Online trading platform for binary options on forex, stock ...

[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]

Welcome to PlotX - Read this to GET STARTED

🌐 Welcome to the Official PlotX Reddit Community
👉 PlotX is a non-custodial prediction protocol that enables users to earn rewards on high-yield prediction markets.
SUMMARY: Dubbed as the Uniswap of Prediction Markets, PlotX uses an Automated Market Making algorithm to settle markets and distribute rewards on the Ethereum Blockchain without any counterparty risk. Markets are focused on crypto-pairs like BTC-USD & ETH-USD and created in intervals of 1h, 1d and 1w.

🤑 Buy $PLOT from Uniswap: https://app.uniswap.org/#/swap?outputCurrency=0x72f020f8f3e8fd9382705723cd26380f8d0c66bb&inputCurrency=ETH

👩‍🌾 Set up a $PLOT Yield Farm (200%+ APY): https://liquidity.plotx.io

Ish Goel Interview with Boxmining
Ish Goel Podcast Interview

📰 PlotX in the News
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✌️PLOT Shill & Chill
◘ Experienced Team - cofounded by ex-CTO & Lead Engineer of Nexus Mutual
◘ Live product with 250+ users & clear token utilization
◘ On-chain community governance already active
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◘ Partnerships with Chainlink, Matic, Elrond, Ankr, 3commas
◘ Super Low Market Cap

Find out more
💪 PlotX Beta | 🌐 Website | 📺 Team Intro | 📄 Whitepaper |⁉️ FAQs | 🤓 PLOT Token | 📙 Tokenomics | 🏁 How to get started with PlotX

👉 FAQ
How is PlotX different from other prediction markets?
What is the Token Utility?

⚠️ Important ⚠️
❌ There are a few fake PlotX tokens listed on Uniswap. Please confirm the token address prior to making a trade - https://etherscan.io/token/0x72f020f8f3e8fd9382705723cd26380f8d0c66bb
❌ Please be on the lookout for scammers who impersonate admins and send private messages with fake addresses (carefully check their usernames or bio), they are fake.
❌ If anyone from PlotX DMs you first, confirm their identity in the official group prior to responding.
❌ Do NOT send tokens to anyone claiming to be from PlotX.

🙏 Community Rules
✔️ No FUD or Spam will be tolerated
✔️ Please only write in English in this group
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✔️ No swearing and show respect for each other
✔️ Share only relevant news and articles

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submitted by ankitnayan to PlotX [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]

