Guide to the 6 best Crypto Trading Bots & platforms ...

Algorithmic Trading

A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. Feel free to submit papers/links of things you find interesting.
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Investment Club

"An investment in knowledge pays the best interest."- B.F.
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AI-powered Trading Ecosystem

AITrading strives to revolutionize the way people manage their financial and crypto assets. We aim at pushing artificial intelligence (AI) based trading to mass markets. We do hope to enrich mass trading with machine learning and AI techniques. AITrading disrupts AI technology application to financial and trading markets redirecting its value and intelligence from selected banks and hedge funds to ordinary people.
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Crypto Trading Machine Review (UPDATE: 2019) • DOES IT WORK?

Crypto Trading Machine Review (UPDATE: 2019) • DOES IT WORK? submitted by bonnies1337 to u/bonnies1337 [link] [comments]

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines submitted by DecentralEN to enigmacatalyst [link] [comments]

Enigma Catalyst Analysis: The Realm of Crypto-trading Machines

Enigma Catalyst Analysis: The Realm of Crypto-trading Machines submitted by bloktgreg to enigmacatalyst [link] [comments]

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines submitted by DecentralEN to cryptoguides [link] [comments]

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines submitted by DecentralEN to ICOAnalysis [link] [comments]

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines

ICO Analysis- Enigma Catalyst: the realm of crypto-trading machines submitted by DecentralEN to CryptoCurrency [link] [comments]

ConsenSys Launches DeFi Compliance Service; Crypto P2P Trading Soars; 2,000 Coke Machines Accept BTC

ConsenSys Launches DeFi Compliance Service; Crypto P2P Trading Soars; 2,000 Coke Machines Accept BTC submitted by afriendofsatoshi to btc [link] [comments]

