Yen places its opponent in check. Analysis as of 04.11.2020
Monthly fundamental forecast for yen
While the greenback is waiting for the election's final results, trading currency cross rates may be worth considering. The US political landscape will undoubtedly affect most currencies, but the pandemic remains a weightier factor in Forex pricing in the medium and long terms. The strategies based on the divergence in epidemiological situations, economic growth, and monetary policies continue to yield profits. Another confirmation is the realization of the targets at 122.9 and 121.8 set in mid-October for shorts in the EURJPY. COVID-19 hit Japan less than the eurozone: in terms of Coronavirus cases per 100,000, Japan is one of the countries that tackle the pandemic most efficiently, along with China, Taiwan, and South Korea. The situation in Belgium, Spain, and Italy looks gloomy, on the contrary.
Recession and pandemic
Source: Financial Times. As a result, Europe is forced to introduce new restrictions, which will cut the eurozone's Q4 GDP by 2.3%, according to Financial Times. Thus, a double recession is certainly in the air. The organization of economic development and cooperation expects that the currency block's economy will reduce 7.9% in 2020, i.e., twice as much as during the previous global crisis. I dare suppose that the second wave may even downgrade those forecasts. The BoJ expects that the Japanese GDP will fall by 5.5% by the end of the 2020/2021 fiscal year in March. Japan's economic loss doesn't look as significant as the eurozone's since the efficiency of anti-pandemic measures in Asia is higher than in Europe.
Source: Financial Times. Christine Lagarde is sure the ECB will expand a monetary stimulus package in December as the coronavirus is spreading fast across Europe. Haruhiko Kuroda and his colleagues are ready to take action if necessary, but the BoJ's Head has not seen such a necessity so far. Both regulators got caught in a liquidity trap where softer monetary policies do not have any positive effect. Both agree to play currency wars, but the ECB's intentions are manifest, and the euro is therefore falling faster than other G10 currencies.
The situation may seriously change soon: vaccines' development will support the global economic recovery and international trade, which is positive news for the euro. The European countries will lift restrictions, and Christine Lagarde's hints about QE expansion will remain mere hints. According to Governor of the Austrian National Bank Robert Holzmann, there is no point in increasing buy volumes as the inflation won't speed up anyway. Instead, a change in the QE program's structure must be in focus. This scenario looks too optimistic, though. But why not hope for the best and use the EURJPY's drawdown to 120.65 for long-term buying? For more information follow the link to the website of the LiteForex https://www.liteforex.com/blog/analysts-opinions/yen-places-its-opponent-in-check-analysis-as-of-04112020/?uid=285861726&cid=62423
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
Fueling The Us Economy's Middle Market Growth Engine
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Job Openings Related To Middle Market Investment Bank
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Stifel Employee Reviews
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The definition of a business financial institution has advanced dramatically up to now a number of decades.
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Are Investment Bankers Rich
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In the previous article, we explained the premise of realizing the trading strategy from the aspects of the introduction of the M language , the basic grammar, the model execution method, and the model classification. In this article, we will continue the previous part, from the commonly used strategy modules and technologies. Indicators, step by step to help you achieve a viable intraday quantitative trading strategy.
Stage increase is calculating the percentage of current K line's closing price compare with previous N periods of closing price's difference. For example: Computing the latest 10 K-lines stage increases, can be written: 1234
CLOSE_0:=CLOSE; //get the current K-line's closing price, and save the results to variable CLOSE_0. CLOSE_10:=REF(CLOSE,10); //get the pervious 10 K-lines' closing price, and save the results to variable CLOSE_10 (CLOSE_0-CLOSE_10)/CLOSE_10*100;//calculating the percentage of current K line's closing price compare with previous N periods of closing price's difference.
New high price
The new high price is calculated by whether the current K line is greater than N cycles' highest price. For example: calculating whether the current K line is greater than the latest 10 K-lines' highest price, can be written: 12
HHV_10:=HHV(HIGH,10); //Get the highest price of latest 10 K-lines, which includes the current K-line. HIGH>REF(HHV_10,1); //Judge whether the current K-line's highest price is greater than pervious K-lines' HHV_10 value.
Price raise with massive trading volume increase
For example: If the current K line's closing price is 1.5 times of the closing price of the previous 10 K-lines, which means in 10 days, the price has risen 50%; and the trading volume also increased more than 5 times of the pervious 10 K-lines. can be written: 1234567
CLOSE_10:=REF(CLOSE,10); //get the 10th K-line closing price IS_CLOSE:=CLOSE/CLOSE_10>1.5; //Judging whether the current K Line closing price is 1.5 times greater than the value of CLOSE_10 VOL_MA_10:=MA(VOL,10); //get the latest 10 K-lines' average trading volume IS_VOL:=VOL>VOL_MA_10*5; //Judging whether the current K-line's trading volume is 5 times greater than the value of VOL_MA_10 IS_CLOSE AND IS_VOL; //Judging whether the condition of IS_CLOSE and IS_VOL are both true.
Price narrow-shock market
Narrow-shock market means that the price is maintained within a certain range in the recent period. For example: If the highest price in 10 cycles minus the lowest price in 10 cycles, the result divided by the current K-line's closing price is less than 0.05. can be written: 1234
HHV_10:=HHV(CLOSE,10); //Get the highest price in 10 cycles(including current K-line) LLV_10:=LLV(CLOSE,10); //Get the lowest price in 10 cycles(including current K-line) (HHV_10-LLV_10)/CLOSE<0.05; //Judging whether the difference between HHV_10 and LLV_10 divided by current k-line's closing price is less than 0.05.
Moving average indicates bull market
Moving Average indicates long and short direction, K line supported by or resisted by 5，10，20，30，60 moving average line, Moving average indicates bull market or bear market. can be written: 123456
MA_5:=MA(CLOSE,5); //get the moving average of 5 cycle closing price. MA_10:=MA(CLOSE,10);//get the moving average of 10 cycle closing price. MA_20:=MA(CLOSE,20);//get the moving average of 20 cycle closing price. MA_30:=MA(CLOSE,30);//get the moving average of 30 cycle closing price. MA_5>MA_10 AND MA_10>MA_20 AND MA_20>MA_30; //determine wether the MA_5 is greater than MA_10, and MA_10 is greater than MA_20, and MA_20 is greater than MA_30.