[ANN][ANDROID MINING][AIRDROP] NewEnglandcoin: Scrypt RandomSpike

New England
New England 6 States Songs: https://www.reddit.com/newengland/comments/er8wxd/new_england_6_states_songs/
NewEnglandcoin
Symbol: NENG
NewEnglandcoin is a clone of Bitcoin using scrypt as a proof-of-work algorithm with enhanced features to protect against 51% attack and decentralize on mining to allow diversified mining rigs across CPUs, GPUs, ASICs and Android phones.
Mining Algorithm: Scrypt with RandomSpike. RandomSpike is 3rd generation of Dynamic Difficulty (DynDiff) algorithm on top of scrypt.
1 minute block targets base difficulty reset: every 1440 blocks subsidy halves in 2.1m blocks (~ 2 to 4 years) 84,000,000,000 total maximum NENG 20000 NENG per block Pre-mine: 1% - reserved for dev fund ICO: None RPCPort: 6376 Port: 6377
NewEnglandcoin has dogecoin like supply at 84 billion maximum NENG. This huge supply insures that NENG is suitable for retail transactions and daily use. The inflation schedule of NengEnglandcoin is actually identical to that of Litecoin. Bitcoin and Litecoin are already proven to be great long term store of value. The Litecoin-like NENG inflation schedule will make NewEnglandcoin ideal for long term investment appreciation as the supply is limited and capped at a fixed number
Bitcoin Fork - Suitable for Home Hobbyists
NewEnglandcoin core wallet continues to maintain version tag of "Satoshi v0.8.7.5" because NewEnglandcoin is very much an exact clone of bitcoin plus some mining feature changes with DynDiff algorithm. NewEnglandcoin is very suitable as lite version of bitcoin for educational purpose on desktop mining, full node running and bitcoin programming using bitcoin-json APIs.
The NewEnglandcoin (NENG) mining algorithm original upgrade ideas were mainly designed for decentralization of mining rigs on scrypt, which is same algo as litecoin/dogecoin. The way it is going now is that NENG is very suitable for bitcoin/litecoin/dogecoin hobbyists who can not , will not spend huge money to run noisy ASIC/GPU mining equipments, but still want to mine NENG at home with quiet simple CPU/GPU or with a cheap ASIC like FutureBit Moonlander 2 USB or Apollo pod on solo mining setup to obtain very decent profitable results. NENG allows bitcoin litecoin hobbyists to experience full node running, solo mining, CPU/GPU/ASIC for a fun experience at home at cheap cost without breaking bank on equipment or electricity.
MIT Free Course - 23 lectures about Bitcoin, Blockchain and Finance (Fall,2018)
https://www.youtube.com/playlist?list=PLUl4u3cNGP63UUkfL0onkxF6MYgVa04Fn
CPU Minable Coin Because of dynamic difficulty algorithm on top of scrypt, NewEnglandcoin is CPU Minable. Users can easily set up full node for mining at Home PC or Mac using our dedicated cheetah software.
Research on the first forked 50 blocks on v1.2.0 core confirmed that ASIC/GPU miners mined 66% of 50 blocks, CPU miners mined the remaining 34%.
NENG v1.4.0 release enabled CPU mining inside android phones.
Youtube Video Tutorial
How to CPU Mine NewEnglandcoin (NENG) in Windows 10 Part 1 https://www.youtube.com/watch?v=sdOoPvAjzlE How to CPU Mine NewEnglandcoin (NENG) in Windows 10 Part 2 https://www.youtube.com/watch?v=nHnRJvJRzZg
How to CPU Mine NewEnglandcoin (NENG) in macOS https://www.youtube.com/watch?v=Zj7NLMeNSOQ
Decentralization and Community Driven NewEnglandcoin is a decentralized coin just like bitcoin. There is no boss on NewEnglandcoin. Nobody nor the dev owns NENG.
We know a coin is worth nothing if there is no backing from community. Therefore, we as dev do not intend to make decision on this coin solely by ourselves. It is our expectation that NewEnglandcoin community will make majority of decisions on direction of this coin from now on. We as dev merely view our-self as coin creater and technical support of this coin while providing NENG a permanent home at ShorelineCrypto Exchange.
Twitter Airdrop
Follow NENG twitter and receive 100,000 NENG on Twitter Airdrop to up to 1000 winners
Graphic Redesign Bounty
Top one award: 90.9 million NENG Top 10 Winners: 500,000 NENG / person Event Timing: March 25, 2019 - Present Event Address: NewEnglandcoin DISCORD at: https://discord.gg/UPeBwgs
Please complete above Twitter Bounty requirement first. Then follow Below Steps to qualify for the Bounty: (1) Required: submit your own designed NENG logo picture in gif, png jpg or any other common graphic file format into DISCORD "bounty-submission" board (2) Optional: submit a second graphic for logo or any other marketing purposes into "bounty-submission" board. (3) Complete below form.
Please limit your submission to no more than two total. Delete any wrongly submitted or undesired graphics in the board. Contact DISCORD u/honglu69#5911 or u/krypton#6139 if you have any issues.
Twitter Airdrop/Graphic Redesign bounty sign up: https://goo.gl/forms/L0vcwmVi8c76cR7m1
Milestones
Roadmap
