Can You Generate Alpha in US Equities Using Crowd Sourced Earnings Data?

By: The Deltix Quantitative Research Team

Introduction

This paper describes the implementation of an automated equity trading strategy based on aggregated company earnings estimates from independent, buy-side, and sell-side analysts, along with those of private investors and students. By sourcing estimates from a diverse community of individuals (“crowd-sourcing”), Estimize provides an alternative view of earnings expectations compared to traditional sell-side analysts.

We implemented the strategy in the Deltix QuantOffice research platform, a purpose-built C# development studio with embedded math, statistics and data libraries.

Estimize Data

The Estimize data used in this research is a set of earnings per share (EPS) estimates. Each data record refers to a certain EPS announcement which is made for a stock every quarter. The data record features a set of fields:

–        Instrument type;

–        Creation time;

–        Estimate_id;

–        Released_id;

–        EPS forecast;

–        Revenue forecast;

–        Date of the release.

 

A sample of the Estimize data as represented in Deltix QuantOffice is shown below.


 

Basis of Research using Estimize data

The purpose of the research described in this paper is to determine if there are opportunities to generate alpha in US equities, using Estimize data as a basis for overnight market movement prediction (the holding period is about 19 hours). We show how with simple mathematical techniques, we can identify pricing inefficiencies which we can exploit to generate excess returns. We take the theory of stock market overreaction as a basis for our model. A consequence of overreaction is the possible profitability of a contrarian strategy, that is, a strategy that exploits negative serial dependence in asset returns. The defining characteristic of a contrarian strategy is the purchase of securities that have performed poorly in the past and the sale of securities that have performed well. Such “selling the winners” and “buying the losers” may earn positive returns because current losers are likely to become future winners and current winners are likely to become future losers when stock returns are negatively autocorrelated. Therefore, it may be said that an implication of stock market overreaction is positive expected profits from a contrarian investment strategy.

This research analyzes how Estimize forecasts change over the period preceding the announcement by a company of its earnings (reporting period). The directionality in estimates can serve as an indicator for “winners” and “losers”. We determine if the short term estimates are more optimistic or pessimistic than the long term ones to predict the change of price after the announcement.

As a start, let us determine which estimates should be considered as short term. To do this, the average number of estimates per reporting period for a ticker universe of the S&P100 is calculated. The results are shown below.

Ticker

Number

Ticker

Number

Ticker

Number

AAPL

186.57

MON

10.43

BMY

4.13

GOOG

70.62

HON

10.33

FCX

4.00

AMZN

53.10

JNJ

9.64

APC

3.88

NKE

40.22

CVS

9.23

MET

3.80

SBUX

33.00

WMT

9.15

UNP

3.78

EBAY

32.85

PG

9.00

WAG

3.67

INTC

30.46

FDX

8.50

RTN

3.43

DIS

28.00

CL

7.50

UPS

3.43

JPM

27.56

UNH

7.50

LLY

3.36

CSCO

25.91

AIG

7.44

AEP

3.33

IBM

24.85

MRK

7.25

DOW

3.00

F

24.45

GILD

7.11

NOV

2.75

MSFT

22.92

ABT

6.78

NSC

2.75

V

22.64

HAL

6.67

MDT

2.67

ORCL

21.30

AMGN

6.63

SO

2.67

QCOM

19.83

BAX

6.57

MMM

2.63

HPQ

19.73

EMR

6.57

MDLZ

2.33

MA

18.38

PEP

6.50

UTX

2.33

HD

17.36

BA

6.44

DVN

2.25

DELL

16.71

T

6.44

EXC

2.25

EMC

16.25

LMT

6.29

COF

2.09

WFC

16.00

CVX

6.09

COP

2.00

LOW

15.88

MS

5.83

MO

2.00

GS

15.62

PFE

5.50

OXY

2.00

TGT

14.56

VZ

5.13

BK

1.88

COST

13.82

TWX

5.00

APA

1.75

TXN

13.77

SLB

4.78

PM

1.50

BAC

13.58

AXP

4.69

WMB

1.00

C

13.00

USB

4.50

ALL

0.00

MCD

13.00

XOM

4.40

BRK.B

0.00

CAT

11.31

DD

4.25

SPG

0.00

GE

11.11

ACN

4.22

KO

10.64

GD

4.20

 

The mean value is 12.5 estimates per reporting period. To designate estimates as a short term, we need to balance between having too large a number of estimates (which may increase imprecision) and too small a number (which may be unrepresentative). Therefore, we expect that the boundary between these short term and long term estimates should be a particular quintile, q, of estimates:

In our implementation q= 0.2, which means that for 100 estimates, we take the mean value of the last 20 as the short term forecast.

To measure the predictive power of this approach we need to collect the estimates prior to the last reporting date and calculate the mean estimate which is our long term forecast:

The last estimate in this summation is at the day close prior to the report.

Similarly, the short term forecast is:

Trading Hypothesis

Our hypothesis is that relatively higher optimism in the short term forecast compared to the long-term forecast usually leads to negative price movement after the actual earnings announcement. In this case, the announcement will probably miss the short term expectation, and the stock price will fall. On the other hand, where long term optimism is followed by short term, pre-announcement, pessimism then this predicts positive stock price movement.

Trading Algorithm

To implement this hypothesis as an automated trading strategy we need to:

1)     Collect the earnings estimates until the day close prior to the earnings announcement and calculate the mean estimate:

meanEPS = sumEPS/EPScount;

 

2)     Calculate the mean of last q values:

k  = Floor(EPScount*q);

meanEPS_q = sumEPS_k/k;

 

3)     Calculate the difference between long and short term forecast, i.e. the difference between the mean Estimize consensus pre-report and mean Estimize consensus of the last 20% values:

 

delta = meanEPS – meanEPS_q;

 

4)     Open position prior to the EPS announcement in accordance with ∆.

 if (delta < 0) goShort; else goLong;

 

5)     Close position on next day open.

In the strategy, we open the position at 15:59 and close the position at 09:35 next day. Through testing, we determined that opening position at the closing auction price has no significant impact on performance.

 

Market Neutrality

We tested strategy performance using dollar (notional) and beta (market) neutral approaches with the goal of creating a portfolio uncorrelated with the movement of the market, i.e. to determine that this Estimize-based strategy generates true alpha.

