Best Automated Forex Trading Software For Traders

Binary Options Review; Best Binary Options Brokers

Binary Options Review; Best Binary Options Brokers

Binary Options Review; Best Binary Options Brokers
We have compared the best regulated binary options brokers and platforms in May 2020 and created this top list. Every binary options company here has been personally reviewed by us to help you find the best binary options platform for both beginners and experts. The broker comparison list below shows which binary trading sites came out on top based on different criteria.
You can put different trading signals into consideration such as using payout (maximum returns), minimum deposit, bonus offers, or if the operator is regulated or not. You can also read full reviews of each broker, helping you make the best choice. This review is to ensure traders don't lose money in their trading account.
How to Compare Brokers and Platforms
In order to trade binary options, you need to engage the services of a binary options broker that accepts clients from your country e.g. check US trade requirements if you are in the United States. Here at bitcoinbinaryoptionsreview.com, we have provided all the best comparison factors that will help you select which trading broker to open an account with. We have also looked at our most popular or frequently asked questions, and have noted that these are important factors when traders are comparing different brokers:
  1. What is the Minimum Deposit? (These range from $5 or $10 up to $250)
  2. Are they regulated or licensed, and with which regulator?
  3. Can I open a Demo Account?
  4. Is there a signals service, and is it free?
  5. Can I trade on my mobile phone and is there a mobile app?
  6. Is there a Bonus available for new trader accounts? What are the Terms and
  7. conditions?
  8. Who has the best binary trading platform? Do you need high detail charts with technical analysis indicators?
  9. Which broker has the best asset lists? Do they offer forex, cryptocurrency, commodities, indices, and stocks – and how many of each?
  10. Which broker has the largest range of expiry times (30 seconds, 60 seconds, end of the day, long term, etc?)
  11. How much is the minimum trade size or amount?
  12. What types of options are available? (Touch, Ladder, Boundary, Pairs, etc)
  13. Additional Tools – Like Early closure or Metatrader 4 (Mt4) plugin or integration
  14. Do they operate a Robot or offer automated trading software?
  15. What is Customer Service like? Do they offer telephone, email and live chat customer support – and in which countries? Do they list direct contact details?
  16. Who has the best payouts or maximum returns? Check the markets you will trade.
The Regulated Binary Brokers
Regulation and licensing is a key factor when judging the best broker. Unregulated brokers are not always scams, or untrustworthy, but it does mean a trader must do more ‘due diligence’ before trading with them. A regulated broker is the safest option.
Regulators - Leading regulatory bodies include:
  • CySec – The Cyprus Securities and Exchange Commission (Cyprus and the EU)
  • FCA – Financial Conduct Authority (UK)
  • CFTC – Commodity Futures Trading Commission (US)
  • FSB – Financial Services Board (South Africa)
  • ASIC – Australia Securities and Investment Commission
There are other regulators in addition to the above, and in some cases, brokers will be regulated by more than one organization. This is becoming more common in Europe where binary options are coming under increased scrutiny. Reputable, premier brands will have regulation of some sort.
Regulation is there to protect traders, to ensure their money is correctly held and to give them a path to take in the event of a dispute. It should therefore be an important consideration when choosing a trading partner.
Bonuses - Both sign up bonuses and demo accounts are used to attract new clients. Bonuses are often a deposit match, a one-off payment, or risk-free trade. Whatever the form of a bonus, there are terms and conditions that need to be read.
It is worth taking the time to understand those terms before signing up or clicking accept on a bonus offer. If the terms are not to your liking then the bonus loses any attraction and that broker may not be the best choice. Some bonus terms tie in your initial deposit too. It is worth reading T&Cs before agreeing to any bonus, and worth noting that many brokers will give you the option to ‘opt-out’ of taking a bonus.
Using a bonus effectively is harder than it sounds. If considering taking up one of these offers, think about whether, and how, it might affect your trading. One common issue is that turnover requirements within the terms, often cause traders to ‘over-trade’. If the bonus does not suit you, turn it down.
How to Find the Right Broker
But how do you find a good broker? Well, that’s where BitcoinBinaryOptionsReview.com comes in. We assess and evaluate binary options brokers so that traders know exactly what to expect when signing up with them. Our financial experts have more than 20 years of experience in the financial business and have reviewed dozens of brokers.
Being former traders ourselves, we know precisely what you need. That’s why we’ll do our best to provide our readers with the most accurate information. We are one of the leading websites in this area of expertise, with very detailed and thorough analyses of every broker we encounter. You will notice that each aspect of any broker’s offer has a separate article about it, which just goes to show you how seriously we approach each company. This website is your best source of information about binary options brokers and one of your best tools in determining which one of them you want as your link to the binary options market.
Why Use a Binary Options Trading Review?
So, why is all this relevant? As you may already know, it is difficult to fully control things that take place online. There are people who only pose as binary options brokers in order to scam you and disappear with your money. True, most of the brokers we encounter turn out to be legit, but why take unnecessary risks?
Just let us do our job and then check out the results before making any major decisions. All our investigations regarding brokers’ reliability can be seen if you click on our Scam Tab, so give it a go and see how we operate. More detailed scam reports than these are simply impossible to find. However, the most important part of this website can be found if you go to our Brokers Tab.
There you can find extensive analyses of numerous binary options brokers irrespective of your trading strategy. Each company is represented with an all-encompassing review and several other articles dealing with various aspects of their offer. A list containing the very best choices will appear on your screen as you enter our website whose intuitive design will allow you to access all the most important information in real-time.
We will explain minimum deposits, money withdrawals, bonuses, trading platforms, and many more topics down to the smallest detail. Rest assured, this amount of high-quality content dedicated exclusively to trading cannot be found anywhere else. Therefore, visiting us before making any important decisions regarding this type of trading is the best thing to do.
CONCLUSION: Stay ahead of the market, and recover from all kinds of binary options trading loss, including market losses in bitcoin, cryptocurrency, and forex markets too. Send your request via email to - [email protected]
submitted by Babyelijah to u/Babyelijah [link] [comments]

Trading economic news

The majority of this sub is focused on technical analysis. I regularly ridicule such "tea leaf readers" and advocate for trading based on fundamentals and economic news instead, so I figured I should take the time to write up something on how exactly you can trade economic news releases.
This post is long as balls so I won't be upset if you get bored and go back to your drooping dick patterns or whatever.

How economic news is released

First, it helps to know how economic news is compiled and released. Let's take Initial Jobless Claims, the number of initial claims for unemployment benefits around the United States from Sunday through Saturday. Initial in this context means the first claim for benefits made by an individual during a particular stretch of unemployment. The Initial Jobless Claims figure appears in the Department of Labor's Unemployment Insurance Weekly Claims Report, which compiles information from all of the per-state departments that report to the DOL during the week. A typical number is between 100k and 250k and it can vary quite significantly week-to-week.
The Unemployment Insurance Weekly Claims Report contains data that lags 5 days behind. For example, the Report issued on Thursday March 26th 2020 contained data about the week ending on Saturday March 21st 2020.
In the days leading up to the Report, financial companies will survey economists and run complicated mathematical models to forecast the upcoming Initial Jobless Claims figure. The results of surveyed experts is called the "consensus"; specific companies, experts, and websites will also provide their own forecasts. Different companies will release different consensuses. Usually they are pretty close (within 2-3k), but for last week's record-high Initial Jobless Claims the reported consensuses varied by up to 1M! In other words, there was essentially no consensus.
The Unemployment Insurance Weekly Claims Report is released each Thursday morning at exactly 8:30 AM ET. (On Thanksgiving the Report is released on Wednesday instead.) Media representatives gather at the Frances Perkins Building in Washington DC and are admitted to the "lockup" at 8:00 AM ET. In order to be admitted to the lockup you have to be a credentialed member of a media organization that has signed the DOL lockup agreement. The lockup room is small so there is a limited number of spots.
No phones are allowed. Reporters bring their laptops and connect to a local network; there is a master switch on the wall that prevents/enables Internet connectivity on this network. Once the doors are closed the Unemployment Insurance Weekly Claims Report is distributed, with a heading that announces it is "embargoed" (not to be released) prior to 8:30 AM. Reporters type up their analyses of the report, including extracting key figures like Initial Jobless Claims. They load their write-ups into their companies' software, which prepares to send it out as soon as Internet is enabled. At 8:30 AM the DOL representative in the room flips the wall switch and all of the laptops are connected to the Internet, releasing their write-ups to their companies and on to their companies' partners.
Many of those media companies have externally accessible APIs for distributing news. Media aggregators and squawk services (like RanSquawk and TradeTheNews) subscribe to all of these different APIs and then redistribute the key economic figures from the Report to their own subscribers within one second after Internet is enabled in the DOL lockup.
Some squawk services are text-based while others are audio-based. FinancialJuice.com provides a free audio squawk service; internally they have a paid subscription to a professional squawk service and they simply read out the latest headlines to their own listeners, subsidized by ads on the site. I've been using it for 4 months now and have been pretty happy. It usually lags behind the official release times by 1-2 seconds and occasionally they verbally flub the numbers or stutter and have to repeat, but you can't beat the price!
Important - I’m not affiliated with FinancialJuice and I’m not advocating that you use them over any other squawk. If you use them and they misspeak a number and you lose all your money don’t blame me. If anybody has any other free alternatives please share them!

How the news affects forex markets

Institutional forex traders subscribe to these squawk services and use custom software to consume the emerging data programmatically and then automatically initiate trades based on the perceived change to the fundamentals that the figures represent.
It's important to note that every institution will have "priced in" their own forecasted figures well in advance of an actual news release. Forecasts and consensuses all come out at different times in the days leading up to a news release, so by the time the news drops everybody is really only looking for an unexpected result. You can't really know what any given institution expects the value to be, but unless someone has inside information you can pretty much assume that the market has collectively priced in the experts' consensus. When the news comes out, institutions will trade based on the difference between the actual and their forecast.
Sometimes the news reflects a real change to the fundamentals with an economic effect that will change the demand for a currency, like an interest rate decision. However, in the case of the Initial Jobless Claims figure, which is a backwards-looking metric, trading is really just self-fulfilling speculation that market participants will buy dollars when unemployment is low and sell dollars when unemployment is high. Generally speaking, news that reflects a real economic shift has a bigger effect than news that only matters to speculators.
Massive and extremely fast news-based trades happen within tenths of a second on the ECNs on which institutional traders are participants. Over the next few seconds the resulting price changes trickle down to retail traders. Some economic news, like Non Farm Payroll Employment, has an effect that can last minutes to hours as "slow money" follows behind on the trend created by the "fast money". Other news, like Initial Jobless Claims, has a short impact that trails off within a couple minutes and is subsequently dwarfed by the usual pseudorandom movements in the market.
The bigger the difference between actual and consensus, the bigger the effect on any given currency pair. Since economic news releases generally relate to a single currency, the biggest and most easily predicted effects are seen on pairs where one currency is directly effected and the other is not affected at all. Personally I trade USD/JPY because the time difference between the US and Japan ensures that no news will be coming out of Japan at the same time that economic news is being released in the US.
Before deciding to trade any particular news release you should measure the historical correlation between the release (specifically, the difference between actual and consensus) and the resulting short-term change in the currency pair. Historical data for various news releases (along with historical consensus data) is readily available. You can pay to get it exported into Excel or whatever, or you can scroll through it for free on websites like TradingEconomics.com.
Let's look at two examples: Initial Jobless Claims and Non Farm Payroll Employment (NFP). I collected historical consensuses and actuals for these releases from January 2018 through the present, measured the "surprise" difference for each, and then correlated that to short-term changes in USD/JPY at the time of release using 5 second candles.
I omitted any releases that occurred simultaneously as another major release. For example, occasionally the monthly Initial Jobless Claims comes out at the exact same time as the monthly Balance of Trade figure, which is a more significant economic indicator and can be expected to dwarf the effect of the Unemployment Insurance Weekly Claims Report.
USD/JPY correlation with Initial Jobless Claims (2018 - present)
USD/JPY correlation with Non Farm Payrolls (2018 - present)
The horizontal axes on these charts is the duration (in seconds) after the news release over which correlation was calculated. The vertical axis is the Pearson correlation coefficient: +1 means that the change in USD/JPY over that duration was perfectly linearly correlated to the "surprise" in the releases; -1 means that the change in USD/JPY was perfectly linearly correlated but in the opposite direction, and 0 means that there is no correlation at all.
For Initial Jobless Claims you can see that for the first 30 seconds USD/JPY is strongly negatively correlated with the difference between consensus and actual jobless claims. That is, fewer-than-forecast jobless claims (fewer newly unemployed people than expected) strengthens the dollar and greater-than-forecast jobless claims (more newly unemployed people than expected) weakens the dollar. Correlation then trails off and changes to a moderate/weak positive correlation. I interpret this as algorithms "buying the dip" and vice versa, but I don't know for sure. From this chart it appears that you could profit by opening a trade for 15 seconds (duration with strongest correlation) that is long USD/JPY when Initial Jobless Claims is lower than the consensus and short USD/JPY when Initial Jobless Claims is higher than expected.
The chart for Non Farm Payroll looks very different. Correlation is positive (higher-than-expected payrolls strengthen the dollar and lower-than-expected payrolls weaken the dollar) and peaks at around 45 seconds, then slowly decreases as time goes on. This implies that price changes due to NFP are quite significant relative to background noise and "stick" even as normal fluctuations pick back up.
I wanted to show an example of what the USD/JPY S5 chart looks like when an "uncontested" (no other major simultaneously news release) Initial Jobless Claims and NFP drops, but unfortunately my broker's charts only go back a week. (I can pull historical data going back years through the API but to make it into a pretty chart would be a bit of work.) If anybody can get a 5-second chart of USD/JPY at March 19, 2020, UTC 12:30 and/or at February 7, 2020, UTC 13:30 let me know and I'll add it here.