NENG v1.4.0 Android Mining, randomSpike Evaluation https://github.com/ShorelineCrypto/NewEnglandCoin/releases/download/NENG_2020_Q3_report/NENG_2020_Q3_report.pdf
RandomSpike - NENG core v1.3.0 Hardfork Upgrade Proposal https://github.com/ShorelineCrypto/NewEnglandCoin/releases/download/2020Q1_Report/Scrypt_RandomSpike_NENGv1.3.0_Hardfork_Proposal.pdf
NENG Security, Decentralization & Valuation
https://github.com/ShorelineCrypto/NewEnglandCoin/releases/download/2019Q2_report/NENG_Security_Decentralization_Value.pdf
Whitepaper v1.0 https://github.com/ShorelineCrypto/NewEnglandCoin/releases/download/whitepaper_v1.0/NENG_WhitePaper.pdf
DISCORD https://discord.gg/UPeBwgs
Explorer
http://www.findblocks.com/exploreNENG http://86.100.49.209/exploreNENG http://nengexplorer.mooo.com:3001/
Step by step guide on how to setup an explorer: https://github.com/ShorelineCrypto/nengexplorer
Github https://github.com/ShorelineCrypto/NewEnglandCoin
Wallet
Android with UserLand App (arm64/armhf), Chromebook (x64/arm64/armhf): https://github.com/ShorelineCrypto/NewEnglandCoin/releases/tag/v1.4.0.5
Linux Wallet (Ubuntu/Linux Mint, Debian/MX Linux, Arch/Manjaro, Fedora, openSUSE): https://github.com/ShorelineCrypto/NewEnglandCoin/releases/tag/v1.4.0.3
MacOS Wallet (10.11 El Capitan or higher): https://github.com/ShorelineCrypto/NewEnglandCoin/releases/tag/v1.4.0.2
Android with GNUroot on 32 bits old Phones (alpha release) wallet: https://github.com/ShorelineCrypto/NewEnglandCoin/releases/tag/v1.4.0
Windows wallet: https://github.com/ShorelineCrypto/NewEnglandCoin/releases/tag/v1.3.0.1
addnode ip address for the wallet to sync faster, frequently updated conf file: https://github.com/ShorelineCrypto/cheetah_cpumineblob/mastenewenglandcoin.conf-example
How to Sync Full Node Desktop Wallet https://www.reddit.com/NewEnglandCoin/comments/er6f0q/how_to_sync_full_node_desktop_wallet/
TWITTER https://twitter.com/newenglandcoin
REDDIT https://www.reddit.com/NewEnglandCoin/
Cheetah CPU Miner Software https://github.com/ShorelineCrypto/cheetah_cpuminer
Solo Mining with GPU or ASIC https://bitcointalk.org/index.php?topic=5027091.msg52187727#msg52187727
How to Run Two Full Node in Same Desktop PC https://bitcointalk.org/index.php?topic=5027091.msg53581449#msg53581449
ASIC/GPU Mining Pools Warning to Big ASIC Miners Due to DynDiff Algo on top of Scrypt, solo mining is recommended for ASIC/GPU miners. Further more, even for mining pools, small mining pool will generate better performance than big NENG mining pool because of new algo v1.2.x post hard fork.
The set up configuration of NENG for scrypt pool mining is same as a typical normal scrypt coin. In other word, DynDiff on Scrypt algo is backward compatible with Scrypt algo. Because ASIC/GPU miners rely on CPU miners for smooth blockchain movement, checkout bottom of "Latest News" section for A WARNING to All ASIC miners before you decide to dump big ASIC hash rate into NENG mining.
(1) Original DynDiff Warning: https://bitcointalk.org/index.php?topic=5027091.msg48324708#msg48324708 (2) New Warning on RandomSpike Spike difficulty (244k) introduced in RandomSpike served as roadblocks to instant mining and provide security against 51% attack risk. However, this spike difficulty like a roadblock that makes big ASIC mining less profitable. In case of spike block to be mined, the spike difficulty immediately serve as base difficulty, which will block GPU/ASIC miners effectively and leave CPU cheetah solo miners dominating mining almost 100% until next base difficulty reset.
FindBlocks http://findblocks.com/
CRpool http://crpool.xyz/
Cminors' Pool http://newenglandcoin.cminors-pool.com/
SPOOL https://spools.online/
Exchange
📷
https://shorelinecrypto.com/
Features: anonymous sign up and trading. No restriction or limit on deposit or withdraw.
The trading pairs available: NewEnglandcoin (NENG) / Dogecoin (DOGE)
Trading commission: A round trip trading will incur 0.10% trading fees in average. Fees are paid only on buyer side. buy fee: 0.2% / sell fee: 0% Deposit fees: free for all coins Withdraw fees: ZERO per withdraw. Mining fees are appointed by each coin blockchain. To cover the blockchain mining fees, there is minimum balance per coin per account: * Dogecoin 2 DOGE * NewEnglandcoin 1 NENG
Latest News Aug 30, 2020 - NENG v1.4.0.5 Released for Android/Chromebook Upgrade with armhf, better hardware support https://bitcointalk.org/index.php?topic=5027091.msg55098029#msg55098029
Aug 11, 2020 - NENG v1.4.0.4 Released for Android arm64 Upgrade / Chromebook Support https://bitcointalk.org/index.php?topic=5027091.msg54977437#msg54977437
Jul 30, 2020 - NENG v1.4.0.3 Released for Linux Wallet Upgrade with 8 Distros https://bitcointalk.org/index.php?topic=5027091.msg54898540#msg54898540
Jul 21, 2020 - NENG v1.4.0.2 Released for MacOS Upgrade with Catalina https://bitcointalk.org/index.php?topic=5027091.msg54839522#msg54839522
Jul 19, 2020 - NENG v1.4.0.1 Released for MacOS Wallet Upgrade https://bitcointalk.org/index.php?topic=5027091.msg54830333#msg54830333
