The Top Technical Indicators for Options Trading

Addressing Canada’s Employment Insurance Gap For Self-Employed Workers

Source: TD
Ksenia Bushmeneva, Economist
Dated July 15th, 2020

Highlights


Chart 1 - Workers in More Precarious Employment See Steep Job Losses

Chart 2 - COVID-19 Self-employed to Cut Hours Worked Drastically

EI Leaves Many Non-Standard Workers Behind


Chart 3 - Self-employed Workers Much More Likely to Apply for CERB

Chart 4 - Prevalence of Self-employment Varies by Province

What Complicates Offering EI Coverage For Non-Standard Workers


Chart 5 - Maternity and Family Benefits Available to Self-employment

Chart 6 - Sickness, Disability, and Work Injury Coverage Available to Self-Employed

Some Solutions Based on The International Experience


Chart 7 - Unemployment Benefits Coverage Options to Self-employed

Chart 8 - Old-age Pensions Coverage Options Available to Self-employed

Concluding Remarks


References

  1. “Employment Insurance Coverage Survey, 2018”. Statistics Canada. https://www150.statcan.gc.ca/n1/daily-quotidien/191114/dq191114a-eng.htm
  2. Sunil Johal & Erich Hartmann. “Facilitating the Future of Work Through Modernizing EI System”. The Mowat Center. https://ppforum.ca/wp-content/uploads/2019/05/PPF-Modernizing-EI-for-Future-of-Work-April-2019-EN.pdf
  3. Antonia Asenjo and Clemente Pignatti. “Unemployment insurance schemes around the world: Evidence and policy options.” International Labour Office. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_723778.pdf
  4. Sung-Hee Jeon and Yuri Ostrovsky. “The impact of COVID-19 on the gig economy: Short- and long-term concerns”. Statistics Canada. https://www150.statcan.gc.ca/n1/en/pub/45-28-0001/2020001/article/00021-eng.pdf?st=x8kZDLV7
  5. Sunil Johal & Erich Hartmann. “Facilitating the Future of Work Through Modernizing EI System”. The Mowat Center. https://ppforum.ca/wp-content/uploads/2019/05/PPF-Modernizing-EI-for-Future-of-Work-April-2019-EN.pdf Ibid.
  6. “Evaluation of the Employment Insurance Special Benefits for Self-employed Workers”. Employment and Social Development Canada. https://www.canada.ca/en/employment-social-development/corporate/reports/evaluations/2016-ei-special-benefits.html
  7. “The Future of Social Protection: what works for non-standard workers?” OECD. https://www.oecd-ilibrary.org/sites/9789264306943-en/1/2/1/index.html?itemId=/content/publication/9789264306943-en&_csp_=60072f6c81e5afb306d1ad580d284396&itemIGO=oecd&itemContentType=book#chapter-d1e549 Ibid.
  8. “Key Small Business Statistics - January 2019”. Statistics Canada. https://www.ic.gc.ca/eic/site/061.nsf/eng/h_03090.html#point1-3 Ibid.
  9. “Government Response To The Fifth Report Of The Standing Committee on The Status of Women. Interim Report on the Maternity and Parental Benefits Under Employment Insurance: the Exclusion of Self-Employed Workers.” https://www.ourcommons.ca/DocumentVieween/39-1/FEWO/report-5/response-8512-391-19
  10. “Evaluation of the Employment Insurance Special Benefits for Self-employed Workers”. Employment and Social Development Canada. https://www.canada.ca/en/employment-social development/corporate/reports/evaluations/2016-ei-special-benefits.html

End Notes

  1. Since 2010 self-employed workers can voluntarily participate in EI Special Benefit for Self-Employed Workers (SBSE) to gain access to many life event-type benefits accessible to regular employees, such as maternity and paternity leave programs, leave due to sickness or to care for an sick family member. In addition to this, current EI system allows certain exceptions for some non-standard workers. For example some individuals who work independently as barbers, hairdressers, taxi drivers, drivers of other passenger vehicles are eligible to receive benefits through the regular EI program. Fishermen are also included as insured persons under the EI Fishing Regulations. In the case of the self- employed fishermen, EI qualification is tied to income. In order to qualify for up to 26 weeks of benefit, they need to have earned between $2,500 to $4,200 in the last 31 weeks.
  2. The two main reasons for not contributing to the EI program were not having worked in the previous 12 months, and non-insurable employment (which includes self-employment).
submitted by AwesomeMathUse to econmonitor [link] [comments]

Binary options signals, for what?

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submitted by vfxAlert to u/vfxAlert [link] [comments]

[OC] Predicting the 2019-20 Coach of the Year

For those interested, this is part of a very long blog post here where I explain my entire thought process and methodology.
This post also contains a series of charts linked to here.

Introduction

Machine Learning models have been used to predict everything in basketball from the All Star Starters to James Harden’s next play. One model that has never been made is a successful Coach of the Year Predictor. The goal of this project is to create such a model.
Of course, creating such a model is challenging because, ultimately, the COY is awarded via voting and inherently adds a human element. As we will discover in this post, accounting for these human elements (e.g. recency bias, weighing storylines, climate around the team) makes it quite challenging. Having said this, I demonstrate how we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.

Methods

Data Aggregation

First, I created a database of all the coaches referred to in Basketball Reference's coachs index
Coach statistics were acquired from the following template url:
f'https://www.basketball- reference.com/leagues/NBA_{season_end_year}_coaches.html'
Team statistics were acquired from the following template url:
f'https://www.basketball- reference.com/teams/{team_abbreviation}/{season_end_year}.html'
I leveraged the new basketball-reference-scraper Python module to simplify the process.
After some data engineering that I describe completely in the post, I settled on the following features.
Non numerical data Coach Statistics Team Data
COACH SEASONS WITH FRANCHISE SEASON
TEAM SEASONS OVERALL FG
CURRENT SEASON GAMES FGA
CURRENT SEASON WINS FG%
FRANCHISE SEASON GAMES 3P
FRANCHISE SEASON WINS 3PA
CAREER SEASON GAMES 3P%
CAREER SEASON WINS 2P
FRANCHISE PLAYOFF GAMES 2PA
FRANCHISE PLAYOFF WINS 2P%
CAREER PLAYOFF GAMES FT
CAREER PLAYOFF WINS FTA
COY FT%
ORB
DRB
TRB
AST
STL
BLK
TOV
PF
PTS
OPP_G
OPP_FG
OPP_FGA
OPP_FG%
OPP_3P
OPP_3PA
OPP_3P%
OPP_2P
OPP_2PA
OPP_2P%
OPP_FT
OPP_FTA
OPP_FT%
OPP_ORB
OPP_DRB
OPP_TRB
OPP_AST
OPP_STL
OPP_BLK
OPP_TOV
OPP_PF
OPP_PTS
AGE
PW
PL
MOV
SOS
SRS
ORtg
DRtg
NRtg
PACE
FTr
TS%
eFG%
TOV%
ORB%
FT/FGA
OPP_eFG%
OPP_TOV%
DRB%
OPP_FT/FGA
For obtaining a full description of each statistic, please refer to Basketball Reference's glossary.

Data Exploration

First, I computed the correlation between the COY label and all the other features and sorted them. Here are some of the top statistics that correlate with the award along with their Pearson correlation coefficient.
Statistic Pearson coefficient
CURRENT SEASON WINS 0.21764609944203592
SRS 0.20748396385759718
MOV 0.20740447792956693
NRtg 0.20613382194841318
PW 0.20282119218684597
PL -0.19850434198291064
DRtg -0.12967106743277185
ORtg 0.11896730313375109
As expected, the one of the most important features appears to be CURRENT SEASON WINS.
It is interesting the PW and PL correlate so much. This correlation indicates that not only does performance matter, but disparity between expected performance and reality matters significantly as well.
The weight put towards SRS, MOV, and NRtg also provide insight into how the COY is selected. Apparently, not only does it matter whether a team wins or not, but it also matters how they win. For example, the Bucks are typically winning games at an average of ~13 ppg this year, which would heavily favor them.
The high weight toward SRS(defined as a rating that takes into account average point differential and strength of schedule) indicates that it is even more important how a team performs against other challenging opponents. For example, no one should and does care about the Bucks crushing the Warriors, but they should care if they beat the Lakers.
Let's explore the CURRENT SEASON WINS statistic a little more using a box plot.
Box Plot
It appears coaches need to win ~50+ games for an 82 game season in order to be eligible. The exception being Mike Dunleavy’s minimum win season, there were only 50 games since it was a lockout season. Hence, that explains the outlier case.
Another interesting data point is the unfortunate coach who won the most games, but did not win the award. This turned out to be Phil Jackson, one year after his 72 win season in 1995-96 appeared to underperform by winning only 69 games. This, once again, indicates that the COY award takes into account historical performance. Who won instead? Pat Riley, with 61 wins.
Here are some histograms of the MOV and SRS where the blue plots indicate COY's and orange plots indicate NON-COY's.
As expected, COY’s are expected to dominate their teams and not just defeat them.

Oversampling

Before we begin, there is one key flaw in our dataset to look into. Namely, the two classes are not balanced at all.
Looking into the counts we have 1686 non-COY's and 43 COY's (as expected). This disparity can lead to a bad model, so how did I fix this?

SMOTE Oversampling

SMOTE (Synthetic Minority Over-sampling Technique) is a method of oversampling to even the distribution of the two classes. SMOTE takes a random sample from the minority class (COY=1 in our case) and computes it k-nearest neighbors. It chooses one of the neighbors and computes the vector between the sample and the neighbor. Next, it multiplies this vector by a random number between 0 and 1 and adds the vector to the original random sample to obtain a new data point.
See more details here.

Model Selection and Metrics

For this binary classification problem, we'll use 5 different models. Each model had its hyperparameters fine tuned using Grid Search Cross Validation to provide the best metrics. Here are all the models with a short description of each one: * Decision Tree Classifier - with Shannon's entropy as the criterion and a maximum depth of 37. * Random Forest Classifier - using the gini index as the criterion, maximum depth of 35 and maximum number of features of 5. * Logistic Classifier - using the simple ordinary least squares method * Support Vector Machine - with a linear kernel and C=1000 * Neural Network - a simple 6 layer network consisting of 80, 40, 20, 10, 5, 1 nodes, respectively (chosen to correspond with the number of features). I also used early stopping and 20% dropouts on each layer to prevent overfitting.
The metrics that will be used to evaluate our models are: NOTE that: TP=True Positives (Predicted COY and was a COY), TN=True Negatives (Predicted Not COY and was Not COY), FP=False Positives (Predicted COY and was Not COY), FN=False Negatives (Predicted Not COY and was COY)
  • Accuracy - % of correctly categorized instances ; Accuracy = (TP+TN)/(TP+TN+FP+FN)
  • Recall - Ability to categorize (+) class (COY) ; Recall = TP/(TP+FN)
  • Precision - How many of TP were correct ; Precision = TP/(TP+FP)
  • F1 - Balances Precision and Recall ; F1 = 2(Precision * Recall) / (Precision + Recall)

Results

Model Accuracy Recall Precision F1
Decision Tree 0.963 0.977 0.952 0.964
Random Forest 0.985 0.997 0.974 0.986
Logistic 0.920 0.980 0.870 0.922
SVC 0.959 0.991 0.932 0.960
Neural Network 0.898 1.0 0.833 0.909
In terms of all metrics the Random Forest outperforms all. Moreover, the Random Forest boasts an extremely high recall which is our most important metric. When predicting the Coach of the Year, we want to be able to predict the positive class best, which is indicated by a high recall.

Confusion Matrices

Confusion Matrices are another way of visualization our models' performances. Confusion Matrices are nxn matrices where the columns represent the actual class and the rows represent the class predicted by the model.
In the case of a binary classification problem, we obtain a 2x2 matrix with the true positives (bottom right), true negatives (top left), false positive (top right), and false negatives (bottom left).
Here are the confusion matrices for the Decision Tree, Random Forest, Logistic Classifier, SVC, and Neural Network.
Looking at the confusion matrices we can clearly see the disparity between the Random Forest Classifier and other classifiers. Evidently, the Random Forest Classifier is the best option.