Previous high price and its locations
To obtain the location of the previous high price and its location, you can use FMZ Quant API directly. can be written: 123
HHV_20:=HHV(HIGH,20); //get the highest price of 20 cycle(including current K line) HHVBARS_20:=HHVBARS(HIGH,20); //get the number of cycles from the highest price in 20 cycles to current K line HHV_60_40:REF(HHV_20,40); //get the highest price between 60 cycles and 40 cycles.
Price gap jumping
The price gap is the case where the highest and lowest prices of the two K lines are not connected. It consists of two K lines, and the price gap is the reference price of the support and pressure points in the future price movement. When a price gap occurs, it can be assumed that an acceleration along the trend with original direction has begun. can be written: 12345678
HHV_1:=REF(H,1); //get the pervious K line's highest price LLV_1:=REF(L,1); //get the pervious K line's lowest price HH:=L>HHV_1; //judging wether the current K line's lowest price is greater than pervious K line's highest price (jump up) LL:=H1.001; //adding additional condition, the bigger of the price gap, the stronger the signal (jump up) LLL:=H/REF(L.1)<0.999; //adding additional condition, the bigger of the price gap, the stronger the signal (jump down) JUMP_UP:HH AND HHH; //judging the overall condition, whether it is a jump up JUMP_DOWN:LL AND LLL; //judging the overall condition, whether it is a jump down
Common technical indicators
Moving average https://preview.redd.it/np9qgn3ywxs41.png?width=811&format=png&auto=webp&s=39a401b5c9498a13d953678c0c452b3b8f6cbe2c From a statistical point of view, the moving average is the arithmetic average of the daily price, which is a trending price trajectory. The moving average system is a common technical tool used by most analysts. From a technical point of view, it is a factor that affects the psychological price of technical analysts. The decision-making factor of thinking trading is a good reference tool for technical analysts. The FMZ Quant tool supports many different types of moving averages, as shown below: 1234567
MA_DEMO:MA(CLOSE,5); // get the moving average of 5 cycle MA_DEMO:EMA(CLOSE,15); // get the smooth moving average of 15 cycle MA_DEMO:EMA2(CLOSE,10);// get the linear weighted moving average of 10 cycle MA_DEMO:EMAWH(CLOSE,50); // get the exponentially weighted moving average of 50 cycle MA_DEMO:DMA(CLOSE,100); // get the dynamic moving average of 100 cycle MA_DEMO:SMA(CLOSE,10,3); // get the fixed weight of 3 moving average of closing price in 10 cycle MA_DEMO:ADMA(CLOSE,9,2,30); // get the fast-line 2 and slow-line 30 Kaufman moving average of closing price in 9 cycle.
https://preview.redd.it/mm0lkv00xxs41.png?width=1543&format=png&auto=webp&s=a87bdb4feecf97cbeef423b935860bfea85ffe6d Bollinger bands is also based on the statistical principle. The middle rail is calculated according to the N-day moving average, and the upper and lower rails are calculated according to the standard deviation. When the BOLL channel starts changing from wide to narrow, which means the price will gradually returns to the mean. When the BOLL channel is changing from narrow to wide, it means that the market will start to change. If the price is up cross the upper rail, it means that the buying power is enhanced. If the price down cross the lower rail, it indicates that the selling power is enhanced. Among all the technical indicators, Bollinger Bands calculation method is one of the most complicated, which introduces the concept of standard deviation in statistics, involving the middle trajectory ( MB ), the upper trajectory ( UP ) and the lower trajectory ( DN ). luckily, you don't have to know the calculation details, you can use it directly on FMZ Quant platform as follows: 1234
MID:MA(CLOSE,100); //calculating moving average of 100 cycle, call it Bollinger Bands middle trajectory TMP2:=STD(CLOSE,100); //calculating standard deviation of closing price of 100 cycle. TOP:MID+2*TMP2; //calculating middle trajectory plus 2 times of standard deviation, call it upper trajectory BOTTOM:MID-2*TMP2; //calculating middle trajectory plus 2 times of standard deviation, call it lower trajectory
https://preview.redd.it/9p3k7y42xxs41.png?width=630&format=png&auto=webp&s=b1b8078325fc142c1563a1cf1cc0f222a13e0bde The MACD indicator is a double smoothing operation using fast (short-term) and slow (long-term) moving averages and their aggregation and separation. The MACD developed according to the principle of moving averages removes the defect that the moving average frequently emits false signals, and also retains the effect of the other good aspect. Therefore, the MACD indicator has the trend and stability of the moving average. It was used to study the timing of buying and selling stocks and predicts stock price change. You can use it as follows:
DIFF:EMA(CLOSE,10)-EMA(CLOSE,50); //First calculating the difference between short-term moving average and long-term moving average. DEA:EMA(DIFF,10); //Then calculating average of the difference.
The above is the commonly used strategy module in the development of quantitative trading strategies. In addition, there are far more than that. Through the above module examples, you can also implement several trading modules that you use most frequently in subjective trading. The methods are the same. Next, we began to write a viable intraday trading strategy.
In the Forex spot market, there is a wellknown strategy called HANS123. Its logic are basically judging wether the price breaks through the highest or lowest price of the number of K lines after the market opening
Ready to enter the market after 30 minutes of opening;
Upper rail = 30 minutes high after opening ;
Lower rail = 30 minutes low after opening ;
When the price breaks above the upper limit, buy and open the position;
When the price falls below the lower rail, the seller opens the position.
