Forex Market Size: A Trader's Advantage

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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]

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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]

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When should I choose a Hedged ETF and when not?

When should I choose a Hedged ETF and when not?
Hi there, a couple of things that might confuse and overwhelm to new investors are currency risk and hedging. If you are not familiar with these terms, here and here you have some reading.
Even experienced investors that known the theory finds the choice (or not) of a Hedged ETF sometimes cumbersome due to the back and forth logic that you have to follow in your head. Usually a graph helps to clarify the relation between Currency, Hedged and UnHedged ETFs.

So, decide between both options (if available) is inconvenient?
Well, there are some rules and general recommendations for different cases based on common sense and empirical evidence. That's why I tried to put those recommendations on a flow diagram.
I would appreciate your feedback because this is the first version and it may contain errors so, as usual, before using any information to invest, double check it!

Points to keep in mind (kind of a disclaimer):
  • Currency hedging is a way to manage risk, not to add return and so is the idea of this flow diagram.
  • You won't always find a Hedged version of the ETF that you want.
  • As always, this is a way to help you taking best decisions based on YOUR broad goal. This is not an exact science and one size NEVER fits all.
  • The flow was thought to compare ETFs replicating the same index but Hedged (if available) and Unhedged versions.
  • The logic of the flow is at ETF level, not at Portfolio level. That means that if you apply it to your Portfolio of, lets say 5 ETFs, that does not imply that you should hedge or unhedge your whole Portfolio.
  • Hedged ETFs are always more expensive than Unheged's of the same Index and Provider. Therefor I used that fact for the last decision box, but is purely subjective and at that point you can use any other parameter to tiebreak: Size, Volume, Spread, etc. or all of them.

Change ideas are welcomed!:

Version 2
EDIT 19/04/2020: Version 2 with 1st block addition. An example of why
submitted by Iam_an_80s_guy to ETFs_Europe [link] [comments]

TATA Consumer Products Shares Complete Fundamental Analysis

TATA Consumer Products was formed when Tata Chemicals de-merged it’s Consumer Products Business (CPB) as a going concern into Tata Global Beverages in an-all equity transaction in February 2020.
The overall aim of the transaction was to create a sizeable Consumer player in the Indian market with enhanced scale and financial strength.
The company currently manufactures Tea, Coffee, Liquid beverages, Salt, Spices and Staples and operates a joint venture with Starbucks in India. Some of their major brands include Tata Tea, Tetley, Himalayan Water, Tata Salt and Tata Sampann.
After the restructuring, the company’s addressable market has increased by 300% and 90% of its total revenue comes from branded business. This will give them a cost advantage due to the economies of scale.
The company owns some popular brands like Tata Salt which is №1 salt brand in India. Their Tata Tea brand is №1 in terms of volume and №2 in terms of value. Tetley brand is among the top 3 in the UK and Canada along with Eight O’ Clock which is the 4th largest player in coffee bags in the US.
Some focused brands like Tata Tea Agni and Spice Mix continue to grow in double digits in India. Overall the company has a wide global presence along with strong branding and backing of the 150-year-old Tata conglomerate. Therefore this category gets 4 stars in TATA consumer shares fundamental analysis.
I have evaluated the company on 10 fundamental categories and each has been given a rating out of 5 stars. From this, I have arrived at a combined stock rating for the company.
This is the summary of the analysis. You can read the detailed analysis and ratings on the source link to my blog.
Source: TATA Consumer Shares Fundamental Analysis and Future Outlook
Some insights for the coming years from management discussion & analysis (MD&A) and con calls are as follows.
The company shows promising growth opportunities and business synergies, but faces headwinds due to weakening demand and the global disruption in supply chains due to COVID-19. The situation may start improving once the demand picks up and supply chains are restored.
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submitted by DhairyaDamjiPatel to EquityResearchIndia [link] [comments]