Backtesting

So without too much effort we determined that (1) USD/JPY is strongly negatively correlated with the Initial Jobless Claims figure for the first 15 seconds after the release of the Unemployment Insurance Weekly Claims Report (when no other major news is being released) and also that (2) USD/JPY is strongly positively correlated with the Non Farms Payroll figure for the first 45 seconds after the release of the Employment Situation report.
Before you can assume you can profit off the news you have to backtest and consider three important parameters.
Entry speed: How quickly can you realistically enter the trade? The correlation performed above was measured from the exact moment the news was released, but realistically if you've got your finger on the trigger and your ear to the squawk it will take a few seconds to hit "Buy" or "Sell" and confirm. If 90% of the price move happens in the first second you're SOL. For back-testing purposes I assume a 5 second delay. In practice I use custom software that opens a trade with one click, and I can reliably enter a trade within 2-3 seconds after the news drops, using the FinancialJuice free squawk.
Minimum surprise: Should you trade every release or can you do better by only trading those with a big enough "surprise" factor? Backtesting will tell you whether being more selective is better long-term or not.
Hold time: The optimal time to hold the trade is not necessarily the same as the time of maximum correlation. That's a good starting point but it's not necessarily the best number. Backtesting each possible hold time will let you find the best one.
The spread: When you're only holding a position open for 30 seconds, the spread will kill you. The correlations performed above used the midpoint price, but in reality you have to buy at the ask and sell at the bid. Brokers aren't stupid and the moment volume on the ECN jumps they will widen the spread for their retail customers. The only way to determine if the news-driven price movements reliably overcome the spread is to backtest.
Stops: Personally I don't use stops, neither take-profit nor stop-loss, since I'm automatically closing the trade after a fixed (and very short) amount of time. Additionally, brokers have a minimum stop distance; the profits from scalping the news are so slim that even the nearest stops they allow will generally not get triggered.
I backtested trading these two news releases (since 2018), using a 5 second entry delay, real historical spreads, and no stops, cycling through different "surprise" thresholds and hold times to find the combination that returns the highest net profit. It's important to maximize net profit, not expected value per trade, so you don't over-optimize and reduce the total number of trades taken to one single profitable trade. If you want to get fancy you can set up a custom metric that combines number of trades, expected value, and drawdown into a single score to be maximized.
For the Initial Jobless Claims figure I found that the best combination is to hold trades open for 25 seconds (that is, open at 5 seconds elapsed and hold until 30 seconds elapsed) and only trade when the difference between consensus and actual is 7k or higher. That leads to 30 trades taken since 2018 and an expected return of... drumroll please... -0.0093 yen per unit per trade.
Yep, that's a loss of approx. $8.63 per lot.
Disappointing right? That's the spread and that's why you have to backtest. Even though the release of the Unemployment Insurance Weekly Claims Report has a strong correlation with movement in USD/JPY, it's simply not something that a retail trader can profit from.
Let's turn to the NFP. There I found that the best combination is to hold trades open for 75 seconds (that is, open at 5 seconds elapsed and hold until 80 seconds elapsed) and trade every single NFP (no minimum "surprise" threshold). That leads to 20 trades taken since 2018 and an expected return of... drumroll please... +0.1306 yen per unit per trade.
That's a profit of approx. $121.25 per lot. Not bad for 75 seconds of work! That's a +6% ROI at 50x leverage.

Make it real

If you want to do this for realsies, you need to run these numbers for all of the major economic news releases. Markit Manufacturing PMI, Factory Orders MoM, Trade Balance, PPI MoM, Export and Import Prices, Michigan Consumer Sentiment, Retail Sales MoM, Industrial Production MoM, you get the idea. You keep a list of all of the releases you want to trade, when they are released, and the ideal hold time and "surprise" threshold. A few minutes before the prescribed release time you open up your broker's software, turn on your squawk, maybe jot a few notes about consensuses and model forecasts, and get your finger on the button. At the moment you hear the release you open the trade in the correct direction, hold it (without looking at the chart!) for the required amount of time, then close it and go on with your day.
Some benefits of trading this way: * Most major economic releases come out at either 8:30 AM ET or 10:00 AM ET, and then you're done for the day. * It's easily backtestable. You can look back at the numbers and see exactly what to expect your return to be. * It's fun! Packing your trading into 30 seconds and knowing that institutions are moving billions of dollars around as fast as they can based on the exact same news you just read is thrilling. * You can wow your friends by saying things like "The St. Louis Fed had some interesting remarks on consumer spending in the latest Beige Book." * No crayons involved.
Some downsides: * It's tricky to be fast enough without writing custom software. Some broker software is very slow and requires multiple dialog boxes before a position is opened, which won't cut it. * The profits are very slim, you're not going to impress your instagram followers to join your expensive trade copying service with your 30-second twice-weekly trades. * Any friends you might wow with your boring-ass economic talking points are themselves the most boring people in the world.
I hope you enjoyed this long as fuck post and you give trading economic news a try!
submitted by thicc_dads_club to Forex [link] [comments]

Looking for someone to collaborate with in exploring some of the fundamental questions in algo trading in relation to quantitative analysis and the Forex market specifically.

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.
Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.
Can quantitative analysis of the Forex market give an edge to a retail trader?
Can a retail trader utilize said edge to make consistent profits, within the market?
Are these profits enough to make a full time living on?
But before we answer these questions, there are even more fundamental questions that need to be answered.
To what degree if any is back-testing useful in generating successful algo strategies?
Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?
What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?
What indicators if any are most successful in helping generate profitable strategies?
What data horizons are best suited to generate most successful strategies?
What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?
What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....
And many many more fundamental questions....
As you can see answering these questions will be no easy or fast task, there is a lot of research and data mining that will have to be done. I like to approach things from a purely scientific method, make no assumptions about anything and use a rigorous approach when testing, validating any and all conclusions. I like to see real data and correlations that are actually there before I start making assumptions.
The reason I am searching for these answers is because, they are simply not available out on the internet. I have read many research papers on-line, and articles on this or that about various topics related to Forex and quantitative analysis, but whatever information there is, its very sparse or very vague (and there is no shortage of disinformation out there). So, I have no choice but to answer these questions myself.
I have and will be spending considerable time on the endeavour, but I am also not delusional, there is only so much 1 man can do and achieve with the resources at his disposal. And at the end of the whole thing, I can at least say I gave it a good try. And along the way learn some very interesting things (already had a few eureka moments).
Mo workflow so far has consisted of using a specific (free) software package that generate strategies. You can either use it to auto generate strategies or create very specific rules yourself and create the strategies from scratch. I am not a coder so I find this tool quite useful. I mainly use this tool to do lots of hypothesis testing as I am capable of checking for any possible correlations in the markets very fast, and then test for the significance if any of said correlations.
Anyways who I am looking for? Well if you are the type of person that has free time on their hands, is keen on the scientific method and rigorous testing and retesting of various hypothesis, hit me up. You don't need to be a coder or have a PHD in statistics. Just someone who is interested in answering the same questions I am.
Whats the end goal? I want to answer enough of these questions with enough certainty, whereby I can generate profitable algo strategies consistently. OR, maybe the answer is that It cant be done by small fry such as a retail trader. And that answer would be just as satisfactory, because It could save me a lot more time and money down the road, because I could close off this particular road and look elsewhere to make money.
submitted by no_witty_username to Forex [link] [comments]

Looking for someone to collaborate with in exploring some of the fundamental questions in algo trading in relation to quantitative analysis and the Forex market specifically.

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.
Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.
Can quantitative analysis of the Forex market give an edge to a retail trader?
Can a retail trader utilize said edge to make consistent profits, within the market?
Are these profits enough to make a full time living on?
But before we answer these questions, there are even more fundamental questions that need to be answered.
To what degree if any is back-testing useful in generating successful algo strategies?
Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?
What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?
What indicators if any are most successful in helping generate profitable strategies?
What data horizons are best suited to generate most successful strategies?
What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?
What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....
And many many more fundamental questions....
As you can see answering these questions will be no easy or fast task, there is a lot of research and data mining that will have to be done. I like to approach things from a purely scientific method, make no assumptions about anything and use a rigorous approach when testing, validating any and all conclusions. I like to see real data and correlations that are actually there before I start making assumptions.
The reason I am searching for these answers is because, they are simply not available out on the internet. I have read many research papers on-line, and articles on this or that about various topics related to Forex and quantitative analysis, but whatever information there is, its very sparse or very vague (and there is no shortage of disinformation out there). So, I have no choice but to answer these questions myself.
I have and will be spending considerable time on the endeavour, but I am also not delusional, there is only so much 1 man can do and achieve with the resources at his disposal. And at the end of the whole thing, I can at least say I gave it a good try. And along the way learn some very interesting things (already had a few eureka moments).
Mo workflow so far has consisted of using a specific (free) software package that generate strategies. You can either use it to auto generate strategies or create very specific rules yourself and create the strategies from scratch. I am not a coder so I find this tool quite useful. I mainly use this tool to do lots of hypothesis testing as I am capable of checking for any possible correlations in the markets very fast, and then test for the significance if any of said correlations.
Anyways who I am looking for? Well if you are the type of person that has free time on their hands, is keen on the scientific method and rigorous testing and retesting of various hypothesis, hit me up. You don't need to be a coder or have a PHD in statistics. Just someone who is interested in answering the same questions I am.
Whats the end goal? I want to answer enough of these questions with enough certainty, whereby I can generate profitable algo strategies consistently. OR, maybe the answer is that It cant be done by small fry such as a retail trader. And that answer would be just as satisfactory, because It could save me a lot more time and money down the road, because I could close off this particular road and look elsewhere to make money.
submitted by no_witty_username to algotrading [link] [comments]

Which are your Top 5 favourite coins out of the Top 100? An analysis.

