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Chance a depressed gap year boy

Note: The mention of O levels and A levels throughout the post refers to the British patterned system of education. O levels is equivalent to the 9th and 10th grade and Alevels is equivalent to the 11th and 12th grade in the American High School System
Hi, I am a Pakistani guy currently on a gap year after doing A levels from one of the most prestigious schools in the country on a 100% scholarship. I did my O levels from a very less renowned school because of my financial condition but my grades allowed me admission into my A levels school. That is why most of my ECAS were done in the last 2 years. I am looking to apply to the following international universities with full scholarship/financial aid:
Majors Intended: Computer Science / Data Science / Artificial Intelligence
Universities: NYUAD (ED), Duke, Rice, Vanderbilt, Northwestern, Case Western Reserve University, Washington and Lee, Colby, Skidmore, CMU Qatar, Georgetown Qatar, NTU, HKUST, HKU, CityU
Academics
Test Scores
Extra-Curriculars / Awards
submitted by Faizan-Raza to chanceme [link] [comments]

eToro: impressions, doubts and (ignored) lessons from copy trading

(no promotional content, no affiliate links)
Hi,
exactly four years ago, I started copying eToro investors / traders that I selected using the broker's built-in search engine (profitable in last two years, already being copied by others), followed by manual filtering, to take into account fluctuations in yearly returns, composition of their portfolios etc. With that, I got a list of 10 people whom I started to copy on a demo account:
https://drive.google.com/file/d/1u52f0XHfr-LauIscKcFDYF0yGTTUr6VY/view?usp=sharing
In the screenshot you can see that in case of the first two of them the amount invested was $10,000, while for the rest it was just $100. This is because I started copying the first two a couple of weeks earlier; eventually I changed this into $100 the same day I made the screenshot and this is when my calculations start - so this thing is irrelevant, I just cannot travel in time to make another screenshot.
What I did after that?
Well, within the next six weeks my profits oscillated between -$11 and +$9.50 (the biggest profit was on Nov 9, a day after US presidential elections). I found this "boring" and discontinued experimenting with copy trading.
Today I looked back at those ten traders. Here is what I found. Firstly, seven of them are not with eToro anymore; investorNo1, Simple-Stock-Mkt, tradingrelax, 4exPirate, primit, Gallojack, xjurokx. The other three traders are:
My observations and thoughts are as follows:
  1. Seven out of ten traders are not with eToro anymore, which makes me wonder why. I have no proof but my guess is they simply performed poorly, lost their copiers and closed their accounts. This is already alarming but what if they opened another account? Or, even worse, multiple accounts? They could be investing small money and try different risky approaches, hoping that at least one account will turn out profitable in the long turn, attracting potential copiers. (I'm not claiming that those 7 particular traders did this, it's just my general suspicion regarding some of eToro traders)
  2. I'm unable to calculate what would be my profit if I never stopped copying them, because I cannot check at what day and with what profit those seven traders left eToro. I'm guessing this would be an immense loss. On the other hand, considering the three traders who are still with eToro, I would lose more than a quarter of my assets!
What now?
I must be a quite adventurous person or at least an incorrigible optimist, because a month ago (exactly on Aug 26th) I started copying three traders with real money. Here is who they are.
rubymza (Heloise Greeff)

OlivierDanvel (Olivier Jean Andre Danvel)

rayvahey (Raymond Noel Vahey)
What was my strategy to hand-pick these particular traders? First I did some basic scanning using eToro's built-in search engine. The most important filter was that the trader was profitable within the last two years: unfortunately, eToro does not allow to reach details of earlier performance automatically. To know how the trader performed before 2019, I had to look at stats in the profile of each of them. I was also taking into account how often they trade (to avoid those who do only a couple of trades yearly), whether they were trading recently and whether they write posts regularly in their feed. With this, I got a list of fifteen candidates to copy:
As you already know, I finally chose three of them. Rubymza seemed to be the most trustworthy stock trader, based on profits, posts feed and regular trading, among other things. Regarding OlivierDanvel, his uniqueness is the ability to record continuous profits with the Forex market. Finally, with rayvahey I wanted to increase my exposure to the commodities market.
Wish me good luck!
Michael

P.S.
You might find those copy-trading related readings interesting:

Disclosures:
submitted by investing-scientist2 to StockMarket [link] [comments]

H1 Backtest of ParallaxFX's BBStoch system

Disclaimer: None of this is financial advice. I have no idea what I'm doing. Please do your own research or you will certainly lose money. I'm not a statistician, data scientist, well-seasoned trader, or anything else that would qualify me to make statements such as the below with any weight behind them. Take them for the incoherent ramblings that they are.
TL;DR at the bottom for those not interested in the details.
This is a bit of a novel, sorry about that. It was mostly for getting my own thoughts organized, but if even one person reads the whole thing I will feel incredibly accomplished.

Background

For those of you not familiar, please see the various threads on this trading system here. I can't take credit for this system, all glory goes to ParallaxFX!
I wanted to see how effective this system was at H1 for a couple of reasons: 1) My current broker is TD Ameritrade - their Forex minimum is a mini lot, and I don't feel comfortable enough yet with the risk to trade mini lots on the higher timeframes(i.e. wider pip swings) that ParallaxFX's system uses, so I wanted to see if I could scale it down. 2) I'm fairly impatient, so I don't like to wait days and days with my capital tied up just to see if a trade is going to win or lose.
This does mean it requires more active attention since you are checking for setups once an hour instead of once a day or every 4-6 hours, but the upside is that you trade more often this way so you end up winning or losing faster and moving onto the next trade. Spread does eat more of the trade this way, but I'll cover this in my data below - it ends up not being a problem.
I looked at data from 6/11 to 7/3 on all pairs with a reasonable spread(pairs listed at bottom above the TL;DR). So this represents about 3-4 weeks' worth of trading. I used mark(mid) price charts. Spreadsheet link is below for anyone that's interested.