Random Forest Evaluation

So what made the Random Forest so good? What features did it use that enabled it to make such accurate predictions?
I charted the feature importances of the Random Forest and plotted them in order here.
Here are some explicit numbers:
Feature % Contribution
CURRENT SEASON WINS 6.569329857214043
SRS 6.368785568654217
PW 6.059094690243399
NRtg 5.5519116066060175
MOV 4.473122672559081
PL 3.643349558354282
... ...
See more in my blog post.
I found it, once again, interesting that SRS such an important feature. It appears that the Random Forest took the correlation predicted earlier into account.
However, we see that other statistics matter significantly too, like CURRENT SEASON WINS, NRtg, and MOV as we predicted.
Something one wouldn’t anticipate is the contribution of factors outside of this season like FRANCHISE and CAREER features. Along these lines, one wouldn’t expect PW or PL to matter too much, but this model indicates that it is one of the most important features.
Let’s also take a look at where the random forest failed. If you recall from the confusion matrix, there was one instance where a COY was classified as NOT COY.
The point is the 1976 COY who was categorized as not COY. This individual was coach Bill Fitch of the 1975-76 Cleveland Cavaliers. He had a modest win record of 49-33 during an overall down year where the top record was the 54-28 Lakers. Looking at the modern era where 60 win records and obscene statistics are put up on a regular basis, I would say that this is not a terrible error on our model's part.
The reason the model may have classified this as a NOT COY instance is due to the fact that the team's statistics aren't all that impressive, but impressive with respect to THAT year. This lack of incorporating other team performances during the year may be the biggest flaw in our model.

Predicting the next Coach of the Year

Unfortunately, we do not have all the statistics for the current year, but we will obtain what we can and modify the data as we did earlier.
Note that all our data is PER GAME, so for all of these statistics, we will just use the PER GAME statistics up to this point (1/21/20)
The only unrealistic statistic is, then, CURRENT SEASON statistics. We will assume CURRENT SEASON GAMES will be 82 for all coaches and obtain CURRENT SEASON WINS from 538's ELO projections on 1/21/20.
Once again, all other stats were acquired via the basketball_reference_scraper Python package.
Team Probability to win COY
MIL 0.49
TOR 0.46
LAC 0.36
BOS 0.31
HOU 0.23
LAL 0.22
DAL 0.22
MIA 0.17
DEN 0.16
IND 0.13
UTA 0.12
PHI 0.12
DET 0.09
NOP 0.07
WAS 0.05
SAS 0.04
ORL 0.04
CHI 0.04
BRK 0.04
POR 0.03
PHO 0.03
OKC 0.03
CHO 0.03
NYK 0.02
SAC 0.01
MIN 0.01
GSW 0.01
ATL 0.01
MEM 0.0
CLE 0.0
This shows the probability of each coach to win COY in the current season. Let's take a look at each of the candidates in order:
1) Milwaukee Bucks & Mike Budenholzer (49%)
Mike Budenholzer was the COY in the 2018-19 season and, objectively, the top candidate for COY this year as well. The Bucks are on a nearly 70-win pace which would automatically elevate him to the top spot.
However, the model is purely objective and fails to incorporate human elements such as the fact that individuals look at the Bucks skeptically as a 'regular season team'. Voters will likely avoid Budenholzer until there is more playoff success.
Moreover, Budenholzer won last year and voters almost never vote for the same candidate twice in a row. In fact, a repeat performance has never occurred in the COY award.
We see here the flaw in the model to not weight the human elements of recency bias against previous COY's and playoff success sufficiently.
2) Toronto Raptors & Nick Nurse (46%)
The Raptors are truly an incredible story this year. No one expected them to be this good. Even the ELO ratings put them at an expected 56 wins this season and be tied for the 3rd best record in the league behind the Lakers and Bucks.
The disparity between what people expected of the Raptors and what has actually transpired (despite injuries to significant players such as Lowry and Siakam) indicates that Nurse would be a viable candidate for COY.
3) Los Angeles Clippers & Doc Rivers (36%)
Despite the model favoring Doc Rivers, I believe it is unlikely that he wins COY due to the current stories circulating around the Clippers.
Everyone came into the season expecting the Clippers to blow everyone out of the water in the playoffs. No one expects the Clippers to exceed expectations during the regular season, especially with their superstars Kawhi Leonard and Paul George being the role models of load management.
4) Boston Celtics & Brad Stevens (31%)
Brad Stevens is another likely candidate for the COY. Not ony are the Celtics objectively impressive, but they also have the narrative on their side. After last year's disappointing performance, people questioned Stevens, but their newfound success without Kyrie Irving has pushed the blame onto Irving over Stevens. Moreover, significant strides have been made by their young players Jaylen Brown and Jayson Tatum vaulting them into Eastern Conference champion contention.
5) Los Angeles Lakers & Frank Vogel (22%)
Being in tune with the current basketball landscape through podcasts and articles, I can tell that Frank Vogel's campaign for the COY is quite strong. Over and over again we hear praises from players like Anthony Davis and Danny Green on the recent Lowe Post on how happy the Lakers are.
With the gaudy record, spotlight and percolating positive energy around the Lakers, Vogel is a very viable pick for the COY.
6) Dallas Mavericks & Rick Carlisle (22%)
Tied with Vogel is Rick Carlisle and the Dallas Mavericks. The Dallas Mavericks, along with the Raptors, are perhaps the most unexpected successful team this season. Looking at their roster, no one stands out except for Porzingis and Doncic, but they still tout a predicted record of 50-32.
Once again, the disparity between expectations and reality puts Carlisle in high contention of the COY.

Conclusion

Overall, I'm quite pleased with the Random Forest model's metrics. The predictions made by the model for the current 2019-20 appear on point as well. The model appears to favor the disparity between what people expected of teams and their performance on the court quite well. However, the flaw in the model is the lack of weighing recent events properly as we saw with coach Budenholzer.
Once again, predicting the COY is a challenging task and we cannot expect the model to be perfect. Yet, we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.
submitted by vagartha to nba [link] [comments]

[OC] Predicting the 2019-20 Coach of the Year

For those interested, this is part of a very long blog post here where I explain my entire thought process and methodology.
This post also contains a series of charts linked to here.

Introduction

Machine Learning models have been used to predict everything in basketball from the All Star Starters to James Harden’s next play. One model that has never been made is a successful Coach of the Year Predictor. The goal of this project is to create such a model.
Of course, creating such a model is challenging because, ultimately, the COY is awarded via voting and inherently adds a human element. As we will discover in this post, accounting for these human elements (e.g. recency bias, weighing storylines, climate around the team) makes it quite challenging. Having said this, I demonstrate how we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.

Methods

Data Aggregation

First, I created a database of all the coaches referred to in Basketball Reference's coachs index
Coach statistics were acquired from the following template url:
f'https://www.basketball- reference.com/leagues/NBA_{season_end_year}_coaches.html'
Team statistics were acquired from the following template url:
f'https://www.basketball- reference.com/teams/{team_abbreviation}/{season_end_year}.html'
I leveraged the new basketball-reference-scraper Python module to simplify the process.
After some data engineering that I describe completely in the post, I settled on the following features.
Non numerical data Coach Statistics Team Data
COACH SEASONS WITH FRANCHISE SEASON
TEAM SEASONS OVERALL FG
CURRENT SEASON GAMES FGA
CURRENT SEASON WINS FG%
FRANCHISE SEASON GAMES 3P
FRANCHISE SEASON WINS 3PA
CAREER SEASON GAMES 3P%
CAREER SEASON WINS 2P
FRANCHISE PLAYOFF GAMES 2PA
FRANCHISE PLAYOFF WINS 2P%
CAREER PLAYOFF GAMES FT
CAREER PLAYOFF WINS FTA
COY FT%
ORB
DRB
TRB
AST
STL
BLK
TOV
PF
PTS
OPP_G
OPP_FG
OPP_FGA
OPP_FG%
OPP_3P
OPP_3PA
OPP_3P%
OPP_2P
OPP_2PA
OPP_2P%
OPP_FT
OPP_FTA
OPP_FT%
OPP_ORB
OPP_DRB
OPP_TRB
OPP_AST
OPP_STL
OPP_BLK
OPP_TOV
OPP_PF
OPP_PTS
AGE
PW
PL
MOV
SOS
SRS
ORtg
DRtg
NRtg
PACE
FTr
TS%
eFG%
TOV%
ORB%
FT/FGA
OPP_eFG%
OPP_TOV%
DRB%
OPP_FT/FGA
For obtaining a full description of each statistic, please refer to Basketball Reference's glossary.

Data Exploration

First, I computed the correlation between the COY label and all the other features and sorted them. Here are some of the top statistics that correlate with the award along with their Pearson correlation coefficient. |Statistic|Pearson coefficient| |--|--| |CURRENT SEASON WINS|0.21764609944203592| |SRS|0.20748396385759718| |MOV|0.20740447792956693| |NRtg|0.20613382194841318| |PW|0.20282119218684597| |PL|-0.19850434198291064| |DRtg|-0.12967106743277185| |ORtg|0.11896730313375109|
As expected, the one of the most important features appears to be CURRENT SEASON WINS.
It is interesting the PW and PL correlate so much. This correlation indicates that not only does performance matter, but disparity between expected performance and reality matters significantly as well.
The weight put towards SRS, MOV, and NRtg also provide insight into how the COY is selected. Apparently, not only does it matter whether a team wins or not, but it also matters how they win. For example, the Bucks are typically winning games at an average of ~13 ppg this year, which would heavily favor them.
The high weight toward SRS(defined as a rating that takes into account average point differential and strength of schedule) indicates that it is even more important how a team performs against other challenging opponents. For example, no one should and does care about the Bucks crushing the Warriors, but they should care if they beat the Lakers.
Let's explore the CURRENT SEASON WINS statistic a little more using a box plot.
Box Plot
It appears coaches need to win ~50+ games for an 82 game season in order to be eligible. The exception being Mike Dunleavy’s minimum win season, there were only 50 games since it was a lockout season. Hence, that explains the outlier case.
Another interesting data point is the unfortunate coach who won the most games, but did not win the award. This turned out to be Phil Jackson, one year after his 72 win season in 1995-96 appeared to underperform by winning only 69 games. This, once again, indicates that the COY award takes into account historical performance. Who won instead? Pat Riley, with 61 wins.
Here are some histograms of the MOV and SRS where the blue plots indicate COY's and orange plots indicate NON-COY's.
As expected, COY’s are expected to dominate their teams and not just defeat them.

Oversampling

Before we begin, there is one key flaw in our dataset to look into. Namely, the two classes are not balanced at all.
Looking into the counts we have 1686 non-COY's and 43 COY's (as expected). This disparity can lead to a bad model, so how did I fix this?

SMOTE Oversampling

SMOTE (Synthetic Minority Over-sampling Technique) is a method of oversampling to even the distribution of the two classes. SMOTE takes a random sample from the minority class (COY=1 in our case) and computes it k-nearest neighbors. It chooses one of the neighbors and computes the vector between the sample and the neighbor. Next, it multiplies this vector by a random number between 0 and 1 and adds the vector to the original random sample to obtain a new data point.
See more details here.

