Binary.com Academy

[Just Launched] Options Domination Binary Trading - [Amazing System] - True Risk Free Trades! [New for 2015]

Many brokers or services will market something called “risk free” trades in which a certain number of your first trades you can get your money back should the signals they give you prove to be of bad quality. In most cases there are many regulations that require you to keep investing a certain amount before you can withdraw your “risk free” trades. This is the sign of a bad signal provider that probably makes more money selling their signals then they do actually implementing them themselves.
In our case study of the system we won 5 out of 7 of the trades and pocketed $250 in profit which is a 25% return on a small investment. We were very impressed with these results. At that time we could have elected to withdraw our original $1,000 and essentially be playing with the $250 “on the house”. CLICK HERE TO GET YOUR RISK FREE TRADES NOW!
CLICK HERE TO GET YOUR RISK FREE TRADES NOW!
Using their basic system of signals we were able to accumulate over $10,000 in our account in just 30 days! These are better results then we have gotten with other binary signals costing 10 times the amount of what options domination is charging. For a simple $50 a month you get multiple daily signals, keep in mind they don’t send you 1,000’s of signals a day like most services as they are focusing on the quality of the signal and not just sending you a bunch of garbage signals like many of the other companies do.
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submitted by optionsdomination to optionsdomination [link] [comments]

I'm looking for a Python API to a binary options trading platform.

I have an algorithm for binary options trading, but I don't feel like manually working a GUI to do my trades.
Could someone point me to a resource for executing my trades via Python?
submitted by metaperl to Python [link] [comments]

Good place to trade bitcoin binary options through API interface?

I did some searching on google, and this subreddit and didn't find much that looked up-to-date or trustworthy....
Just looking for a reputable trading site that offers binary options and has an API for access. And, yes, I know that binary trading is essentially pure speculation. :)
submitted by sigma_noise to BitcoinMarkets [link] [comments]

Using Deep Learning to Predict Earnings Outcomes

Using Deep Learning to Predict Earnings Outcomes
(Note: if you were following my earlier posts, I wrote a note at the end of this post explaining why I deleted old posts and what changed)
Edit: Can't reply to comments since my account is still flagged as new :\. Thank you everyone for your comments. Edit: Made another post answering questions here.
  • Test data is untouched during training 10:1:1 train:val:test.
  • Yes, I consider it "deep" learning from what I learned at my institution. I use LSTMs at one point in my pipeline, feel free to consider that deep or not.
  • I'll be making daily posts so that people can follow along.
  • Someone mentioned RL, yes I plan on trying that in the future :). This would require a really clever way to encode the current state parameters. Haven't thought about it too much yet.
  • Someone mentioned how companies beat earnings 61% anyway, so my model must be useless right? Well if you look at the confusion matrix you can see I balanced classes before training (with some noise). This means that the data had roughly 50/50 beat/miss and had a 58% test accuracy.
TLDR:
Not financial advice.
  • I created a deep learning algorithm trained on 2015-2019 data to predict whether a company will beat earning estimates.
  • Algorithm has an accuracy of 58%.
  • I need data and suggestions.
  • I’ll be making daily posts for upcoming earnings.
Greetings everyone,
I’m Bunga, an engineering PhD student at well known university. Like many of you, I developed an interest in trading because of the coronavirus. I lost a lot of money by being greedy and uninformed about how to actually trade options. With all the free time I have with my research slowing down because of the virus, I’ve decided to use what I’m good at (being a nerd, data analytics, and machine learning) to help me make trades.
One thing that stuck out to me was how people make bets on earnings reports. As a practitioner of machine learning, we LOVE binary events since the problem can be reduced to a simple binary classification problem. With that being said, I sought out to develop a machine learning algorithm to predict whether a company will beat earnings estimates.
I strongly suggest TO NOT USE THIS AS FINANCIAL ADVICE. Please, I could just be a random guy on the internet making things up, and I could have bugs in my code. Just follow along for some fun and don’t make any trades based off of this information 😊
Things other people have tried:
A few other projects have tried to do this to some extent [1,2,3], but some are not directly predicting the outcome of the earnings report or have a very small sample size of a few companies.
The data
This has been the most challenging part of the project. I’m using data for 4,000 common stocks.
Open, high, low, close, volume stock data is often free and easy to come by. I use stock data during the quarter (Jan 1 – Mar 31 stock data for Q1 for example) in a time series classifier. I also incorporate “background” data from several ETFs to give the algorithm a feel for how the market is doing overall (hopefully this accounts for bull/bear markets when making predictions).
I use sentiment analyses extracted from 10K/10Q documents from the previous quarter as described in [4]. This gets passed to a multilayer perceptron neural network.
Data that I’ve tried and doesn’t work to well:
Scraping 10K/10Q manually for US GAAP fields like Assets, Cash, StockholdersEquity, etc. Either I’m not very good at processing the data or most of the tables are incomplete, this doesn’t work well. However, I recently came across this amazing API [5] which will ameliorate most of these problems, and I plan on incorporating this data sometime this week.
Results
After training on about 34,000 data points, the model achieves a 58% accuracy on the test data. Class 1 is beat earnings, Class 2 is miss earnings.. Scroll to the bottom for the predictions for today’s AMC estimates.