 

1)     Dollar-neutral:

The portfolio is hedged using the S&P 500 index via the SPY. We calculate the dollar amount of all opened positions at 15:59 and open the opposite position in SPY. If the dollar amounts invested in each of the stocks are  , then we invest in SPY:  

 

 2)     Beta-neutral:

We performed a regression of stock returns on the SPY return over a period of 250 days:

 

Where denotes the daily return of a stock i or SPY index respectively, R = log(CurrentDayClose/prevDayClose). We used Ordinary Least Squares method to estimate betas for each stock.

If the dollar amounts invested in each of the stocks are , then the dollar amount invested in SPY is such that:

 

which keeps the overall portfolio beta-neutral.

These variations of the algorithm are available via strategy input parameters, in QuantOffice.


Consolidated Report

The strategy was run on securities:

1) S&P100;

2) 100 companies from S&P500 with the greatest turnover;

3) 200 companies from S&P500 with the greatest turnover.

Back-testing period: from 12/26/2011 to 12/31/2013.

 

 

S&P100

100 companies from S&P500 

200 companies from S&P500 

Strategy

Stocks

Dollar neutral

Beta neutral

Stocks

Dollar neutral

Beta neutral

Stocks

Dollar neutral

Beta neutral

Net Profit/Loss

22,903.79

18,235.72

17,099.26

28,831.43

24,290.54

21,602.58

42,221.01

34,195.22

28,497.21

Total Profit

88,561.18

92,850.19

88,344.25

121,216.22

125,820.11

121,110.75

211,465.74

219,339.25

207,103.44

Total Loss

-65,657.39

-74,614.47

-71,244.99

-92,384.79

-101,529.57

-99,508.17

-169,244.74

-185,144.03

-178,606.22

Cumulated Profit %

22.90 %

18.24 %

17.10 %

28.83 %

24.29 %

21.60 %

42.22 %

34.20 %

28.50 %

 

 

 

 

 

 

 

 

 

 

Max Drawdown

-4,050.98

-4,064.70

-4,130.32

-6,097.02

-6,023.01

-5,886.81

-6,847.01

-6,567.76

-6,992.91

Max Drawdown %

-3.44 %

-3.54 %

-3.62 %

-4.80 %

-4.84 %

-4.82 %

-4.87 %

-4.73 %

-5.23 %

Max Drawdown Time

1/28/2013

1/28/2013

1/28/2013

1/23/2013

1/23/2013

1/23/2013

3/19/2013

11/27/2013

11/29/2013

Return/Drawdown Ratio

5.65

4.49

4.14

4.73

4.03

3.67

6.17

5.21

4.08

Drawdown Days %

69.17 %

72.33 %

48.17 %

72.33 %

72.92 %

49.12 %

80.43 %

82.61 %

55.37 %

Max Drawdown Duration

81

81

81

137

139

139

78

133

133

 

 

 

 

 

 

 

 

 

 

CAGR

11.18 %

8.99 %

5.71 %

13.90 %

11.82 %

7.12 %

19.84 %

16.31 %

9.27 %

Sharpe Ratio

2.08

1.72

1.36

2.12

1.83

1.33

2.19

1.88

1.33

Sortino Ratio

5.38

5.22

4.20

6.56

6.47

5.34

9.06

8.66

6.99

UPI

3.43

2.74

2.16

3.77

3.13

2.26

3.90

3.14

2.14

Annualized Volatility

0.72

0.54

0.40

0.65

0.46

0.32

0.64

0.46

0.30

Information Ratio

1.93

1.62

1.29

1.88

1.63

1.22

1.87

1.62

1.18

Optimal f

38.65

33.01

32.41

32.33

28.21

24.98

24.20

21.77

19.01

 

 

 

 

 

 

 

 

 

 

All Trades #

603

784

771

657

838

823

1148

1389

1344

Profitable Trades Ratio

0.54

0.52

0.52

0.54

0.52

0.52

0.53

0.52

0.52

Winning Trades #

328

409

401

355

433

425

607

718

693

Losing Trades #

275

375

370

302

405

398

541

671

651

 

 

 

 

 

 

 

 

 

 

Average Trade

37.98

23.26

22.18

43.88

28.99

26.25

36.78

24.62

21.20

Average Winning Trade

270.00

227.02

220.31

341.45

290.58

284.97

348.38

305.49

298.85

Average Losing Trade

-238.75

-198.97

-192.55

-305.91

-250.69

-250.02

-312.84

-275.92

-274.36

Avg. Win/ Avg. Loss Ratio

1.13

1.14

1.14

1.12

1.16

1.14

1.11

1.11

1.09

Average Profit per Share

0.17

0.12

0.11

0.19

0.14

0.13

0.15

0.11

0.09

 

 

 

 

 

 

 

 

 

 

Max Conseq. Winners

8

10

10

7

8

7

10

8

7

Max Conseq. Losers

6

6

7

9

7

7

8

8

8

 

Conclusion

We presented an approach that measures the relative optimism or pessimism of short term versus long term Estimize estimates as a strong predictive factor of the price directionality after EPS announcements. We developed a trading strategy that implements an algorithm based on this approach. We also constructed dollar-neutral and beta-neutral portfolios to demonstrate that the achieved effect is uncorrelated with the market.

The main contribution of the paper is back-testing and comparison of the unhedged strategy with dollar-neutral and beta-neutral ones. On stocks in the S&P100, back-testing shows that the unhedged strategy has an Information ratio of 1.93 over the period 2012-2013, with a stronger performance in 2012. Dollar-neutral and beta-neutral strategies achieved Information Ratios of 1.62 and 1.29 respectively, which indicates that the strategy does generate alpha.