Model Selection and Metrics

For this binary classification problem, we'll use 5 different models. Each model had its hyperparameters fine tuned using Grid Search Cross Validation to provide the best metrics. Here are all the models with a short description of each one: * Decision Tree Classifier - with Shannon's entropy as the criterion and a maximum depth of 37. * Random Forest Classifier - using the gini index as the criterion, maximum depth of 35 and maximum number of features of 5. * Logistic Classifier - using the simple ordinary least squares method * Support Vector Machine - with a linear kernel and C=1000 * Neural Network - a simple 6 layer network consisting of 80, 40, 20, 10, 5, 1 nodes, respectively (chosen to correspond with the number of features). I also used early stopping and 20% dropouts on each layer to prevent overfitting.
The metrics that will be used to evaluate our models are: NOTE that: TP=True Positives (Predicted COY and was a COY), TN=True Negatives (Predicted Not COY and was Not COY), FP=False Positives (Predicted COY and was Not COY), FN=False Negatives (Predicted Not COY and was COY)
  • Accuracy - % of correctly categorized instances ; Accuracy = (TP+TN)/(TP+TN+FP+FN)
  • Recall - Ability to categorize (+) class (COY) ; Recall = TP/(TP+FN)
  • Precision - How many of TP were correct ; Precision = TP/(TP+FP)
  • F1 - Balances Precision and Recall ; F1 = 2(Precision * Recall) / (Precision + Recall)

Results

Model Accuracy Recall Precision F1
Decision Tree 0.963 0.977 0.952 0.964
Random Forest 0.985 0.997 0.974 0.986
Logistic 0.920 0.980 0.870 0.922
SVC 0.959 0.991 0.932 0.960
Neural Network 0.898 1.0 0.833 0.909
In terms of all metrics the Random Forest outperforms all. Moreover, the Random Forest boasts an extremely high recall which is our most important metric. When predicting the Coach of the Year, we want to be able to predict the positive class best, which is indicated by a high recall.

Confusion Matrices

Confusion Matrices are another way of visualization our models' performances. Confusion Matrices are nxn matrices where the columns represent the actual class and the rows represent the class predicted by the model.
In the case of a binary classification problem, we obtain a 2x2 matrix with the true positives (bottom right), true negatives (top left), false positive (top right), and false negatives (bottom left).
Here are the confusion matrices for the Decision Tree, Random Forest, Logistic Classifier, SVC, and Neural Network.
Looking at the confusion matrices we can clearly see the disparity between the Random Forest Classifier and other classifiers. Evidently, the Random Forest Classifier is the best option.

Random Forest Evaluation

So what made the Random Forest so good? What features did it use that enabled it to make such accurate predictions?
I charted the feature importances of the Random Forest and plotted them in order here.
Here are some explicit numbers:
Feature % Contribution
CURRENT SEASON WINS 6.569329857214043
SRS 6.368785568654217
PW 6.059094690243399
NRtg 5.5519116066060175
MOV 4.473122672559081
PL 3.643349558354282
... ...
See more in my blog post.
I found it, once again, interesting that SRS such an important feature. It appears that the Random Forest took the correlation predicted earlier into account.
However, we see that other statistics matter significantly too, like CURRENT SEASON WINS, NRtg, and MOV as we predicted.
Something one wouldn’t anticipate is the contribution of factors outside of this season like FRANCHISE and CAREER features. Along these lines, one wouldn’t expect PW or PL to matter too much, but this model indicates that it is one of the most important features.
Let’s also take a look at where the random forest failed. If you recall from the confusion matrix, there was one instance where a COY was classified as NOT COY.
The point is the 1976 COY who was categorized as not COY. This individual was coach Bill Fitch of the 1975-76 Cleveland Cavaliers. He had a modest win record of 49-33 during an overall down year where the top record was the 54-28 Lakers. Looking at the modern era where 60 win records and obscene statistics are put up on a regular basis, I would say that this is not a terrible error on our model's part.
The reason the model may have classified this as a NOT COY instance is due to the fact that the team's statistics aren't all that impressive, but impressive with respect to THAT year. This lack of incorporating other team performances during the year may be the biggest flaw in our model.

Predicting the next Coach of the Year

Unfortunately, we do not have all the statistics for the current year, but we will obtain what we can and modify the data as we did earlier.
Note that all our data is PER GAME, so for all of these statistics, we will just use the PER GAME statistics up to this point (1/21/20)
The only unrealistic statistic is, then, CURRENT SEASON statistics. We will assume CURRENT SEASON GAMES will be 82 for all coaches and obtain CURRENT SEASON WINS from 538's ELO projections on 1/21/20.
Once again, all other stats were acquired via the basketball_reference_scraper Python package.
Team Probability to win COY
MIL 0.49
TOR 0.46
LAC 0.36
BOS 0.31
HOU 0.23
LAL 0.22
DAL 0.22
MIA 0.17
DEN 0.16
IND 0.13
UTA 0.12
PHI 0.12
DET 0.09
NOP 0.07
WAS 0.05
SAS 0.04
ORL 0.04
CHI 0.04
BRK 0.04
POR 0.03
PHO 0.03
OKC 0.03
CHO 0.03
NYK 0.02
SAC 0.01
MIN 0.01
GSW 0.01
ATL 0.01
MEM 0.0
CLE 0.0
This shows the probability of each coach to win COY in the current season. Let's take a look at each of the candidates in order:
1) Milwaukee Bucks & Mike Budenholzer (49%)
Mike Budenholzer was the COY in the 2018-19 season and, objectively, the top candidate for COY this year as well. The Bucks are on a nearly 70-win pace which would automatically elevate him to the top spot.
However, the model is purely objective and fails to incorporate human elements such as the fact that individuals look at the Bucks skeptically as a 'regular season team'. Voters will likely avoid Budenholzer until there is more playoff success.
Moreover, Budenholzer won last year and voters almost never vote for the same candidate twice in a row. In fact, a repeat performance has never occurred in the COY award.
We see here the flaw in the model to not weight the human elements of recency bias against previous COY's and playoff success sufficiently.
2) Toronto Raptors & Nick Nurse (46%)
The Raptors are truly an incredible story this year. No one expected them to be this good. Even the ELO ratings put them at an expected 56 wins this season and be tied for the 3rd best record in the league behind the Lakers and Bucks.
The disparity between what people expected of the Raptors and what has actually transpired (despite injuries to significant players such as Lowry and Siakam) indicates that Nurse would be a viable candidate for COY.
3) Los Angeles Clippers & Doc Rivers (36%)
Despite the model favoring Doc Rivers, I believe it is unlikely that he wins COY due to the current stories circulating around the Clippers.
Everyone came into the season expecting the Clippers to blow everyone out of the water in the playoffs. No one expects the Clippers to exceed expectations during the regular season, especially with their superstars Kawhi Leonard and Paul George being the role models of load management.
4) Boston Celtics & Brad Stevens (31%)
Brad Stevens is another likely candidate for the COY. Not ony are the Celtics objectively impressive, but they also have the narrative on their side. After last year's disappointing performance, people questioned Stevens, but their newfound success without Kyrie Irving has pushed the blame onto Irving over Stevens. Moreover, significant strides have been made by their young players Jaylen Brown and Jayson Tatum vaulting them into Eastern Conference champion contention.
5) Los Angeles Lakers & Frank Vogel (22%)
Being in tune with the current basketball landscape through podcasts and articles, I can tell that Frank Vogel's campaign for the COY is quite strong. Over and over again we hear praises from players like Anthony Davis and Danny Green on the recent Lowe Post on how happy the Lakers are.
With the gaudy record, spotlight and percolating positive energy around the Lakers, Vogel is a very viable pick for the COY.
6) Dallas Mavericks & Rick Carlisle (22%)
Tied with Vogel is Rick Carlisle and the Dallas Mavericks. The Dallas Mavericks, along with the Raptors, are perhaps the most unexpected successful team this season. Looking at their roster, no one stands out except for Porzingis and Doncic, but they still tout a predicted record of 50-32.
Once again, the disparity between expectations and reality puts Carlisle in high contention of the COY.

Conclusion

Overall, I'm quite pleased with the Random Forest model's metrics. The predictions made by the model for the current 2019-20 appear on point as well. The model appears to favor the disparity between what people expected of teams and their performance on the court quite well. However, the flaw in the model is the lack of weighing recent events properly as we saw with coach Budenholzer.
Once again, predicting the COY is a challenging task and we cannot expect the model to be perfect. Yet, we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.
submitted by vagartha to nbadiscussion [link] [comments]

Chromie's latest rework - a mathematical breakdown, where it failed, and what it can improve on moving forward.

So, as I'm sure some of you have noticed, I post a lot about Chromie, and frankly have for a very long time. To clear the air about my position and expose any bias I might have, I will state that I don't think the hero has ever been OP or "unfun to play against", probably because I played her a lot and thus when going up against one on the enemy team had a pretty good idea of how they would play and how to counter them. I do respect the viewpoint though, and that's why my upcoming post on her rework and possible changes that could be made to it does it's best to keep that in mind. I'm not even close to GM, I'm not even the highest-leveled Chromie on this sub by a long shot, I'm just someone super passionate about the hero who nerded out over the past few days and did some MATHS and shit.

Thus, the stated goals of this post:


-Make Chromie more fun to play for the people who enjoyed playing her prior to this or other reworks. From my general anecdotal experiences in various Twitch chats, it feels like most players including many former pros and GM players say that while the hero is stronger numerically, she's much less fun to play, and IMO her pick rate sitting at a fairly lackluster 11% in Storm League confirms that. She only has a 50% winrate, so it's also clear that she's not excelling even when played by those still enthusiastic about the hero or in situations that would be good for her to be drafted, as is the case with most niche picks. She's not the WORST hero in the game by a long margin, but if the dev comments on her 8-28 buff patch are any indication, it's clear that Blizzard's internal data probably has her struggling a bit too.
-Make her emphasis fully on spells rather than her current combination of spells and sand clone auto-attacks. She's a mage hero, and one with the longest range in the game. Nobody plays her to be a right-click bot, and yet right-clicks are a pretty big part of her damage - more than half her Q DPS if we're being exact here. This obviously isn't Whitemane rework levels of AA-focus, but it's still pretty high and if we look at other mage AA talents as any indication, it's obvious that people don't prefer or like those talents and prefer the focus for mages remain firmly on their spells. Yet because Chromie's AA gimmick is a baseline part of her kit, and not just an individual talent, obviously other parts of her kit were made deliberately weaker to compensate for that. I'd like to see that addressed, and think it would go a loooong way towards achieving stated goal number 1, which is to make her more fun to play.
-Buff her slightly in ways that aren't straight numerical changes. See my above point about why she could probably use some slight tweaking. I think throwing numbers at Chromie is exactly why her last rework (with Temporal Loop/Timely Surprise one-shot shenanigans) grew to be so problematic, and I don't want to see that become the case here and see this sub fill up with posts again about how she needs to be dumpstered. Frankly she's finally almost at a good place in the game and I think with some slight changes she could basically be almost perfect, or at least as perfect as she can be without pissing off people en masse.
-Fix her problematic level 1 and level 18 talent tiers. There are no real choices at 1 or 18, and I personally think this is a shame because if those tiers were fixed, the hero could actually have THREE separate, viable builds IMO - a Q build, a W build, and a generic "spellpower" build that doesn't hyperfocus on either ability but rather buff them both mildly.
-What these changes are NOT proposing are increasing her burst, range, or other factors that players typically associate with frustration when playing against her. I can't stress that enough. I think her current damage is fine for the most part, albeit a bit too reliant on Q pierce at 18 to do anything meaningful. I think Loop is a nice setup ability but the full one-shot potential behind it again, isn't realized until 18 and even then only on the squishiest heroes in the game. As I've said already, Chromie is MOSTLY fine and my goal isn't to make her OP, just improve her talent diversity on certain tiers and make her a bit more fun to play for people who liked playing her previously.