Jul 15, 2020 - NENG v1.4.0 Released for Android Mining, Ubuntu 20.04 support https://bitcointalk.org/index.php?topic=5027091.msg54803639#msg54803639
Jul 11, 2020 - NENG v1.4.0 Android Mining, randomSpike Evaluation https://bitcointalk.org/index.php?topic=5027091.msg54777222#msg54777222
Jun 27, 2020 - Pre-Announce: NENG v1.4.0 Proposal for Mobile Miner Upgrade, Android Mining Start in July 2020 https://bitcointalk.org/index.php?topic=5027091.msg54694233#msg54694233
Jun 19, 2020 - Best Practice for Futurebit Moonlander2 USB ASIC on solo mining mode https://bitcointalk.org/index.php?topic=5027091.msg54645726#msg54645726
Mar 15, 2020 - Scrypt RandomSpike - NENG v1.3.0.1 Released for better wallet syncing https://bitcointalk.org/index.php?topic=5027091.msg54030923#msg54030923
Feb 23, 2020 - Scrypt RandomSpike - NENG Core v1.3.0 Relased, Hardfork on Mar 1 https://bitcointalk.org/index.php?topic=5027091.msg53900926#msg53900926
Feb 1, 2020 - Scrypt RandomSpike Proposal Published- NENG 1.3.0 Hardfork https://bitcointalk.org/index.php?topic=5027091.msg53735458#msg53735458
Jan 15, 2020 - NewEnglandcoin Dev Team Expanded with New Kickoff https://bitcointalk.org/index.php?topic=5027091.msg53617358#msg53617358
Jan 12, 2020 - Explanation of Base Diff Reset and Effect of Supply https://www.reddit.com/NewEnglandCoin/comments/envmo1/explanation_of_base_diff_reset_and_effect_of/
Dec 19, 2019 - Shoreline_tradingbot version 1.0 is released https://bitcointalk.org/index.php?topic=5121953.msg53391184#msg53391184
Sept 1, 2019 - NewEnglandcoin (NENG) is Selected as Shoreline Tradingbot First Supported Coin https://bitcointalk.org/index.php?topic=5027091.msg52331201#msg52331201
Aug 15, 2019 - Mining Update on Effect of Base Difficulty Reset, GPU vs ASIC https://bitcointalk.org/index.php?topic=5027091.msg52169572#msg52169572
Jul 7, 2019 - CPU Mining on macOS Mojave is supported under latest Cheetah_Cpuminer Release https://bitcointalk.org/index.php?topic=5027091.msg51745839#msg51745839
Jun 1, 2019 - NENG Fiat project is stopped by Square, Inc https://bitcointalk.org/index.php?topic=5027091.msg51312291#msg51312291
Apr 21, 2019 - NENG Fiat Project is Launched by ShorelineCrypto https://bitcointalk.org/index.php?topic=5027091.msg50714764#msg50714764
Apr 7, 2019 - Announcement of Fiat Project for all U.S. Residents & Mobile Miner Project Initiation https://bitcointalk.org/index.php?topic=5027091.msg50506585#msg50506585
Apr 1, 2019 - Disclosure on Large Buying on NENG at ShorelineCrypto Exchange https://bitcointalk.org/index.php?topic=5027091.msg50417196#msg50417196
Mar 27, 2019 - Disclosure on Large Buying on NENG at ShorelineCrypto Exchange https://bitcointalk.org/index.php?topic=5027091.msg50332097#msg50332097
Mar 17, 2019 - Disclosure on Large Buying on NENG at ShorelineCrypto Exchange https://bitcointalk.org/index.php?topic=5027091.msg50208194#msg50208194
Feb 26, 2019 - Community Project - NewEnglandcoin Graphic Redesign Bounty Initiated https://bitcointalk.org/index.php?topic=5027091.msg49931305#msg49931305
Feb 22, 2019 - Dev Policy on Checkpoints on NewEnglandcoin https://bitcointalk.org/index.php?topic=5027091.msg49875242#msg49875242
Feb 20, 2019 - NewEnglandCoin v1.2.1 Released to Secure the Hard Kork https://bitcointalk.org/index.php?topic=5027091.msg49831059#msg49831059
Feb 11, 2019 - NewEnglandCoin v1.2.0 Released, Anti-51% Attack, Anti-instant Mining after Hard Fork https://bitcointalk.org/index.php?topic=5027091.msg49685389#msg49685389
Jan 13, 2019 - Cheetah_CpuMiner added support for CPU Mining on Mac https://bitcointalk.org/index.php?topic=5027091.msg49218760#msg49218760
Jan 12, 2019 - NENG Core v1.1.2 Released to support MacOS OSX Wallet https://bitcointalk.org/index.php?topic=5027091.msg49202088#msg49202088
Jan 2, 2019 - Cheetah_Cpuminer v1.1.0 is released for both Linux and Windows https://bitcointalk.org/index.php?topic=5027091.msg49004345#msg49004345
Dec 31, 2018 - Technical Whitepaper is Released https://bitcointalk.org/index.php?topic=5027091.msg48990334#msg48990334
Dec 28, 2018 - Cheetah_Cpuminer v1.0.0 is released for Linux https://bitcointalk.org/index.php?topic=5027091.msg48935135#msg48935135
Update on Dec 14, 2018 - NENG Blockchain Stuck Issue https://bitcointalk.org/index.php?topic=5027091.msg48668375#msg48668375
Nov 27, 2018 - Exclusive for PC CPU Miners - How to Steal a Block from ASIC Miners https://bitcointalk.org/index.php?topic=5027091.msg48258465#msg48258465
Nov 28, 2018 - How to CPU Mine a NENG block with window/linux PC https://bitcointalk.org/index.php?topic=5027091.msg48298311#msg48298311
Nov 29, 2018 - A Warning to ASIC Miners https://bitcointalk.org/index.php?topic=5027091.msg48324708#msg48324708
Disclosure: Dev Team Came from ShorelineCrypto, a US based Informatics Service Business offering Fee for service for Coin Creation, Coin Exchange Listing, Blockchain Consulting, etc.
submitted by honglu69 to NewEnglandCoin [link] [comments]

