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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]

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
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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
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Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press
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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
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Where is Bitcoin Going and When?

Where is Bitcoin Going and When?

The Federal Reserve and the United States government are pumping extreme amounts of money into the economy, already totaling over $484 billion. They are doing so because it already had a goal to inflate the United States Dollar (USD) so that the market can continue to all-time highs. It has always had this goal. They do not care how much inflation goes up by now as we are going into a depression with the potential to totally crash the US economy forever. They believe the only way to save the market from going to zero or negative values is to inflate it so much that it cannot possibly crash that low. Even if the market does not dip that low, inflation serves the interest of powerful people.
The impending crash of the stock market has ramifications for Bitcoin, as, though there is no direct ongoing-correlation between the two, major movements in traditional markets will necessarily affect Bitcoin. According to the Blockchain Center’s Cryptocurrency Correlation Tool, Bitcoin is not correlated with the stock market. However, when major market movements occur, they send ripples throughout the financial ecosystem which necessary affect even ordinarily uncorrelated assets.
Therefore, Bitcoin will reach X price on X date after crashing to a price of X by X date.

Stock Market Crash

The Federal Reserve has caused some serious consternation with their release of ridiculous amounts of money in an attempt to buoy the economy. At face value, it does not seem to have any rationale or logic behind it other than keeping the economy afloat long enough for individuals to profit financially and politically. However, there is an underlying basis to what is going on which is important to understand in order to profit financially.
All markets are functionally price probing systems. They constantly undergo a price-discovery process. In a fiat system, money is an illusory and a fundamentally synthetic instrument with no intrinsic value – similar to Bitcoin. The primary difference between Bitcoin is the underlying technology which provides a slew of benefits that fiat does not. Fiat, however, has an advantage in being able to have the support of powerful nation-states which can use their might to insure the currency’s prosperity.
Traditional stock markets are composed of indices (pl. of index). Indices are non-trading market instruments which are essentially summaries of business values which comprise them. They are continuously recalculated throughout a trading day, and sometimes reflected through tradable instruments such as Exchange Traded Funds or Futures. Indices are weighted by market capitalizations of various businesses.
Price theory essentially states that when a market fails to take out a new low in a given range, it will have an objective to take out the high. When a market fails to take out a new high, it has an objective to make a new low. This is why price-time charts go up and down, as it does this on a second-by-second, minute-by-minute, day-by-day, and even century-by-century basis. Therefore, market indices will always return to some type of bull market as, once a true low is formed, the market will have a price objective to take out a new high outside of its’ given range – which is an all-time high. Instruments can only functionally fall to zero, whereas they can grow infinitely.
So, why inflate the economy so much?
Deflation is disastrous for central banks and markets as it raises the possibility of producing an overall price objective of zero or negative values. Therefore, under a fractional reserve system with a fiat currency managed by a central bank – the goal of the central bank is to depreciate the currency. The dollar is manipulated constantly with the intention of depreciating its’ value.
Central banks have a goal of continued inflated fiat values. They tend to ordinarily contain it at less than ten percent (10%) per annum in order for the psyche of the general populace to slowly adjust price increases. As such, the markets are divorced from any other logic. Economic policy is the maintenance of human egos, not catering to fundamental analysis. Gross Domestic Product (GDP) growth is well-known not to be a measure of actual growth or output. It is a measure of increase in dollars processed. Banks seek to produce raising numbers which make society feel like it is growing economically, making people optimistic. To do so, the currency is inflated, though inflation itself does not actually increase growth. When society is optimistic, it spends and engages in business – resulting in actual growth. It also encourages people to take on credit and debts, creating more fictional fiat.
Inflation is necessary for markets to continue to reach new heights, generating positive emotional responses from the populace, encouraging spending, encouraging debt intake, further inflating the currency, and increasing the sale of government bonds. The fiat system only survives by generating more imaginary money on a regular basis.
Bitcoin investors may profit from this by realizing that stock investors as a whole always stand to profit from the market so long as it is managed by a central bank and does not collapse entirely. If those elements are filled, it has an unending price objective to raise to new heights. It also allows us to realize that this response indicates that the higher-ups believe that the economy could crash in entirety, and it may be wise for investors to have multiple well-thought-out exit strategies.

Economic Analysis of Bitcoin

The reason why the Fed is so aggressively inflating the economy is due to fears that it will collapse forever or never rebound. As such, coupled with a global depression, a huge demand will appear for a reserve currency which is fundamentally different than the previous system. Bitcoin, though a currency or asset, is also a market. It also undergoes a constant price-probing process. Unlike traditional markets, Bitcoin has the exact opposite goal. Bitcoin seeks to appreciate in value and not depreciate. This has a quite different affect in that Bitcoin could potentially become worthless and have a price objective of zero.
Bitcoin was created in 2008 by a now famous mysterious figure known as Satoshi Nakamoto and its’ open source code was released in 2009. It was the first decentralized cryptocurrency to utilize a novel protocol known as the blockchain. Up to one megabyte of data may be sent with each transaction. It is decentralized, anonymous, transparent, easy to set-up, and provides myriad other benefits. Bitcoin is not backed up by anything other than its’ own technology.
Bitcoin is can never be expected to collapse as a framework, even were it to become worthless. The stock market has the potential to collapse in entirety, whereas, as long as the internet exists, Bitcoin will be a functional system with a self-authenticating framework. That capacity to persist regardless of the actual price of Bitcoin and the deflationary nature of Bitcoin means that it has something which fiat does not – inherent value.
Bitcoin is based on a distributed database known as the “blockchain.” Blockchains are essentially decentralized virtual ledger books, replete with pages known as “blocks.” Each page in a ledger is composed of paragraph entries, which are the actual transactions in the block.
Blockchains store information in the form of numerical transactions, which are just numbers. We can consider these numbers digital assets, such as Bitcoin. The data in a blockchain is immutable and recorded only by consensus-based algorithms. Bitcoin is cryptographic and all transactions are direct, without intermediary, peer-to-peer.
Bitcoin does not require trust in a central bank. It requires trust on the technology behind it, which is open-source and may be evaluated by anyone at any time. Furthermore, it is impossible to manipulate as doing so would require all of the nodes in the network to be hacked at once – unlike the stock market which is manipulated by the government and “Market Makers”. Bitcoin is also private in that, though the ledge is openly distributed, it is encrypted. Bitcoin’s blockchain has one of the greatest redundancy and information disaster recovery systems ever developed.
Bitcoin has a distributed governance model in that it is controlled by its’ users. There is no need to trust a payment processor or bank, or even to pay fees to such entities. There are also no third-party fees for transaction processing. As the ledge is immutable and transparent it is never possible to change it – the data on the blockchain is permanent. The system is not easily susceptible to attacks as it is widely distributed. Furthermore, as users of Bitcoin have their private keys assigned to their transactions, they are virtually impossible to fake. No lengthy verification, reconciliation, nor clearing process exists with Bitcoin.
Bitcoin is based on a proof-of-work algorithm. Every transaction on the network has an associated mathetical “puzzle”. Computers known as miners compete to solve the complex cryptographic hash algorithm that comprises that puzzle. The solution is proof that the miner engaged in sufficient work. The puzzle is known as a nonce, a number used only once. There is only one major nonce at a time and it issues 12.5 Bitcoin. Once it is solved, the fact that the nonce has been solved is made public.
A block is mined on average of once every ten minutes. However, the blockchain checks every 2,016,000 minutes (approximately four years) if 201,600 blocks were mined. If it was faster, it increases difficulty by half, thereby deflating Bitcoin. If it was slower, it decreases, thereby inflating Bitcoin. It will continue to do this until zero Bitcoin are issued, projected at the year 2140. On the twelfth of May, 2020, the blockchain will halve the amount of Bitcoin issued when each nonce is guessed. When Bitcoin was first created, fifty were issued per block as a reward to miners. 6.25 BTC will be issued from that point on once each nonce is solved.
Unlike fiat, Bitcoin is a deflationary currency. As BTC becomes scarcer, demand for it will increase, also raising the price. In this, BTC is similar to gold. It is predictable in its’ output, unlike the USD, as it is based on a programmed supply. We can predict BTC’s deflation and inflation almost exactly, if not exactly. Only 21 million BTC will ever be produced, unless the entire network concedes to change the protocol – which is highly unlikely.
Some of the drawbacks to BTC include congestion. At peak congestion, it may take an entire day to process a Bitcoin transaction as only three to five transactions may be processed per second. Receiving priority on a payment may cost up to the equivalent of twenty dollars ($20). Bitcoin mining consumes enough energy in one day to power a single-family home for an entire week.