The numbers breakdown:

-Q = 122 damage per second, reliant on a line skillshot that can be blocked by PVE, structures, or other enemy heroes other than your intended target. 20 mana cost, 15 range. Q damage per second calculated assuming you land both the primary Q missile as well as the sand clone Q missile that does 40% damage.
-W, assuming all 3 drops land on a single-target = 43.71 damage per second. Unlikely that all 3 drops will hit, however has the benefit of being AOE. That said, even assuming a unicorn situation where all 5 members of the enemy team potato into all 3 hits of the ability it is still only a baseline 218.55 DPS...that's sad, IMO. Slightly less than double damage per second compared to hitting both Q missiles, a situation that is far more likely to occur, though obviously significantly more mana-friendly due to W's cooldown vs Q's cooldown. 65 mana cost, 13 range, but due to the nature of the vector can reach around 21 range (rough estimate). I wouldn't consider the max range that valuable given the drop delay - enemies will be able to predict the drop of the third hit and dodge it completely, making that range useless.
-AAs = 76.5 damage per second during Q setup period (basically casting a Q first to set up the sand clone), then 114.8 damage per second for every second thereafter, point and click, can't miss barring blinds or evade abilities/talents. No mana cost, 7.5 range.
So we can already see two things: number one, that Chromie's AAs are not an insignificant part of her damage, even assuming you are a god and can constantly land both the primary Q missile AND the secondary Q missile, and number two, that her W unless operating under basically impossible conditions is very lackluster damage-wise. This leads to a pretty boring playstyle where your Q feels like a clone setup for braindead free AA damage until level 18 when suddenly your power spikes off the charts, and your W feels like something you just use for waveclear, and MAYBE to pressure an enemy team in a chokepoint.
A "waveclear-only" ability isn't strictly a problem for a mage except that Chromie's has the unique advantage of getting completely dicked by long CC chains - unless someone is actually capable of moving you will only hit someone with 2 W hits (there's a small Venn diagram area where the hitboxes of two Ws overlap). This "Venn diagram" area is actually so small that you cannot fit two hero hitboxes into it, meaning you'll only get that particular value on a singular target, even if there are multiple people caught in something like a RoF/Mosh Pit. Other mages do not have this issue, not even Gul'dan due to the size and drop speed of his vector spell. Note that I use Corruption as the comparison here because not only are Corruption and Dragon's Breath both triple-drop circular vector spells, they also share an identical cooldown and identical damage on each hit (lmao). The original comparisons to new Dragon's Breath being "Sand Corruption" were not far off, even though Chromie mains were called hyperbolic for saying as such.

Fundamental problems with Chromie's rework:

-To put it simply, auto-attacks on sand clone are too strong and thus make the level 1 Q talent where you gain a third sand clone auto-attack too valuable to pass up. They also make Mobius Loop at 5 wholly unappealing because it does fuck-all for your sand clone autos, though Proper Greeting and Bronze Talons mathematically both have their strong suits and have a healthy competition with one another (tl;dr Talons is good if you have a target you want to hit who is unlikely to be affected by CC or your spells and the enemy has an easy fat target like tanks/Azmodan/Fenix that you can nail with Q to get the proc - so basically hypermobile heroes and heroes with spell shields who can pop them to as a response to Proper Greeting procs, OR in comps where you have literally 0 CC outside of your own Trap slow and Slowing Sands).
-Because the level 1 Q quest is so good, it basically makes taking Piercing Sands at 18 somewhat inevitable to get maximum value out of the triple Qs you can set up. Bear in mind, I think Chromie's Q hitting PVE was fundamentally a nerf and I don't think it was a good idea in retrospect. I get that the goal was to "increase counterplay/reduce frustration" by making it so that players could juke behind minion waves or their buildings and not get gibbed over walls and whatnot, but because of this change the hero is now very reliant on her AA gimmick to deal consistent damage AND you could argue that she honestly doesn't feel like a real hero until you get pierce at 18. Regardless, I understand that this change is probably here to stay and I think there are ways we can work around her jank power distribution while still keeping the PVE Q block in. (More on this in "Proposed Changes" below.)
-Building into W just feels like shit because of how lackluster the damage is. It's clear that her power pie was distributed based on the rare unicorn chance that she will hit all 3 Ws on a single target, but given the winrate and pickrate of W talents we can honestly see that this isn't a winning strategy, especially when Q gives you more consistent value (and probably would even if sand clone AAs were removed tbh). To clarify, W would have to hit at least 6 times (as in, any combination of players in any combination of hits that would equal 6) to match the DPS of just Chromie's primary Q missile...saying nothing of her sand clone AA damage and secondary Q missile damage. Obviously Chromie talenting into W doesn't mean she can't use Q, so I did the math on simply the bonus 3rd sand clone AA and tertiary Q missile, and landing each one is an increase of 67.66 DPS. W talents are only an increase of 32.14 DPS, though bear in mind this is for 3 hits on a single target only. Here's where it gets stupid: the third sand clone AA by itself is an increase of 32.8 DPS. This means that unless you are consistently hitting at least two people with W for multiple hits of the ability or you simply cannot stay in 7.5 range to AA* at any given time it is universally better to build into Q talent at 1 solely for the extra AA, even if you never land a single tertiary Q missile in your goddamned life. See where this is a problem?

The Proposed Changes (aka the meat of this post):

Suffice it to say, I think Dragon's Breath at this point needs a fundamental change to its design to not only be effective at what it is supposed to do, but also in general just to be fun to use. Its current design is a move that largely punishes potatoes or players stuck in a "no-win" situation where they either have to eat the second and third W hits or face greater threats like CC chains for trying to facetank it by not moving during the first hit. My proposed change is to simply turn Chromie's W into a single ability with 3 separate charges, keeping its current damage, radius, and drop speed, giving each charge an individual mana cost and allowing all charges to return at once similar to Junkrat Q/Zarya grenade. What this does by default is make W talents more appealing by allowing good Chromie players who can "read" their opponents correctly the option to build into that move and deal respectable damage, especially as an alternative to matchups where Q may not be a reliable source of damage (into summoners, Anub'arak, Nova/Samuro/Rexxar, etc). What this would NOT do is bring back Loop one-shots, because with a .75 drop delay the Looped target will only eat one charge of the damage same as they do now...they will have to be more careful on how they move immediately after Loop though to make sure Chromie doesn't read their movement and plop another in their path. I think this is a fair change, even if it comes with slight numerical nerfs to W to compensate. (I don't think she will need this though due to other proposed changes I have below.)
To follow to that, I think that the sand clone gimmick should either be scrapped, or if nothing else the AA component of it needs to be removed. I actually like the sand clone thematically and am fine with it mirroring Q - I think it would be neat if it could go further and also mimic her W, adding further dimension to Chromie's "prediction mage" theme. I get that two invisible Ws dropping anywhere would definitely be tilting AF for the enemy team though, so this change could either come with W returning to being visible or, as I stated earlier, simply keep the sand clone mirroring only Chromie's Q and nothing else.
What this change would aim to do is frankly tone down how stupidly effective her AAs are compared to her actual abilities. I can't think of a single person who originally picked up this hero to be an auto-bot; IMO there's Hanzo and Junkrat for that long-range/AA hybrid flavor and they were both designed around that purpose (Junkrat with splash AAs, Hanzo with AA-related talents/build). Nothing about Chromie's 2016 release hinted at "right-click guys for the same DPS as your spells", not even OG Bronze Talons which for the record I do think suffered from a lack of meaningful competitive options until Timely Surprise in 2018 and that's why it was picked all the time.
Regardless, I think Sand Blast should have 1-target pierce made baseline if sand clone autos are removed. I think if you remove clone autos the damage numbers will simply default to W being unilaterally the best build outside of playing for the very binary power spike at level 18 from Piercing Sands, which I don't think is super healthy for her design. Chromie's autos paired with clone autos were actually one of the few ways the hero could damage people standing behind a Zagara minion, and if you remove the strength of those she'll need some sort of extra power to compensate, IMO. That said, this is a change that might not wholly be necessary if other aspects of her kit are made better, such as the W change I proposed above. This could be a wait-and-see sort of change, I admit.
Alternately, if 2-target Q is too strong baseline, it could be an alternate reward to her level 1 quest, removing the tertiary missile altogether. Incidentally this would give her more power in the early game by completing the quest, while not completely dumpstering the 18 talent's viability, while also giving players the freedom to pick something else on 18 if they feel like 2-target Qs are good enough to pressure the enemy team. I think this is a pretty healthy change that makes her a little less hot-and-cold when it comes to damage dealing, especially when it comes to early game vs late game.
Her level 5 talent tier will need a bit more parity, especially if clone autos are removed. Bronze Talons' power budget currently factors in the extra autos from clone, including if you complete the level 1 Q quest - while I like the synergy between the two talents, I think if clone autos are removed Bronze Talons as a whole will need to be redesigned. I think the simplest change is to bake in a functionality that mirrors the strength of her old sand clone when this rework originally went live, which is that sand clone's Q damage is increased from 40% to 50%. This was originally nerfed in an attempt to increase parity between her level 1 talents, but failed miserably, and I think with the other changes I've proposed here the extra 10% clone damage could probably come back.
Mobius Loop in its current form is really bad, and even with a full mana refund or something added in will probably continue to be bad. While my proposed changes to W would make it get more value by default, I think we can do better by making the ability an active on a 60-90 second cooldown that returns all her W charges at once instead. This seems kind of strong and I'm open to feedback on this one, but I think without some bold changes to W and W talents that Q will still be the dominant build and this was just one thing I could think of to make W more appealing.
For her heroics, I think Slowing Sands could probably use the level 18 functionality made baseline to make it more appealing compared to Loop. I like Sands as an easy way to proc her current Greeting talent at 5, but since the straight nerfs that came with her latest rework that made it only a 5% slow baseline it just has really minimal use outside of Greeting procs, and you can rely on your Time Trap slow talent or allies for those procs. It could probably use some slight buffs. I honestly have no idea what to make the 18 upgrade if the spell armor reduction is added to the level 8 version. Suggestions more than welcome here.
Her survivability options on 11 are actually okay except for Here and There. This is where I actually make a pretty bold change for her baseline Q sand clone, which is actually that rather than leaving a clone where Chromie previously cast her last Q, sand clone is now an active that Chromie can choose to place anywhere within 7.5 radius of her, with say a 10-20 second cooldown. I think this is a bit of a "berf" in that it's probably a nerf to fairly aggressive/mobile Chromie players who are constantly moving and placing new Qs in very forward locations, but probably a buff to more strategic Chromies who want to aim their shots at the perfect angle to hit their intended target. The change is kind of playstyle-defining and I'm not 100% sure it's what she needs, but I also think this could not only serve as a way of making Here and There more appealing (by giving more control over where you teleport to), but also a decent workaround to Q being blocked by various things until level 18. This might serve to reduce reliance on her level 18 pierce talent as well, which I always welcome.
Fast Forward is in a similarly poor spot compared to the other 14 options. By 14 Chromie basically has zero mana tension, making half the talent effectively useless. With some simple MATHS we can see what sort of DPS increase Fast Forward is vs the other two:
-Fast Forward, assuming you always get the proc and ALWAYS hit your secondary Q missile from sand clone, turns Q from a 122 DPS move to an 282 DPS move. This however doesn't affect her W, so the overall total DPS is 282 from this talent under perfect conditions - basically spamming because the CDR is so fast, never moving, and always hitting Q WITH also hitting the secondary Q missile. Bear in mind that this talent does not proc from a long-range sand clone Q, meaning you must position in such a way that the primary missile will always travel 50% of its distance. Shifting Sands works similarly so I don't factor that into DPS contributions, though Quantum Overdrive does in fact get CDR from all sand clone hits, making its actual DPS higher than what I calculated in theory.
-Quantum Overdrive, assuming you always achieve the CDR from Sand Blasts for it (again, we're assuming perfect conditions for everything here), will be available every 24 seconds and is a flat 25% spellpower increase for 8 seconds - or basically, two Q casts and one W cast (we won't factor in Loop usage here for simplicity's sake). Factoring in the CD, this is a 54.1 DPS increase - not nearly as impressive, though worth noting that the upfront damage is much larger AND and as I stated above, you actually get CDR from every sand clone missile, meaning its value increases exponentially with the Q quest on level 1 as well as how good you are at landing multi-Q hits.
-Shifting Sands has math that is much harder to calculate due to its stacking increase, but because I love you all I did. Suffice it to say, assuming you do not let the stacking spellpower buff fall off, it is a 151 DPS increase on Q until it ramps up at 38.5 seconds (not directly, mind you, this is just on average, I wasn't calculating 10 individual DPS increases and pasting them here lol), then a 181 DPS increase for each second it remains fully stacked. This is actually less than Fast Forward, though you can also add in 58ish DPS for W hits (again, assuming all 3 hits on a single target only), making the total value of the talent 209 DPS until stacked, then 249 DPS after.
So this is still less than Fast Forward, right? So why the hell are people picking it, you might ask? Simply because at any point if you miss a Q, the entire value of Fast Forward is lost, whereas with Shifting Sands you retain the spellpower unless you whiff a second Q hit in that original 8-second window. Basically, you have to land 1 Q every 8 seconds to get value out of Shifting Sands. Fast Forward, if you ever don't proc it, you don't have a level 14 talent during that and the window between your next Q.
(That said, doing this math actually made me realize that Fast Forward might be better than I think it is and I might give it a try in my next match, hehe.)
To be honest, all Fast Forward might need to be competitive is for the CDR to occur even if a secondary or tertiary Q missile hits, rather than requiring it to be the primary one. Since Fast Forward only buffs Q and not W, I think this is a fair buff that would reward consistently good aim with more sustained damage relative to Q, while still leaving Shifting Sands the favorite for a more forgiving slow spellpower increase and Quantum Overdrive for Loop/burst shenanigans.
As a final point, while I think that a lot of my proposed baseline changes to the hero would by default make Piercing Sands mildly less appealing at 18, I'm still not sure what to do to completely increase her talent diversity on this tier, especially because her ulti upgrades are frankly kind of lackluster. This is an area where I sort of ran out of steam brainstorming, to be completely honest with you. I actually think both of her ultis are really boring baseline and couldn't come up with interesting 18 upgrades for either. Lend me your brains on this one, HotS subreddit! What would you do to make Chromie's Piercing Sands talent less appealing while making her ulti upgrades more appealing? I'd love to see what we can all come up with!