Your /r/javascript recap for the week of August 24 - August 30

Monday, August 24 - Sunday, August 30

Top Posts

score comments title & link
441 34 comments ztext.js - a clever new JS library (3.9 kb) that makes any font 3D
438 107 comments TIL, "JavaScript" is a trademark of Oracle Corporation in the United States
335 30 comments Visualize your Data Structures in VS Code
269 16 comments Making WAVs: Understanding a Binary File Format by Parsing and Creating WAV Files from Scratch in JavaScript
232 135 comments Why I Don’t Use GraphQL Anymore
217 12 comments ePaper.js - Node.js library for easily creating an ePaper display on a Raspberry PI using HTML and Javascript
183 67 comments I created a plugin for ESLint that sorts imports in a beautiful way
148 23 comments I built a website where you can guess the total number of npm dependencies and also display them in a tree view
143 6 comments React Internals (Part 2) - Reconciliation algorithm until React 15
141 19 comments Probably more than what you want to know about node shebang (medium, not paywalled)
 

Most Commented Posts

score comments title & link
18 38 comments [AskJS] [AskJS] Is it industry practice NOT to handle network errors?
78 31 comments Midway Serverless - A Node.js framework for Serverless - Interview with Harry Chen
32 29 comments [AskJS] [AskJS] How do you guys expose internals of a module for testing without adding it to the API surface?
39 25 comments Setting up a Micro Frontend architecture with Vue and single-spa
6 24 comments [AskJS] [AskJS] object destructuring vs dot notation. Performance and cohesiveness.
 

Top Ask JS

score comments title & link
11 19 comments [AskJS] [AskJS] To Deno, or Not to Deno?
10 14 comments [AskJS] [AskJS] When are service workers worth it?
2 19 comments [AskJS] [AskJS] Is RPC the future?
 

