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A place for redditors to discuss quantitative trading, statistical methods, econometrics, programming, implementation, automated strategies, and bounce ideas off each other for constructive criticism. Feel free to submit papers/links of things you find interesting.
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.
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.
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:
I'm using the stop entry version - so I wait for the price to trade beyond the confirmation candle(in the direction of my trade) before entering. I don't have any data to support this decision, but I've always preferred this method over retracement-limit entries. Maybe I just like the feeling of a higher winrate even though there can be greater R:R using a limit entry. Variety is the spice of life.
I put my stop loss right at the opposite edge of the confirmation candle. NOT at the edge of the 2-candle pattern that makes up the system. I'll get into this more below - not enough trades are saved to justify the wider stops. (Wider stop means less $ per pip won, assuming you still only risk 1%).
All my profit/loss statistics are based on a 1% risk per trade. Because 1 is real easy to multiply.
There are definitely some questionable trades in here, but I tried to make it as mechanical as possible for evaluation purposes. They do fit the definitions of the system, which is why I included them. You could probably improve the winrate by being more discretionary about your trades by looking at support/resistance or other techniques.
I didn't use MBB much for either entering trades, or as support/resistance indicators. Again, trying to be pretty mechanical here just for data collection purposes. Plus, we all make bad trading decisions now and then, so let's call it even.
As stated in the title, this is for H1 only. These results may very well not play out for other time frames - who knows, it may not even work on H1 starting this Monday. Forex is an unpredictable place.
I collected data to show efficacy of taking profit at three different levels: -61.8%, -100% and -161.8% fib levels described in the system using the passive trade management method(set it and forget it). I'll have more below about moving up stops and taking off portions of a position.
And now for the fun. Results!
Total Trades: 241
TP at -61.8%: 177 out of 241: 73.44%
TP at -100%: 156 out of 241: 64.73%
TP at -161.8%: 121 out of 241: 50.20%
Adjusted Proft % (takes spread into account):
TP at -61.8%: 5.22%
TP at -100%: 23.55%
TP at -161.8%: 29.14%
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):
7pm-4am: Fewer setups, but winrate high.
5am-6am: Lots of setups, but but winrate low.
12pm-3pm Medium number of setups, but winrate low.
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.
Moving SL up to 0% when the price hits -61.8%, TP at -100%
Adjusted Proft % (takes spread into account): 5.36%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%
Adjusted Proft % (takes spread into account): -1.01% (yes, a net loss)
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:
Moving SL up to 0% when the price hits -61.8%, TP at -100%
Winrate(breakeven doesn't count as a win): 46.4%
Adjusted Proft % (takes spread into account): 17.97%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%
Winrate(breakeven doesn't count as a win): 65.97%
Adjusted Proft % (takes spread into account): 11.60%
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.
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.
Total Trades: 75
TP at -61.8%: 84.00%
TP at -100%: 73.33%
TP at -161.8%: 60.00%
Moving SL up to 0% when the price hits -61.8%, TP at -100%: 53.33%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%: 53.33% (yes, oddly the exact same winrate. but different trades/profits)
Adjusted Proft % (takes spread into account):
TP at -61.8%: 18.13%
TP at -100%: 26.20%
TP at -161.8%: 34.01%
Moving SL up to 0% when the price hits -61.8%, TP at -100%: 19.20%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%: 17.29%
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):
"System Details" I described above.
TP at -161.8%
Static SL at opposite side of confirmation candle - I won't move stops up to breakeven.
Trade only 7am-11am and 4pm-11pm signals.
Nothing where spread is more than 25% of trade width.
Looking at the data for these rules, test results are:
Adjusted Proft % (takes spread into account): 47.43%
I'll be sure to let everyone know how it goes!
Other Technical Details
ATR is only slightly elevated in this date range from historical levels, so this should fairly closely represent reality even after the COVID volatility leaves the scalpers sad and alone.
The sample size is much too small for anything really meaningful when you slice by hour or pair. I wasn't particularly looking to test a specific pair here - just the system overall as if you were going to trade it on all pairs with a reasonable spread.
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.
I'm on the East Coast in the US, so the timestamps are Eastern time.
Time stamp is from the confirmation candle, not the indecision candle. So 7am would mean the indecision candle was 6:00-6:59 and the confirmation candle is 7:00-7:59 and you'd put in your order at 8:00.
I found a couple AM/PM typos as I was reviewing the data, so let me know if a trade doesn't make sense and I'll correct it.
Insanely detailed spreadsheet notes
For you real nerds out there. Here's an explanation of what each column means:
Pair - duh
Date/Time - Eastern time, confirmation candle as stated above
Win to -61.8%? - whether the trade made it to the -61.8% TP level before it hit the original SL.
Win to -100%? - whether the trade made it to the -100% TP level before it hit the original SL.
Win to -161.8%? - whether the trade made it to the -161.8% TP level before it hit the original SL.
Retracement level between -61.8% and -100% - how deep the price retraced after hitting -61.8%, but before hitting -100%. Be careful to look for the negative signs, it's easy to mix them up. Using the fib% levels defined in ParallaxFX's original thread. A plain hyphen "-" means it did not retrace, but rather went straight through -61.8% to -100%. Positive 100 means it hit the original SL.
Retracement level between -100% and -161.8% - how deep the price retraced after hitting -100%, but before hitting -161.8%. Be careful to look for the negative signs, it's easy to mix them up. Using the fib% levels defined in ParallaxFX's original thread. A plain hyphen "-" means it did not retrace, but rather went straight through -100% to -161.8%. Positive 100 means it hit the original SL.
Trade Width(Pips) - the size of the confirmation candle, and thus the "width" of your trade on which to determine position size, draw fib levels, etc.
Loser saved by 2 candle stop? - for all losing trades, whether or not the 2-candle stop loss would have saved the trade and how far it ended up getting if so. "No" means it didn't save it, N/A means it wasn't a losing trade so it's not relevant.
Spread(ThinkorSwim) - these are typical spreads for these pairs on ToS.
Spread % of Width - How big is the spread compared to the trade width? Not used in any calculations, but interesting nonetheless.
True Risk(Trade Width + Spread) - I set my SL at the opposite side of the confirmation candle knowing that I'm actually exposing myself to slightly more risk because of the spread(stop order = market order when submitted, so you pay the spread). So this tells you how many pips you are actually risking despite the Trade Width. I prefer this over setting the stop inside from the edge of the candle because some pairs have a wide spread that would mess with the system overall. But also many, many of these trades retraced very nearly to the edge of the confirmation candle, before ending up nicely profitable. If you keep your risk per trade at 1%, you're talking a true risk of, at most, 1.25% (in worst-case scenarios with the spread being 25% of the trade width as I am going with above).