I am putting together my investment portfolio for 2018 and made a complete summary of the current Top 100. Interestingly, I noticed that all coins can be categorized into 12 markets. Which markets do you think will play the biggest role in the coming year?
Here is a complete overview of all coins in an excel sheet including name, market, TPS, risk profile, time since launch (negative numbers mean that they are launching that many months in the future) and market cap. You can also sort by all of these fields of course. Coins written in bold are the strongest contenders within their market either due to having the best technology or having a small market cap and still excellent technology and potential. https://docs.google.com/spreadsheets/d/1s8PHcNvvjuy848q18py_CGcu8elRGQAUIf86EYh4QZo/edit#gid=0
The 12 markets are
  1. Currency 13 coins
  2. Platform 25 coins
  3. Ecosystem 9 coins
  4. Privacy 10 coins
  5. Currency Exchange Tool 8 coins
  6. Gaming & Gambling 5 coins
  7. Misc 15 coins
  8. Social Network 4 coins
  9. Fee Token 3 coins
  10. Decentralized Data Storage 4 coins
  11. Cloud Computing 3 coins
  12. Stable Coin 2 coins
Before we look at the individual markets, we need to take a look of the overall market and its biggest issue scalability first:
Cryptocurrencies aim to be a decentralized currency that can be used worldwide. Its goal is to replace dollar, Euro, Yen, all FIAT currencies worldwide. The coin that will achieve that will be worth several trillion dollars.
Bitcoin can only process 7 transactions per second (TPS). In order to replace all FIAT, it would need to perform at at least VISA levels, which usually processes around 3,000 TPS, up to 25,000 TPS during peak times and a maximum of 64,000 TPS. That means that this cryptocurrency would need to be able to perform at least several thousand TPS. However, a ground breaking technology should not look at current technology to set a goal for its use, i.e. estimating the number of emails sent in 1990 based on the number of faxes sent wasn’t a good estimate.
For that reason, 10,000 TPS is the absolute baseline for a cryptocurrency that wants to replace FIAT. This brings me to IOTA, which wants to connect all 80 billion IoT devices that are expected to exist by 2025, which constantly communicate with each other, creating 80 billion or more transactions per second. This is the benchmark that cryptocurrencies should be aiming for. Currently, 8 billion devices are connected to the Internet.
With its Lightning network recently launched, Bitcoin is realistically looking at 50,000 possible soon. Other notable cryptocurrencies besides IOTA and Bitcoin are Nano with 7,000 TPS already tested, Dash with several billion TPS possible with Masternodes, Neo, LISK and RHOC with 100,000 TPS by 2020, Ripple with 50,000 TPS, Ethereum with 10,000 with Sharding.
However, it needs to be said that scalability usually goes at the cost of decentralization and security. So, it needs to be seen, which of these technologies can prove itself resilient and performant.
Without further ado, here are the coins of the first market

Market 1 - Currency:

  1. Bitcoin: 1st generation blockchain with currently bad scalability currently, though the implementation of the Lightning Network looks promising and could alleviate most scalability concerns, scalability and high energy use.
  2. Ripple: Centralized currency that might become very successful due to tight involvement with banks and cross-border payments for financial institutions; banks and companies like Western Union and Moneygram (who they are currently working with) as customers customers. However, it seems they are aiming for more decentralization now.https://ripple.com/dev-blog/decentralization-strategy-update/. Has high TPS due to Proof of Correctness algorithm.
  3. Bitcoin Cash: Bitcoin fork with the difference of having an 8 times bigger block size, making it 8 times more scalable than Bitcoin currently. Further block size increases are planned. Only significant difference is bigger block size while big blocks lead to further problems that don't seem to do well beyond a few thousand TPS. Opponents to a block size argue that increasing the block size limit is unimaginative, offers only temporary relief, and damages decentralization by increasing costs of participation. In order to preserve decentralization, system requirements to participate should be kept low. To understand this, consider an extreme example: very big blocks (1GB+) would require data center level resources to validate the blockchain. This would preclude all but the wealthiest individuals from participating.Community seems more open than Bitcoin's though.
  4. Litecoin : Little brother of Bitcoin. Bitcoin fork with different mining algorithm but not much else.Copies everything that Bitcoin does pretty much. Lack of real innovation.
  5. Dash: Dash (Digital Cash) is a fork of Bitcoin and focuses on user ease. It has very fast transactions within seconds, low fees and uses Proof of Service from Masternodes for consensus. They are currently building a system called Evolution which will allow users to send money using usernames and merchants will find it easy to integrate Dash using the API. You could say Dash is trying to be a PayPal of cryptocurrencies. Currently, cryptocurrencies must choose between decentralization, speed, scalability and can pick only 2. With Masternodes, Dash picked speed and scalability at some cost of decentralization, since with Masternodes the voting power is shifted towards Masternodes, which are run by Dash users who own the most Dash.
  6. IOTA: 3rd generation blockchain called Tangle, which has a high scalability, no fees and instant transactions. IOTA aims to be the connective layer between all 80 billion IOT devices that are expected to be connected to the Internet in 2025, possibly creating 80 billion transactions per second or 800 billion TPS, who knows. However, it needs to be seen if the Tangle can keep up with this scalability and iron out its security issues that have not yet been completely resolved.
  7. Nano: 3rd generation blockchain called Block Lattice with high scalability, no fees and instant transactions. Unlike IOTA, Nano only wants to be a payment processor and nothing else, for now at least. With Nano, every user has their own blockchain and has to perform a small amount of computing for each transaction, which makes Nano perform at 300 TPS with no problems and 7,000 TPS have also been tested successfully. Very promising 3rd gen technology and strong focus on only being the fastest currency without trying to be everything.
  8. Decred: As mining operations have grown, Bitcoin’s decision-making process has become more centralized, with the largest mining companies holding large amounts of power over the Bitcoin improvement process. Decred focuses heavily on decentralization with their PoW Pos hybrid governance system to become what Bitcoin was set out to be. They will soon implement the Lightning Network to scale up. While there do not seem to be more differences to Bitcoin besides the novel hybrid consensus algorithm, which Ethereum, Aeternity and Bitcoin Atom are also implementing, the welcoming and positive Decred community and professoinal team add another level of potential to the coin.
  9. Aeternity: We’ve seen recently, that it’s difficult to scale the execution of smart contracts on the blockchain. Crypto Kitties is a great example. Something as simple as creating and trading unique assets on Ethereum bogged the network down when transaction volume soared. Ethereum and Zilliqa address this problem with Sharding. Aeternity focuses on increasing the scalability of smart contracts and dapps by moving smart contracts off-chain. Instead of running on the blockchain, smart contracts on Aeternity run in private state channels between the parties involved in the contracts. State channels are lines of communication between parties in a smart contract. They don’t touch the blockchain unless they need to for adjudication or transfer of value. Because they’re off-chain, state channel contracts can operate much more efficiently. They don’t need to pay the network for every time they compute and can also operate with greater privacy. An important aspect of smart contract and dapp development is access to outside data sources. This could mean checking the weather in London, score of a football game, or price of gold. Oracles provide access to data hosted outside the blockchain. In many blockchain projects, oracles represent a security risk and potential point of failure, since they tend to be singular, centralized data streams. Aeternity proposes decentralizing oracles with their oracle machine. Doing so would make outside data immutable and unchangeable once it reaches Aeternity’s blockchain. Of course, the data source could still be hacked, so Aeternity implements a prediction market where users can bet on the accuracy and honesty of incoming data from various oracles.It also uses prediction markets for various voting and verification purposes within the platform. Aeternity’s network runs on on a hybrid of proof of work and proof of stake. Founded by a long-time crypto-enthusiast and early colleague of Vitalik Buterin, Yanislav Malahov. Promising concept though not product yet
  10. Bitcoin Atom: Atomic Swaps and hybrid consenus. This looks like the only Bitcoin clone that actually is looking to innovate next to Bitcoin Cash.
  11. Dogecoin: Litecoin fork, fantastic community, though lagging behind a bit in technology.
  12. Bitcoin Gold: A bit better security than bitcoin through ASIC resistant algorithm, but that's it. Not that interesting.
  13. Digibyte: Digibyte's PoS blockchain is spread over a 100,000+ servers, phones, computers, and nodes across the globe, aiming for the ultimate level of decentralization. DigiByte rebalances the load between the five mining algorithms by adjusting the difficulty of each so one algorithm doesn’t become dominant. The algorithm's asymmetric difficulty has gained notoriety and been deployed in many other blockchains.DigiByte’s adoption over the past four years has been slow. It’s still a relatively obscure currency compared its competitors. The DigiByte website offers a lot of great marketing copy and buzzwords. However, there’s not much technical information about what they have planned for the future. You could say Digibyte is like Bitcoin, but with shorter blocktimes and a multi-algorithm. However, that's not really a difference big enough to truly set themselves apart from Bitcoin, since these technologies could be implemented by any blockchain without much difficulty. Their decentralization is probably their strongest asset, however, this also change quickly if the currency takes off and big miners decide to go into Digibyte.
  14. Bitcoin Diamond Asic resistant Bitcoin and Copycat

Market 2 - Platform

Most of the cryptos here have smart contracts and allow dapps (Decentralized apps) to be build on their platform and to use their token as an exchange of value between dapp services.
  1. Ethereum: 2nd generation blockchain that allows the use of smart contracts. Bad scalability currently, though this concern could be alleviated by the soon to be implemented Lightning Network aka Plasma and its Sharding concept.
  2. EOS: Promising technology that wants to be able do everything, from smart contracts like Ethereum, scalability similar to Nano with 1000 tx/second + near instant transactions and zero fees, to also wanting to be a platform for dapps. However, EOS doesn't have a product yet and everything is just promises still. Highly overvalued right now. However, there are lots of red flags, have dumped $500 million Ether over the last 2 months and possibly bought back EOS to increase the size of their ICO, which has been going on for over a year and has raised several billion dollars. All in all, their market cap is way too high for that and not even having a product.
  3. Cardano: Similar to Ethereum/EOS, however, only promises made with no delivery yet, highly overrated right now. Interesting concept though. Market cap way too high for not even having a product. Somewhat promising technology.
  4. VeChain: Singapore-based project that’s building a business enterprise platform and inventory tracking system. Examples are verifying genuine luxury goods and food supply chains. Has one of the strongest communities in the crypto world. Most hyped token of all, with merit though.
  5. Neo: Neo is a platform, similar to Eth, but more extensive, allowing dapps and smart contracts, but with a different smart contract gas system, consensus mechanism (PoS vs. dBfT), governance model, fixed vs unfixed supply, expensive contracts vs nearly free contracts, different ideologies for real world adoption. There are currently only 9 nodes, each of which are being run by a company/entity hand selected by the NEO council (most of which are located in china) and are under contract. This means that although the locations of the nodes may differ, ultimately the neo council can bring them down due to their legal contracts. In fact this has been done in the past when the neo council was moving 50 million neo that had been locked up. Also dbft (or neo's implmentation of it) has failed underload causing network outages during major icos. The first step in decentralization is that the NEO Counsel will select trusted nodes (Universities, business partners, etc.) and slowly become less centralized that way. The final step in decentralization will be allowing NEO holders to vote for new nodes, similar to a DPoS system (ARK/EOS/LISK). NEO has a regulation/government friendly ideology. Finally they are trying to work undewith the Chinese government in regards to regulations. If for some reason they wanted it shut down, they could just shut it down.
  6. Stellar: PoS system, similar goals as Ripple, but more of a platform than only a currency. 80% of Stellar are owned by Stellar.org still, making the currency centralized.
  7. Ethereum classic: Original Ethereum that decided not to fork after a hack. The Ethereum that we know is its fork. Uninteresing, because it has a lot of less resources than Ethereum now and a lot less community support.
  8. Ziliqa: Zilliqa is building a new way of sharding. 2400 tpx already tested, 10,000 tps soon possible by being linearly scalable with the number of nodes. That means, the more nodes, the faster the network gets. They are looking at implementing privacy as well.
  9. QTUM: Enables Smart contracts on the Bitcoin blockchain. Useful.
  10. Icon: Korean ethereum. Decentralized application platform that's building communities in partnership with banks, insurance providers, hospitals, and universities. Focused on ID verification and payments. No big differentiators to the other 20 Ethereums, except that is has a product. That is a plus. Maybe cheap alternative to Ethereum.
  11. LISK: Lisk's difference to other BaaS is that side chains are independent to the main chain and have to have their own nodes. Similar to neo whole allows dapps to deploy their blockchain to. However, Lisk is currently somewhat centralized with a small group of members owning more than 50% of the delegated positions. Lisk plans to change the consensus algorithm for that reason in the near future.
  12. Rchain: Similar to Ethereum with smart contract, though much more scalable at an expected 40,000 TPS and possible 100,000 TPS. Not launched yet. No product launched yet, though promising technology. Not overvalued, probably at the right price right now.
  13. ARDR: Similar to Lisk. Ardor is a public blockchain platform that will allow people to utilize the blockchain technology of Nxt through the use of child chains. A child chain, which is a ‘light’ blockchain that can be customized to a certain extent, is designed to allow easy self-deploy for your own blockchain. Nxt claims that users will "not need to worry" about security, as that part is now handled by the main chain (Ardor). This is the chief innovation of Ardor. Ardor was evolved from NXT by the same company. NEM started as a NXT clone.
  14. Ontology: Similar to Neo. Interesting coin
  15. Bytom: Bytom is an interactive protocol of multiple byte assets. Heterogeneous byte-assets (indigenous digital currency, digital assets) that operate in different forms on the Bytom Blockchain and atomic assets (warrants, securities, dividends, bonds, intelligence information, forecasting information and other information that exist in the physical world) can be registered, exchanged, gambled and engaged in other more complicated and contract-based interoperations via Bytom.
  16. Nxt: Similar to Lisk
  17. Stratis: Different to LISK, Stratis will allow businesses and organizations to create their own blockchain according to their own needs, but secured on the parent Stratis chain. Stratis’s simple interface will allow organizations to quickly and easily deploy and/or test blockchain functionality of the Ethereum, BitShares, BitCoin, Lisk and Stratis environements.
  18. Status: Status provides access to all of Ethereum’s decentralized applications (dapps) through an app on your smartphone. It opens the door to mass adoption of Ethereum dapps by targeting the fastest growing computer segment in the world – smartphone users.16. Ark: Fork of Lisk that focuses on a smaller feature set. Ark wallets can only vote for one delegate at a time which forces delegates to compete against each other and makes cartel formations incredibly hard, if not impossible.
  19. Neblio: Similar to Neo, but 30x smaller market cap.
  20. NEM: Is similar to Neo No marketing team, very high market cap for little clarilty what they do.
  21. Bancor: Bancor is a Decentralized Liquidity Network that allows you to hold any Ethereum token and convert it to any other token in the network, with no counter party, at an automatically calculated price, using a simple web wallet.
  22. Dragonchain: The Purpose of DragonChain is to help companies quickly and easily incorporate blockchain into their business applications. Many companies might be interested in making this transition because of the benefits associated with serving clients over a blockchain – increased efficiency and security for transactions, a reduction of costs from eliminating potential fraud and scams, etc.
  23. Skycoin: Transactions with zero fees that take apparently two seconds, unlimited transaction rate, no need for miners and block rewards, low power usage, all of the usual cryptocurrency technical vulnerabilities fixed, a consensus mechanism superior to anything that exists, resistant to all conceivable threats (government censorship, community infighting, cybenucleaconventional warfare, etc). Skycoin has their own consensus algorithm known as Obelisk written and published academically by an early developer of Ethereum. Obelisk is a non-energy intensive consensus algorithm based on a concept called ‘web of trust dynamics’ which is completely different to PoW, PoS, and their derivatives. Skywire, the flagship application of Skycoin, has the ambitious goal of decentralizing the internet at the hardware level and is about to begin the testnet in April. However, this is just one of the many facets of the Skycoin ecosystem. Skywire will not only provide decentralized bandwidth but also storage and computation, completing the holy trinity of commodities essential for the new internet. Skycion a smear campaign launched against it, though they seem legit and reliable. Thus, they are probably undervalued.