In Closing:

These changes, whether implemented as a whole or by their parts in certain areas, would IMO serve to give the hero three viable build paths:
-1st build path, "Q path": Q talent at 1 for second pierce target, newly-designed Bronze Talons at 5 for the extra clone Q damage, Here and There at 11 for mobility shenanigans thanks to being able to manually place your clone target in advantageous locations, Fast Forward at 14 to put a further emphasis on landing good Qs and edging out W as a big part of your damage pie, and Piercing Sands at 18 for obvious reasons.
-2nd build path, "W path": W talent at 1, Mobius Loop at 5, any survivability option at 11, Quantum Overdrive at 14 to take advantage of the new Mobius Loop W charge reset, Piercing Sands at 18 could still be appealing with this build but I also think Blessing of the Bronze or Temporal Loop upgrade depending on your ulti choice could be good here.
And a final "generic spellpower path" that doesn't build strictly into improving Q or W separately and would instead opt to modestly increase the damage of both, with perhaps less binary gains than building fully into one spell: which would feature Timewalker's Pursuit at 1, Proper Greeting at 5, any survivability option at 11, Shifting Sands at 14 (though I think Quantum could work here too), and maybe Q pierce or Blessing at 18.
While I appreciate anyone who has read this far, for those who don't have the fortitude here is a simple tl;dr:
-W is no longer 3 vectored hits that drop at the same time but rather 3 circular AOEs that can be dropped individually but all return on the same cooldown similar to Junk Q/Zarya Q. If this is OP, reduce the size of the AOE so opponents have greater windows to dodge, or (possibly) make the landing point visible again (less a fan of this though tbh).
-sand clone autos removed to put more damage back into her spells and less into boring right-click talents/builds (her right-click damage is about 2/3rds the DPS of Q!). Her current design with sand clone AAs means that the level 1 Q quest that adds an extra sand clone is dramatically more value than the other two quests simply for the extra AA damage, which is a bongos DPS increase in comparison. Rather than gut the entire sand clone thematic, which I think is cool, I think simply removing the AAs would do loads for her talent diversity.
-manual sand clone placement on a modest cooldown to work around the newly-implemented Q PVE block. An alternate solution is to change her level 1 Q quest from a third sand clone to a singular pierce upon completion, allowing her once the quest is completed to have Q hit two targets for each missile instead of just 1. Either way if sand clone placement is made manual I think the level 1 Q quest will have to be changed, because otherwise manually placing two clones would probably make her a bit too "busy", and I don't want her to be butt-blastingly difficult to play for anyone who isn't a main. I think both manual Q placement and changing the Q quest at 1 to reward one-unit pierce would be fine, without sending her balance out of control, but obviously I can't say for sure.
-Some talent retooling and rebalancing to promote better diversity on certain tiers (namely 1, 14 and 18). I could use some help on brainstorming better ult upgrades at 18 than what she currently has.
-NO increases to damage or range except incidental increases from things like easier Q secondary missile hits or more control over W landing points. These would be offset by the removal of sand clone autos, as well as less reliance on Q pierce talent at 18, making her power curve less exponential.
-no changes to Time Trap, which I think after the rework is a beautifully-designed ability as-is and the talent tier representing it (level 2) is one of the most balanced talent tiers we actually have in the entire Heroes of the Storm game.
If you made it through all this without rage-quitting over the mere mention of Chromie, I appreciate your time, and am looking forward to having some discussions here. Are these changes too much? What do you think the hero needs to become a bit more fun to play, while not dramatically increasing her power level or the frustration factor that other people have playing against her? How would you improve her talent diversity with her current design? I look forward to seeing what everyone has to say!
submitted by Thundermelons to heroesofthestorm [link] [comments]

Results of the /r/MBTI survey (on religious and/or spiritual beliefs)!

Hello everyone, I am proud to present to you the results of the survey I conducted on May 3rd. The survey was posted both here and on /Samplesize. The survey consisted of four questions: your MBTI type, your gender, your nationality and your self-stated religious/spiritual belief (or lack thereof). The aim of the survey was to determine whether some types were more likely to identify as having religious and/or spiritual beliefs. However, it turns out the survey is also great as a general demographic survey (which types people identify most as on this subreddit and so on. I don't remember the last time such a survey was done here, but if any clever user knows, comment it below please!)
So, without further ado, here are the results. View the full results (includes pie charts, mmh, pie) of the survey here and here.
Overall, 125 people have filled out the survey. Thank you so much to all of those that did - as well as all those reading this willing to participate in future surveys!

Type representation:

Participants of each of the 16 types have responded (luckily!), although, as is to be expected, not everyone is equally represented. These results may give you an idea of what types are over- or underrepresented in this community.
Here are the top three most and least represented types:
Most represented:
  1. INFP (26 respondents, 20,8% of all respondents)
  2. INTP (22 respondents, 17,2%)
  3. INFJ (15 respondents, 11,2%)
Least represented:
  1. ENFJ and ENTJ (2 respondents each, equals to 1,6% of all respondents)
  2. ISFJ (3 respondents, 2,4%)
  3. ESTJ, ESTP and ISTP (4 respondents of each, 3,2%)
Overall, Thinking and Feeling-types were equally well-represented, and there's only a reasonably small majority of Perceiving-types (65,6%). However, Sensors (20,8%) and Extroverts (26,4%) are especially underrepresented. It's possible that there are simply less Sensor and Extrovert-types on the internet (or on /mbti), but it could also have to do with the bias against Sensation, and the phenomenon of Extroverts self-identifying as Introverts. To give you an idea of underrepresentation, 8 of the 16 MBTI types made up only 22% of the participants!

Gender differences

Contrary to the general trend on Reddit, a majority of participants were female (58,5%) as opposed to 39% male participants. Additionally, two persons identified as non-binary and one as having no gender.
I was not able to find many significant gender differences other than that. Females and males were about as likely to identify as introverts, intuitives, or perceiving types. There was also little gender difference in the percentage of those who identify as irreligious, or as spiritual/religious.
However, males were about 9% likelier than females to explicitly identify as atheist (as opposed to merely agnostic or secular). Additionally, females were significantly (24,3%) more likely to identify as a Feeling-type (xxFx) than males, confirming Carl Jung's original theory that the feeling-types are typically more common in women.

Nationality

There have been participants from 28 different countries, however half (51,2%) of them were from the USA and 70,4% come from English-speaking countries.

Religious and/or spiritual beliefs

Atheism was by far the most frequent response (41,6%), with agnosticism second (13,6%). Overall, the irreligious options (atheism, agnosticism, and secular humanism) amounted to a significant majority (62,4%) of all answers. The remaining options, which do imply at least some sort of religious and/or spiritual belief, were then 32%. Among the established religions, Christianity and Islam make up 15,2% and 6,4% of replies respectively. There have been no Hindu, Buddhist or Jewish responses.
Interestingly, two respondents identify as Wiccan, and one (the final response) as Unitarian Universalist! And I'd like to give a shoutout to the female US ISFP who wrote this in the answer box: 'I believe we are a baby universe created by someone else in wth an atom smasher in another universe'.
While gender appears to make little difference predicting religious belief, the same can't neccesarily be said for type. Here are the three types most likely to identify as atheist (when compared to the rest of responses by respondents of the same type. For instance, if ten ENTPs participate, and nine of them choose 'Atheism', that type is 90% likely to choose atheism. For representative reasons, types with less than 5 participants were excluded from these comparisons)
  1. ISTJ (100% of 6 respondents)
  2. ENTP (63,6% of 7 respondents)
  3. INTJ (54,5% of 6 respondents)
This tendency becomes even clearer if we also include replies indicating agnosticism and secular humanism:
  1. ISTJ (100% of 6 respondents)
  2. INTJ (90,9% of 10 respondents)
  3. INTP (85,7% of 22 respondents)

Notes for future survey

It has been rightly pointed out that it'd been better if I made it possible to select multiple stated convictions (e.g. secular humanism + atheism, or Christianity + pantheism). Also, I was thinking of a scale question, where you would have to indicate how important religiosity is in your personal life.

Finally, I've been thinking, perhaps it could also be worthwhile instead of a followup survey about religion, to do one about another theme. But what could it be? About political preference? Sexuality? Music taste? Personal interests? Favourite type of pizza? All of this at once? Any suggestions in the comments would be greatly appreciated!
submitted by RockoRocks to mbti [link] [comments]

Beginner’s Guide to BitMEX

Beginner’s Guide to BitMEX

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Founded by HDR Global Trading Limited (which in turn was founded by former bankers Arthur Hayes, Samuel Reed and Ben Delo) in 2014, BitMEX is a trading platform operating around the world and registered in the Seychelles.
Meaning Bitcoin Mercantile Exchange, BitMEX is one of the largest Bitcoin trading platforms currently operating, with a daily trading volume of over 35,000 BTC and over 540,000 accesses monthly and a trading history of over $34 billion worth of Bitcoin since its inception.

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Unlike many other trading exchanges, BitMEX only accepts deposits through Bitcoin, which can then be used to purchase a variety of other cryptocurrencies. BitMEX specialises in sophisticated financial operations such as margin trading, which is trading with leverage. Like many of the exchanges that operate through cryptocurrencies, BitMEX is currently unregulated in any jurisdiction.
Visit BitMEX

How to Sign Up to BitMEX

In order to create an account on BitMEX, users first have to register with the website. Registration only requires an email address, the email address must be a genuine address as users will receive an email to confirm registration in order to verify the account. Once users are registered, there are no trading limits. Traders must be at least 18 years of age to sign up.
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However, it should be noted that BitMEX does not accept any US-based traders and will use IP checks to verify that users are not in the US. While some US users have bypassed this with the use of a VPN, it is not recommended that US individuals sign up to the BitMEX service, especially given the fact that alternative exchanges are available to service US customers that function within the US legal framework.
How to Use BitMEX
BitMEX allows users to trade cryptocurrencies against a number of fiat currencies, namely the US Dollar, the Japanese Yen and the Chinese Yuan. BitMEX allows users to trade a number of different cryptocurrencies, namely Bitcoin, Bitcoin Cash, Dash, Ethereum, Ethereum Classic, Litecoin, Monero, Ripple, Tezos and Zcash.
The trading platform on BitMEX is very intuitive and easy to use for those familiar with similar markets. However, it is not for the beginner. The interface does look a little dated when compared to newer exchanges like Binance and Kucoin’s.
Once users have signed up to the platform, they should click on Trade, and all the trading instruments will be displayed beneath.
Clicking on the particular instrument opens the orderbook, recent trades, and the order slip on the left. The order book shows three columns – the bid value for the underlying asset, the quantity of the order, and the total USD value of all orders, both short and long.
The widgets on the trading platform can be changed according to the user’s viewing preferences, allowing users to have full control on what is displayed. It also has a built in feature that provides for TradingView charting. This offers a wide range of charting tool and is considered to be an improvement on many of the offering available from many of its competitors.
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Once trades are made, all orders can be easily viewed in the trading platform interface. There are tabs where users can select their Active Orders, see the Stops that are in place, check the Orders Filled (total or partially) and the trade history. On the Active Orders and Stops tabs, traders can cancel any order, by clicking the “Cancel” button. Users also see all currently open positions, with an analysis if it is in the black or red.
BitMEX uses a method called auto-deleveraging which BitMEX uses to ensure that liquidated positions are able to be closed even in a volatile market. Auto-deleveraging means that if a position bankrupts without available liquidity, the positive side of the position deleverages, in order of profitability and leverage, the highest leveraged position first in queue. Traders are always shown where they sit in the auto-deleveraging queue, if such is needed.
Although the BitMEX platform is optimized for mobile, it only has an Android app (which is not official). There is no iOS app available at present. However, it is recommended that users use it on the desktop if possible.
BitMEX offers a variety of order types for users:
  • Limit Order (the order is fulfilled if the given price is achieved);
  • Market Order (the order is executed at current market price);
  • Stop Limit Order (like a stop order, but allows users to set the price of the Order once the Stop Price is triggered);
  • Stop Market Order (this is a stop order that does not enter the order book, remain unseen until the market reaches the trigger);
  • Trailing Stop Order (it is similar to a Stop Market order, but here users set a trailing value that is used to place the market order);
  • Take Profit Limit Order (this can be used, similarly to a Stop Order, to set a target price on a position. In this case, it is in respect of making gains, rather than cutting losses);
  • Take Profit Market Order (same as the previous type, but in this case, the order triggered will be a market order, and not a limit one)
The exchange offers margin trading in all of the cryptocurrencies displayed on the website. It also offers to trade with futures and derivatives – swaps.