 

 

 

Appendix A: Detailed Reports

Only S&P100 stocks:

Net Profit/Loss

22,903.79

13,834.76

9,069.03

Total Profit

88,561.18

58,373.43

30,187.75

Total Loss

-65,657.39

-44,538.67

-21,118.72

Cumulated Profit %

22.90 %

13.83 %

9.07 %

Max Drawdown

-4,050.98

-2,535.09

-2,528.59

Max Drawdown %

-3.44 %

-2.28 %

-2.32 %

Max Drawdown Date

1/28/2013

1/24/2013

7/31/2013

Return/Drawdown Ratio

5.65

5.46

3.59

Drawdown Days %

69.17 %

78.26 %

73.91 %

Max Drawdown Duration

81

83

137

CAGR

11.18 %

6.88 %

4.56 %

Sharpe Ratio

2.08

1.49

1.33

Annualized Volatility

5.38

4.62

3.43

Sortino Ratio

3.43

2.43

2.32

UPI

0.72

0.52

0.34

Information Ratio

1.93

1.42

1.29

Optimal f

38.65

32.26

38.88

All Trades #

603

441

162

Profitable Trades Ratio

0.54

0.54

0.56

Winning Trades #

328

238

90

Losing Trades #

275

203

72

Average Trade

37.98

31.37

55.98

Average Winning Trade

270.00

245.27

335.42

Average Losing Trade

-238.75

-219.40

-293.32

Avg. Win/ Avg. Loss Ratio

1.13

1.12

1.14

Average Profit per Share

0.17

0.15

0.23

Max Conseq. Winners

8

7

7

Max Conseq. Losers

6

6

5

Hedged by SPY S&P100 stocks:

 

Net Profit/Loss

18,235.72

13,519.25

4,716.47

Total Profit

92,850.19

58,917.72

33,932.47

Total Loss

-74,614.47

-45,398.47

-29,216.00

Cumulated Profit %

18.24 %

13.52 %

4.72 %

Max Drawdown

-4,064.70

-2,553.26

-3,319.65

Max Drawdown %

-3.54 %

-2.30 %

-3.13 %

Max Drawdown Date

1/28/2013

1/24/2013

8/1/2013

Return/Drawdown Ratio

4.49

5.29

1.42

Drawdown Days %

72.33 %

83.79 %

90.91 %

Max Drawdown Duration

81

107

169

CAGR

8.99 %

6.73 %

2.40 %

Sharpe Ratio

1.72

1.45

0.63

Annualized Volatility

5.22

4.63

3.80

Sortino Ratio

2.74

2.37

0.99

UPI

0.54

0.50

0.12

Information Ratio

1.62

1.39

0.62

Optimal f

33.01

31.44

16.63

All Trades #

784

483

301

Profitable Trades Ratio

0.52

0.53

0.50

Winning Trades #

409

257

152

Losing Trades #

375

226

149

Average Trade

23.26

27.99

15.67

Average Winning Trade

227.02

229.25

223.24

Average Losing Trade

-198.97

-200.88

-196.08

Avg. Win/ Avg. Loss Ratio

1.14

1.14

1.14

Average Profit per Share

0.12

0.14

0.08

Max Conseq. Winners

10

7

6

Max Conseq. Losers

6

6

8

 

 

Beta neutral S&P100 stocks:

Net Profit/Loss

17,099.26

13,140.04

3,959.22

Total Profit

88,344.25

55,753.20

32,591.05

Total Loss

-71,244.99

-42,613.16

-28,631.83

Cumulated Profit %

17.10 %

13.14 %

3.96 %

Max Drawdown

-4,130.32

-2,589.82

-3,407.28

Max Drawdown %

-3.62 %

-2.34 %

-3.24 %

Max Drawdown Date

1/28/2013

1/24/2013

8/1/2013

Return/Drawdown Ratio

4.14

5.07

1.16

Drawdown Days %

48.17 %

54.53 %

60.76 %

Max Drawdown Duration

81

129

169

CAGR

5.71 %

4.44 %

1.38 %

Sharpe Ratio

1.36

1.20

0.44

Annualized Volatility

4.20

3.71

3.13

Sortino Ratio

2.16

1.93

0.67

UPI

0.40

0.39

0.08

Information Ratio

1.29

1.15

0.44

Optimal f

32.41

32.23

14.08

All Trades #

771

469

302

Profitable Trades Ratio

0.52

0.54

0.49

Winning Trades #

401

253

148

Losing Trades #

370

216

154

Average Trade

22.18

28.02

13.11

Average Winning Trade

220.31

220.37

220.21

Average Losing Trade

-192.55

-197.28

-185.92

Avg. Win/ Avg. Loss Ratio

1.14

1.12

1.18

Average Profit per Share

0.11

0.15

0.07

Max Conseq. Winners

10

7

6

Max Conseq. Losers

7

6

6

 

Only 100 stocks from S&P500:

Net Profit/Loss

28,831.43

23,711.21

5,120.22

Total Profit

121,216.22

80,130.00

41,086.22

Total Loss

-92,384.79

-56,418.79

-35,966.00

Cumulated Profit %

28.83 %

23.71 %

5.12 %

Max Drawdown

-6,097.02

-3,303.87

-5,767.64

Max Drawdown %

-4.80 %

-2.60 %

-5.24 %

Max Drawdown Date

1/23/2013

12/20/2013

5/21/2013

Return/Drawdown Ratio

4.73

7.18

0.89

Drawdown Days %

72.33 %

65.42 %

78.46 %

Max Drawdown Duration

137

97

301

CAGR

13.90 %

11.55 %

2.60 %

Sharpe Ratio

2.12

2.17

0.57

Annualized Volatility

6.56

5.32

4.59

Sortino Ratio

3.77

4.18

0.87

UPI

0.65

0.83

0.05

Information Ratio

1.88

1.98

0.54

Optimal f

32.33

40.79

12.33

All Trades #

657

451

206

Profitable Trades Ratio

0.54

0.55

0.52

Winning Trades #

355

247

108

Losing Trades #

302

204

98

Average Trade

43.88

52.57

24.86

Average Winning Trade

341.45

324.41

380.43

Average Losing Trade

-305.91

-276.56

-367.00

Avg. Win/ Avg. Loss Ratio

1.12

1.17

1.04

Average Profit per Share

0.19

0.23

0.11

Max Conseq. Winners

7

8

7

Max Conseq. Losers

9

10

5

 

 

Hedged by SPY 100 stocks:

 