Trading or Investing?

The fundamental divide in trading revolves around the question of market structure. Many feel that the market operates totally randomly and its’ behavior cannot be predicted. For the purposes of this article, we will assume that the market has a structure, but that that structure is not perfect. That market structure naturally generates chart patterns as the market records prices in time. In order to determine when the stock market will crash, causing a major decline in BTC price, we will analyze an instrument, an exchange traded fund, which represents an index, as opposed to a particular stock. The price patterns of the various stocks in an index are effectively smoothed out. In doing so, a more technical picture arises. Perhaps the most popular of these is the SPDR S&P Standard and Poor 500 Exchange Traded Fund ($SPY).
In trading, little to no concern is given about value of underlying asset. We are concerned primarily about liquidity and trading ranges, which are the amount of value fluctuating on a short-term basis, as measured by volatility-implied trading ranges. Fundamental analysis plays a role, however markets often do not react to real-world factors in a logical fashion. Therefore, fundamental analysis is more appropriate for long-term investing.
The fundamental derivatives of a chart are time (x-axis) and price (y-axis). The primary technical indicator is price, as everything else is lagging in the past. Price represents current asking price and incorrectly implementing positions based on price is one of the biggest trading errors.
Markets and currencies ordinarily have noise, their tendency to back-and-fill, which must be filtered out for true pattern recognition. That noise does have a utility, however, in allowing traders second chances to enter favorable positions at slightly less favorable entry points. When you have any market with enough liquidity for historical data to record a pattern, then a structure can be divined. The market probes prices as part of an ongoing price-discovery process. Market technicians must sometimes look outside of the technical realm and use visual inspection to ascertain the relevance of certain patterns, using a qualitative eye that recognizes the underlying quantitative nature
Markets and instruments rise slower than they correct, however they rise much more than they fall. In the same vein, instruments can only fall to having no worth, whereas they could theoretically grow infinitely and have continued to grow over time. Money in a fiat system is illusory. It is a fundamentally synthetic instrument which has no intrinsic value. Hence, the recent seemingly illogical fluctuations in the market.
According to trade theory, the unending purpose of a market or instrument is to create and break price ranges according to the laws of supply and demand. We must determine when to trade based on each market inflection point as defined in price and in time as opposed to abandoning the trend (as the contrarian trading in this sub often does). Time and Price symmetry must be used to be in accordance with the trend. When coupled with a favorable risk to reward ratio, the ability to stay in the market for most of the defined time period, and adherence to risk management rules; the trader has a solid methodology for achieving considerable gains.
We will engage in a longer term market-oriented analysis to avoid any time-focused pressure. The Bitcoin market is open twenty-four-hours a day, so trading may be done when the individual is ready, without any pressing need to be constantly alert. Let alone, we can safely project months in advance with relatively high accuracy. Bitcoin is an asset which an individual can both trade and invest, however this article will be focused on trading due to the wide volatility in BTC prices over the short-term.

Technical Indicator Analysis of Bitcoin

Technical indicators are often considered self-fulfilling prophecies due to mass-market psychology gravitating towards certain common numbers yielded from them. They are also often discounted when it comes to BTC. That means a trader must be especially aware of these numbers as they can prognosticate market movements. Often, they are meaningless in the larger picture of things.
  • Volume – derived from the market itself, it is mostly irrelevant. The major problem with volume for stocks is that the US market open causes tremendous volume surges eradicating any intrinsic volume analysis. This does not occur with BTC, as it is open twenty-four-seven. At major highs and lows, the market is typically anemic. Most traders are not active at terminal discretes (peaks and troughs) because of levels of fear. Volume allows us confidence in time and price symmetry market inflection points, if we observe low volume at a foretold range of values. We can rationalize that an absolute discrete is usually only discovered and anticipated by very few traders. As the general market realizes it, a herd mentality will push the market in the direction favorable to defending it. Volume is also useful for swing trading, as chances for swing’s validity increases if an increase in volume is seen on and after the swing’s activation. Volume is steadily decreasing. Lows and highs are reached when volume is lower.
Therefore, due to the relatively high volume on the 12th of March, we can safely determine that a low for BTC was not reached.
  • VIX – Volatility Index, this technical indicator indicates level of fear by the amount of options-based “insurance” in portfolios. A low VIX environment, less than 20 for the S&P index, indicates a stable market with a possible uptrend. A high VIX, over 20, indicates a possible downtrend. VIX is essentially useless for BTC as BTC-based options do not exist. It allows us to predict the market low for $SPY, which will have an indirect impact on BTC in the short term, likely leading to the yearly low. However, it is equally important to see how VIX is changing over time, if it is decreasing or increasing, as that indicates increasing or decreasing fear. Low volatility allows high leverage without risk or rest. Occasionally, markets do rise with high VIX.
As VIX is unusually high, in the forties, we can be confident that a downtrend for the S&P 500 is imminent.
  • RSI (Relative Strength Index): The most important technical indicator, useful for determining highs and lows when time symmetry is not availing itself. Sometimes analysis of RSI can conflict in different time frames, easiest way to use it is when it is at extremes – either under 30 or over 70. Extremes can be used for filtering highs or lows based on time-and-price window calculations. Highly instructive as to major corrective clues and indicative of continued directional movement. Must determine if longer-term RSI values find support at same values as before. It is currently at 73.56.
  • Secondly, RSI may be used as a high or low filter, to observe the level that short-term RSI reaches in counter-trend corrections. Repetitions based on market movements based on RSI determine how long a trade should be held onto. Once a short term RSI reaches an extreme and stay there, the other RSI’s should gradually reach the same extremes. Once all RSI’s are at extreme highs, a trend confirmation should occur and RSI’s should drop to their midpoint.