Market 3 - Ecosystem

The 3rd market with 11 coins is comprised of ecosystem coins, which aim to strengthen the ease of use within the crypto space through decentralized exchanges, open standards for apps and more
  1. Nebulas: Similar to how Google indexes webpages Nebulas will index blockchain projects, smart contracts & data using the Nebulas rank algorithm that sifts & sorts the data. Developers rewarded NAS to develop & deploy on NAS chain. Nebulas calls this developer incentive protocol – basically rewards are issued based on how often dapp/contract etc. is used, the more the better the rewards and Proof of devotion. Works like DPoS except the best, most economically incentivised developers (Bookkeeppers) get the forging spots. Ensuring brains stay with the project (Cross between PoI & PoS). 2,400 TPS+, DAG used to solve the inter-transaction dependencies in the PEE (Parallel Execution Environment) feature, first crypto Wallet that supports the Lightening Network.
  2. Waves: Decentralized exchange and crowdfunding platform. Let’s companies and projects to issue and manage their own digital coin tokens to raise money.
  3. Salt: Leveraging blockchain assets to secure cash loands. Plans to offer cash loans in traditional currencies, backed by your cryptocurrency assets. Allows lenders worldwide to skip credit checks for easier access to affordable loans.
  4. CHAINLINK: ChainLink is a decentralized oracle service, the first of its kind. Oracles are defined as an ‘agent’ that finds and verifies real-world occurrences and submits this information to a blockchain to be used in smart contracts.With ChainLink, smart contract users can use the network’s oracles to retrieve data from off-chain application program interfaces (APIs), data pools, and other resources and integrate them into the blockchain and smart contracts. Basically, ChainLink takes information that is external to blockchain applications and puts it on-chain. The difference to Aeternity is that Chainlink deploys the smart contracts on the Ethereum blockchain while Aeternity has its own chain.
  5. WTC: Combines blockchain with IoT to create a management system for supply chains Interesting
  6. Ethos unifyies all cryptos. Ethos is building a multi-cryptocurrency phone wallet. The team is also building an investment diversification tool and a social network
  7. Aion: Aion is the token that pays for services on the Aeternity platform.
  8. USDT: is no cryptocurrency really, but a replacement for dollar for trading After months of asking for proof of dollar backing, still no response from Tether.

Market 4 - Privacy

The 4th market are privacy coins. As you might know, Bitcoin is not anonymous. If the IRS or any other party asks an exchange who is the identity behind a specific Bitcoin address, they know who you are and can track back almost all of the Bitcoin transactions you have ever made and all your account balances. Privacy coins aim to prevent exactly that through address fungability, which changes addresses constantly, IP obfuscation and more. There are 2 types of privacy coins, one with completely privacy and one with optional privacy. Optional Privacy coins like Dash and Nav have the advantage of more user friendliness over completely privacy coins such as Monero and Enigma.
  1. Monero: Currently most popular privacy coin, though with a very high market cap. Since their privacy is all on chain, all prior transactions would be deanonymized if their protocol is ever cracked. This requires a quantum computing attack though. PIVX is better in that regard.
  2. Zcash: A decentralized and open-source cryptocurrency that hide the sender, recipient, and value of transactions. Offers users the option to make transactions public later for auditing. Decent privacy coin, though no default privacy
  3. Verge: Calls itself privacy coin without providing private transactions, multiple problems over the last weeks has a toxic community, and way too much hype for what they have.
  4. Bytecoin: First privacy-focused cryptocurrency with anonymous transactions. Bytecoin’s code was later adapted to create Monero, the more well-known anonymous cryptocurrency. Has several scam accusations, 80% pre-mine, bad devs, bad tech
  5. Bitcoin Private: A merge fork of Bitcoin and Zclassic with Zclassic being a fork of Zcash with the difference of a lack of a founders fee required to mine a valid block. This promotes a fair distribution, preventing centralized coin ownership and control. Bitcoin private offers the optional ability to keep the sender, receiver, and amount private in a given transaction. However, this is already offered by several good privacy coins (Monero, PIVX) and Bitcoin private doesn't offer much more beyond this.
  6. Komodo: The Komodo blockchain platform uses Komodo’s open-source cryptocurrency for doing transparent, anonymous, private, and fungible transactions. They are then made ultra-secure using Bitcoin’s blockchain via a Delayed Proof of Work (dPoW) protocol and decentralized crowdfunding (ICO) platform to remove middlemen from project funding. Offers services for startups to create and manage their own Blockchains.
  7. PIVX: As a fork of Dash, PIVX uses an advanced implementation of the Zerocoin protocol to provide it’s privacy. This is a form of zeroknowledge proofs, which allow users to spend ‘Zerocoins’ that have no link back to them. Unlike Zcash u have denominations in PIVX, so they can’t track users by their payment amount being equal to the amount of ‘minted’ coins, because everyone uses the same denominations. PIVX is also implementing Bulletproofs, just like Monero, and this will take care of arguably the biggest weakness of zeroknowledge protocols: the trusted setup.
  8. Zcoin: PoW cryptocurrency. Private financial transactions, enabled by the Zerocoin Protocol. Zcoin is the first full implementation of the Zerocoin Protocol, which allows users to have complete privacy via Zero-Knowledge cryptographic proofs.
  9. Enigma: Monero is to Bitcoin what enigma is to Ethereum. Enigma is for making the data used in smart contracts private. More of a platform for dapps than a currency like Monero. Very promising.
  10. Navcoin: Like bitcoin but with added privacy and pos and 1,170 tps, but only because of very short 30 second block times. Though, privacy is optional, but aims to be more user friendly than Monero. However, doesn't really decide if it wants to be a privacy coin or not. Same as Zcash.Strong technology, non-shady team.
  11. Tenx: Raised 80 million, offers cryptocurrency-linked credit cards that let you spend virtual money in real life. Developing a series of payment platforms to make spending cryptocurrency easier. However, the question is if full privacy coins will be hindered in growth through government regulations and optional privacy coins will become more successful through ease of use and no regulatory hindrance.

Market 5 - Currency Exchange Tool

Due to the sheer number of different cryptocurrencies, exchanging one currency for the other it still cumbersome. Further, merchants don’t want to deal with overcluttered options of accepting cryptocurrencies. This is where exchange tool like Req come in, which allow easy and simple exchange of currencies.
  1. Cryptonex: Fiat and currency exchange between various blockchain services, similar to REQ.
  2. QASH: Qash is used to fuel its liquid platform which will be an exchange that will distribute their liquidity pool. Its product, the Worldbook is a multi-exchange order book that matches crypto to crypto, and crypto to fiat and the reverse across all currencies. E.g., someone is selling Bitcoin is USD on exchange1 not owned by Quoine and someone is buying Bitcoin in EURO on exchange 2 not owned by Quoine. If the forex conversions and crypto conversions match then the trade will go through and the Worldbook will match it, it'll make the sale and the purchase on either exchange and each user will get what they wanted, which means exchanges with lower liquidity if they join the Worldbook will be able to fill orders and take trade fees they otherwise would miss out on.They turned it on to test it a few months ago for an hour or so and their exchange was the top exchange in the world by 4x volume for the day because all Worldbook trades ran through it. Binance wants BNB to be used on their one exchange. Qash wants their QASH token embedded in all of their partners. More info here https://www.reddit.com/CryptoCurrency/comments/8a8lnwhich_are_your_top_5_favourite_coins_out_of_the/dwyjcbb/?context=3
  3. Kyber: network Exchange between cryptocurrencies, similar to REQ. Features automatic coin conversions for payments. Also offers payment tools for developers and a cryptocurrency wallet.
  4. Achain: Building a boundless blockchain world like Req .
  5. Req: Exchange between cryptocurrencies.
  6. Bitshares: Exchange between cryptocurrencies. Noteworthy are the 1.5 second average block times and throughput potential of 100,000 transactions per second with currently 2,400 TPS having been proven. However, bitshares had several Scam accusations in the past.
  7. Loopring: A protocol that will enable higher liquidity between exchanges and personal wallets.
  8. ZRX: Open standard for dapps. Open, permissionless protocol allowing for ERC20 tokens to be traded on the Ethereum blockchain. In 0x protocol, orders are transported off-chain, massively reducing gas costs and eliminating blockchain bloat. Relayers help broadcast orders and collect a fee each time they facilitate a trade. Anyone can build a relayer.

Market 6 - Gaming

With an industry size of $108B worldwide, Gaming is one of the largest markets in the world. For sure, cryptocurrencies will want to have a share of that pie.
  1. Storm: Mobile game currency on a platform with 9 million players.
  2. Fun: A platform for casino operators to host trustless, provably-fair gambling through the use of smart contracts, as well as creating their own implementation of state channels for scalability.
  3. Electroneum: Mobile game currency They have lots of technical problems, such as several 51% attacks
  4. Wax: Marketplace to trade in-game items

Market 7 - Misc

There are various markets being tapped right now. They are all summed up under misc.
  1. OMG: Omise is designed to enable financial services for people without bank accounts. It works worldwide and with both traditional money and cryptocurrencies.
  2. Power ledger: Australian blockchain-based cryptocurrency and energy trading platform that allows for decentralized selling and buying of renewable energy. Unique market and rather untapped market in the crypto space.
  3. Populous: A platform that connects business owners and invoice buyers without middlemen. Invoice sellers get cash flow to fund their business and invoice buyers earn interest. Similar to OMG, small market.
  4. Monacoin: The first Japanese cryptocurrency. Focused on micro-transactions and based on a popular internet meme of a type-written cat. This makes it similar to Dogecoin. Very niche, tiny market.
  5. Revain: Legitimizing reviews via the blockchain. Interesting concept, though market not as big.
  6. Augur: Platform to forecast and make wagers on the outcome of real-world events (AKA decentralized predictions). Uses predictions for a “wisdom of the crowd” search engine. Not launched yet.
  7. Substratum: Revolutionzing hosting industry via per request billing as a decentralized internet hosting system. Uses a global network of private computers to create the free and open internet of the future. Participants earn cryptocurrency. Interesting concept.
  8. Veritaseum: Is supposed to be a peer to peer gateway, though it looks like very much like a scam.
  9. TRON: Tronix is looking to capitalize on ownership of internet data to content creators. However, they plagiarized their white paper, which is a no go. They apologized, so it needs to be seen how they will conduct themselves in the future. Extremely high market cap for not having a product, nor proof of concept.
  10. Syscoin: A cryptocurrency with a decentralized marketplace that lets people buy and sell products directly without third parties. Trying to remove middlemen like eBay and Amazon.
  11. Hshare: Most likely scam because of no code changes, most likely pump and dump scheme, dead community.
  12. BAT: An Ethereum-based token that can be exchanged between content creators, users, and advertisers. Decentralized ad-network that pays based on engagement and attention.
  13. Dent: Decentralizeed exchange of mobile data, enabling mobile data to be marketed, purchased or distributed, so that users can quickly buy or sell data from any user to another one.
  14. Ncash: End to end encrypted Identification system for retailers to better serve their customers .
  15. Factom Secure record-keeping system that allows companies to store their data directly on the Blockchain. The goal is to make records more transparent and trustworthy .

