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PhD Tested

Ted W. Warnock, PhD

Aerospace engineer Ted Warnock is a real rocket scientist whose career includes applying neural network technology to tethered satellites so he knows it’s not easy to set up neural networks to find solutions. But he calls TradeShark’s 80% accuracy rate over 40 markets he tested “extremely impressive.” And you don’t have to understand the neural network process to take advantage of TradeShark’s trend forecasts. See more at http://www.TradeShark.com
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Ted W. Warnock, PhD

Video
Aerospace engineer Ted Warnock is a real rocket scientist whose career includes applying neural network technology to tethered satellites so he knows it’s not easy to set up neural networks to find...
Aerospace engineer Ted Warnock is a real rocket scientist whose career includes applying neural network technology to tethered satellites so he knows it’s not easy to set up neural networks to find solutions. But he calls TradeShark’s 80% accuracy rate over 40 markets he tested “extremely impressive.” And you don’t have to understand the neural network process to take advantage of TradeShark’s trend forecasts. See more at http://www.TradeShark.com

Dr. Warnock did an extensive test and put TradeShark through the paces.  He tested the predictive accuracy for the period of January 15, 2013 – October 1, 2013.  Forty markets were selected at random and the results of these assessments indicate the accuracy and consistency of TradeShark’s Predicted Neural Index.  The overall (combined) average accuracy for all markets tested was demonstrated to be in excess of 80% with a high (Apple stock) of 87% and a low (Corn futures) of 75%.

Accuracy Assessment of TradeShark Intermarket Analysis Software

By Ted W. Warnock, Ph.D.
October 2, 2013

Background

This report describes the methodology applied and results obtained in an accuracy assessment I conducted of TradeShark's Neural Index (NI) on October 1, 2013. For this assessment, the accuracy of NI forecasts in 40 randomly selected markets was examined, using TradeShark Version 1.0.01.0220 with data spanning the time period January 15, 2013 to October 1, 2013. This time period was selected for this assessment because it allows evaluation of the software's performance in a "forward test" setting. Data from the period January 15, 2008 thru January 15, 2013 were used to train the neural networks imbedded in the TradeShark software.

TradeShark uses neural networks to combine intermarket data with each market of interest's price action to provide predictive forecasts. The NI is a proprietary indicator included in the software that predicts whether or not a three-day simple moving average of typical price (average of the high, low, and closing prices) will be higher or lower two days in the future than it is on the current day. The NI compares two three-day simple moving average values — the current moving average value and predicted moving average value, which is generated by the software.

When the predicted three-day simple moving average value of typical prices (3SMAtp) is appreciably greater than the current 3SMAtp, the NI value reported is "up", indicating the market is expected to move higher over the next two days. When the predicted 3SMAtp is appreciably less than the current 3SMAtp, the NI value reported is "down", indicating the market is expected to move lower over the next two days. In some cases, the NI reports a value of "n/a", indicating there are only relatively weak and inconclusive indications of future price movement - and so no call of "up" or "down" is made.

Markets

TradeShark provides forecasts for over 2,000 markets in the categories of ETFs; Forex; Futures; and Stocks. The 40 markets selected for this assessment spanned the categories available in TradeShark, and are listed below.

ETFs

iShares BRIC Index Fund (BKF); iShares MSCI South Korea IDX (EWY); ProShares Short S&P500 (SH); Vanguard REIT (VNQ); Health Care SPDR (XLV)

Forex

AUD/USD; CAD/JPY; EUR/AUD; EUR/USD; GBP/USD; USD/INR

Futures

("continuous" in all cases) Cocoa; Corn; Emini DJIA; Emini Euro FX; Emini Natural Gas; Emini Japanese Yen; FTSE100; Gold; Heating Oil; Lean Hogs; Nikkei 225; Orange Juice; Palladium; S&P 500; Soybeans; 10 year U.S. Treasury Bonds

Stocks

Apple (AAPL); Cheesecake Factory (CAKE); CSX (CSX); FLIR Systems (FLIR); Ford (F); Kellog (K); Netflix (NFLX); Newmont Mining (NEM); Southwest Airlines (LUV); TASER (TASR); Walgreens (WAG); Yahoo (YHOO)

Methodology

Daily price data provided by the TradersOnly data service spanning the time period of interest for this assessment was ingested into TradeShark. Selected columns of TradeShark's History Report for each market were exported to Microsoft Excel for analysis. The data exported were the Date, the NI, and the High, Low, and Closing Prices for each of the 40 markets each day. Each of these data elements were placed into separate columns in Excel workbook spreadsheets, with one spreadsheet in the workbook per market.

Using mathematical operations available in Excel, the typical price for each day for each market was calculated by averaging the day's high, low, and closing prices and stored in a new spreadsheet column. Next, the 3SMAtp was calculated for each day by averaging the typical price of the current day with the typical prices of the two immediately prior trading days, and stored in a new column. To complete the data preparation, an additional spreadsheet column was populated with values of the 3SMAtp that actually occurred two days in the future. Hence, for each day, for each market, the spreadsheet contained neighboring columns containing the NI; the day's 3SMAtp; and the 3SMAtp that occurred two trading days later.

NI performance in the long (Nl="up") and short (NI="down") directions was evaluated separately. For days when the NI value was "up", a performance column was populated with a '1' if the 3SMAtp two days in the future was greater than the current day's 3SMAtp. A '0' was placed in the column if NI was "up" and the 3SMAtp two days in the future was not greater than the current day's 3SMAtp. Additionally, this performance column was populated with a '0' if the current day's NI value was "down".

Similarly, a second performance column was populated with a '1' if the day's NI was "down" and the 3SMAtp two days in the future was less than the current day's 3SMAtp. A '0' was inserted in this column if NI was "down" and the 3SMAtp two days in the future was greater than the current day's 3SMAtp. Additionally, this performance column was populated with a '0' if the current day's NI value was "up".

If the NI for a day was "n/a", a '0' was entered into both the performance columns.

The values contained in each performance column were summed for use in determining the number of correct NI predictions in each trading direction. Excel's 'COUNTIF' function was used to determine the number of NI="up" days and also the number of N1="down" days that occurred in the assessment time period. The sums of the "Up" and "Down" performance columns were divided by these totals, respectively, to determine the percentage correct in each trading direction. The combined performance for all days was determined by summing the two performance columns and dividing the result by the total number of non-NI="n/a" trading days evaluated, i.e., the sum of the number of NI="up" and NI="down" days.

Finally, for each market the number of NI="n/a" days that occurred was determined and divided by the total number of trading data days in the assessment period to determine the percentage of time the NI made neither an "up" nor "down" prediction.