Futures and Swaps

A futures contract is an agreement to buy or sell a given asset in the future at a predetermined price. On BitMEX, users can leverage up to 100x on certain contracts.
Perpetual swaps are similar to futures, except that there is no expiry date for them and no settlement. Additionally, they trade close to the underlying reference Index Price, unlike futures, which may diverge substantially from the Index Price.
BitMEX also offers Binary series contracts, which are prediction-based contracts which can only settle at either 0 or 100. In essence, the Binary series contracts are a more complicated way of making a bet on a given event.
The only Binary series betting instrument currently available is related to the next 1mb block on the Bitcoin blockchain. Binary series contracts are traded with no leverage, a 0% maker fee, a 0.25% taker fee and 0.25% settlement fee.

Bitmex Leverage

BitMEX allows its traders to leverage their position on the platform. Leverage is the ability to place orders that are bigger than the users’ existing balance. This could lead to a higher profit in comparison when placing an order with only the wallet balance. Trading in such conditions is called “Margin Trading.”
There are two types of Margin Trading: Isolated and Cross-Margin. The former allows the user to select the amount of money in their wallet that should be used to hold their position after an order is placed. However, the latter provides that all of the money in the users’ wallet can be used to hold their position, and therefore should be treated with extreme caution.
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The BitMEX platform allows users to set their leverage level by using the leverage slider. A maximum leverage of 1:100 is available (on Bitcoin and Bitcoin Cash). This is quite a high level of leverage for cryptocurrencies, with the average offered by other exchanges rarely exceeding 1:20.

BitMEX Fees

For traditional futures trading, BitMEX has a straightforward fee schedule. As noted, in terms of leverage offered, BitMEX offers up to 100% leverage, with the amount off leverage varying from product to product.
However, it should be noted that trading at the highest leverages is sophisticated and is intended for professional investors that are familiar with speculative trading. The fees and leverage are as follows:
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However, there are additional fees for hidden / iceberg orders. A hidden order pays the taker fee until the entire hidden quantity is completely executed. Then, the order will become normal, and the user will receive the maker rebate for the non-hidden amount.

Deposits and Withdrawals

BitMEX does not charge fees on deposits or withdrawals. However, when withdrawing Bitcoin, the minimum Network fee is based on blockchain load. The only costs therefore are those of the banks or the cryptocurrency networks.
As noted previously, BitMEX only accepts deposits in Bitcoin and therefore Bitcoin serves as collateral on trading contracts, regardless of whether or not the trade involves Bitcoin.
The minimum deposit is 0.001 BTC. There are no limits on withdrawals, but withdrawals can also be in Bitcoin only. To make a withdrawal, all that users need to do is insert the amount to withdraw and the wallet address to complete the transfer.
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Deposits can be made 24/7 but withdrawals are processed by hand at a recurring time once per day. The hand processed withdrawals are intended to increase the security levels of users’ funds by providing extra time (and email notice) to cancel any fraudulent withdrawal requests, as well as bypassing the use of automated systems & hot wallets which may be more prone to compromise.

Supported Currencies

BitMEX operates as a crypto to crypto exchange and makes use of a Bitcoin-in/Bitcoin-out structure. Therefore, platform users are currently unable to use fiat currencies for any payments or transfers, however, a plus side of this is that there are no limits for trading and the exchange incorporates trading pairs linked to the US Dollar (XBT), Japanese Yen (XBJ), and Chinese Yuan (XBC).
BitMEX supports the following cryptocurrencies:
  • Bitcoin (XBT)
  • Bitcoin Cash (BCH)
  • Ethereum (ETH)
  • Ethereum Classic (ETC)
  • Litecoin (LTC)
  • Ripple Token (XRP)
  • Monero (XMR)
  • Dash (DASH)
  • Zcash (ZEC)
  • Cardano (ADA)
  • Tron (TRX)
  • EOS Token (EOS)
BitMEX also offers leverage options on the following coins:
  • 5x: Zcash (ZEC)
  • 20x : Ripple (XRP),Bitcoin Cash (BCH), Cardano (ADA), EOS Token (EOS), Tron (TRX)
  • 25x: Monero (XMR)
  • 33x: Litecoin (LTC)
  • 50x: Ethereum (ETH)
  • 100x: Bitcoin (XBT), Bitcoin / Yen (XBJ), Bitcoin / Yuan (XBC)

Trading Technologies International Partnership

HDR Global Trading, the company which owns BitMEX, has recently announced a partnership with Trading Technologies International, Inc. (TT), a leading international high-performance trading software provider.
The TT platform is designed specifically for professional traders, brokers, and market-access providers, and incorporates a wide variety of trading tools and analytical indicators that allow even the most advanced traders to customize the software to suit their unique trading styles. The TT platform also provides traders with global market access and trade execution through its privately managed infrastructure and the partnership will see BitMEX users gaining access to the trading tools on all BitMEX products, including the popular XBT/USD Perpetual Swap pairing.
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The BitMEX Insurance Fund

The ability to trade on leverage is one of the exchange’s main selling points and offering leverage and providing the opportunity for traders to trade against each other may result in a situation where the winners do not receive all of their expected profits. As a result of the amounts of leverage involved, it’s possible that the losers may not have enough margin in their positions to pay the winners.
Traditional exchanges like the Chicago Mercantile Exchange (CME) offset this problem by utilizing multiple layers of protection and cryptocurrency trading platforms offering leverage cannot currently match the levels of protection provided to winning traders.
In addition, cryptocurrency exchanges offering leveraged trades propose a capped downside and unlimited upside on a highly volatile asset with the caveat being that on occasion, there may not be enough funds in the system to pay out the winners.
To help solve this problem, BitMEX has developed an insurance fund system, and when a trader has an open leveraged position, their position is forcefully closed or liquidated when their maintenance margin is too low.
Here, a trader’s profit and loss does not reflect the actual price their position was closed on the market, and with BitMEX when a trader is liquidated, their equity associated with the position drops down to zero.
In the following example, the trader has taken a 100x long position. In the event that the mark price of Bitcoin falls to $3,980 (by 0.5%), then the position gets liquidated with the 100 Bitcoin position needing to be sold on the market.
This means that it does not matter what price this trade executes at, namely if it’s $3,995 or $3,000, as from the view of the liquidated trader, regardless of the price, they lose all the equity they had in their position, and lose the entire one Bitcoin.
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Assuming there is a fully liquid market, the bid/ask spread should be tighter than the maintenance margin. Here, liquidations manifest as contributions to the insurance fund (e.g. if the maintenance margin is 50bps, but the market is 1bp wide), and the insurance fund should rise by close to the same amount as the maintenance margin when a position is liquidated. In this scenario, as long as healthy liquid markets persist, the insurance fund should continue its steady growth.
The following graphs further illustrate the example, and in the first chart, market conditions are healthy with a narrow bid/ask spread (just $2) at the time of liquidation. Here, the closing trade occurs at a higher price than the bankruptcy price (the price where the margin balance is zero) and the insurance fund benefits.
Illustrative example of an insurance contribution – Long 100x with 1 BTC collateral
https://preview.redd.it/is89ep924cc41.png?width=699&format=png&auto=webp&s=f0419c68fe88703e594c121b5b742c963c7e2229
(Note: The above illustration is based on opening a 100x long position at $4,000 per BTC and 1 Bitcoin of collateral. The illustration is an oversimplification and ignores factors such as fees and other adjustments.
The bid and offer prices represent the state of the order book at the time of liquidation. The closing trade price is $3,978, representing $1 of slippage compared to the $3,979 bid price at the time of liquidation.)
The second chart shows a wide bid/ask spread at the time of liquidation, here, the closing trade takes place at a lower price than the bankruptcy price, and the insurance fund is used to make sure that winning traders receive their expected profits.
This works to stabilize the potential for returns as there is no guarantee that healthy market conditions can continue, especially during periods of heightened price volatility. During these periods, it’s actually possible that the insurance fund can be used up than it is built up.
Illustrative example of an insurance depletion – Long 100x with 1 BTC collateral
https://preview.redd.it/vb4mj3n54cc41.png?width=707&format=png&auto=webp&s=0c63b7c99ae1c114d8e3b947fb490e9144dfe61b
(Notes: The above illustration is based on opening a 100x long position at $4,000 per BTC and 1 Bitcoin of collateral. The illustration is an oversimplification and ignores factors such as fees and other adjustments.
The bid and offer prices represent the state of the order book at the time of liquidation. The closing trade price is $3,800, representing $20 of slippage compared to the $3,820 bid price at the time of liquidation.)
The exchange declared in February 2019, that the BitMEX insurance fund retained close to 21,000 Bitcoin (around $70 million based on Bitcoin spot prices at the time).
This figure represents just 0.007% of BitMEX’s notional annual trading volume, which has been quoted as being approximately $1 trillion. This is higher than the insurance funds as a proportion of trading volume of the CME, and therefore, winning traders on BitMEX are exposed to much larger risks than CME traders as:
  • BitMEX does not have clearing members with large balance sheets and traders are directly exposed to each other.
  • BitMEX does not demand payments from traders with negative account balances.
  • The underlying instruments on BitMEX are more volatile than the more traditional instruments available on CME.
Therefore, with the insurance fund remaining capitalized, the system effectively with participants who get liquidated paying for liquidations, or a losers pay for losers mechanism.
This system may appear controversial as first, though some may argue that there is a degree of uniformity to it. It’s also worth noting that the exchange also makes use of Auto Deleveraging which means that on occasion, leveraged positions in profit can still be reduced during certain time periods if a liquidated order cannot be executed in the market.
More adventurous traders should note that while the insurance fund holds 21,000 Bitcoin, worth approximately 0.1% of the total Bitcoin supply, BitMEX still doesn’t offer the same level of guarantees to winning traders that are provided by more traditional leveraged trading platforms.
Given the inherent volatility of the cryptocurrency market, there remains some possibility that the fund gets drained down to zero despite its current size. This may result in more successful traders lacking confidence in the platform and choosing to limit their exposure in the event of BitMEX being unable to compensate winning traders.

How suitable is BitMEX for Beginners?