Trend Definition Analysis of Bitcoin

Trend definition is highly powerful, cannot be understated. Knowledge of trend logic is enough to be a profitable trader, yet defining a trend is an arduous process. Multiple trends coexist across multiple time frames and across multiple market sectors. Like time structure, it makes the underlying price of the instrument irrelevant. Trend definitions cannot determine the validity of newly formed discretes. Trend becomes apparent when trades based in counter-trend inflection points continue to fail.
Downtrends are defined as an instrument making lower lows and lower highs that are recurrent, additive, qualified swing setups. Downtrends for all instruments are similar, except forex. They are fast and complete much quicker than uptrends. An average downtrend is 18 months, something which we will return to. An uptrend inception occurs when an instrument reaches a point where it fails to make a new low, then that low will be tested. After that, the instrument will either have a deep range retracement or it may take out the low slightly, resulting in a double-bottom. A swing must eventually form.
A simple way to roughly determine trend is to attempt to draw a line from three tops going upwards (uptrend) or a line from three bottoms going downwards (downtrend). It is not possible to correctly draw a downtrend line on the BTC chart, but it is possible to correctly draw an uptrend – indicating that the overall trend is downwards. The only mitigating factor is the impending stock market crash.

Time Symmetry Analysis of Bitcoin

Time is the movement from the past through the present into the future. It is a measurement in quantified intervals. In many ways, our perception of it is a human construct. It is more powerful than price as time may be utilized for a trade regardless of the market inflection point’s price. Were it possible to perfectly understand time, price would be totally irrelevant due to the predictive certainty time affords. Time structure is easier to learn than price, but much more difficult to apply with any accuracy. It is the hardest aspect of trading to learn, but also the most rewarding.
Humans do not have the ability to recognize every time window, however the ability to define market inflection points in terms of time is the single most powerful trading edge. Regardless, price should not be abandoned for time alone. Time structure analysis It is inherently flawed, as such the markets have a fail-safe, which is Price Structure. Even though Time is much more powerful, Price Structure should never be completely ignored. Time is the qualifier for Price and vice versa. Time can fail by tricking traders into counter-trend trading.
Time is a predestined trade quantifier, a filter to slow trades down, as it allows a trader to specifically focus on specific time windows and rest at others. It allows for quantitative measurements to reach deterministic values and is the primary qualifier for trends. Time structure should be utilized before price structure, and it is the primary trade criterion which requires support from price. We can see price structure on a chart, as areas of mathematical support or resistance, but we cannot see time structure.
Time may be used to tell us an exact point in the future where the market will inflect, after Price Theory has been fulfilled. In the present, price objectives based on price theory added to possible future times for market inflection points give us the exact time of market inflection points and price.
Time Structure is repetitions of time or inherent cycles of time, occurring in a methodical way to provide time windows which may be utilized for inflection points. They are not easily recognized and not easily defined by a price chart as measuring and observing time is very exact. Time structure is not a science, yet it does require precise measurements. Nothing is certain or definite. The critical question must be if a particular approach to time structure is currently lucrative or not.
We will measure it in intervals of 180 bars. Our goal is to determine time windows, when the market will react and when we should pay the most attention. By using time repetitions, the fact that market inflection points occurred at some point in the past and should, therefore, reoccur at some point in the future, we should obtain confidence as to when SPY will reach a market inflection point. Time repetitions are essentially the market’s memory. However, simply measuring the time between two points then trying to extrapolate into the future does not work. Measuring time is not the same as defining time repetitions. We will evaluate past sessions for market inflection points, whether discretes, qualified swings, or intra-range. Then records the times that the market has made highs or lows in a comparable time period to the future one seeks to trade in.
What follows is a time Histogram – A grouping of times which appear close together, then segregated based on that closeness. Time is aligned into combined histogram of repetitions and cycles, however cycles are irrelevant on a daily basis. If trading on an hourly basis, do not use hours.
  • Yearly Lows (last seven years): 1/1/13, 4/10/14, 1/15/15, 1/17/16, 1/1/17, 12/15/18, 2/6/19
  • Monthly Mode: 1, 1, 1, 1, 2, 4, 12
  • Daily Mode: 1, 1, 6, 10, 15, 15, 17
  • Monthly Lows (for the last year): 3/12/20 (10:00pm), 2/28/20 (7:09am), 1/2/20 (8:09pm), 12/18/19 (8:00am), 11/25/19 (1:00am), 10/24/19 (2:59am), 9/30/19 (2:59am), 8/29,19 (4:00am), 7/17/19 (7:59am), 6/4/19 (5:59pm), 5/1/19 (12:00am), 4/1/19 (12:00am)
  • Daily Lows Mode for those Months: 1, 1, 2, 4, 12, 17, 18, 24, 25, 28, 29, 30
  • Hourly Lows Mode for those Months (Military time): 0100, 0200, 0200, 0400, 0700, 0700, 0800, 1200, 1200, 1700, 2000, 2200
  • Minute Lows Mode for those Months: 00, 00, 00, 00, 00, 00, 09, 09, 59, 59, 59, 59
  • Day of the Week Lows (last twenty-six weeks):
Weighted Times are repetitions which appears multiple times within the same list, observed and accentuated once divided into relevant sections of the histogram. They are important in the presently defined trading time period and are similar to a mathematical mode with respect to a series. Phased times are essentially periodical patterns in histograms, though they do not guarantee inflection points
Evaluating the yearly lows, we see that BTC tends to have its lows primarily at the beginning of every year, with a possibility of it being at the end of the year. Following the same methodology, we get the middle of the month as the likeliest day. However, evaluating the monthly lows for the past year, the beginning and end of the month are more likely for lows.
Therefore, we have two primary dates from our histogram.
1/1/21, 1/15/21, and 1/29/21
2:00am, 8:00am, 12:00pm, or 10:00pm
In fact, the high for this year was February the 14th, only thirty days off from our histogram calculations.
The 8.6-Year Armstrong-Princeton Global Economic Confidence model states that 2.15 year intervals occur between corrections, relevant highs and lows. 2.15 years from the all-time peak discrete is February 9, 2020 – a reasonably accurate depiction of the low for this year (which was on 3/12/20). (Taking only the Armstrong model into account, the next high should be Saturday, April 23, 2022). Therefore, the Armstrong model indicates that we have actually bottomed out for the year!
Bear markets cannot exist in perpetuity whereas bull markets can. Bear markets will eventually have price objectives of zero, whereas bull markets can increase to infinity. It can occur for individual market instruments, but not markets as a whole. Since bull markets are defined by low volatility, they also last longer. Once a bull market is indicated, the trader can remain in a long position until a new high is reached, then switch to shorts. The average bear market is eighteen months long, giving us a date of August 19th, 2021 for the end of this bear market – roughly speaking. They cannot be shorter than fifteen months for a central-bank controlled market, which does not apply to Bitcoin. (Otherwise, it would continue until Sunday, September 12, 2021.) However, we should expect Bitcoin to experience its’ exponential growth after the stock market re-enters a bull market.
Terry Laundy’s T-Theory implemented by measuring the time of an indicator from peak to trough, then using that to define a future time window. It is similar to an head-and-shoulders pattern in that it is the process of forming the right side from a synthetic technical indicator. If the indicator is making continued lows, then time is recalculated for defining the right side of the T. The date of the market inflection point may be a price or indicator inflection date, so it is not always exactly useful. It is better to make us aware of possible market inflection points, clustered with other data. It gives us an RSI low of May, 9th 2020.
The Bradley Cycle is coupled with volatility allows start dates for campaigns or put options as insurance in portfolios for stocks. However, it is also useful for predicting market moves instead of terminal dates for discretes. Using dates which correspond to discretes, we can see how those dates correspond with changes in VIX.
Therefore, our timeline looks like:
  • 2/14/20 – yearly high ($10372 USD)
  • 3/12/20 – yearly low thus far ($3858 USD)
  • 5/9/20 – T-Theory true yearly low (BTC between 4863 and 3569)
  • 5/26/20 – hashrate difficulty halvening
  • 11/14/20 – stock market low
  • 1/15/21 – yearly low for BTC, around $8528
  • 8/19/21 – end of stock bear market
  • 11/26/21 – eighteen months from halvening, average peak from halvenings (BTC begins rising from $3000 area to above $23,312)
  • 4/23/22 – all-time high
Taken from my blog: http://aliamin.info/2020/
submitted by aibnsamin1 to Bitcoin [link] [comments]