FUD Slaying: Why “DYOR” is More Important Than YouTube Videos and Internet FUD

Hello everyone,
I am here to discuss the recent FUD presented by a relatively unknown YouTube reviewer. I intend to discuss his methodology and the actual points themselves.
https://www.youtube.com/watch?time_continue=1&v=1hH5_FAEzyo
This is his YouTube video based on the document in question. He wrote the document. https://docs.google.com/document/d/1XQlAGIDPjDoQNHtzEWGdbO9i8MUkc4lZFKYLTZzMpYU/edit
First, to get this out of the way, the reviewer has only been around on the social media scene for a short while. The views of his videos are only in the hundreds and his twitter was created a week ago. He is basically a "nobody" at this point. I don't mean that to be disparaging. He literally came out of nowhere. He is unproven and his methodology is inconsistent and extremely questionable.
With that said, just because he came out of nowhere doesn't mean he might not have a point, so let's look at his rating methodology to get a better idea of his process.
Oh and if you do not want to read all this, here is the TL:DR: The guy doesn't know what he is talking about. He doesn't has much idea of what he is doing when writing reviews. His research is lazy. I actually feel I wasted my time responding to this, but I am going to do it anyway.
When rating a project, he uses the following categories: MVP (minimum viable product), ease of research, team, roadmap, community (bonus), solving a problem, does it need blockchain, token use, red flags, competition, presentation, token vesting, demand/value, scarcity, customer service, best in field (bonus), active use, size of market, development (bonus)
These are pretty good things to look at, but he failed to look at GitHub contributions (or other source code related sites), so he can't really tell if a project is scammy or not. So, how well did he check this stuff out?
Rating the team:
When looking at his review of GVT, the only way to get an idea of this person's methodology is to look at his reviews of other projects. When rating the team there are basically two basic routes a person can take. You can analyze the team itself, or you can bundle the team and the advisors together and rate the project as a whole.
The reviewer is inconsistent in his reviews. In this category he bundles the entire team and advisors on some projects whereas he just looks solely at the team in other reviews.
His research is absolutely lazy. He gave Polymath a 0 rating for their team, but their website links to their company LinkedIn page and lists all 26 employees. It was not hard to find this. Even if it weren't on the site, a simple google search would have revealed who the team is. Polymath has a great team with some decent “stars” on it. It makes no sense to give them a 0. The reviewer doesn't know what he is doing.
Difficulty in finding the team deserves docking points in "ease of research", and it does not deserve giving the entire category a 0. The point of this category should be to evaluate the merits of the team members, which is something he does not do in most of his reviews.
He gave Selfkey a perfect score stating: "Team: 20 Points - Superstar team and advisors" This means he is bundling the team and advisors together. If so, any issues with advisors deserves docking points from that category, not docking at additional 20 points because of one advisor.
Looking at Selfkey, I don't know where the he gets the idea that they have a "superstar team". What does that even mean? I checked their profiles. Some of them only came onto the project recently and their LinkedIn pages are nothing to write home about. Some of them don't even have LinkedIn pages.
He gave the GVT team 13 points, but then docked 20 points because he didn't like Charlie Shrem.
Do you realize the ridiculousness of this? The GV team category effectively gets -7/20 points because the reviewer does not like Charlie Shrem. That is worse than giving the team 0/20. Charlie is only one advisor with no actual power over the GVT team's operations. He cannot execute any commands over the GV team or force them to do anything. The GV team can fire Charlie. Charlie cannot dismantle the GV team. That power balance is important. The rating makes no sense at all. Also, he docked the Changelly advisor because his company has bad customer service? Really? What does that have to do with his ability to advise the GV team on the things they need from him? Fact of the matter is his business is still running. The same cannot be said for advisors of other projects (more on that soon).
If you are going to rate the team and include the advisors, the value should be 3:1 or even 2:1. Even if you gave the advisors a score of 0, the category score should not be that low. GVT's advisors are absolutely amazing. To call them weak is ridiculous.
With regard to Nuls: "Asian team, isn’t on LinkedIn. No way to research." They get 0 points because they are Asian and don't use the sites you like to use? The language used allows that statement to be interpreted in a very negative way. There are non-Asians on that team as well. There is a way to research them. There are bios of each team member if you scroll over the pictures. You can then use that information to do more research on them. You are just too lazy.
Looking at The Key, their members are definitely not "all-stars". Their team is unknown and they have 3 relatively unknown advisors, only one of which has a LinkedIn page. Love him or hate him, Charlie Shrem is a crypto superstar compared to these people. Interestingly they are more of an "Asian team" than Nuls. That didn't seem to affect the score much though.
He gave the Bounty0x team a perfect score, but he obvious didn't bother to research every member of the team or their advisors with much effort. As an example, Terry Li is the Bounty0x solidity developer. If you check his LinkedIn page you will find a few serious red flags. He hasn't held a job for over a year. He has no visible programming experience. He has been a solidity developer for 10 months with no prior history or proof that he can program well. I cannot stress this enough: you do not want your solidity developer to be a programming newbie. This will spell disaster for your project.
When you look at their advisors there are some serious red flags as well. I picked two advisors to research and I found out that both of them have had their companies fail. One of them even declared themselves unsuccessful in a Facebook post. I don't want a project to be advised by people with a bunch of failed startups. Changelly having bad customer service pales in comparison to advisors whose project's failed. Bounty0x's advisor team is filled with failed entrepreneurs and members of their team lack experience in the jobs they are assigned. Also, their "Backend and Solidity engineer" has only been with the project for a month, and his blockchain programming experience is nonexistent. They do not deserve a perfect score in this category.
GVT has a team with years of programming experience, but more importantly, they have years of experience programming financial software. These are exactly the type of people you need on your team.
To the reviewer: Either bundle the advisors into the team rating or give them a separate category. Do not be inconsistent in this category. Do not bring a team's ethnicity into play as a factor for anything. Please do actual research on all the members, and please define what it means to be a "superstar". Please learn to navigate websites. Polymath's team is there. Your inconsistency and lack of research in this makes you appear incapable of judging a team. There is no clear methodology here. All your reviews are questionable because of this.