Top Showoffs

score comment
3 samdawsondev said Wrote an article on [How not to GraphQL](https://www.samdawson.dev/article/how-not-to-graphql)
3 Jaskys said Rebuilt my portfolio recently, would like to get some feedback https://dev.jaska.dev/
3 hp4k1h5 said iexcli is somewhat stable now. would appreciate feedback. https://github.com/HP4k1h5/iexcli
 

Top Comments

score comment
274 anlumo said ECMAScript is the correct term which sadly nobody uses (probably because it’s so clunky).
92 596F75206E65726421 said JavaScript is a terrible name anyways. It implies it has something to do with Java. JS is nothing like Java other than the fact that they both use C style syntax.
80 ghostfacedcoder said GraphQL is an optimization, and like any optimization you trade one thing to get another. GraphQL makes it harder to build on the server: to a server dev they are an inherently worse option. But t...
77 OmnipotentMug said Testing private internals is a code smell. It's only public behavior that matters.
73 himdel said I would go package imports first, then local imports (./), all sorted by the from part.
 
submitted by subredditsummarybot to javascript [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]

New DAPP for TRON, trustless binary options

We are halfway through development for a new trustless dapp on the TRON blockchain. The dapp supports binary options trading for a selection of markets with up to 85% payout for winning trades. We use a trustless on-chain custom TRON oracle for connecting to the price API from Kaiko. We also have more crypto based social betting and a few automated trading bots which connect to polonidex for algorithmic trend trading (TRX/JST, TRX/USDT)
The native currency is USDT-TRON and trade expiries range from 10 seconds to 1 hour thanks to the TRON 4.0 upgrade cutting block times down to a few seconds. Currently developing the UI and later creating a non-web3.0 version so you can trade without a dapp browser like TRONlink. This uses Burner Wallet which is in-browser to sign txs on the platforms smart contract wallet/in-app balance.
Let us know what you think and if you have any questions! If you know about binary options and the bad reputation they have, this solves all the fraud and price manipulation thanks to the blockchain. A real killer app in my opinion. We are all very proud to unveil it when it’s complete and will be releasing thousands of dollars of free bet tokens via Airdrop (with a minimum wager requirement of course) so everyone can get involved and see what it’s all about.
Follow us on Twitter optionblitz_io to keep up to date with the project
submitted by Optionblitz to dapps [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]

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]

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!
submitted by qwasz123 to algotrading [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]

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]

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]

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]