All calculations were executed at the full numerical precision implemented by Microsoft Excel. Results were rounded to the nearest whole number and reported as percentages.

Results

Tables containing the accuracy results obtained for markets in each of the categories studied in this assessment - ETFs, Forex, Futures, and Stocks - are presented in this section. As shown in the summary Table below, the computed mean accuracy for the markets studied was 80%, with remarkable consistency across the categories. In each category, the NI provided a prediction of "n/a" on average on only 10.5% of the trading days.

In the Tables below, all values shown are percentages.

ETFs

Forex

Stocks

Futures

Conclusions

The Neural Index has demonstrated that on average, across all market categories studied, it correctly predicts changes in the 3SMAtp two days in the future 80% of the time, with a maximum standard deviation in Combined Accuracy of 2.8 percentage points.

The highest Combined Accuracy of the Neural Index in markets included in this assessment was 87% (AAPL: 91% accurate in "up" cases, 82% accurate in "down" cases, 10% "n/a" days). The lowest Combined Accuracy in markets included in this assessment was 75% (Corn futures: 65% accurate in "up" cases, 87% accurate in "down" cases, 11% "n/a" days). The variation in the mean accuracy of the Neural Index predictions in "up" cases versus "down" cases ranged from "none" in the Futures markets studied to 8 percentage points in the Stocks markets studied. The maximum percentage of trading days on which the Neural Index reported a value of "n/a" for markets in this assessment was 17% (iShares MSCI BRIC Index Fund (BKF)) and the minimum was 6% (10 year U.S. Treasury Bonds futures); the mean for all markets studied was 10.5%.

The results of these assessments indicate the very impressive accuracy and consistency of performance of TradeShark's Neural Index. Overall, TradeShark has demonstrated itself to be a very reliable tool that traders should find extremely useful and valuable in engaging any market of interest

About the Author

Dr. Ted W. Warnock has over 30 years experience in the planning, analysis, research, development, prototyping and testing of advanced technology systems. He has earned bachelor's and Ph.D. degrees in Aerospace Engineering from Auburn University; a master's degree in Aeronautical and Astronautical Engineering from Stanford University; and a master's degree in Systems Management from the University of Southern California. He is an engineering consultant, a retired U.S. Air Force officer, a former RAND Research Fellow, and a former Assistant Professor and Laboratories Director in Astronautical Engineering at the U.S. Air Force Academy. He has contributed to numerous Government programs ranging from ICBM operations to advanced technology airborne and space systems development and testing, to studies and analyses of the warfighting effectiveness of intelligence collection and information warfare, to research and development of adaptive machine learning technologies, social network analysis algorithms for tactical environments, and bleeding edge data exploitation capabilities. He also pioneered the successful application of neural networks to the real world problem of predicting the orbital lifetimes of tethered satellites.

Phillip A. Arcuri, PhD

Video
Dr. Phillip A. Arcuri has worked in areas of advanced technology for over 30 years, including modeling of neutral beam injectors for tokamaks at Oak Ridge National Laboratory, MATLAB...

Dr. Phillip A. Arcuri has worked in areas of advanced technology for over 30 years, including modeling of neutral beam injectors for tokamaks at Oak Ridge National Laboratory, MATLAB model development at AERE Harwell, MUMPS programming at the John Radcliffe Hospital, Oxfordshire, development of large-scale personnel dispatch systems for Verizon, and Public Key Infrastructure security products for bTrade. He holds an A.B. degree from Wabash College and both an M.Sc. and PhD. in mathematics from Oxford University, England.

Dr. Arcuri did an extensive 3-year test to verify and certify the actual accuracy of the TradeShark forecasting software. The three year time period was from September 2010 to September 2013. He randomly selected 369 of the markets TradeShark forecasts for spanning futures, forex, stocks and ETFs and in all cases, the accuracy is better than 70% with nearly a third of the markets over 80%.  The average accuracy over all thirty markets is 78.89%, with a low value of 73.01% for Diebold stock, and a high value of 83.53% for Singapore Straits Times which is an overseas stock index futures market.

Verification of Accuracy of TradeShark Predictive Neural Index

For the three year period September, 2010 to September, 2013
Prepared by Phillip A. Arcuri, Ph.D. Mathematics

As part of an independent verification of TradeShark Software, I have been requested to randomly select a number of markets for which TradeShark provides forecasts and to calculate the accuracy of the predicted TradeShark Neural Index values for each of these markets. The Neural Index is a proprietary indicator computed by TradeShark that predicts whether or not a three-day simple moving average of price data will be higher or lower two days in the future.

In order to accomplish this task, I utilized the TradersOnly Data Service (integrated with TradeShark) to download the required price data on demand. I chose to use three years of data for each market (roughly 750 data points) in order to provide statistically meaningful results, and I chose to test 359 different randomly selected markets using TradeShark, including all available Futures, Forex and ETF markets, as well as 110 randomly selected U.S. Stocks. The stocks were selected by randomly choosing 10 in each of the available U.S. Stock categories of Basic Materials, Capital Goods, Conglomerates, Consumer/Cyclical, Consumer/Non-Cyclical, Energy, Financial, Healthcare, Services, Technology, Transportation, and Utilities.

Methodology:

  1. Installed TradeShark software on my desktop.

  2. Configured TradeShark to use the TradersOnly Data Service to obtain historical data.

  3. Configured TradeShark to use the most recent 3 years of daily data for each market.

  4. Configured the TradeShark History Grid template to display the Date, High Price, Low Price, Close Price, Neural Index, and Neural Index Strength value for each day for which price data was available.

  5. Working in groups of 10 to 20 markets at a time, downloaded each markets price data, opened a History Grid for each market, and exported the 10 to 20 History Grids to Excel spreadsheet. Each tab in the exported spreadsheet contained the price data and Neural Index and Neural Strength Index values for three years of data.

  6. For each market/tab in a spreadsheet, I added columns to compute the following:

    • -TP = Typical Price [ = (High + Low + Close) / 3] for each day;

    • -SMA(TP,3) = 3-day Simple Moving Average of the Typical Price;

    • -Actual Neural Index Strength = SMA(TP,3) from two days in the future less SMA(TP,3) from today;

    • -Actual Neural Index = ‘up’ or ‘down’ if the Actual Neural Index Strength is > or < 0;

    • -Winning prediction (1) or not (0), if the Actual Neural Index agrees or disagrees with the predicted value computed by TradeShark.