Win or Loss in %(1% risk) including spread TP -61.8% - not going to go into huge detail, see the spreadsheet for calculations if you want. But, in a nutshell, if the trade was a win to 61.8%, it returns a positive # based on 61.8% of the trade width, minus the spread. Otherwise, it returns the True Risk as a negative. Both normalized to the 1% risk you started with.
Win or Loss in %(1% risk) including spread TP -100% - same as the last, but 100% of Trade Width.
Win or Loss in %(1% risk) including spread TP -161.8% - same as the last, but 161.8% of Trade Width.
Win or Loss in %(1% risk) including spread TP -100%, and move SL to breakeven at 61.8% - uses the retracement level columns to calculate profit/loss the same as the last few columns, but assuming you moved SL to 0% fib level after price hit -61.8%. Then full TP at 100%.
Win or Loss in %(1% risk) including spread take off half of position at -61.8%, move SL to breakeven, TP 100% - uses the retracement level columns to calculate profit/loss the same as the last few columns, but assuming you took of half the position and moved SL to 0% fib level after price hit -61.8%. Then TP the remaining half at 100%.
Overall Growth(-161.8% TP, 1% Risk) - pretty straightforward. Assuming you risked 1% on each trade, what the overall growth level would be chronologically(spreadsheet is sorted by date).
Based on the reasonable rules I discovered in this backtest:
Date range: 6/11-7/3
Adjusted Proft % (takes spread into account): 47.43%
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!
Date range: 7/9-7/30
Adjusted Proft % (takes spread into account): 20.73%
Starting Balance: $5,000
Ending Balance: $6,036.51
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.
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
The exact mechanism of drain, or transfers from India to Britain was quite simple.
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.
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.
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.
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 seeRajan 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.
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'.
Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times Tuovila, Alicia (2019). Expenditure method. Investopedia Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History
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Nonfarm payroll employment is a compiled name for goods, construction and manufacturing companies in the US. It does not include farm workers, private household employees, or non-profit organization employees. It is an influential statistic and economic indicator released monthly by the United States Department of Labor as part of a comprehensive report on the state of the labor market. The Bureau of Labor Statistics releases data on the first Friday of the month, at 8:30 a.m. Eastern Time. This data is analyzed closely because of its importance in identifying the rate of economic growth and inflation. Nonfarm payroll is included in the monthly Employment Situation or informally the jobs report and affects the US dollar, the Foreign exchange market, the bond market, and the stock market. The markets react very quickly and most of the time in a very volatile fashion around the time the NFP data is released. The short-term market moves indicate that there is a very strong correlation between the NFP data and the strength of the US dollar. Historical price movement data shows a small negative correlation between the NFP data and the US dollar Index. The figure released is the change in nonfarm payrolls (NFP), compared to the previous month, and is usually between +10,000 and +250,000 during non-recessional times. The NFP number is meant to represent the number of jobs added or lost in the economy over the last month, not including jobs relating to the farming industry. As with other indicators, the difference between the actual non-farm data and expected figures will determine the overall impact on the market. If the non-farm payroll is expanding, this is a good indication that the economy is growing, and vice versa. However, if increases in non-farm payroll occur at a fast rate, this may lead to an increase in inflation. In forex, the level of actual non-farm payroll compared to payroll estimates is taken very seriously. If the actual data comes in lower than economists' estimates, forex traders will usually sell U.S. dollars in anticipation of a weakening currency. The opposite is true when the data is higher than economists' expectations.
Nonfarm payroll employment is a compiled name for goods, construction and manufacturing companies in the US. It does not include farm workers, private household employees, or non-profit organization employees. https://preview.redd.it/7xp41ft401a51.jpg?width=750&format=pjpg&auto=webp&s=467c40fa530f23c5a425c3aa449b3e306a027381 It is an influential statistic and economic indicator released monthly by the United States Department of Labor as part of a comprehensive report on the state of the labor market. The Bureau of Labor Statistics releases data on the first Friday of the month, at 8:30 a.m. Eastern Time. This data is analyzed closely because of its importance in identifying the rate of economic growth and inflation. Nonfarm payroll is included in the monthly Employment Situation or informally the jobs report and affects the US dollar, the Foreign exchange market, the bond market, and the stock market. The markets react very quickly and most of the time in a very volatile fashion around the time the NFP data is released. The short-term market moves indicate that there is a very strong correlation between the NFP data and the strength of the US dollar. Historical price movement data shows a small negative correlation between the NFP data and the US dollar Index. The figure released is the change in nonfarm payrolls (NFP), compared to the previous month, and is usually between +10,000 and +250,000 during non-recessional times. The NFP number is meant to represent the number of jobs added or lost in the economy over the last month, not including jobs relating to the farming industry. As with other indicators, the difference between the actual non-farm data and expected figures will determine the overall impact on the market. If the non-farm payroll is expanding, this is a good indication that the economy is growing, and vice versa. However, if increases in non-farm payroll occur at a fast rate, this may lead to an increase in inflation. In forex, the level of actual non-farm payroll compared to payroll estimates is taken very seriously. If the actual data comes in lower than economists' estimates, forex traders will usually sell U.S. dollars in anticipation of a weakening currency. The opposite is true when the data is higher than economists' expectations.