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
  1. JavaScript & LeetCode | The Ultimate Interview Bootcamp
  2. Learn Angular 8 By Creating A Simple Full Stack Web App
  3. Learn How To Make Trading Card Game Menus With Unity 3D
20. Learn React JS And Web API By Creating A Full Stack Web App
  1. Learn To Code Trading Card Game Battle System With Unity 3D
  2. Learn To Code With Python 3!
  3. Linux For Absolute Beginners!
  4. Linux Shell Terminal Command Basics
  5. Machine Learning iOS 11
  6. MapReduce Architecture For Big Data
  7. QuickChat 2.0 (WhatsApp Like Chat) iOS10 And Swift 3
  8. Random Forest Algorithm In Machine Learning
  9. Scrum Advanced: Software Development & Program Management
  10. Scrum Certification Prep + Scrum Master + Agile Scrum Training
  11. Simple And Advanced Topics Of Animating 2D Characters
  12. SSL Complete Guide: HTTP To HTTPS
  13. Start your own online store now for FREE
  14. Swift Weather (Meteorology) Application With REST API
  15. The Complete jQuery Course 2019: Build Real World Projects!
  16. Understanding On Google Charts
  17. User Stories For Agile Scrum + Product Owner + Business Analysis
  18. WP Plugin Development - Build Your Own Plugin!
  19. Double Your Office Productivity Using Google Apps
  20. How to become a much better & safer driver & avoid accidents
  21. Leadership Wisdom - Advanced Leadership Strategies
  22. Use your perfectionism to be more successful at work
  23. 3D Animation Film-Making With Plotagon: Ultra-Speed 2019 Design
  24. Blender Beginners Guide To 3D Modeling Game Asset Pipeline Design
  25. Citrix 1Y0-371 Designing Deploying Managing Citrix Exam IT & Software
  26. Complete Whiteboard Video Creation With VideoScribe: 2019 Design
  27. Create Lightning Fast Videos With InVideo: AI Video Making Design
  28. Learn Cinema 4D: Low Poly Tree Design
  29. Learn Illustrator CC: Create Simple Flat Vector Characters Design
  30. The Illustration Masterclass Design
  31. The Open Source Multimedia Masterclass Design
  32. Camtasia Studio 9: Become a Video Editing Guru With Camtasia
  33. 10 Copywriting Hacks That Work In 2019
  34. 10 Facebook Marketing Hacks That Work In 2019
  35. Certified Facebook Marketing 2019 (Complete Masterclass)
  36. Certified Network Marketer (Network Marketing & MLM Mastery)
  37. ClickBank Affiliate Marketing Secrets Home Business Success
  38. ClickBank Affiliate Marketing: NO Cost, No Website - Proven
  39. Competitor Analysis Tools For 2019: Part 1
  40. Digital Marketing Secrets For Beginners
  41. Email Blasting For Commissions [CPA & Affiliate Marketing]
  42. Email Marketing Mastery to Earn More & Build a Huge List
63. Facebook Ads 101. Complete Facebook Ads & Marketing Course
  1. Facebook Marketing: Advanced Targeting Strategies
  2. Facebook Marketing: How To Build A List With Lead Ads
  3. Facebook Marketing: How To Build A Targeted Email List
  4. Fraud Analytics Using R & Microsoft Excel
  5. Gamification: Use Gamification In Marketing
  6. Google Analytics For Beginners 2019
  7. Google Analytics For WordPress to Track Your Website Traffic
  8. Home Business: CPA Marketing From Scratch
  9. How To Get Your First 1,000 Facebook Fans: For Beginners
  10. How To Promote CPA Offers With Bing Ads
  11. Influencer Content Marketing: Killer Tactics For 2019
  12. Instagram Marketing Growth Tips [Influencer Shortcuts]
  13. Marketing Analytics Using R And Excel
  14. Master ClickFunnels & Create Sales Funnels Like a Boss
  15. Modern Social Media Marketing - Complete Certificate Course
  16. Powerpoint 4 Video Part A - Introduction + Character Animation
  17. Secrets Exposed: Find The Most Profitable Niches Of 2019
  18. Talking Robots: Artificial Intelligence Audiobook Creation
  19. The Complete Social Media Marketing Agency Masterclass
  20. VideoScribe: Whiteboard Animation From Zero To Hero
  21. VideoScribe Whiteboard Animation: Create Amazing Promo Video
  22. Viral Content Buzz - Killer Tactics For Blog Promotions
  23. YouTube Creator Tips [Grow A Channel-Get More Subs & Views]
  24. Youtube SEO Course: How TO Rank # 1 On YouTube In 2019
  25. YouTube Video Marketing For Domination: ViralNomics 2019
  26. Artificial Intelligence Music Creation & Remixing 2019
  27. STRUMMING SIMPLIFIED: 51 Guitar Rhythms For All Styles!
  28. Agile Project Management: Scrum Step By Step With Examples
  29. Amazon Dropship Mastery
  30. Amazon FBA Tycoon - The Ultimate Private Label Masterclass
  31. Artificial Intelligence And Predictive Analysis Using Python
  32. Binary Options Trading Ninja: The Bandit Strategy
  33. Bitcoin Valuation: Methods And Frameworks
  34. Business Education: Guide To Blockchain And Cryptocurrencies
  35. Certified Network Marketer (Network Marketing & MLM Mastery)
  36. ClickBank Affiliate Marketing Secrets Home Business Success
  37. Dropshipping With WordPress: Create A Dropship Business Fast
  38. eCommerce Business: Set Up Your Own Business From Home
  39. Entrepreneurship: Complete Guide To Business Model Creation
  40. Entrepreneurship Bootcamp: Create Work At Home Business
  41. Entrepreneurship Tips For Success
  42. Futures Trading Ninja: DIY Futures Trading Course (12 Hour)
  43. Gamification: Use Gamification In Marketing
  44. Home Business: CPA Marketing From Scratch
  45. How To Be Lucky In Business And Life
  46. Lean Six Sigma Applications In Information Technology
  47. Online Business: How I Make 5 Figure Passive Income on JVZoo
  48. Pandas With Python Tutorial
  49. Personal / Business Networking Skills For Maximum Success!
  50. Project Management: Deliver On Time + Scrum Project Delivery
  51. Scrum Master Training: Case Studies And Confessions
  52. Start Making Passive Income Online: The Complete Bundle
  53. The BeLive Studio2 Course For Live Broadcasters
  54. The Complete Personal Productivity Course - Business & Life
  55. Transformational Leadership - Ultimate Leadership Course
  56. Ultimate Time Management - BEST Time Management Course
  57. User Stories For Agile Scrum + Product Owner + Business Analysis
  58. Your Complete Guide To Agile, Scrum, Kanban
  59. Your Ultimate Blueprint To Sell Products Online