System Details

I'm pretty much using ParallaxFX's system textbook, but since there are a few options in his writeups, I'll include all the discretionary points here:

And now for the fun. Results!

As you can see, a higher target ended up with higher profit despite a much lower winrate. This is partially just how things work out with profit targets in general, but there's an additional point to consider in our case: the spread. Since we are trading on a lower timeframe, there is less overall price movement and thus the spread takes up a much larger percentage of the trade than it would if you were trading H4, Daily or Weekly charts. You can see exactly how much it accounts for each trade in my spreadsheet if you're interested. TDA does not have the best spreads, so you could probably improve these results with another broker.
EDIT: I grabbed typical spreads from other brokers, and turns out while TDA is pretty competitive on majors, their minors/crosses are awful! IG beats them by 20-40% and Oanda beats them 30-60%! Using IG spreads for calculations increased profits considerably (another 5% on top) and Oanda spreads increased profits massively (another 15%!). Definitely going to be considering another broker than TDA for this strategy. Plus that'll allow me to trade micro-lots, so I can be more granular(and thus accurate) with my position sizing and compounding.

A Note on Spread

As you can see in the data, there were scenarios where the spread was 80% of the overall size of the trade(the size of the confirmation candle that you draw your fibonacci retracements over), which would obviously cut heavily into your profits.
Removing any trades where the spread is more than 50% of the trade width improved profits slightly without removing many trades, but this is almost certainly just coincidence on a small sample size. Going below 40% and even down to 30% starts to cut out a lot of trades for the less-common pairs, but doesn't actually change overall profits at all(~1% either way).
However, digging all the way down to 25% starts to really make some movement. Profit at the -161.8% TP level jumps up to 37.94% if you filter out anything with a spread that is more than 25% of the trade width! And this even keeps the sample size fairly large at 187 total trades.
You can get your profits all the way up to 48.43% at the -161.8% TP level if you filter all the way down to only trades where spread is less than 15% of the trade width, however your sample size gets much smaller at that point(108 trades) so I'm not sure I would trust that as being accurate in the long term.
Overall based on this data, I'm going to only take trades where the spread is less than 25% of the trade width. This may bias my trades more towards the majors, which would mean a lot more correlated trades as well(more on correlation below), but I think it is a reasonable precaution regardless.

Time of Day

Time of day had an interesting effect on trades. In a totally predictable fashion, a vast majority of setups occurred during the London and New York sessions: 5am-12pm Eastern. However, there was one outlier where there were many setups on the 11PM bar - and the winrate was about the same as the big hours in the London session. No idea why this hour in particular - anyone have any insight? That's smack in the middle of the Tokyo/Sydney overlap, not at the open or close of either.
On many of the hour slices I have a feeling I'm just dealing with small number statistics here since I didn't have a lot of data when breaking it down by individual hours. But here it is anyway - for all TP levels, these three things showed up(all in Eastern time):
I don't have any reason to think these timeframes would maintain this behavior over the long term. They're almost certainly meaningless. EDIT: When you de-dup highly correlated trades, the number of trades in these timeframes really drops, so from this data there is no reason to think these timeframes would be any different than any others in terms of winrate.
That being said, these time frames work out for me pretty well because I typically sleep 12am-7am Eastern time. So I automatically avoid the 5am-6am timeframe, and I'm awake for the majority of this system's setups.

Moving stops up to breakeven

This section goes against everything I know and have ever heard about trade management. Please someone find something wrong with my data. I'd love for someone to check my formulas, but I realize that's a pretty insane time commitment to ask of a bunch of strangers.
Anyways. What I found was that for these trades moving stops up...basically at all...actually reduced the overall profitability.
One of the data points I collected while charting was where the price retraced back to after hitting a certain milestone. i.e. once the price hit the -61.8% profit level, how far back did it retrace before hitting the -100% profit level(if at all)? And same goes for the -100% profit level - how far back did it retrace before hitting the -161.8% profit level(if at all)?
Well, some complex excel formulas later and here's what the results appear to be. Emphasis on appears because I honestly don't believe it. I must have done something wrong here, but I've gone over it a hundred times and I can't find anything out of place.
Now, you might think exactly what I did when looking at these numbers: oof, the spread killed us there right? Because even when you move your SL to 0%, you still end up paying the spread, so it's not truly "breakeven". And because we are trading on a lower timeframe, the spread can be pretty hefty right?
Well even when I manually modified the data so that the spread wasn't subtracted(i.e. "Breakeven" was truly +/- 0), things don't look a whole lot better, and still way worse than the passive trade management method of leaving your stops in place and letting it run. And that isn't even a realistic scenario because to adjust out the spread you'd have to move your stoploss inside the candle edge by at least the spread amount, meaning it would almost certainly be triggered more often than in the data I collected(which was purely based on the fib levels and mark price). Regardless, here are the numbers for that scenario:
From a literal standpoint, what I see behind this behavior is that 44 of the 69 breakeven trades(65%!) ended up being profitable to -100% after retracing deeply(but not to the original SL level), which greatly helped offset the purely losing trades better than the partial profit taken at -61.8%. And 36 went all the way back to -161.8% after a deep retracement without hitting the original SL. Anyone have any insight into this? Is this a problem with just not enough data? It seems like enough trades that a pattern should emerge, but again I'm no expert.
I also briefly looked at moving stops to other lower levels (78.6%, 61.8%, 50%, 38.2%, 23.6%), but that didn't improve things any. No hard data to share as I only took a quick look - and I still might have done something wrong overall.
The data is there to infer other strategies if anyone would like to dig in deep(more explanation on the spreadsheet below). I didn't do other combinations because the formulas got pretty complicated and I had already answered all the questions I was looking to answer.