https://preview.redd.it/fqapvx2z1tv41.png?width=875&format=png&auto=webp&s=05ea5cae25ee5689edea334f2814e1fa73aa195d
Future Directions
Things I’m going to try:
  • Financial twitter sentiment data (need data for this)
  • Data on options (ToS apparently has stuff that you can use)
  • Using data closer to the earnings report itself rather than just the data within the quarterly date
A note to the dozen people who were following me before
Thank you so much for the early feedback and following. I had a bug in my code which was replicating datapoints, causing my accuracy to be way higher in reality. I’ve modified some things to make the network only output a single value, and I’ve done a lot of bug fixing.
Predictions for 4/30/20 AMC:
A value closer to 1 means that the company will be more likely to beat earnings estimates. Closer to 0 means the company will be more likely to miss earnings estimates. (People familiar with machine learning will note that neural networks don’t actually output a probability distribution so the values don’t actually represent a confidence).
  • Tkr: AAPL NN: 0.504
  • Tkr: AMZN NN: 0.544
  • Tkr: UAL NN: 0.438
  • Tkr: GILD NN: 0.532
  • Tkr: TNDM NN: 0.488
  • Tkr: X NN: 0.511
  • Tkr: AMGN NN: 0.642
  • Tkr: WDC NN: 0.540
  • Tkr: WHR NN: 0.574
  • Tkr: SYK NN: 0.557
  • Tkr: ZEN NN: 0.580
  • Tkr: MGM NN: 0.452
  • Tkr: ILMN NN: 0.575
  • Tkr: MOH NN: 0.500
  • Tkr: FND NN: 0.542
  • Tkr: TWOU NN: 0.604
  • Tkr: OSIS NN: 0.487
  • Tkr: CXO NN: 0.470
  • Tkr: BLDR NN: 0.465
  • Tkr: CASA NN: 0.568
  • Tkr: COLM NN: 0.537
  • Tkr: COG NN: 0.547
  • Tkr: SGEN NN: 0.486
  • Tkr: FMBI NN: 0.496
  • Tkr: PSA NN: 0.547
  • Tkr: BZH NN: 0.482
  • Tkr: LOCO NN: 0.575
  • Tkr: DLA NN: 0.460
  • Tkr: SSNC NN: 0.524
  • Tkr: SWN NN: 0.476
  • Tkr: RMD NN: 0.499
  • Tkr: VKTX NN: 0.437
  • Tkr: EXPO NN: 0.526
  • Tkr: BL NN: 0.516
  • Tkr: FTV NN: 0.498
  • Tkr: ASGN NN: 0.593
  • Tkr: KNSL NN: 0.538
  • Tkr: RSG NN: 0.594
  • Tkr: EBS NN: 0.483
  • Tkr: PRAH NN: 0.598
  • Tkr: RRC NN: 0.472
  • Tkr: ICBK NN: 0.514
  • Tkr: LPLA NN: 0.597
  • Tkr: WK NN: 0.630
  • Tkr: ATUS NN: 0.530
  • Tkr: FBHS NN: 0.587
  • Tkr: SWI NN: 0.521
  • Tkr: TRUP NN: 0.570
  • Tkr: AJG NN: 0.509
  • Tkr: BAND NN: 0.618
  • Tkr: DCO NN: 0.514
  • Tkr: BRKS NN: 0.490
  • Tkr: BY NN: 0.502
  • Tkr: CUZ NN: 0.477
  • Tkr: EMN NN: 0.532
  • Tkr: VICI NN: 0.310
  • Tkr: GLPI NN: 0.371
  • Tkr: MTZ NN: 0.514
  • Tkr: SEM NN: 0.405
  • Tkr: SPSC NN: 0.465
[1] https://towardsdatascience.com/forecasting-earning-surprises-with-machine-learning-68b2f2318936
[2] https://zicklin.baruch.cuny.edu/wp-content/uploads/sites/10/2019/12/Improving-Earnings-Predictions-with-Machine-Learning-Hunt-Myers-Myers.pdf
[3] https://www.euclidean.com/better-than-human-forecasts
[4] https://cran.r-project.org/web/packages/edgaedgar.pdf.
[5] https://financialmodelingprep.com/developedocs/
submitted by xXx_Bunga_xXx to wallstreetbets [link] [comments]

ABI Breaks: Not just about rebuilding

Related reading:
What is ABI, and What Should WG21 Do About It?
The Day The Standard Library Died

Q: What does the C++ committee need to do to fix large swaths of ABI problems?

A: Absolutely nothing

On current implementations, std::unique_ptr's calling convention causes some inefficiencies compared to raw pointers. The standard doesn't dictate the calling convention of std::unique_ptr, so implementers could change that if they chose to.
On current implementations, std::hash will return the same result for the same input, even across program invocations. This makes it vulnerable to cache poisoning attacks. Nothing in the standard requires that different instances of a program produce the same output. An implementation could choose to have a global variable with a per-program-instance seed in it, and have std::hash mix that in.
On current implementations, std::regex is extremely slow. Allegedly, this could be improved substantially without changing the API of std::regex, though most implementations don't change std::regex due to ABI concerns. An implementation could change if it wanted to though. However, very few people have waded into the guts of std::regex and provided a faster implementation, ABI breaking or otherwise. Declaring an ABI break won't make such an implementation appear.
None of these issues are things that the C++ committee claims to have any control over. They are dictated by vendors and by the customers of the vendors. A new vendor could come along and have a better implementation. For customers that prioritize QoI over ABI stability, they could switch and recompile everything.
Even better, the most common standard library implementations are all open source now. You could fork the standard library, tweak the mangling, and be your own vendor. You can then be in control of your own destiny ABI, and without taking the large up-front cost of reinventing the parts of the standard library that you are satisfied with. libc++ has a LIBCXX_ABI_UNSTABLE configuration flag, so that you always get the latest and greatest optimizations. libstdc++ has a --enable-symvers=gnu-versioned-namespace configuration flag that is ABI unstable, and it goes a long way towards allowing multiple libstdc++ instances coexist simultaneously. Currently the libc++ and libstdc++ unstable ABI branches don't have many new optimizations because there aren't many contributions and few people use it. I will choose to be optimistic, and assume that they are unused because people were not aware of them.
If your only concern is ABI, and not API, then vendors and developers can fix this on their own without negatively affecting code portability or conformance. If the QoI gains from an ABI break are worth a few days / weeks to you, then that option is available today.

Q: What aspects of ABI makes things difficult for the C++ committee.

A: API and semantic changes that would require changes to the ABI are difficult for the C++ committee to deal with.

There are a lot of things that you can do to a type or function to make it ABI incompatible with the old type. The C++ committee is reluctant to make these kinds of changes, as they have a substantially higher cost. Changing the layout of a type, adding virtual methods to an existing class, and changing template parameters are the most common operations that run afoul of ABI.

Q: Are ABI changes difficult for toolchain vendors to deal with?

A1: For major vendors, they difficulty varies depending on the magnitude of the break.

Since GCC 5 dealt with the std::string ABI break, GCC has broken the language ABI 6 other times, and most people didn't even notice. There were several library ABI breaks (notably return type changes for std::complex and associative container erase) that went smoothly as well. Quite a few people noticed the GCC 5 std::string ABI changes though.
In some cases, there are compiler heroics that can be done to mitigate an library ABI change. You will get varying responses as to whether this is a worthwhile thing to do, depending on the vendor and the change.
If the language ABI changes in a large way, then it can cause substantially more pain. GCC had a major language ABI change in GCC 3.4, and that rippled out into the library. Dealing with libstdc++.so.5 and libstdc++.so.6 was a major hassle for many people, myself included.

A2: For smaller vendors, the difficulty of an ABI break depends on their customer base.

These days, it's easier than ever to be your own toolchain vendor. That makes you a vendor with excellent insight into how difficult an ABI change would be.

Q: Why don't you just rebuild after an ABI change?

A1: Are you rebuilding the standard library too?