Net Profit/Loss

24,290.54

23,623.01

667.54

Total Profit

125,820.11

81,115.72

44,704.39

Total Loss

-101,529.57

-57,492.71

-44,036.86

Cumulated Profit %

24.29 %

23.62 %

.67 %

Max Drawdown

-6,023.01

-3,452.13

-7,171.01

Max Drawdown %

-4.84 %

-2.72 %

-6.68 %

Max Drawdown Date

1/23/2013

12/20/2013

10/28/2013

Return/Drawdown Ratio

4.03

6.84

0.09

Drawdown Days %

72.92 %

75.30 %

86.56 %

Max Drawdown Duration

139

100

301

CAGR

11.82 %

11.51 %

.34 %

Sharpe Ratio

1.83

2.16

0.07

Annualized Volatility

6.47

5.32

4.93

Sortino Ratio

3.13

4.17

0.10

UPI

0.46

0.79

0.01

Information Ratio

1.63

1.97

0.07

Optimal f

28.21

40.61

1.41

All Trades #

838

503

335

Profitable Trades Ratio

0.52

0.54

0.49

Winning Trades #

433

270

163

Losing Trades #

405

233

172

Average Trade

28.99

46.96

1.99

Average Winning Trade

290.58

300.43

274.26

Average Losing Trade

-250.69

-246.75

-256.03

Avg. Win/ Avg. Loss Ratio

1.16

1.22

1.07

Average Profit per Share

0.14

0.22

0.01

Max Conseq. Winners

8

9

6

Max Conseq. Losers

7

8

6

Beta neutral 100 stocks

 

Net Profit/Loss

21,602.58

22,202.86

-600.28

Total Profit

121,110.75

77,638.42

43,472.33

Total Loss

-99,508.17

-55,435.57

-44,072.61

Cumulated Profit %

21.60 %

22.20 %

-.60 %

Max Drawdown

-5,886.81

-3,471.30

-7,440.96

Max Drawdown %

-4.82 %

-2.76 %

-6.99 %

Max Drawdown Date

1/23/2013

12/20/2013

10/28/2013

Return/Drawdown Ratio

3.67

6.40

-0.08

Drawdown Days %

49.12 %

50.88 %

59.00 %

Max Drawdown Duration

139

100

301

CAGR

7.12 %

7.31 %

-.21 %

Sharpe Ratio

1.33

1.68

-0.05

Annualized Volatility

5.34

4.35

4.12

Sortino Ratio

2.26

3.21

-0.07

UPI

0.32

0.59

0.00

Information Ratio

1.22

1.56

-0.05

Optimal f

24.98

38.58

-1.25

All Trades #

823

490

333

Profitable Trades Ratio

0.52

0.54

0.47

Winning Trades #

425

267

158

Losing Trades #

398

223

175

Average Trade

26.25

45.31

-1.80

Average Winning Trade

284.97

290.78

275.14

Average Losing Trade

-250.02

-248.59

-251.84

Avg. Win/ Avg. Loss Ratio

1.14

1.17

1.09

Average Profit per Share

0.13

0.22

-0.01

Max Conseq. Winners

7

9

6

Max Conseq. Losers

7

8

7

Only 200 stocks from S&P500:

 

Net Profit/Loss

42,221.01

35,965.79

6,255.22

Total Profit

211,465.74

148,821.21

62,644.53

Total Loss

-169,244.74

-112,855.42

-56,389.31

Cumulated Profit %

42.22 %

35.97 %

6.26 %

Max Drawdown

-6,847.01

-5,999.18

-6,399.88

Max Drawdown %

-4.87 %

-4.60 %

-5.73 %

Max Drawdown Date

3/19/2013

2/20/2013

5/21/2013

Return/Drawdown Ratio

6.17

6.00

0.98

Drawdown Days %

80.43 %

82.21 %

87.15 %

Max Drawdown Duration

78

109

299

CAGR

19.84 %

17.10 %

3.17 %

Sharpe Ratio

2.19

2.09

0.55

Annualized Volatility

9.06

8.18

5.72

Sortino Ratio

3.90

3.90

0.87

UPI

0.64

0.62

0.07

Information Ratio

1.87

1.85

0.53

Optimal f

24.20

25.58

9.69

All Trades #

1148

841

307

Profitable Trades Ratio

0.53

0.54

0.50

Winning Trades #

607

453

154

Losing Trades #

541

388

153

Average Trade

36.78

42.77

20.38

Average Winning Trade

348.38

328.52

406.78

Average Losing Trade

-312.84

-290.86

-368.56

Avg. Win/ Avg. Loss Ratio

1.11

1.13

1.10

Average Profit per Share

0.15

0.18

0.09

Max Conseq. Winners

10

9

5

Max Conseq. Losers

8

8

5

 

 

Hedged by SPY 200 stocks:

 

Net Profit/Loss

34,195.22

35,888.21

-1,692.99

Total Profit

219,339.25

149,819.75

69,519.49

Total Loss

-185,144.03

-113,931.54

-71,212.48

Cumulated Profit %

34.20 %

35.89 %

-1.69 %

Max Drawdown

-6,567.76

-5,997.23

-9,581.66

Max Drawdown %

-4.73 %

-4.60 %

-8.91 %

Max Drawdown Date

11/27/2013

2/20/2013

11/8/2013

Return/Drawdown Ratio

5.21

5.98

-0.18

Drawdown Days %

82.61 %

83.99 %

92.29 %

Max Drawdown Duration

133

109

299

CAGR

16.31 %

17.07 %

-.87 %

Sharpe Ratio

1.88

2.09

-0.14

Annualized Volatility

8.66

8.18

6.47

Sortino Ratio

3.14

3.89

-0.19

UPI

0.46

0.62

-0.01

Information Ratio

1.62

1.85

-0.13

Optimal f

21.77

25.53

-2.09

All Trades #

1389

892

497

Profitable Trades Ratio

0.52

0.54

0.48

Winning Trades #

718

479

239

Losing Trades #

671

413

258

Average Trade

24.62

40.23

-3.41

Average Winning Trade

305.49

312.78

290.88

Average Losing Trade

-275.92

-275.86

-276.02

Avg. Win/ Avg. Loss Ratio

1.11

1.13

1.05

Average Profit per Share

0.11

0.17

-0.02

Max Conseq. Winners

8

9

7

Max Conseq. Losers

8

8

6

 

 

Beta neutral 200 stocks:

 