What Is a Forex Robot? These days, it is becoming more and more common for traders to utilize modern methods of technology in their trading and there are many advantages to doing so. Traders are increasingly likely to use trading systems or software that allows them to automate the trading process — thus reducing the problems of emotional attachment to a trade or a lack of trading discipline. A Forex robot does exactly that and one of the most popular ways to use one is via the MetaTrader 4 platform. The MetaTrader 4 platform offers a complete solution to a trader’s needs, consisting of charts, news feeds, and more. And, by coding in the native MQL4 programming language, it is also possible to write custom built indicators or even trading strategies — also known as expert advisors. Free Ex4 to Mq4 decompiler!! Top EA List: https://best-forex-trading-robots.com/ Expert Advisors An expert advisor (EA) is another name for a Forex robot, one that has been developed to be used on the MetaTrader 4 platform. Since it can be custom built, an expert advisor can be designed to implement any trading strategy or risk management system so long as the designer knows how to code it into the program. For example, a trader may design an EA to open positions in the market at a certain size after a moving average crossover. Has anyone tried Forex robot trading? Best-forex-robots Benefits The main advantage of using a Forex robot is that it takes the emotion out of trading, which if not addressed, can be a big barrier to many traders. Fear, greed, and stress can build up in manual trading all too easily, leading a trader to lose money and get frustrated with the game. A robot on the other hand, will implement the chosen strategy flawlessly every time and with a high degree of accuracy. It will also make difficult risk management calculations in the blink of an eye, much faster than a human trader. Not only that, but robots can be designed to trade around the clock and on different markets at once, meaning that you need not have to sit in front of your screen all day and all night. In short, a Forex robot can take much of the hard work out of trading — that hard work is done beforehand — developing and testing the trading idea. Limitations Of course, there are no shortcuts to making money on the Forex markets and working with a Forex robot brings with it its own inherent limitations. For one thing, Forex markets are fiercely competitive and coming up with a robot that is able to beat the market is a notoriously difficult thing to do. Indeed, it is not enough to design a robot that works over a couple of weeks data, the robot must work over several months, if not years, of historical data and undergo rigorous statistical testing to prove that it works. Because if a trader cannot be confident that the robot works, they will more than likely abandon it when times get tough.
When going for an automated trading platform it is very important to look for some important features before you decide on the automated trading platform you want to trade on. Different automated trading platforms offer different services which have their own pros and cons and might suit certain strategies and better than the others. We have discussed important features that you should consider while choosing an algorithmic trading platform.
A backtest is a historical simulation of an algorithmic trading strategy to see how it would’ve performed on the data in the past. Backtest results usually show the strategy’s performance in terms of profits and losses and some popular performance statistics like Sharpe Ratio or Information ratio which help to quantify the strategy’s return on risk. Hence a good backtesting software can be a great plus for an automated trading platform. Backtests can be divided into two categories ‘Research Backtesters’ and ‘Event-Driven Backtesting’.
Choice of programming language is very important while deciding which platform to use for automating your trading strategy. Different languages have different pros and cons. Most commonly used programming languages used for algorithmic trading are C++, C#, Java, R, Python, and MATLAB. You can refer to one of our recent posts on top backtesting platforms where we’ve discussed popular programming languages.
Different automated trading platforms provide access to/support trading/backtesting of certain securities only; some provide specific access to data feeds like Bloomberg and Thomson/Reuters. For instance, there are platforms dedicated to Forex trading or Equities trading only that too in specific markets. You need to make sure what the automated trading platform offers and then decide based on your needs. The frequency of data that you would need should also be taken into account. Some strategies would require daily EOD data while some other strategies might require intraday trading data.
Different automated stock trading platforms vary in ease of use. Some platforms may require actual programming expertise while others may not. Most platforms provide a demo version which can help you decide what fits your comfort level. The complexity of platforms can be different for different assets traded, and one should check the different tools & features available to analyze the specific asset class.
Number of Strategies Allowed
Sometimes there might be restrictions on the number of long or short strategies loaded on a particular account and you might need extra accounts for more strategies. You should also check if you have enough memory on your computer for multiple accounts if required as it can be memory intensive. Some platforms also offer their own trading strategies as add-ons which can be subscribed by paying a periodic or one-time fee.
Trading commissions can impact your profits to a great extent. Carefully choose the plan which suits your trading requirements. Also, check if there are initial and/or monthly fees and what is offered against it to make sure you are only paying for services which you actually want.
Technical Support & Customer Service
Automated Trading platforms are expected to have an extremely high “up-time” and rarely go out of service. Before choosing the platform you should check the history of outages and if there have been any other issues in the past, how soon were those resolved, and how knowledgeable and helpful was the support team.
The proven oil reserves in Venezuela are recognized as the LARGEST in the world, totaling 297 billion barrels. While ignoring (and even supporting) the atrocities of authoritarian regimes in places like Saudi Arabia, Bahrain and Uzbekistan, US oligarchs have targeted Venezuela for “regime-change” in the name of “democracy”. Currently, the US is engaging in economic warfare against Venezuela to foment a coup and remove its democratically elected president Nicolás Maduro. Without providing solid evidence, our corporate-controlled government and mainstream media portray Maduro as a corrupt, repressive, and illegitimate leader with little to no support.
Why is the US Corporatocracy so Keen to Remove Maduro?
While Venezuela’s economy is not a strictly-state-run economy, its oil industry is nationalized and uses its revenues for the benefit of its citizens (especially the poor). After years of crippling US sanctions Maduro stepped over a crucial line in October when his government announced that Venezuela was abandoning the US dollar and would be make all future transactions on the Venezuelan exchange market in euro. Saddam Hussein also went off the dollar in favor of the euro in 2003 – we started dropping bombs on him the next month. A similar decision by the Gadhafi government in Libya (2011) was quickly followed by a devastating US-orchestrated conflict - culminating in Gadhafi's capture by radical Islamists who sodomized him with a bayonet before killing him. Since then, Libya has gone from Africa's wealthiest country to a truly failed-state complete with a slave trade! To make matters worse, after the collapse of the Libyan government, its military arms were smuggled out of that country and into the hands of ISIS fighters in Iraq and Syria - enabling US-orchestrated chaos in those countries.
Who cares what currency a country uses to trade petroleum?
Answer: US oligarchy
The US dollar is central to US world economic domination. Like all other modern currencies, it is a fiat currency – backed by no real assets to prop up its value. In lieu of a “gold standard” we know operate on a de-facto “oil-standard”: "After the collapse of the Bretton Woods gold standard in the early 1970s, the United States struck a deal with Saudi Arabia to standardize oil prices in dollar terms. Through this deal, the petrodollar system was born, along with a paradigm shift away from pegged exchanged rates and gold-backed currencies to non-backed, floating rate regimes. The petrodollar system elevated the U.S. dollar to the world's reserve currency and, through this status, the United States enjoys persistent trade deficits and is a global economic hegemony." Investopedia “The central banking Ponzi scheme requires an ever-increasing base of demand and the immediate silencing of those who would threaten its existence. Perhaps that is what the hurry [was] in removing Gaddafi in particular and those who might have been sympathetic to his monetary idea.” Anthony Wile
US Foreign Policy is about Oligarchy Not Democracy
Since World War II, the US has attempted to over-throw the 52 foreign governments. Aside from a handful of exceptions (China, Cuba, Vietnam, etc.), the US has been successful in the vast majority of these attempts. US foreign policy is not about democracy – it is about exploiting the world’s resources in the interests of a small, ultra-wealthy global elite. This exploitation benefits a small percentage of people at the top of the economic pyramid while the costs are born by those at the bottom.
