“Neuromation is a distributed synthetic data platform for deep learning applications. Unblocking game-changing computing capacity for wide AI adoption”
A couple weeks ago, like most people, I had zero clue what this meant. Since I started researching this project my eyes have been opened, and my mind blown. Three key definitions to help understand the project.
Synthetic Data: Any production doata applicable to a given situation, not obtained by direct measurement.
AI neural network: An interconnected group of nodes, similar to the large network of neurons in a brain.
Machine learning: AI that provides computers with the ability to learn without being explicitly programmed. Solving giant math problems and self adapting when exposed to new data. The process is similar to data mining.
Neuromation is where data scientists and companies can get all their AI data analysis needs met faster and cheaper than anywhere in the world. This is possible because they reward GPU miners for using their computational power to mine this synthetic data. After the synthetic data is generated, scientists can specify how they want to train their machines to analyze the data in record time on a separate group of nodes.
Here’s how Neuromation describes themselves:
“Neuromation is a technology platform that creates synthetic learning environments for deep learning of neural network. These simulations are then used for training better algorithms.
We are building the platform of distributed computing for creating artificial worlds where AI algorithms are trained on simulated sensory inputs. These synthetic worlds also have a virtually infinite supply of perfectly labeled training data. AI plays, as with video games, to learn specific tasks in the real world.
Our technology is crucial to making Deep Learning-based systems useful in the real world as they are taken up by industry. With Neuromation, the future has arrived where computers teach computers to perceive.
Our strategy is not to develop our platform in isolation, but to work with partners in select industries in order to try to bring our vision organically to life. We are developing “Neuromation Labs” that would develop synthetic data and train deep learning models on live applications. Each “lab” like Retail Automation Lab, Industrial Automation Lab, Pharma/Medicine/Biotech Lab will be a study on a specific problem in a partnership with a category leader. As our platform is fleshed out we will be moving parts of generation and training there, allowing us to organically test parts of our vision in real-life scenarios. The Labs will seed the Neuromation Platform market with initial data generator and data sets. We will also encourage our Labs partners to transact farther services through the platform, thus building the initial market for services.”
In order to do transaction on the platform you must use Neurotokens. To make it easy, Neuromation will provide a portal with a one click token buying process. The platform offers 5 services; Data generation, data labeling, data purchase, model training, and model purchase. The price customers pay for each service will depend on how much it costs for the nodes. The platform will determine the resources required for each task, then select the most efficient node pool (minimizing the cost to the costumer, while still making it extremely profitable for the nodes/miners. This brings me to the juice of this project.
Mining Knowledge instead of ETH: The reason why synthetic dataset generations haven’t seen widespread adoption yet is because of the huge deficiency of computing power. Neuromation plans to change this by offering cryptocurrency GPU miners 3-5 times what they make per hour mining crypto, to mine Neuro(synthetic data/machine learning) instead How this works is in addition to their existing mining software miners load up a neuromation computation node. This node is special because it allows miners to mine their normal cryptocurrency until Neuromation has a task available, at which time the miners will switch over to mining neuro until the task is complete.
Since I believe this to be the heart of the project, I emailed the neuromation team and asked them to break down further how their mining/node system works. Here’s what they said.
“To mine neurotokens, you will need to load our node. If your system is eligible (depending on processing capacity, bandwidth) the node will activate and wait for tasks. The nodes take on available tasks if they win an internal auction. That auction is run in tiers. Tiers are numbered from 0 to 4. Nodes in tier 0 get a higher priority (the auction will first try to distribute the task among lower tier nodes) then nodes in tier 1 and so on. When our token sale is running you only tier 4 nodes will be available.
We have a program where miners can invest various amounts into out pre-sale and secure a certain number of lower tier nodes. It is:
— 3,000 Ether for tier 0 (500 node keys)
— 1,000 Ether for tier 1 (100 node keys)
— 600 Ether for tier 2 (100 node keys)
— 200 Ether for tier 3 (50 node keys)”
Why Blockchain? Today if a data scientist wants access to a large amount of computer power he can go to Amazon and pay for it. They will charge him 12 times what it costs for GPU miners to generate it. This is a giant opportunity for Neuromation to slide in.