Market 8 - Social network

Web 2.0 is still going strong and Web 3.0 is not going to ignore it. There are several gaming tokens already out there and a few with decent traction already, such as Steem, which is Reddit with voting through money is a very interesting one.
  1. Mithril: As users create content via social media, they will be rewarded for their contribution, the better the contribution, the more they will earn
  2. Steem: Like Reddit, but voting with money. Already launched product and Alexa rank 1,000 Thumbs up.
  3. Rdd: Reddcoin makes the process of sending and receiving money fun and rewarding for everyone. Reddcoin is dedicated to one thing – tipping on social networks as a way to bring cryptocurrency awareness and experience to the general public.
  4. Kin: Token for the platform Kik. Kik has a massive user base of 400 million people. Replacing paying with FIAT with paying with KIN might get this token to mass adoption very quickly.

Market 9 - Fee token

Popular exchanges realized that they can make a few billion dollars more by launching their own token. Owning these tokens gives you a reduction of trading fees. Very handy and BNB (Binance Coin) has been one of the most resilient tokens, which have withstood most market drops over the last weeks and was among the very few coins that could show growth.
  1. BNB: Fee token for Binance
  2. Gas: Not a Fee token for an exchange, but it is a dividend paid out on Neo and a currency that can be used to purchase services for dapps.
  3. Kucoin: Fee token for Kucoin

Market 10 - Decentralized Data Storage

Currently, data storage happens with large companies or data centers that are prone to failure or losing data. Decentralized data storage makes loss of data almost impossible by distributing your files to numerous clients that hold tiny pieces of your data. Remember Torrents? Torrents use a peer-to-peer network. It is similar to that. Many users maintain copies of the same file, when someone wants a copy of that file, they send a request to the peer-to-peer network., users who have the file, known as seeds, send fragments of the file to the requester., he requester receives many fragments from many different seeds, and the torrent software recompiles these fragments to form the original file.
  1. Gbyte: Byteball data is stored and ordered using directed acyclic graph (DAG) rather than blockchain. This allows all users to secure each other's data by referencing earlier data units created by other users, and also removes scalability limits common for blockchains, such as blocksize issue.
  2. Siacoin: Siacoin is decentralized storage platform. Distributes encrypted files to thousands of private users who get paid for renting out their disk space. Anybody with siacoins can rent storage from hosts on Sia. This is accomplish via "smart" storage contracts stored on the Sia blockchain. The smart contract provides a payment to the host only after the host has kept the file for a given amount of time. If the host loses the file, the host does not get paid.
  3. Maidsafecoin: MaidSafe stands for Massive Array of Internet Disks, Secure Access for Everyone.Instead of working with data centers and servers that are common today and are vulnerable to data theft and monitoring, SAFE’s network uses advanced P2P technology to bring together the spare computing capacity of all SAFE users and create a global network. You can think of SAFE as a crowd-sourced internet. All data and applications reside in this network. It’s an autonomous network that automatically sets prices and distributes data and rents out hard drive disk space with a Blockchain-based storage solutions.When you upload a file to the network, such as a photo, it will be broken into pieces, hashed, and encrypted. The data is then randomly distributed across the network. Redundant copies of the data are created as well so that if someone storing your file turns off their computer, you will still have access to your data. And don’t worry, even with pieces of your data on other people’s computers, they won’t be able to read them. You can earn MadeSafeCoins by participating in storing data pieces from the network on your computer and thus earning a Proof of Resource.
  4. Storj: Storj aims to become a cloud storage platform that can’t be censored or monitored, or have downtime. Your files are encrypted, shredded into little pieces called 'shards', and stored in a decentralized network of computers around the globe. No one but you has a complete copy of your file, not even in an encrypted form.

Market 11 - Cloud computing

Obviously, renting computing power, one of the biggest emerging markets as of recent years, e.g. AWS and Digital Ocean, is also a service, which can be bought and managed via the blockchain.
  1. Golem: Allows easy use of Supercomputer in exchange for tokens. People worldwide can rent out their computers to the network and get paid for that service with Golem tokens.
  2. Elf: Allows easy use of Cloud computing in exchange for tokens.

Market 12 - Stablecoin

Last but not least, there are 2 stablecoins that have established themselves within the market. A stable coin is a coin that wants to be independent of the volatility of the crypto markets. This has worked out pretty well for Maker and DGD, accomplished through a carefully diversified currency fund and backing each token by 1g or real gold respectively. DO NOT CONFUSE DGD AND MAKER with their STABLE COINS DGX and DAI. DGD and MAKER are volatile, because they are the companies of DGX and DAI. DGX and DAI are the stable coins.
  1. DGD: Platform of the Stablecoin DGX. Every DGX coin is backed by 1g of gold and make use proof of asset consensus.
  2. Maker: Platform of the Stablecoin DAI that doesn't vary much in price through widespread and smart diversification of assets.
EDIT: Added a risk factor from 0 to 10. The baseline is 2 for any crypto. Significant scandals, mishaps, shady practices, questionable technology, increase the risk factor. Not having a product yet automatically means a risk factor of 6. Strong adoption and thus strong scrutiny or positive community lower the risk factor.
EDIT2: Added a subjective potential factor from 0 to 10, where its overall potential and a small or big market cap is factored in. Bitcoin with lots of potential only gets a 9, because of its massive market cap, because if Bitcoin goes 10x, smaller coins go 100x, PIVX gets a 10 for being as good as Monero while carrying a 10x smaller market cap, which would make PIVX go 100x if Monero goes 10x.
submitted by galan77 to CryptoCurrency [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]

Crypto exchange trade. Remember psychology!