Roadmap:
He gave 0 points to GVT for their roadmap being hard to read. But the key point is this: They have a roadmap. There is no reason to give 0 points in this category. Not only that, the roadmap is decently detailed with many goals and objectives. The roadmap isn't some simple points on a line like Enigma's roadmap. Speaking of which...
He gave Enigma 0 points for not having a roadmap at all.... But they do have a roadmap. The guy didn't do his research.
https://en.decentral.news/2017/12/27/ico-analysis-enigma-catalyst-realm-crypto-trading-machines/
It can be found here.
MVP:
Having a minimum viable product be worth only 10 points is ludicrous. Any project that has an MVP basically utterly destroys a project that doesn't. More importantly, the reviewer didn't actually bother to use the MVP on what he reviews.
He gave Polymath 0 points for their demo, but gave GVT 10 points for theirs.
I am going to be blunt about this. GVT's demo is a non-functional interface demo. GVT's MVP comes on April 1. Polymath does not deserve a 0, and GVT does not (as of 3/21) deserve a 10. They both deserve a 5. He didn't bother to actually check out GVT's demo, which goes to show he doesn't actually research things properly.
He gave Enigma a 3 for an MVP not available to the public and Selfkey a 5 for an MVP not used by the public. Eh?
He gave the Authorship a 10 for their MVP but claims he cannot find any info about them. How is that supposed to work?
He gave Po.Et 0 points for their MVP because he couldn't find it.
Here you go buddy: https://github.com/poetapp/wordpress-plugin
It's right there. You just failed to find it. It isn't their fault your research is bad.
Ease of Research:
The reviewer either needs to dock points for research being difficult in their respective categories or dock research being difficult in this category. Do not "double dip" and dock points in both categories. This category is irrelevant since the reviewer already docks points in their respective categories. Also, this category is subjective because it is based on the reviewer's research skillset.
Community:
He uses coingecko's score or numbers from their telegram channel but there isn’t much evidence that he actually bothered to check out their communities much. Reeks of laziness and has nothing to do with the quality of a community. This really shouldn't even be a category if he is going to give points based on this. High telegram channel members has little meaning.
Solving a problem:
The reviewer’s inability to understand the problem that a project solves should not be held against it. Polymath is quite clear in the problem it solves.
He gives projects that solve problems of identifying people a 10, but gives projects that solve problems of identifying intellectual property a 3. That makes no sense. Those are both problems that need to be solved by the blockchain. The idea that he finds one more important than the other is clear bias.
Token Use:
The author does not understand the GV product. GV is platform agnostic, and more importantly GVT needs as little outside influence as possible. There is a very specific reason why GVT has to be used in place of ETH. ETH would technically be a middleman in this sense. GV's success is not meant the be tied to ETH's success or ETH token price manipulation. GV's success isn't even meant to be tied to crypto's success. GV is designed to succeed even if ETH or crypto fails.
GVT actually deserves a 10 in this category. GVT is needed to use the platform. Money is transferred using GVT. Profit is returned using GVT. Other services such as GV Markets will also function using GVT as gas. The utility of GVT is needed in all aspects of the platform. This gives the token great utility and investment value. If 1 Billion is invested through the GV platform, GV's market cap includes that 1 billion because the token is needed to transfer that 1 Billion around. This provides great incentive to invest in the platform and a great reason for the token price to grow in value. No other project that this much incentive or ways to bring value to their token as much as GVT. I am surprised the reviewer cannot see this.
GVT is also market agnostic. The entire crypto market can fail and GVT can still maintain value through profits brought in from the Forex and stock markets. This will make it extremely resilient over time.
Presentation:
The purpose of GVT is quite clear. It is broken down on the website and the presentation clearly explains why it is needed as all levels of trust management including the brokers, customers and managers. All that info is very clear on the front page of the site. 0/10? GVT presentation isn't the problem here. It seems the reviewer only watched the video which is just one part of the presentation. Everything is on the site and in the whitepaper, which the reviewer apparently didn't even fully read.
Token vesting:
He colors it yellow for GVT but green for other projects that also get 5 points... visual bias is apparent. He gave one project a 10 for an 18 month vesting period and a 6 to another project for the same period with little justification for such a disparity.
Supply/Scarcity:
GVT receives 3 points because 44M tokens were available during ICO but only sold about 4M. This makes him believe that they didn’t create much demand. “Everyone who wanted GVT got it.” The US and Singapore could not participate. Also, Bounty0x failed to reach their soft cap, but the reviewer didn’t dock any points for that. If everyone who wanted GVT got it then the marketcap wouldn’t be where it is today. What a terrible assumption he made.
Competition: He gave GV a 5/10, but his reasoning made little sense. “Covesting and coindash are used to trade cryptocurrencies while GVT is for cryptocurrency AND non-crypto trading. They will still compete for a portion of the same market. People will have only so much fiat to invest.” You do not use fiat to invest in Covesting or Coindash. Also, GV will allow people who are into stocks or forex to bring their money into crypto. No other coin is doing what GVT does. Covesting and coindash, arguably, are projects that try to compete against just one part of the entire GV platform. GVT is more than that and should have a higher score because there is basically no competition. There is competition for some of its features, but not for the platform as a whole. He gave Bounty0x a 20-point bonus for "Best in Field"... but they are the best because they have no competition. As a matter of fact, there is no reason for a 20 point "best in field category" when you already have a competition category worth 10 points.
He gave Funfair a 5/10 even though he states "No competition in FunFair’s niche"... That would automatically make it the best in its field if it has no competition as well.
Why does a project that has no competition effectively get 30 points (10/10 + 20), while another project with no competition get only 5 (5/10 + 0)? I will tell you why. It's because the author doesn't know what he is doing.
Guy's I am going to be honest. I am tired of doing this. You get my point. His reviews are an inconsistent and poorly researched mess. I've written around 8 pages worth of content covering this. If there is anything else you need me to compare, please write it in the comment section.
submitted by novadaemon to genesisvision [link] [comments]