Many people will recommend not passing standard library types around, and not throwing exceptions across shared library boundaries. They often forget that at least one very commonly used shared library does exactly that... your C++ standard library.
On many platforms, there is usually a system C++ standard library. If you want to use that, then you need to deal with standard library types and exceptions going across shared library boundaries. If OS version N+1 breaks ABI in the system C++ standard library, the program you shipped and tested with for OS version N will not work on the upgraded OS until you rebuild.

A2: Sometimes, rebuilding isn't enough

Suppose your company distributes pre-built programs to customers, and this program supports plugins (e.g. Wireshark dissector plugins). If the plugin ABI changes, in the pre-built program, then all of the plugins need to rebuild. The customer that upgrades the program is unlikely to be the one that does the rebuilding, but they will be responsible for upgrading all the plugins as well. The customer cannot effectively upgrade until the entire ecosystem has responded to the ABI break. At best, that takes a lot of time. More likely, some parts of the ecosystem have become unresponsive, and won't ever upgrade.
This also requires upgrading large swaths of a system at once. In certain industries, it is very difficult to convince a customer to upgrade anything at all, and upgrading an entire system would be right out.
Imagine breaking ABI on a system library on a phone. Just getting all of the apps that your company owns upgraded and deployed at the same time as the system library would be a herculean effort, much less getting all the third party apps to upgrade as well.
There are things you can do to mitigate these problems, at least for library and C++ language breaks on Windows, but it's hard to mitigate this if you are relying on a system C++ standard library. Also, these mitigations usually involve writing more error prone code that is less expressive and less efficient than if you just passed around C++ standard library types.

A3: Sometimes you can't rebuild everything.

Sometimes, business models revolve around selling pre-built binaries to other people. It is difficult to coordinate ABI changes across these businesses.
Sometimes, there is a pre-built binary, and the company that provided that binary is no longer able to provide updates, possibly because the company no longer exists.
Sometimes, there is a pre-built binary that is a shared dependency among many companies (e.g. OpenSSL). Breaking ABI on an upgrade of such a binary will cause substantial issues.

Q: What tools do we have for managing ABI changes?

A: Several, but they all have substantial trade-offs.

The most direct tool is to just make a new thing and leave the old one alone. Don't like std::unordered_map? Then make std::open_addressed_hash_map. This technique allows new and old worlds to intermix, but the translations between new and old must be done explicitly. You don't get to just rebuild your program and get the benefits of the new type. Naming the new things becomes increasingly difficult, at least if you decide to not do the "lazy" thing and just name the new class std::unordered_map2 or std2::unordered_map. Personally, I'm fine with slapping a version number on most of these classes, as it gives a strong clue to users that we may need to revise this thing again in the future, and it would mean we might get an incrementally better hash map without needing to wait for hashing research to cease.
inline namespaces are another tool that can be used, but they solve far fewer ABI problems than many think. Upgrading a type like std::string or std::unordered_map via inline namespaces generally wouldn't work, as user types holding the upgraded types would also change, breaking those ABIs. inline namespaces can probably help add / change parameters to functions, and may even extend to updating empty callable objects, but neither of those are issues that have caused many problems in the C++ committee in the past.
Adding a layer of indirection, similar to COM, does a lot to address stability and extensibility, at a large cost to performance. However, one area that the C++ committee hasn't explored much in the past is to look at the places where we already have a layer of indirection, and using COM-like techniques to allow us to add methods in the future. Right now, I don't have a good understanding of the performance trade-offs between the different plug-in / indirect call techniques that we could use for things like std::pmr::memory_resource and std::error_category.

Q: What can I do if I don't want to pay the costs for ABI stability?

A: Be your own toolchain vendor, using the existing open-source libraries and tools.

If you are able to rebuild all your source, then you can point all your builds at a custom standard library, and turn on (or even make your own) ABI breaking changes. You now have a competitive advantage, and you didn't even need to amend an international treaty (the C++ standard) to make it happen! If your changes were only ABI breaking and not API breaking, then you haven't even given up on code portability.
Note that libc++ didn't need to get libstdc++'s permission in order to coexist on Linux. You can have multiple standard libraries at the same time, though there are some technical challenges created when you do that.

Q: What can I do if I want to change the standard in a way that is ABI breaking?

A1: Consider doing things in a non-breaking way.

A2: Talk to compiler vendors and the ABI Review Group (ARG) to see if there is a way to mitigate the ABI break.

A3: Demonstrate that your change is so valuable that the benefit outweighs the cost, or that the cost isn't necessarily that high.

Assorted points to make before people in the comments get them wrong

submitted by ben_craig to cpp [link] [comments]

Using Deep Learning to Predict Earnings Outcomes

Using Deep Learning to Predict Earnings Outcomes
(Note: if you were following my earlier posts, I wrote a note at the end of this post explaining why I deleted old posts and what changed)
TLDR:
Not financial advice.
  • I created a deep learning algorithm trained on 2015-2019 data to predict whether a company will beat earning estimates.
  • Algorithm has an accuracy of 58%.
  • I need data and suggestions.
  • I’ll be making daily posts for upcoming earnings.
Greetings everyone,
I’m Bunga, an engineering PhD student at well known university. Like many of you, I developed an interest in trading because of the coronavirus. I lost a lot of money by being greedy and uninformed about how to actually trade options. With all the free time I have with my research slowing down because of the virus, I’ve decided to use what I’m good at (being a nerd, data analytics, and machine learning) to help me make trades.
One thing that stuck out to me was how people make bets on earnings reports. As a practitioner of machine learning, we LOVE binary events since the problem can be reduced to a simple binary classification problem. With that being said, I sought out to develop a machine learning algorithm to predict whether a company will beat earnings estimates.
I strongly suggest TO NOT USE THIS AS FINANCIAL ADVICE. Please, I could just be a random guy on the internet making things up, and I could have bugs in my code. Just follow along for some fun and don’t make any trades based off of this information 😊
Things other people have tried:
A few other projects have tried to do this to some extent [1,2,3], but some are not directly predicting the outcome of the earnings report or have a very small sample size of a few companies.
The data
This has been the most challenging part of the project. I’m using data for 4,000 common stocks.
Open, high, low, close, volume stock data is often free and easy to come by. I use stock data during the quarter (Jan 1 – Mar 31 stock data for Q1 for example) in a time series classifier. I also incorporate “background” data from several ETFs to give the algorithm a feel for how the market is doing overall (hopefully this accounts for bull/bear markets when making predictions).
I use sentiment analyses extracted from 10K/10Q documents from the previous quarter as described in [4]. This gets passed to a multilayer perceptron neural network.
Data that I’ve tried and doesn’t work to well:
Scraping 10K/10Q manually for US GAAP fields like Assets, Cash, StockholdersEquity, etc. Either I’m not very good at processing the data or most of the tables are incomplete, this doesn’t work well. However, I recently came across this amazing API [5] which will ameliorate most of these problems, and I plan on incorporating this data sometime this week.
Results
After training on about 34,000 data points, the model achieves a 58% accuracy on the test data. Class 1 is beat earnings, Class 2 is miss earnings.. Scroll to the bottom for the predictions for today’s AMC estimates.