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What Is Capitalism?

Capitalism is an economic system in which private individuals or businesses own capital goods. The production of goods and services is based on supply and demand in the general market—known as a market economy—rather than through central planning—known as a planned economy or command economy.
The purest form of capitalism is free market or laissez-faire capitalism. Here, private individuals are unrestrained. They may determine where to invest, what to produce or sell, and at which prices to exchange goods and services. The laissez-faire marketplace operates without checks or controls.
Today, most countries practice a mixed capitalist system that includes some degree of government regulation of business and ownership of select industries.
Volume 75% 2:05

Capitalism

Understanding Capitalism

Functionally speaking, capitalism is one process by which the problems of economic production and resource distribution might be resolved. Instead of planning economic decisions through centralized political methods, as with socialism or feudalism, economic planning under capitalism occurs via decentralized and voluntary decisions.

KEY TAKEAWAYS

  • Capitalism is an economic system characterized by private ownership of the means of production, especially in the industrial sector.
  • Capitalism depends on the enforcement of private property rights, which provide incentives for investment in and productive use of productive capital.
  • Capitalism developed historically out of previous systems of feudalism and mercantilism in Europe, and dramatically expanded industrialization and the large-scale availability of mass-market consumer goods.
  • Pure capitalism can be contrasted with pure socialism (where all means of production are collective or state-owned) and mixed economies (which lie on a continuum between pure capitalism and pure socialism).
  • The real-world practice of capitalism typically involves some degree of so-called “crony capitalism” due to demands from business for favorable government intervention and governments’ incentive to intervene in the economy.

Capitalism and Private Property

Private property rights are fundamental to capitalism. Most modern concepts of private property stem from John Locke's theory of homesteading, in which human beings claim ownership through mixing their labor with unclaimed resources. Once owned, the only legitimate means of transferring property are through voluntary exchange, gifts, inheritance, or re-homesteading of abandoned property.
Private property promotes efficiency by giving the owner of resources an incentive to maximize the value of their property. So, the more valuable the resource is, the more trading power it provides the owner. In a capitalist system, the person who owns the property is entitled to any value associated with that property.
For individuals or businesses to deploy their capital goods confidently, a system must exist that protects their legal right to own or transfer private property. A capitalist society will rely on the use of contracts, fair dealing, and tort law to facilitate and enforce these private property rights.
When a property is not privately owned but shared by the public, a problem known as the tragedy of the commons can emerge. With a common pool resource, which all people can use, and none can limit access to, all individuals have an incentive to extract as much use value as they can and no incentive to conserve or reinvest in the resource. Privatizing the resource is one possible solution to this problem, along with various voluntary or involuntary collective action approaches.

Capitalism, Profits, and Losses

Profits are closely associated with the concept of private property. By definition, an individual only enters into a voluntary exchange of private property when they believe the exchange benefits them in some psychic or material way. In such trades, each party gains extra subjective value, or profit, from the transaction.
Voluntary trade is the mechanism that drives activity in a capitalist system. The owners of resources compete with one another over consumers, who in turn, compete with other consumers over goods and services. All of this activity is built into the price system, which balances supply and demand to coordinate the distribution of resources.
A capitalist earns the highest profit by using capital goods most efficiently while producing the highest-value good or service. In this system, information about what is highest-valued is transmitted through those prices at which another individual voluntarily purchases the capitalist's good or service. Profits are an indication that less valuable inputs have been transformed into more valuable outputs. By contrast, the capitalist suffers losses when capital resources are not used efficiently and instead create less valuable outputs.

Free Enterprise or Capitalism?

Capitalism and free enterprise are often seen as synonymous. In truth, they are closely related yet distinct terms with overlapping features. It is possible to have a capitalist economy without complete free enterprise, and possible to have a free market without capitalism.
Any economy is capitalist as long as private individuals control the factors of production. However, a capitalist system can still be regulated by government laws, and the profits of capitalist endeavors can still be taxed heavily.
"Free enterprise" can roughly be understood to mean economic exchanges free of coercive government influence. Although unlikely, it is possible to conceive of a system where individuals choose to hold all property rights in common. Private property rights still exist in a free enterprise system, although the private property may be voluntarily treated as communal without a government mandate.
Many Native American tribes existed with elements of these arrangements, and within a broader capitalist economic family, clubs, co-ops, and joint-stock business firms like partnerships or corporations are all examples of common property institutions.
If accumulation, ownership, and profiting from capital is the central principle of capitalism, then freedom from state coercion is the central principle of free enterprise.

Feudalism the Root of Capitalism

Capitalism grew out of European feudalism. Up until the 12th century, less than 5% of the population of Europe lived in towns. Skilled workers lived in the city but received their keep from feudal lords rather than a real wage, and most workers were serfs for landed nobles. However, by the late Middle Ages rising urbanism, with cities as centers of industry and trade, become more and more economically important.
The advent of true wages offered by the trades encouraged more people to move into towns where they could get money rather than subsistence in exchange for labor. Families’ extra sons and daughters who needed to be put to work, could find new sources of income in the trade towns. Child labor was as much a part of the town's economic development as serfdom was part of the rural life.