Источник: Телеграм-канал WScoupon
submitted by abbelrus to Pikabu [link] [comments]

List of AMA answers Hero Design and Balance.

EDIT: Formatting.
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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 binary options may not be suitable for everyone. Trading CFDs carries a high level of risk since leverage can work both to your advantage and disadvantage. As a result, the products offered on this website may not be suitable for all investors because of the risk of losing all of your invested capital. You should never invest money that you cannot afford to lose, and never trade with ... Trading binary options may not be suitable for everyone, so please ensure that you fully understand the risks involved. Your losses can exceed your initial deposit and you do not own or have any interest in the underlying asset. In regards to binary options which are gambling products, remember that gambling can be addictive - please play responsibly. Read about So binary options trading in the US, generally delivers the same level of choice trading in Europe, but in a more closely regulated market. Some of the links to third party websites included on our website are affiliate links. This means that we may receive commission or a fee if you click on a link that takes you through to a third party website or if you purchase a product from a third party ... EUR/USD binary options. Also, are you working with a clearing firm or are you trying to find a retail platform that will allow API access to enter your bids and offers? Well the company I was planning to manually trade at was Traderush.. I think that would be a retail platform. Trading binary options may not be suitable for everyone. Trading CFDs carries a high level of risk since leverage can work both to your advantage and disadvantage. As a result, the products offered on this website may not be suitable for all investors because of the risk of losing all of your invested capital. You should never invest money that you cannot afford to lose, and never trade with ... Binary Options Trading. Binary options is a simple trading instrument that can be used to earn money by guessing the future of the Forex, stocks, commodity and other prices. With binary options you either win if you guessed it right, or lose if you guessed it wrong. BinaryTrading.com is here to help you to win more often than lose. You will find here information on binary trading brokers, some ... Both TD Ameritrade and Interactive Brokers offer API's to connect directly and trade. Below are links for both sites: https://www.tdameritrade.com/api.page https ... Trading binary options may not be suitable for everyone. Trading CFDs carries a high level of risk since leverage can work both to your advantage and disadvantage. As a result, the products offered on this website may not be suitable for all investors because of the risk of losing all of your invested capital. You should never invest money that you cannot afford to lose, and never trade with ... 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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The Best Binary Options Trading Strategy - Here's how I ...

How Binary Options Work. To get the transcript and MP3, go to: https://www.rockwelltrading.com/coffee-with-markus/how-binary-options-work/ Try it for yoursel... Are binary options a good idea? If you're thinking about trading binary options, watch this video first. Check out our FREE training for traders https://bi... 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 t... Binary options trading Tutorial for the beginners Hey guys! Today I'm gonna show you my binary options trading strategy that I usually use in my binary tra... Chapter 1 - Introduction to binary options trading: brokers, how it works, example of trade Chapter 2 - Bid/offer levels from the brokers: what it means in t... Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations... 💰💲FULL BEGINNER? Join My PERSONAL TRAINING!💴💵 BLW Trading Academy: http://www.blwtradingacademy.com/ Live Trading Signals HERE!🔙💲💹Join My ... BINARY OPTIONS TRADING - New Binary Options Trading Strategy ★ TRY STRATEGY HERE http://iqopts.com/demo ★ WORK ON REAL MONEY http://iqopts.com/register ★... Need a Binary Options Trading Strategy? To get the transcript and MP3, go to: https://www.rockwelltrading.com/uncategorized/binary-options-trading-strategy-h... This is how I have traded Binary for the past 3 years. Thank you for watching my videos, hit the subscribe button for more content. Check out our members res...

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