Concerns on DeFi

Hello,
Just wanted to share some of my legitimate concerns around decentralised finance with the broader community. To be quite clear - I am a huge fan of Ethereum and DeFi and believe this could lead to the future of finance. However, I do worry if there is a circle jerk within the community that could lead to a lack of adoption in the coming months. I will try and keep this as short as possible. By all means, do understand I am coming from the pov of sharing constructive criticism and not dissing on the efforts of those building.
If you are solving for these problems in particular, please ping me and I'd love to talk further with you
  1. On-ramps The largest problem for much of the developing world is the fact that while DAI can without doubt give dollar exposure, acquiring them is quite a difficult task. In fact if DAI demand goes up substantially in a region, it could have premiums of upto 25% which makes it a bad on-ramp tool without necessary liquidity in place. (check Wazir X p2p USDT rates in India for context). This problem is not endemic to DAI alone but is applicable to stable tokens of all kinds. With regional regulations in nations like Thailand, Vietnam, Indonesia, Phillipines, Malaysia and India not being clear on stable tokens in particular, it becomes an uphill task for developers to build on it. More importantly, it becomes less appealing for the average individual to use. Now typically this wouldnt matter if the point of DeFi was to be a niche project aimed at a small community. However, DeFi has the power to be the first mass market blockchain tool for the world. Consider it to be the "e-mail" or "napster" moment for blockchain based applications. IF we are to scale then on-ramps and off-ramps need to be solved for. This can happen only and if the community begins engaging with regional regulators and exchanges begin providing solutions. In an ideal world, acquiring stable tokens should be as easy as venmo'ing someone $10 dollar and receiving say $9.90 (1% fee) in Incento (incento.io seems interesting, not shilling but do check them out!)
  2. Incumbent Efficiency In order for a system to scale past a certain point, the value add it brings needs to be considerably higher than the incumbent. Depending on the size of the remittance market, there exists multiple payments and wire transfer corridors set up by startups today to solve for quick transfers. In fact during times when a blockchain like those of Ethereum's or Bitcoin's are clogged - transferwise can prove to be a cheaper, better alternative than tokens. This is not to diss on the fact that decentralisation and immutability has a price attached to them, but for the average user today alternatives are far better than token based products. The challenge when it comes to scaling - especially towards L2 is whether products can be incrementally better than their incumbents in exchange for some trade offs (eg: relative centralisation in lightning for minimal fees and quicker confirmation). Today's DeFi apps have to make a call between being ideological and efficient because it seems there is a price attached to ideology and retail users aren't willing to pay that price.
  3. Slippage Much props to Kyber and Uniswap for solving for this on most DeFi apps but there remains challenges in how settlements for defi instruments today happen. As the scale of volume on products like DyDx and Nuo increase and the expected accuracy at which trade settlements are anticipated to be limited to, there will come a point in time where traditional market-makers will have to enter the system. At $500 million the DeFi space's largest traders constantly reel from price slippages and a lack of liquidity. How can we scale to $10 billion or $1 trillion without the kind of liquidity that could instill confidence in large whales. In order to solve this, there will come a point in time where hedge funds and dark pool service providers from traditional markets begin targetting DeFi instruments. The community will likely see this as an all out assault on the principles DeFi has been built upon but to be honest, this will be a quintessential requirement for the space to grow. We are seeing an early variant of this already with the likes of Cred raising $50 million to re-issue as debt (yes, not entirely DeFi) or with MakerDAO having VC partners that come from traditional backgrounds. Even in the case of products like Dharma and compound, the market-makers are hedge funds. We will see a convergence of traditional market products and DeFi soon. That will be an exciting phase imo.
  4. Product-Market Fit Debt is one of the oldest financial innovations in the markets. Quite literally. Some of the first ever tablets recorded debt obligations and as such have been quintessential to the growth of human civilisation. MakerDAO's proposition of issuing token backed debt is by all means revolutionary but in order to see true scale, DeFi has to grow beyond the individuals that can give assets as collateral. I reckon there will be a new layer of growth for DeFi soon that will be powered with open-data and AI. One where an individual's credit worthiness could be checked with the individual's permission on basis of on-chain tx activity and self sovereign identity. I also see a market for AI based lending rate predictions and forex management by central banks. Autonomous agents can realistically analyse tx's in and out of a country, account for macro-economic indicators and optimise internal lending rates and foreign currency reserves. Ofcourse it is too early for any of this to take place but within the next decade our markets will be far more (i) closer due to globalisation and (ii) automated due to improvements in AI. DeFi is all well and good but if we are going to beat the same old drums of economic instruments that were created thousands of years back, there may be no real value proposition here. LsDAI, rDAI, CDAI, DAI... are all interesting but the average user sees no value yet. Which makes me wonder if we are sitting around patting each other's back before we see something productive (a unicorn from the DeFi ecosystem perhaps?)
  5. Scale 4.5 billion. That's the number of unbanked individuals that can be catered to with an L2 payments solution powered by Ethereum. Challenges? On-ramp, storage of private keys, user education and bloody hell - marketing and user education. Emphasis on the last 2 because I feel not much focus is given on it. We can no longer build and hope the markets come. We are in an era of Zombie startups where startups with north of $100 million+ valuations in Mcap, that raised north of $10million in 2017 from ICOs are sitting on ~1000 users a month. People think the alts blood seepage is done but it is likely that that bleeding wont stop until we find users. And when we do find users, we cant expect them to be using a gazillion tokens, each with weird token economics and even more complex functioning to be using them. Standardising of token interactions through wallets and interoperability will solve for these challenges but its time we asked what are the biggest problems DeFi can solve today? Here are some hints.. NFT based Income share agreements -Non collateralised debt for gig economy corporations that are registered as DAOs -DAO treasury management -Forex off-ramps for tourists (P2P) More on these later..
Just wanted to share my $0.02.
submitted by WiseAcanthisitta5 to ethfinance [link] [comments]