Mercantilism Replaces Feudalism

Mercantilism gradually replaced the feudal economic system in Western Europe and became the primary economic system of commerce during the 16th to 18th centuries. Mercantilism started as trade between towns, but it was not necessarily competitive trade. Initially, each town had vastly different products and services that were slowly homogenized by demand over time.
After the homogenization of goods, trade was carried out in broader and broader circles: town to town, county to county, province to province, and, finally, nation to nation. When too many nations were offering similar goods for trade, the trade took on a competitive edge that was sharpened by strong feelings of nationalism in a continent that was constantly embroiled in wars.
Colonialism flourished alongside mercantilism, but the nations seeding the world with settlements were not trying to increase trade. Most colonies were set up with an economic system that smacked of feudalism, with their raw goods going back to the motherland and, in the case of the British colonies in North America, being forced to repurchase the finished product with a pseudo-currency that prevented them from trading with other nations.
It was Adam Smith who noticed that mercantilism was not a force of development and change, but a regressive system that was creating trade imbalances between nations and keeping them from advancing. His ideas for a free market opened the world to capitalism.

Growth of Industrial Capitalism

Smith's ideas were well-timed, as the Industrial Revolution was starting to cause tremors that would soon shake the Western world. The (often literal) gold mine of colonialism had brought new wealth and new demand for the products of domestic industries, which drove the expansion and mechanization of production. As technology leaped ahead and factories no longer had to be built near waterways or windmills to function, industrialists began building in the cities where there were now thousands of people to supply ready labor.
Industrial tycoons were the first people to amass their wealth in their lifetimes, often outstripping both the landed nobles and many of the money lending/banking families. For the first time in history, common people could have hopes of becoming wealthy. The new money crowd built more factories that required more labor, while also producing more goods for people to purchase.
During this period, the term "capitalism"—originating from the Latin word "capitalis," which means "head of cattle"—was first used by French socialist Louis Blanc in 1850, to signify a system of exclusive ownership of industrial means of production by private individuals rather than shared ownership.
Contrary to popular belief, Karl Marx did not coin the word "capitalism," although he certainly contributed to the rise of its use.

Industrial Capitalism's Effects

Industrial capitalism tended to benefit more levels of society rather than just the aristocratic class. Wages increased, helped greatly by the formation of unions. The standard of living also increased with the glut of affordable products being mass-produced. This growth led to the formation of a middle class and began to lift more and more people from the lower classes to swell its ranks.
The economic freedoms of capitalism matured alongside democratic political freedoms, liberal individualism, and the theory of natural rights. This unified maturity is not to say, however, that all capitalist systems are politically free or encourage individual liberty. Economist Milton Friedman, an advocate of capitalism and individual liberty, wrote in Capitalism and Freedom (1962) that "capitalism is a necessary condition for political freedom. It is not a sufficient condition."
A dramatic expansion of the financial sector accompanied the rise of industrial capitalism. Banks had previously served as warehouses for valuables, clearinghouses for long-distance trade, or lenders to nobles and governments. Now they came to serve the needs of everyday commerce and the intermediation of credit for large, long-term investment projects. By the 20th century, as stock exchanges became increasingly public and investment vehicles opened up to more individuals, some economists identified a variation on the system: financial capitalism.

Capitalism and Economic Growth

By creating incentives for entrepreneurs to reallocate away resources from unprofitable channels and into areas where consumers value them more highly, capitalism has proven a highly effective vehicle for economic growth.
Before the rise of capitalism in the 18th and 19th centuries, rapid economic growth occurred primarily through conquest and extraction of resources from conquered peoples. In general, this was a localized, zero-sum process. Research suggests average global per-capita income was unchanged between the rise of agricultural societies through approximately 1750 when the roots of the first Industrial Revolution took hold.
In subsequent centuries, capitalist production processes have greatly enhanced productive capacity. More and better goods became cheaply accessible to wide populations, raising standards of living in previously unthinkable ways. As a result, most political theorists and nearly all economists argue that capitalism is the most efficient and productive system of exchange.

Capitalism vs. Socialism

In terms of political economy, capitalism is often pitted against socialism. The fundamental difference between capitalism and socialism is the ownership and control of the means of production. In a capitalist economy, property and businesses are owned and controlled by individuals. In a socialist economy, the state owns and manages the vital means of production. However, other differences also exist in the form of equity, efficiency, and employment.

Equity

The capitalist economy is unconcerned about equitable arrangements. The argument is that inequality is the driving force that encourages innovation, which then pushes economic development. The primary concern of the socialist model is the redistribution of wealth and resources from the rich to the poor, out of fairness, and to ensure equality in opportunity and equality of outcome. Equality is valued above high achievement, and the collective good is viewed above the opportunity for individuals to advance.

Efficiency

The capitalist argument is that the profit incentive drives corporations to develop innovative new products that are desired by the consumer and have demand in the marketplace. It is argued that the state ownership of the means of production leads to inefficiency because, without the motivation to earn more money, management, workers, and developers are less likely to put forth the extra effort to push new ideas or products.

Employment

In a capitalist economy, the state does not directly employ the workforce. This lack of government-run employment can lead to unemployment during economic recessions and depressions. In a socialist economy, the state is the primary employer. During times of economic hardship, the socialist state can order hiring, so there is full employment. Also, there tends to be a stronger "safety net" in socialist systems for workers who are injured or permanently disabled. Those who can no longer work have fewer options available to help them in capitalist societies.

Mixed System vs. Pure Capitalism

When the government owns some but not all of the means of production, but government interests may legally circumvent, replace, limit, or otherwise regulate private economic interests, that is said to be a mixed economy or mixed economic system. A mixed economy respects property rights, but places limits on them.
Property owners are restricted with regards to how they exchange with one another. These restrictions come in many forms, such as minimum wage laws, tariffs, quotas, windfall taxes, license restrictions, prohibited products or contracts, direct public expropriation, anti-trust legislation, legal tender laws, subsidies, and eminent domain. Governments in mixed economies also fully or partly own and operate certain industries, especially those considered public goods, often enforcing legally binding monopolies in those industries to prohibit competition by private entities.
In contrast, pure capitalism, also known as laissez-faire capitalism or anarcho-capitalism, (such as professed by Murray N. Rothbard) all industries are left up to private ownership and operation, including public goods, and no central government authority provides regulation or supervision of economic activity in general.
The standard spectrum of economic systems places laissez-faire capitalism at one extreme and a complete planned economy—such as communism—at the other. Everything in the middle could be said to be a mixed economy. The mixed economy has elements of both central planning and unplanned private business.
By this definition, nearly every country in the world has a mixed economy, but contemporary mixed economies range in their levels of government intervention. The U.S. and the U.K. have a relatively pure type of capitalism with a minimum of federal regulation in financial and labor markets—sometimes known as Anglo-Saxon capitalism—while Canada and the Nordic countries have created a balance between socialism and capitalism.
Many European nations practice welfare capitalism, a system that is concerned with the social welfare of the worker, and includes such policies as state pensions, universal healthcare, collective bargaining, and industrial safety codes.