Net Profit/Loss

28,497.21

33,793.76

-5,296.55

Total Profit

207,103.44

141,269.44

65,834.00

Total Loss

-178,606.22

-107,475.67

-71,130.55

Cumulated Profit %

28.50 %

33.79 %

-5.30 %

Max Drawdown

-6,992.91

-5,998.04

-10,306.36

Max Drawdown %

-5.23 %

-4.68 %

-9.85 %

Max Drawdown Date

11/29/2013

2/20/2013

11/8/2013

Return/Drawdown Ratio

4.08

5.63

-0.51

Drawdown Days %

55.37 %

56.46 %

62.86 %

Max Drawdown Duration

133

109

299

CAGR

9.27 %

10.85 %

-1.91 %

Sharpe Ratio

1.33

1.64

-0.34

Annualized Volatility

6.99

6.61

5.55

Sortino Ratio

2.14

3.07

-0.47

UPI

0.30

0.47

-0.03

Information Ratio

1.18

1.48

-0.34

Optimal f

19.01

24.81

-6.19

All Trades #

1344

853

491

Profitable Trades Ratio

0.52

0.54

0.47

Winning Trades #

693

460

233

Losing Trades #

651

393

258

Average Trade

21.20

39.62

-10.79

Average Winning Trade

298.85

307.11

282.55

Average Losing Trade

-274.36

-273.47

-275.70

Avg. Win/ Avg. Loss Ratio

1.09

1.12

1.02

Average Profit per Share

0.09

0.17

-0.05

Max Conseq. Winners

7

9

7

Max Conseq. Losers

8

8

6

 

Tactical Equity Allocation Using Conference Call Sentiment Indicators

THEORETICAL BACKGROUND 

The dialogue between management teams and Wall Street analysts on earnings conference calls is an important tool for information dissemination from asymmetrically informed management teams to the investing public.  This data point is primarily used by fundamental analysts to understand the current issues concerning a public company’s performance.  However, the linguistic content of these dialogues can also provide valuable quantitative data points if the market is not fully efficient in immediately digesting positive and negative stories.  Route 3 Capital and its predecessor firm, HudsonView Capital Management, have worked to prove this is the case, by quantifying and employing sentiment measures from these conference calls to predict security returns.

Sentiment Average Construction 

Route 3 Capital uses proprietary measures of conference call sentiment to systematically guide short to medium term tactical equity allocation for managed account clients.  To this end, the firm maintains short and medium term moving averages of sentiment for industry ETFs using weighted sentiment scores of constituent company conference call dialogues.  The following chart shows clear delineation in next day returns (morning to morning) in four beta driven industry ETFs by percentile of the sentiment averages:

Average Next Day ETF Returns by Sentiment Score Percentile

   

TACTICAL ETF STRATEGY BACKTEST

In November 2012, Route 3 began live trading its Tactical ETF Strategy.  The model is systematic, using the sentiment averages to determine a long, short, or neutral position in each ETF each trading day.   Route 3 used minute bar pricing data from TickData, adjusted for dividends, and the Deltix QuantOffice strategy development environment utilizing TimeBase’s integrated message bus to backtest the strategy from Jan 1, 2007 through the present.  The risk adjusted returns are encouraging and, with a negative 21% monthly correlation to the S&P 500, show particular value as an equity hedge or short-bias strategy:

Tactical ETF Strategy: Backtested Model Time Series (1/1/2007 – 1/31/2014),  $10m starting equity)

 

Tactical ETF Strategy: Backtested Monthly Returns (1/1/2007 – 1/31/2014)

 

 

Tactical ETF Strategy: Backtested Summary Performance Data (1/1/2007 –1/31/2014)

 

 

Principal- Joseph Graham, CFA

Joe currently operates Route 3 Capital and is a Senior Risk Officer at LibertyView Capital Management.  He most recently founded HudsonView Capital Management, which developed quantitative testing systems and research related to linguistic signals.  The company used this research to manage systematic long/short strategies for a prominent Geneva-based family office.  Prior to HudsonView, Joe was part of the SPAC portfolio management team at Millennium Partners.  Joe’s team was one of the largest SPAC common and warrant investors from 2008 to 2010.  Prior to Millennium, Joe was an Analyst and Portfolio Manager at LibertyView in the equity group, focusing on private placements, merger arbitrage, and SPACs.  He has an MBA with honors from the Wharton School of Business at the University of Pennsylvania and graduated summa cum laude with majors in Finance and Philosophy from Washington University in St. Louis.

Contact info:

Email: [email protected]

Phone: 646-660-3167

Automated Trading Strategy using Social Media Sentiment

Introduction

This paper describes the implementation of an automated equity trading strategy based on social media derived bullishness and bearishness sentiment indicators and the results from back-testing this strategy. The sentiment factors were provided by PsychSignal who have developed natural language processing software with the purpose of calculating bullishness and bearishness indices from StockTwits, Twitter and chat rooms.

The PsychSignal data and market data used in this research, together with the QuantOffice implementation of this strategy, are available from Deltix Cloud Services (DCS). 

Trading logic description

We developed the trading strategy based on intraday PsychSignal bullish/bearish indices using two approaches. In both cases, we were operating on the hypothesis that past bullish/bearish indicators for a specific security provide predictive power on the future price of that security and that we can profitably trade using this insight.

1. Regression based model

We took as predictors the two most recent days of daily bullish and bearish indicators data. These daily predictors were smoothed using an EMA (Exponential Moving Average) indicator and acted as the regressors (independent variables) in our model. As the regressand (dependent variable), we calculated the close-to-next-day-open price change for each security in the S&P500 over a period of 50 days (the training period).  We applied linear regression to the resultant matrix of regressor values and the vector of the regressand values. The regression parameters were set as weights in the indicator formula and the following signal logic was applied:

If indicator  is positive and current price is lower than the 50 day EMA-smoothed day close price, then open a long position at market close and close that position at next market open.

If indicator is negative and current price is higher than 50 day EMA-smoothed day close price with then open a short position at market close and close that position at next market open.

The order size for opening order is calculated by the following formula:

where: retON = list of overnight returns collected for the last 50 days.

The following variations of this model are available via input parameters of the strategy:

  •   Using the return calculated as 15 min before close price, as an extra parameter in the regression model.
  • Instead of comparing the indicator to 0, we can compare it to some enhanced boundaries. As an example of such boundaries, +/- absolute value of the 30 day SMA (Simple Moving Average) of the indicator was used: if indicator is greater than the absolute value of the SMA, then open long position, if it is less then open a short position.

2. RSI based model

In this model, daily predictors are smoothed by collecting data for each predictor into an EMA indicator. The day close EMA values are smoothed with EMA daily indicators and the RSI (Relative Strength Index) is calculated as:

If RSI<15  and current price is lower than 50 day EMA-smoothed day close price then open long position at market close and close that position at next open.

If RSI>85  and current price is higher than 50 day EMA-smoothed day close price then open short position at market close and close that position at next open.