Stockmarket 2020 - Random 🇵🇹

Parece que existe uma montanha de emoções actualmente nos mercados financeiros. (Seja forex, opções...) Se repararmos também podemos utilizar a análise fundamental o que indica que potenciais investidores neste momento estão de volta. No entanto existe um ditado muito antigo aqui na tuga "tudo o que sobe muito rápido caí muito rápido" Veremos o comportamento dos investidores face a estes novos pacotes que o governo apresentou e esperemos olhar também para a análise técnica para perceber o volume de pessoas a investir neste momento (situação importante).
submitted by Drop_Out_Director to u/Drop_Out_Director [link] [comments]

MAME 0.210

MAME 0.210

It’s time for the delayed release of MAME 0.210, marking the end of May. This month, we’ve got lots of fixes for issues with supported systems, as well as some interesting additions. Newly added hand-held and tabletop games include Tronica’s Shuttle Voyage and Space Rescue, Mattel’s Computer Chess, and Parker Brothers’ Talking Baseball and Talking Football. On the arcade side, we’ve added high-level emulation of Gradius on Bubble System hardware and a prototype of the Neo Geo game Viewpoint. For this release, Jack Li has contributed an auto-fire plugin, providing additional functionality over the built-in auto-fire feature.
A number of systems have had been promoted to working, or had critical issues fixed, including the Heathkit H8, Lola 8A, COSMAC Microkit, the Soviet PC clone EC-1840, Zorba, and COMX 35. MMU issues affecting Apollo and Mac operating systems have been addressed. Other notable improvements include star field emulation in Tutankham, further progress on SGI emulation, Sega Saturn video improvements, write support for the CoCo OS-9 disk image format, and preliminary emulation for MP3 audio on Konami System 573 games.
There are lots of software list additions this month. Possibly most notable is the first dump of a Hanimex Pencil II cartridge, thanks to the silicium.org team. Another batch of cleanly cracked and original Apple II software has been added, along with more ZX Spectrum +3 software, and a number of Colour Genie cassette titles.
That’s all we’ve got space for here, but there are lots more bug fixes, alternate versions of supported arcade games, and general code quality improvements. As always, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

MAME 0.210

MAME 0.210

It’s time for the delayed release of MAME 0.210, marking the end of May. This month, we’ve got lots of fixes for issues with supported systems, as well as some interesting additions. Newly added hand-held and tabletop games include Tronica’s Shuttle Voyage and Space Rescue, Mattel’s Computer Chess, and Parker Brothers’ Talking Baseball and Talking Football. On the arcade side, we’ve added high-level emulation of Gradius on Bubble System hardware and a prototype of the Neo Geo game Viewpoint. For this release, Jack Li has contributed an auto-fire plugin, providing additional functionality over the built-in auto-fire feature.
A number of systems have had been promoted to working, or had critical issues fixed, including the Heathkit H8, Lola 8A, COSMAC Microkit, the Soviet PC clone EC-1840, Zorba, and COMX 35. MMU issues affecting Apollo and Mac operating systems have been addressed. Other notable improvements include star field emulation in Tutankham, further progress on SGI emulation, Sega Saturn video improvements, write support for the CoCo OS-9 disk image format, and preliminary emulation for MP3 audio on Konami System 573 games.
There are lots of software list additions this month. Possibly most notable is the first dump of a Hanimex Pencil II cartridge, thanks to the silicium.org team. Another batch of cleanly cracked and original Apple II software has been added, along with more ZX Spectrum +3 software, and a number of Colour Genie cassette titles.
That’s all we’ve got space for here, but there are lots more bug fixes, alternate versions of supported arcade games, and general code quality improvements. As always, you can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

Clones promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to MAME [link] [comments]

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Estrategia Precio y Volumen en Forex

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