https://preview.redd.it/qmeig6of3tv41.png?width=875&format=png&auto=webp&s=c8ba4a34294b7388bf1b9e64150d7375da959ac2
Future Directions
Things I’m going to try:
  • Financial twitter sentiment data (need data for this)
  • Data on options (ToS apparently has stuff that you can use)
  • Using data closer to the earnings report itself rather than just the data within the quarterly date
A note to the dozen people who were following me before
Thank you so much for the early feedback and following. I had a bug in my code which was replicating datapoints, causing my accuracy to be way higher in reality. I’ve modified some things to make the network only output a single value, and I’ve done a lot of bug fixing.
Predictions for 4/29/20 AMC:
A value closer to 1 means that the company will be more likely to beat earnings estimates. Closer to 0 means the company will be more likely to miss earnings estimates. (People familiar with machine learning will note that neural networks don’t actually output a probability distribution so the values don’t actually represent a confidence).
  • Tkr: AAPL NN: 0.504
  • Tkr: AMZN NN: 0.544
  • Tkr: UAL NN: 0.438
  • Tkr: GILD NN: 0.532
  • Tkr: TNDM NN: 0.488
  • Tkr: X NN: 0.511
  • Tkr: AMGN NN: 0.642
  • Tkr: WDC NN: 0.540
  • Tkr: WHR NN: 0.574
  • Tkr: SYK NN: 0.557
  • Tkr: ZEN NN: 0.580
  • Tkr: MGM NN: 0.452
  • Tkr: ILMN NN: 0.575
  • Tkr: MOH NN: 0.500
  • Tkr: FND NN: 0.542
  • Tkr: TWOU NN: 0.604
  • Tkr: OSIS NN: 0.487
  • Tkr: CXO NN: 0.470
  • Tkr: BLDR NN: 0.465
  • Tkr: CASA NN: 0.568
  • Tkr: COLM NN: 0.537
  • Tkr: COG NN: 0.547
  • Tkr: SGEN NN: 0.486
  • Tkr: FMBI NN: 0.496
  • Tkr: PSA NN: 0.547
  • Tkr: BZH NN: 0.482
  • Tkr: LOCO NN: 0.575
  • Tkr: DLA NN: 0.460
  • Tkr: SSNC NN: 0.524
  • Tkr: SWN NN: 0.476
  • Tkr: RMD NN: 0.499
  • Tkr: VKTX NN: 0.437
  • Tkr: EXPO NN: 0.526
  • Tkr: BL NN: 0.516
  • Tkr: FTV NN: 0.498
  • Tkr: ASGN NN: 0.593
  • Tkr: KNSL NN: 0.538
  • Tkr: RSG NN: 0.594
  • Tkr: EBS NN: 0.483
  • Tkr: PRAH NN: 0.598
  • Tkr: RRC NN: 0.472
  • Tkr: ICBK NN: 0.514
  • Tkr: LPLA NN: 0.597
  • Tkr: WK NN: 0.630
  • Tkr: ATUS NN: 0.530
  • Tkr: FBHS NN: 0.587
  • Tkr: SWI NN: 0.521
  • Tkr: TRUP NN: 0.570
  • Tkr: AJG NN: 0.509
  • Tkr: BAND NN: 0.618
  • Tkr: DCO NN: 0.514
  • Tkr: BRKS NN: 0.490
  • Tkr: BY NN: 0.502
  • Tkr: CUZ NN: 0.477
  • Tkr: EMN NN: 0.532
  • Tkr: VICI NN: 0.310
  • Tkr: GLPI NN: 0.371
  • Tkr: MTZ NN: 0.514
  • Tkr: SEM NN: 0.405
  • Tkr: SPSC NN: 0.465
[1] https://towardsdatascience.com/forecasting-earning-surprises-with-machine-learning-68b2f2318936
[2] https://zicklin.baruch.cuny.edu/wp-content/uploads/sites/10/2019/12/Improving-Earnings-Predictions-with-Machine-Learning-Hunt-Myers-Myers.pdf
[3] https://www.euclidean.com/better-than-human-forecasts
[4] https://cran.r-project.org/web/packages/edgaedgar.pdf.
[5] https://financialmodelingprep.com/developedocs/
submitted by xXx_Bunga_xXx to u/xXx_Bunga_xXx [link] [comments]

HXROBOT The easiest trading bot to config and run

HXROBOT The easiest trading bot to config and run
I would like to introduce you to HXROBOT

This FREE bot works in conjunction with HXRO website.
HXRO is a binary option website which allows you to bet a few BTC or erc20 HXRO tokens on the color of the next candle.

HXROBOT allows via an API to automate this process according to different indicators directly available (RSI, shochastic, bollinger ...).

https://preview.redd.it/w94aqm0g8fv41.png?width=1657&format=png&auto=webp&s=8f4d1e333b27ae7ab17fb9598925c6f5b06d7538
It is very easy to use and no coding skills are required. you just need to master the basic knowledge of trading indicators.
From there you can start to configure your strategy directly on the web page.
HXROBOT will take care of placing the bets for you on the HXRO site.

https://preview.redd.it/bzg8fzev8fv41.png?width=1039&format=png&auto=webp&s=bece73cd22a20b1188aea0db08945810d5904e07
You can also place bets manually.