The order size for opening order is calculated by the following formula:

where: retON means list of overnight returns collected for the last 30 days.

Results

The strategy was run on securities in the S&P100.

Consolidated results

ParameterRegression basedSMA boundariesReturn in regression, SMA boundariesRSI
Net Profit/Loss390,804.54404,459.44325,423.34137,914.56
Total Profit2,176,093.571,734,422.431,614,855.21326,873.83
Total Loss-1,785,289.03-1,329,962.99-1,289,431.87-188,959.27
Cumulated Profit %390.80%404.46%325.42%137.91%
Max Drawdown-106,957.11-95,237.32-62,589.54-20,407.60
Max Drawdown %-18.80%-16.77%-13.00%-19.80%
Max Drawdown Date7/24/20137/23/20137/25/201312/31/2012
Return/Drawdown Ratio3,654,255,26,76
Drawdown Days %46.07%41.88%50.26%54.97%
Max Drawdown Duration21212129
CAGR771.97%805.16%617.77%225.39%
Sharpe Ratio2,883,133,12,7
Annualized Volatility184,48175,93143,0369,47
Sortino Ratio5,555,615,755,76
UPI5,186,794,134,14
Information Ratio2,883,133,12,7
Optimal f2,272,63,024,67
All Trades #565743414226739
Profitable Trades Ratio0,510,540,510,58
Winning Trades #289723382172427
Losing Trades #276020032054312
Average Trade69,0893,1777,01186,62
Average Winning Trade751,15741,84743,49765,51
Average Losing Trade-646,84-663,99-627,77-605,64
Avg. Win/ Avg. Loss Ratio1,161,121,181,26
Average Profit per Share0,020,030,030,05
Max Conseq. Winners13261947
Max Conseq. Losers15161413

Detailed results

1. Regression based algorithm

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss390,804.54378,866.6211,937.92
Total Profit2,176,093.57845,980.891,330,112.68
Total Loss-1,785,289.03-467,114.27-1,318,174.76
Cumulated Profit %390.80%378.87%11.94%
Max Drawdown-106,957.11-53,779.96-158,289.04
Max Drawdown %-18.80%-15.74%-60.24%
Max Drawdown Date7/24/20136/24/20137/24/2013
Return/Drawdown Ratio3,657,040,08
Drawdown Days %46.07%45.03%64.40%
Max Drawdown Duration211627
CAGR771.97%743.23%16.59%
Sharpe Ratio2,883,080,07
Annualized Volatility184,48167,3242,09
Sortino Ratio5,555,570,11
UPI5,1810,020,05
Information Ratio2,883,080,07
Optimal f2,272,660,03
All Trades #565718593798
Profitable Trades Ratio0,510,610,47
Winning Trades #289711251772
Losing Trades #27607342026
Average Trade69,08203,83,14
Average Winning Trade751,15751,98750,63
Average Losing Trade-646,84-636,4-650,63
Avg. Win/ Avg. Loss Ratio1,161,181,15
Average Profit per Share0,020,070
Max Conseq. Winners135534
Max Conseq. Losers152344
 

2. +/- SMA value as boundaries

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss404,459.44377,455.0827,004.36
Total Profit1,734,422.431,047,071.96687,350.47
Total Loss-1,329,962.99-669,616.88-660,346.11
Cumulated Profit %404.46%377.46%27.00%
Max Drawdown-95,237.32-70,438.24-99,399.48
Max Drawdown %-16.77%-16.54%-43.90%
Max Drawdown Date7/23/20136/24/201308.01.2013
Return/Drawdown Ratio4,255,360,27
Drawdown Days %41.88%45.55%60.73%
Max Drawdown Duration211628
CAGR805.16%739.85%38.46%
Sharpe Ratio3,132,540,28
Annualized Volatility175,93202,45131,01
Sortino Ratio5,614,30,43
UPI6,797,180,18
Information Ratio3,132,540,28
Optimal f2,61,810,22
All Trades #434122182123
Profitable Trades Ratio0,540,60,48
Winning Trades #233813251013
Losing Trades #20038931110
Average Trade93,17170,1812,72
Average Winning Trade741,84790,24678,53
Average Losing Trade-663,99-749,85-594,91
Avg. Win/ Avg. Loss Ratio1,121,051,14
Average Profit per Share0,030,060
Max Conseq. Winners263423
Max Conseq. Losers164034
 

3. Return in regression, SMA boundaries

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss404,459.44377,455.0827,004.36
Total Profit1,734,422.431,047,071.96687,350.47
Total Loss-1,329,962.99-669,616.88-660,346.11
Cumulated Profit %404.46%377.46%27.00%
Max Drawdown-95,237.32-70,438.24-99,399.48
Max Drawdown %-16.77%-16.54%-43.90%
Max Drawdown Date7/23/20136/24/201308.01.2013
Return/Drawdown Ratio4,255,360,27
Drawdown Days %41.88%45.55%60.73%
Max Drawdown Duration211628
CAGR805.16%739.85%38.46%
Sharpe Ratio3,132,540,28
Annualized Volatility175,93202,45131,01
Sortino Ratio5,614,30,43
UPI6,797,180,18
Information Ratio3,132,540,28
Optimal f2,61,810,22
All Trades #434122182123
Profitable Trades Ratio0,540,60,48
Winning Trades #233813251013
Losing Trades #20038931110
Average Trade93,17170,1812,72
Average Winning Trade741,84790,24678,53
Average Losing Trade-663,99-749,85-594,91
Avg. Win/ Avg. Loss Ratio1,121,051,14
Average Profit per Share0,030,060
Max Conseq. Winners263423
Max Conseq. Losers164034
 

4.  RSI

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss325,423.34304,738.1520,685.20
Total Profit1,614,855.21603,891.161,010,964.04
Total Loss-1,289,431.87-299,153.02-990,278.85
Cumulated Profit %325.42%304.74%20.69%
Max Drawdown-62,589.54-26,493.13-113,048.33
Max Drawdown %-13.00%-8.72%-48.37%
Max Drawdown Date7/25/20136/24/201308.01.2013
Return/Drawdown Ratio5,211,50,18
Drawdown Days %50.26%43.98%63.35%
Max Drawdown Duration211037
CAGR617.77%570.69%29.17%
Sharpe Ratio3,13,580,15
Annualized Volatility143,03115,91184,57
Sortino Ratio5,756,890,24
UPI4,139,860,09
Information Ratio3,13,580,15
Optimal f3,024,250,09
All Trades #422613012925
Profitable Trades Ratio0,510,620,47
Winning Trades #21728041368
Losing Trades #20544971557
Average Trade77,01234,237,07
Average Winning Trade743,49751,11739,01
Average Losing Trade-627,77-601,92-636,02
Avg. Win/ Avg. Loss Ratio1,181,251,16
Average Profit per Share0,030,080
Max Conseq. Winners193526
Max Conseq. Losers141746
 

·        Using the return calculated as 15 min before close price, as an extra parameter in the regression model.