US CIA Coup Playbook:
How to Plunder Resources from Foreign Countries While Pretending to Support Democracy
Find a country with resources you want.
Send in an “Economic Hitman” to offer bribes the country’s leader in the form of personally lucrative business deals. If he accepts the deal, the leader will amass a personal fortune in exchange for "privatizing” the resources you wish to extract.
If the leader will not accept your bribes, begin the regime-change process. 3) Engage in economic warfare by imposing crippling sanctions on the country and blame the ensuing shortages on the leader’s “socialist” policies. 4) Work with right-wing allies inside country to fund and organize an “astroturf” opposition group behind a corporate-friendly puppet. 5) Hire thugs inside country to incite unrest and violence against the government in coordination with your opposition group. Use corporate media to publicize the orchestrated outbursts as popular outrage and paint a picture of a “failed state” mired in corruption and chaos. 6) When the government arrests your thugs, decry the response as the brutal repression. Use corporate-owned media to demonize the target government as a despotic regime while praising your puppet opposition as champions of democracy. 7) Work with right-wing military leaders to organize the overthrow the government (offer them the same business deals the current leader refused). 8) If a military-led coup cannot be organized, create a mercenary army to carry out acts of terrorism against the government and its supporters. Portray the mercenaries as “freedom fighters” and their acts of terrorism as a “civil war”. 9) If the target government has popular and military support and is too well-defended for your mercenaries to over-throw: label the country a “rouge state” and wait for the right time to invade. Meanwhile, continue to wear the country’s government and populace down using steps 3 – 8. 10) Escalate the terror campaign within the country to provoke a military response from the country against the US. If they won’t take the bait , fabricate an attack or threat that you can sell to the US population as justification for an invasion. 11) Once the government is removed, set up your puppet regime to provide the illusion of sovereignty. The regime will facilitate and legitimize your appropriation of the country’s resources under the guise of "free" trade. 12) As you continue to extract the country’s resources, provide intelligence and military support to the puppet regime to suppress popular dissent within the country. 13) Use the demise of the former government as yet another example of the impracticality of “socialism.” What Can I Do? Call your senators and representatives to voice your opposition to US regime-change efforts in Venezuela. https://www.commoncause.org/find-your-representative/ Please share this message with others. Sources included at: https://link.medium.com/8DiA5xzx4T
ALAN MACLEOD FEBRUARY 8, 2019 A recent Gallup poll (8/13/18) found that a majority of millennials view socialism favorably, preferring it to capitalism. Democratic socialist Bernie Sanders is the most popular politician in the United States, while new leftist Rep. Alexandria Ocasio-Cortez’s (AOC) policies of higher taxes on the wealthy, free healthcare and public college tuition are highly popular—even among Republican voters (FAIR.org,1/23/19). Alarmed by the growing threat of progressive policies at home, the establishment has found a one-word weapon to deploy against the rising tide: Venezuela. The trick is to attack any political figure or movement even remotely on the left by claiming they wish to turn the country into a “socialist wasteland” (Fox News, 2/2/19) run by a corrupt dictatorship, leaving its people hungry and devastated. Leading the charge have been Fox News and other conservative outlets. One Fox opinion piece (1/25/19) claimed that Americans should be “absolutely disgusted” by the “fraud” of Bernie Sanders and Democrats like Alexandria Ocasio-Cortez, Elizabeth Warren and Cory Booker, as they “continue to promote a system that is causing mass starvation and the collapse of a country,” warning that is exactly what their failed socialist policies would bring to the US. (Back in the real world, while Sanders and Ocasio-Cortez identify as socialists, Warren is a self-described capitalist, and Booker is noted for his ties to Wall Street, whose support for his presidential bid he has reportedly been soliciting.) A second Fox Newsarticle (1/27/19) continues in the same vein, warning that, “At the heart of Venezuela’s collapse is a laundry list of socialist policies that have decimated its economy.” TheWall Street Journal(1/28/19) describes calls for negotiations in Venezuela as “siding with the dictator.” In an article entitled “Bernie Sanders, Jeremy Corbyn and the Starving Children of Venezuela,” the Washington Examiner (6/15/17) warned its readers to “beware the socialist utopia,” describing it as a dystopia where children go hungry thanks to socialism. The Wall Street Journal (1/28/19) recently condemned Sanders for his support of a “dictator,” despite the fact Bernie has strongly criticized Venezuelan President Nicolás Maduro, and dismissed Maduro’s predecessor, Hugo Chavez, as a “dead Communist dictator” (Reuters, 6/1/16). More supposedly centrist publications have continued this line of attack. The New York Times’ Bret Stephens (1/25/19) argued: “Venezuela is a socialist catastrophe. In the age of AOC, the lesson must be learned again”—namely, that “socialism never works,” as “20 years of socialism” has led to “the ruin of a nation.” The Miami Herald(2/1/19) cast shame on Sanders and AOC for arguing for socialism in the face of such overwhelming evidence against it, describing the left’s refusal to back self-appointed president Juan Guaidó, someone whomless than 20 percentof Venezuelans had even heard of, let alone voted for, as “morally repugnant.” This useful weapon to be used against the left can only be sustained by withholding a great number of key facts—chief among them, the US role in Venezuela’s devastation. US sanctions, according to the Venezuelan opposition’s economics czar, are responsible for a halving of the country’s oil output (FAIR.org, 12/17/18). The UN Human Rights Council has formally condemned the US and discussed reparations to be paid, with one UN special rapporteur describing Trump’s sanctions as a possible “crime against humanity” (London Independent, 1/26/19). This has not been reported by any the New York Times, Washington Post, CNN or any other national US “resistance” news outlet, which have been only too quick to support Trump’s regime change plans (FAIR.org, 1/25/19). Likewise, the local US-backed opposition’s role in the economic crisis is barely mentioned. The opposition, which controls much of the country’s food supply, has officially accepted responsibility for conducting an “economic war” by withholding food and other key goods. For example, the monolithic Empresas Polar controls the majority of the flour production and distribution crucial for making arepa cornbread, Venezuela’s staple food. Polar’s chair is Leopoldo Lopez, national coordinator of Juan Guaidó’s Popular Will party, while its president is Lorenzo Mendoza, who considered running for president against Maduro in the 2018 elections that caused pandemonium in the media (FAIR.org, 5/23/18). Conspicuously, it’s the products that Polar has a near-monopoly in that are often in shortest supply. This is hardly a secret, but never mentioned in the copious stories (CNN, 5/14/14, Bloomberg, 3/16/17, Washington Post, 5/22/17, NPR, 4/7/17) focusing on bread lines in the country. Also rarely commented on was the fact