Intraday trading strategy, closing before closing;
// Data Calculation Q:=BARSLAST(DATA<>REF(DATA,1))+1; //Calculating the number of period from the first K line of the current trading day to current k line, and assign the results to N HH:=VALUEWHEN(TIME=0930,HHV(H,Q)); //when time is 9:30, get the highest price of N cycles, and assign the results to HH LL:=VALUEWHEN(TIME=0930,LLV(L,Q)); //When time is 9:30, get the lowest price of N cycles, and assign the results to LL //Placing Orders TIME>0930 AND TIME<1445 AND C>HH,BK; //If the time is greater than 9:30 and lesser than 14:45, and the closing price is greater than HH, opening long position. TIME>0930 AND TIME<1445 AND C=1445,CLOSEOUT; //If the time is greater or equal to 14:45, close all position. //Filtering the signals AUTOFILTER; //opening the filtering the signals mechanism
To sum up
Above we have learned the concept of the strategy module. Through several commonly used strategy module cases, we had a general idea of the FMZ Quant programming tools, it can be said that learning to write strategy modules and improve programming logic thinking is a key step in advanced quantitative trading. Finally, we used the FMZ Quant tool to implement the trading strategy according a classical Forex trading strategy.
Next section notice
Maybe there are still some confusion for some people, mainly because of the coding part. Don't worry, we have already thought of that for you. On the FMZ Quant platform, there is another even easier programming tool for beginners. It is the visual programming, let's learn it soon!
A wild MAME 0.215 appears! Yes, another month has gone by, and it’s time to check out what’s new. On the arcade side, Taito’s incredibly rare 4-screen top-down racer Super Dead Heat is now playable! Joining its ranks are other rarities, such as the European release of Capcom‘s 19XX: The War Against Destiny, and a bootleg of Jaleco’s P-47 – The Freedom Fighter using a different sound system. We’ve got three newly supported Game & Watch titles: Lion, Manhole, and Spitball Sparky, as well as the crystal screen version of Super Mario Bros. Two new JAKKS Pacific TV games, Capcom 3-in-1 and Disney Princesses, have also been added. Other improvements include several more protection microcontrollers dumped and emulated, the NCR Decision Mate V working (now including hard disk controllers), graphics fixes for the 68k-based SNK and Alpha Denshi games, and some graphical updates to the Super A'Can driver. We’ve updated bgfx, adding preliminary Vulkan support. There are some issues we’re aware of, so if you run into issues, check our GitHub issues page to see if it’s already known, and report it if it isn’t. We’ve also improved support for building and running on Linux systems without X11. You can get the source and Windows binary packages from the download page.
apple2_flop_orig: Alibi, American Government (Micro Learningware), Apple Stellar Invaders, Battlefront, Beach Landing, Carriers at War, The Coveted Mirror, Crime Stopper, Decisive Battles of the American Civil War: Volume Three, Decisive Battles of the American Civil War: Volume Two, Decisive Battles of the Civil War: Volume One, Dogfight II, Europe Ablaze, Galactic Wars, Gauntlet, Ghostbusters, Go (Hayden), Guderian, Halls of Montezuma, The Haunted Palace, I, Damiano, Leisure Suit Larry in The Land of The Lounge Lizards, The Mask of the Sun (Version 2.1), MacArthur's War, Muppet Learning Keys: The Muppet Discovery Disk, Oil Rig, Panzer Battles, Pulsar ][, Questprobe featuring Spider-Man, Reach For The Stars (Version 1.0), Reach For The Stars (Version 2.0), Reach For The Stars (Version 3.0), Reversal, Russia, Sherlock Holmes in Another Bow, Simultaneous Linear Equations, Space Kadet, Tapper, Ulysses and the Golden Fleece, Vaults of Zurich, Winter Games [4am, Firehawke]
fmtowns_cd: CG Syndicate Vol. 1 - Lisa Northpoint, CubicSketch V1.1 L10, New Horizon CD Learning System II - English Course 1, Shanghai, Space Museum, TownsSOUND V1.1 L20, Z's Triphony DigitalCraft Towns [redump.org, r09]
hp9825b_rom: 9885/9895 ROM for 9825, 9885 ROM for 9825, Matrix ROM for 9825, SSS mass storage ROM [F.Ulivi]
ibm5150: Action Service (Smash16 release) (3.5"), International Karate, Italy '90 Soccer, Joe Blade (Smash16 release), Out Run (Kixx release), Starflight [ArcadeShadow]
ibm5170: Corridor 7: Alien Invasion, Links - The Challenge of Golf (5.25"HD) [ArcadeShadow]
midi_flop: Dansbandshits nr 3 (Sweden) [FakeShemp]
