Forex indicators for beginners, forex strategies based on moving average, forex patterns and formations, forex strategies, forex configurations, Forex technical analysis for beginners and novices of forex includes moving averages
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I am a Software Engineer / Data Scientist and I decided to give a go at automating a strategy based on the ParallaxFX strategy floating around and backtests the results, also due to some inspiration by Vanguer
I backtested on the majors 4H timeframe between January 2015 to January 2020.
I am only considering trades from the top and bottom bands for now.
[Guide] Hal-hal esensial yang wajib dimiliki mahasiswa.
Selamat pagi! Salam mahasiswa! Terinspirasi dari komen-komen di thread gua sebelumnya, gua ingin compile beberapa must-have tools, stuff, and websites untuk kalian yang baru saja jadi mahasiswa atau sedang menjalani studi. Gue akan memisahkan ke beberapa kategori, yaitu Wajib Punya, Wajib Punya Untuk Anak [Jurusan], Boleh Punya, Cukup Tau, dan Jangan Pernah Sentuh. Dalam kategori tersebut akan diisi dengan kombinasi apps, website, dan alat-alat fisik. Untuk yang bersifat bajakan, sorry to say gua gak akan link di sini, kecuali Sci Hub atau Gen Lib. Bagi redditor yang bukan anak psikologi, tolong bantuin gua ya dengan comment berisi suggestion kalian.
WhatsApp, LINE, dan sometimes Telegram. : Ya menurut lo aja deh, hari gini masih SMS?
Flash drive : Get an 8GB stick, walaupun sekarang udah serba digital, kadang dosen masih minta print-out tugas. Plus, tukang fotokopi pasti sibuk dan gak ada waktu buka e-mail (walaupun ada), akan lebih praktis kalau data yang mau lo print atau submit pindahin dulu ke sini. Side note : Untuk anak DKV, Arsitektur, Desain Produk, Musik, dan Film, sepertinya kalian wajib beli external hard-drive minimal 500GB. Kalau bisa SSD ya, biar file terus protected (tapi agak mahal).
Google Drivedan isinya (Sheets, Docs, Draw, Slides) : Lo akan mobile for most of your campus life, GDrive gunanya bukan hanya sebagai backup tapi sebagai base of operations dari perkuliahan lo. Separate folders into semesters, lalu di dalamnya bikin folder per matkul, dan di dalamnya pun ada folder buku, tugas, class notes, and etc.
Google Calendar : Start planning through this app. Its highly underrated and I suggest you take time and learn how GCal works. Most people only use this after they started working, getting a head start is always better.
Mendeleyatau reference manager lain : Lu akan menghabiskan waktu 4 tahun baca artikel ilmiah, kadang mereka suka aneh formatting filenya kalo di-download dan mereka udah pasti gak appealing untuk di-save di laptop. Mendeley cuts off all of the problems and puts all of your references in one place. (Available on desktop and mobile)
Google Scholar: Berhubungan dengan sebelumnya, Google Scholar akan menjadi wikipedia elu di perguruan tinggi. You will access this site almost every day in uni.
Genesis Library : Adalah perpustakaan terlengkap di jagad internet. Gak usah beli textbook kalau lu gak mampu, download aja di sini.
Side points : Perpusnas punya akses e-book gratis pula, mostly koleksi mereka ada di situ. Appnya bisa dicari di Google Play Store (iOS setau gua belom ada).
Sci-hub : This is the scalpel of academia, the tool of a true mahasiswa. Sometimes lo akan ketemu artikel yang BAGUS, tapi sayang lo harus bayar ke publishernya. Nah, this bypasses that and you can have the PDF for FREEEEEEEEEEEEEEEEEEEEEEEEEE. Add extensionnya https://github.com/allanino/sci-hub-fy
E-book manager like Calibre (for PC and iOS) and Aldiko (for Android) : Pretty self-explanatory karena most of the time mahasiswa tingkat awal itu gak tau cara manage folder di laptop.
m-Banking app from your bank : Sekarang apa-apa sudah serba digital, belom lagi kalau lo butuh bayar-bayar atau patungan sama temen. Dengan adanya mbanking app, lo udah gak butuh ke ATM. Bahkan, sekarang mbanking bisa bayar ke OVO, Gopay, or Shoppee Pay lewat QRIS.