·        Instead of comparing the indicator to 0, we can compare it to some enhanced boundaries. As an example of such boundaries, +/- absolute value of the 30 day SMA (Simple Moving Average) of the indicator was used: if indicator is greater than the absolute value of the SMA, then open long position, if it is less then open a short position.

Automated FX Trading Strategy Using Macro News Events

Introduction

This paper describes the implementation of an automated quantitative FX trading strategy based on macro news data provided by RavenPack.  RavenPack sources news from a variety of sources from which it produces an array of analytics (including sentiment, relevance and novelty) in real-time, and which are available historically.

We undertook the research and implemented the strategy in the Deltix QuantOffice research platform, a purpose-built C# development studio with embedded math, statistics and data libraries.

Our thesis was: does the arrival of macroeconomic news from the world’s largest economies bring additional volatility to the market? The historical data set used is described below:

  •      News Data from 1 March 2012 till 1 August 2012:

–       More than 1.1 million messages

–       Used subset of macro-economic news  for US (287,000 records), Germany (7,800), EU (3,700) and Japan (14,400).

  •       Market Data from 1 March 2012 till 1 August 2012:

–       Three currency pairs: EURUSD, USDJPY, EURJPY (bid/ask quotes)

–       Approx 100 million market data messages.

  •       News data was filtered by the following news types:

–       consumer-price-index

–       producer-price-index

–       unemployment

–       retail-sales

–       gross-domestic-product

–       durable-goods

–       interest-rate

–       consumer-confidence

–       home-sales-existing, home-sales-new

–       current-account, current-account-surplus, current-account-deficit

–       consumer-spending

–       jobless-claims

–       inflation

–       trade-balance, trade-balance-deficit, trade-balance-surplus

  •    Extra filters applied were:            

–       news relevance: RELEVANCE = 100 (maximum relevance)

–       news novelty: ENS = 100 (maximum novelty) 

Testing the Thesis

As a measure of volatility, we calculated the annualized standard deviation of log returns inside 5 minute windows of 10-sec bars (i.e. 30 bars). We also calculated variance ratio[i]:

               VR = HILO (N) / ( ATR(N) * SQRT(N))

where

               N = 30; HILO (N) is high/low price range and ATR(N) is average true range over the N bars period

All statistics were calculated for 5 minutes before the time of the news release time and for 5 minutes after. For example, for US announcements scheduled for 8:30am, the time intervals were 8:25am to 8:30am and 8:30am to 8:35am. The results, pertaining to US economic news data, are show below:

automated1

automated2

Trading Strategy

It is clear from the results above that there is a significant change in short-term volatility of FX rates after the announcement of economic data. The next step in our research was to design and test a trading strategy which utilizes this observation.

The strategy defines breakout buy/sell levels in the five minute interval preceding the scheduled event. Upon receiving the news event, the strategy creates a long position if the price exceeds the buy level, and creates a short position if the market moves below the sell level. The strategy then closes positions five minutes after receiving the news event.

In back-testing, order execution simulation was done using the relatively conservative Best Bid Offer mode: buy at best ask price; sell at best bid price. The lot size for all trades was $100,000.

Results

automated3

automated4


[i] Variance Ratio statistics introduced by Lo and MacKinlay (1988):

VR close to 1 indicates that market is in a random walk regime;

VR > 1 indicates that market is in a trending regime (with positive autocorrelation of price returns);

VR < 1 indicates that market is in a mean reversion regime (with negative autocorrelation of price returns).

 

 

Automated Trading Strategy Using Twitter Sentiment

By: The Deltix Quantitative Research Team

Introduction

This paper describes the implementation of an automated quantitative equity trading strategy based on sentiment factors derived from Twitter and the results from back-testing this strategy. The sentiment factors were provided by Social Market Analytics (SMA).  SMA produces a family of metrics, called S-Factors™, designed to capture the signature of financial market sentiment. SMA applies these metrics to data captured from Twitter to estimate sentiment for indices, sectors, and individual securities; yielding and recording time series of these measurements on daily and intraday time scales.

We implemented the strategy in the Deltix QuantOffice research platform, a purpose-built C# development studio with embedded math, statistics and data libraries.

The strategy, which uses SMA’s intraday S-Scores, generates trading signals based on the S-Score trend-lines. We applied industry standard moving average methodology for S-Score time-series data and established trend-lines by comparing slow and fast moving averages. In addition we generated several filters to improve signal accuracy.

We made a series of experiments on the members of S&P100 index from December 2011 till March 2013. We compared the following scenarios:

a)      Open a position using day open price

b)      Open a position at 9:32am using current price

In addition, we added a hedging rule against the market using the SPY ETF and investigated the impact of hedging for each of the above scenarios. In the consolidated results table below, “H+” means with hedging, “H-”means without.

The results of the backtesting showed Information Ratios between 3.0 and 3.72 across the four scenarios tested. The primary effects of hedging were to reduce average profit per share and drawdown amounts.