You will find more explanations by watching this video
https://www.youtube.com/watch?v=oy-2n8el3XQ

A tutorial created by another user is also available
https://medium.com/@van_alek6/hxrobot-the-ultimate-tool-to-trade-on-hxro-1fc5c0da3f21

I allow myself to present this site to you because it allowed me to make a substantial profit after a few months of use.

https://preview.redd.it/9ktafw63afv41.png?width=1605&format=png&auto=webp&s=de7107ea30a63961b2e3c2defbfd8eb8a1c4e520

You can also join us on discord. The team will be happy to welcome new users and answer any questions regarding HXROBOT

HXRO link : https://beta.hxro.io/register?code=mathiews&campaign=default
HXROBOT link : https://hxrobot.io/?affiliate=1768fc77aa
Discord link : https://discord.gg/MndBFQ

See you soon
submitted by mathiews39 to u/mathiews39 [link] [comments]

Fairlearn - A Python package to assess AI system's fairness

In 2015, Claire Cain Miller wrote on The New York Times that there was a widespread belief that software and algorithms that rely on data were objective. Five years later, we know for sure that AI is not free of human influence. Data is created, stored, and processed by people, machine learning algorithms are written and maintained by people, and AI applications simply reflect people’s attitudes and behavior.
Data scientists know that no longer accuracy is the only concern when developing machine learning models, fairness must be considered as well. In order to make sure that machine learning solutions are fair and the value of their predictions easy to understand and explain, it is essential to build tools that developers and data scientists can use to assess their AI system’s fairness and mitigate any observed unfairness issues.
This article will focus on AI fairness, by explaining the following aspects and tools:
  1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues
  2. How to use Fairlearn in Azure Machine Learning
  3. What we mean by fairness
  4. Fairlearn algorithms
  5. Fairlearn dashboard
  6. Comparing multiple models
  7. Additional resources and how to contribute

1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues

Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system’s fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as a Jupyter widget for model assessment. The Fairlearn package has two components:
There is also a collection of Jupyter notebooks and an a detailed API guide, that you can check to learn how to leverage Fairlearn for your own data science scenario.

2. How to use Fairlearn in Azure Machine Learning

The Fairlearn package can be installed via:
pip install fairlearn
or optionally with a full feature set by adding extras, e.g. pip install fairlearn[customplots], or you can clone the repository locally via:
git clone [email protected]:fairlearn/fairlearn.git
In Azure Machine Learning, there are a few options to use Jupyter notebooks for your experiments:

a) Get Fairlearn samples on your notebook server

If you’d like to bring your own notebook server for local development, follow these steps:
  1. Use the instructions at Azure Machine Learning SDK to install the Azure Machine Learning SDK for Python
  2. Create an Azure Machine Learning workspace.
  3. Write a configuration file
  4. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Start the notebook server from your cloned directory.
jupyter notebook
For more information, see Install the Azure Machine Learning SDK for Python.
b) Get Fairlearn samples on DSVM
The Data Science Virtual Machine (DSVM) is a customized VM image built specifically for doing data science. If you create a DSVM, the SDK and notebook server are installed and configured for you. However, you’ll still need to create a workspace and clone the sample repository.
  1. Create an Azure Machine Learning workspace.
  2. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Add a workspace configuration file to the cloned directory using either of these methods:
  1. Start the notebook server from your cloned directory:
jupyter notebook

3. What we mean by fairness

Fighting against unfairness and discrimination has a long history in philosophy and psychology, and recently in machine learning. However, in order to be able to achieve fairness, we should first define the notion of it. An AI system can behave unfairly for a variety of reasons and many different fairness explanations have been used in literature, making this definition even more challenging. In general, fairness definitions fall under three different categories as follows:
In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harms. We focus on two kinds of harms:
We follow the approach known as group fairness, which asks: Which groups of individuals are at risk of experiencing harm? The relevant groups need to be specified by the data scientist and are application-specific. Group fairness is formalized by a set of constraints, which require that some aspect (or aspects) of the AI system’s behavior be comparable across the groups. The Fairlearn package enables the assessment and mitigation of unfairness under several common definitions.

4. Fairlearn algorithms

Fairlearn contains the following algorithms for mitigating unfairness in binary classification and regression:
https://preview.redd.it/5fzg767oh5051.png?width=898&format=png&auto=webp&s=731eab09b421c2dd3233ea9e184df136bf066739

5. Fairlearn dashboard

Fairlearn dashboard is a Jupyter notebook widget for assessing how a model’s predictions impact different groups (e.g., different ethnicities), and also for comparing multiple models along different fairness and accuracy metrics.
To assess a single model’s fairness and accuracy, the dashboard widget can be launched within a Jupyter notebook as follows:
from fairlearn.widget import FairlearnDashboard
# A_test containts your sensitive features (e.g., age, binary gender)
# sensitive_feature_names containts your sensitive feature names
# y_true contains ground truth labels
# y_pred contains prediction labels
FairlearnDashboard(sensitive_features=A_test,
sensitive_feature_names=['BinaryGender', 'Age'],
y_true=Y_test.tolist(),
y_pred=[y_pred.tolist()])
After the launch, the widget walks the user through the assessment set-up, where the user is asked to select:
  1. the sensitive feature of interest (e.g., binary gender or age)
  2. the accuracy metric (e.g., model precision) along which to evaluate the overall model performance as well as any disparities across groups.
These selections are then used to obtain the visualization of the model’s impact on the subgroups (e.g., model precision for females and model precision for males). The following figures illustrate the set-up steps, where binary gender is selected as a sensitive feature and the accuracy rate is selected as the accuracy metric:
After the set-up, the dashboard presents the model assessment in two panels, as summarized in the table, and visualized in the screenshot below:
https://preview.redd.it/juxlrmrkh5051.png?width=900&format=png&auto=webp&s=d92da30619369f5ab5109834ff7ff4ec3ad7f33d

6. Comparing multiple models

An additional feature that this dashboard offers is the comparison of multiple models, such as the models produced by different learning algorithms and different mitigation approaches, including:
As before, the user is first asked to select the sensitive feature and the accuracy metric. The model comparison view then depicts the accuracy and disparity of all the provided models in a scatter plot. This allows the user to examine trade-offs between algorithm accuracy and fairness. Moreover, each of the dots can be clicked to open the assessment of the corresponding model.
The figure below shows the model comparison view with binary gender selected as a sensitive feature and accuracy rate selected as the accuracy metric.

7. Additional resources and how to contribute

For references and additional resources, please refer to:
To contribute please check this contributing guide.
submitted by frlazzeri to deeplearning [link] [comments]

How much does it cost to develop a cryptocurrency exchange software?