Go-Jek or Grab (and OVO) : Kemana-mana dan bayar apa-apa lebih gampang.
Kartu emoney, Flazz, Brizzi, dan sejenis : Silahkan beli salah satu dari kartu ini untuk kalian yang harus menggunakan moda transportasi seperti KRL atau Transjakarta. Plus, very handy untuk beli air putih di Indo/Alfamart. Kalau bisa yang satu jenis dengan bank kalian, agar top-up dapat dilakukan secara mudah di ATM atau app mbanking (bagi yang memiliki NFC hpnya)
Cheap OEM earphones : You will have some solace from annoying pieces of shit when you're reading or doing assignments. Browse through any ecommerce site and search for "headset samsung/iphone grosir" and buy 10.
Masker : Well, duh.
Zoom/Skype/Hangouts/Microsoft Teams : Please check on your faculty's specification, sekarang lagi pandemi and I don't think you guys are going back to school any soon.
Powerbank : Trust me, you will forget to charge your phone. One powerbank on the ready will be a life saver, especially during late nights.
OpenOfficeorLibreOffice : I do not condone the piracy of a certain word processing software. Get open-source and just relax. Alternatively, you can go all-out with Google's existing apps inside Drive.
JASP : I also do not condone the piracy of a certain statistics software.
Canva : Untuk anak-anak non-design yang gak bisa design, ditambah gak punya duit untuk hire designer (ya menurut lo), please take time to learn Canva. I would recommend GIMP a few years ago, but Canva has been gold standard of designing for non-designers.
CamScanner : For scanning documents. Available on iOS and Android
Condoms : Just, bring it.
MSDN : Kadang Microsoft kerjasama dengan kampus, check on your faculty.
Tar tambah lagiiiii.......
WAJIB PUNYA UNTUK ANAK.....
Kalkulator scientific : Bisa cari di toko buku atau e-commerce. Get Texas Instrument or Casio.
nanti kali ya
Kopi sachet yang banyak
Penggaris segitiga atau meteran
APA Publication Manual : Sebagai S.Psi gua akan menekankan PENTINGNYA MEMILIKI PDF INI DI SEMUA DEVICE ELU. Pelajarin dan cross-check semua style tulis dengan editorial style APA. Dosen PASTI BAKAL PERIKSA GAYA TULISAN ELU DENGAN APA.
KBBI : Dosen Psikologi paling terkenal dengan penulisan dan artikulasi kata, tolong pelajari bentuk baku kata-kata bahasa kita.
3D Brain : Untuk bantu Psiko Abnormal dan Faal.
Buku KUHP dan KUHPER, e-book or printed.
UU yang berkaitan dengan kelas, e-book or printed.
Printer dengan tinta isi ulang alias nyuntik
CompSci, Teknik Informatika, or Sistem Informatika
Spotify Premium : Check if your school is eligible for student discount! I do not condone using modified APK for Spotify Premium.
Audacity : Boleh lah punya kalau mau coba-coba bikin podcast.
Da Vinci Resolve : Kalian akan sewaktu-waktu dapet tugas buat edit video, either untuk kelas atau organisasi. Ini software open source yang lumayan powerful untuk editing.
SSDs for laptops : This is me speaking from experience, you'll need this if your risk of being in an accident is high. Upgrading to an SSD is 0-1, not only you get great booting and transfer speeds, but your data is almost always protected if amit-amit ketabrak atau laptop kenapa-napa.
Powerstrip : Ini bisa wajib, bisa enggak. Kadang berguna kalau kalian nugas di cafe, tapi kalian gak mati juga kalau gak punya.
Write Monkey : Ini dapat meng-enhance pengalaman kalian menulis, gue menggunakan program ini saat skripsi. Fungsinya cuma satu : Biar nulis lebih enak. Cocok bagi yang jurusannya rajin ngetik. Again, lo gak akan mati kalo gak punya ini.
Eventbrite : Cocok buat yang pengen cari group activities atau seminar gratisan.
TIX.ID : For the time being, jangan ke bioskop dulu. Tapi TIX suka banyak promo buy1get1. Lumayan buat irit duit.