  7. To compute the performance over the three year period the count of the successful predictions was divided by the total number of predictions and multiplied by 100.

  8. TradeShark predicts the Neural Index to be either ‘up’ or ‘down’ or ‘n/a’. It predicts the ‘n/a’ value when its computations do not clearly favor ‘up’ or ‘down’. Days when ‘n/a’ is output are omitted from the accuracy statistics. To determine how often this occurs, I counted the number of ‘n/a’ predictions for the Neural Index in a small random selection of (26) markets, and computed the percentage of days when ‘n/a’ was output.

Summary of All Results:

The range of all 359 markets studied had a low of 73.01% correct for Diebold (symbol DBD in U.S. Stock, Technology category) to a high of 83.53% correct for Singapore Straits Times (symbol STY in Futures, Indices category). The mean of all 359 markets is 78.89% correct with a standard deviation of 1.90. The small standard deviation shows how consistent the predictions are.

The occurrence of a predicted Neural Index value of ‘n/a’ was seen to be between 7.81% and 13.53%, with a mean value of 10.84% of the days for which predictions could be made.

The detailed results showing overall percentage correct for each market is given below in Appendix A. A quick summary of the accuracy for all markets is given below in Figure 1.

Chart 1: Accuracy of the 359 tested markets, from least to most accurate.

Impressions from the test results:

First, the TradeShark product is extremely easy to use, with a very intuitive User Interface, the ability to process/predict multiple markets at once, and a powerful export capability that will allow TradeShark users to do any type of desired post-processing to develop and refine their market strategies.

Second, the accuracy of the predicted Neural Index is consistently very high, being correct nearly 80% of the time. As the following chart shows, only ten or so of the 359 tested markets are less than 75% accurate over 3 years of data, and nearly a third of the markets are over 80% accurate over this period. When one considers the various ups and downs in market conditions experienced over the last three years, it is amazing that the underlying neural network algorithms can consistently produce such accurate predictions.

Biographical Sketch

Dr. Phillip A. Arcuri has worked in areas of advanced technology for over 30 years, including modeling of neutral beam injectors for tokamaks at Oak Ridge National Laboratory, MATLAB model development at AERE Harwell, U.K, MUMPS programming at the John Radcliffe Hospital, Oxfordshire, U.K., development of large-scale personnel dispatch systems for Verizon, managing application security compliance for Verizon, and development of Public Key Infrastructure security products for bTrade and DiCentral Corp. He holds an A.B. degree from Wabash College, IN and both M.Sc. and D.Phil. degrees in Mathematics from Oxford University, England.