2-Candle vs Confirmation Candle Stops

Another interesting point is that the original system has the SL level(for stop entries) just at the outer edge of the 2-candle pattern that makes up the system. Out of pure laziness, I set up my stops just based on the confirmation candle. And as it turns out, that is much a much better way to go about it.
Of the 60 purely losing trades, only 9 of them(15%) would go on to be winners with stops on the 2-candle formation. Certainly not enough to justify the extra loss and/or reduced profits you are exposing yourself to in every single other trade by setting a wider SL.
Oddly, in every single scenario where the wider stop did save the trade, it ended up going all the way to the -161.8% profit level. Still, not nearly worth it.

Correlated Trades

As I've said many times now, I'm really not qualified to be doing an analysis like this. This section in particular.
Looking at shared currency among the pairs traded, 74 of the trades are correlated. Quite a large group, but it makes sense considering the sort of moves we're looking for with this system.
This means you are opening yourself up to more risk if you were to trade on every signal since you are technically trading with the same underlying sentiment on each different pair. For example, GBP/USD and AUD/USD moving together almost certainly means it's due to USD moving both pairs, rather than GBP and AUD both moving the same size and direction coincidentally at the same time. So if you were to trade both signals, you would very likely win or lose both trades - meaning you are actually risking double what you'd normally risk(unless you halve both positions which can be a good option, and is discussed in ParallaxFX's posts and in various other places that go over pair correlation. I won't go into detail about those strategies here).
Interestingly though, 17 of those apparently correlated trades ended up with different wins/losses.
Also, looking only at trades that were correlated, winrate is 83%/70%/55% (for the three TP levels).
Does this give some indication that the same signal on multiple pairs means the signal is stronger? That there's some strong underlying sentiment driving it? Or is it just a matter of too small a sample size? The winrate isn't really much higher than the overall winrates, so that makes me doubt it is statistically significant.
One more funny tidbit: EUCAD netted the lowest overall winrate: 30% to even the -61.8% TP level on 10 trades. Seems like that is just a coincidence and not enough data, but dang that's a sucky losing streak.
EDIT: WOW I spent some time removing correlated trades manually and it changed the results quite a bit. Some thoughts on this below the results. These numbers also include the other "What I will trade" filters. I added a new worksheet to my data to show what I ended up picking.
To do this, I removed correlated trades - typically by choosing those whose spread had a lower % of the trade width since that's objective and something I can see ahead of time. Obviously I'd like to only keep the winning trades, but I won't know that during the trade. This did reduce the overall sample size down to a level that I wouldn't otherwise consider to be big enough, but since the results are generally consistent with the overall dataset, I'm not going to worry about it too much.
I may also use more discretionary methods(support/resistance, quality of indecision/confirmation candles, news/sentiment for the pairs involved, etc) to filter out correlated trades in the future. But as I've said before I'm going for a pretty mechanical system.
This brought the 3 TP levels and even the breakeven strategies much closer together in overall profit. It muted the profit from the high R:R strategies and boosted the profit from the low R:R strategies. This tells me pair correlation was skewing my data quite a bit, so I'm glad I dug in a little deeper. Fortunately my original conclusion to use the -161.8 TP level with static stops is still the winner by a good bit, so it doesn't end up changing my actions.
There were a few times where MANY (6-8) correlated pairs all came up at the same time, so it'd be a crapshoot to an extent. And the data showed this - often then won/lost together, but sometimes they did not. As an arbitrary rule, the more correlations, the more trades I did end up taking(and thus risking). For example if there were 3-5 correlations, I might take the 2 "best" trades given my criteria above. 5+ setups and I might take the best 3 trades, even if the pairs are somewhat correlated.
I have no true data to back this up, but to illustrate using one example: if AUD/JPY, AUD/USD, CAD/JPY, USD/CAD all set up at the same time (as they did, along with a few other pairs on 6/19/20 9:00 AM), can you really say that those are all the same underlying movement? There are correlations between the different correlations, and trying to filter for that seems rough. Although maybe this is a known thing, I'm still pretty green to Forex - someone please enlighten me if so! I might have to look into this more statistically, but it would be pretty complex to analyze quantitatively, so for now I'm going with my gut and just taking a few of the "best" trades out of the handful.
Overall, I'm really glad I went further on this. The boosting of the B/E strategies makes me trust my calculations on those more since they aren't so far from the passive management like they were with the raw data, and that really had me wondering what I did wrong.

What I will trade

Putting all this together, I am going to attempt to trade the following(demo for a bit to make sure I have the hang of it, then for keeps):
Looking at the data for these rules, test results are:
I'll be sure to let everyone know how it goes!

Other Technical Details

Raw Data

Here's the spreadsheet for anyone that'd like it. (EDIT: Updated some of the setups from the last few days that have fully played out now. I also noticed a few typos, but nothing major that would change the overall outcomes. Regardless, I am currently reviewing every trade to ensure they are accurate.UPDATE: Finally all done. Very few corrections, no change to results.)
I have some explanatory notes below to help everyone else understand the spiraled labyrinth of a mind that put the spreadsheet together.

Insanely detailed spreadsheet notes

For you real nerds out there. Here's an explanation of what each column means:

Pairs

  1. AUD/CAD
  2. AUD/CHF
  3. AUD/JPY
  4. AUD/NZD
  5. AUD/USD
  6. CAD/CHF
  7. CAD/JPY
  8. CHF/JPY
  9. EUAUD
  10. EUCAD
  11. EUCHF
  12. EUGBP
  13. EUJPY
  14. EUNZD
  15. EUUSD
  16. GBP/AUD
  17. GBP/CAD
  18. GBP/CHF
  19. GBP/JPY
  20. GBP/NZD
  21. GBP/USD
  22. NZD/CAD
  23. NZD/CHF
  24. NZD/JPY
  25. NZD/USD
  26. USD/CAD
  27. USD/CHF
  28. USD/JPY

TL;DR

Based on the reasonable rules I discovered in this backtest:

Demo Trading Results

Since this post, I started demo trading this system assuming a 5k capital base and risking ~1% per trade. I've added the details to my spreadsheet for anyone interested. The results are pretty similar to the backtest when you consider real-life conditions/timing are a bit different. I missed some trades due to life(work, out of the house, etc), so that brought my total # of trades and thus overall profit down, but the winrate is nearly identical. I also closed a few trades early due to various reasons(not liking the price action, seeing support/resistance emerge, etc).
A quick note is that TD's paper trade system fills at the mid price for both stop and limit orders, so I had to subtract the spread from the raw trade values to get the true profit/loss amount for each trade.
I'm heading out of town next week, then after that it'll be time to take this sucker live!