Trello or Asana : Nah, sebenarnya ini wajib untuk orang kantoran (depends industrinya), tapi menurut gua kalau kalian coba aja pelajarin agile project management, mungkin performance group akan lebih naik. Ditambah ini lagi pandemi, nugas akan lebih gampang menurut gua dengan ini. Kakak-kakak yang udah kerja di kantor agile pasti bisa jelasin.
Jobstreet, Kalibrr, JobsDB, Glints : For work opportunities.
Halodoc : Truth be told, this app have saved my life multiple times. I would suggest a healthy diet, but having this on your phone will not hurt one bit.
Pisau lipat Victorinox : Handy untuk yang berencana jadi anak alam atau bocah camping. But basically handy untuk segala situasi, sih.
Aplikasi sekuritas : Bisa mulai belajar, setau gua macem MNC Sekuritas bisa mulai trading dengan Rp100.000.
Discord : Lumayan handy untuk jadi basis chat angkatan. Tapi, mereka lebih cater ke gaming crowd, walaupun fiturnya sebagus Slack Enterprise, tapi entah kenapa susah banget penetrate mainstream user.
To be added later...........
Netflix : Bisa patungan sama temen-temen. I don't suggest buy shady accounts.
Premier League app : Seru loh bikin liga fantasy sama temen-temen.
Former investment bank FX trader: Risk management part 3/3
Welcome to the third and final part of this chapter. Thank you all for the 100s of comments and upvotes - maybe this post will take us above 1,000 for this topic! Keep any feedback or questions coming in the replies below. Before you read this note, please start with Part I and then Part II so it hangs together and makes sense. Part III
Squeezes and other risks
Crap trades, timeouts and monthly limits
Squeezes and other risks
We are going to cover three common risks that traders face: events; squeezes, asymmetric bets.
Economic releases can cause large short-term volatility. The most famous is Non Farm Payrolls, which is the most widely watched measure of US employment levels and affects the price of many instruments.On an NFP announcement currencies like EURUSD might jump (or drop) 100 pips no problem. This is fine and there are trading strategies that one may employ around this but the key thing is to be aware of these releases.You can find economic calendars all over the internet - including on this site - and you need only check if there are any major releases each day or week. For example, if you are trading off some intraday chart and scalping a few pips here and there it would be highly sensible to go into a known data release flat as it is pure coin-toss and not the reason for your trading. It only takes five minutes each day to plan for the day ahead so do not get caught out by this. Many retail traders get stopped out on such events when price volatility is at its peak.
Short squeezes bring a lot of danger and perhaps some opportunity. The story of VW and Porsche is the best short squeeze ever. Throughout these articles we've used FX examples wherever possible but in this one instance the concept (which is also highly relevant in FX) is best illustrated with an historical lesson from a different asset class. A short squeeze is when a participant ends up in a short position they are forced to cover. Especially when the rest of the market knows that this participant can be bullied into stopping out at terrible levels, provided the market can briefly drive the price into their pain zone. There's a reason for the car, don't worry Hedge funds had been shorting VW stock. However the amount of VW stock available to buy in the open market was actually quite limited. The local government owned a chunk and Porsche itself had bought and locked away around 30%. Neither of these would sell to the hedge-funds so a good amount of the stock was un-buyable at any price. If you sell or short a stock you must be prepared to buy it back to go flat at some point. To cut a long story short, Porsche bought a lot of call options on VW stock. These options gave them the right to purchase VW stock from banks at slightly above market price. Eventually the banks who had sold these options realised there was no VW stock to go out and buy since the German government wouldn’t sell its allocation and Porsche wouldn’t either. If Porsche called in the options the banks were in trouble. Porsche called in the options which forced the shorts to buy stock - at whatever price they could get it. The price squeezed higher as those that were short got massively squeezed and stopped out. For one brief moment in 2008, VW was the world’s most valuable company. Shorts were burned hard. Incredible event Porsche apparently made $11.5 billion on the trade. The BBC described Porsche as “a hedge fund with a carmaker attached.” If this all seems exotic then know that the same thing happens in FX all the time. If everyone in the market is talking about a key level in EURUSD being 1.2050 then you can bet the market will try to push through 1.2050 just to take out any short stops at that level. Whether it then rallies higher or fails and trades back lower is a different matter entirely. This brings us on to the matter of crowded trades. We will look at positioning in more detail in the next section. Crowded trades are dangerous for PNL. If everyone believes EURUSD is going down and has already sold EURUSD then you run the risk of a short squeeze. For additional selling to take place you need a very good reason for people to add to their position whereas a move in the other direction could force mass buying to cover their shorts. A trading mentor when I worked at the investment bank once advised me: Always think about which move would cause the maximum people the maximum pain. That move is precisely what you should be watching out for at all times.