What's the consensus in this sub on machine learning for crypto trading?

Is there a consensus among experts on whether it's a waste of time / scam or it is legit?
I've seen a few sites selling signals or auto traders based on machine learning. I'm very familiar with ML but I've never used it for crypto trading and I'm wondering what you guys think In particular I'm not interested in high frequency trading, I think server proximity is too much of a factor there, I'm looking more at a few trades per month.
submitted by boffum to algotrading [link] [comments]

Crypto trading and Machine Learning?

Hello guys,
I have experience with modeling Machine Learning algorithms for trading stocks and futures for a hedge fund. Since I have been involved in the crypto bull market 2017, participated in many ICO's and etc, I'm now thinking of possible Machine Learning implementation for crypto algo trading.
Is there room for ML in crypto trading? I have a lot of concerns about the crypto market as a whole , i know that most of the volumes in many exchanges are fake, no regulations, high volatility and etc, it's still Wild West.
What are your opinions?
submitted by zandana to algotrading [link] [comments]

Capitalise Crypto developed a trading tool that lets you plan like a human and trade like a machine

Capitalise Crypto developed a trading tool that lets you plan like a human and trade like a machine submitted by leftycatchersmit to CryptoCurrency [link] [comments]

Crypto trading platform launch this fall including AI and machine learning (Cryzen).

Crypto trading platform launch this fall including AI and machine learning (Cryzen). submitted by nat777poop to algorithmictrading [link] [comments]

What machine learning libraries and methods would you recommend for creating a predictive trading bot with 3-10TB of crypto market data?

I know not all of them scale well. I'm learning to do some stuff with scikit-learn, but will it work on enormous data sets? Is there something else that would scale better? Basically, it's a bunch of crypto trading data, probably something like 500-900 million trades, and probably half as many order book snapshots (trades that have not met a match yet) for a few different currencies, which each have between 200 and 8000 orders each.
Anyways, I'm planning on getting into using gpu's for this purpose as well later on if that factors into the preferred library.
submitted by hekoshi to learnpython [link] [comments]

@Timccopeland Yes, it is law. People are liable. The act of trading is a human decision, not a machine Blockchain… https://t.co/gTur8a7oDO - Crypto Insider Info - Whales's

Posted at: November 9, 2018 at 03:11PM
By:
@Timccopeland Yes, it is law. People are liable. The act of trading is a human decision, not a machine Blockchain… https://t.co/gTur8a7oDO
Automate your Trading via Crypto Bot : https://ift.tt/2EU8PEX
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submitted by cryptotradingbot to cryptobots [link] [comments]

How Machine Learning can help the development of crypto-trading with Signals Network

If you're interested in the #machinelearning aspect of Signals platform, be sure to check out this blog post!
https://blog.signals.network/what-is-machine-learning-and-how-do-we-use-it-in-signals-6797e720d636
If you needs more info about Signals Network ICO
submitted by bitmanija to icocrypto [link] [comments]

How to Use Machine Learning to Trade Bitcoin and Crypto

How to Use Machine Learning to Trade Bitcoin and Crypto submitted by grindbehind to CryptoMarkets [link] [comments]

How I applied Buffet's strategies to my own portfolio, +70% networth, beat SP500 by 40%

I believe I did pretty well in the market this year. My networth increased ~65% since its lowest point in March, ~350k to 620k. 20k from the car I bought in March. I rolled over a 401k and it messed up Mint's reporting, hence the spike from Jul -> Aug.
I beat the SP500 by 40% in my YOLO account, my FAANG account went from 180->300
I did this by following some basic investing principles, buying and holding for the most part, being patient, and only investing in areas which I have expertise in.
I did not buy into the TSLA hype, nor do I play options, nor do I play crypto.