Live Trading Results

I started live-trading this system on 8/10, and almost immediately had a string of losses much longer than either my backtest or demo period. Murphy's law huh? Anyways, that has me spooked so I'm doing a longer backtest before I start risking more real money. It's going to take me a little while due to the volume of trades, but I'll likely make a new post once I feel comfortable with that and start live trading again.
submitted by ForexBorex to Forex [link] [comments]

The Next Crypto Wave: The Rise of Stablecoins and its Entry to the U.S. Dollar Market

The Next Crypto Wave: The Rise of Stablecoins and its Entry to the U.S. Dollar Market

Author: Christian Hsieh, CEO of Tokenomy
This paper examines some explanations for the continual global market demand for the U.S. dollar, the rise of stablecoins, and the utility and opportunities that crypto dollars can offer to both the cryptocurrency and traditional markets.
The U.S. dollar, dominant in world trade since the establishment of the 1944 Bretton Woods System, is unequivocally the world’s most demanded reserve currency. Today, more than 61% of foreign bank reserves and nearly 40% of the entire world’s debt is denominated in U.S. dollars1.
However, there is a massive supply and demand imbalance in the U.S. dollar market. On the supply side, central banks throughout the world have implemented more than a decade-long accommodative monetary policy since the 2008 global financial crisis. The COVID-19 pandemic further exacerbated the need for central banks to provide necessary liquidity and keep staggering economies moving. While the Federal Reserve leads the effort of “money printing” and stimulus programs, the current money supply still cannot meet the constant high demand for the U.S. dollar2. Let us review some of the reasons for this constant dollar demand from a few economic fundamentals.

Demand for U.S. Dollars

Firstly, most of the world’s trade is denominated in U.S. dollars. Chief Economist of the IMF, Gita Gopinath, has compiled data reflecting that the U.S. dollar’s share of invoicing was 4.7 times larger than America’s share of the value of imports, and 3.1 times its share of world exports3. The U.S. dollar is the dominant “invoicing currency” in most developing countries4.

https://preview.redd.it/d4xalwdyz8p51.png?width=535&format=png&auto=webp&s=9f0556c6aa6b29016c9b135f3279e8337dfee2a6

https://preview.redd.it/wucg40kzz8p51.png?width=653&format=png&auto=webp&s=71257fec29b43e0fc0df1bf04363717e3b52478f
This U.S. dollar preference also directly impacts the world’s debt. According to the Bank of International Settlements, there is over $67 trillion in U.S. dollar denominated debt globally, and borrowing outside of the U.S. accounted for $12.5 trillion in Q1 20205. There is an immense demand for U.S. dollars every year just to service these dollar debts. The annual U.S. dollar buying demand is easily over $1 trillion assuming the borrowing cost is at 1.5% (1 year LIBOR + 1%) per year, a conservative estimate.

https://preview.redd.it/6956j6f109p51.png?width=487&format=png&auto=webp&s=ccea257a4e9524c11df25737cac961308b542b69
Secondly, since the U.S. has a much stronger economy compared to its global peers, a higher return on investments draws U.S. dollar demand from everywhere in the world, to invest in companies both in the public and private markets. The U.S. hosts the largest stock markets in the world with more than $33 trillion in public market capitalization (combined both NYSE and NASDAQ)6. For the private market, North America’s total share is well over 60% of the $6.5 trillion global assets under management across private equity, real assets, and private debt investments7. The demand for higher quality investments extends to the fixed income market as well. As countries like Japan and Switzerland currently have negative-yielding interest rates8, fixed income investors’ quest for yield in the developed economies leads them back to the U.S. debt market. As of July 2020, there are $15 trillion worth of negative-yielding debt securities globally (see chart). In comparison, the positive, low-yielding U.S. debt remains a sound fixed income strategy for conservative investors in uncertain market conditions.

Source: Bloomberg
Last, but not least, there are many developing economies experiencing failing monetary policies, where hyperinflation has become a real national disaster. A classic example is Venezuela, where the currency Bolivar became practically worthless as the inflation rate skyrocketed to 10,000,000% in 20199. The recent Beirut port explosion in Lebanon caused a sudden economic meltdown and compounded its already troubled financial market, where inflation has soared to over 112% year on year10. For citizens living in unstable regions such as these, the only reliable store of value is the U.S. dollar. According to the Chainalysis 2020 Geography of Cryptocurrency Report, Venezuela has become one of the most active cryptocurrency trading countries11. The demand for cryptocurrency surges as a flight to safety mentality drives Venezuelans to acquire U.S. dollars to preserve savings that they might otherwise lose. The growth for cryptocurrency activities in those regions is fueled by these desperate citizens using cryptocurrencies as rails to access the U.S. dollar, on top of acquiring actual Bitcoin or other underlying crypto assets.

