I saw a post by krishm97 of his positive experience purchasing from mecha.store and I bit the bullet.
Before I talk about the delivery process, here are my thoughts on the keyboard(It's my first mech): * It's built like a brick(I got the aluminum version), the first thing that gets you is how heavy it is. * The key travel just feels amazing chef's kiss. Especially since I've used chiclet keyboards all my life. * Overall I love the keyboard but if you're buying browns thinking it's going to be tactile, you're mistaken, as was I. The tactility is barely noticeable, I had bought Kailh blues for a janky hand wire and they feel more tactile. though I think their sound will get overwhelmed if you bottom out.

Purchase experience

I placed the order on 15th October for aluminum frame + RGB, and it shipped 4 days later. Since it was taking so long to ship I had contacted them, so pro tip: Contact them on whatsapp rather than email, they respond to WA much quicker.
The customs actually took an entire day to clear my package since it got flagged for regulatory review and it arrived two days after that, so all in all it took 11 days.

Pricing

1. Keyboard + Shipping from mecha store: INR 8585.55 (Actual conversion for $112.22 is 8292.4, so the rest was forex charges)
2. Customs Charges: INR 2250(~30% effective)

Of note: They had declared a lower value on the shipment so if your package does not get flagged you might get away with paying less.
If you have any questions feel free to post it down below.