vz_snap: Ace of Aces, Adventure, Airstrip, Arkaball v1, Arkaball v2, Arrgh, Assembly Language for Beginners, Asteroids, Attack of the Killer Tomatoes, Backgammon, Backgammon Instructions, Battleships v1, Battleships v2, Bezerk, Binary Tape Copier v1.0, Bomber, Breakproof File Copier, Bust Out, Camel, Card Andy, Casino Roulette v1, Casino Roulette v2, Catch, Challenger, Chasm Capers, Check Disk, Checkers, Chess, Circus, Compgammon, Computer Learjet, Concentration, Cos Res, Craps, Crash, Curses, Dawn Patrol, Decoy v1, Decoy v2, Defence Penetrator, Dig Out, Disassembler v2, Disassemmbler v1, Disk Copier, Disk Copy V2.0, Disk Editor-Assembler V6.0X, Disk Menu, Disk Ops 4, Disk Sector Editor v1, Disk Sector Editor v2, Dog Fight, Dracula's Castle, The Dynasty Derby, Editor-Assembler V.1.2, Editor-Assembler V.1.2B, Electric Tunnel, Electronic Blackjack, Extended DOS V1.3, Extended VZ Basic V2.5, Factory, Fastdisk V1.0, Fastdisk V1.1, Fastdisk V1.2, Fastdisk V1.2 demo, Filesearch 2.0, Filesearch V2.0, Formula One v1, Formula One v2, Formula Uno, Frog, Galactic Invasion, Galactic Raiders, Galactic Trade, Galaxon, Game Instructions, Ghost Blasters, Ghost Hunter (hacked), Ghost Hunter instructions, Ghost Hunter v1, Ghost Hunter v2, Golf, Grand Prix, Grave Digger, Gunfight, Hamburger Sam, Hangman v1, Hangman v3, Hangman v4, Hex Maths, Hex Utilities, The High Mountains, High Scores, Hoppy v1, Hoppy v2, Hunt the Wumpus, Instructions for Asteroid Dodge, Instructions for Invaders, Instructions for Ladder Challenge, Invaders v1, Invaders v2, Inventory, Kamikaze Invaders, Key Hunt, Knights and Dragons, Ladder Challenge, Laser, Laser Pong, Lunar Lander, Mad Max VI, Madhouse, Mars Patrol, Mastermind, Match Box, Match Box Instructions, Maths Armada, Maze Generator, Meat Pies, Melbourne Cup, Meteor, Missile Attack, Missile Command v1, Missile Command v2, Missing Number, Moon, Moon Lander, Moonlander, Moving Targets, Number Sequence, Number Slide, Othello, Othello Instructions, Painter v1, Painter v2, Painter v3, Panik, Panik Instructions, Penguin, Planet Patrol, Poker Machine, Punch v1, Punch v2, Pursuit, The Quest, The Return of Defense Command, Rocket Command, Shootout, Space, Space Ram, Space Station Defender, Space Vice, Star Blaster, Submarine, Super Snake, Super Snake Trapper, The Ten Commandments, Tennis v1, Tennis v2, Tone Generator, Totaliser Derby, Tower, Triffids 2040 AD, Twisting Road, VZ 200-300 Diskette Monitor, VZ Panik, VZ cave, VZ-200 Cup, Vzetris, Worm, Write a Story [Robbbert]
Software list items promoted to working
dmv: MS-DOS v2.11 HD, MS-DOS v2.11 HD (Alt 2), MS-DOS v2.11 HD (Alt 3), MS-DOS v2.11 HD (Alt), Z-Com v2.0 HD [Sandro Ronco, rfka01]
evio: Anime Mix 1, Chisako Takashima Selection, evio Challenge!, evio Selection 02, evio Selection 03, Hard Soul 1, I Love Classic 1, Pure Kiss 1 [David Haywood, Peter Wilhelmsen, ShouTime, Sean Riddle]
Debian GNU/Linux 1.3.1 with Debian-JP Packages, Debian GNU/Linux 2.0r2 with Hamm-JP [akira_2020, Tokugawa Corporate Forums, r09]
Air Warrior V1.2, Fujitsu Habitat V2.1L10, Hyper Media NHK Zoku Kiso Eigo - Dai-3-kan, Nobunaga no Yabou - Sengoku Gun'yuuden, Taito Chase H.Q. (Demo), TownsFullcolor V2.1 L10, Video Koubou V1.4 L10 [redump.org, r09]
leapfrog_ltleappad_cart: Baby's First Words (USA), Disney Pooh Loves You! (USA), If I were... (USA) [ClawGrip, TeamEurope]
ins8250: Only clear transmitter holding register empty interrupt on reading IIR if it’s the highest priority pending interrupt. [68bit]
bus/ss50/mps2.cpp: Connected RS-232 control lines. [68bit]
machine/ie15.cpp: Cleaned up RS-232 interface. [68bit]
bus/rs232: Delay pushing initial line state to reset time. [68bit]
bus/rs232/null_modem.cpp: Added configuration option for DTR flow control. [68bit]
tv990.cpp: Improved cursor position calculation. [68bit]
tilemap.cpp: Improved assert conditions, fixing tilemap viewer, mtrain and strain in debug builds. [AJR]
spbactn.cpp: Use raw screen timing parameters for spbactn. [AJR]
laz_aftrshok.cpp: Added aftrshok DIP switch documentation from the manual. [AJR]
ELAN RISC II updates: [AJR]
Identified CPU type used by vreadere as ePG3231.
Added preliminary port I/O handlers and callbacks.
Added stub handlers and state variables for interrupt controller, timers, synthesizer, UART and SPI.
Fixed TBRD addressing of external data memory.
Fixed calculation of carry flag for normal adder operations.
Implemented multi-byte carry/borrow for applicable registers.
Implemented signed multiplication option.
Added internal stack buffer for saving PCH during calls/interrupts.
alpha68k_n.cpp: Replaced sstingry protection simulation with microcontroller emulation. [AJR]
sed1330: Implemented character drawing from external ROM, fixed display on/off command, and fixed screen area definition. [AJR]
tlcs90: Separated TMP90840 and TMP90844 disassemblers. [AJR]
z180 updates: [AJR]
Split Z180 device into subtypes; HD647180X now implements internal PROM, RAM and parallel ports.
Added internal clock dividers adjust CPU clocks in many drivers to compensate.
Reduced logical address width to 16 bits.
h8: Made debug PC adjustment and breakpoints actually work. [AJR]
subsino2.cpp: Added save state support and cleaned up code a little. [AJR]
Wall Street Week Ahead for the trading week beginning July 22nd, 2019
Good morning and happy Saturday to all of you here on wallstreetbets. I hope everyone on this subreddit made out pretty nicely in the market this past week, and is ready for the new trading week ahead. Here is everything you need to know to get you ready for the trading week beginning July 22nd, 2019.