Distribution of Token
60 million of the 100 million total supply will be sold during the ICO.
base price = .001 or .30 cents per token + bonuses
The presale is taking place right now thru November 28 (25% bonus). you must sign up for the whitelist https://ico.neuromation.io/en/
The regular ICO starts November 28th and runs 4 weeks:
week 1 (15% bonus) week 2 (10%) week 3 (5%)
Important to note: Over the next 3 years, neuromation will burn 50% of the total supply!
2018 =30% 2019= 20% 2020=10% (hmm, this is actually 60%, i think they made an error)
This is a seriously talented team They are out there putting in a lot of work too. Winning competitions, attending conferences, and forming partnerships (hacken.io, TaaS).
CEO- Max Prasolov has been a big time player since 2001. Here’s a short informative interview he recently did. https://cryptosrus.com/interview-neuromation-ceo-maxim-prasolov/ . I’m actually just going to copy and paste his introduction to the rest of his team. Note, I did look fairly deep into each one of these guys, all legit, max just did such a good job summing them up I will use his words.
“I’m a serial entrepreneur and TOP manager with 15 years of experience in different sectors from natural resources mining to industrial multimedia development. I was a part of the team who made an IPO of the iron ore company, blue chip on the London Stock Exchange. But all my life I’ve been in love with graphic novels and animation. I have written and produced several animation movies. While making them I have found out that AI can be trained by showing it the cartoons. This metaphor is very close to what we are doing in Neuromation.
I am glad to work with very smart and talented people involved in our company. Our mentor and advisor Andrew Rabinovich, who is a creator of key deep learning algorithm of Google image. My partner, investor and founder of Neuromation Constantine Goltcev, who believes in me from the beginning and he brings powerful engineers to our team. A deep learning scientist and mathematician Sergey Nikolenko, who is checking our crazy ideas with the proof of scientific method.
Fedor Savchenko, experienced CGI expert who creates synthetic data generator. Kyryl Truskovskyi, a talented researcher and engineer, who implements our hypotheses in deep learning applications. Denis Popov, former CTO of Viewdle, who is helping us to hire strongest software developers all over the world. All of these people are my partners and shareholders at Neuromation”
They have 2 absolute beasts on their advisory board.
Andrew Rabinovich – has studied machine learning for over 15 years. has many patents and peer reviewed publication. He is the Director of Deep Learning at Google, Magic Leap!!
David Orban (Singularity University) apparently this guy has been a pretty big deal for the last 20 years. He’s definitely good at hyping Neuromation.
Because it currently costs so much to “knowledge mine”, this giant futuristic AI market has yet to take off. Neuromation has the answer. They incorporate the mathematical power of blockchain miners to make synthetic data mining and machine training cheaper and faster than ever before. Its now possible that in the near future, thousands of deep learning projects and b2b clients will use neuromation to develop things we haven’t even heard about yet.
The team already has many labs in the works, and has recently boasted about their new Retail Lab, that is already providing “image recognition services to major retail brands”
When just reading the nerdy worded explanations of what synthetic data machine learning is all about, it could be hard to fully grasp what changes this can have on our entire system. I recommend watching youtube videos demonstrating the power of these self learning machines.
- It will take millions of micro-transactions for this platform to run once fully live and running. Like every project in the space right now, scalability is still a giant issue. I emailed the team asking how they plan to deal with this, their reply, “We are looking at HashGraph currently. EOS is also an option. For version 1 of the platform we will run an auction internally on our system and not on the blockchain, so transaction volume is not an issue until the end of 2018. For now, we will likely use HashGraph. “
- Data is the new oil, and there will be businesses aggressively competing in all sectors . Luckily for Neuromation, and their extra cheap/fast computational power, they are far ahead of the pack when it comes to knowledge mining.
- If they cant get the miners to adapt their nodes, then the project dies. I don’t think it will be hard to get them to switch though if neuros pays way better than anything else
- With the timing of this ICO coming out during this fork, it may be possible they don’t sell enough tokens to reach their final goal (which requires 60k eth) world wide adoption.