Also known as picking up pennies in front of a steamroller. This risk has caught out many a retail trader. Sometimes it is referred to as a "negative skew" strategy. Ideally what you are looking for is asymmetric risk trade set-ups: that is where the downside is clearly defined and smaller than the upside. What you want to avoid is the opposite. A famous example of this going wrong was the Swiss National Bank de-peg in 2012. The Swiss National Bank had said they would defend the price of EURCHF so that it did not go below 1.2. Many people believed it could never go below 1.2 due to this. Many retail traders therefore opted for a strategy that some describe as ‘picking up pennies in front of a steam-roller’. They would would buy EURCHF above the peg level and hope for a tiny rally of several pips before selling them back and keep doing this repeatedly. Often they were highly leveraged at 100:1 so that they could amplify the profit of the tiny 5-10 pip rally. Then this happened. Something that changed FX markets forever The SNB suddenly did the unthinkable. They stopped defending the price. CHF jumped and so EURCHF (the number of CHF per 1 EUR) dropped to new lows very fast. Clearly, this trade had horrific risk : reward asymmetry: you risked 30% to make 0.05%. Other strategies like naively selling options have the same result. You win a small amount of money each day and then spectacularly blow up at some point down the line.
We have talked about short squeezes. But how do you know what the market position is? And should you care? Let’s start with the first. You should definitely care. Let’s imagine the entire market is exceptionally long EURUSD and positioning reaches extreme levels. This makes EURUSD very vulnerable. To keep the price going higher EURUSD needs to attract fresh buy orders. If everyone is already long and has no room to add, what can incentivise people to keep buying? The news flow might be good. They may believe EURUSD goes higher. But they have already bought and have their maximum position on. On the flip side, if there’s an unexpected event and EURUSD gaps lower you will have the entire market trying to exit the position at the same time. Like a herd of cows running through a single doorway. Messy. We are going to look at this in more detail in a later chapter, where we discuss ‘carry’ trades. For now this TRYJPY chart might provide some idea of what a rush to the exits of a crowded position looks like. A carry trade position clear-out in action Knowing if the market is currently at extreme levels of long or short can therefore be helpful. The CFTC makes available a weekly report, which details the overall positions of speculative traders “Non Commercial Traders” in some of the major futures products. This includes futures tied to deliverable FX pairs such as EURUSD as well as products such as gold. The report is called “CFTC Commitments of Traders” ("COT"). This is a great benchmark. It is far more representative of the overall market than the proprietary ones offered by retail brokers as it covers a far larger cross-section of the institutional market. Generally market participants will not pay a lot of attention to commercial hedgers, which are also detailed in the report. This data is worth tracking but these folks are simply hedging real-world transactions rather than speculating so their activity is far less revealing and far more noisy. You can find the data online for free and download it directly here. Raw format is kinda hard to work with However, many websites will chart this for you free of charge and you may find it more convenient to look at it that way. Just google “CFTC positioning charts”. But you can easily get visualisations You can visually spot extreme positioning. It is extremely powerful. Bear in mind the reports come out Friday afternoon US time and the report is a snapshot up to the prior Tuesday. That means it is a lagged report - by the time it is released it is a few days out of date. For longer term trades where you hold positions for weeks this is of course still pretty helpful information. As well as the absolute level (is the speculative market net long or short) you can also use this to pick up on changes in positioning. For example if bad news comes out how much does the net short increase? If good news comes out, the market may remain net short but how much did they buy back? A lot of traders ask themselves “Does the market have this trade on?” The positioning data is a good method for answering this. It provides a good finger on the pulse of the wider market sentiment and activity. For example you might say: “There was lots of noise about the good employment numbers in the US. However, there wasn’t actually a lot of position change on the back of it. Maybe everyone who wants to buy already has. What would happen now if bad news came out?” In general traders will be wary of entering a crowded position because it will be hard to attract additional buyers or sellers and there could be an aggressive exit. If you want to enter a trade that is showing extreme levels of positioning you must think carefully about this dynamic.