that multiple international election observer missions declared the 2018 elections free and fair, and that Venezuelan government spending as a proportion of GDP (often considered a barometer of socialism) is actually lower than the US’s, and far lower than most of Europe’s, according to the conservative Heritage Foundation. The LondonDaily Express(2/3/19) demonstrates that redbaiting works equally well on either side of the Atlantic. Regardless of these bothersome facts, the media has continued to present Venezuela’s supposedly socialist dictatorship as solely responsible for its crisis as a warning to any progressives who get the wrong idea. So useful is this tool that it is being used to attack progressive movements around the world. The Daily Express (2/3/19) and Daily Mail (2/3/19) condemned UK Labour Party leader Jeremy Corbyn for his “defense” of a “dictator,” while the Daily Telegraph(2/3/19) warned that the catastrophe of Venezuela is Labour’s blueprint for Britain. Meanwhile, the Greek leftist party Syriza’s support for Maduro (the official position of three-quarters of UN member states) was condemned as “shameful” (London Independent, 1/29/19). “Venezuela” is also used as a one-word response to shut down debate and counter any progressive idea or thought. While the panel on ABC’s The View (7/23/18) discussed progressive legislation like Medicare for All and immigration reform, conservative regular Meghan McCain responding by invoking Venezuela: “They’re starving to death” she explained, leaving the other panelists bemused. President Trump has also used it. In response to criticism from Senator Elizabeth Warren over his “Pocahontas” jibe, he replied that she would “make our country into Venezuela” (Reuters, 10/15/18). The weapon’s effectiveness can only be sustained through a media in lockstep with the government’s regime-change goals. That the media is fixated on the travails of a relatively small and unimportant country in America’s “backyard,” and that the picture of Venezuela is so shallow, is not a mistake. Rather, the simplistic narrative of a socialist dictatorship starving its own people provides great utility as a weapon for the establishment to beat back the domestic “threat” of socialism, by associating movements and figures such as Bernie Sanders, Alexandria Ocasio-Cortez and Jeremy Corbyn with an evil caricature they have carefully crafted.
Corporate Propaganda Blitz Against Venezuela’s Elected President: MSM Will Not Let Facts Interfere With Coup Agenda
Facts Don’t Interfere With Propaganda Blitz Against Venezuela’s Elected PresidentJoe Emersberger Guaidó, anointed by Trump and a new Iraq-style Coalition of the Willing, did not even run in Venezuela’s May 2018 presidential election. In fact, shortly before the election, Guaidó was not even mentioned by the opposition-aligned pollster Datanálisis when it published approval ratings of various prominent opposition leaders. Henri Falcón, who actually did run in the election (defying US threats against him) was claimed by the pollster to basically be in a statistical tie for most popular among them. It is remarkable to see the Western media dismiss this election as “fraudulent,” without even attempting to show that it was “stolen“ from Falcón. Perhaps that’s because it so clearly wasn’t stolen. Graph: Approval Ratings of Main Venezuelan Leaders Nov 2016 - July 2018 Data from the opposition-aligned pollsters in Venezuela (via Torino Capital) indicates that Henri Falcón was the most popular of the major opposition figures at the time of the May 2018 presidential election. Nicolás Maduro won the election due to widespread opposition boycotting and votes drawn by another opposition candidate, Javier Bertucci. The constitutional argument that Trump and his accomplices have used to “recognize” Guaidó rests on the preposterous claim that Maduro has “abandoned” the presidency by soundly beating Falcón in the election. Caracas-based journalist Lucas Koerner took apart that argument in more detail. What about the McClatchy-owned Miami Herald's claim that Maduro “continues to reject international aid”? In November 2018, following a public appeal by Maduro, the UN did authorize emergency aid for Venezuela. It was even reported by Reuters (11/26/18), whose headlines have often broadcast the news agency’s contempt for Maduro’s government. It’s not unusual for Western media to ignore facts they have themselves reported when a major “propaganda blitz” by Washington is underway against a government. For example, it was generally reported accurately in 1998 that UN weapons inspectors were withdrawn from Iraq ahead of air strikes ordered by Bill Clinton, not expelled by Iraq’s government. But by 2002, it became a staple of pro-war propaganda that Iraq had expelled weapons inspectors (Extra! Update, 10/02). And, incidentally, when a Venezuelan NGO requested aid from the UN-linked Global Fund in 2017, it was turned down. Setting aside how effective foreign aid is at all (the example of Haiti hardly makes a great case for it), it is supposed to be distributed based on relative need, not based on how badly the US government wants somebody overthrown. But the potential for “aid” to alleviate Venezuela’s crisis is negligible compared to the destructive impact of US economic sanctions. Near the end of the Miami Herald article, author Jim Wyss cited an estimate from the thoroughly demonized Venezuelan government that US sanctions have cost it $30 billion, with no time period specified for that estimate. Again, this calls to mind the run-up to the Iraq invasion, when completely factual statements that Iraq had no WMDs were attributed to the discredited Iraqi government. Quoting Iraqi denials supposedly balanced the lies spread in the media by US officials like John Bolton, who now leads the charge to overthrow Maduro. Wyss could have cited economists independent of the Maduro government on the impact of US sanctions—like US economist Mark Weisbrot, or the emphatically anti-Maduro Venezuelan economist Francisco Rodríguez. Illegal US sanctions were first imposed in 2015 under a fraudulent “state of emergency” declared by Obama, and subsequently extended by Trump. The revenue lost to Venezuela’s government due to US economic sanctions since August 2017, when the impact became very easy to quantify, is by nowwell over $6 billion. That’s enormous in an economy that was only able to import about $11 billion of goods in 2018, and needs about $2 billion per year in medicines. Trump’s “recognition” of Guaidó as “interim president” was the pretext for making the already devastating sanctions much worse. Last month, Francisco Rodríguez revised his projection for the change in Venezuela’s real GDP in 2019, from an 11 percent contraction to 26 percent, after the intensified sanctions were announced. The $20 million in US “aid” that Wyss is outraged Maduro won’t let in is a rounding error compared to the billions already lost from Trump’s sanctions. Former US Ambassador to Venezuela William Brownfield, who pressed for more sanctions on Venezuela, dispensed with the standard “humanitarian” cover that US officials have offered for them (Intercept, 2/10/19):
And if we can do something that will bring that end quicker, we probably should do it, but we should do it understanding that it’s going to have an impact on millions and millions of people who are already having great difficulty finding enough to eat, getting themselves cured when they get sick, or finding clothes to put on their children before they go off to school. We don’t get to do this and pretend as though it has no impact there. We have to make the hard decision—the desired outcome justifies this fairly severe punishment.