- These machines are already learning things from this synthetic data that no human has ever even thought of before. This is freaky.
- Truly unlimited
- Neuromation plans to open an Enterprise Automation Lab where the synthetic data approach will help implement solutions in manufacturing, supply-chain, financial services and agricultural industries, to name just a few
- According to Gartner, by 2020 85% of customer interactions in retail will be managed by artificial intelligence.
Here’s a quote from Neuromation that sums up their potential better than I can
“What is especially fascinating is that in only about a month of working on the inventory recognition problem we have achieved 95+% accuracy, a result others have spent years of effort and millions of dollars on. Significantly, the model performs well on real-life data without seeing anything but synthetic datasets during training. This breakthrough proves the viability and efficiency of our approach. With Neuromation Platform, the gateway to easy AI training at scale will be finally opened.
The potential demand for image recognition alone in the retail industry is enormous. It amounts to more than 40 billion images per year, according to ECR research of 72 of the biggest retailers and suppliers. Going further, we plan to create datasets that mimic human interaction with the shelf. We will be able to track customer flow and intent, creating a full simulator of the retail store, with a multitude of possible applications”
I have to admit, after watchin numerous machine learning neural network demos on YouTube, I feel like a teenager who just touched his first real life boob. My mind is filled with wonder and amazement. That being said, you should do your own research. I’m clearly smitten, and could be ranking her too high.
This project is unique, revolutionary, and cheaper/faster than every competitor. The science they are accelerating with these large data mines could change the world in a lot of ways. If they grow their community, and get those miners to switch over their GPU power, the possibilities are endless At only .30 plus bonus and a lower supply of only 100 million , I feel very comfortable recommending this ICO. I wouldn’t sell it anytime soon either. This is a newborn baby giraffe, with the sexy genes she got from her mother! 7.8 out 10
- Symbol: NEURO
- Presale : now until nov 28th join the whitelist here https://ico.neuromation.io/en/
- Opening sale: Nov 28
- End Date : January 1 , 2018
- Supply : 100 million .001 eth per + bonus
Featured image courtesy of Shutterstock.
ICO Analysis: Deepbrain Chain
Deepbrain Chain will provide a low-cost, private, flexible, secure, and decentralized artificial intelligence computing platform for artificial intelligence products.
Coming out of China, based on NEO, Deepbrain Chain is an incredibly difficult whitepaper to read. Many concepts are both lost in translation on paper, and in my simple brain.
I came across a Reddit post from a man named crypto_oxford, who does a great job summarizing.
“It is a data computation platform and a Data trading platform that uses distributed spare computing ressources, makes AI computational demands cheaper, protects against data leakage via hacking, secures the seperation of data ownership and usage rights, and secures intellectual property for the data and for the products.”
They figure to reduce the cost of AI by 70% by making it minable on the blockchain. I cannot verify these claims, I am no expert in this field. Here’s a good example possible investors face when trying to learn about this project. It sounds great, but what does it really mean?
“The founding team believes that DeepBrain Chain is a project that has been verified by the market, has huge market scale and significant application value, lets the process and economic value associated, and is gradually issued with the core business sharing storage and the mechanism of computation capacity of mining. Each token corresponds to the computational value of the service provided during its issue, and is a truly valuable asset and digital currency that has already landed. Due to the difficulty of issuing, the value of the flow needed by every new token will increase. The earlier one holds, the more the expected value of the market will be.”
- NEO platform (nep 5 token)
- A max hard cap of $15 million,
- There’s a total supply of 10 billion Deepbrain Chain (DBT) tokens. 50% of these will be mined over time.
- 1.5 billion tokens will be for sale
- 600 million sold during the Presale, which ended 10 days ago, and was almost impossible to get in.
- Token sale stars Dec 15th. You must fill out a KYC form to be eligible. No USA or China allowed
Only 1.5 billion of the total 10 billion tokens will be for sale.
600 million in the presale, and 900 million in the public sale
Use of funds. 55% R&D, 25% marketing, 10% daily operations, 8 % incentives, 2% patent fees
Based in China? The team is doing things. They recently won 1st and 2nd place prizes in Academic Sector & Enterprise of SMP, at the Chinese Man-Machine Dialogue Field Authority Evaluation Contest. This contest had over 30 of Chinas best competing.