High level advice:

https://www.simplysafedividends.com/intelligent-income/posts/37-top-10-pieces-of-investment-advice-from-warren-buffett
I picked the 7 I agree with.
  1. Invest in what you know…and nothing more.
  2. Never compromise on business quality
  3. When you buy a stock, plan to hold it forever
  4. Diversification can be dangerous
  5. Most news is noise, not news (don't read articles about investing)
  6. The best moves are usually boring (buy and hold)
  7. Only listen to those you know and trust
I firmly believe that anyone who follows those concepts, they will find success in investing.

General mindset:

Application:

I was very specific in the types of companies I would choose to invest in within tech. I decided to follow my strengths. As a data engineer, I'm very intimate with cloud technologies, and I think I generally have pretty sharp business acumen and good strategic direction.
As a result, my day to day work had me using a ton of technologies in the cloud space. I've used Splunk, NewRelic, Twilio, AWS, GCP, Hortonworks/Cloudera, Oracle, Tableau, Datadog, Sendgrid (bought by Twilio), Dropbox/box, Slack, Salesforce, Marketo, Databricks, Snowflake, HP Vertica, just to name a few. I was familiar with CDN services like Fastly and Cloudflare because sometimes, I worked with the DevOps and IT guys.
Based on industry hearsay, day to day work, eventually, I got a good "feel" of what technologies were widely adopted, easy to use, and had a good reputation in the industry. Similarly, I also got a feel for what tech were being considered 'dated' or not widely used (HP, Oracle, Cloudera, Dropbox, Box).
I tend to shy away from companies that I don't understand. In the past, most times I've done that-- I got burned. My biggest losers this year was betting on $NAT and $JMNA (10k total loss). After learning from those mistakes, I decided to only focus on investing in companies that either I or my peers have intimate first hand experience with using. Because of this rationale, the majority of stocks in my portfolio are products which I believe in, I thoroughly enjoy using, and I would recommend to my friends, family, and colleagues.
Post COVID, due to the shift to remote work and increase in online shopping I decided to double down on tech. I already knew that eCommerce was the next big thing. I made very early investments into SHOP and Amazon in 2017 for that reason.
My hypothesis was that post-COVID, the shift on increased online activity, remote work, and eCommerce would mean that companies which build tools to support increased online activity should also increase. I decided to choose three sectors within tech to narrow down-- these were three sectors that I had a good understanding of, due to the nature of my work and personal habits.
  1. eCommerce + AdTech
  2. IT/DevOps (increased online activity means higher need for infra)
  3. FinTech (increased shopping activity means more transactions)
These are the points I consider before I consider jumping into a stock:
  1. Do I feel good about using the company? Do I believe in the company's vision?
  2. Where do I see this company in 5 years? 10 years? Do I see my potential children being around to use these companies?
  3. What does YoY, QoQ growth look like for this company?
  4. Is/Will this product be a core part of how businesses or people operate?
  5. Who are their customers and target demographic?
  6. (SaaS) Customer testimonials, white papers, case studies. If it's for a technology, I'm going to want to read a paper or use case.
In March, I took what I believe to be an "educated gamble". When the market crashed, I liquefied most of my non tech assets and reinvested them into tech. Some of the holdings I already had, some holdings were newly purchased.
EDIT ^ this isn't called timing the market you /wsb imbeciles. Timing the market would be trying to figure out when to PULL OUT during ATH and then buying the dip. I SOLD at the lowest point, and I with the cash I sold AT A LOSS, I reinvested that cash and doubled down into tech. If I sold in Feb, and bought back in March, that would be calling timing the market. What I am doing is called REINVESTING/REBALANCING... not timing the market.
I have 50% of my networth in AMZN, MSFT, AAPL, GOOG, FB, NFLX, and the rest in individual securities/mutual funds. I have 3 shares of TSLA that I got in @1.5.
Here are the non FAANGs I chose.
  1. $SQ. I had already been invested in SQ since 2016. I made several bad trades, holding when it first blew past 90 until I sold it at 70... bought in again last year at 60s, after noticing that more and more B&M stores were getting rid of their clunky POS systems and replacing it with Square's physical readers. After COVID, I noticed a lot of pop up vendors, restaurants doing take out. A Square reader made transactions very easy to make post-COVID.
  2. $ATVI. Call of Duty and Candy Crush print money for them. I've been a Blizzard fanboy since I was a kid, so I have to keep this just out of principle.
  3. $SHOP. They turned a profit this year, and I think there is still a lot more room to grow. It's become somewhat of a household name. I've met quite a few people who mentioned that they have a Shopify site set up to do their side hustle. I've tried the product myself, and can definitely attest that it's pretty easy to get an online shop up and running within a day. I 5.5xed my return here.
  4. $BIGC. I bought into this shortly after IPO. I'm very excited to see an American Shopify. BigC focuses on enterprise customers right now, and Shopify independent merchants, so I don't see them directly competing. I'm self aware this is essentially a gamble. I got in at 90, sold at 140, and added more in 120s. I def got lucky here... it's not common for IPOs to pop so suddenly. I honestly wasn't expecting it to pop so soon.
  5. $OKTA. Best in class SSO tool. Amazing tool that keeps tracks of all of my sign-ons at work.
  6. $DDOG. Great monitoring tool. Widely adopted and good recommendations throughout the industry. Always had a nice looking booth at GoogleNext.
  7. $ZM. Zoom was the only video conf tool at work which I had a good time using. Adoption had blown up pre-COVID already in the tech world, and post-COVID, they somehow became a noun. "Zoom parties" and "Zoom dates" somehow became a thing interwoven into peoples' day to day lives.
  8. $TWLO. Twilio sells APIs which allow applications to send messages like text, voice, and video chat. For example, when DoorDash sends you a text at 1 AM reminding you that your bad decision has arrived, that text is powered by Twilio. In March, New York announced that they were going to use Twilio to send SMS notifs for COVID contact tracing.
  9. $NET/$FSTY. These two two seem like the ones best poised for growth in the CDN space. This is based off of industry exposure and chatting with people who work in DevOps.
  10. $DOCU. people aren't going to office to sign stuff, super easy to use, I like their product.
  11. $WMT. eComm, streaming, and a very substantial engineering investment makes me think they have room to grow. Also I really need to diversify.
  12. $COST. When is the last time you heard someone say "Man I hate going to Costco and paying $1.50 for a hotdog and soda?" Diversification. Also cheap hotdogs.
  13. $NVDA/AMD. GPUs are the present and the future. Not only are they used for video games, but Machine Learning now uses GPU instead of CPU to do compute (Tensorflow for example). Crypto is still a thing as well, and there will always been a constant need for GPUs.
Mutual funds/ETFs 1. $FSCSX. MF which focuses on FinTech.
  1. $VTSAX Pretty much moves with the SP500.
  2. $WCLD. Holdings include Salesforce, Workday, Zuora, Atlassian, Okta, New Relic, Fastly...
Titanvest: I was an early access user, and I was able to secure 0% fees for my accout. 36% gains so far. I like them, because their portfolio happens to include shares of tech giants that I either don't have individual stocks for or my stake is low (CRM, PPYL). It nicely complements my existing portfolio.