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Get started today by signing up online or call us to learn more about investing at 312-600-2883. DISCLAIMER: Please consult your finance and tax professionals to learn more about investing and taxes. Back to top
Get ready for the trading week of February 25th, 2019!
Hey what's happening wallstreetbets! Good morning and happy Saturday to all of you on this subreddit. I hope everyone made out pretty nicely in the market last week, and are ready for the new trading week ahead! :) Here is everything you need to know to get you ready for the trading week beginning February 25th, 2019.
Next week will be pivotal for markets with trade deadline, Powell, Trump-Kim and more - (Source)
The coming week could be one of the most pivotal for the Trump White House and the markets, depending on how President Donald Trump chooses to proceed with China trade tariffs. U.S.-China trade talks apparently have been making progress, and in a positive sign, sources said a possible meeting between Trump and Chinese President Xi Jinping is being discussed for late March. Strategists expect some eventual deal to be reached, but first and foremost, the March 2 deadline on new tariffs looms at the end of the week. For now, it looks like the deadline could be extended. Trump, in fact, Friday reiterated that he could extend the deadline if progress is being made. He also said there was a very good chance a deal could be reached with China, and that he and Xi would make the big decisions. The week is packed with major events that could be market moving, including two days of economic testimony from Federal Reserve Chairman Jerome Powell. He appears before the Senate Banking Committee on Tuesday, and then a House committee Wednesday for the semiannual testimony. Trump also heads to Vietnam for a summit with North Korean leader Kim Jong Un on Wednesday and Thursday, and U.K. Prime Minister Theresa May faces another Brexit vote in parliament. The markets are also closely watching U.S. economic data after a string of misses on manufacturing and consumer data rattled stocks in the past couple of weeks. The lack of government data during the 35-day government shutdown has made it more difficult than usual to get a handle on the economy, and some economists now see fourth-quarter and first-quarter growth running at just 2 percent or below. Fourth-quarter GDP, delayed because of the shutdown, is finally released on Thursday.
Though earnings season is winding down, quite a few earnings releases are expected, including from retailers Home Depot, Macy'sand Nordstrom. "To me, the biggest story next week for markets is China. Do they announce an agreement or do they at least extend the deadline? That's the one that has the most immediate market impact. The markets are pricing in good news on China next week," said Tom Block, Washington policy strategist at Fundstrat. There were some news reports that Special Counsel Robert Mueller's report on the Trump campaign and Russia would be provided to the attorney general next week, but a Justice Department official Friday afternoon said that was not true.Whether the Trump campaign was involved with Russia or not matters much less than whether the president himself was involved. "This is of course great for American political drama but as for the $4.3 trillion foreign exchange market or what does this mean for the value of corporate America, it's not a big deal unless there's a smoking gun, and people think Trump is going to get impeached," said Marc Chandler, chief market strategist at Bannockburn Global Forex. "Why this is important is it might paralyze other policy. … The only way it is a really big factor is if it's used as fodder to pursue further investigations that paralyze the administration like Watergate did." Chandler said while the geopolitical events in the coming week could add to tension, they could all remain unresolved. "We want some closure. Next week is not going to bring some closure. We're going to get extensions," said Chandler. The uncertainty around China trade has been impacting the economic data, and business leaders have called on the White House to end the tariffs on China. The farm belt has been hurt as China retaliated against U.S. products. Cowen analysts said the talks are nearing a "term sheet" between Chinese and U.S. trade negotiators. The memorandums are expected to touch on a half-dozen key areas, including forced technology transfers and cybertheft; intellectual property rights; opening up of Chinese financial services to U.S. companies; currency; agriculture, and nontariff barriers to trade. Those barriers include industrial subsidies, licensing procedures and other regulations. The talks are also expected to focus on a list of 10 goods and commodities that China will buy to help narrow the trade balance. That could include an additional $30 billion per year of U.S. farm products including soybeans, corn, and wheat, the Cowen analysts said. Fundstrat's Block said the president understands the political impact of continuing tariffs or raising them to 25 percent by March 2, as he has threatened.
Trade deadline, North Korea, Brexit
Trump has said the deadline could be extended. "The road to 270 electoral votes for Trump goes through the farm states of the Midwest. There's no road map for Trump to get 270 electoral votes if he doesn't carry all those Midwestern farm states," Block said. "China is very big for lots of reasons. …Trump's people have to figure out, at a minimum, how to extend the truce. … The biggest threat to those states is continued trade war with China focused on agricultural products exported from the U.S." Besides China and trade and the Mueller report, Trump plans to meet North Korean leader Kim Jong Un in Vietnam in the week ahead, and Trump has said it is not to be his last meeting with Kim. The U.S. and North Korea are expected to seek a common understanding of what is expected in denuclearization, and Trump is expected to push Kim to give up his nuclear ambitions. Block said it's unclear what will come of those talks. "Trump overstates what he does, but the world is a little safer with us talking with North Korea rather than saber rattling with North Korea. That seems to be Trump's approach. Regardless of what his thought process is, the net result is better than not doing it," said Block. Investors are also looking to Europe where the U.K. Parliament votes on a no-deal Brexit, which critics say would disrupt trade and commerce . Prime Minister Theresa May continues to push for Britain's exit from the European Union on March 29. On Wednesday, there will be a vote on an amendment that would give the House of Commons the power to block a no-exit deal if May has not secured the approval by Parliament for a revised Brexit deal by the middle of March. "They're trying to force her to give up the no deal exit. The EU is expecting a request for a 60-day extension," said Chandler.
As for U.S. data, reports on personal income and spending are coming on Friday and fourth-quarter GDP on Thursday. December's disappointing durable goods data showed slower business spending, so analysts are watching closely to see whether there was any improvement in consumer spending. "The U.S. growth slowdown is seen intensifying in the first quarter too. We forecast U.S. GDP growth at a modest 1.5% annual rate in Q1. Slowing global manufacturing activity, tighter financial conditions, sluggish business equipment spending, and lackluster federal government spending (due in part to the government shutdown in January) are all contributing to the weakest quarter for U.S. growth in two years," wrote Scott Anderson, chief economist at Bank of the West. Anderson expects fourth-quarter growth at 2.2 percent. He also said if uncertainties in the U.S. around China trade talks and the Brexit negotiations go away, there is a good chance U.S. economic growth will bounce back in the second quarter. "I should note this is our base case forecast, as none of the parties involved in the negotiations want to see the worst case outcomes realized. If for some reason either of the negotiations go seriously off-track, however, the 2019 U.S. and global economic outlook will become considerably bleaker," he wrote.