Their resume’s check out pretty well. And just look at these faces… JACKPOT!
When it comes to the technical side of this project, I am out of my element. They have a hard cap of $15 million, $6 million of that already came from private investors, one of which being NEO, who funded them $1 million.This gives them serious street cred.
AI data computation, neuro networks, machine learning, all these concepts are no doubt where our world is going. On Deepbrain Chains platform, one can compute and trade data. They have a working platform with more than 1,000 semantic skills.
- This being a Chinese project, on NEO, makes it more susceptible to regulations than other projects. It doesn’t seem likely, but is a risk nonetheless -1
- The token metrics are funny. Only 15% for sale. They have a whitelist for the presale (which may be filled up by the time readers see this.) They didn’t limit the amount people bought during the presale, and won’t for the public sale either. This could lead to whales owning most of the supply. -2
- They are having KYC implementation difficulties with their sale. It has been a huge issue in their telegram the last 24 hours. What looks like is happening, is there is no way to verify what customer is connected to what KYC. This could be an in for investors who currently aren’t signed up for the KYC to buy these tokens. These issues could be a bad sign of things to come.-2
- The Deepbrain whitepaper states; there have been over 5k startups since 2012, collecting over $22 billion. This is without counting the money large existing companies put into ai, which makes the total amount of money over $100 billion. It is certain that this is just the beginning.+4
- NEO partnership. NEO alone has an endless amount of growth potential. They have a large community that gets exdcited and involved with the projects NEO backs. This partnership is worth a lot. +3
- The ICO has a strict KYC rule. This is going to create a tremendous amount of demand for this once it hits exchanges.+2
- This is a $20 billion industry, that is only growing from here on out.+2
The 10 billion supply with only 1.5 billion being sold is scary. However, the rest of the ICO seems to make up for this. 5.8 out of 10
Sale starts Dec 15th, however, you need to fill out the whitelist/KYC app in order to get in. This application is having technical issues which may allow anyone to buy in without previously being KYC whitelisted. It’s worth a shot, but need to hurry!
Sign up here https://www.deepbrainchain.org/pc/kycEnglish.html
Cover image courtesy of Shutterstock.com.
ICO Analysis: The Game Machine
In recent years passionate gamers have been exploited by huge game development companies that hold a monopoly over the industry. The recent EA Star Wars Battlefront catastrophe brought a lot of attention to an issue that gamers are all too familiar with.
Gamers have to dig deeper and deeper into their pockets to pay for the expansion packs, DLC, and additional features that are excluded from the main game. And these games aren’t cheap.
It’s increasingly becoming apparent that there are fundamental issues with how the gaming industry works today. Fortunately for gamers, the blockchain is already beginning to form a new paradigm in the way games are funded, developed and purchased.
The Game Machine is an open source platform that seeks to decentralize the gaming industry. It aims to provide sleek software that will empower gamers and game developers alike.
How are they planning on doing this?
The platform has four foundational layers that are stepping stones for this innovative new project. The first layer is the game machine client. It will work as a wallet to store and send Gamefuel tokens and will come with a built in mining interface so that all users can participate in securing the Game Machine’s blockchain.
The second step is to develop their “Rise Machine” that will allow members of the Game Machine community to invest funds into games they see promise in – funds that go directly to the developers so they can create their game independent of the EAs and other oligarchies.
This is perhaps the most powerful innovation suggested by the platform. It gives everyone from the small game studios, with a only a few developers, to the prominent developer, who wants to deviate from the script, the chance to create and sell great games to the community at a fair price when they otherwise could not.
The third layer of the platform is the “Ads Machine” a decentralized advertising market that will live inside the Game Machine client so that game publishers or advertisers can market their products to a gamer specific demographic. Advertisers have been experimenting for years with in-game, native advertising, and it’s a powerful use case for the game machine, just as a stand alone feature. Expect this element of their platform to bring in huge revenue if they can build up their user base.