Some things I do that that are against the grain:

One example was how I applied the above principle was to WalMart. In 2018 I noticed that I was getting targeted by a lot of Data engineering job listing for WalMartLabs-- WarMart's tech division. The role was to build out a big data pipeline to support their eCommerce platform. WalMart's online store released in Q3 of 2019. Post COVID, I used their online store and it was a seamless experience. They even offer a 5% cash back card like Amazon. They reported strong Q4 sales last year, and they did very well post COVID. Why did I choose to invest in $WMT? Because I believe that Wal-Mart has room to grow for their online platform.
Lastly... remember that wealth isn't accrued over time. It takes years to build. The quickest way to increase your wealth is by investing in yourself-- your career and earning potential. The sooner my income increased, the quicker I had more capital to buy into stocks.
Also, if you've gotten this far, the point of my post isn't to say that you should invest into tech. The message I'm trying to get across is-- when picking companies, pick companies in fields or verticals you have good knowledge in. Heed Buffet's advice to only pick companies you believe in and understand. Play to your strengths, don't mindless toss money based on one person's posts on Reddit-- always do your own due diligence. Use DD as a guide and use personal research and experience to drive your decision.
submitted by fire_water76 to stocks [link] [comments]

Thomson Reuters Uses Machine Learning to Predict Crypto-Trading

Thomson Reuters Uses Machine Learning to Predict Crypto-Trading submitted by ArtificialLawyer to CryptoCurrency [link] [comments]

How can we use AI. and Machine Learning when trading crypto-currencies?