Retail traders often drastically underestimate how correlated their bets are. Through bitter experience, I have learned that a mistake in position correlation is the root of some of the most serious problems in trading. If you have eight highly correlated positions, then you are really trading one position that is eight times as large. Bruce Kovner of hedge fund, Caxton Associates For example, if you are trading a bunch of pairs against the USD you will end up with a simply huge USD exposure. A single USD-trigger can ruin all your bets. Your ideal scenario — and it isn’t always possible — would be to have a highly diversified portfolio of bets that do not move in tandem. Look at this chart. Inverted USD index (DXY) is green. AUDUSD is orange. EURUSD is blue. Chart from TradingView So the whole thing is just one big USD trade! If you are long AUDUSD, long EURUSD, and short DXY you have three anti USD bets that are all likely to work or fail together. The more diversified your portfolio of bets are, the more risk you can take on each. There’s a really good video, explaining the benefits of diversification from Ray Dalio. A systematic fund with access to an investable universe of 10,000 instruments has more opportunity to make a better risk-adjusted return than a trader who only focuses on three symbols. Diversification really is the closest thing to a free lunch in finance. But let’s be pragmatic and realistic. Human retail traders don’t have capacity to run even one hundred bets at a time. More realistic would be an average of 2-3 trades on simultaneously. So what can be done? For example:
You might diversify across time horizons by having a mix of short-term and long-term trades.
You might diversify across asset classes - trading some FX but also crypto and equities.
You might diversify your trade generation approach so you are not relying on the same indicators or drivers on each trade.
You might diversify your exposure to the market regime by having some trades that assume a trend will continue (momentum) and some that assume we will be range-bound (carry).
And so on. Basically you want to scan your portfolio of trades and make sure you are not putting all your eggs in one basket. If some trades underperform others will perform - assuming the bets are not correlated - and that way you can ensure your overall portfolio takes less risk per unit of return. The key thing is to start thinking about a portfolio of bets and what each new trade offers to your existing portfolio of risk. Will it diversify or amplify a current exposure?
Crap trades, timeouts and monthly limits
One common mistake is to get bored and restless and put on crap trades. This just means trades in which you have low conviction. It is perfectly fine not to trade. If you feel like you do not understand the market at a particular point, simply choose not to trade. Flat is a position. Do not waste your bullets on rubbish trades. Only enter a trade when you have carefully considered it from all angles and feel good about the risk. This will make it far easier to hold onto the trade if it moves against you at any point. You actually believe in it. Equally, you need to set monthly limits. A standard limit might be a 10% account balance stop per month. At that point you close all your positions immediately and stop trading till next month. Be strict with yourself and walk away Let’s assume you started the year with $100k and made 5% in January so enter Feb with $105k balance. Your stop is therefore 10% of $105k or $10.5k . If your account balance dips to $94.5k ($105k-$10.5k) then you stop yourself out and don’t resume trading till March the first. Having monthly calendar breaks is nice for another reason. Say you made a load of money in January. You don’t want to start February feeling you are up 5% or it is too tempting to avoid trading all month and protect the existing win. Each month and each year should feel like a clean slate and an independent period. Everyone has trading slumps. It is perfectly normal. It will definitely happen to you at some stage. The trick is to take a break and refocus. Conserve your capital by not trading a lot whilst you are on a losing streak. This period will be much harder for you emotionally and you’ll end up making suboptimal decisions. An enforced break will help you see the bigger picture. Put in place a process before you start trading and then it’ll be easy to follow and will feel much less emotional. Remember: the market doesn’t care if you win or lose, it is nothing personal. When your head has cooled and you feel calm you return the next month and begin the task of building back your account balance.