Appendix A. TradeShark Neural Index accuracy results for the 359 tested markets

Market Type

Market Category

Symbol / Contract

Wins

Count

Accuracy

ETFs

Canada

CBQ

506

655

77.25%

ETFs

Canada

CDZ

562

689

81.57%

ETFs

Canada

CRQ

535

657

81.43%

ETFs

Canada

HEU

553

685

80.73%

ETFs

Canada

HFD

517

675

76.59%

ETFs

Canada

HFU

460

621

74.07%

ETFs

Canada

HGU

517

650

79.54%

ETFs

Canada

HXD

517

666

77.63%

ETFs

Canada

HXU

488

652

74.85%

ETFs

Canada

XCB

514

660

77.88%

ETFs

Canada

XDV

543

685

79.27%

ETFs

Canada

XEG

542

676

80.18%

ETFs

Canada

XFN

538

680

79.12%

ETFs

Canada

XGD

517

664

77.86%

ETFs

Canada

XIC

536

666

80.48%

ETFs

Canada

XIN

529

673

78.60%

ETFs

Canada

XIT

536

661

81.09%

ETFs

Canada

XIU

528

672

78.57%

ETFs

Canada

XMA

536

670

80.00%

ETFs

Canada

XRB

509

638

79.78%

ETFs

Canada

XRE

543

673

80.68%

ETFs

Canada

XSB

520

678

79.69%

ETFs

Canada

XSP

526

658

79.93%

ETFs

Canada

XTR

524

677

77.40%

ETFs

Commodity

DBA

514

663

77.53%

ETFs

Commodity

DBB

531

664

79.97%

ETFs

Commodity

DBC

512

657

77.93%

ETFs

Commodity

DBE

524

659

79.51%

ETFs

Commodity

DBO

512

666

76.88%

ETFs

Commodity

DBP

533

674

79.08%

ETFs

Commodity

DBS

497

656

75.76%

ETFs

Commodity

DGL

528

677

77.99%

ETFs

Commodity

DJP

518

660

78.48%

ETFs

Commodity

GDX

538

682

78.89%

ETFs

Commodity

GLD

531

679

78.20%

ETFs

Commodity

GSG

518

661

78.37%

ETFs

Commodity

GSP

523

661

79.12%

ETFs

Commodity

IAU

528

662

79.76%

ETFs

Commodity

OIH

540

694

77.81%

ETFs

Commodity

OIL

537

669

80.27%

ETFs

Commodity

SLV

513

662

77.49%

ETFs

Commodity

SLX

519

664

78.16%

ETFs

Commodity

UNG

521

668

77.99%

ETFs

Commodity

USO

529

675

78.37%

ETFs

Commodity

XLE

521

666

78.23%

ETFs

Currency

DBV

508

667

76.17%

ETFs

Currency

FXA

480

647

74.19%

ETFs

Currency

FXB

520

673

77.27%

ETFs

Currency

FXC

507

667

76.01%

ETFs

Currency

FXE

529

657

80.52%

ETFs

Currency

FXF

537

662

81.12%

ETFs

Currency

FXS

503

647

77.74%

ETFs

Currency

FXY

504

654

77.06%

ETFs

Currency

UDN

539

687

78.46%

ETFs

Currency

UUP

534

665

80.30%

ETFs

International

EEM

526

682

77.13%

ETFs

International

EFA

510

665

76.69%

ETFs

International

EWA

507

668

75.90%

ETFs

International

EWG

515

656

78.51%

ETFs

International

EWH

500

656

76.22%

ETFs

International

EWJ

487

649

75.04%

ETFs

International

EWL

491

645

76.12%

ETFs

International

EWM

507

657

77.17%

ETFs

International

EWO

522

681

76.65%

ETFs

International

EWT

523

665

78.65%

ETFs

International

EWU

504

653

77.18%

ETFs

International

EWW

510

664

76.81%

ETFs

International

EWY

516

660

78.18%

ETFs

International

EWZ

533

681

78.27%

ETFs

International

EZU

512

655

78.17%

ETFs

International

FXI

509

656

77.59%

ETFs

International

IEV

511

665

76.84%

ETFs

International

ILF

538

671

80.18%

ETFs

International

IXC

490

654

74.92%

ETFs

International

PGJ

528

662

79.76%

ETFs

International

VWO

488

640

76.25%

ETFs

Short & Ultra Short

DOG

504

675

74.67%

ETFs

Short & Ultra Short

DUG

524

653

80.25%

ETFs

Short & Ultra Short

DXD

529

674

78.49%

ETFs

Short & Ultra Short

MYY

509

655

77.71%

ETFs

Short & Ultra Short

MZZ

508

680

74.71%

ETFs

Short & Ultra Short

PSQ

528

686

76.97%

ETFs

Short & Ultra Short

QID

556

682

81.52%

ETFs

Short & Ultra Short

REW

548

684

80.12%

ETFs

Short & Ultra Short

RWM

542

666

81.38%

ETFs

Short & Ultra Short

RXD

455

611

74.47%

ETFs

Short & Ultra Short

SBB

521

695

74.96%

ETFs

Short & Ultra Short

SDD

535

685

78.10%

ETFs

Short & Ultra Short

SDP

510

654

77.98%

ETFs

Short & Ultra Short

SDS

511

670

76.27%

ETFs

Short & Ultra Short

SH

536

676

79.29%

ETFs

Short & Ultra Short

SKF

517

665

77.74%

ETFs

Short & Ultra Short

SKK

526

668

78.74%

ETFs

Short & Ultra Short

SMN

555

697

79.63%

ETFs

Short & Ultra Short

SRS

511

683

74.82%

ETFs

Short & Ultra Short

SSG

506

660

76.67%

ETFs

Short & Ultra Short

TWM

532

677

78.58%

ETFs

United Kingdom

EUE

538

683

78.77%

ETFs

United Kingdom

IEEM

510

665

76.69%

ETFs

United Kingdom

IEUX

523

674

77.60%

ETFs

United Kingdom

IFFF

538

672

80.06%

ETFs

United Kingdom

IJPN

524

671

78.09%

ETFs

United Kingdom

ISF

524

667

78.56%

ETFs

United Kingdom

IUSA

552

669

82.51%

ETFs

United Kingdom

IWDP

524

662

79.15%

ETFs

United Kingdom

IWRD

538

689

78.08%

ETFs

United Kingdom

MIDD

546

673

81.13%

ETFs

United Kingdom

SLXX

487

633

76.94%

ETFs

United States

AGG

528

661

79.88%

ETFs

United States

DIA

533

673

79.20%

ETFs

United States

DVY

524

684

76.61%

ETFs

United States

IBB

522

658

79.33%

ETFs

United States

ICF

533

670

79.55%

ETFs

United States

IJR

528

671

78.69%

ETFs

United States

IVE

536

672

79.76%

ETFs

United States

IWD

508

670

75.82%

ETFs

United States

IWF

540

683

79.06%

ETFs

United States

IWM

533

666

80.03%

ETFs

United States

IWN

524

679

77.17%

ETFs

United States

IYK

546

681

80.18%

ETFs

United States

IYR

515

651

79.11%

ETFs

United States

IYT

537

678

79.20%

ETFs

United States

MDY

530

661

80.18%

ETFs

United States

OEF

505

657

76.86%

ETFs

United States

PBW

550

693

79.37%

ETFs

United States

PKB

527

673

78.31%

ETFs

United States

QQQ

530

658

80.55%

ETFs

United States

RTH

520

684

76.02%

ETFs

United States

RWR

527

662

79.61%

ETFs

United States

SHY

498

656

75.91%

ETFs

United States

SMH

527

676

77.96%

ETFs

United States

SPY

529

668

79.19%

ETFs

United States

TLT

521

663

78.58%

ETFs

United States

VAW

533

677

78.73%

ETFs

United States

VNQ

530

668

79.34%

ETFs

United States

XLB

518

672

77.08%

ETFs

United States

XLF

514

658

78.12%

ETFs

United States

XLI

519

657

79.00%

ETFs

United States

XLK

530

671

78.99%

ETFs

United States

XLP

511

669

76.38%

ETFs

United States

XLU

527

677

77.84%

ETFs

United States

XLV

495

667

74.21%

ETFs

United States

XLY

494

