Parameter Robustness Curve – A novel measure to evaluate parameter influence on trading systems, strategies and expert advisors

Sven Schmidt*

*Corresponding author: Scientific-Trading.Com, Germany, Munich, Email: scientific-trading@web.de
September 2011. All rights reserved.

Abstract

Parameters of indicators and expert systems used in technical analysis and automated trading can have a huge impact on their usefulness and performance. Many systems are published and evaluated with parameters that perform optimal on selected data. However, this gives no information of the influence of the parameter space on the performance of the indicator, expert advisor or trading system. Even worse, no intuitive measure addresses the question how likely it is that the chosen parameters will lead to a positive result of the trading system.

Here, we systematically evaluated a simple trading system based on the Super-Trend indicator for the major currencies EUR, GBP, USD, CHF and JPY. The analysis was run for 12 years and six different time frames on each of the ten currency pairs. For each currency pairs and time frame, 640 back test runs with different parameters were performed and the result noted.

In this study, we introduce the Parameter Robustness Curve (PRC) and demonstrate its usage on a large-scale evaluation of a Super-Trend based trading system. The PRC gives an intuitive impression about the probability that a random parameter combination of a trading system would lead to a positive result. Its application on different markets shows the generality of the used strategy by the Parameter Robustness Curve’s down-grade. The PRC may allow the comparison of the influence of parameter space on the performance on expert advisors and trading strategies. Here, we demonstrated on which markets our Super Trend strategy performs most robust and that this observation is very little influenced by the applied time frame.

Introduction

Parameters of indicators and expert systems used in technical analysis and automated trading can have a huge impact on their usefulness and performance. Many systems are published and evaluated with parameters that performed optimal on their tested data. However, this gives no information of the influence of the parameter space on the performance of the indicator, expert advisor or trading system. Even worse, no intuitive measure addresses the question how likely it is that the chosen parameters will lead to a positive result of the trading system.

Although various measures to evaluate the performance of a single run of an expert advisor system are available like Profit Factor and Risk of Ruin, no information about the influence of the parameters is available usually. Here, we evaluate a simple trading strategy based on the Super-Trend indicator (Robinson, 2008) and introduce a novel measure the Parameter Robustness Curve (PRC). We demonstrate that the PCR gives an impression about the likelihood to obtain a positive net result using the trading system with a random parameter choice. In addition, we show that the slope of the PRC can be used to estimate the generality of the strategy in different markets. Besides, we provide the best parameters for the Super-Trend indicator on 6 time frames. Finally, we show that parameter robustness is mainly dominated by the market and less by the time frame in our analyzed strategy.

Methods

Data

Date data was obtained from MetaQuotes Historical data download (MetaQuotes) for the currencies EUR, GBP, USD, CHF, JPY and its pairs (Table 1). For all pairs, data on the time frames 5, 15, and 30 minutes, hourly, all 4 hours and daily were used starting from January 1999 until December 2010. Here, open prices were used only.

Table 1 Currency pairs analyzed

  EUR GBP USD CHF JPY
EUR / X X X X
GBP   / X X X
USD     / X X
CHF       / X

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 20] step size 1 and [0.5 to 20] step size 0.5, respectively. Shorter windows than five are hardly informative. Thus, in total 16*40 = 640 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 and 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 was 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 an 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

Here, we evaluated a simple Super-Trend (Robinson, 2008) based trading strategy. 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. In total 640 parameter pairs on 10 FOREX currency pairs were evaluated on six time frames starting from January 1999 until December 2010. Details about the data are shown in (Table 1).

We found that the choice of the applied parameter multiplier and window size influence the outcomes significantly. This observation supports our findings on the daily time frame (Schmidt, 2011). Therefore, we addressed the question how likely it is that a random parameter combination would result in a positive result on different markets with the Parameter Robustness Curve. Beyond, we revealed that the market is the major determinant for the performance of the analyzed strategy and less the chosen time frame.

Parameter Robustness Curve (PRC)

Any trading strategy – as our Super-Trend example here – should return a positive net win. This is the first criteria which we applied for a parameter choice to be counted as successful. Further, we consider all parameters as ‘failed’/’no-success’ that yielded a draw-back of more than 30% or a single loss of 10% on any open trade (percentages relative to the order open price).