https://medium.com/@sergiygolubyev/crypto-exchange-trade-remember-psychology-6d4433569d9d
Crypto Exchange is a high-tech platform in which all trade transactions are conducted using modern software created based on the latest IT solutions. The emergence of new types of currencies, in particular cryptocurrencies, gives a chance for the rapid development of the world economy as a whole. In turn, structural changes in the international economic system gave impetus to the emergence and development of new types of exchange technologies. Thus, crypto exchanges appeared which allowed its participants anywhere in the world to buy, sell and exchange one cryptocurrency for others, or for fiat of other countries. Each crypto exchange tries to offer customers convenient ways to convert financial instruments, and provides the ability to conduct transactions on its own terms. The high rates of development and distribution of cryptocurrencies, which are based on Blockchain, as well as the gradual wide recognition by the world community and leading economists, ensure the further improvement of exchange technologies. This means that in an effort to provide the most comfortable conditions for its customers, each crypto exchange will take them to an ever-higher quality level of service with innovative nuances. But at the same time, within the framework of the technological process of stock trading, which is available to users (from professional traders to amateurs), the question of psychology and its role in the decision making has not been canceled. Successful trading depends on 70% primarily on the psychology of a trader and only 30% on the trading scheme/strategy.
Trading on the exchange, it is necessary to develop discipline, self-control and be able to respond quickly to changing stock charts. All this will allow you to earn and minimize your losses more effectively. Everyone should remember, from the amateur to the professional, that in the financial markets you can not only earn money, but also lose money. Cryptocurrency rates are still subject to political and regulatory influences; their value is influenced by the reputation of the company's founders, informational insertions about blockchain projects and plans for their further development, scandals and disclosures. Nevertheless, there are simple rules for successful trading from the field of psychology, which will reduce the risks when trying to make money on cryptocurrency and not only. There are a number of problems that always hinder every beginner - amateur:
· Excitement
· Fear
· Greed
· Unwillingness to learn new things
· Imaginary visualization of results
All these problems have psychological aspects. Emotions, feelings and desires significantly influence the trading decisions made by the trader. This happens all the time, not only on traditional exchanges, but also in the cryptocurrency sphere as well. Excitement is an emotional state when it seems to a person that he is lucky, and as the series of successful transactions continues, he performs larger by volume financial transactions. Often, the excitement motivates to turn away from long-term transactions and trends, and look towards short-term operations. After all, it seems that the more often you successfully complete operations, the more capital you earn. Not at all! The more often you make mistakes, leading to a default on your account. Money only is earned on long-term trends and operations. Traders are often worried, fearing an unsuccessful deal closing.
Of course, a loss is bad, but sometimes it is better to close a position in minus than to lose a large amount only because of the hope of a quick price reversal. Therefore, fear often pushes for the wrong strategic decisions. Fear of loss as a result becomes a sentence for your positioning in profit. On the same face with fear, if not strange, is the factor of greed. Having essentially a different source of inspiration, greed, like fear, leads to a generally pitiable result — to the default of your trading account. The reluctance to learn new strategies, technologies, and denial of forecasting also leads to failure. Successful is who always strives to learn new things, and perceives the fact and necessity of continuous learning. Since learning is a process of striving for the progress of its results and professional qualities. Another scourge - Wish list or visualization. Everyone wants to see the price move in the right direction. This is pretty dangerous. By visualizing the price jump in the right direction, you can dream and invest too much in cryptocurrency. This will lead to losses. Here you should always remember to diversify your investments. Remember your psychological portrait even when you program your trading strategies, algorithms and bots. After all, your algorithm is essentially your psychological portrait. Finally, the above-mentioned flaws, especially in the strategy can dominate and damage your deposit and reputation. The main signs of competent crypto-trade are the same as on other exchanges (such as FOREX). This is a kind of algorithm for a sustainable profit strategy:
· Risk no more than 10% of the deposit
· Use risk per trade of 5% or less
· Do not close profitable deals too early
· Do not accumulate losing trades
· Fix quick speculative profit
· Respect the trend
· Pay more attention to liquid assets (cryptocurrency)
· Set your personal entry and exit rules for trades and stick to them
· Long-term trading strategy gives you maximum steady profits
· Do not use the principles of Martingale tactics if there is no experience. You cannot double the volume of the transaction, if it closed in the red zone. If a loss was incurred, then the cryptocurrency market situation was predicted incorrectly and it was necessary to work on improving the analytical skills, and not to conclude a larger deal, which probably also closes in the negative
It is obvious that the psychology of trading significantly affects the performance of stock speculation both in the traditional market and in the field of cryptocurrency. It is important to remember that the success of a person in any field of activity depends on the emotional component, namely the internal balance. Exchange trading is a nervous activity, and if you do not learn to take emotions under control, the results can be disastrous. The basis for achieving success in stock trading, in my opinion, are two fundamental factors. The first factor relates to the field of formulation of the trading idea, and the second - to the area of ​​its implementation.
To formulate a trading idea, on the one hand, methods of technical and fundamental analysis are used to select an exchange instrument and determine the moment of opening and closing a position on it. On the other hand, capital management methods are used to determine the optimal size of the position being opened. As you know, without these two crucial moments it is impossible to achieve stable success in stock trading. As experience shows, for the most part, people have enough intelligence to master all the necessary theoretical knowledge of technical and fundamental analysis in a few months of intensive training. There are no special intellectual difficulties. But, as the same experience shows, this is clearly not enough for successful exchange trading, since all knowledge may turn out to be a useless load if the second success factor is not sufficiently present - the practical implementation of trading ideas, which is no longer based on the intellectual sphere, and psycho-emotional. It is within this area that the main problem arises for many traders, which prevents the receipt of stable profits. As a rule, this is due to the psycho-emotional profile of a person. It depends on how the trader will behave in the psychologically stressful situations that the exchange trading is full of. Inherent in all human emotions and feelings - fear, greed, excitement, envy, hope, etc. very often have a decisive influence on the behavior of traders, not allowing them to follow strictly the trading strategy and plan, even if they have one. From a psychological point of view, the process of stock exchange activity can be divided into stages, after which the trader can return to the starting point. The above scenarios and risk factors are one of the options for the behavior of an exchange speculator; however, it often happens exactly the opposite. Having suffered losses from his first transactions in the market, the trader loses interest in exchange trading, he gives up and he falls into despair. In this case, the first step to victory is the admission of defeat. It would seem silly and ridiculous, but it works. After that, there are two options: either the trader leaves the exchange forever, or returns to the battlefield. Such “returns” may occur more than once. In addition, at some other time, after repeated analysis of his actions, mistakes made and their consequences, a person from a beginner begins to turn into an experienced trader, which is marked by the stability of his activity and, perhaps, by slow, but surely growth of his deposit and profit. The psychological basis for success in trading, which leads to victory and the absence of which is equivalent to defeat, are as follows:
· It is not only the lack of self-control, discipline and focus on the process that causes the defeat
· Self-control, discipline and ability to concentrate is not enough to achieve success
· To achieve success, it is equally important to be able to adapt to changes
In principle, one can consider the idea that traditional approaches to the psychology of trading are limited. In the majority of benefits for traders, the key qualities necessary for successful exchange trading are only self-control and discipline. Of course, these qualities are necessary in any field of business activities. Trading is not an exception, especially considering that it is in the risk zone. But self-control and discipline are not enough to achieve success. Trading is a business. Moreover, any business does not stand still. You cannot find a formula for success and use it forever. You will need to monitor trends and constantly look for new successful solutions.
The main feature of a successful trader is adaptability to changes. The lack of development leads to defeat, large monetary losses. Many technology companies continued to produce stationary computers when laptops became popular. The same companies continued to produce laptops when tablets appeared and became popular. The products of these companies were of high quality, and their employees organized pre-set tasks in an organized manner. But they lost large sums due to the fact that they could not adapt to changes in demand. If we draw a parallel with the sphere of investment, the similarities will become noticeable. The stock market, like any other subject to change. One period is replaced by another. Those methods that allowed achieving success in the previous period can lead to failure in the current. The key concept in stock trading is volatility. The change in this indicates the onset of a new period. When volatility increases, trade becomes more risky. Accordingly, with a decrease in this indicator, the degree of risk during trading operations decreases. With a high level of volatility, trends most often unfold. Strong and weak positions can be swapped out. With a high level of volatility, trends continue for some time. From the foregoing, it should be concluded that market processes and methods during periods of high and low volatility differ strongly. You cannot use the same methods during changing market trends. Often it is the adherence to the previous methods, excessive discipline leads to collapse as well. The fact that the investor was defeated does not mean that he suddenly became morally unstable, unorganized. Trading is trading.
Therefore, we have every right to assert that under the psychology of trade in the markets is meant human preparedness for the risks that inevitably accompany any activity. Trading on the stock exchange is based on the interaction of the three most important components: capital management, analysis, and the psychology of trading (which cannot be considered in conjunction with the other aspects of trading). The psychology of human behavior is a source for understanding what is happening in financial markets. The source for understanding the events occurring in the financial markets and the behavior of traders during exchange trading is the psychology of the human person. Emotions — greed, fear, doubt, hope, a sense of self-preservation — are peculiar to any person in life — are clearly manifested in the hard rhythm of decision-making during the dynamic course of exchange trading (which was partially considered above). Knowledge of the human psychology and their behavioral characteristics must be used to achieve success. The psychology of a trader is formed from a multitude of grains - it is a belief in what one does in the stock market, in one’s actions, in own system of one’s decisions, in trading method. In addition, the psychology of a trader is that one can unload oneself emotionally, one does not accept the intellectual challenge that the stock market carries. On the contrary, becomes restrained, calm when making decisions on operations in the stock market. There are many situations where a trader expresses his attention and focus; he does not disperse it on the tracking of news factors or on the receipt of stimuli from the news agencies. Consequently, the crowd psychology is the factor that makes prices move, therefore, in addition to assessing one's own psychological state, one must be sensitive to changes in the mood of other market participants, move in the flow, not against it, and then success will not take long.
Of course, you can argue that why do I need this psychology? After all, besides creating your own strategies and individual work, some exchanges (including crypto exchanges) allow minimizing risks by following the strategies of experienced traders; this service is called a PAMM account. PAMM provides an opportunity for clients (Subscribers) to follow the trading strategy of experienced and professional traders (Providers). Provider's trading results are publicly available. With the help of the rating of accounts, graphs of profitability and reviews of other traders, you can choose the most suitable Provider and begin to follow his strategy. Again, in this case, the provider is a human with all the ensuing consequences. And psychological aspects are not foreign to professionals as well, including victories and mistakes. The financial market attracts people the possibility of obtaining independence, including financial. A successful trader can live and work in any country in the world without having either a boss or subordinates. The motivation of people on the exchanges can be different: from getting a higher percentage than from a bank to making several thousand dollars a day. At the same time, there are two main categories of people in the financial market (including cryptocurrencies): investors who acquire assets or currency for a relatively long period, and speculators who profit from changes in the prices of certain assets for short periods. Many believe, an easy way to make money is not for everybody. First, the skillful use and manipulation of the psychological aspects of a human make it possible to become a speculator. And this, of course, in addition to knowledge and analytical skills. Experience shows that successful speculation is the right state of mind. It would seem that this is the simplest thing that can be acquired by human. But in fact, this self-tuning is available to very few. It is also necessary to distinguish the psychology of the market and the personal psychology of the trader. The behavior of the market as a whole depends on people, since it is the stock market crowd that determines its direction. However, quite often traders lose sight of the most important component of victory - managing their personal emotions, that is, their psychology. Without control over oneself, there can be no control over one’s trading capital. If a trader is not tuned to the trend range of the stock crowd, if he does not pay attention to changes in her psychology, then he will also not achieve significant success in trading. To succeed on the exchange, one needs to take a sober look at exchange trading, recognize its trends and their changes, and not waste time on dreams or lamenting about failures.
Any price of a financial instrument is a momentary agreement on its value, reached by a market crowd and expressed in the fact of a transaction, i.e. it is the equilibrium point between the players for a rise and a fall, or the "equilibrium" price. Crowds of traders create asset prices: buyers, sellers and fluctuating market watchers. Charts of prices and trading volumes reflect the psychology of the exchange. In addition, this is always worth remembering! After all, the main purpose of the presence of the analysis of psychology in stock trading is not the quantity, but the quality of transactions. A person striving to become a good trader needs to remember the words of DiNapoli, a well-known stock exchange trader: “The most important trading tool is not a computer, not a service for supplying information, or even methods developed by a trader. It is he himself! If a trader is not suitable for this - he should not trade at all”! Therefore, before pushing orders on the trading platform, think about whether you are suitable for this role.
Join chat — https://t.me/joinchat/AAAAAE84vCXg5PK-VpHADg
Sergiy Golubyev (Сергей Голубев)
EU structural funds, ICO projects, NGO & investment projects, project management, comprehensive support of business
submitted by Golubyev_Sergiy to u/Golubyev_Sergiy [link] [comments]

Subreddit Stats: cs7646_fall2017 top posts from 2017-08-23 to 2017-12-10 22:43 PDT

Period: 108.98 days
Submissions Comments
Total 999 10425
Rate (per day) 9.17 95.73
Unique Redditors 361 695
Combined Score 4162 17424