BitMEX generates high Bitcoin trading levels, and also attracts good levels of volume across other crypto-to-crypto transfers. This helps to maintain a buzz around the exchange, and BitMEX also employs relatively low trading fees, and is available round the world (except to US inhabitants).
This helps to attract the attention of people new to the process of trading on leverage and when getting started on the platform there are 5 main navigation Tabs to get used to:
  • **Trade:**The trading dashboard of BitMEX. This tab allows you to select your preferred trading instrument, and choose leverage, as well as place and cancel orders. You can also see your position information and view key information in the contract details.
  • **Account:**Here, all your account information is displayed including available Bitcoin margin balances, deposits and withdrawals, and trade history.
  • **Contracts:**This tab covers further instrument information including funding history, contract sizes; leverage offered expiry, underlying reference Price Index data, and other key features.
  • **References:**This resource centre allows you to learn about futures, perpetual contracts, position marking, and liquidation.
  • **API:**From here you can set up an API connection with BitMEX, and utilize the REST API and WebSocket API.
BitMEX also employs 24/7 customer support and the team can also be contacted on their Twitter and Reddit accounts.
In addition, BitMEX provides a variety of educational resources including an FAQ section, Futures guides, Perpetual Contracts guides, and further resources in the “References” account tab.
For users looking for more in depth analysis, the BitMEX blog produces high level descriptions of a number of subjects and has garnered a good reputation among the cryptocurrency community.
Most importantly, the exchange also maintains a testnet platform, built on top of testnet Bitcoin, which allows anyone to try out programs and strategies before moving on to the live exchange.
This is crucial as despite the wealth of resources available, BitMEX is not really suitable for beginners, and margin trading, futures contracts and swaps are best left to experienced, professional or institutional traders.
Margin trading and choosing to engage in leveraged activity are risky processes and even more advanced traders can describe the process as a high risk and high reward “game”. New entrants to the sector should spend a considerable amount of time learning about margin trading and testing out strategies before considering whether to open a live account.

Is BitMEX Safe?

BitMEX is widely considered to have strong levels of security. The platform uses multi-signature deposits and withdrawal schemes which can only be used by BitMEX partners. BitMEX also utilises Amazon Web Services to protect the servers with text messages and two-factor authentication, as well as hardware tokens.
BitMEX also has a system for risk checks, which requires that the sum of all account holdings on the website must be zero. If it’s not, all trading is immediately halted. As noted previously, withdrawals are all individually hand-checked by employees, and private keys are never stored in the cloud. Deposit addresses are externally verified to make sure that they contain matching keys. If they do not, there is an immediate system shutdown.
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In addition, the BitMEX trading platform is written in kdb+, a database and toolset popular amongst major banks in high frequency trading applications. The BitMEX engine appears to be faster and more reliable than some of its competitors, such as Poloniex and Bittrex.
They have email notifications, and PGP encryption is used for all communication.
The exchange hasn’t been hacked in the past.

How Secure is the platform?

As previously mentioned, BitMEX is considered to be a safe exchange and incorporates a number of security protocols that are becoming standard among the sector’s leading exchanges. In addition to making use of Amazon Web Services’ cloud security, all the exchange’s systems can only be accessed after passing through multiple forms of authentication, and individual systems are only able to communicate with each other across approved and monitored channels.
Communication is also further secured as the exchange provides optional PGP encryption for all automated emails, and users can insert their PGP public key into the form inside their accounts.
Once set up, BitMEX will encrypt and sign all the automated emails sent by you or to your account by the [[email protected]](mailto:[email protected]) email address. Users can also initiate secure conversations with the support team by using the email address and public key on the Technical Contact, and the team have made their automated system’s PGP key available for verification in their Security Section.
The platform’s trading engine is written in kdb+, a database and toolset used by leading financial institutions in high-frequency trading applications, and the speed and reliability of the engine is also used to perform a full risk check after every order placement, trade, settlement, deposit, and withdrawal.
All accounts in the system must consistently sum to zero, and if this does not happen then trading on the platform is immediately halted for all users.
With regards to wallet security, BitMEX makes use of a multisignature deposit and withdrawal scheme, and all exchange addresses are multisignature by default with all storage being kept offline. Private keys are not stored on any cloud servers and deep cold storage is used for the majority of funds.
Furthermore, all deposit addresses sent by the BitMEX system are verified by an external service that works to ensure that they contain the keys controlled by the founders, and in the event that the public keys differ, the system is immediately shut down and trading halted. The exchange’s security practices also see that every withdrawal is audited by hand by a minimum of two employees before being sent out.

BitMEX Customer Support

The trading platform has a 24/7 support on multiple channels, including email, ticket systems and social media. The typical response time from the customer support team is about one hour, and feedback on the customer support generally suggest that the customer service responses are helpful and are not restricted to automated responses.
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The BitMEX also offers a knowledge base and FAQs which, although they are not necessarily always helpful, may assist and direct users towards the necessary channels to obtain assistance.
BitMEX also offers trading guides which can be accessed here

Conclusion

There would appear to be few complaints online about BitMEX, with most issues relating to technical matters or about the complexities of using the website. Older complaints also appeared to include issues relating to low liquidity, but this no longer appears to be an issue.
BitMEX is clearly not a platform that is not intended for the amateur investor. The interface is complex and therefore it can be very difficult for users to get used to the platform and to even navigate the website.
However, the platform does provide a wide range of tools and once users have experience of the platform they will appreciate the wide range of information that the platform provides.
Visit BitMEX
submitted by bitmex_register to u/bitmex_register [link] [comments]