The Rise of Crypto Dollars

Due to the highly volatile nature of cryptocurrencies, USD stablecoin, a crypto-powered blockchain token that pegs its value to the U.S. dollar, was introduced to provide stable dollar exposure in the crypto trading sphere. Tether is the first of its kind. Issued in 2014 on the bitcoin blockchain (Omni layer protocol), under the token symbol USDT, it attempts to provide crypto traders with a stable settlement currency while they trade in and out of various crypto assets. The reason behind the stablecoin creation was to address the inefficient and burdensome aspects of having to move fiat U.S. dollars between the legacy banking system and crypto exchanges. Because one USDT is theoretically backed by one U.S. dollar, traders can use USDT to trade and settle to fiat dollars. It was not until 2017 that the majority of traders seemed to realize Tether’s intended utility and started using it widely. As of April 2019, USDT trading volume started exceeding the trading volume of bitcoina12, and it now dominates the crypto trading sphere with over $50 billion average daily trading volume13.

https://preview.redd.it/3vq7v1jg09p51.png?width=700&format=png&auto=webp&s=46f11b5f5245a8c335ccc60432873e9bad2eb1e1
An interesting aspect of USDT is that although the claimed 1:1 backing with U.S. dollar collateral is in question, and the Tether company is in reality running fractional reserves through a loose offshore corporate structure, Tether’s trading volume and adoption continues to grow rapidly14. Perhaps in comparison to fiat U.S. dollars, which is not really backed by anything, Tether still has cash equivalents in reserves and crypto traders favor its liquidity and convenience over its lack of legitimacy. For those who are concerned about Tether’s solvency, they can now purchase credit default swaps for downside protection15. On the other hand, USDC, the more compliant contender, takes a distant second spot with total coin circulation of $1.8 billion, versus USDT at $14.5 billion (at the time of publication). It is still too early to tell who is the ultimate leader in the stablecoin arena, as more and more stablecoins are launching to offer various functions and supporting mechanisms. There are three main categories of stablecoin: fiat-backed, crypto-collateralized, and non-collateralized algorithm based stablecoins. Most of these are still at an experimental phase, and readers can learn more about them here. With the continuous innovation of stablecoin development, the utility stablecoins provide in the overall crypto market will become more apparent.

Institutional Developments

In addition to trade settlement, stablecoins can be applied in many other areas. Cross-border payments and remittances is an inefficient market that desperately needs innovation. In 2020, the average cost of sending money across the world is around 7%16, and it takes days to settle. The World Bank aims to reduce remittance fees to 3% by 2030. With the implementation of blockchain technology, this cost could be further reduced close to zero.
J.P. Morgan, the largest bank in the U.S., has created an Interbank Information Network (IIN) with 416 global Institutions to transform the speed of payment flows through its own JPM Coin, another type of crypto dollar17. Although people argue that JPM Coin is not considered a cryptocurrency as it cannot trade openly on a public blockchain, it is by far the largest scale experiment with all the institutional participants trading within the “permissioned” blockchain. It might be more accurate to refer to it as the use of distributed ledger technology (DLT) instead of “blockchain” in this context. Nevertheless, we should keep in mind that as J.P. Morgan currently moves $6 trillion U.S. dollars per day18, the scale of this experiment would create a considerable impact in the international payment and remittance market if it were successful. Potentially the day will come when regulated crypto exchanges become participants of IIN, and the link between public and private crypto assets can be instantly connected, unlocking greater possibilities in blockchain applications.
Many central banks are also in talks about developing their own central bank digital currency (CBDC). Although this idea was not new, the discussion was brought to the forefront due to Facebook’s aggressive Libra project announcement in June 2019 and the public attention that followed. As of July 2020, at least 36 central banks have published some sort of CBDC framework. While each nation has a slightly different motivation behind its currency digitization initiative, ranging from payment safety, transaction efficiency, easy monetary implementation, or financial inclusion, these central banks are committed to deploying a new digital payment infrastructure. When it comes to the technical architectures, research from BIS indicates that most of the current proofs-of-concept tend to be based upon distributed ledger technology (permissioned blockchain)19.

https://preview.redd.it/lgb1f2rw19p51.png?width=700&format=png&auto=webp&s=040bb0deed0499df6bf08a072fd7c4a442a826a0
These institutional experiments are laying an essential foundation for an improved global payment infrastructure, where instant and frictionless cross-border settlements can take place with minimal costs. Of course, the interoperability of private DLT tokens and public blockchain stablecoins has yet to be explored, but the innovation with both public and private blockchain efforts could eventually merge. This was highlighted recently by the Governor of the Bank of England who stated that “stablecoins and CBDC could sit alongside each other20”. One thing for certain is that crypto dollars (or other fiat-linked digital currencies) are going to play a significant role in our future economy.

Future Opportunities

There is never a dull moment in the crypto sector. The industry narratives constantly shift as innovation continues to evolve. Twelve years since its inception, Bitcoin has evolved from an abstract subject to a familiar concept. Its role as a secured, scarce, decentralized digital store of value has continued to gain acceptance, and it is well on its way to becoming an investable asset class as a portfolio hedge against asset price inflation and fiat currency depreciation. Stablecoins have proven to be useful as proxy dollars in the crypto world, similar to how dollars are essential in the traditional world. It is only a matter of time before stablecoins or private digital tokens dominate the cross-border payments and global remittances industry.
There are no shortages of hypes and experiments that draw new participants into the crypto space, such as smart contracts, new blockchains, ICOs, tokenization of things, or the most recent trends on DeFi tokens. These projects highlight the possibilities for a much more robust digital future, but the market also needs time to test and adopt. A reliable digital payment infrastructure must be built first in order to allow these experiments to flourish.
In this paper we examined the historical background and economic reasons for the U.S. dollar’s dominance in the world, and the probable conclusion is that the demand for U.S. dollars will likely continue, especially in the middle of a global pandemic, accompanied by a worldwide economic slowdown. The current monetary system is far from perfect, but there are no better alternatives for replacement at least in the near term. Incremental improvements are being made in both the public and private sectors, and stablecoins have a definite role to play in both the traditional and the new crypto world.
Thank you.