The last layer of development in their platform is the “Exchange Machine”. This will simplify the process of buying and selling tokens for gamers who use or hold multiple ingame currencies. This way, gamers can sell their Gamefuel and easily move a variety of coins in and out of the game machine.
The Game Machine team is using an Erc20 token called GMIT, which stands for Game Machine Initial Token. Each token is currently valued at 2,500 GMIT per ETH, or $0.32 USD. The token will be tradeable for actual Gamefuel at a ratio where 1 Gamefuel= 0.5 GMIT. Thiswill occur once the platform officially launches in May or June of next year.
The GMIT token is issued by Game Machine OÜ, incorporated in Estonia. A total of 140 million tokens will be created during the various stages of the token sale. The pre-sale has already been conducted and an equivalent of 751 Ethereum were invested, which means roughly 1,870,000 GMIT have already been bought. There are bonuses for early investors during the crowdsale where day 1=+15%, day 2=+10% and day3 =+5%.
There is also another coin that can be mined called GMC or Game Machine Client token, which will be exchangeable for GMIT tokens before the official platform launch at a ratio where 1 GMC = 0.0002 GMIT. The GMC token is given to miners who are being rewarded for securing the network during the Game Machine’s beta testing stage so they can earn Gamefuel. The official Gamefuel token will have its own blockchain that runs on two key components, Limited Proof of Work, and Proof of Authority. Limited proof of work is an energy friendly implementation of the traditional proof of work protocol that bitcoin uses.
Proof of Authority is used to enable faster confirmations of crowdfunding transactions where the authority level of a user confirming transactions is determined through analyzing metrics such as time of use, the amount of purchases and sales of games on the platform made and how positive or negative the feedback of other users were about their contributions to the platform. This can also include how long they have been mining for and how fast. One can imagine this is useful for fending off bad actors that might just try to crowdsource Gamefuel and then commit an exit scam without contributing anything. This blockchain is inspired by the Scorex 2 framework devised by the Scorex foundation, which was also implemented by the Waves decentralized exchange platform.
The three co-founders of Game Machine have over 17 years of combined experience in project development, IT consulting, video game marketing and development.
The entire team consists of 19 full time employees who are busy working on many different parts of the Game Machine platform. If that’s not impressive enough then look at the history of two of the co-founders Taras Dogval and Alexandr Isaev who were both previous board members of Hakk, which is an interactive agency that has done marketing for huge European companies such as Volvo, Tallink Silja Line and Neste. The other co-founder Maria Suvorina has six years of experience in marketing and promoting games on computers and phones. She’s worked for companies such as Suricate Games, TMA and AminiLab.
Although these companies aren’t that well known, most of their work is out of the public’s eye, and they have actually made contributions to famous games. Aminilab for example has participated in development for games such as Alone in the Dark, FIFA, Dragon Age, Mass Effect, Doodle God and Doodle Devil.
The Game Machine is an extremely ambitious project that, if successful, will truly revolutionize the industry. The team behind the platform is experienced, has a great track record and is big enough to polish and refine the Game Machine into a fantastic platform for gamers and developers. However, the existing industry players already have huge advantages when it comes to funding, marketing, development and most importantly building a big reputation and brand awareness. It’s difficult to predict if a community driven effort from gamers and developers combined on an open source platform, will be enough to break into the existing market and convince everyday gamers to switch to an entirely new platform.