  1. Strategy optimization
Even if you decide not to use machine learning and to define your strategy manually, methods from computer science and statistics, which are closely related to machine learning, can help you.
  1. Signals extraction
In trading, technical analysis indicators are popular. There are hundreds of these indicators and they are built on years of research in time series processing as well as on years of experience of day traders. You can find them implemented in most trading software. Traders mostly use these indicators to indicate buy or sell signals and they usually use just a few of these indicators. At Signals Network, we will give you an opportunity to use the technical indicators as features for your machine learning algorithm.
For more info about Signals Network ICO: visit website.
submitted by bitmanija to u/bitmanija [link] [comments]

How can we use AI and Machine Learning when trading crypto-currencies?

  1. Strategy optimization
Even if you decide not to use machine learning and to define your strategy manually, methods from computer science and statistics, which are closely related to machine learning, can help you.
  1. Signals extraction
In trading, technical analysis indicators are popular. There are hundreds of these indicators and they are built on years of research in time series processing as well as on years of experience of day traders. You can find them implemented in most trading software. Traders mostly use these indicators to indicate buy or sell signals and they usually use just a few of these indicators. At Signals Network, we will give you an opportunity to use the technical indicators as features for your machine learning algorithm.
For more info about Signals Network ICO: visit web..
submitted by bitmanija to BitRussia [link] [comments]

Funguana Is an Upcoming Trading Bot Combining TA With Machine Learning #cryptocurrency #crypto #ico… https://t.co/KjyDCNFmfy - Crypto Insider Info - Whales's

Posted at: April 30, 2018 at 01:33AM
By:
Funguana Is an Upcoming Trading Bot Combining TA With Machine Learning #cryptocurrency #crypto #ico… https://t.co/KjyDCNFmfy
Automate your Trading via Crypto Bot : https://ift.tt/2EU8PEX
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How can we use AI. and Machine Learning when trading crypto-currencies..

  1. Strategy optimization
Even if you decide not to use machine learning and to define your strategy manually, methods from computer science and statistics, which are closely related to machine learning, can help you.
  1. Signals extraction
In trading, technical analysis indicators are popular. There are hundreds of these indicators and they are built on years of research in time series processing as well as on years of experience of day traders. You can find them implemented in most trading software. Traders mostly use these indicators to indicate buy or sell signals and they usually use just a few of these indicators. At Signals Network, we will give you an opportunity to use the technical indicators as features for your machine learning algorithm.
For more info about Signals Network ICO: visit signals website.
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AI Trader  The Machine Learning Bot Crypto-ML Introduction and Overview - Machine Learning for ... How To Use The Crypto Money Machine Crypto Trading Basics Trading AI Bot Live Account 5 Days - 2079 Trades

Comfort / Ease Of Use: Ease of use is the fundamental thing behind selecting a trading bot. In making crypto trading easier, using software for metric measurement can help. A result-driven crypto trading machine should be simple to use. Traders should check if the bots can operate with a couple of clicks. Algorithmic Trading and Machine Learning for Crypto Traders. Algorithmic trading is a procedural method whereby a computer opens and closes trades based on a set of instructions. Simply put, it is the joining of a computer-based strategy with computer-based trade execution. Crypto Trading Machine-Review 2018 Learn more : Turn Your Computer into a Crypto Currency Trading Machine… Bitcoin, Ethereum, More! Subscribe to Get more stuff like this. Subscribe to our mailing list and get interesting stuff and updates to your email inbox. There are 2 types of trading algorithms characterized by different speeds, turnover rates, and order-to-trade ratios that leverages different frequency financial data and electronic trading tools. Machine learning tools for cryptocurrency traders and investors. Clear signals and deep market insights. Trading signals and crypto bot trading for Bitcoin.

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AI Trader The Machine Learning Bot

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