Top Submitters' Top Submissions

  1. 296 points, 24 submissions: tuckerbalch
    1. Project 2 Megathread (optimize_something) (33 points, 475 comments)
    2. project 3 megathread (assess_learners) (27 points, 1130 comments)
    3. For online students: Participation check #2 (23 points, 47 comments)
    4. ML / Data Scientist internship and full time job opportunities (20 points, 36 comments)
    5. Advance information on Project 3 (19 points, 22 comments)
    6. participation check #3 (19 points, 29 comments)
    7. manual_strategy project megathread (17 points, 825 comments)
    8. project 4 megathread (defeat_learners) (15 points, 209 comments)
    9. project 5 megathread (marketsim) (15 points, 484 comments)
    10. QLearning Robot project megathread (12 points, 691 comments)
  2. 278 points, 17 submissions: davebyrd
    1. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes (37 points, 10 comments)
    2. Project 1 Megathread (assess_portfolio) (34 points, 466 comments)
    3. marketsim grades are up (25 points, 28 comments)
    4. Midterm stats (24 points, 32 comments)
    5. Welcome to CS 7646 MLT! (23 points, 132 comments)
    6. How to interact with TAs, discuss grades, performance, request exceptions... (18 points, 31 comments)
    7. assess_portfolio grades have been released (18 points, 34 comments)
    8. Midterm grades posted to T-Square (15 points, 30 comments)
    9. Removed posts (15 points, 2 comments)
    10. assess_portfolio IMPORTANT README: about sample frequency (13 points, 26 comments)
  3. 118 points, 17 submissions: yokh_cs7646
    1. Exam 2 Information (39 points, 40 comments)
    2. Reformat Assignment Pages? (14 points, 2 comments)
    3. What did the real-life Michael Burry have to say? (13 points, 2 comments)
    4. PSA: Read the Rubric carefully and ahead-of-time (8 points, 15 comments)
    5. How do I know that I'm correct and not just lucky? (7 points, 31 comments)
    6. ML Papers and News (7 points, 5 comments)
    7. What are "question pools"? (6 points, 4 comments)
    8. Explanation of "Regression" (5 points, 5 comments)
    9. GT Github taking FOREVER to push to..? (4 points, 14 comments)
    10. Dead links on the course wiki (3 points, 2 comments)
  4. 67 points, 13 submissions: harshsikka123
    1. To all those struggling, some words of courage! (20 points, 18 comments)
    2. Just got locked out of my apartment, am submitting from a stairwell (19 points, 12 comments)
    3. Thoroughly enjoying the lectures, some of the best I've seen! (13 points, 13 comments)
    4. Just for reference, how long did Assignment 1 take you all to implement? (3 points, 31 comments)
    5. Grade_Learners Taking about 7 seconds on Buffet vs 5 on Local, is this acceptable if all tests are passing? (2 points, 2 comments)
    6. Is anyone running into the Runtime Error, Invalid DISPLAY variable when trying to save the figures as pdfs to the Buffet servers? (2 points, 9 comments)
    7. Still not seeing an ML4T onboarding test on ProctorTrack (2 points, 10 comments)
    8. Any news on when Optimize_Something grades will be released? (1 point, 1 comment)
    9. Baglearner RMSE and leaf size? (1 point, 2 comments)
    10. My results are oh so slightly off, any thoughts? (1 point, 11 comments)
  5. 63 points, 10 submissions: htrajan
    1. Sample test case: missing data (22 points, 36 comments)
    2. Optimize_something test cases (13 points, 22 comments)
    3. Met Burt Malkiel today (6 points, 1 comment)
    4. Heads up: Dataframe.std != np.std (5 points, 5 comments)
    5. optimize_something: graph (5 points, 29 comments)
    6. Schedule still reflecting shortened summer timeframe? (4 points, 3 comments)
    7. Quick clarification about InsaneLearner (3 points, 8 comments)
    8. Test cases using rfr? (3 points, 5 comments)
    9. Input format of rfr (2 points, 1 comment)
    10. [Shameless recruiting post] Wealthfront is hiring! (0 points, 9 comments)
  6. 62 points, 7 submissions: swamijay
    1. defeat_learner test case (34 points, 38 comments)
    2. Project 3 test cases (15 points, 27 comments)
    3. Defeat_Learner - related questions (6 points, 9 comments)
    4. Options risk/reward (2 points, 0 comments)
    5. manual strategy - you must remain in the position for 21 trading days. (2 points, 9 comments)
    6. standardizing values (2 points, 0 comments)
    7. technical indicators - period for moving averages, or anything that looks past n days (1 point, 3 comments)
  7. 61 points, 9 submissions: gatech-raleighite
    1. Protip: Better reddit search (22 points, 9 comments)
    2. Helpful numpy array cheat sheet (16 points, 10 comments)
    3. In your experience Professor, Mr. Byrd, which strategy is "best" for trading ? (12 points, 10 comments)
    4. Industrial strength or mature versions of the assignments ? (4 points, 2 comments)
    5. What is the correct (faster) way of doing this bit of pandas code (updating multiple slice values) (2 points, 10 comments)
    6. What is the correct (pythonesque?) way to select 60% of rows ? (2 points, 11 comments)
    7. How to get adjusted close price for funds not publicly traded (TSP) ? (1 point, 2 comments)
    8. Is there a way to only test one or 2 of the learners using grade_learners.py ? (1 point, 10 comments)
    9. OMS CS Digital Career Seminar Series - Scott Leitstein recording available online? (1 point, 4 comments)
  8. 60 points, 2 submissions: reyallan
    1. [Project Questions] Unit Tests for assess_portfolio assignment (58 points, 52 comments)
    2. Financial data, technical indicators and live trading (2 points, 8 comments)
  9. 59 points, 12 submissions: dyllll
    1. Please upvote helpful posts and other advice. (26 points, 1 comment)
    2. Books to further study in trading with machine learning? (14 points, 9 comments)
    3. Is Q-Learning the best reinforcement learning method for stock trading? (4 points, 4 comments)
    4. Any way to download the lessons? (3 points, 4 comments)
    5. Can a TA please contact me? (2 points, 7 comments)
    6. Is the vectorization code from the youtube video available to us? (2 points, 2 comments)
    7. Position of webcam (2 points, 15 comments)
    8. Question about assignment one (2 points, 5 comments)
    9. Are udacity quizzes recorded? (1 point, 2 comments)
    10. Does normalization of indicators matter in a Q-Learner? (1 point, 7 comments)
  10. 56 points, 2 submissions: jan-laszlo
    1. Proper git workflow (43 points, 19 comments)
    2. Adding you SSH key for password-less access to remote hosts (13 points, 7 comments)
  11. 53 points, 1 submission: agifft3_omscs
    1. [Project Questions] Unit Tests for optimize_something assignment (53 points, 94 comments)
  12. 50 points, 16 submissions: BNielson
    1. Regression Trees (7 points, 9 comments)
    2. Two Interpretations of RFR are leading to two different possible Sharpe Ratios -- Need Instructor clarification ASAP (5 points, 3 comments)
    3. PYTHONPATH=../:. python grade_analysis.py (4 points, 7 comments)
    4. Running on Windows and PyCharm (4 points, 4 comments)
    5. Studying for the midterm: python questions (4 points, 0 comments)
    6. Assess Learners Grader (3 points, 2 comments)
    7. Manual Strategy Grade (3 points, 2 comments)
    8. Rewards in Q Learning (3 points, 3 comments)
    9. SSH/Putty on Windows (3 points, 4 comments)
    10. Slight contradiction on ProctorTrack Exam (3 points, 4 comments)
  13. 49 points, 7 submissions: j0shj0nes
    1. QLearning Robot - Finalized and Released Soon? (18 points, 4 comments)
    2. Flash Boys, HFT, frontrunning... (10 points, 3 comments)
    3. Deprecations / errata (7 points, 5 comments)
    4. Udacity lectures via GT account, versus personal account (6 points, 2 comments)
    5. Python: console-driven development (5 points, 5 comments)
    6. Buffet pandas / numpy versions (2 points, 2 comments)
    7. Quant research on earnings calls (1 point, 0 comments)
  14. 45 points, 11 submissions: Zapurza
    1. Suggestion for Strategy learner mega thread. (14 points, 1 comment)
    2. Which lectures to watch for upcoming project q learning robot? (7 points, 5 comments)
    3. In schedule file, there is no link against 'voting ensemble strategy'? Scheduled for Nov 13-20 week (6 points, 3 comments)
    4. How to add questions to the question bank? I can see there is 2% credit for that. (4 points, 5 comments)
    5. Scratch paper use (3 points, 6 comments)
    6. The big short movie link on you tube says the video is not available in your country. (3 points, 9 comments)
    7. Distance between training data date and future forecast date (2 points, 2 comments)
    8. News affecting stock market and machine learning algorithms (2 points, 4 comments)
    9. pandas import in pydev (2 points, 0 comments)
    10. Assess learner server error (1 point, 2 comments)
  15. 43 points, 23 submissions: chvbs2000
    1. Is the Strategy Learner finalized? (10 points, 3 comments)
    2. Test extra 15 test cases for marketsim (3 points, 12 comments)
    3. Confusion between the term computing "back-in time" and "going forward" (2 points, 1 comment)
    4. How to define "each transaction"? (2 points, 4 comments)
    5. How to filling the assignment into Jupyter Notebook? (2 points, 4 comments)
    6. IOError: File ../data/SPY.csv does not exist (2 points, 4 comments)
    7. Issue in Access to machines at Georgia Tech via MacOS terminal (2 points, 5 comments)
    8. Reading data from Jupyter Notebook (2 points, 3 comments)
    9. benchmark vs manual strategy vs best possible strategy (2 points, 2 comments)
    10. global name 'pd' is not defined (2 points, 4 comments)
  16. 43 points, 15 submissions: shuang379
    1. How to test my code on buffet machine? (10 points, 15 comments)
    2. Can we get the ppt for "Decision Trees"? (8 points, 2 comments)
    3. python question pool question (5 points, 6 comments)
    4. set up problems (3 points, 4 comments)
    5. Do I need another camera for scanning? (2 points, 9 comments)
    6. Is chapter 9 covered by the midterm? (2 points, 2 comments)
    7. Why grade_analysis.py could run even if I rm analysis.py? (2 points, 5 comments)
    8. python question pool No.48 (2 points, 6 comments)
    9. where could we find old versions of the rest projects? (2 points, 2 comments)
    10. where to put ml4t-libraries to install those libraries? (2 points, 1 comment)
  17. 42 points, 14 submissions: larrva
    1. is there a mistake in How-to-learn-a-decision-tree.pdf (7 points, 7 comments)
    2. maximum recursion depth problem (6 points, 10 comments)
    3. [Urgent]Unable to use proctortrack in China (4 points, 21 comments)
    4. manual_strategynumber of indicators to use (3 points, 10 comments)
    5. Assignment 2: Got 63 points. (3 points, 3 comments)
    6. Software installation workshop (3 points, 7 comments)
    7. question regarding functools32 version (3 points, 3 comments)
    8. workshop on Aug 31 (3 points, 8 comments)
    9. Mount remote server to local machine (2 points, 2 comments)
    10. any suggestion on objective function (2 points, 3 comments)
  18. 41 points, 8 submissions: Ran__Ran
    1. Any resource will be available for final exam? (19 points, 6 comments)
    2. Need clarification on size of X, Y in defeat_learners (7 points, 10 comments)
    3. Get the same date format as in example chart (4 points, 3 comments)
    4. Cannot log in GitHub Desktop using GT account? (3 points, 3 comments)
    5. Do we have notes or ppt for Time Series Data? (3 points, 5 comments)
    6. Can we know the commission & market impact for short example? (2 points, 7 comments)
    7. Course schedule export issue (2 points, 15 comments)
    8. Buying/seeking beta v.s. buying/seeking alpha (1 point, 6 comments)
  19. 38 points, 4 submissions: ProudRamblinWreck
    1. Exam 2 Study topics (21 points, 5 comments)
    2. Reddit participation as part of grade? (13 points, 32 comments)
    3. Will birds chirping in the background flag me on Proctortrack? (3 points, 5 comments)
    4. Midterm Study Guide question pools (1 point, 2 comments)
  20. 37 points, 6 submissions: gatechben
    1. Submission page for strategy learner? (14 points, 10 comments)
    2. PSA: The grading script for strategy_learner changed on the 26th (10 points, 9 comments)
    3. Where is util.py supposed to be located? (8 points, 8 comments)
    4. PSA:. The default dates in the assignment 1 template are not the same as the examples on the assignment page. (2 points, 1 comment)
    5. Schedule: Discussion of upcoming trading projects? (2 points, 3 comments)
    6. [defeat_learners] More than one column for X? (1 point, 1 comment)
  21. 37 points, 3 submissions: jgeiger
    1. Please send/announce when changes are made to the project code (23 points, 7 comments)
    2. The Big Short on Netflix for OMSCS students (week of 10/16) (11 points, 6 comments)
    3. Typo(?) for Assess_portfolio wiki page (3 points, 2 comments)
  22. 35 points, 10 submissions: ltian35
    1. selecting row using .ix (8 points, 9 comments)
    2. Will the following 2 topics be included in the final exam(online student)? (7 points, 4 comments)
    3. udacity quiz (7 points, 4 comments)
    4. pdf of lecture (3 points, 4 comments)
    5. print friendly version of the course schedule (3 points, 9 comments)
    6. about learner regression vs classificaiton (2 points, 2 comments)
    7. is there a simple way to verify the correctness of our decision tree (2 points, 4 comments)
    8. about Building an ML-based forex strategy (1 point, 2 comments)
    9. about technical analysis (1 point, 6 comments)
    10. final exam online time period (1 point, 2 comments)
  23. 33 points, 2 submissions: bhrolenok
    1. Assess learners template and grading script is now available in the public repository (24 points, 0 comments)
    2. Tutorial for software setup on Windows (9 points, 35 comments)
  24. 31 points, 4 submissions: johannes_92
    1. Deadline extension? (26 points, 40 comments)
    2. Pandas date indexing issues (2 points, 5 comments)
    3. Why do we subtract 1 from SMA calculation? (2 points, 3 comments)
    4. Unexpected number of calls to query, sum=20 (should be 20), max=20 (should be 1), min=20 (should be 1) -bash: syntax error near unexpected token `(' (1 point, 3 comments)
  25. 30 points, 5 submissions: log_base_pi
    1. The Massive Hedge Fund Betting on AI [Article] (9 points, 1 comment)
    2. Useful Python tips and tricks (8 points, 10 comments)
    3. Video of overview of remaining projects with Tucker Balch (7 points, 1 comment)
    4. Will any material from the lecture by Goldman Sachs be covered on the exam? (5 points, 1 comment)
    5. What will the 2nd half of the course be like? (1 point, 8 comments)
  26. 30 points, 4 submissions: acschwabe
    1. Assignment and Exam Calendar (ICS File) (17 points, 6 comments)
    2. Please OMG give us any options for extra credit (8 points, 12 comments)
    3. Strategy learner question (3 points, 1 comment)
    4. Proctortrack: Do we need to schedule our test time? (2 points, 10 comments)
  27. 29 points, 9 submissions: _ant0n_
    1. Next assignment? (9 points, 6 comments)
    2. Proctortrack Onboarding test? (6 points, 11 comments)
    3. Manual strategy: Allowable positions (3 points, 7 comments)
    4. Anyone watched Black Scholes documentary? (2 points, 16 comments)
    5. Buffet machines hardware (2 points, 6 comments)
    6. Defeat learners: clarification (2 points, 4 comments)
    7. Is 'optimize_something' on the way to class GitHub repo? (2 points, 6 comments)
    8. assess_portfolio(... gen_plot=True) (2 points, 8 comments)
    9. remote job != remote + international? (1 point, 15 comments)
  28. 26 points, 10 submissions: umersaalis
    1. comments.txt (7 points, 6 comments)
    2. Assignment 2: report.pdf (6 points, 30 comments)
    3. Assignment 2: report.pdf sharing & plagiarism (3 points, 12 comments)
    4. Max Recursion Limit (3 points, 10 comments)
    5. Parametric vs Non-Parametric Model (3 points, 13 comments)
    6. Bag Learner Training (1 point, 2 comments)
    7. Decision Tree Issue: (1 point, 2 comments)
    8. Error in Running DTLearner and RTLearner (1 point, 12 comments)
    9. My Results for the four learners. Please check if you guys are getting values somewhat near to these. Exact match may not be there due to randomization. (1 point, 4 comments)
    10. Can we add the assignments and solutions to our public github profile? (0 points, 7 comments)
  29. 26 points, 6 submissions: abiele
    1. Recommended Reading? (13 points, 1 comment)
    2. Number of Indicators Used by Actual Trading Systems (7 points, 6 comments)
    3. Software Install Instructions From TA's Video Not Working (2 points, 2 comments)
    4. Suggest that TA/Instructor Contact Info Should be Added to the Syllabus (2 points, 2 comments)
    5. ML4T Software Setup (1 point, 3 comments)
    6. Where can I find the grading folder? (1 point, 4 comments)
  30. 26 points, 6 submissions: tomatonight
    1. Do we have all the information needed to finish the last project Strategy learner? (15 points, 3 comments)
    2. Does anyone interested in cryptocurrency trading/investing/others? (3 points, 6 comments)
    3. length of portfolio daily return (3 points, 2 comments)
    4. Did Michael Burry, Jamie&Charlie enter the short position too early? (2 points, 4 comments)
    5. where to check participation score (2 points, 1 comment)
    6. Where to collect the midterm exam? (forgot to take it last week) (1 point, 3 comments)
  31. 26 points, 3 submissions: hilo260
    1. Is there a template for optimize_something on GitHub? (14 points, 3 comments)
    2. Marketism project? (8 points, 6 comments)
    3. "Do not change the API" (4 points, 7 comments)
  32. 26 points, 3 submissions: niufen
    1. Windows Server Setup Guide (23 points, 16 comments)
    2. Strategy Learner Adding UserID as Comment (2 points, 2 comments)
    3. Connect to server via Python Error (1 point, 6 comments)
  33. 26 points, 3 submissions: whoyoung99
    1. How much time you spend on Assess Learner? (13 points, 47 comments)
    2. Git clone repository without fork (8 points, 2 comments)
    3. Just for fun (5 points, 1 comment)
  34. 25 points, 8 submissions: SharjeelHanif
    1. When can we discuss defeat learners methods? (10 points, 1 comment)
    2. Are the buffet servers really down? (3 points, 2 comments)
    3. Are the midterm results in proctortrack gone? (3 points, 3 comments)
    4. Will these finance topics be covered on the final? (3 points, 9 comments)
    5. Anyone get set up with Proctortrack? (2 points, 10 comments)
    6. Incentives Quiz Discussion (2-01, Lesson 11.8) (2 points, 3 comments)
    7. Anyone from Houston, TX (1 point, 1 comment)
    8. How can I trace my error back to a line of code? (assess learners) (1 point, 3 comments)
  35. 25 points, 5 submissions: jlamberts3
    1. Conda vs VirtualEnv (7 points, 8 comments)
    2. Cool Portfolio Backtesting Tool (6 points, 6 comments)
    3. Warren Buffett wins $1M bet made a decade ago that the S&P 500 stock index would outperform hedge funds (6 points, 12 comments)
    4. Windows Ubuntu Subsystem Putty Alternative (4 points, 0 comments)
    5. Algorithmic Trading Of Digital Assets (2 points, 0 comments)
  36. 25 points, 4 submissions: suman_paul
    1. Grade statistics (9 points, 3 comments)
    2. Machine Learning book by Mitchell (6 points, 11 comments)
    3. Thank You (6 points, 6 comments)
    4. Assignment1 ready to be cloned? (4 points, 4 comments)
  37. 25 points, 3 submissions: Spareo
    1. Submit Assignments Function (OS X/Linux) (15 points, 6 comments)
    2. Quantsoftware Site down? (8 points, 38 comments)
    3. ML4T_2017Spring folder on Buffet server?? (2 points, 5 comments)
  38. 24 points, 14 submissions: nelsongcg
    1. Is it realistic for us to try to build our own trading bot and profit? (6 points, 21 comments)
    2. Is the risk free rate zero for any country? (3 points, 7 comments)
    3. Models and black swans - discussion (3 points, 0 comments)
    4. Normal distribution assumption for options pricing (2 points, 3 comments)
    5. Technical analysis for cryptocurrency market? (2 points, 4 comments)
    6. A counter argument to models by Nassim Taleb (1 point, 0 comments)
    7. Are we demandas to use the sample for part 1? (1 point, 1 comment)
    8. Benchmark for "trusting" your trading algorithm (1 point, 5 comments)
    9. Don't these two statements on the project description contradict each other? (1 point, 2 comments)
    10. Forgot my TA (1 point, 6 comments)
  39. 24 points, 11 submissions: nurobezede
    1. Best way to obtain survivor bias free stock data (8 points, 1 comment)
    2. Please confirm Midterm is from October 13-16 online with proctortrack. (5 points, 2 comments)
    3. Are these DTlearner Corr values good? (2 points, 6 comments)
    4. Testing gen_data.py (2 points, 3 comments)
    5. BagLearner of Baglearners says 'Object is not callable' (1 point, 8 comments)
    6. DTlearner training RMSE none zero but almost there (1 point, 2 comments)
    7. How to submit analysis using git and confirm it? (1 point, 2 comments)
    8. Passing kwargs to learners in a BagLearner (1 point, 5 comments)
    9. Sampling for bagging tree (1 point, 8 comments)
    10. code failing the 18th test with grade_learners.py (1 point, 6 comments)
  40. 24 points, 4 submissions: AeroZach
    1. questions about how to build a machine learning system that's going to work well in a real market (12 points, 6 comments)
    2. Survivor Bias Free Data (7 points, 5 comments)
    3. Genetic Algorithms for Feature selection (3 points, 5 comments)
    4. How far back can you train? (2 points, 2 comments)
  41. 23 points, 9 submissions: vsrinath6
    1. Participation check #3 - Haven't seen it yet (5 points, 5 comments)
    2. What are the tasks for this week? (5 points, 12 comments)
    3. No projects until after the mid-term? (4 points, 5 comments)
    4. Format / Syllabus for the exams (2 points, 3 comments)
    5. Has there been a Participation check #4? (2 points, 8 comments)
    6. Project 3 not visible on T-Square (2 points, 3 comments)
    7. Assess learners - do we need to check is method implemented for BagLearner? (1 point, 4 comments)
    8. Correct number of days reported in the dataframe (should be the number of trading days between the start date and end date, inclusive). (1 point, 0 comments)
    9. RuntimeError: Invalid DISPLAY variable (1 point, 2 comments)
  42. 23 points, 8 submissions: nick_algorithm
    1. Help with getting Average Daily Return Right (6 points, 7 comments)
    2. Hint for args argument in scipy minimize (5 points, 2 comments)
    3. How do you make money off of highly volatile (high SDDR) stocks? (4 points, 5 comments)
    4. Can We Use Code Obtained from Class To Make Money without Fear of Being Sued (3 points, 6 comments)
    5. Is the Std for Bollinger Bands calculated over the same timespan of the Moving Average? (2 points, 2 comments)
    6. Can't run grade_learners.py but I'm not doing anything different from the last assignment (?) (1 point, 5 comments)
    7. How to determine value at terminal node of tree? (1 point, 1 comment)
    8. Is there a way to get Reddit announcements piped to email (or have a subsequent T-Square announcement published simultaneously) (1 point, 2 comments)
  43. 23 points, 1 submission: gong6
    1. Is manual strategy ready? (23 points, 6 comments)
  44. 21 points, 6 submissions: amchang87
    1. Reason for public reddit? (6 points, 4 comments)
    2. Manual Strategy - 21 day holding Period (4 points, 12 comments)
    3. Sharpe Ratio (4 points, 6 comments)
    4. Manual Strategy - No Position? (3 points, 3 comments)
    5. ML / Manual Trader Performance (2 points, 0 comments)
    6. T-Square Submission Missing? (2 points, 3 comments)
  45. 21 points, 6 submissions: fall2017_ml4t_cs_god
    1. PSA: When typing in code, please use 'formatting help' to see how to make the code read cleaner. (8 points, 2 comments)
    2. Why do Bollinger Bands use 2 standard deviations? (5 points, 20 comments)
    3. How do I log into the [email protected]? (3 points, 1 comment)
    4. Is midterm 2 cumulative? (2 points, 3 comments)
    5. Where can we learn about options? (2 points, 2 comments)
    6. How do you calculate the analysis statistics for bps and manual strategy? (1 point, 1 comment)
  46. 21 points, 5 submissions: Jmitchell83
    1. Manual Strategy Grades (12 points, 9 comments)
    2. two-factor (3 points, 6 comments)
    3. Free to use volume? (2 points, 1 comment)
    4. Is MC1-Project-1 different than assess_portfolio? (2 points, 2 comments)
    5. Online Participation Checks (2 points, 4 comments)
  47. 21 points, 5 submissions: Sergei_B
    1. Do we need to worry about missing data for Asset Portfolio? (14 points, 13 comments)
    2. How do you get data from yahoo in panda? the sample old code is below: (2 points, 3 comments)
    3. How to fix import pandas as pd ImportError: No module named pandas? (2 points, 4 comments)
    4. Python Practice exam Question 48 (2 points, 2 comments)
    5. Mac: "virtualenv : command not found" (1 point, 2 comments)
  48. 21 points, 3 submissions: mharrow3
    1. First time reddit user .. (17 points, 37 comments)
    2. Course errors/types (2 points, 2 comments)
    3. Install course software on macOS using Vagrant .. (2 points, 0 comments)
  49. 20 points, 9 submissions: iceguyvn
    1. Manual strategy implementation for future projects (4 points, 15 comments)
    2. Help with correlation calculation (3 points, 15 comments)
    3. Help! maximum recursion depth exceeded (3 points, 10 comments)
    4. Help: how to index by date? (2 points, 4 comments)
    5. How to attach a 1D array to a 2D array? (2 points, 2 comments)
    6. How to set a single cell in a 2D DataFrame? (2 points, 4 comments)
    7. Next assignment after marketsim? (2 points, 4 comments)
    8. Pythonic way to detect the first row? (1 point, 6 comments)
    9. Questions regarding seed (1 point, 1 comment)
  50. 20 points, 3 submissions: JetsonDavis
    1. Push back assignment 3? (10 points, 14 comments)
    2. Final project (9 points, 3 comments)
    3. Numpy versions (1 point, 2 comments)
  51. 20 points, 2 submissions: pharmerino
    1. assess_portfolio test cases (16 points, 88 comments)
    2. ML4T Assignments (4 points, 6 comments)