https://preview.redd.it/rev67s9hs5u41.jpg?width=2048&format=pjpg&auto=webp&s=07b035b2926c73a59f4c361a36a6eda122184b1d
Cryptocurrency and crypto exchanges are the top trending businesses in the current digitally evolving sphere. Every budding entrepreneur aspires to enter the cryptoverse with their crypto exchange, and the demand and competition are rapidly increasing with each passing day. But one common question that intrigues them and not being addressed often is the cost of building an exchange software. In this article, we are going to quickly learn about all the factors that help determine and decide the cost for cryptocurrency exchange software development.
Factors that help shape the cost of an exchange software
The first step is to choose the type of exchange you want to build for your business. There are different types of crypto exchanges, each with its own characteristics and features. Therefore, the cost of building each platform differs from one another, depending on the requirements. Below is a quick glance at the types of exchanges.
1. Centralized exchanges
A centralized exchange is where a central authority manages the exchange orders and user funds. They will have complete control over the functionalities of the exchange and the transactions that happen on the platform.
2. Decentralized exchanges
With decentralized exchanges, there is no involvement from any third party having control over the transactions in the exchange. Users can conduct direct peer-to-peer transactions in a decentralized platform.
3. Hybrid exchanges
Hybrid exchanges are a combination of both centralized and decentralized exchanges. It eliminates the glitches in both the exchanges and provides a better optimized solution for traders enabling an efficient business experience.
These are the most common crypto exchange types. Other than these, there are also order book exchanges, ad-based exchanges, and binary exchanges which also possess their own characteristics.
There are two ways to go about developing a cryptocurrency exchange platform. The first one is,
1. Building from scratch
Building an exchange from scratch requires a ton of effort to gather the requirements for development, deployment, etc and needs technical assistance. This will take up a lot of time and cost you a fortune. Whereas, the second method eliminates these hassles.
2. Obtaining whitelabel cryptocurrency exchange software
The second option is to choose the right company and buy their whitelabel cryptocurrency exchange software. Whitelabel solutions are readily available solutions that are 100% tried and pre-tested. The customers just have to buy and install whitelabel solutions to kickstart the exchange. Whitelabel solutions are easy to deploy and cost way less when compared to building the exchange from the ground up.

Another factor is the features. The cost of the exchange also depends upon the features that the customers choose. However, the essential features that cannot be ignored while building an exchange are as follows,
  1. Multi-currency support integration
  2. Multi-language integration
  3. Secured multi-signature wallet
  4. Powerful trade matching engine
  5. Automated KYC/AML
  6. Integrated Liquidity API
  7. Investor dashboard
  8. Admin Backend panel
  9. Blockchain technology and smart contracts
  10. High volume TPS
  11. Payment gateway integration
  12. Automic swap option
  13. Trading Bots
  14. Integrated referral program
  15. Mobile applications support
  16. Enhanced security integration
These are the crucial factors that decide the cost of an exchange. Other than this, the personal customization preferences of the user also makes a difference in the cost of an exchange. However, as discussed earlier in this article, whitelabel solutions are comparatively cost-effective than an exchange built from scratch. The price of a whitelabel exchange with every essential feature, technical, security integrations, etc, ranges from $20,000 to $40,000, slightly differing from customer to customer based on their personal requirements.
CES is one of the experienced cryptocurrency exchange software development companies that will offer reliable whitelabel solutions for your exchange that can be launched within a jiffy at the best market prices. You can also avail their crypto exchange scripts that come with 100% source codes and help with quick, efficient deployment at lower costs.
If you are looking to launch your own exchange, get in touch with our team of experts to figure out the explicit quote!
submitted by AnnaLisbeth to AppDevelopment [link] [comments]

Fairlearn - A Python package to assess AI system's fairness

Fairlearn - A Python package to assess AI system's fairness
In 2015, Claire Cain Miller wrote on The New York Times that there was a widespread belief that software and algorithms that rely on data were objective. Five years later, we know for sure that AI is not free of human influence. Data is created, stored, and processed by people, machine learning algorithms are written and maintained by people, and AI applications simply reflect people’s attitudes and behavior.
Data scientists know that no longer accuracy is the only concern when developing machine learning models, fairness must be considered as well. In order to make sure that machine learning solutions are fair and the value of their predictions easy to understand and explain, it is essential to build tools that developers and data scientists can use to assess their AI system’s fairness and mitigate any observed unfairness issues.
This article will focus on AI fairness, by explaining the following aspects and tools:
  1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues
  2. How to use Fairlearn in Azure Machine Learning
  3. What we mean by fairness
  4. Fairlearn algorithms
  5. Fairlearn dashboard
  6. Comparing multiple models
  7. Additional resources and how to contribute

1. Fairlearn: a tool to assess AI system’s fairness and mitigate any observed unfairness issues

Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system’s fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as a Jupyter widget for model assessment. The Fairlearn package has two components:
  • A dashboard for assessing which groups are negatively impacted by a model, and for comparing multiple models in terms of various fairness and accuracy metrics.
  • Algorithms for mitigating unfairness in a variety of AI tasks and along a variety of fairness definitions.
There is also a collection of Jupyter notebooks and an a detailed API guide, that you can check to learn how to leverage Fairlearn for your own data science scenario.

2. How to use Fairlearn in Azure Machine Learning

The Fairlearn package can be installed via:
pip install fairlearn
or optionally with a full feature set by adding extras, e.g. pip install fairlearn[customplots], or you can clone the repository locally via:
git clone [email protected]:fairlearn/fairlearn.git
In Azure Machine Learning, there are a few options to use Jupyter notebooks for your experiments:

a) Get Fairlearn samples on your notebook server

If you’d like to bring your own notebook server for local development, follow these steps:
  1. Use the instructions at Azure Machine Learning SDK to install the Azure Machine Learning SDK for Python
  2. Create an Azure Machine Learning workspace.
  3. Write a configuration file
  4. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Start the notebook server from your cloned directory.
jupyter notebook
For more information, see Install the Azure Machine Learning SDK for Python.
b) Get Fairlearn samples on DSVM
The Data Science Virtual Machine (DSVM) is a customized VM image built specifically for doing data science. If you create a DSVM, the SDK and notebook server are installed and configured for you. However, you’ll still need to create a workspace and clone the sample repository.
  1. Create an Azure Machine Learning workspace.
  2. Clone the GitHub repository.
git clone [email protected]:fairlearn/fairlearn.git
  1. Add a workspace configuration file to the cloned directory using either of these methods:
  • In the Azure portal, select Download config.json from the Overview section of your workspace.
  • Create a new workspace using code in the configuration.ipynb notebook in your cloned directory
  1. Start the notebook server from your cloned directory:
jupyter notebook

3. What we mean by fairness

Fighting against unfairness and discrimination has a long history in philosophy and psychology, and recently in machine learning. However, in order to be able to achieve fairness, we should first define the notion of it. An AI system can behave unfairly for a variety of reasons and many different fairness explanations have been used in literature, making this definition even more challenging. In general, fairness definitions fall under three different categories as follows:
  • Individual Fairness – Give similar predictions to similar individuals.
  • Group Fairness – Treat different groups equally.
  • Subgroup Fairness – Subgroup fairness intends to obtain the best properties of the group and individual notions of fairness.
In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harms. We focus on two kinds of harms:
  • Allocation harms. These harms can occur when AI systems extend or withhold opportunities, resources, or information. Some of the key applications are in hiring, school admissions, and lending.
  • Quality-of-service harms. Quality of service refers to whether a system works as well for one person as it does for another, even if no opportunities, resources, or information are extended or withheld.
We follow the approach known as group fairness, which asks: Which groups of individuals are at risk of experiencing harm? The relevant groups need to be specified by the data scientist and are application-specific. Group fairness is formalized by a set of constraints, which require that some aspect (or aspects) of the AI system’s behavior be comparable across the groups. The Fairlearn package enables the assessment and mitigation of unfairness under several common definitions.