- One risk for this project is the quality of its design in terms of how friendly the user interface will be. If the platform is too difficult for technically illiterate people to use then it will not have wheels to get going anywhere. -1
- Another threat to the game machine is the plethora of other competitors that are already working on blockchain innovations in the gaming industry. For example, Enjincoin is an existing game development company founded in 2009 that recently completed its ICO, raising $20 million to kick start a platform that boasts features very similar to the ones offered on Game Machine. -2
- Besides the long list of other game-based ICOs that have been launched this year, there is also stiff competition from massive conventional gaming markets. In addition, newer platforms such as Steam have already attractive hundreds of millions of users. -2.5
- The Game Machine has a lot of potential for quickly stacking up a big user base, and one reason is due to the strong alignment of incentives between gamers and game creators. The traditional game development giants on the other hand are ignoring what their consumers and even some of their own developers have had to say about how games should be created, distributed or sold. Instead of focusing on quality and a fair deal for customers, these development companies have opted to lined their pockets instead. This is why gamers and developers would flock to the Game Machine overnight if the platform works well. +3
- The project’s potential for increasing the value of the underlying gamefuel token is actually quite immense in scope. Just the crowdsourcing and kickstarting mechanism built into the platform would induce a scenario where a large sum of people would continually purchase gamefuel tokens to lock into smart contracts. Once enough gamers are participating in this process the money locked in gamefuel tokens at any given time will only rise, thus reducing the supply of tokens in circulation and consequently increasing gamefuel’s value.+3
- With the plans to integrate a digital advertising market directly into the platform, gamefuel has a secondary source of revenue because advertising slots on the game machine platform can only be purchased with gamefuel.+3
- The “Exchange machine” that’s built into the Game Machine client is a nice approach to sourcing liquidity that will allow many other game based cryptocurrency holders to sell their tokens to purchase gamefuel. Attracting a wide range of gamers who are interested in different blockchain based gaming platforms is a unique approach to marketing that many readers may not have considered as a form of advertising. +2
The Game Machine is a solid project overall; the team is large, has experience and will have raised additional funds to expand their efforts once their crowdsale is completed. That being said, stiff competition from new and existing gaming avenues, not to mention luring a dedicated gaming community to an entirely new platform. These risks must be weighed carefully before entering into Game Machine. As such, this ICO has been granted a score of 5.5 out of 10.
Unfortunately, the presale period of the Game Machine ended a few days ago; however, the final crowdsale period will open for everyone to participate from Dec. 14 through Jan. 31, 2018..
There will only ever be 140 million gamefuel tokens created in the ICO, and 70% of them will be available for token sale participants. The rest of the tokens will be divided into portions and used to fund various parts of the project:
- 14.2% token storage for starting in-game items withdrawals.
- 1.4% for bounty program.
- 1.4% for advisors.
- 4.5% for referral program.
- 7.1% for team.
The team’s portion of tokens is utilized to pay for development and split in the following arrangement below.
- 10% Legal maintenance.
- 5% Operating expenses.
- 35% Marketing and PR.
- 50% Development of a product.
You can learn more about their token and ICO here.
Featured image courtesy of Shutterstock.
ICO Analysis: Gimmer Token
The impeccable rise of algorithmic trading has ushered in a new wave of do-it-yourself (DIY) algorithmic trading bots. With the success of these DIY bots in traditional financial markets, it was only a matter of time until they entered the cryptocurrency market.
For algorithmic trading, volatility creates opportunity sets. And with cryptocurrencies still trading in an inefficient market, volatility runs rampant. This level of volatility creates an ideal environment for even the most rudimentary algorithmic trading strategies. However, there is a lack of DIY automated trading bots that are available for use by amatuer cryptocurrency traders. With this in mind, Gimmer is looking to take advantage of this need.
According to the company’s website, “Gimmer offers easy-to-use advanced algorithmic trading bots that require no programming skills, no previous trading experience and no in-depth knowledge of cryptocurrencies.”
Essentially, Gimmer is hoping to position itself as the leading DIY algorithmic trading bots for individual cryptocurrency traders. While the company may never be the “Quantopian” of the cryptocurrency space, Gimmer does provide a novel solution for amateur traders.
The Gimmer token (GMR) will be implemented using the Ethereum ERC20. While GMR tokens will be visible in participants’ ERC20 wallet, the tokens will not be tradable until the close of the public sale on January 31, 2018. GMR tokens will issued starting from January 3, 2018. GMR holders generate value from the token as a form of payment for the rental cost of Gimmer’s trading bots. For users, the rental cost scales proportionately to the level of sophistication desired – more sophistication equals higher return (at least in theory).
According to the whitepaper, 45% of the funds raised will go towards development and operations, 35% towards marketing and acquisition, 15% towards the founders and team, with the remainder of the pot (5%) going to legal and compliance.