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  85. gcalma3 (47 points, 25 comments)
  86. krushnatmore (44 points, 32 comments)
  87. sn_48 (43 points, 22 comments)
  88. thenewprofessional (43 points, 16 comments)
  89. urider (42 points, 33 comments)
  90. gatech-raleighite (42 points, 30 comments)
  91. chrisong2017 (41 points, 26 comments)
  92. ProudRamblinWreck (41 points, 24 comments)
  93. kramey8 (41 points, 24 comments)
  94. coderafk (40 points, 28 comments)
  95. niufen (40 points, 23 comments)
  96. tholladay3 (40 points, 23 comments)
  97. SaberCrunch (40 points, 22 comments)
  98. gnr11 (40 points, 21 comments)
  99. nadav3 (40 points, 18 comments)
  100. gt7431a (40 points, 16 comments)

Top Submissions

  1. [Project Questions] Unit Tests for assess_portfolio assignment by reyallan (58 points, 52 comments)
  2. [Project Questions] Unit Tests for optimize_something assignment by agifft3_omscs (53 points, 94 comments)
  3. Proper git workflow by jan-laszlo (43 points, 19 comments)
  4. Exam 2 Information by yokh_cs7646 (39 points, 40 comments)
  5. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes by davebyrd (37 points, 10 comments)
  6. Project 1 Megathread (assess_portfolio) by davebyrd (34 points, 466 comments)
  7. defeat_learner test case by swamijay (34 points, 38 comments)
  8. Project 2 Megathread (optimize_something) by tuckerbalch (33 points, 475 comments)
  9. project 3 megathread (assess_learners) by tuckerbalch (27 points, 1130 comments)
  10. Deadline extension? by johannes_92 (26 points, 40 comments)

Top Comments

  1. 34 points: jgeiger's comment in QLearning Robot project megathread
  2. 31 points: coolwhip1234's comment in QLearning Robot project megathread
  3. 30 points: tuckerbalch's comment in Why Professor is usually late for class?
  4. 23 points: davebyrd's comment in Deadline extension?
  5. 20 points: jason_gt's comment in What would be a good quiz question regarding The Big Short?
  6. 19 points: yokh_cs7646's comment in For online students: Participation check #2
  7. 17 points: i__want__piazza's comment in project 3 megathread (assess_learners)
  8. 17 points: nathakhanh2's comment in Project 2 Megathread (optimize_something)
  9. 17 points: pharmerino's comment in Midterm study Megathread
  10. 17 points: tuckerbalch's comment in Midterm grades posted to T-Square
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