[MEA Spoilers] Mass Effect Andromeda Romance Survey Results

A week ago I posted a survey where you were asked to report your Andromeda lock-in romances and casual romances, and rate the attractiveness of Ryder's potential love interests. More than 1500 people responded, I have spreadsheets for days, and I have analysis prepared for presentation.
Demographics
Let's start with the basics.
73% reported as male. 26.3% reported as female. 0.7% reported as non-binary.
74.8% reported as heterosexual. 16.2% reported as bisexual. 7.3% reported as homosexual. 1.7% reported as another sexuality.
60.8% played as male Ryder, whilst 39.2% played as female Ryder. Who played as who? 70.6% of males played as male Ryder, and 90.1% of females played as female Ryder.
Locking In a Romance
Respondent demographics 1 2 3 4 5 6 7 8 9
All Males Peebee (32.4%) Cora (29.9%) Vetra (21.4%) Suvi (4.4%) Gil (3.9%) None (3%) Reyes (2.5%) Jaal (1.3%); Liam (1.3%)
Male Heterosexual Peebee (35.6%) Cora (32%) Vetra (22.4%) Suvi (4%) None (3%) Liam (1.1%) Jaal (0.9%) Reyes (0.8%) Gil (0.3%)
Male Bisexual Cora (31.9%) Peebee (22.7%) Vetra (21%) Jaal (5.9%); Suvi (5.9%) Gil (5%); Reyes (5%) None (1.7%) Liam (0.8%)
Male Homosexual Gil (43.4%) Reyes (17.1%) Peebee (10.5%); Vetra (10.5%) Suvi (6.6%) None (5.4%) Liam (3.9%) Cora (2.3%)
Male Other Peebee (22.2%); Reyes; (22.2%); Vetra (22.2%) Cora (11.1%); Gil (11.1%); Suvi (11.1%)
All Females Jaal (35%) Reyes (28.3%) Peebee (11.9%) Vetra (8.9%) Liam (8.7%) Cora (4.5%) Suvi (1.5%) Gil (1%) None (0.2%)
Female Heterosexual Reyes (41.5%) Jaal (39.3%) Liam (9.6%) Peebee (3.5%) Vetra (3.1%) Cora (1.7%) Gil (0.9%) None (0.4%)
Female Bisexual Jaal (36%) Peebee (17.6%) Reyes (12%); Vetra (12%) Cora (10.4%) Liam (9.6%) Gil (1.6%) Suvi (0.8%)
Female Homosexual Peebee (48.6%) Vetra (31.4%) Suvi (14.3%) Jaal (5.7%)
Female Other Jaal (28.6%); Reyes (28.6%) Vetra (21.4%) Cora (7.1%); Liam (7.1%); Peebee (7.1%)
Non-Binary Vetra (46.2%) Jaal (23.1%) Peebee (15.4%) Cora (7.7%); Suvi (7.7%)
All Respondents Peebee (26.8%) Cora (22.9%) Vetra (18.3%) Jaal (10.4%) Reyes (9.3%) Suvi (3.7%) Liam (3.2%) Gil (3.1%) None (2.2%)
When looking at all respondents, the top slots are no surprise; the top four are squadmates afforded more screentime than their brethren, and the top three are female characters more likely to appeal to the male heterosexual majority of respondents. The surprising outcome here is that Liam is languishing as far back as he is, considering he’s a squadmate option. Bioware might wish to reconsider how they approached Liam’s romance.
This result aligns fairly closely with the prediction of one of our Redditors, AutumnLantern, who is a screenwriter and assessed the romances available to a male Ryder only, predicting that due to imbalanced screentime and character pros and fails, Peebee, with over one hour of relationship content, would be preferred over Cora (49 min), Vetra (36 min), Reyes (26 min), and Gil (19 min). See here for AutumnLantern’s detailed breakdown of why other character romances are set to fail in comparison to Peebee. In short, Bioware might want to think about more screentime and better character balancing in order to round out and create more satisfying romances amongst the lesser picked characters.
Casual Romances
In addition to looking at who was popular with whom for a serious relationship, we asked respondents who they had casual relationships with. Multiple choices were allowed.
Technically the winner here is no one, as 664 people who locked in a romance indicated that they did not pursue any other relationship. Going down the list, Peebee rules the roost here in casual relationships as she is most people’s second choice even if they chose someone else to get serious with. This is again not surprising given Peebee’s screentime. The second, third, and fourth most popular picks here are all non-squadmates, suggesting that Ryders playing the field like to do so on the down low. In the case of Peebee and Reyes, who can both be locked in with, both characters gain more votes here than they did in the locked in vote, suggesting that even when not chosen they were many people’s second choice. Jaal, Vetra, and Cora’s numbers are considerably lower than their locked in scores, suggesting that when people did pursue them they preferred to get serious rather than keep it casual.
Discounting sample sizes of less than 20 (which tend to produce extreme results), which groups only had eyes for their locked-in romance and no other? Male heterosexual respondents whose beloved was Vetra stuck with her 53.69% of the time. The group most interested in spreading the love around were male bisexual respondents who had romanced Cora, who only inspired 15.79% to choose her alone. Both this most faithful group and this most flirtatious group, when they did decide to share the love, most often chose Peebee as the subject of their affections. As a whole, regardless of player demographics, the characters who inspired the most fidelity were Reyes, followed closely by Vetra. Cora attracted the most people looking to play the field.
Character Percentage of Romancers With No Other Romances
Reyes Vidal 61.76%
Vetra Nyx 58.43%
Peebee 48.89%
Jaal Ama Darav 46.21%
Liam Kosta 45.81%
Suvi Anwar 37.04%
Gil Brodie 34.78%
Cora Harper 28.45%
Who is the Fairest of them All? Ryder’s Romances Rated by Attractiveness
The table below shows the results from all respondents, not disaggregated by gender identity or sexual orientation. The mean is the average score. The median is the exact middle value of the ratings – 50% of responses were above it, and 50% of ratings were below it.
Character Mean Median Respondents
Peebee 7.0 7 1400
Suvi Anwar 7.0 7 1388
Cora Harper 6.8 7 1400
Vetra Nyx 6.5 7 1380
Reyes Vidal 6.4 7 1390
Keri T’Vessa 6.0 6 1380
Jaal Ama Darav 5.3 6 1371
Avela Kjar 5.1 5 1348
Liam Kosta 4.9 5 1370
Gil Brodie 4.6 5 1373
Peebee and Suvi are in a dead heat for the lead. Reyes is the best-liked male character, in the middle of the table. Gil comes in last, behind Liam. As with DarylZer0’s “How Attractive Are Shepard’s Love Interests” survey, the results are heavily skewed due to the fact that male heterosexuals are the single largest group (57.4%) among respondents and have the strongest tendency to give male characters ‘1’ ratings. This tended to drag down the mean of male characters. In fact, male heterosexuals handed out the lowest rating to characters far more than any other group, at 10.5% frequency. The group least inclined to give a ‘1’ was male bisexuals, at just 3.8% of all scores given by that demographic.
Breaking down the results by gender reveals just how these factors affect ratings. First, the results from males:
Character Mean Median Respondents
Cora Harper 7.3 8 1038
Peebee 7.3 8 1033
Suvi Anwar 7.3 8 1030
Vetra Nyx 6.4 7 1023
Keri T’Vessa 6.3 7 1025
Reyes Vidal 5.6 6 1010
Avela Kjar 5.2 5 992
Jaal Ama Darav 4.7 5 1013
Liam Kosta 4.4 5 1011
Gil Brodie 4.3 5 1011
As might be expected, the results are fairly similar to the aggregated scores. Cora, Peebee, and Suvi are all within a hair’s breadth of each other and the top slot. Male characters languish in the second half of the table. The results are pretty heavily influenced by the fact that heterosexual males made up 81.6% of all male respondents.
Here are the results from females:
Character Mean Median Respondents
Reyes Vidal 8.2 8 366
Vetra Nyx 6.9 7 343
Jaal Ama Darav 6.6 7 354
Liam Kosta 6.3 6 345
Suvi Anwar 6.2 7 344
Peebee 6.1 7 353
Cora Harper 5.6 6 348
Gil Brodie 5.4 6 348
Keri T’Vessa 5.2 5 341
Avela Kjar 5.0 5 343
Males trended towards the top of the table as the majority of female respondents were heterosexual (56.8%). Cora, Peebee, and Suvi are pushed down the mid-table, whilst Reyes, Jaal, and Liam shoot to the top of the board, all three leaping up five places from their previous positions.
And the results from non-binary respondents:
Character Mean Median Respondents
Vetra Nyx 7.1 8 14
Reyes Vidal 6.9 8 14
Peebee 6.8 7 14
Jaal Ama Darav 6.2 5.5 13
Cora Harper 6 6.5 14
Liam Kosta 5.6 6.5 14
Suvi Anwar 5.6 6 14
Keri T’Vessa 5.2 5 14
Avela Kjar 5.1 5 13
Gil Brodie 3.6 4.5 14
The sample size from non-binary respondents is so small that it’s doubtful useful conclusions can be drawn, but Vetra and Reyes, favourites with the females, and Peebee, favourite with the males, fill out the top three.
How does sexual orientation affect results? Here are the results from heterosexual males:
Character Mean Median Respondents
Peebee 7.5 8 840
Suvi Anwar 7.5 8 837
Cora Harper 7.4 8 845
Vetra Nyx 6.6 7 831
Keri T’Vessa 6.3 7 834
Reyes Vidal 5.3 6 816
Avela Kjar 5.2 5 809
Jaal Ama Darav 4.7 5 819
Liam Kosta 4.2 5 818
Gil Brodie 3.9 4 817
This is almost exactly as you’d expect, and closely mirrors the overall results. Keri and Reyes have swapped places, female characters’ scores are higher, and male characters’ scores are lower. There aren’t really any surprises here.
Bisexual males:
Character Mean Median Respondents
Peebee 7.2 7 114
Reyes Vidal 7.1 7 113
Cora Harper 7.0 7.5 114
Suvi Anwar 6.7 7 114
Keri T’Vessa 6.4 7 114
Vetra Nyx 5.8 6.5 114
Gil Brodie 5.7 6 113
Liam Kosta 5.6 6 113
Avela Kjar 5.2 5 109
Jaal Ama Darav 4.9 5 113
Things are shaken up quite a bit with the bisexual males. The range of means is less broad, and whilst females still dominate the upper half of the table, Reyes has shot up to second place, whilst Jaal has fallen into last place.
Homosexual males:
Character Mean Median Respondents
Reyes Vidal 7.6 8 73
Gil Brodie 6.5 7 73
Suvi Anwar 6.3 7 71
Vetra Nyx 6.1 6.5 70
Cora Harper 5.9 6 71
Jaal Ama Darav 5.8 6 73
Peebee 5.7 6 71
Liam Kosta 5.5 6 72
Keri T’Vessa 5.3 5 69
Avela Kjar 4.4 5 66
We get a different picture again from the homosexual males. It’s not surprising that Reyes and Gil – the only two male romances available to a male Ryder – are occupying the top, but Suvi’s popular too in third. Cora has slipped to fifth, and Peebee has crashed down into seventh.
Other orientation males:
Character Mean Median Respondents
Vetra Nyx 6.8 7 8
Suvi Anwar 6.4 6 8
Reyes Vidal 6.3 6 8
Cora Harper 6.1 6 8
Keri T’Vessa 6 5 8
Gil Brodie 5.9 7 8
Peebee 5.6 6 8
Jaal Ama Darav 5.3 5 8
Avela Kjar 5 5 8
Liam Kosta 4.8 5 8
Again the sample size is really too small to draw useful conclusions, but Cora and Peebee seem to be less popular, whilst Reyes and Gil gain a small boost, as do Vetra and Suvi.
Heterosexual females:
Character Mean Median Respondents
Reyes Vidal 8.4 8 194
Jaal Ama Darav 7.2 8 173
Vetra Nyx 6.6 7 171
Liam Kosta 6.4 7 173
Gil Brodie 5.7 6 176
Suvi Anwar 5.7 6 172
Peebee 5.1 6 182
Cora Harper 5.1 5 176
Avela Kjar 4.9 5 172
Keri T’Vessa 4.8 5 170
The heterosexual females expressed very different preferences from the overall scores. In a reversal of the heterosexual males’ results, this table has female characters bar Vetra occupying the second half of the table, whilst males dominate the top. Jaal and Reyes are absolutely storming into the lead, and Gil and Liam have risen to mid-table, a considerable jump compared to their aggregate scores.
Bisexual females:
Character Mean Median Respondents
Reyes Vidal 8.0 8 124
Vetra Nyx 7.0 8 124
Peebee 6.9 7 123
Liam Kosta 6.6 7 124
Suvi Anwar 6.6 7 124
Jaal Ama Darav 6.5 7 124
Cora Harper 6.0 6.5 124
Keri T’Vessa 5.5 6 123
Gil Brodie 5.3 5.5 124
Avela Kjar 5.1 5 124
Reyes is still in the lead for bisexual females, but Vetra proves to be the second favourite. Gil loses the popularity he had with heterosexual females when it comes to bisexual females.
Homosexual females:
Character Mean Median Respondents
Peebee 7.9 8 35
Vetra Nyx 7.4 8 35
Suvi Anwar 7.1 8 35
Cora Harper 6.2 6 34
Keri T’Vessa 5.8 6 35
Reyes Vidal 5.4 6 35
Avela Kjar 5.0 5 34
Jaal Ama Darav 4.9 5 35
Gil Brodie 4.8 5 35
Liam Kosta 4.5 5 35
The table for homosexual females naturally prioritises female characters romanceable by a female Ryder, though Cora still comes in fairly strong in fourth, and the male characters are back in the second half of the table.
Other orientation females:
Character Mean Median Respondents
Reyes Vidal 8.8 9 13
Jaal Ama Darav 8.2 8 13
Vetra Nyx 8.1 8 13
Suvi Anwar 7.1 7 13
Liam Kosta 6.7 7 13
Cora Harper 6.2 6 13
Peebee 5.9 6 13
Avela Kjar 5.6 5 13
Keri T’Vessa 4.9 5 13
Gil Brodie 4.8 5 13
This is another sample size that is too small to make useful conclusions from, however, it bears similarities to the heterosexual females, with Reyes and Jaal at the top and Cora and Peebee mid-table.
TL;DR - Final Summary
When examining Andromeda’s romances, it’s worth keeping in mind that the overall results are wildly skewed due to the fact that 73% were male, and 81.6% of them were heterosexual. Who is actually the most popular serious romance, casual romance, and rated the most attractive varies enormously depending on respondent gender and sexual orientation, and it is more helpful to look at the disaggregated scores in order to understand who is appealing to whom.
Cora – It’s no surprise that Cora is most attractive to heterosexual males, and second to bisexual males. With other groups she tends to be rated middle of the table in attractiveness. Heterosexual males and bisexual males locked in romances with her with almost the exact same frequency – although straight males still preferred Peebee, but for bisexual males Cora was the number one romance pick. However, she was also the character most likely to not be the player’s only romance, and she was not often picked as a casual romance. All in all, Cora’s results are what you’d expect given the demographics, and her screentime.
Gil – Gil was the most attractive to homosexual males, although both bisexual males and heterosexual females both rated him moderately attractive at 5.7. For other groups, Gil often languished in the bottom three in terms of attractiveness. He was rarely chosen as a casual romance pick, and as far as romance lock-ins go he was the first choice, by a long way (43.7%) for male homosexuals but didn’t make it into the top three for any other group. It’s pretty obvious the ball has been dropped here in terms of Gil’s screentime compared to other characters, and in terms of available male-male romance options. Bioware might want to think about that.
Jaal – Reactions to Jaal are very spread across genders and orientations. Heterosexual males, bisexual males, and homosexual females rated him low, heterosexual females let it be known that they rated him highly, and for everyone else Jaal tended to be seen as moderately attractive. Jaal’s second place on straight females’ attractiveness table was also reflected by his second place amongst their romance lock-in picks. His performance is not that surprising given his screentime and status as a squadmate.
Liam – Liam is another character that has lost out somewhat on the romance front. Heterosexual and bisexual females rated him the highest of any groups in attractiveness, but he still never rose above fourth place with either, and with everyone else he shuttles between mid and bottom of the table. He was, however, the third most popular pick for straight females to lock in, though his percentage is a bit low on that (9.6%). Liam is quite clearly underperforming given his screentime and the fact that he is a squadmate. Gil’s problems are explicable in part by his lack of screentime, but that is not the case with Liam.
Peebee – Peebee is fairly dominant in the romance stakes. Heterosexual males, bisexual males, and homosexual females all have her topping their attractiveness charts, with the top rating (7.9) given by homosexual females. She is least attractive to heterosexual females, who rated her 5.1, and otherwise hovers mid-table. She’s the most popular lock-in with straight males and gay females, although bi males prefer to get serious with Cora. She’s also the most frequently picked casual romance when the respondents had a different locked-in romance. This is absolutely not a surprising result in any way. The romantic odds are stacked heavily in Peebee’s favour since she is available to both male and female Ryders, commands the most screentime in her romance of any character (well over an hour, compared to Cora’s 49 minutes as her nearest rival), and lacks the same kind of character flaws that the others have.
Reyes – Well, this is another surprising result, but for the opposite reasons to Gil and Liam. Reyes was considered the most attractive character by heterosexual females, bisexual females, and homosexual males. In fact, of any character across any group, he absolutely crushes the opposition into the ground with a 8.4 rating from straight females. The lowest rating he ever gets is 5.3 from the heterosexual males, and bisexual males gave him 7.1 and only preferred Peebee in the attractiveness stakes. He’s the third most picked casual romance, and when it comes to locking in, he was the most locked in for heterosexual females, second place for homosexual males, and third place for bisexual females – the discrepancy likely owing to the character’s divisive turning point. And this is from a character who is not a squadmate, and whose 26 minute screentime is well below others. Reyes punches well above his weight, and Bioware should probably take notice of this fan favourite.
Suvi – Suvi is equal with Peebee in straight males’ attractiveness ratings, and though she doesn’t top anyone else’s tables, she performs consistently well. She never drops below 5.7 (from heterosexual females), and consistently sits in the top half of all tables except straight females. However, she’s not often locked-in as a romance, the most frequently being 14.8% with gay females. She is doing fairly well considering she’s a non-squadmate. Must be the Scottish accent.
Vetra – Vetra’s a consistent performer in the romance stakes. Straight and gay males place her fourth on their attractiveness tables, though she drops to sixth for bisexual males. She’s in second for gay and bi females, however, and third for straight females. Her highest rating is 7.4 from gay females, and the lowest is 5.8 from bi males. In terms of locking in, straight, bi, and gay males, and bi females make her their third pick, whilst she’s second choice for gay females. Further, straight males who chose her tended not to romance anyone else casually. In short, whilst Vetra isn’t a favourite, people tend to agree that she’s attractive and a desirable romance.
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