4. Fairlearn algorithms

Fairlearn contains the following algorithms for mitigating unfairness in binary classification and regression:
https://preview.redd.it/2inmvd6g75051.png?width=899&format=png&auto=webp&s=3386410974a9e3640ef8ef8a409a2f19f989330a

5. Fairlearn dashboard

Fairlearn dashboard is a Jupyter notebook widget for assessing how a model’s predictions impact different groups (e.g., different ethnicities), and also for comparing multiple models along different fairness and accuracy metrics.
To assess a single model’s fairness and accuracy, the dashboard widget can be launched within a Jupyter notebook as follows:
from fairlearn.widget import FairlearnDashboard
# A_test containts your sensitive features (e.g., age, binary gender)
# sensitive_feature_names containts your sensitive feature names
# y_true contains ground truth labels
# y_pred contains prediction labels
FairlearnDashboard(sensitive_features=A_test,
sensitive_feature_names=['BinaryGender', 'Age'],
y_true=Y_test.tolist(),
y_pred=[y_pred.tolist()])
After the launch, the widget walks the user through the assessment set-up, where the user is asked to select:
  1. the sensitive feature of interest (e.g., binary gender or age)
  2. the accuracy metric (e.g., model precision) along which to evaluate the overall model performance as well as any disparities across groups.
These selections are then used to obtain the visualization of the model’s impact on the subgroups (e.g., model precision for females and model precision for males). The following figures illustrate the set-up steps, where binary gender is selected as a sensitive feature and the accuracy rate is selected as the accuracy metric:
After the set-up, the dashboard presents the model assessment in two panels, as summarized in the table, and visualized in the screenshot below:

https://preview.redd.it/enskhh7i75051.png?width=900&format=png&auto=webp&s=db98cb058029655757df1946e42bca4831170451

6. Comparing multiple models

An additional feature that this dashboard offers is the comparison of multiple models, such as the models produced by different learning algorithms and different mitigation approaches, including:
  • fairlearn.reductions.GridSearch
  • fairlearn.reductions.ExponentiatedGradient
  • fairlearn.postprocessing.ThresholdOptimizer
As before, the user is first asked to select the sensitive feature and the accuracy metric. The model comparison view then depicts the accuracy and disparity of all the provided models in a scatter plot. This allows the user to examine trade-offs between algorithm accuracy and fairness. Moreover, each of the dots can be clicked to open the assessment of the corresponding model.
The figure below shows the model comparison view with binary gender selected as a sensitive feature and accuracy rate selected as the accuracy metric.

7. Additional resources and how to contribute

For references and additional resources, please refer to:
To contribute please check this contributing guide.
submitted by frlazzeri to learnmachinelearning [link] [comments]

Derivatives (futures/swaps/options) at one site: ContractMarketCap

Derivatives (futures/swaps/options) at one site: ContractMarketCap
We are launching world's first crypto derivatives market data portal https://contractmarketcap.com - like coinmarketcap but only strong focused for derivatives. All from top exchanges, such as Huobi, OKEx, Binance, Kraken have derivatives, up to 10 projects launched as dedicated derivative exchanges. And yes, BitMEX ist'n the biggest market, only closes top-3.
Now we have 100% market coverage. Products: Futures - vanilla, inverse, quanto, perpetual, Swap and Options (European, binary etc., coming soon). Also, we can provide market data API (~ETA Q1'2020).
  • 16 exchange connected
  • 174 products
  • 122 indices for mark-to-market and settlement
  • Top coin markets: BTC, ETH, Ripple, ETC, LTC, EOS, BNB, BCH, BSV, TRX
  • Exotic - 3 tradable wide market indices at Delta.Exchange (available soon)
  • 24h trading volume: 22B$(BTC domination: 80%)
  • Open Interest: 11.2B$ (BTC:30%)
For shortly example:
BTC derivatives market
Looks interesting? Yours feedback? What we can do to be the better?
submitted by tntneal7 to CryptoCurrency [link] [comments]

Marchero

With two cryptocurrency integrations under my belt I've set out with a solid plan for Monero.
Instead of jumping in and figuring it out as I go along I want to make sure that I have the solutions I'll need, well defined and scoped out. It's a good thing I'm going this path, too.
Turns out that there isn't really much in the way of JavaScript code for Monero out there. Probably the two best (and possibly only), solutions at the moment are monero-javascript and MyMonero.
Although there's no official Monero JavaScript library, the monero-javascript project is probably the closest to the original C++ client. This derived code comes in the form of WebAssembly, a low-level language that runs a lot closer to the metal than JavaScript. This means that WebAssembly is generally faster when doing things like calculations, but with some trade-offs like ease of use. JavaScript is a lot easier to code and understand, but it tends to run slower. In modern browsers, both run side by side so developers can decide which parts need to work fast, and which parts need to be easier to change and maintain.
The MyMonero project is actually a set of supporting libraries for a Monero wallet. It seems to have a lot more "real world" mileage but has a smaller dev team and hasn't been updated for many months, unlike monero-javascript which was updated as recently as today. MyMonero also uses WebAssembly for some of the core wallet functionality but the "bindings", or how this functionality is exposed to JavaScript, are different.
Between the two solutions, I've had more luck in getting support for monero-javascript. Neither project is well documented so having someone to run questions by is, at least at this point, a winning feature.
This will be my first time working with WebAssembly so factoring in some learning time is prudent. I'll only be learning to use existing code rather than learning how to write it but still, it's a new thing for me.
As I mentioned, support for Monero via JavaScript is surprisingly rare, so I may end up contributing some original code back to the project(s) I'll be using. I haven't yet decided which of them will find its way into CypherPoker.JS because there are still some open questions about how certain things are done and if they're even possible. However, right now monero-javascript is looking like the best choice.
Once I'm satisfied that all the building blocks are viable, I'll add the cryptocurrency handler and integrate it all the way through to the front end. As with BTC and BCH, a Monero testnet option will be available along with a faucet so that you can test it out without using any actual XMR. A full client option that uses the downloadable monerod client, the equivalent of a full Bitcoin node, will be available alongside the "light" option which will use external APIs. After that I'll upgrade the live demo (and server), update the wallet generator, post v0.5.2 code documentation, create a new release, and write a wrap-up post.
I want to stress again that this is all contingent on whether or not the JavaScript libraries actually do what I need them to do, and if there exists at least one public API that can be used in place of the daemon (monerod). But based on the help I've received from the Monero community so far I'm feeling optimistic.
Probably the most interesting thing about the Monero integration is that it represents the final, Rumsfeldian, "known unknown" of the CypherPoker.JS project. It's the last thing I'm embarking on with essentially zero prior knowledge; I know that I know nothing.
The rest of the project, the other cryptocurrencies, smart contracts, peer-to-peer communications - those are all things I'd at least had an introduction to if not outright practical knowledge of. When it comes time to add Ethereum support I'll be able to confidently say that it's nothing new. Incorporating Tor anonymization ... been there, done that.
When it comes to Monero, though, I'm a wide-eyed ignoramus. Never owned any XMR, never ran a Monero wallet, never tried out a Monero block explorer; seems I'm even getting some of the technical terminology wrong. Basically, it's the last part of the project's core vision that comes with a steep learning curve and a not-insignificant chance of failure. I mean, I don't think I'm gonna fail but I can't point to any definitive reason why I should think that.
There's no pragmatic reason to believe that March and (the) Monero (integration) will be contemporaneous but then again, why not?
submitted by monican_agent to cypherpoker [link] [comments]