Week ahead: Earnings, GDP expected to show sluggish growth as investors await rate cut - (Source)
Sluggish economic and earnings growth will be a theme in markets in the week ahead, as investors await a Fed interest rate cut at the end of the month. More than a quarter of the S&P 500 companies report earnings in the coming week, the second big week of the second quarter reporting season. FAANG names, like Alphabet and Amazon, and blue chips from McDonald’s to Boeingand United Technologies are among the more than 130 companies reporting. There is also some key economic data, including Friday’s second quarter GDP, which should show a slowing to 1.8% from the first quarter’s 3.1% pace, according to Refinitiv. On Thursday, durable goods are reported and will include an update on businesses investment. There are also existing home sales Tuesday, new home sales Wednesday and advance economic indicators Thursday. But there will be no Fed speakers, after a parade of central bank officials in the past week, including Fed Chair Jerome Powell. The most impactful comments, however, came Thursday from New York Fed President John Williams, who set off a debate about how much the Fed could cut rates at its July 30-31 meeting — 25 or 50 basis points. Even as the New York Fed later said Williams comments were not about current policy, market pros took heed of his words about how central bankers should “act quickly.” Fed dominates Fed officials do not speak publicly in the days ahead of policy meetings, but market pros will find plenty to debate. Fed funds futures were predicting a 43% chance of a 50 basis point cut in July, after shooting as high as 70% Thursday afternoon. “For sure, the Fed is going to dominate for next week. I think we’ll get at least a 25 basis point cut. I’m thinking we’re not going to get 50 basis point cut...The Fed has been burned when it’s been bold,” said Tony Roth, chief investment officer at Wilmington Trust. Roth said he believes the market is already pricing in a quarter-point cut, and he does not see the Fed’s rate cut as much of a longer-term catalyst for stocks. If it trims by a half percentage point, he expects just a short-term pop. Economists believe the Fed will cut interest rates even though recent data has improved. That’s in part because Powell has stressed the Fed is focused on the global economic slowdown, trade wars and low inflation, and that it will do what it takes to keep the economy expanding. “The only real catalyst that would really help the market would be if there was a trade deal with China,” Roth said. “I think the likelihood of that is less than > 10%. We’re very pessimistic on the possibility of a real deal with China prior to the [2020 presidential] election.” So, in the void ahead of the Fed’s meeting, the market will be watching earnings. As earnings rolled out this past week, stocks took a rest from their record-setting streak, as some companies lowered forecasts and most beat earnings and revenue estimates. As of Friday morning, 77% of the roughly 80 companies reporting had beaten earnings estimates, and 65% topped revenue forecasts, according to Refinitiv. Based on actual reports and forecasts, earnings per share for the S&P companies are expected to be up 1% in the second quarter. That is up from expectations that the profit growth would be slightly negative this quarter. “If you look at the numbers, we’re above the averages for top and bottom line beats, but at the same time when you look at revisions, every day we’re getting revisions for third and fourth quarter, and they’re coming down.There’s a real worry of an earnings recession, when you get out into the third and fourth quarter and out to next year,” Roth said. Roth said he’s currently neutral on risk assets, and he sees a slowdown brewing in the smallest U.S. companies that could spread up the food chain. “We do see those fundamental cracks in the economy in small business and the small business labor market, and on top of that you have these big macro risks out there,” such as trade and the upcoming election, Roth said. Slower economy As earnings growth was muted in the second quarter, so was the pace of economic gains. If growth comes in as expected, it would be the first quarter where growth was under 2% since the first quarter of 2017. Economists are watching to see how consumer spending fared in the quarter, after a recent pickup and also whether business inventories are declining. “The data we need is not Q2. What’s at risk is the growth and magnitude of the Fed rate cut. I don’t think Q2 is going to have much impact on the Fed’s thinking,” said Marc Chandler, chief market strategist at Bannockburn Global Forex. “It’s really how Q3 is progressing. It seems to me the economy softened in April and May and picked up in June with jobs data, retail sales and manufacturing sector.” Chandler said investors will also be focused on the European Central Bank, which some economists believe could cut its overnight deposit rate to negative 0.5% from negative 0.4% currently when it meets Thursday. Chandler said odds are about 50% for the rate cut, which many also expect in September. “While we’re waiting for the Fed to figure out whether it’s 25 or 50 basis points, and we’re waiting for the ECB to get all its forms sorted out ... the emerging markets are pushing ahead,” said Chandler, noting Russia and Turkey could cut rates in the next several days, after similar moves in the past week by South Africa, South Korea and Indonesia. “It just makes the story more global. You’re seeing the trade numbers from China, Japan, Singapore and South Korea weaken. You’re seeing exports form China suffer. Exports from all of Asia are suffering,” he said. “The big surprise for China and Japan has also been on the import side. The declines in their imports is really someone else’s [drop in] exports.” Rate cuts and currency wars Dollar strength has been a consequence of the trade war, and Fed action could help turn it around. “If the Fed fails to move, you’re going to end up with an increasingly stronger dollar,” which impacts corporate earnings, Roth said. “The dollar is quite strong and is increasingly going to be a headwind for U.S. companies. It hasn’t appreciated that much in 12 months, but if we see a divergence in monetary policy between the U.S. and the rest of the world, you would see a carry trade develop where people would want to buy assets in the U.S.,” he said. The dollar index was slightly higher on the week, but Wall Street has been focused on President Donald Trump’s negative comments on the currency’s strength. As Trump has criticized the Fed, he also complains that other central banks manipulate their currencies to give them an edge in trade. Trump has said the Fed should already be cutting rates, something it hasn’t done since December 2008. A number of Wall Street strategists have said they now believe it is possible that the U.S. government could intervene to weaken the dollar, but that would be unlikely.
This past week saw the following moves in the S&P:
Lagging Small-caps: Seasonal and Economic Factors Weigh
Small-caps measured by the performance of the Russell 2000 have been lagging since mid-March with the gap in performance widening in June and continuing into July. At yesterday’s close the Russell 2000 was up 15.35% year-to-date compared to a gain of 19.87% for the Russell 1000. Based upon historical trends this is not unusual for this time of the year nor during times when U.S. economic data is mixed. In the following chart the one-year seasonal pattern of the Russell 2000/Russell 1000 has been plotted (solid black line with grey fill) along with 2019 year-to-date (blue line). This chart is similar to the chart found on page 110 of the 2019 Stock Trader’s Almanac. When the lines are rising small-caps are outperforming, when the lines are falling small-caps are lagging. Small-caps exhibited typical seasonal strength during the first quarter but have been fading ever since. In some years, small-cap strength can last until mid-June however, that is not the case this year. Going forward, small-cap underperformance is likely to persist until early in the fourth quarter with possible a hint of strength at the end of August.