Crony Capitalism

Crony capitalism refers to a capitalist society that is based on the close relationships between business people and the state. Instead of success being determined by a free market and the rule of law, the success of a business is dependent on the favoritism that is shown to it by the government in the form of tax breaks, government grants, and other incentives.
In practice, this is the dominant form of capitalism worldwide due to the powerful incentives both faced by governments to extract resources by taxing, regulating, and fostering rent-seeking activity, and those faced by capitalist businesses to increase profits by obtaining subsidies, limiting competition, and erecting barriers to entry. In effect, these forces represent a kind of supply and demand for government intervention in the economy, which arises from the economic system itself.
Crony capitalism is widely blamed for a range of social and economic woes. Both socialists and capitalists blame each other for the rise of crony capitalism. Socialists believe that crony capitalism is the inevitable result of pure capitalism. On the other hand, capitalists believe that crony capitalism arises from the need of socialist governments to control the economy.
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https://preview.redd.it/grfmt8oe4le41.png?width=1199&format=png&auto=webp&s=49d71283e37563aff53287dff7c1f99f993fb8b5
submitted by MattPetroski to ItalicoIntegralism [link] [comments]

WolfpackBOT - The world's fastest and most secure trading bot

WolfpackBOT - The world's fastest and most secure trading bot

https://preview.redd.it/n7wutgsuzfd21.png?width=800&format=png&auto=webp&s=d0dac7147b8e70584305f997732a248d6b088ff9

INTRODUCTION

Cryptocurrency is essentially digital money traded from one person to another through the use of pseudonyms. There are no intermediaries like banks, no governmental oversight or authority, and no fees. The “crypto” in cryptocurrency refers to the use of cryptography to ensure the security and privacy of every transaction.
New coins are created through a technique called mining. The process requires powerful computers that solve complex math problems. Each problem should take about 10 minutes to solve, and results in the creation of a predetermined number of coins. The total number of coins that can be created is fixed — there’s a limit of 21 million bitcoins that can be created. The number of coins rewarded for solving each problem dwindles as time goes on.
Bitcoin is believed to have been created in 2009 by Satoshi Nakamoto, an enigmatic figure who has so far proven all but impossible to definitively identify. By using cryptography to control the creation and tracking of a digital currency, Nakamoto took that power away from central authorities like governments.
Bitcoin was the first and most famous digital currency, but you can choose from more than 1,500, including ether, litecoin and even cryptokitties. For awhile, you saw these currencies only in the darkest corners of the internet, where people used them for all sorts of questionable, even illegal, activities. Drug dealers liked them because they made transactions all but invisible, and trolls at the Kremlin-backed Internet Research Agency used bitcoin to finance their campaign to influence the 2016 election.
That started to change in 2014, when Overstock became the first major US retailer to accept bitcoin. Companies like Expedia and Microsoft followed suit.
One of the biggest misconceptions about cryptocurrencies is that you need thousands of dollars to invest. It’s an easy assumption to make, especially in the case of bitcoin, which stayed under $1,000 from about 2010 to 2017. But then it took off, surpassing thousand-dollar milestones at a pace that seemed quicker than you could refresh your phone.
The staggering value is off-putting to many. But unlike most stocks, you can buy a fraction of a bitcoin so you don’t need thousands to get into the crypto game.

OVERVIEW OF WolfpackBOT

WolfpackBOT is a highly advanced cryptocurrency trading software that allows for the execution of trades at lightning speed using proprietary trading algorithms, proprietary “Werewolf” Trading Analysis configurations, or user customized settings based on personal trading style. WolfpackBOT also allows for simultaneous trading access to all compatible cryptocurrency exchanges that are available to the bot, and all trading pairs with the WerewolfBOT subscription package.
WolfpackBOT is introducing an industry first, a beautiful automated cryptocurrency trading console: The WolfBOX. This efficient and sleek piece of hardware will conveniently allow for the full utilization of a bot subscription without the need for a VPS or dedicated computer. The WolfBOX will also include a built-in secure Hardware Wallet and RFID card reader to optimize ease-of-use and functionality.
WolfpackBOT trading software is enabled with limit, market, and “Wolf Trade” orders on all trading candles, including one-minute candles, with the widest array of technical trading indicators available on the market. WolfpackBOT's proprietary “Wolf Trade” orders provide superior market sell orders with a bite! WolfpackBOT is the only trading bot to feature live price scanning on your positions and also handles partial fills with ease, meaning you don’t miss out on orders. WolfpackBOT is incredibly fast and can fulfill up to 10,000 trades per day depending on market conditions and subscription package.
WolfpackBOT allows simultaneous trading access to all cryptocurrency exchanges that are available to the bot, and all trading pairs through the WerewolfBOT subscription plan. Not only do inferior bots allow limited access to one exchange and one trading pair per bot, they also store your API keys remotely on servers which are potentially susceptible to hacks and pump and dump attacks. User security and API key protection holds a high priority within the WolfpackBOT framework which is why it is the only trading bot that gives users full control with local management of their API keys.
Masternode and Proof of Work X11 Blockchain
Wolfcoin Blockchain with X11 Proof of Work Mining and Masternode Reward Systems The Wolfcoin blockchain and network are both designed and engineered to ensure store of value, transactional speed and security, and fungibility. The main goal of the Wolfcoin blockchain is to facilitate fast and secure transactions with a governance that helps sustain the network for the benefit of all users. The Wolfcoin blockchain is a two-tier network comprised of a Proof of Work (PoW) consensus mechanism powered by miners and a Proof of Service (PoSe) system powered by masternodes.
The Wolfcoin blockchain is secured through Proof of Work (PoW) in which miners attempt to solve difficult problems with specialized computers. When a problem is solved, the miner receives the right to add a new block to the blockchain. If the problem was solved correctly, the miner is rewarded once the block is added.
The second tier, which is powered by masternodes, enables Wolfcoin to facilitate private and instant transactions with Private Send and Instant Send. Masternodes are also rewarded when miners discover new blocks.
The block reward is distributed with 80% going to the masternodes and 20% going to miners. The masternode system is referred to as Proof of Service (PoSe), since the masternodes provide crucial services that support the features of the network.
Masternodes also oversee the network and have the power to reject improperly formed blocks from miners. If a miner tried to take the entire block reward for themselves, the masternode network would orphan the block ensuring that it would not be added to the blockchain.
In short, miners power the first tier, which is the basic sending and receiving of funds and prevention of double spending. Masternodes power the second tier, which provide the added features that make Wolfcoin different from other cryptocurrencies. Masternodes do not mine, and mining computers cannot serve as masternodes.
Additionally, each masternode is “secured” by 10,000 WOLF. Those WOLF remain under the sole control of their owner at all times. The funds are not locked in any way; however, if enough of the funds are moved or spent to cause the user’s holdings to drop below 10,000 Wolfcoin, the associated masternode will go offline and stop receiving rewards.
By pre-ordering your WolfpackBOT subscription, you will also receive Wolfcoin as a reward that can be utilized in the following ways:
  • Redeemable for WolfpackBOT subscriptions
  • Redeemable for the WolfBOX Console
  • Redeemable for WolfpackBOT and Wolfcoin apparel and merchandise
  • Fungible utility that can be exchanged for like value on exchanges
When you hold at least 10,000 Wolfcoin in your Wolfcoin wallet connected to a static IP address, you will become a masternode, meaning you will have a chance to receive 80 percent of the block reward every sixty seconds.

THE FEATURES

WolfpackBOT Automated Trading Software:

After the crowdsale, Wolfcoin will be the exclusive method of payment for WolfpackBOT Automated Trading Software subscriptions.