Gimmer Tokens are valued at 1 Ether (ETH) per 1,000 GMR (plus applicable bonuses). The total amount of tokens to be sold is capped at 100,000,000 GMR. However, an additional 6,000,000 GMR will be created for advisors, reserves, and the team, with another 4,000,000 GMR created for bounties.
The company has not yet stated its intention to list the GMR tokens on any major crypto exchanges.
Gimmer’s core team consists of two senior developers, a global macro hedge fund manager, and a creative design veteran. As compared with the majority of ICOs, Gimmer’s team is in-line with the relative standard – the quality of team meets basic expectations.
The company’s CEO, Philipe Comini, is a senior-level UX/UI designer who is also balancing two other jobs (according to LinkedIn) – typically, not a good sign. The company’s CTO, Persio Flexa, is also a senior developer who recently launched 2 other start-ups – again, not a good sign. The company’s COO, Paul Lindsell, is a creative design veteran with over 12 years experience that is seemingly committed to his role – not balancing multiple jobs. The company’s CIO, Masaichi Hasegawa, is currently a global macro hedge fund manager and an executive of a shoe manufacturing company – the third C-suite executive of Gimmer to balance two other jobs.
The rest of Gimmer’s team consists of a marketing director, a user experience director, two developers, a customer researcher, a commercial director, and a journalist.
Gimmer presents a highly speculative buying opportunity for investors interested in short-term capital appreciation.
Creating profitable algorithmic trading strategies is incredibly difficult. Hedge funds typically employ a large staff of mathematicians, experienced machine learning engineers, data scientists, and the like – Wall Street refers to them as “quants.” Quants typically hold a PhD in finance or quantitative mathematics and have years of hands-on experience with both statistical analysis and engineering (Python and C++). Does Gimmer employ any quants? No, not even by the slightest measure.
Overall, Gimmer’s DIY algorithmic trading bots are likely just a novel tool-kit for amatuer cryptocurrency traders, nothing more, nothing less.
Gimmer provides no data on slippage modeling, meaning users have no idea of all the transaction costs that are associated with a higher frequency of trading (including: fees, commission, and slippage). These costs can be significant and add up quickly. -1
Gimmer’s core team does not seem to be dedicated (balancing multiple jobs) or qualified in any sense. With Gimmer’s team lacking any real trading platform experience, unforeseen issues with their algorithms may lead to sizable losses for users. -1.5
Gimmer provides no data on latency, meaning users do not know if the company’s algorithms are deployed to proximity-based execution servers in attempt to achieve low-latency performance no matter where the user is located. For all trading strategies, latency must be measured and managed in order to maximize the probability of success. -1
Provided that Gimmer’s trading bots run successfully without any technical glitches, users could benefit from enhanced risk management protocols, thereby insuring their principal investment through more downside protection. +2
Copy trading techniques could benefit novice traders, as they can publicly see high level information such as start date, running period, currency pairs and percent gained. Based on the public information, users can copy seemingly successful trading strategies and rent the same bots. +3
Automated trading strategies will allow a larger pool of traders to invest in cryptocurrencies. Since the market is still subject to large, volatile price swings, more passive traders could use Gimmer’s platform to execute automated trades (based on pre-set parameters) without having to monitor the market on a day-to-day basis. +2.5
While algorithmic trading in the cryptocurrency space is a smart strategy, Gimmer lacks the sophistication of even the most basic trading platforms. The biggest concern beyond Gimmer’s lack of sophistication, is the pedigree of the core team. With no quants on staff and a couple UI/UX designers creating the algorithms, technical issues are likely to occur. And with that in mind, faulty algorithms or platform glitches could easily lead to the loss of principal investment for users.
For amateur traders interested in novel tool to play around with, Gimmer is a great choice. For veteran traders with solid programming and statistical skills, move on to a better platform.
Against this backdrop, we believe that a score of 4.0 out of 10 is warranted.
- Type: Crowdsale
- Symbol: GMR
- Pre-ICO Sale: November 24, 2017
- Public Sale: January 3, 2018
- Payments Accepted: ETH
Disclaimer: no position in Gimmer at the time of writing.
Featured image courtesy of Shutterstock.
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