It’s usually about this time of the year, when trading volumes begin to slump and markets meander that we begin to hear talk of the infamous “Summer Rally” featured on page 74 of the Stock Trader’s Almanac 2019. The “Summer Rally” is usually the weakest seasonal rally of them all. We looked at the current Summer Rally and found it to be above average already, up 10.2% from the Spring low on May 31, and that does portend well for the Summer and Fall Corrections. We lined up the Summer Rallies ranked from weakest to strongest since 1964. Over the past 55 years prior to this year DJIA has rallied and average of 9.1% from its May/June low until its Q3 high. The Fall Rally averages 10.9% and the Summer and Fall Corrections average a loss of just under 9% for a net average gain of a few percentage points over the summer and fall. As shown in the table below, when the Summer Rally is greater than or equal to the 55-year 9.1% average, the summer and fall correction tend to be bit milder, -6.2% and -8.2%, respectively. Summer Rally gains beyond 12.5% historically had the smallest summer and fall corrections. One prominent exception being 1987.
Earnings (and Guidance) Likely to Make or Break the Rally
Once again today, DJIA, S&P 500 and NASDAQ closed at new all-time highs. With today’s modest gains, DJIA is up 17.3% year-to-date. S&P 500 is even better at 20.2% while NASDAQ is still best at 24.5%. Compared to historical average performance in pre-election years at this time of the year, DJIA and S&P 500 are comfortably above average. NASDAQ’s impressive 24.5% gain is just average (since 1971). NASDAQ’s Midyear Rally delivered again, but officially ended last Friday. The seasonal pattern charts, above and below, along with July’s typical performance over the last 21 years suggest further gains during the balance of July and the third quarter could be limited. For the market to make meaningful gains in the near-term earnings will need to decent and forward guidance will also need to be firm.
Yesterday was another one of those days that makes you scratch your head. In a relatively busy day for economic data, Initial Jobless Claims came in within 25K of a 50-year low, and the Philly Fed Manufacturing report saw its largest m/m increase in a decade. That follows other data last week where Retail Sales were very strong and CPI and PPI both came in ahead of consensus forecasts. The trend of better than expected data since the June employment report on July 5th is reflected in recent moves of the Citi Economic Surprise Index which has rallied from -68.3 up to -41.5. Granted, it’s still negative, but what was looking like a real dismal backdrop for the economy just three weeks ago seems to be showing signs of improvement.
On top of the economic data, two notable interviews from FOMC officials Williams from New York and Vice Chair Clarida moved markets. Given the strong tone of economic data, one would expect both officials to try and tone down rising market expectations regarding any aggressive policy moves at the July meeting. Well, markets don’t always make sense. In their respective interviews, both Williams and Clarida not only didn’t tone down expectations, but they added fuel to the fire. Williams noted that “it pays to act quickly to lower rates" and "vaccinate” the economy "against further ills." Clarida was even more direct when he said that “Research shows you act preemptively when you can.” In other words, the data-dependent Fed is casting the data aside and ready to move anyway. In his interview on Fox Business, Clarida almost got a chuckle when asked whether there was any chance the Fed wouldn’t cut rates in July. The dovish turn from the Fed was immediately reflected in market expectations for rate policy at the July meeting. Back in June, market expectations for a 50 basis points (bps) cut at the next meeting peaked out at under 50%. Then, in the days following the June employment report, expectations dropped all the way down to 3%. In the last ten days, though, the trend has completely reversed, and as of yesterday’s close topped out at 71% versus just a 29% chance for a 25 bps cut. Probabilities for a 50 bps cut came in a bit overnight but are still at about 50/50. Yesterday alone, though, expectations for a 25 bps cut and a 50 bps cut more than completely reversed from the prior day, and remember, that’s after what was a good day of economic data! Can you imagine what expectations would be like if the data was actually bad?
The Bloomberg World index is a cap-weighted index made up of nearly 5,000 stocks from around the world (including US stocks). While the S&P 500 has been hitting new all-time highs over the last week, the Bloomberg World index remains 7% below highs that it last made back in January 2018.
Below is a chart showing the ratio of the S&P 500 to the Bloomberg World index since the World index's inception back in August 2003. While the World index outperformed the US for five years in the mid-2000s, the US has been outperforming since the end of 2007, which includes both the Financial Crisis and the bull market that has been in place since the 2009 lows.
Along with the relative strength chart between the two indices above, below we show the price change of the S&P 500 versus the Bloomberg World index since August 2003. Through today, the S&P was up 203% versus a gain of 142% for the Bloomberg World index.
Since the November 2016 election, the S&P 500 is up 40% versus a gain of 26% for the Bloomberg World index. Notably, the World index kept up with the S&P through early 2018, but weakness for the World index in mid-2018 and a failure to bounce back as much as the US this year has left the World index well behind.
The S&P 500 is up over 20% YTD, but over the last 12 months, it is up just under 10% on a total return basis. And within the S&P 1500, there are only 44 stocks that are up more than 50% on a total return basis over the last 12 months. These 44 stocks are listed below. Innovative Industrials (IIPR) -- a cannabis REIT -- has been the best performing stock in the S&P 1500 over the last year with a total return of 302%. In second place is eHealth (EHTH) with a gain of 269%, followed by Avon Products (AVP) at +174.8% and Coca-Cola Bottling (COKE) at +128.58%. Coca-Cola Bottling is probably one of the last names you would have guessed as a top five performer over the last year! Other notables on the list of biggest winners include Advanced Micro (AMD), LendingTree (TREE), Starbucks (SBUX), AutoZone (AZO), Chipotle (CMG), Hershey (HSY), and Procter & Gamble (PG). Some names that aren't on the list that you may have expected to see? AMZN, NFLX, MSFT? Nope. None of the mega-cap Tech companies are on the list of biggest winners due to serious weakness from this group in Q4 2018.