This past week saw the following moves in the S&P:
Pre-Election Year March: Small-Caps Perfect 10 for 10
Turbulent March markets tend to drive prices up early in the month and batter stocks at month end. Julius Caesar failed to heed the famous warning to “beware the Ides of March” but investors have been served well when they have. Stock prices have a propensity to decline, sometimes rather precipitously, during the latter days of the month. In March 2001, DJIA plunged 1469 points (-11.8%) from March 9 to the 22. Normally a decent performing market month, March performs even better in pre-election years (see Vital Statistics table below). In pre-election years March ranks: 4th best for DJIA, S&P 500, NASDAQ and Russell 1000 (January, April and December are better). Pre-election year March rank #3 for Russell 2000. Pre-election year March has been up 13 out of the last 14 for DJIA. In fact, since inception in 1979, the Russell 2000 has a perfect, 10-for-10 winning record.
What more can we say about the amazing rebound of the stock market since December 24? For the first time since 1997, the S&P 500 Index is up more than 10% for the year through this point in February. Of course, it was the worst December for stocks since the Great Depression—making a larger bounce possible—but the rebound over the past two months has been historic. That begs the question: What does it mean when stocks are overbought on many short-term levels? “Yes, stocks are quite extended near -term,” explained LPL Senior Market Strategist Ryan Detrick, “but historically, extended markets have tended to deliver continued outperformance over the next several months.” We can see this by looking at the number of stocks in the S&P 500 that are above their 50-day moving average and the subsequent performance of the index. That number recently cleared 90%, which was one of the highest readings ever. And after 90% of stocks in the S&P 500 go above their 50-day moving average, their 1-, 3-, and 6-month returns actually have shown continued strength. In fact, as the LPL Chart of the Day shows, three months after hitting that 90% mark, the S&P 500 has been higher 12 of the previous 13 times going back to 1990.
This tells us the easy part of the recent rally is over, and we do see reasons to expect some type of consolidation or well-deserved pullback at some point, but we still think the stage is potentially set for new highs later this year.
More Good News
As this week’s Weekly Market Commentary suggested, over the near term equities appear quite stretched, but overall we continue to think the bull market has plenty of life left. Today, we’ll take a look at market breadth—one of our favorite technical indicators—to explore whether it may be pointing to better times ahead for equities. Market breadth measures how many stocks are participating in the movement of broader indexes. One of the easiest ways to measure this is via advance/decline (A/D) lines on various exchanges. An A/D line is a ratio of how many stocks go up versus down each day. The thinking is, if gains are caused by increases in many stocks, then there are plenty of buyers and the upward trend should likely continue, all else equal. On the other hand, if an upward move in a broad market gauge is driven by relatively few stocks, this can be a warning sign of cracks in the bull’s armor. Today’s LPL Chart of the Day shows that the NYSE Common Stock Only A/D line has broken out to a new all-time high. “This is another clue to market participants that things are actually quite healthy under the surface. When advance/decline lines are breaking out to new highs, history tells us that stocks usually aren’t too far behind,” explained LPL Senior Market Strategist Ryan Detrick.
One aspect of the rally in stocks this year that we can’t stress enough is how strong breadth has been. Besides the fact that the equal-weighted S&P 500 is outperforming the market cap weighted index by close to three percentage points YTD, the vast majority of S&P 500 Industry Groups are also either right at or very close to YTD highs. The table below lists S&P 500 Industry Groups that, along with the S&P 500, hit YTD highs so far today. Of the 60 Industry Groups, 26 hit YTD highs today and five of them are already up 20% YTD!
In addition to the 26 Industry Groups above, another 16 Industry Groups traded within 1% of a YTD high today and three of those are also up over 20% YTD. Adding both lists together, 70% of S&P 500 Industry Groups either traded at or came within 1% of hitting a YTD high this morning. That’s broad!
Square, Inc. (SQ) is confirmed to report earnings at approximately 4:05 PM ET on Wednesday, February 27, 2019. The consensus earnings estimate is $0.13 per share on revenue of $908.21 million and the Earnings Whisper ® number is $0.16 per share. Investor sentiment going into the company's earnings release has 80% expecting an earnings beat The company's guidance was for earnings of $0.12 to $0.13 per share on revenue of $895.00 million to $905.00 million. Consensus estimates are for year-over-year earnings growth of 62.50% with revenue increasing by 47.43%. Short interest has increased by 8.9% since the company's last earnings release while the stock has drifted lower by 4.2% from its open following the earnings release to be 8.3% above its 200 day moving average of $70.25. Overall earnings estimates have been revised lower since the company's last earnings release. On Wednesday, February 13, 2019 there was some notable buying of 5,812 contracts of the $75.00 put and 5,392 contracts of the $75.00 call expiring on Thursday, April 18, 2019. Option traders are pricing in a 8.4% move on earnings and the stock has averaged a 4.5% move in recent quarters.
Home Depot, Inc. (HD) is confirmed to report earnings at approximately 6:00 AM ET on Tuesday, February 26, 2019. The consensus earnings estimate is $2.16 per share on revenue of $26.56 billion and the Earnings Whisper ® number is $2.21 per share. Investor sentiment going into the company's earnings release has 76% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 27.81% with revenue increasing by 11.21%. Short interest has decreased by 13.1% since the company's last earnings release while the stock has drifted higher by 8.5% from its open following the earnings release to be 2.2% above its 200 day moving average of $188.29. Overall earnings estimates have been revised lower since the company's last earnings release. On Tuesday, February 12, 2019 there was some notable buying of 11,051 contracts of the $165.00 put expiring on Friday, March 15, 2019. Option traders are pricing in a 3.6% move on earnings and the stock has averaged a 1.1% move in recent quarters.
Chesapeake Energy Corp. (CHK) is confirmed to report earnings at approximately 7:00 AM ET on Wednesday, February 27, 2019. The consensus earnings estimate is $0.17 per share on revenue of $1.04 billion and the Earnings Whisper ® number is $0.20 per share. Investor sentiment going into the company's earnings release has 71% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 43.33% with revenue decreasing by 58.71%. Short interest has increased by 117.9% since the company's last earnings release while the stock has drifted lower by 22.2% from its open following the earnings release to be 33.4% below its 200 day moving average of $3.91. Overall earnings estimates have been revised lower since the company's last earnings release. On Friday, January 11, 2019 there was some notable buying of 5,346 contracts of the $7.00 call expiring on Friday, January 15, 2021. Option traders are pricing in a 14.4% move on earnings and the stock has averaged a 8.6% move in recent quarters.