633

78.04%

Forex

Cross Pairs

AUD/CAD

552

690

80.00%

Forex

Cross Pairs

AUD/JPY

544

694

78.39%

Forex

Cross Pairs

AUD/NZD

555

706

78.61%

Forex

Cross Pairs

CAD/JPY

541

687

78.75%

Forex

Cross Pairs

CHF/JPY

574

709

80.96%

Forex

Cross Pairs

EUR/AUD

547

702

77.92%

Forex

Cross Pairs

EUR/CAD

580

716

81.01%

Forex

Cross Pairs

EUR/CHF

531

679

78.20%

Forex

Cross Pairs

EUR/GBP

572

689

83.02%

Forex

Cross Pairs

EUR/JPY

569

709

80.25%

Forex

Cross Pairs

GBP/AUD

531

691

76.85%

Forex

Cross Pairs

GBP/CAD

549

687

79.91%

Forex

Cross Pairs

GBP/CHF

556

684

81.29%

Forex

Cross Pairs

GBP/JPY

570

708

80.51%

Forex

Major Pairs

AUD/USD

560

715

78.32%

Forex

Major Pairs

EUR/USD

589

706

83.43%

Forex

Major Pairs

GBP/USD

578

712

81.18%

Forex

Major Pairs

NZD/USD

554

701

79.03%

Forex

Major Pairs

USD/BRL

561

695

80.72%

Forex

Major Pairs

USD/CAD

541

690

78.41%

Forex

Major Pairs

USD/CHF

576

696

82.76%

Forex

Major Pairs

USD/ILS

567

695

81.58%

Forex

Major Pairs

USD/INR

575

700

82.14%

Forex

Major Pairs

USD/JPY

543

697

77.91%

Forex

Major Pairs

USD/MXN

564

708

79.66%

Forex

Major Pairs

ZAR/USD

569

711

80.03%

Futures

Currencies

AD - Continuous

528

696

75.86%

Futures

Currencies

BP - Continuous

541

676

80.03%

Futures

Currencies

CD - Continuous

521

669

77.88%

Futures

Currencies

DX - Continuous

581

706

82.29%

Futures

Currencies

E7 - Continuous

566

682

82.99%

Futures

Currencies

EC - Continuous

548

680

80.59%

Futures

Currencies

J7 - Continuous

534

667

80.06%

Futures

Currencies

JY - Continuous

550

684

80.41%

Futures

Currencies

MP - Continuous

519

674

77.00%

Futures

Currencies

SF - Continuous

560

681

82.23%

Futures

Energies

BZ - Continuous

533

701

76.03%

Futures

Energies

CL - Continuous

549

673

81.58%

Futures

Energies

HO - Continuous

532

666

79.88%

Futures

Energies

NG - Continuous

541

680

79.56%

Futures

Energies

QG - Continuous

548

687

79.77%

Futures

Energies

QM - Continuous

522

652

80.06%

Futures

Energies

RB - Continuous

545

676

80.62%

Futures

Grains

KW - Continuous

545

685

79.56%

Futures

Grains

RS - Continuous

557

676

82.40%

Futures

Grains

YC - Continuous

527

679

77.61%

Futures

Grains

YK - Continuous

534

676

78.99%

Futures

Grains

YW - Continuous

537

681

78.85%

Futures

Grains

ZC - Continuous

539

680

79.26%

Futures

Grains

ZL - Continuous

550

694

79.25%

Futures

Grains

ZM - Continuous

552

688

80.23%

Futures

Grains

ZO - Continuous

526

661

79.58%

Futures

Grains

ZR - Continuous

526

665

79.10%

Futures

Grains

ZS - Continuous

557

681

81.79%

Futures

Grains

ZW - Continuous

541

680

79.56%

Futures

Indices

AP - Continuous

580

696

83.33%

Futures

Indices

DUX - Cash

530

690

76.81%

Futures

Indices

ES - Continuous

556

678

82.01%

Futures

Indices

FCE - Continuous

550

690

79.71%

Futures

Indices

FDAX - Continuous

546

686

79.59%

Futures

Indices

FESX - Continuous

545

680

80.15%

Futures

Indices

HHA - Continuous

571

736

77.58%

Futures

Indices

IXI - Cash

561

694

80.84%

Futures

Indices

LZ - Continuous

534

674

79.23%

Futures

Indices

ND - Continuous

539

656

82.16%

Futures

Indices

NK - Continuous

544

686

79.30%

Futures

Indices

NQ - Continuous

565

695

81.30%

Futures

Indices

OEX - Cash

518

658

78.72%

Futures

Indices

SP - Continuous

567

689

82.29%

Futures

Indices

STY00 - Cash

213

255

83.53%

Futures

Indices

TF - Continuous

559

690

81.01%

Futures

Indices

XAO - Cash

581

699

83.12%

Futures

Indices

YM - Continuous

518

679

76.29%

Futures

Indices

ZD - Continuous

526

688

76.45%

Futures

Interest Rates

ED - Continuous

514

663

77.53%

Futures

Interest Rates

FGBL - Continuous

566

693

81.67%

Futures

Interest Rates

FV - Continuous

539

667

80.81%

Futures

Interest Rates

TU - Continuous

515

660

78.03%

Futures

Interest Rates

TY - Continuous

559

695

80.43%

Futures

Interest Rates

US - Continuous

543

684

79.39%

Futures

Meats

FC - Continuous

549

684

80.26%

Futures

Meats

LC - Continuous

536

668

80.24%

Futures

Meats

LH - Continuous

549

681

80.62%

Futures

Metals

GC - Continuous

553

676

81.80%

Futures

Metals

HG - Continuous

561

678

82.74%

Futures

Metals

PA - Continuous

549

676

81.21%

Futures

Metals

PL - Continuous

550

676

81.36%

Futures

Metals

QI - Continuous

531

674

78.78%

Futures

Metals

QO - Continuous

551

689

79.97%

Futures

Metals

SI - Continuous

550

680

80.88%

Futures

Softs

CC - Continuous

676

548

81.07%

Futures

Softs

CT - Continuous

569

696

81.75%

Futures

Softs

KC - Continuous

528

673

78.45%

Futures

Softs

LB - Continuous

539

676

79.73%

Futures

Softs

OJ - Continuous

541

666

81.23%

Futures

Softs

SB - Continuous

540

668

80.84%

US Stocks

Basic Materials

AGU

517

674

76.71%

US Stocks

Basic Materials

APA

536

663

80.84%

US Stocks

Basic Materials

AUY

522

655

79.69%

US Stocks

Basic Materials

CCK

524

667

78.56%

US Stocks

Basic Materials

CENX

548

669

81.91%

US Stocks

Basic Materials

FMC

534

675

79.11%

US Stocks

Basic Materials

IP

539

672

80.21%

US Stocks

Basic Materials

MOS

536

675

79.41%

US Stocks

Basic Materials

POT

543

671

80.92%

US Stocks

Basic Materials

STO

498

649

76.73%

US Stocks

Capital Goods

AGCO

565

700

80.71%

US Stocks

Capital Goods

BEAV

546

682

80.06%

US Stocks

Capital Goods

CAT

560

682

82.11%

US Stocks

Capital Goods

CNH

515

652

78.99%

US Stocks

Capital Goods

DE

536

680

78.82%

US Stocks

Capital Goods

ETN

527

680

77.50%

US Stocks

Capital Goods

FLR

519

659

78.76%

US Stocks

Capital Goods

MLM

541

677

79.91%

US Stocks

Capital Goods

PH

538

673

79.94%

US Stocks

Capital Goods

ROP

519

665

78.05%

US Stocks

Conglomerates

ABB

519

665

78.05%

US Stocks

Conglomerates

DHR

527

680

77.50%

US Stocks

Conglomerates

DOV

545

686

79.45%

US Stocks

Conglomerates

FSS

544

670

81.19%

US Stocks

Conglomerates

GE

512

665

76.99%

US Stocks

Conglomerates

ITT

503

649

77.50%

US Stocks