As Parameter Robustness, we now define the percentage of parameter combinations relative to the tested total number of parameter combinations (here 640) that fulfilled these success criteria. A high robustness indicates the likelihood that a random choice of the parameters in the given market would result in a positive trading performance. Figure 1 visualizes the robustness of the Super-Trend trading strategy on major FOREX markets and on the time frames all 5 minutes (M5), all 15 minutes (M15), all 30 minutes (M30), hourly (H1), all four hours (H4) and daily (D1).

Figure 1 Parameter Robustness Curve (PRC) of the Super-Trend trading strategy. Robustness shows the percentage of possible parameters (on the evaluated parameter space and markets) that yielded a positive total net win without inacceptable drawbacks or single losses. Color and shape of the dots represent the applied time frame (M5= 5 minutes, H1=hourly, H4=all four hours, D1=daily). A high robustness indicates that almost all parameter combinations will result in a positive performance of the trading. For example a robustness of 84.1% for the currency pair EUR/USD and 15 minutes time freame means that a random choice of the parameters (within the parameter range) will lead with a likelihood of 84% to a positive result on that market and time frame. The slope of the curve further shows how well the strategy is applicable to different market types. An interesting observation is that the parameter robustness of the Super Trend strategy is comparable within a currency pair no matter which time frame was analyzed.

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

The Parameter Robustness Curve as shown for the Super-Trend example gives an immediate visualization of the likelihood that a parameter choice is successful. In Table 2 the optimal parameters for the Super-Trend indicator are shown for the 15 minutes time frame. A complete listing of all data and time frames can be found in the supplementary material.

It should be noted that leverage and margin was assumed to be 1, therefore the resulting performance values of Table 2 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.

Table 2 Best parameters of the Super-Trend trading system on M15 time frame

Symbol

Best-Mult.1

Best-Period1

Performance2

Trends3

Failed4

Number-Params-Loss5

Failed-Total6

Robust-ness

CHF/JPY

0

0

0

0

640

0

640

0.0%

EUR/CHF

13

9

0.49418

407

0

388

388

39.4%

EUR/GBP

7.5

17

0.50674

980

0

333

333

48.0%

EUR/JPY

6

5

150.126

2023

0

267

267

58.3%

EUR/USD

12.5

20

0.90632

733

0

102

102

84.1%

GBP/CHF

6

18

1.3284

1433

0

356

356

44.4%

GBP/JPY

9

14

213.087

963

0

240

240

62.5%

GBP/USD

3.5

19

0.93325

4563

0

162

162

74.7%

USD/CHF

3

20

0.9797

5541

0

328

328

48.8%

USD/JPY

5

8

60.956

2516

0

461

461

28.0%

1 Multiplier and window denotes the best multiplier and window size parameter of the Super-Trend indicator on the 5 minutes timeframe for the data used in this study.
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 for the data and timeframe evaluated would thus yield around 60,110 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 and may serve as indicator for parameter robustness.
5 Loss denotes the number of parameter pairs that resulted in a negative performance result.
6 Failed total is the sum of ‘failed’ and ‘loss’.

Discussion

In this article, we presented a novel measure of parameter robustness on trading systems. Our defined Parameter Robustness denotes the percentage of ‘successful’ parameter choices in a parameter search space. We recommend to use i) positive total net result ii) no draw-backs greater than 30% and iii) no single losses greater than 10% as criteria for a successful run of the trading strategy. It should be noted that Parameter Robustness not only depends on the ‘success’ criteria but also on the number of evaluated time points and the parameter space. Therefore, a sufficient large parameter and rate data space is obligatory. Given comparable criteria and data, the Parameter Robustness allows comparing the likelihood of choosing ‘good’ parameters of a trading system. Even if a parameter space is not covered completely, a representative and sufficiently large- and fine grained sampling would allow estimating a pseudo-probability.

To address the comparison of different markets, we further introduced the Parameter Robustness Curve (PRC) which allows estimating the influence of parameter choice on different markets. Considering the success-criteria of a trade system evaluation as constant and parameter space equivalently large, PRC allows to compare trading systems with regard to their parameter influence on various markets at a single glance easily.

References

CodeGear. (n.d.). Delphi 2009. Retrieved from http://www.embarcadero.com/products/delphi

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/4725

Schmidt, S.-O. (2011, 08 15). Evaluation of Super-Trend indicator’s parameters for all major FOREX pairs over 12 years. Retrieved from TradingResearch Blog: http://wp.me/1M1iZ

Supplementary Material

Robustness_Curve_SuperTrend.xlsx is available on request from http://www.scientific-trading.com/contact.php

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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