Etsy, Inc. (ETSY) is confirmed to report earnings at approximately 4:05 PM ET on Monday, February 25, 2019. The consensus earnings estimate is $0.26 per share on revenue of $194.88 million and the Earnings Whisper ® number is $0.28 per share. Investor sentiment going into the company's earnings release has 75% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 73.33% with revenue increasing by 43.01%. Short interest has increased by 2.6% since the company's last earnings release while the stock has drifted higher by 22.6% from its open following the earnings release to be 25.1% above its 200 day moving average of $45.29. Overall earnings estimates have been revised higher since the company's last earnings release. On Tuesday, February 5, 2019 there was some notable buying of 2,590 contracts of the $55.00 put expiring on Friday, March 15, 2019. Option traders are pricing in a 11.6% move on earnings and the stock has averaged a 10.9% move in recent quarters.
JD.com, Inc. (JD) is confirmed to report earnings at approximately 5:25 AM ET on Thursday, February 28, 2019. The consensus estimate is for a loss of $0.04 per share on revenue of $19.15 billion and the Earnings Whisper ® number is ($0.02) per share. Investor sentiment going into the company's earnings release has 60% expecting an earnings beat. Consensus estiamtes are for year-over-year revenue growth of 13.10%. Short interest has increased by 25.4% since the company's last earnings release while the stock has drifted higher by 15.6% from its open following the earnings release to be 9.9% below its 200 day moving average of $28.80. Overall earnings estimates have been revised lower since the company's last earnings release. On Friday, February 15, 2019 there was some notable buying of 17,853 contracts of the $30.00 call expiring on Thursday, April 18, 2019. Option traders are pricing in a 7.9% move on earnings and the stock has averaged a 4.4% move in recent quarters.
Macy's, Inc. (M) is confirmed to report earnings at approximately 8:00 AM ET on Tuesday, February 26, 2019. The consensus earnings estimate is $2.65 per share on revenue of $8.46 billion and the Earnings Whisper ® number is $2.60 per share. Investor sentiment going into the company's earnings release has 28% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 6.03% with revenue decreasing by 2.38%. Short interest has decreased by 12.4% since the company's last earnings release while the stock has drifted lower by 31.6% from its open following the earnings release to be 28.4% below its 200 day moving average of $33.59. Overall earnings estimates have been revised lower since the company's last earnings release. On Friday, February 22, 2019 there was some notable buying of 3,804 contracts of the $24.50 call expiring on Friday, March 1, 2019. Option traders are pricing in a 10.0% move on earnings and the stock has averaged a 9.8% move in recent quarters.
McDermott International Inc. (MDR) is confirmed to report earnings at approximately 7:30 AM ET on Monday, February 25, 2019. The consensus earnings estimate is $0.21 per share on revenue of $2.70 billion and the Earnings Whisper ® number is $0.18 per share. Investor sentiment going into the company's earnings release has 62% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 110.00% with revenue increasing by 275.99%. Short interest has increased by 9.7% since the company's last earnings release while the stock has drifted lower by 15.0% from its open following the earnings release to be 48.2% below its 200 day moving average of $14.94. Overall earnings estimates have been revised lower since the company's last earnings release. On Wednesday, February 20, 2019 there was some notable buying of 22,689 contracts of the $8.00 call expiring on Friday, May 17, 2019. Option traders are pricing in a 17.4% move on earnings and the stock has averaged a 25.9% move in recent quarters.
PG&E Corp. (PCG) is confirmed to report earnings at approximately 8:45 AM ET on Thursday, February 28, 2019. The consensus earnings estimate is $0.62 per share on revenue of $4.29 billion. Investor sentiment going into the company's earnings release has 18% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 1.59% with revenue increasing by 4.63%. Short interest has increased by 122.1% since the company's last earnings release while the stock has drifted lower by 60.9% from its open following the earnings release to be 47.6% below its 200 day moving average of $35.85. Overall earnings estimates have been revised lower since the company's last earnings release. On Thursday, January 24, 2019 there was some notable buying of 10,702 contracts of the $20.00 call expiring on Friday, January 17, 2020. Option traders are pricing in a 11.5% move on earnings and the stock has averaged a 2.1% move in recent quarters.
Fitbit, Inc. (FIT) is confirmed to report earnings at approximately 4:05 PM ET on Wednesday, February 27, 2019. The consensus earnings estimate is $0.07 per share on revenue of $567.68 million and the Earnings Whisper ® number is $0.08 per share. Investor sentiment going into the company's earnings release has 80% expecting an earnings beat The company's guidance was for earnings of at least $0.07 per share on revenue of at least $560.00 million. Consensus estimates are for year-over-year earnings growth of 200.00% with revenue decreasing by 0.54%. Short interest has decreased by 27.1% since the company's last earnings release while the stock has drifted higher by 22.3% from its open following the earnings release to be 9.2% above its 200 day moving average of $6.13. Overall earnings estimates have been revised higher since the company's last earnings release. On Tuesday, February 5, 2019 there was some notable buying of 6,274 contracts of the $6.50 call expiring on Friday, March 1, 2019. Option traders are pricing in a 14.7% move on earnings and the stock has averaged a 13.0% move in recent quarters.
Amarin Corporation plc (AMRN) is confirmed to report earnings at approximately 5:00 AM ET on Wednesday, February 27, 2019. The consensus estimate is for a loss of $0.08 per share on revenue of $74.45 million and the Earnings Whisper ® number is ($0.08) per share. Investor sentiment going into the company's earnings release has 71% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 0.00% with revenue increasing by 38.21%. Short interest has increased by 15.4% since the company's last earnings release while the stock has drifted lower by 4.8% from its open following the earnings release to be 89.6% above its 200 day moving average of $10.48. Overall earnings estimates have been revised lower since the company's last earnings release. On Friday, February 22, 2019 there was some notable buying of 35,406 contracts of the $20.00 call expiring on Thursday, April 18, 2019. Option traders are pricing in a 17.3% move on earnings and the stock has averaged a 4.6% move in recent quarters.
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3 EMA Crossover Trading Secrets For Any Market - YouTube
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