Conglomerates

MMM

530

676

78.40%

US Stocks

Conglomerates

PPG

524

673

77.86%

US Stocks

Conglomerates

TXT

527

666

79.13%

US Stocks

Conglomerates

UTX

531

657

80.82%

US Stocks

Consumer/Cyclical

COH

522

666

78.38%

US Stocks

Consumer/Cyclical

FNP

516

661

78.06%

US Stocks

Consumer/Cyclical

HOG

515

653

78.87%

US Stocks

Consumer/Cyclical

IFF

510

650

78.46%

US Stocks

Consumer/Cyclical

MAT

536

671

79.88%

US Stocks

Consumer/Cyclical

NWL

523

664

78.77%

US Stocks

Consumer/Cyclical

SKX

533

663

80.39%

US Stocks

Consumer/Cyclical

TIF

539

673

80.09%

US Stocks

Consumer/Cyclical

VFC

535

686

77.99%

US Stocks

Consumer/Cyclical

WWW

529

672

78.72%

US Stocks

Consumer/Non-Cyclical

ADM

513

651

78.80%

US Stocks

Consumer/Non-Cyclical

ANDE

541

679

79.67%

US Stocks

Consumer/Non-Cyclical

CL

534

676

78.99%

US Stocks

Consumer/Non-Cyclical

DF

525

666

78.83%

US Stocks

Consumer/Non-Cyclical

MO

542

675

80.30%

US Stocks

Consumer/Non-Cyclical

PEP

517

675

76.59%

US Stocks

Consumer/Non-Cyclical

PG

518

632

81.96%

US Stocks

Consumer/Non-Cyclical

SJM

539

670

80.45%

US Stocks

Consumer/Non-Cyclical

TSN

512

665

76.99%

US Stocks

Consumer/Non-Cyclical

WM

519

663

78.28%

US Stocks

Energy

APC

518

667

77.66%

US Stocks

Energy

CAM

532

662

80.36%

US Stocks

Energy

DNR

504

664

75.90%

US Stocks

Energy

ECA

534

681

78.41%

US Stocks

Energy

FTI

514

647

79.44%

US Stocks

Energy

HES

533

673

79.20%

US Stocks

Energy

NOV

531

656

80.95%

US Stocks

Energy

OXY

539

661

81.54%

US Stocks

Energy

PXD

528

664

79.52%

US Stocks

Energy

PZE

527

672

78.42%

US Stocks

Financial

AET

532

673

79.05%

US Stocks

Financial

ALL

533

687

77.58%

US Stocks

Financial

BCS

484

640

75.63%

US Stocks

Financial

CBG

514

642

80.06%

US Stocks

Financial

GS

550

679

81.00%

US Stocks

Financial

IBN

516

656

78.66%

US Stocks

Financial

NDAQ

511

660

77.42%

US Stocks

Financial

PRU

525

666

78.83%

US Stocks

Financial

TSS

507

654

77.52%

US Stocks

Financial

XL

535

679

78.79%

US Stocks

Healthcare

AGN

530

658

80.55%

US Stocks

Healthcare

AZN

509

663

76.77%

US Stocks

Healthcare

BAX

519

653

79.48%

US Stocks

Healthcare

CTXS

528

665

79.40%

US Stocks

Healthcare

DGX

511

679

75.26%

US Stocks

Healthcare

JNJ

533

661

80.64%

US Stocks

Healthcare

LLY

519

660

78.64%

US Stocks

Healthcare

SIGA

526

672

78.27%

US Stocks

Healthcare

VAR

527

675

78.07%

US Stocks

Healthcare

WLP

526

658

79.94%

US Stocks

Services

AN

546

670

81.49%

US Stocks

Services

BBBY

502

651

77.11%

US Stocks

Services

FDO

508

668

76.05%

US Stocks

Services

GCI

536

678

79.06%

US Stocks

Services

MDP

540

672

80.36%

US Stocks

Services

NFLX

529

660

80.15%

US Stocks

Services

SBUX

521

686

75.95%

US Stocks

Services

SVU

487

652

74.69%

US Stocks

Services

WAG

524

662

79.15%

US Stocks

Services

WFM

549

678

80.97%

US Stocks

Technology

AMT

520

667

77.96%

US Stocks

Technology

BSX

510

657

77.63%

US Stocks

Technology

DBD

487

667

73.01%

US Stocks

Technology

FFIV

536

651

82.33%

US Stocks

Technology

MSFT

526

665

79.10%

US Stocks

Technology

SYMC

504

649

77.66%

US Stocks

Technology

TIBX

535

671

79.73%

US Stocks

Technology

TSM

520

676

76.92%

US Stocks

Technology

VRSN

500

646

77.40%

US Stocks

Technology

XRX

507

658

77.05%

US Stocks

Transportation

ALEX

514

668

76.95%

US Stocks

Transportation

CHRW

532

658

80.85%

US Stocks

Transportation

DAL

522

672

77.68%

US Stocks

Transportation

FDX

546

684

79.82%

US Stocks

Transportation

HOS

524

663

79.03%

US Stocks

Transportation

KSU

537

677

79.32%

US Stocks

Transportation

LUV

545

668

81.57%

US Stocks

Transportation

NSC

531

680

78.09%

US Stocks

Transportation

UTIW

527

665

79.25%

US Stocks

Transportation

WERN

510

662

77.04%

US Stocks

Utilities

AEE

522

670

77.91%

US Stocks

Utilities

CNP

540

662

81.57%

US Stocks

Utilities

ED

493

649

75.96%

US Stocks

Utilities

EIX

502

652

76.99%

US Stocks

Utilities

EXC

496

662

74.92%

US Stocks

Utilities

GAS

510

650

78.46%

US Stocks

Utilities

KEP

509

647

78.67%

US Stocks

Utilities

PCG

506

672

75.30%

US Stocks

Utilities

SCG

529

671

78.84%

US Stocks

Utilities

WMB

523

659

79.36%

Jerry Meyer, PhD

Video
Dr. Gerald H. Meyer, currently a full professor and Director of Computer Science at LaGuardia Community College, who served as chair of the Computer Information Systems Department...

Dr. Gerald H. Meyer, currently a full professor and Director of Computer Science at LaGuardia Community College, who served as chair of the Computer Information Systems Department for more than twenty years and is a PhD Mathematician.  

Fifty-four markets were randomly selected from the markets and stocks forecasted by TradeShark. Detailed methodology was then used to analyze data for a five-year period to certify the accuracy rate of the predicted neural index, which compares an actual three-day moving average with a predicted three-day moving average to forecast whether a three-day simple moving average of the typical price (average of the high, low and close) will be higher or lower in two days than it is today. Dr. Meyer found that the range of accuracy for all fifty-four markets selected was from a low 73.8% to a high of 83.6%, with the mean of all fifty-four markets at 79.26%.  

Verification of TradeShark Predictive Neural Index Accuracy

For the five year period August 27, 2008 to August 9, 2013
Prepared by: Gerald H. Meyer, Ph.D. Applied Mathematics

As part of an independent verification of TradeShark Software, I have been requested to randomly select at least 40 of the over 2000 markets of which TradeShark provides forecasts for and to calculate the accuracy of its proprietary Neural Index prediction for each of these markets over a five year time period. In order to accomplish this task, I downloaded five years of data (less in a few markets where five years of data was not available) for each of 54 different markets chosen at random using TradeShark.