Reference:
[1] How the US dollar became the world’s reserve currency, Investopedia
[2] The dollar is in high demand, prone to dangerous appreciation, The Economist
[3] Dollar dominance in trade and finance, Gita Gopinath
[4] Global trades dependence on dollars, The Economist & IMF working papers
[5] Total credit to non-bank borrowers by currency of denomination, BIS
[6] Biggest stock exchanges in the world, Business Insider
[7] McKinsey Global Private Market Review 2020, McKinsey & Company
[8] Central banks current interest rates, Global Rates
[9] Venezuela hyperinflation hits 10 million percent, CNBC
[10] Lebanon inflation crisis, Reuters
[11] Venezuela cryptocurrency market, Chainalysis
[12] The most used cryptocurrency isn’t Bitcoin, Bloomberg
[13] Trading volume of all crypto assets, coinmarketcap.com
[14] Tether US dollar peg is no longer credible, Forbes
[15] New crypto derivatives let you bet on (or against) Tether’s solvency, Coindesk
[16] Remittance Price Worldwide, The World Bank
[17] Interbank Information Network, J.P. Morgan
[18] Jamie Dimon interview, CBS News
[19] Rise of the central bank digital currency, BIS
[20] Speech by Andrew Bailey, 3 September 2020, Bank of England
submitted by Tokenomy to tokenomyofficial [link] [comments]

Copy trading with eToro: impressions, doubts and (ignored) lessons

(no promotional content, no affiliate links)
Hi,
exactly four years ago, I started copying eToro investors / traders that I selected using the broker's built-in search engine (profitable in last two years, already being copied by others), followed by manual filtering, to take into account fluctuations in yearly returns, composition of their portfolios etc. With that, I got a list of 10 people whom I started to copy on a demo account:
https://drive.google.com/file/d/1u52f0XHfr-LauIscKcFDYF0yGTTUr6VY/view?usp=sharing
In the screenshot you can see that in case of the first two of them the amount invested was $10,000, while for the rest it was just $100. This is because I started copying the first two a couple of weeks earlier; eventually I changed this into $100 the same day I made the screenshot and this is when my calculations start - so this thing is irrelevant, I just cannot travel in time to make another screenshot.
What I did after that?
Well, within the next six weeks my profits oscillated between -$11 and +$9.50 (the biggest profit was on Nov 9, a day after US presidential elections). I found this "boring" and discontinued experimenting with copy trading.
Today I looked back at those ten traders. Here is what I found. Firstly, seven of them are not with eToro anymore; investorNo1, Simple-Stock-Mkt, tradingrelax, 4exPirate, primit, Gallojack, xjurokx. The other three traders are:
My observations and thoughts are as follows:
  1. Seven out of ten traders are not with eToro anymore, which makes me wonder why. I have no proof but my guess is they simply performed poorly, lost their copiers and closed their accounts. This is already alarming but what if they opened another account? Or, even worse, multiple accounts? They could be investing small money and try different risky approaches, hoping that at least one account will turn out profitable in the long turn, attracting potential copiers. (I'm not claiming that those 7 particular traders did this, it's just my general suspicion regarding some of eToro traders)
  2. I'm unable to calculate what would be my profit if I never stopped copying them, because I cannot check at what day and with what profit those seven traders left eToro. I'm guessing this would be an immense loss. On the other hand, considering the three traders who are still with eToro, I would lose more than a quarter of my assets!
What now?
I must be a quite adventurous person or at least an incorrigible optimist, because a month ago (exactly on Aug 26th) I started copying three traders with real money. Here is who they are.
rubymza (Heloise Greeff)

OlivierDanvel (Olivier Jean Andre Danvel)

rayvahey (Raymond Noel Vahey)
What was my strategy to hand-pick these particular traders? First I did some basic scanning using eToro's built-in search engine. The most important filter was that the trader was profitable within the last two years: unfortunately, eToro does not allow to reach details of earlier performance automatically. To know how the trader performed before 2019, I had to look at stats in the profile of each of them. I was also taking into account how often they trade (to avoid those who do only a couple of trades yearly), whether they were trading recently and whether they write posts regularly in their feed. With this, I got a list of fifteen candidates to copy:
As you already know, I finally chose three of them. Rubymza seemed to be the most trustworthy stock trader, based on profits, posts feed and regular trading, among other things. Regarding OlivierDanvel, his uniqueness is the ability to record continuous profits with the Forex market. Finally, with rayvahey I wanted to increase my exposure to the commodities market.
Wish me good luck!
Michael

P.S.
You might find those copy-trading related readings interesting:

Disclosures:
submitted by investing-scientist2 to InvestmentClub [link] [comments]

No, the British did not steal $45 trillion from India

This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.
How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.
This is begging the question.
After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.
This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.
She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain
Moving on:
The exact mechanism of drain, or transfers from India to Britain was quite simple.
Convenient.
Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.
- Chandra et al. (1989)
The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.
The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):
the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).
Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.
The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.
So what's going on here? Well Roy (2019) explains it better:
Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.
So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):
It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.
While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:
she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.
Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.
The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.
While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.
When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.
So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:
Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.
Indeed, if paying for what you buy is drain, then several billions of us are drained every day.
The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.
It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):
The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.
Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):
Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.
It doesn't appear that others appreciate their insight Roy (2019):
K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.
Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).
If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.
This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual1). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):
This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.
A view echoed in Raychaudhuri (1983):
Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.
So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):
All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.
The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):
Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable
So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
1. Several authors have affirmed that Indian identity is a colonial artefact. For example see Rajan 1969:
Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.
or see Bryant 2000:
But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist. [...] Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
Tuovila, Alicia (2019). Expenditure method. Investopedia
Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review
Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books
Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press
Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire
Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press
Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press
Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press
Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy
Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal
Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review
Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly
Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press
Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History
Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press
Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History
Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History
submitted by GaslightEveryone to u/GaslightEveryone [link] [comments]