 

Consolidated results

 SP100, H-,S&P100, H+,S&P100, H-,S&P100, H+,
gap 0.5%gap 0.5%9.32, gap 0.5%9.32, gap 0.5%
HedgingFALSETRUEFALSETRUE
Average Profit per Share0,080,040,080,03
Information Ratio3,313,723,313,3
Net Profit/Loss3,601.192,089.433,668.971,711.99
Total Profit9,058.9211,376.368,788.8610,922.18
Total Loss-5,457.73-9,286.93-5,119.88-9,210.19
Cumulated Profit %3.60%2.09%3.67%1.71%
Max Drawdown-338,39-126,02-339,28-171,73
Max Drawdown %-0.33%-0.12%-0.33%-0.17%
Max Drawdown Date4-May-20124-May-20124-May-201213-Dec-2012
Return/Drawdown Ratio10,6416,5810,819,97
Drawdown Days %62.46%63.36%62.46%66.67%
Max Drawdown Duration36394239
CAGR2.80%1.63%2.85%1.33%
Sharpe Ratio3,313,723,313,3
Annualized Volatility0,850,440,870,41
Sortino Ratio6,178,316,67,19
UPI1,812,281,771,51
Information Ratio3,313,723,313,3
Optimal f388,54845,22380,3812,9
All Trades #1598184215991844
Profitable Trades Ratio0,580,550,570,55
Winning Trades #92210209151019
Losing Trades #676822684825
Average Trade2,251,132,290,93
Average Winning Trade9,8311,159,6110,72
Average Losing Trade-8,07-11,3-7,49-11,16
Avg. Win/ Avg. Loss Ratio1,220,991,280,96
Average Profit per Share0,080,040,080,03
 

 

Detailed results

1. Open position at day open time

twitter1

Performance report:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss3,601.193,768.95-167,77
Total Profit9,058.928,316.09742,82
Total Loss-5,457.73-4,547.14-910,59
Cumulated Profit %3.60%3.77%-0.17%
Max Drawdown-338,39-338,39-206,71
Max Drawdown %-0.33%-0.33%-0.21%
Max Drawdown Date4-May-20124-May-20123-Aug-2012
Return/Drawdown Ratio10,6411,14-0,81
Drawdown Days %62.46%60.66%89.79%
Max Drawdown Duration3640299
CAGR2.80%2.93%-0.13%
Sharpe Ratio3,313,53-0,73
Annualized Volatility0,850,830,18
Sortino Ratio6,176,69-0,93
UPI1,812,31-0,05
Information Ratio3,313,53-0,73
Optimal f388,54422,1-404,48
All Trades #15981449149
Profitable Trades Ratio0,580,590,49
Winning Trades #92284973
Losing Trades #67660076
Average Trade2,252,6-1,13
Average Winning Trade9,839,810,18
Average Losing Trade-8,07-7,58-11,98
Avg. Win/ Avg. Loss Ratio1,221,290,85
Average Profit per Share0,080,1-0,03
Max Conseq. Winners21207
Max Conseq. Losers20207
 

 

2.  Open position at day open time, hedged version

twitter2

Performance report:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss2,089.433,819.76-1,730.33
Total Profit11,376.368,610.222,766.13
Total Loss-9,286.93-4,790.47-4,496.46
Cumulated Profit %2.09%3.82%-1.73%
Max Drawdown-126,02-382,58-1,730.33
Max Drawdown %-0.12%-0.37%-1.73%
Max Drawdown Date4-May-20127-Nov-20121-Mar-2013
Return/Drawdown Ratio16,589,98-1
Drawdown Days %63.36%61.86%89.79%
Max Drawdown Duration3941299
CAGR1.63%2.97%-1.35%
Sharpe Ratio3,723,55-2,04
Annualized Volatility0,440,840,66
Sortino Ratio8,316,7-2,71
UPI2,282,02-0,06
Information Ratio3,723,55-2,04
Optimal f845,22421,79-307,84
All Trades #18421499343
Profitable Trades Ratio0,550,580,43
Winning Trades #1020872148
Losing Trades #822627195
Average Trade1,132,55-5,04
Average Winning Trade11,159,8718,69
Average Losing Trade-11,3-7,64-23,06
Avg. Win/ Avg. Loss Ratio0,991,290,81
Average Profit per Share0,040,1-0,12
Max Conseq. Winners12205
Max Conseq. Losers162011
 

3. Open positions at 9.32am

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Performance report:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss3,668.973,861.32-192,34
Total Profit8,788.868,073.09715,77
Total Loss-5,119.88-4,211.77-908,11
Cumulated Profit %3.67%3.86%-0.19%
Max Drawdown-339,28-339,28-200,74
Max Drawdown %-0.33%-0.33%-0.20%
Max Drawdown Date4-May-20124-May-201221-Feb-2013
Return/Drawdown Ratio10,8111,38-0,96
Drawdown Days %62.46%59.76%89.79%
Max Drawdown Duration4242299
CAGR2.85%3.00%-0.15%
Sharpe Ratio3,313,55-0,84
Annualized Volatility0,870,850,18
Sortino Ratio6,67,22-1,05
UPI1,772,13-0,06
Information Ratio3,313,55-0,84
Optimal f380,3415,71-466,28
All Trades #15991450149
Profitable Trades Ratio0,570,580,48
Winning Trades #91584372
Losing Trades #68460777
Average Trade2,292,66-1,29
Average Winning Trade9,619,589,94
Average Losing Trade-7,49-6,94-11,79
Avg. Win/ Avg. Loss Ratio1,281,380,84
Average Profit per Share0,080,1-0,04
Max Conseq. Winners21207
Max Conseq. Losers202010
 

4. Open positions at 9.32am, hedged version

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Performance report:

ParameterAll TradesLong TradesShort Trades
Net Profit/Loss1,711.993,900.98-2,188.99
Total Profit10,922.188,352.472,569.72
Total Loss-9,210.19-4,451.49-4,758.71
Cumulated Profit %1.71%3.90%-2.19%
Max Drawdown-171,73-414,77-2,188.99
Max Drawdown %-0.17%-0.40%-2.19%
Max Drawdown Date13-Dec-20128-Nov-20121-Mar-2013
Return/Drawdown Ratio9,979,41-1
Drawdown Days %66.67%60.96%89.79%
Max Drawdown Duration3942299
CAGR1.33%3.03%-1.71%
Sharpe Ratio3,33,56-2,5
Annualized Volatility0,410,860,68
Sortino Ratio7,197,21-3,1
UPI1,511,87-0,07
Information Ratio3,33,56-2,5
Optimal f812,9414,81-367,24
All Trades #18441500344
Profitable Trades Ratio0,550,580,44
Winning Trades #1019867152
Losing Trades #825633192
Average Trade0,932,6-6,36
Average Winning Trade10,729,6316,91
Average Losing Trade-11,16-7,03-24,78
Avg. Win/ Avg. Loss Ratio0,961,370,68
Average Profit per Share0,030,1-0,15
Max Conseq. Winners19205
Max Conseq. Losers17209

1.      Open positions at 9.32