Временно бесплатные курсы Udemy

Временно бесплатные курсы Udemy

https://preview.redd.it/se7zt100k9c31.jpg?width=700&format=pjpg&auto=webp&s=b7d9eb97754935764b044d2dd31900c6106efab5
Подборка временно бесплатных курсов Udemy.122 шт. Промокоды, вшиты в ссылки.Все курсы на английском.

  1. Agile Retrospective: Continuous Improvement + Kaizen Wth Scrum
  2. Artificial Intelligence Concepts - AI 101
  3. Build Interactive Apps Using VueJS, Vuex And VueRouter
  4. C Programming 2019
  5. CloverETL Data Integration
  6. Create A SHMUP With Unity 3D
  7. Google Cloud Platform Associate Cloud Engineer Practice Test
  8. How To Create Android Apps Without Coding Advance Course
  9. How to Install Linux Mint (Cinnamon) on a Virtual Machine
  10. How to Install Ubuntu Linux on a Virtual Machine
  11. How To Uv Unwrap Models In Blender
12. Introduction To SAS
13. iOS 12 Chat Application Like WhatsApp And Viber
14. iOS App Grocery List (Swift 3.1, iOS10.3) From 0 To AppStore
  1. iOS12 Animations, Learn Swift Animation With UIKit
16. iOS12 Bootcamp From Beginner To Professional iOS Developer
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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.

https://preview.redd.it/coenpm4k3cc41.jpg?width=808&format=pjpg&auto=webp&s=8832dcafa5bd615b511bbeb6118ef43d73ed785e
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.
https://preview.redd.it/fabg1nxo3cc41.jpg?width=808&format=pjpg&auto=webp&s=6d939889c3eac15ab1e78ec37a8ccd13fc5e0573
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.
https://preview.redd.it/wel3rka04cc41.png?width=669&format=png&auto=webp&s=3f93dac2d3b40aa842d281384113d2e26f25947e
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.
https://preview.redd.it/t04qs3484cc41.jpg?width=808&format=pjpg&auto=webp&s=a3b106cbc9116713dcdd5e908c00b555fd704ee6
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.
https://preview.redd.it/8k81zl0a4cc41.jpg?width=808&format=pjpg&auto=webp&s=e30e5b7ca93d2931f49e2dc84025f2fda386eab1
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
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Trading platform to use for Automating Forex Trading

Hello everyone! I've just spent the last two weeks researching different trading platforms attempting to determine which would be the best for my use cases. I have scoped out Metatrader 4, Quantopian's Zipline, and many other platforms.
I would like to use a platform that allows me to use Python or C++ to leverage my Computer Science background as well as work with many Forex Brokers. Ideally I would like to use enterprise level software that would work with the Forex Market as well as the Cryptocurrency, equities and options markets. Though as a private investor just getting into the space I would prefer a cheaper solution.
If anyone can point me in the proper directly I would greatly appreciate the help!
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List of AMA answers Hero Design and Balance.

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Binary Options Trading for Beginners 2020! How to create an API Token in binary.com 2. How to create a token API with Binary com to Copy trade signals Binary.com API bridge Binary Options Trading: What is it?? How does it work??

Binary.com is an award-winning online trading provider that helps its clients to trade on financial markets through binary options and CFDs. Trading binary options and CFDs on Synthetic Indices is classified as a gambling activity. Remember that gambling can be addictive – please play responsibly. Learn more about Responsible Trading. Some There are two trading methods implemented on TRIBTC’s platforms: – Call/Put orders – Touch/No Touch orders Trades can be placed on timeframes of 1, 5, 15, 30 minutes, 1 or 4 hours, 1, 2, 7 or 15 days, a month or custom intervals. Users can place their own trades, or can bet against trades placed […] Binary.com is an award-winning online trading provider that helps its clients to trade on financial markets through binary options and CFDs. Trading binary options and CFDs on Synthetic Indices is classified as a gambling activity. Remember that gambling can be addictive – please play responsibly. Learn more about Responsible Trading. Some Trading binary options has large potential rewards, but also large potential risks. You must be aware of the risks and be willing to accept them in order to trade binary options. Don’t trade with money you can’t afford to lose. Day trading, short term trading, options trading, and futures trading are risky undertakings. trading trading-bot trading-api trading-strategies trading-algorithms forex-trading forex-prediction trading-systems forexconnect-api binary-options Updated Sep 25, 2019 C++

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Binary Options Trading for Beginners 2020!

How to create an API Token in binary.com Creating an API Token in binary.com is the same for both a demo account and a real account. Log into your account and select the small drop down arrow next ... Make 10 usd Every 50 Seconds Trading Binary Options 100% WINS - Profitable 2018 Trading strategies - Duration: 5:58. Proudly Tech Money General Tips And Tricks 42,435 views The road to success through trading IQ option Best Bot Reviews Iq Option 2020 ,We make videos using this softwhere bot which aims to make it easier for you to trade, because to use the usual ... #binary_options_trading #binary_options_strategy_2020 #binary_options #binary_options_strategy #binary_options_strategies #binary_options_signals Loading... Autoplay When autoplay is enabled, a ... Binary.com API bridge It Lab. Loading... Unsubscribe from It Lab? ... How To Use MetaTrader 4 - For Binary Options Trading - Duration: 21:18. CommuniTraders Live 14,966 views.

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