That's a wrap on risk management
Thanks for taking time to read this three-part chapter on risk management. I hope you enjoyed it. Do comment in the replies if you have any questions or feedback. Remember: the most important part of trading is not making money. It is not losing money. Always start with that principle. I hope these three notes have provided some food for thought on how you might approach risk management and are of practical use to you when trading. Avoiding mistakes is not a sexy tagline but it is an effective and reliable way to improve results. Next up I will be writing about an exciting topic I think many traders should look at rather differently: news trading. Please follow on here to receive notifications and the broad outline is below. News Trading Part I
Why use the economic calendar
Reading the economic calendar
Knowing what's priced in
First order thinking vs second order thinking
News Trading Part II
Preparing for quantitative and qualitative releases
Data surprise index
Using recent events to predict future reactions
Buy the rumour, sell the fact
The mysterious 'position trim' effect
Some key FX releases
*** Disclaimer:This content is not investment advice and you should not place any reliance on it. The views expressed are the author's own and should not be attributed to any other person, including their employer.
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
Access Part I here: https://www.reddit.com/Forex/comments/h0iwbu/part_i_my_10_minuteday_trading_strategy/ Welcome to Part II of this ongoing series. How many parts will there be? No idea. At least 4-5, I guess. I'd rather have this broken down into digestible chunks than just fire hose you with information. Part I was really just a primer. If I'm using the whole baking a cake analogy, then in Part I we covered what kind of cake we're baking. I will not cover in this post where we look for entries and exits, that's coming next. Part II is going to cover what ingredients we need and why we need those ingredients in greater detail. What Kind Of Strategy Is This Again?It's my 10 minutes per day, trading strategy. I think the beauty of this strategy is that it allows you to take a good number of trader per week without having to commit an inordinate amount of time to the screens. This is both a mean reversion and trend-continuation based strategy. It is dead simple to learn and apply. I'd expect a 10 year old to be able to make money with this. The List Of Ingredients & Why We Use These Particular Ingredients *I will have an image at the end of the post showing a textbook long and short setup* Bollinger Bands: Bollinger Bands (BB) have a base line (standard is the 20SMA, which is also what we will use for this strategy) and two other trend lines (known as the upper Bollinger band [UBB] and lower Bollinger band [LBB]) plotted 2 standard deviations away from the 20SMA. The idea behind BB is deviously simple - the vast majority of price action, approx. 90%, takes place in between the two bands. In other words, when price trades off the UBB or LBB, you could consider prices to be overbought/oversold. However, just because something is OVERbought does NOT mean its run is OVER. Therefore we need additional tools to make sure we are using the BB as effectively as possible. TLDR: BBhelp contextualize where to look for our technical setups using this strategy. Finding the candle/bar pattern is not enough. We need to make sure the setup is in the 'right' part of the chart. We accomplish that using the BB. Stochastic Oscillator: The Stochastic Oscillator (Stochs) is a secondary momentum indicator. Because it is an oscillator that means the signals it generates are range-bound between 0 and 100. There are tons of momentum indicators out there. Theoretically you could swap out the Stochs for RSI or MACD. My hunch is that you won't see a measurable statistical difference in performance if you do. So why Stochs? Because I like the fact you have the %K and %D lines (you can think of them as moving averages) and the fact that the %K and %D lines crossover is a helpful visual aid. Like any other momentum indicator, the Stochs will generate overbought and oversold signals. We use the Stochs to help back up what the BB are telling us. If price is trading at, or even broken out of, the UBB and Stochs are also veeeery overbought that can be potentially useful information. It doesn't mean we have a trade necessarily, but it is a helpful piece of data. Fibonacci Retracement & Extension Tool: This tool is OPTIONAL. The only reason I use this tool for this strategy is to integrate a mechanistic means of entry and exit. In other words, we can use fibonacci levels to place limit orders for entry and profit taking, and a stop order to get us out for our pre-defined risk allocation to each particular trade. If you DON'T want to use the fibs, that is perfectly okay. It just means you will add a more discretionary layer to this strategy Candlestick/Bar Patterns: There isn't a whole lot to say here. We look for ONE formation over, and over, and over again. An indecision bar (small body, doesn't close on its highs or lows) followed by the setup bar which is an outside bar or an engulfing bar. It doesn't particularly matter if the setup bar is an engulfing bar or outside bar. What matters is that for a long trade the setup bar makes a HIGHER HIGH and has a HIGHER CLOSE relative to the indecision bar. The opposite for a short trade setup. The bar formation is what ultimately serves as the trigger for placing orders to take a trade. *MOVING ON* Now We Get Into The Setup Itself:There are 3 places where we look for trades using this strategy:
Short off the UBB (Here we want to see Stochastics overbought and crossing down. Bearish divergence is even better)
Long off the LBB (Here we want to see Stochastics oversold and crossing up. Bullish divergence is even better)
Long/Short off the Middle Bollinger Band (Here if you are looking for a short trade off the MBB you ideally want Stochs overbought. Vice versa for a long trade. NOTE: Often when taking trades off the MBB, Stochs WON'T go overbought/oversold. Because this doesn't happen often, I don't let it stop me from taking trades off the MBB.)