Multiple Technical Analysis Indicators:

WolfpackBOT offers the widest array of multiple Technical Analysis indicators, oscillators, configurations and settings available in the world of Automated Cryptocurrency Trading Bots. WolfpackBOT provides Bollinger Bands, Double EMA, Elliot Wave, EMA, EMA Cross, Fibonacci Sequence, KAMA, MA Cross, MACD, RSI, SMA, Stochastic, Stochastic RSI, Triple EMA, and many more!

Shorting Features:

WolfpackBOT includes Cryptocurrency Shorting Features that allow users to short their positions and buy them back at the lower price to maximize their returns.

Copyrighted Crash Protection:

Crash Protection, one of WolfpackBOT's most advanced features, enables users the option to automatically scan and convert all positions to a stable coin at the sign of our proprietary Hidden Bear Divergence Indicator, and then buy back into base currency to resume trading at the sign of our proprietary Hidden Bull Divergence Indicator.

Language Translator:

WolfpackBOT has a built in Language Translator that instantly translates the entire BOT into Dutch, English, French, German, or Spanish.

All Trading Pairs on all available Exchanges:

WolfpackBOT allows our customers to simultaneously trade on multiple cryptocurrency exchanges, and with all the exchange’s trading pairs available for trading. The best part is that it’s all possible on one bot with one subscription to the WerewolfBOT package!

Coin Selector:

While other automated trading platforms only allow for a limited amount of coins per subscription, WolfpackBOT allows all trading pairs and all coins to be traded on all the available major exchanges with the WerewolfBOT subscription. WolfpackBOT's proprietary Coin Selector allows for users to choose whether to trade all cryptocurrencies or blacklist some, thus not trading them at all, as well as search for the highest volume, greatest performing, or a specific volatility range of coins for a given timeframe.

Werewolf Configurations and Settings:

Werewolf Configurations and Settings are copyrighted trading algorithms that use proprietary optimum settings for trading: the perfect configuration for experienced and inexperienced traders alike. These settings can be adjusted to the current market trend, with preset configurations for bear, sideways, and bull markets.

Werewolf Ultimate:

Werewolf Ultimate is the ultimate choice when trading. It doesn't trade a particular trading pair or particular coins, it trades them all. It goes in for the kill to increase the potential returns. Crash Protection is a built-in feature in Werewolf Ultimate.

Werewolf Bull Market:

Werewolf Bull Market are preset settings and configurations that are usable when your Base Trading Pair is in a Bull Run. Werewolf Bull Market settings are optimized for such conditions and should only be used in a Bull Run Market.

Werewolf Sideways Market:

Werewolf Sideways Market are preset settings and configurations that are usable when your Base Trading Pair is trading sideways. Werewolf Sideways Market settings are optimized for such conditions and should only be used in a Sideways Trading Market.

Werewolf Bear Market:

Werewolf Bear Market are preset settings and configurations that are usable when your Base Trading Pair is in a Bear Run. Werewolf Bear Market settings are optimized for such conditions and should only be used in a Bear Run Market.

The WolfBOX Hardware Console:

WolfpackBOT also offers an industry first: a beautiful hardware console, The WolfBOX. Our console comes preloaded with WolfpackBOT Automated Trading Software and also includes a built-in secure hardware wallet. Some of the key features of the WolfBOX include our high-speed CPU, solid-state hard drive, built-in RFID card reader, and integrated Bitpay and Coinbase wallets.

Wolfpack Consulting

Our company offers its services and expertise as Cryptocurrency and Blockchain Specialists to individuals and companies. We offer consulting services in the fields of blockchain and cryptocurrency development and management.

Wolfpack Philanthropy

We are dedicated to the proposition that we have a responsibility to use a portion of our company’s revenue to help create a better world and a brighter future. As we move forward, our philanthropic efforts include environmental stewardship, renewable energy, human rights, economic development, as well as animal and wildlife rescue and conservation with an emphasis on dogs and wolves.

Wolfcoin Information

THE WOLFCOIN Wolfcoin is the coin that fuels all WolfpackBOT's projects.
This utility, coupled with the reward systems with mining and Masternoding capabilities, makes the use of Wolfcoin potentially appealing to all WolfpackBOT users whom are interested in receiving additional Wolfcoin for subscriptions, merchandise and other rewards such as passive cryptocurrency portfolio growth.
THE WOLFCOIN WALLET WolfpackBOT uses our proprietary Wolfcoin Core QT wallet.
February 2018 Conceptual development of WolfpackBOT Software
May 2018 Company Roadmap development Alpha models of WolfpackBOT Software
June 2018 Ongoing research, development, and testing
October 2018 Advertising and Marketing Campaign Starts Wallets available for payment; BTC, BTG, DASH, DOGE, ETC, ETH, LTC October 15 - Pre-registration begins
November 2018 November 1 - Crowdsale Stage I begins
December 2018 Official presentation of WolfpackBOT beta Software Preview Creation of Wolfcoin (WOLF: 300,000,000 coins pre-mined on Genesis Block) WolfpackBOT beta Software release to selected customers
December 21 - Launch network and mine Genesis block
December 22 - PoW / Mainnet
December 23 - Blockchain and network testing
December 28 - Iquidis Wolfcoin Block Explorer released on our website
January 2019 January 1 - Wolfcoin Core wallets available for download on the website January 1 - Wallet and Masternode Tutorial available January 1 - Masternode and PoW instructional videos available January 1 - Subscription Pre-order Coin Rewards disbursed Announcement listing WOLF on top-10 Exchange
February 2019 February 1 - Crowdsale Stage I Ends February 1 - Crowdsale Stage II Begins
March 2019 March 15 - Crowdsale Stage II Ends March 15 - Crowdsale Stage III Begins WolfpackBOT Software roll-out to contributors WolfBOX Console available for Pre-order
April 2019 WolfpackBOT Subscriptions available for customers First Major version released: automated, manual, and paper trading WolfpackBOT Live support center April 30 - Crowdsale Stage III Ends
May 2019 WolfBOX Consoles Pre-orders first shipment
June 2019 New trading features such as new exchanges, strategy options and indicators
July 2019 New trading features such as new exchanges, strategy options or indicators
August 2019 WolfpackBOT Software Trading Platform V2.0 Second major release: Strategy Marketplace and Back-testing
September 2019 New trading features such as new exchanges, strategy options or indicators
October 2019 WolfpackBOT Software Trading Platform V3.0 Third major release: Signals Marketplace (Supporting 3rd Party App Signals) Mobile Application for WolfpackBOT Software and Trading Platform
November 2019 New trading features such as new exchanges, strategy options or indicator
December 2019 WolfpackBOT Software Trading Platform V4.0
January 2020 WolfpackBOT Software Trading Platform V5.0 Fourth major release: Machine Learning Strategy Optimization