Chance a hopeless gap year guy for NYUAD

Note: The mention of O levels and A levels throughout the post refers to the British patterned system of education. O levels is equivalent to the 9th and 10th grade and Alevels is equivalent to the 11th and 12th grade in the American High School System
Hi, I am a Pakistani guy currently on a gap year after doing A levels from one of the most prestigious schools in the country on a 100% scholarship. I did my O levels from a very less renowned school because of my financial condition but my grades allowed me admission into my A levels school. That is why most of my ECAS were done in the last 2 years. I am looking to apply to the following international universities with full scholarship/financial aid:
Majors Intended: Computer Science / Data Science / Artificial Intelligence
Universities: NYUAD (ED), Duke, Rice, Vanderbilt, Northwestern, Case Western Reserve University, Washington and Lee, Colby, Skidmore, CMU Qatar, Georgetown Qatar, NTU, HKUST, HKU, CityU
Academics
Test Scores
Extra-Curriculars / Awards
submitted by Faizan-Raza to chanceme [link] [comments]

WikiFX: the murky business and the murkier methods

WikiFX: the murky business and the murkier methods
https://preview.redd.it/1rf74ljv34l51.png?width=960&format=png&auto=webp&s=566235871ce22dd3078f0532dfb672bff6eb0707
The irony of financial markets is that this business that officially has got as much regulation as arms trafficking, has also got the same problem –- numerous illegal entities that evolve around the niche.
Scam brokers, funds recovery services that rob the robbed traders, HYIPs, “learn how to make millions overnight” trading courses and a number of other schemes all tend to exploit the weak point of human nature – the belief that there is the magic device with the “MORE MONEY” button out there, that someone can sell you.

A thief shouting “Thief!”

Considering the above there is a high demand in society for truthful and unbiased information about the market players. WikiFX claims to be the provider of such honest information about brokers but in fact, makes money by blackmailing brokers and promoting any company that offers to pay enough in their rankings.
WikiFX is a classic illustration of a thief shouting “Get the thief!” louder than anybody else in the crowd. The strategy works unfortunately and traders tend to trust WikiFx broker’s ratings without questioning what these ratings are based on and who sponsors this global brokers’ database.

Paving the road with some good intentions

Even the most horrible crimes against humanity were done under the cover of best intentions. Starting with the first crusades and ending with the holocaust. There are always some sound arguments, protected people and reliable methods.
Ask any trader whether each forex broker must be regulated by a third party? The answer will be “yes” with a near 100% probability and this answer is totally correct. Know-your-customer procedures and some unbiased third-party control are essential for maintaining the overall transparency of any business in a sphere of finance. This is the argument that WikiFX starts with when promoting its service and there is absolutely no point to argue. Starting with an indisputable truth is a good strategy to win the debate.
“The long-term presence on the market adds credibility”, – says WikiFX, and hears “yes” again.
“Don’t you agree that the longer the company is in the business, the better?”. “Sure”, – the trader agrees one more time.
The mission is completed. This is when the broker ranker can add any other criteria to their appraisal methods. Traders will tend to trust the service because they’ve agreed upon the most important criteria. The rest are minor details.
But what if the rest of the appraisal methods are not just minor issues? What if these details can be the means to manipulate the facts as much as they want to?

Can WikiFX appraisal criteria be trusted?

If we take a look at any broker’s WikiFX rating, we can see that the criteria of appraisal are the following:
  • The year of registration
  • Regulations
  • Market Making license
  • Software license
For example, this is what the top-rated broker’s summary looks like at WikiFX:
WikiFX Forex com example
https://preview.redd.it/t4ugtbt344l51.png?width=625&format=png&auto=webp&s=95fddf8434faf8938d1a3f18bbd5f1da2ceb47e4
Looks good. Really. Regardless of the attitude to this particular brokerage, the work seems to be done fine. All the regulators are listed below, the information on the used software, licensing, and years of operation is included.
But what if we take some other random brokerage with one of the lowest rankings at WikiFX?
NinjaTraderBrokerage WIkiFX Ranking
https://preview.redd.it/pgyqp0u644l51.png?width=631&format=png&auto=webp&s=eb268faac83608a494c31a39eb1621f7132e3520
This is where the truth reveals itself. Once again, regardless of the attitude to this particular brokerage this is really easy to find out what they do, what licenses they’ve got and what kind of software they use.
Suspicious clone? Seriously? If WikiFX staff cared enough to do any investigation prior to stamping that “Suspicious” mark on the brokerage, they would have seen that both domains, nijatrader com and ninjatraderbrokerage com belong to the same entity.
NinyaTrader whois data
https://preview.redd.it/2097lkw944l51.png?width=563&format=png&auto=webp&s=079cc4248b825a3cd941c6b691a67bb9769f4f7f
If they cared enough to collect information on the brokerage from at least one reliable source, like Investopedia or any other similarly known database, they would also have found out that the company not only provides the brokerage service, but also is known for its trading platform with advanced technical analysis tools. But the only trading software that WikiFX considers reliable seems to be MT4/MT5. They simply ignore the fact that trading does not evolve around MetaTrader products, no matter how good and popular they are. WikiFX lowers the score of any brokerage with custom-developed software. We can clearly see this with the above example.
Other criteria that WikiFX is proud to use for the broker’s appraisal are regulations. Using the same example let’s see how well they do the appraisal in this field. As you can see above, WikiFX used the “Suspicious Regulatory License” stamp for NinjaTrader Brokerage.
And here is what The National Futures Association, that NinjaTrader is registered with as a futures broker has on its record:
NFA regulation of NTB proof that WikiFX did not consider to be trustworthy

https://preview.redd.it/di8fwkdd44l51.png?width=629&format=png&auto=webp&s=2de618d5df26bd8fcca99c51a6030f4bdfa7f776
We can’t expect every trader to know that any futures broker that wants to operate on the US market must be a member of NFA. This is the requirement of the Commodity Futures Trading Commission regarding the futures broker’s operations. But this is totally unacceptable for a broker ranking website, which WikiFX claims to be, to mark NFA-registered futures brokerage as non-reliable.
By the way, did you notice on the above screenshot that NTB has obtained the NFA license in 2004? Yet, this does not prevent WikiFX from claiming that the brokerage has only been providing its services for 1-2 years only, instead of the factual 16 years of operations.
We can long discuss the reasons that lie behind such selectivity of WikiFX but this random example clearly shows that any brokerage that provides access to non-forex derivatives trading or dares to suggest custom-developed software to its traders is in danger of receiving a negative review at WikiFX regardless of the factual reliability and regulations.