Parsing a structured email body and automatically updating spreadsheet with data?
I receive emails in a specific outlook folder, Stock Distributions, periodically. These are structured as follows: Security Name: XYZ Inc Distribution Date: 09-03-2020 Security identifier type: Ticker Security Identifier: XYZ Investment Date: 01-01-2001 Distribution Agent: Financial Institution Distribution cost: 5.5 Currency: USD Fund Name: ABC Inc Distribution Amount: 1050 USD Distribution price: 10.50 Number of shares: 100 Forex Rate: 1.00 All of the data is variable, but the identifiers are always in the same order, and the date is in the same format. The identifiers preceded the colon on each line, and the data I need after is proceeding the colon. For the distribution amount, I’d need to delete the currency part and leave the integer value. The price per share and distribution costs can be any amount 0 to infinity and also have more than 2 decimal places after, for example cost could be 2.34678 Would there be an easy way to use VBA to extract the data from emails as they come in and populate an excel file with the following columns: Fund Name, Security Name, Currency, Forex Rate, Distribution Date, Distribution Amount, Number of Shares, Distribution Price, Distribution Cost Basically I want to parse the data and have it auto populate a spreadsheet based on the data identifier, and use the identifiers I need (the others aren’t relevant to me) as the column headers and get the data to the right of the identifiers into the rows under their appropriate headers. Is that doable? Is it easy enough that a beginner could do it over a weekend?
Summarizing some free trading idea resources I've been using
I've been following many free resources on youtube and twitter to generate trading ideas. Some of them are suspicious; some are more like boasting their wining trades but never post any losing trades. I see many people ask about trading ideas/resources, so I want to briefly share some resources I find useful. Twitter resources:
Instrument: Mostly SPX/SPY/ES
Highlights: TicTocTick is amazingly good at levels, spotting sellers and buyers levels. Everyday he posts his plan for the next day of the following format: If open above X, long/short bias, target Y. If open below X, short/long bias, target Z. Intraday he sometimes send "warnings" of potential big sellers / buyers at certain level. His price target and long/short bias is often right in my experience. His levels are useful for day trades IMHO.
Notes: (1) even with his plan, one needs an actionable plan. (2) He sometimes delete his tweets. His day-by-day and intraday tweets are more actionable than his longer term view. (3) he sometimes tweets political and controversial non-stock related things.
Trade transparency: 0/5 (doesn't post any trades)
Live update in-time: 5/5 (updates very frequently)
Actionable trading plan: 1/5 (good at levels and price targets. need your own plan)
Live interaction: 0/5 (no interaction)
Educational: 2/5 (can learn the technique from other resources. TicTock doesn't teach you directly)
Instrument: Mainly SPY/SPX/ES
Technique: candlestick patterns, Fib levels, support and resistance levels etc
Style: only day trading
Highlights: he diligently post daily plan and many educational resources, sometimes intraday updates. Had many good trades.
Notes: I haven't followed him long but so far so good. He also recently has educational youtube videos.
Trade transparency: X/5 (hard to measure)
Live update in-time: 2.5/5 (updates frequently)
Actionable trading plan: 3.5/5
Live interaction: X/5
Educational: 5/5 (youtube videos)
Technique: candlestick patterns, support and resistance levels, trendlines, channels etc
Instrument: SPX/SPY, Forex, Cryptocurrency,, Gold and Silver.
Style: holding for a few hours for SPX/SPY, swing trade for all
Timeframe: 8H for analysis. Lower time frame for entry.