THE AMAZING TEAM

Philip Longhurst Chief Executive Officer The leader of our pack and the man behind the WolfpackBOT trading bot, Philip Longhurst is a mathematical genius, engineer, day trader, and animal rescuer. As an account manager for J.P. Morgan and MBNA Bank, Phil managed the accounts of several high-profile clients and businesses. He has been successfully trading stocks for over twenty-five years and has successfully applied his trading expertise and mathematical acumen to the cryptocurrency market since 2013.
Philip holds bachelor's degrees in mechanical engineering and business administration and is a loving husband, father, and family man who has been rescuing dogs since 1995. His driving desire is to use the success of Wolfpack Group to create a brighter future for humanity. He currently resides in the United States of America with his wife, daughter, and dogs.
Rogier Pointl Chief Financial Officer Rogier Pointl is a successful entrepreneur with nearly twenty-five years of experience in business management, marketing, financial administration, economics, and fintech. Rogier holds bachelor's degrees in Business Communications and Financial Administration. He is a pioneer in the field of virtual reality, having served as CEO and owner of Simworld, the first virtual reality racing center in Europe, where he oversaw the development of advanced simulator and virtual reality hardware and software.
Rogier is an experienced trader and has been trading stocks since 2007. He began applying his expertise to the cryptocurrency market in 2010, gaining experience as a Bitcoin miner along the way. Rogier is a loving husband and father and currently resides in the Netherlands with his wife and two daughters.
Jason Cormier Chief Technical Officer Jason Cormier is a humble -but extraordinary- individual who is blessed with a Mensa IQ of 151, he is continually driven by a desire for knowledge and self-growth. He is self-taught in Visual Basics, C#, C++, HTML, and CSS and began developing programs and applications at the age of 14, including the TCB Wallet, which was the first ever wallet program that held its users' log in names and passwords. Jason is a cryptocurrency guru whose expertise includes cryptocurrency mining farms, proof-of-stake, masternodes, and cryptocurrency trading.
Jason holds Associate degrees in Computer Science and Psychology, and currently resides in the United States of America with his wife and son.
Jay McKinney Chief Web Development and Design Officer Jay is a veteran of the Iraq War who put his life on the line in combat to protect our freedoms. To center himself while stationed in the Iraqi warzone, he taught himself C# as he knew honing his Web Development skills would help him provide a better future for himself and his family. Upon returning home safely, he worked his way through college and holds bachelor's degrees in Computer Programming and Web Development & Design.
Jay has worked for the Kentucky Housing Corporation, serving as a software engineer and web developer. He is a loving family man who currently resides in the United States of America with his wife and two children.
David Johnson Chief Software Development Officer David holds a Master of Science degree in Information Systems and a Bachelor's degree in Business Administration with a specialization in Information Systems, graduating with Magna Cum Laude status. He has worked for the Kentucky Housing Corporation, serving as a network analyst and software engineer. As an entrepreneur, he has owned his own web and software development company since 2009, creating and maintaining several websites in C# and PHP, and has been operating the crypto-oriented YouTube channel BigBits since 2017, where he discusses automated Cryptocurrency trading strategies.
David is a proud father of two and resides in the United States of America with his wife and children. Like any good Kentuckian, he is a huge fan of the University of Kentucky's college sports teams.
Gabriel Condrea Software and Web Development Officer Gabriel Condrea holds a bachelor's degree in electrical and computer engineering and has worked as a software developer and senior systems engineer in both the United States and the United Kingdom, working with a variety of programming languages and IDEs. He has used his expertise to create Manufacturing and SCADA systems in industrial applications.
Gabriel also applies his engineering skills to cryptocurrency day trading, seeking to automate the process. He loves to travel and currently resides in the United States with his girlfriend.
Igor Otorepec Chief Hardware Development Officer Igor is an engineer with twenty years of experience specializing in advanced PLC programming and industrial robotics. He is also an IT security expert and a CEC Certified Ethical Cracker who uses his skills to expose and patch security vulnerabilities in blockchain codes.
Igor is an advanced cryptocurrency trader and Kung Fu master who uses bio-hacking as a way of life to keep his 'chi' constantly centered. He currently resides in Austria with his loving wife.
Manik Ehhsan Director of Marketing and Public Relations Manik holds a Bachelor's degree in Computer Science and has over five years of experience in Web Development, Digital Marketing and Graphics Design. He has also managed the marketing for more than 30 successful Cryptocurrency start-ups and projects, and specializes in SEO and ASO. Manik is also a Cryptocurrency project promotion expert with an emphasis on Masternodes and building Social Media Communities.
Manik has focused his life on Cryptocurrency and currently resides in Bangladesh with his loving family.
Rance Garrison Chief Marketing Officer Rance Garrison holds a bachelor's degree in Business Administration and specialized in Seminary Studies for his Master's degree. He served as an AmeriCorps VISTA at WMMT-FM, the radio station owned by Appalshop, an arts and education center in Kentucky, and has also specialized in local cable television advertising. Rance is also a musician who has released several albums independently over the last decade.
Rance is very dedicated to his local community and is most excited by the potential implications of cryptocurrencies and blockchain technology for rural and remote economies. He currently resides in the United States of America with his wife, dog, and cats.
Paul Gabens Chief Public Relations Officer A master negotiator with a penchant for strategy, Paul Gabens brings more than twenty years of marketing and promotional experience in the automotive, hospitality, and entertainment industries to the Wolfpack. He is also an avid stock and cryptocurrency trader, having first entered into the cryptocurrency market two years ago, embracing his passion for crypto with the same vigor as his love for travel, classic cars, extreme roller coasters, and surfing.
Paul holds degrees in business management, marketing, and automotive aftermarket. He currently resides in the United States with his fiancé and two cats.
Blake Stanley Marketing and Social Media Officer Blake Stanley is a cryptocurrency enthusiast who also has over six years of experience managing both government and private sector client and customer relations. A strategic thinker and expert in the field of social media-based advertising, Blake also owns and manages his own online marketing company where he has been successfully curating and implementing online marketing and advertising strategies for his clients for the past three years.
Blake is a proud father and family man and currently lives in the United States with his daughter and fiancé.
Martin Kilgore Market and Trading Analyst Martin Kilgore holds bachelor’s degrees in both accounting and mathematics, having researched Knot Theory and the Jones Polynomial during his undergraduate studies, giving him a firm edge when analyzing market conditions. He has worked as a staff accountant for several governmental organizations.
Martin lives in the United States with his fiancé.
Jonathan McDonald Chief Trading Strategy Officer Jonathan has honed his trading skills over the past five years by studying and implementing economics, financial strategy, Forex trading analysis and trading bots. Through his constant learning, he discovered Cryptocurrency after seeing the difference in market volatility and high yield trading. His fine-tuned trading strategies complement Crypto markets perfectly, and he has been implementing trading strategies to the Cryptocurrency market for over a year with phenomenal results. Jonathan is constantly improving his trading skills with an emphasis on scalping techniques. He has applied his trading skillset to the WolfpackBOT and enjoys working alongside the Wolfpack in creating the fastest trading bot on the market.
Jonathan currently resides in Canada with his supportive girlfriend and family.
Web site: https://www.wolfpackbot.com/
Technical document: https://www.wolfpackbot.com/Pdf/whitepaper_en.pdf
Bounty0x username: idrixoxo
submitted by idrixoxo2015 to u/idrixoxo2015 [link] [comments]

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