What lies beneath WikiFX selectivity?

WikiFX claims to have a team of professionals that are all involved in objective appraisal of broker’s services, licenses and used software. The methods used by these professionals remain unrevealed and as we see from the above comparison two similarly reliable brokerages can get any score from 1.0 and up to 10.0 at WikiFX, no matter what regulations they’ve got, for how long they’ve been in the business and what kind of software they use.
This is difficult to say what lies behind such selectivity with 100% confidence. The first thing that comes to mind is that WikiFX might be affiliated with some brokers. The hypothesis gets even more realistic if we try to understand who sponsors WikiFX.
There are no transparent built-in ads neither on the web-version of the website nor in its applications. There are no paid subscriptions for access to the database. This means that users sponsor the service with neither their attention to ads nor directly. Being the non-charity and non-governmental organization WikiFX can’t be sponsored with donations or a government. The only option that we have left is that brokers sponsor this ranking system directly, which automatically makes the whole system non-reliable and highly biased.
The only transparent method that we know WikiFX uses to collect money is sponsorship fees they collect from their offline events participants. Let’s have a look at the exhibitors of the recent WikiFX Expo in Thailand.
WikiFX Expo Exhibitors

  • TLC is a non-regulated investment platform that was founded in 2019
  • Samtrade FX is not regulated by any of the agencies that WikiFX itself lists as reliable
  • Forex4you is not regulated by any of the agencies that WikiFX itself lists as reliable
  • B2 Broker is a non-regulated broker
  • XDL FX is a non-regulated broker
  • VAT FX is a non-regulated broker
    Six out of sixteen WikiFX recent expo exhibitors do not have proper legal status according to the “standards” of WikiFX itself. This fact does not prevent them from promoting the services of these companies at their offline events. This conspicuous fact tells a lot about the attitude of WikiFX to common traders looking for reliable partners. Reputation is nothing but a sale item for this brokers’ ranking system.

Murky & Murkier

So far we’ve only discussed the facts that anyone can check himself using free tools and sources.
It was not that difficult to discover that WikiFX uses non-transparent standards for brokers’ appraisal. It ignores the specifics of some brokerages lowering their scores due to non-standard derivatives they offer to trade or custom trading software. It also promotes non-regulated and non-licensed brokerages, which is 100% against the declared WikiFX values and mission.
The rumors are that this company was also noticed blackmailing brokers with the purpose of making them pay for better reviews at WikiFX. There are also some signs that indicate suspicious promotion of WikiFX platform through social media and Quora. Some of the WikiFX positive reviews also look highly suspicious. All of the above is a matter of further investigation.
Nevertheless, thousands of users keep relying on the information provided by this scam ranking system. It may even look like all these users are satisfied. WikiFX has got 4.5 starts at Google Play, which sounds good enough. However, positive WikiFX reviews use similar semantics and are also highly suspicious. Despite the high average grade, Google Play finds the following messages to be most relevant and brings them to the top of WikiFX reviews:
Google Play most relevant WikiFX reviews

https://preview.redd.it/kftutvcl44l51.png?width=532&format=png&auto=webp&s=1ccb74ee156388285a2fab711dd604945c04377c

You’ve got the facts now and it’s time to make your own conclusions.

submitted by WorriedXVanilla to u/WorriedXVanilla [link] [comments]

HOW TO TRADE BINARY OPTIONS ON THISOPTION BINARY EXCHANGE

HOW TO TRADE BINARY OPTIONS ON THISOPTION BINARY EXCHANGE
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USEFUL LINKS ▶ ️ Extons: https://www.extons.io ▶ ️ THISOPTION: https://thisoption.com ▶ ️ Whitepaper: https://www.extons.io/whitepaper ▶ ️ Telegram: https://t.me/thisoption ▶ ️ ANN : https://bitcointalk.org/index.php?topic=5263768 ▶ ️ Medium: https://medium.com/@thisoption.com AUTHOR’S DETAILS Bitcointalk Username- Vunchan Bitcointalk Profile link- https://bitcointalk.org/index.php?action=profile;u=2774784;sa=summary
https://preview.redd.it/f0rivf1w2ck51.jpg?width=1080&format=pjpg&auto=webp&s=14cb21972b5af5d52d51390cf5b9d02f7ca88969
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submitted by Irishsommy to Cryptocoin [link] [comments]

forex factory

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There are numerous advantages of the Forex market over several kinds of financial trading. There is 1 type that springs to mind, when speaking about different investments that are accessible to everyone. Even the Forex or foreign exchange market has many advantages over other types of trading. Because it is an OTC (over-the-counter) market, the Forex market is open 24 hours daily, unlike the regular stock or commodity markets. Most investments require a substantial quantity of money until you're able to make the most of this investment opportunity. Everyone can enter the marketplace with just as little as 1 to trade a"micro account", which enables you to open positions of 1,000 units. One lot of 1,000 units of currency is equivalent to 1 contract from account that is micro. Each"pip" or"tick" (lowest currency rate movement down or up ) is worth $0.10 profit or decrease, depending on wheather you are going with the Marketplace or against it Forex is among the most liquid markets. best forex brokers in uk
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submitted by elzajohn265 to u/elzajohn265 [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]

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