Trading frequency: 1-2 trades per week.
Highlights: For SPX, he rode the big drop down in March; rode the rally up, and rode some pullbacks down in April. Got chopped in May. Now he's positinoning long. He also did well in Gold and Silverthis month. He only uses candle sticks, support and resistance lines, trendlines, and sometimes true trend indicator. He doesn't use volume though.
Youtube style: 2 videos every trading day: (1) live at 9am ET for 1-2 hours and talk about his plan and market analysis. Sometimes he trades during the live session (enter / exit). (2) after market closes he summarizes the day, and talks about plans for the next day. (3) Every weekend he gives out his technical analysis for the next week.
What I like: His levels on the chart are very good. He is also very transparent about his trades no matter whether it's winning or losing. He also explains the general economic environment.
Trade transparency: 4/5 (not knowing trading size; but knowing entry/exit)
Real-time update: 2.5/5 (two times a day)
Actionable trading plan: 5/5
Live interaction: 3.5/5 (some interaction on youtube live; Jordan responses to youtube comments)
Timeframe: all time frames. Mostly 5min, 1H, 1D, 1W, 1M.
Trading frequency: very frequent. multiple trades per day.
Highlights: Justin is very good at seeing through market maker manipulation and highly manipulated stocks. He often explained his plan and his outlook (especially in OPEX days) in his YouTube channel. The stocks on their weekly watchlist tend to do very well. He does live Q&A on youtube as well everyday where one can ask him to look at a chart.
Youtube style: Three videos by his team every trading day: (1) live at 9:30am ET; does 1-2 live scalping trades. Explains what he thinks of the market. (might discontinue) (2) at noon: summarizes what happened and what he sees is happening later in the day. Some of his trading plans. (3) 4:15pm ET: summarizes today and looking forward to the rest of the week. Videos (1) and (2) include live Q&A. I've asked many questions on youtube. Every weekend has two videos talking about plans for the next week.
What I like: The Q&A and Justin's outlook of the market, his team's stock pick.
The scalping trades in the morning is not very suitable for small accounts since they will trade for example 100 shares of BA (~160) to scalp a few dollars per share.
Even though the stocks on their weekly watchlist does well very, one still need to come up with an actionable plan. Very often say they recommend stock A on Sunday, and on Monday it already gaps up big. They sometimes do YOLO options -- big risk big rewards-- options can go to 0.
Besides the free content, everyone can get a free one-week trial for their paid membership, or a 2-week free trial by winning a lottery game on their youtube ( what I did) or knowing someone in their group and get a referral. What I like about the group: (i) very frequently updates each day on SPY and stocks on the watchlist. (ii) all their positions, Profit / Loss are very transparent. I learned a lot about how to manage trades by observing their live trades. (iii) There are many very experienced traders in the group posting their trading ideas, plans, entry/exit, and there are many live discussions. (iv) There's a "helpdesk" in the group where members' questions will be answered in minutes. I often ask about my trading plan, entries/ targets.
Trade transparency: 0/5 (free content: not knowing entry/exit nor position size);5+/5 (membership\*)*
Live update in-time: 3.5/5 (free content: three times a day);5+/5 (membership\*)*
Highlights: I follow their free Shadow trader swing newsletter, where every few days they post some trading ideas and analysis with actionable plan. Their twitter account will also real-time update their entry/exit and trade management.
What I like: I enjoyed learning what they look at to find a good set-up and how to manage a trade. They also have a spreadsheet tracking all their positions and profit/loss. All the winning/losing trades are transparent.
Notes: Because of the current market volatility, during certain weeks the swing trading performance is quite shaky. Profits (per 100K account with no more than 30K invested each time): 2020YTD: +9K, 2019: +6K; 2018: +30K; 2017: +3K; 2016: +2.5K; 2015: -1.8K.
Trade transparency: 5/5
Live update in-time: 5/5 (updates frequently)
Actionable trading plan: 5/5
Live interaction: 0/5 (newsletter and twitter alerts only)
Educational: 4.5/5 (the newsletter explains set-ups, what sectors they are looking at)
I've spent much time looking for free contents, and I like the ones above. Also looking forward to hearing about other good/bad resources. I might also update this post if there are enough interests. NFA
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