – colour BLUE
    • S.T (200 – 1:5) – colour RED
    • No other technical indicators required. ( I used Candlestick & Bollinger Bands 20:3)

    STRATEGY
    • S.T (10 – 2:8) – TREND
    • S.T (200 – 1:4) – Overbought / Oversold indicator

    ENTRY POINTS
    • (10 – 2.8) Only trade in the direction of the TREND (33%)max
    • (200 – 1.5) Regardless of signal. Only trade in the direction of the TREND (66%)
    • Only open a trade every 4 hours. Don’t jump the gun! (GMT: 24.4.8.12.16.20.24.)
    • Close a profitable trade at any time. Close a losing trade if the TREND changes.
    • If you’re standard trade is 100. Then 33% = 33 and 66% = 66. Use the 1% to buy a beer or 2!
    • Just in case you forgot. A profit is a profit. NOT A LOSS.

    This system created good entry points in 2013. I have no idea what it did in 2012.

    PLEASE: Only risk money you can afford to lose.

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