Although the last two trading days have seen exceptionally narrow daily ranges, today we wanted to take a quick look at the S&P 500's frequency of 2% daily moves (either up or down) in the post-WWII period. The chart below breaks out the frequency of 2% days by year, and years with more than 25 one-day moves of 2% are notated accordingly. Overall, there have been an average of 11 daily 2% moves in a given year. After five straight years from 2007 to 2011 where we saw an above-average number of 2% days, the last seven years have only seen one year with an above-average number of occurrences (2018, 21). Remember, in 2017 there wasn't one single trading day that saw the S&P move up or down 2%! So far this year, there have only been four 2% days, but with the most volatile part of the year on tap, we are likely to see that number increase in the months ahead. Don't expect the relative calm that we have seen in the last few trading days to last forever. Volatility is unpredictable and usually comes up and surprises you when you least expect it!
([CLICK HERE FOR FRIDAY'S AFTER-MARKET EARNINGS TIME & ESTIMATES!]())
Amazon.com, Inc. $1,964.52
Amazon.com, Inc. (AMZN) is confirmed to report earnings at approximately 4:00 PM ET on Thursday, July 25, 2019. The consensus earnings estimate is $5.29 per share on revenue of $62.51 billion and the Earnings Whisper ® number is $5.70 per share. Investor sentiment going into the company's earnings release has 78% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 4.34% with revenue increasing by 18.20%. Short interest has increased by 14.0% since the company's last earnings release while the stock has drifted higher by 1.8% from its open following the earnings release to be 13.0% above its 200 day moving average of $1,737.93. Overall earnings estimates have been revised lower since the company's last earnings release. On Thursday, July 11, 2019 there was some notable buying of 3,494 contracts of the $2,000.00 call expiring on Friday, August 16, 2019. Option traders are pricing in a 4.4% move on earnings and the stock has averaged a 4.0% move in recent quarters.
Facebook Inc. (FB) is confirmed to report earnings at approximately 4:05 PM ET on Wednesday, July 24, 2019. The consensus earnings estimate is $1.90 per share on revenue of $16.45 billion and the Earnings Whisper ® number is $2.01 per share. Investor sentiment going into the company's earnings release has 82% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 9.20% with revenue increasing by 24.33%. Short interest has increased by 21.7% since the company's last earnings release while the stock has drifted higher by 0.7% from its open following the earnings release to be 20.8% above its 200 day moving average of $164.17. Overall earnings estimates have been revised higher since the company's last earnings release. On Wednesday, July 17, 2019 there was some notable buying of 16,697 contracts of the $290.00 call expiring on Friday, September 20, 2019. Option traders are pricing in a 6.5% move on earnings and the stock has averaged a 8.6% move in recent quarters.
Tesla, Inc. (TSLA) is confirmed to report earnings at approximately 5:15 PM ET on Wednesday, July 24, 2019. The consensus estimate is for a loss of $0.52 per share on revenue of $6.38 billion and the Earnings Whisper ® number is ($0.44) per share. Investor sentiment going into the company's earnings release has 33% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 84.80% with revenue increasing by 59.41%. Short interest has increased by 26.5% since the company's last earnings release while the stock has drifted higher by 1.2% from its open following the earnings release to be 8.1% below its 200 day moving average of $280.96. Overall earnings estimates have been revised higher since the company's last earnings release. On Tuesday, July 16, 2019 there was some notable buying of 30,445 contracts of the $50.00 put expiring on Friday, August 16, 2019. Option traders are pricing in a 7.8% move on earnings and the stock has averaged a 7.4% move in recent quarters.
Boeing Co. (BA) is confirmed to report earnings at approximately 7:30 AM ET on Wednesday, July 24, 2019. The consensus earnings estimate is $1.89 per share on revenue of $20.27 billion and the Earnings Whisper ® number is $1.91 per share. Investor sentiment going into the company's earnings release has 17% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 43.24% with revenue decreasing by 16.44%. Short interest has increased by 11.2% since the company's last earnings release while the stock has drifted lower by 0.1% from its open following the earnings release to be 4.0% above its 200 day moving average of $362.82. Overall earnings estimates have been revised lower since the company's last earnings release. On Monday, July 8, 2019 there was some notable buying of 6,176 contracts of the $325.00 put expiring on Friday, August 16, 2019. Option traders are pricing in a 3.8% move on earnings and the stock has averaged a 3.0% move in recent quarters.
AT&T Corp. (T) is confirmed to report earnings at approximately 6:50 AM ET on Wednesday, July 24, 2019. The consensus earnings estimate is $0.89 per share on revenue of $45.02 billion and the Earnings Whisper ® number is $0.90 per share. Investor sentiment going into the company's earnings release has 66% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 2.20% with revenue increasing by 15.48%. Short interest has increased by 16.4% since the company's last earnings release while the stock has drifted higher by 5.5% from its open following the earnings release to be 4.5% above its 200 day moving average of $31.37. Overall earnings estimates have been revised lower since the company's last earnings release. On Monday, July 8, 2019 there was some notable buying of 144,398 contracts of the $28.00 call expiring on Friday, January 17, 2020. Option traders are pricing in a 4.1% move on earnings and the stock has averaged a 4.5% move in recent quarters.
Snap Inc. (SNAP) is confirmed to report earnings at approximately 4:10 PM ET on Tuesday, July 23, 2019. The consensus estimate is for a loss of $0.10 per share on revenue of $358.48 million and the Earnings Whisper ® number is ($0.08) per share. Investor sentiment going into the company's earnings release has 61% expecting an earnings beat The company's guidance was for revenue of $335.00 million to $360.00 million. Consensus estimates are for year-over-year earnings growth of 9.09% with revenue increasing by 36.69%. Short interest has decreased by 3.8% since the company's last earnings release while the stock has drifted higher by 13.5% from its open following the earnings release to be 36.9% above its 200 day moving average of $10.24. Overall earnings estimates have been revised lower since the company's last earnings release. On Friday, July 5, 2019 there was some notable buying of 7,449 contracts of the $19.00 call expiring on Friday, July 26, 2019. Option traders are pricing in a 13.7% move on earnings and the stock has averaged a 19.1% move in recent quarters.