Methodology:

  1. 1.Installed TradeShark software on my desktop.
  2. 2.Downloaded data for the five year period showing the date, open, high, low and close price as well as the Neural Index for each of the 54 markets.
  3. 3.Converted the history file to an excel workbook using the built-inexport to excel feature in the TradeShark Software.
  4. Deleted the header for each workbook and saved as a comma delimited file (.csv)
  5. Wrote a thoroughly tested Java application program [Appendix A] to generate a spreadsheet for each of the 54 markets. The spreadsheets include the date, open, high, low, close and predictive neural index. In addition, the program calculated and output the average daily price of the low, high and close (typical price). It also calculated and printed the 3-day simple moving average of this typical daily price. When the Neural Index was “up” or “down” the program calculated whether it was a correct prediction or not based on the 3-daysma two days later. If the Neural Index was “n/a” then there was no determination made. Output for each market included the number of correct predictions, the number of incorrect predictions and the winning percentage.

Note: the first two days and the last two days of the time period were excluded from the calculations since TradeShark needs a minimum number of data points to generate a prediction.

Summary of All Results:

The range of all 54 markets selected is a low of 73.8% (HFU) to a high of 83.6% (SHLD)

The mean of all 54 markets is an astounding 79.26% with a median value of 79.47 and a mode of 79.5 (all values rounded to two decimal places). Also, the standard deviation is just under 2.0.

The accuracy of the predictions does not differ by much for “up” predictions (79.55) and “down” predictions (78.95). Furthermore, the total number of “up” and “down” predictions are nearly equal (27,469 and 25,721 respectively).

The overall percent for each market is given below:

Market

#correct

#incorrect

Win Pct

AAPL

927

194

82.69

AKS

909

214

80.94

AMZN

886

223

79.89

AUD_CAD

930

214

81.29

BA

891

229

79.55

BAC

847

215

79.76

BK

835

247

77.17

BP

880

242

78.43

BZ_V13

872

277

75.89

C

891

225

79.84

CAT

915

206

81.62

CSCO

857

253

77.21

DAL

885

229

79.44

DGL

870

250

77.68

DXU13

153

38

80.1

E7U13

59

15

79.73

ECYU13

245

56

81.4

EUR_GBP

944

204

82.23

EWZ

874

251

77.69

FCEU13

145

39

78.8

FDAXU13

96

29

76.8

FXE

861

222

79.5

GBP_JPY

936

227

80.48

GBP_USD

948

226

80.75

EUR_USD

968

205

82.52

GLW

870

237

78.59

HFU

766

272

73.8

HL

896

240

78.87

IAU

879

231

79.19

MAS

922

203

81.96

MMM

863

245

77.89

NGU13

829

260

76.12

NVDA

894

220

80.25

PAAS

854

243

77.85

PCG

843

271

75.67

POT

907

204

81.64

SH

893

230

79.52

SHLD

926

182

83.57

SLV

860

238

78.32

SLW

890

238

78.9

TLT

861

242

78.06

TYU13

200

51

79.68

UNG

872

236

78.7

USD_CAD

927

239

79.5

USD_CHF

944

197

82.73

USD_JPY

919

237

79.5

VLO

877

232

79.08

WY

861

249

77.57

X

882

232

79.17

XLF

849

243

77.75

XOM

834

254

76.65

YKU13

478

137

77.72

ZCU13

466

119

79.66

ZLU13

474

118

80.07

Totals

42160

11030 79.26

Impressions from the test results:

I am very impressed by the consistency of the results (small deviation) as well as its amazing accuracy. It works equally well over all markets and astonishingly, the accuracy percent is nearly the same for “up” predictions as well as “down”. In my opinion, this is an indication of an extremely good algorithm based on a neural network (as advertised), since it works equally well going long as it does going short.

Biographical Sketch

Dr. Gerald H. Meyer is currently a full professor and Director of Computer Science at a major New York University. He has lectured at the graduate and undergraduate levels in mathematics and computer science and has been invited to give lectures on Encryption Algorithms and Programming Languages. He has published in Stocks and Commodities magazine as well as academic journals. He has devised algorithms, together with others to smooth waves and predict market trends. His consulting includes an efficient algorithm for computation of the elementary functions, and an extensive statistical informational retrieval system for doctors at Albert Einstein College of Medicine. Before returning to academia, Dr. Meyer was director of IT training at Blue Cross Blue Shield of Greater New York. He holds a B.S. degree in Mathematics from Brooklyn College and both an M.S. and Ph.D. in applied mathematics from Adelphi University in New York.

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.Scanner;
import java.util.StringTokenizer;
import javax.swing.JOptionPane;
public class Mkt4 {
    /**
    * @param args */
    static int tctrTot=0,fctrTot=0,tctrTotL=0,fctrTotL=0,tctrTotS=0,fctrTotS=0;
    int lineCtr=0,tctr=0,fctr=0,totLong=0,totShort=0,winLong=0,winShort=0;
    double[] open=new double[70000];
    double[] close = new double[70000];
    double[] low = new double[70000];
    double[] high = new double[70000];
    String[] date=new String[70000];
    String[] ni=new String[70000];
    int[] ind = new int[70000];
    double avg[] =new double[70000];
    double sma[]= new double [70000];
    int verified[]=new int[70000];
    // 0 is incorrect, 1 is correct, 2 is n/a String file;
    double pct=0;
    double pct1;
    double pct2;
    double avgPct;
    static int days=5020;
    // 20 years of data to be verified---251 days is approximately one year
    public Mkt4(String fileName) throws IOException{ file = fileName;
        fillArrays();
        findSma();
        verify();
        printResults();
        // this prints a line on the results3.csv file
        printSheet(); // this prints a .csv file containing 5 years of data for each mkt and the resulting accuracy percent
    }
    void printSheet() throws FileNotFoundException, IOException{
        {
            String path = "C:/dataL/"+file+".csv";
            FileWriter fileWriter = new FileWriter(path);
            BufferedWriter bufferFileWriter = new BufferedWriter(fileWriter);
            fileWriter.append(",,,,,symbol: "+file+"n");
            fileWriter.append("date "+","+"open"+","+"high"+","+"low"+"," +"close"+ ","+"ni,"+"avg Price,sma,sma delta,accuracy,#correct,#incorrect,winning pctn");
            //+","+ Math.round(pct1*100.0)/100.0+"," +winShort+","+(totShort-winShort)+","+ Math.round(pct2*100.0)/100.0+"," + Math.round(avgPct*100.0)/100.0+"n");
            for (int i=0; i=sma[i-2]) {tctr++;
            totShort++;
            winShort++;
            verified[i]=1;
            // true
        }
        else {fctr++;
            totShort++;
            verified[i]=0;
        }
        else
        verified [i]=2;
        // this means skip it
    }
    void findSma(){
        for (int i=0; i<=lineCtr-3; i++)
        sma[i]=(avg[i] +avg[i+1]+ avg[i+2])/3;
    }
    void fillArrays() throws FileNotFoundException
    {// this assumes that the file exported from TradeShark is saved in folder c:/inputL as a .csv file with the header deleted
        Scanner in=new Scanner(new File("c:/inputL/"+file+".csv"));
        while(in.hasNextLine())
        {
            String[] n= in.nextLine().split(",");
            //split each line and identify token by "CSV" comma-separated values low[lineCtr]=Double.parseDouble(n[3]);
            //fill array Low[] from .csv file column 4. in each line high[lineCtr]=Double.parseDouble(n[2]);
            //fill array High[] from .csv file column 5. in each line close[lineCtr]=Double.parseDouble(n[4]);
            //fill array Close[] from .csv file column 6. in each line open[lineCtr]=Double.parseDouble(n[1]);
            date[lineCtr]=n[0];
            ni[lineCtr]=n[5];
            avg[lineCtr]=(low[lineCtr]+ high[lineCtr]+close[lineCtr])/3;
            if (n[5].contains("up"))
            ind[lineCtr]=1;
            else
            if (n[5].contains("down")) ind[lineCtr]=0;
            else
            ind[lineCtr]=2;
            // skip
            lineCtr++;
        //increments the csv file's line counter }
        //close while loop
        lineCtr=Math.min(lineCtr,days);
        in.close();
        //After reading file, we should close it
    }
    // end FillArrays()
    public static void main(String args[])throws IOException{
        // this assumes that the folder dataL has been created on the c drive String path = "C:/dataL/results3.csv";
        //creating file object from given path FileWriter fileWriter3 = new FileWriter(path);
        BufferedWriter bufferFileWriter3 = new BufferedWriter(fileWriter3);
        fileWriter3.append("Market,#correct,#incorrect,Win Pct,Win Long,Lose Long,Win Pct Long,Win Short,Lose Short,Win Pct Short,Avg. G and Jn");
        bufferFileWriter3.close();
        // create objects of Mkt4
        Mkt4 d3=new Mkt4("AAPL");
        Mkt4 d31=new Mkt4("AKS");
        Mkt4 d32=new Mkt4("AMZN");
        Mkt4 d33=new Mkt4("AUD_CAD");
        Mkt4 d34=new Mkt4("BA");
        Mkt4 d35=new Mkt4("BAC");
        Mkt4 d36=new Mkt4("BK");
        Mkt4 d37=new Mkt4("BP");
        Mkt4 d38=new Mkt4("BZ_V13");
        Mkt4 d39=new Mkt4("C");
        Mkt4 d311=new Mkt4("CAT");
        Mkt4 d312=new Mkt4("CSCO");
        Mkt4 d313=new Mkt4("DAL");
        Mkt4 d314=new Mkt4("DGL");
        Mkt4 d315=new Mkt4("DXU13");
        Mkt4 d316=new Mkt4("E7U13");
        Mkt4 d317=new Mkt4("ECYU13");
        Mkt4 d318=new Mkt4("EUR_GBP");
        Mkt4 d319=new Mkt4("EWZ");
        Mkt4 d2=new Mkt4("FCEU13");
        Mkt4 d321=new Mkt4("FDAXU13");
        Mkt4 d323=new Mkt4("FXE");
        Mkt4 d324=new Mkt4("GBP_JPY");
        Mkt4 d325=new Mkt4("GBP_USD");
        Mkt4 d326=new Mkt4("EUR_USD");
        Mkt4 d327=new Mkt4("GLW");
        Mkt4 d329=new Mkt4("HFU");
        Mkt4 d330=new Mkt4("HL");
        Mkt4 d331=new Mkt4("IAU");
        Mkt4 d332=new Mkt4("MAS");
        Mkt4 d333=new Mkt4("MMM");
        Mkt4 d334=new Mkt4("NGU13");
        Mkt4 d335=new Mkt4("NVDA");
        Mkt4 d336=new Mkt4("PAAS");
        Mkt4 d337=new Mkt4("PCG");
        Mkt4 d338=new Mkt4("POT");
        Mkt4 d339=new Mkt4("SH");
        Mkt4 d40=new Mkt4("SHLD");
        Mkt4 d41=new Mkt4("SLV");
        Mkt4 d340=new Mkt4("SLW");
        Mkt4 d341=new Mkt4("TLT");
        Mkt4 d342=new Mkt4("TYU13");
        Mkt4 d343=new Mkt4("UNG");
        Mkt4 d344=new Mkt4("USD_CAD");
        Mkt4 d345=new Mkt4("USD_CHF");
        Mkt4 d346=new Mkt4("USD_JPY");
        Mkt4 d347=new Mkt4("VLO");
        Mkt4 d348=new Mkt4("WY");
        Mkt4 d349=new Mkt4("X");
        Mkt4 d350=new Mkt4("XLF");
        Mkt4 d351=new Mkt4("XOM");
        Mkt4 d352=new Mkt4("YKU13");
        Mkt4 d353=new Mkt4("ZCU13");
        Mkt4 d354=new Mkt4("ZLU13");
        days=Integer.parseInt(JOptionPane.showInputDialog("Enter number of days to verify (251 is about a year), 99999 for max"));
        //creating file object from given path File file2 = new File(path);
        FileWriter fileWriter = new FileWriter(file2,true);
        BufferedWriter bufferFileWriter = new BufferedWriter(fileWriter);
        //calculate percents and then
        //write the final total line on the results3.csv file
        double pct1=Math.round((double)tctrTot/(tctrTot + fctrTot)*100*100.0)/100.0;
        double pct2=Math.round((double)tctrTotL/(tctrTotL + fctrTotL)*100*100.0)/100.0;
        double pct3=Math.round((double)tctrTotS/(tctrTotS + fctrTotS)*100*100.0)/100.0;
        double pct4=Math.round((pct2 + pct3)/2 * 100.0)/100.0;
        fileWriter.append("n"+"Totals"+","+tctrTot+","+fctrTot+","+pct1);
        fileWriter.append(","+tctrTotL+","+fctrTotL+","+pct2);
        fileWriter.append(","+tctrTotS+","+fctrTotS+","+pct3+","+pct4);
        bufferFileWriter.close();
    }
}

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