ShiftPixy, Inc. (PIXY) is confirmed to report earnings at approximately 8:00 AM ET on Monday, July 22, 2019. The consensus estimate is for a loss of $0.08 per share on revenue of $14.39 million. Investor sentiment going into the company's earnings release has 44% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 33.33% with revenue increasing by 53.48%. Short interest has decreased by 8.2% since the company's last earnings release while the stock has drifted lower by 50.9% from its open following the earnings release to be 63.8% below its 200 day moving average of $1.74. Overall earnings estimates have been revised higher since the company's last earnings release. The stock has averaged a 16.9% move on earnings in recent quarters.
Halliburton Company (HAL) is confirmed to report earnings at approximately 6:45 AM ET on Monday, July 22, 2019. The consensus earnings estimate is $0.30 per share on revenue of $5.97 billion and the Earnings Whisper ® number is $0.29 per share. Investor sentiment going into the company's earnings release has 60% expecting an earnings beat. Consensus estimates are for earnings to decline year-over-year by 48.28% with revenue decreasing by 2.88%. Short interest has increased by 39.2% 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 25.7% below its 200 day moving average of $29.27. Overall earnings estimates have been revised lower since the company's last earnings release. On Tuesday, July 16, 2019 there was some notable buying of 9,264 contracts of the $20.00 put expiring on Friday, August 16, 2019. Option traders are pricing in a 5.3% move on earnings and the stock has averaged a 3.5% move in recent quarters.
Twitter, Inc. (TWTR) is confirmed to report earnings at approximately 7:00 AM ET on Friday, July 26, 2019. The consensus earnings estimate is $0.19 per share on revenue of $828.49 million and the Earnings Whisper ® number is $0.24 per share. Investor sentiment going into the company's earnings release has 75% expecting an earnings beat The company's guidance was for revenue of $770.00 million to $830.00 million. Consensus estimates are for earnings to decline year-over-year by 0.00% with revenue increasing by 16.60%. Short interest has increased by 9.0% since the company's last earnings release while the stock has drifted lower by 0.4% from its open following the earnings release to be 10.1% above its 200 day moving average of $33.39. Overall earnings estimates have been revised higher since the company's last earnings release. On Monday, July 15, 2019 there was some notable buying of 7,151 contracts of the $60.00 call expiring on Friday, January 15, 2021. Option traders are pricing in a 10.4% move on earnings and the stock has averaged a 12.7% move in recent quarters.
Visa Inc (V) is confirmed to report earnings at approximately 4:05 PM ET on Tuesday, July 23, 2019. The consensus earnings estimate is $1.33 per share on revenue of $5.70 billion and the Earnings Whisper ® number is $1.37 per share. Investor sentiment going into the company's earnings release has 79% expecting an earnings beat. Consensus estimates are for year-over-year earnings growth of 10.83% with revenue increasing by 8.78%. Short interest has decreased by 6.9% since the company's last earnings release while the stock has drifted higher by 11.7% from its open following the earnings release to be 19.5% above its 200 day moving average of $150.03. Overall earnings estimates have been revised higher since the company's last earnings release. On Tuesday, July 16, 2019 there was some notable buying of 4,839 contracts of the $165.00 put expiring on Friday, August 16, 2019. Option traders are pricing in a 3.1% move on earnings and the stock has averaged a 2.6% move in recent quarters.
The problem with trading forex is that you are trading against your own broker and not the Forex Market or other Brokers. Either you have to lose or your broker. It is exactly like gambling in a Casino. You are gambling against the House. And as you know, the House at the end of the day always wins! But this does not mean you can not win. Occasionally you will win and then you keep trading ... Volume is a one of the biggest way to success in Forex trading business market. in any MT4 you find some volumes indicators and strategy. in this Best Top 3 MT4 Volume indicators list all candle indicator 100% free and very easy for installation. First and best indicator name ” volume ” 2nd volume indicator ” on balance volume”. Forex trading is the buying or selling of one country’s currency in exchange for another. Forex is one of the most liquid markets in the world, with a trading volume of $6 trillion per day. The US dollar is the most widely traded currency in the world. Benefits of FX trading. Forex is traded on margin, meaning you can gain a potentially higher market exposure by putting down just a small ... Forex trading strategies Strategy №1 . Implementing the best momentum forex trading strategy can be the ideal way to build and manage your trading account. Our team at Trading Strategy Guides believes that a momentum indicator strategy can reduce risk. It can also enhance your overall returns. We featured this strategy in our comprehensive guide for the best trading strategies we have ... Volume is the number of contracts, shares, or forex lots that are traded during a particular time frame. Daily volume is the number of contracts that are traded during one trading day. One-minute volume is the number of contracts traded within 60 seconds. High, Low, and Relative Volume . High volume is an indication that a market is actively traded, and low volume is an indication that ... Forex Volumes Secret Strategy Volume Trading Indicator Tutorial in Urdu and Hindi By Tani Forex Disclaimer: Any advice or information on this website is education and general purpose only. By viewing any materiel or using the information on this site. please do not trade or invest based solely on… VOLUME PROFILE INDICATOR v0.5 beta Volume Profile is suitable for day and swing trading on stock and futures markets, is a volume based indicator that gives you 6 key values for each session: POC, VAH, VAL, profile HIGH, LOW and MID levels. This project was born on the idea of plotting the RTH sessions Value Areas for /ES in an automated way, but you can select... Trading with Volume indicator offers the following features: ... traders need to know how the data for volume indicator is gathered in Forex. Forex volume cannot be measured precisely as it is done, for example, in Equity market, where every share traded equals 1 volume, and selling 200 shares means 200 in volume. Forex by nature cannot count how many contracts and what sizes of contracts were ... A falling volume and a falling open interest depict a congestion phase. Volume and open interest can be used in a practical sense to guide one's trades as follows: Open interest increases during a period of an exhibited trend. During the accumulation phase, volume may decline while open interest builds, but volume occasionally spikes. Forex.Academy is a free news and research website, offering educational information to those who are interested in Forex trading. Forex Academy is among the trading communities’ largest online sources for news, reviews, and analysis on currencies, cryptocurrencies, commodities, metals, and indices.
Volume in the Forex Markets - Useful or Not? ☝️ - YouTube
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