Disclaimer: This DD is to help start forming a market view as per RBI announcements. Also a gentle reminder that fundamentals play out over a longer time frame than intraday. The authors take no responsiblity for your yolos.
With contributions by Asli Bakchodi, Bran OP & dragononweed!
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What is the RBI?
RBI is the central bank of India. They are one of the key players who affect India’s economic trajectory. They control currency supply, banking rules and more. This means that it is not a bank in which retailers or corporates can open an account with. Instead they are a bank for bankers and the Government of India.
Their functions can be broadly classified into 6.
· Monetary authority
· Financial supervisor for financial system
· Issuer of currency
· Manages Foreign exchange
· Bankers bank
· Banker to the government
This DD will take a look at each of these functions. It will be followed by a list of rates the RBI sets, and how changes in them can affect the market.
1. Monetary Authority
One of RBI’s functions is to achieve the goal of “Price Stability” in the economy. This essentially means achieving an inflation rate that is within a desired limit.
A monetary policy committee (MPC) decides on the desired inflation rate and its limits through majority vote of its 6 members, in consultation with the GoI.
The current inflation target for RBI is as follows
Consumer Price Inflation (CPI): 4%
Upper Limit: 6%
Lower Limit: 2%
An increase in CPI means less purchasing power. Generally speaking, if inflation is too high, the public starts cutting down on spending, leading to a negative impact on the markets. And vice versa. Lower inflation leads to more purchasing power, more spending, more investments leading to a positive impact on the market.
2. Financial Supervisor For Financial System
A financial system consists of financial markets (Capital market, money market, forex market etc.), financial institutions (banks, stock exchanges, NBFC etc) & financial assets (currencies, bills, bonds etc)
RBI supervises this entire system and lays down the rules and regulations for it. It can also use further ‘Selective Credit Controls’ to regulate banks.
3. Issues of currency
The RBI is responsible for the printing of currency notes. RBI is free to print as much as it wants as long as the minimum reserve of Rs 200 Cr (Gold 112 Cr) is maintained. The RBI has total assets or a balance size sheet of Rs. 51 trillion (April 2020). (1 Trillion = 1 Lakh crore)
India’s current reserves mean our increase in currency circulation is well managed.
4. Manages Foreign Exchange
RBI regulates all of India’s foreign exchange transactions. It is the custodian of all of foreign currencies in India. It allows for the foreign exchange value of the rupee to be controlled. RBI also buy and sell rupees in the foreign exchange market at its discretion.
In case of any currency movement, a country’s central bank can directly intervene to either push the currency up, as India has been doing, or to keep it artificially low, as the Chinese central bank does. To push up a currency, a central bank can sell dollars, which is the global reserve currency, or the currency against which all others are measured. To push down a currency, a central bank can buy dollars.
The RBI deciding this depends on the import/export and financial health of the country. Generally a weaker rupee means imports are more expensive, but are favourable for exports. And a stronger rupee means imports are cheaper but are unfavourable for exports.
A weaker rupee can make foreign investment more lucrative driving up FII. A stronger rupee can have an adverse effect of FII investing in markets.
5. Banker’s Bank
Every bank has to maintain a certain amount of reserve with the RBI. A certain percentage of a bank’s liabilities (anywhere between 3-15% as decided by RBI) has to be maintained in this account. This is called the Cash Reserve Ratio. This is determined by the MPC during the monetary policy review (which happens every six weeks at present).
It lends money from this reserve to other banks if they are short on cash, but generally, it is seen as a last resort move. Banks are encouraged to meet their shortfalls of cash from other resources.
6. Banker to the government
RBI is the entity that carries out ALL monetary transactions on behalf of the Government. It holds custody of the cash balance of the Government, gives temporary loans to both central and state governments and manages the debt operations of the central Government, through instruments of debt and the interest rates associated with them - like bonds.
The different rates set & managed by RBI
- Repo rate
The rate at which RBI is willing to lend to commercial banks is called as Repo Rate.
Banks sometimes need money for emergency or to maintain the SLR and CRR (explained below). They borrow this from RBI but have to pay some interest on it. The interest that is to be paid on the amount to the RBI is called as Repo Rate.
It does not function like a normal loan but acts like a forward contract. Banks have to provide collateral like government bonds, T-bills etc. Repo means Repurchase Option is the true meaning of Repo an agreement where the bank promises to repurchase these government securities after the repo period is over.
As a tool to control inflation, RBI increases the Repo Rate making it more expensive for banks to borrow from the RBI with a view to restrict availability of money. Exact opposite stance shall be taken in case of deflationary environment.
The change of repo rate is aimed to affect the flow of money in the economy. An increase in repo rate decreases the flow of money in the economy, while the decrease in repo rate increases the flow of money in the economy. RBI by changing these rates shows its stance to the economy at large whether they prioritize growth or inflation.
- Reverse Repo Rate
The rate at which the RBI is willing to borrow from the Banks is called as Reverse Repo Rate. If the RBI increases the reverse repo rate, it means that the RBI is willing to offer lucrative interest rate to banks to park their money with the RBI. Banks in this case agree to resell government securities after reverse repo period.
Generally, an increase in reverse repo rate that banks will have a higher incentive to park their money with RBI. It decreases liquidity, affecting the market in a negative manner. Decrease in reverse repo rate increases liquidity affecting the market in a positive manner.
Both the repo rate and reverse repo rate fall under the Liquidity Adjustment Facility tools for RBI.
- Cash reserve ratio (CRR)
Banks in India are required to deposit a specific percentage of their net demand and time liabilities (NDTL) in the form of CASH with the RBI. This minimum ratio (that is the part of the total deposits to be held as cash) is stipulated by the RBI and is known as the CRR or Cash Reserve Ratio. These reserves will not be in circulation at any point in time.
For example, if a bank had a NDTL (like current Account, Savings Account and Fixed Deposits) of 100Cr and the CRR is at 3%, it would have to keep 3Cr as Cash reserve ratio to the RBI. This amount earns no interest.
Currently it is at 3%. A lower cash ratio means banks can deposit just a lower amount and use the remaining money leading to higher liquidity. This translates to more money to invest which is seen as positive for the market. Inversely, a higher cash ratio equates to lower liquidity which translates to a negative market sentiment.
Thus, the RBI uses the CRR to control excess money flow and regulate liquidity in the economy.
- Statutory liquidity ratio (SLR)
Banks in India have to keep a certain percentage of their net demand and time liabilities WITH THEMSELVES. And this can be in the form of liquid assets like gold and government securities, not just cash. A lot of banks keep them in government bonds as they give a decent interest.
The current SLR ratio of 18.25%, which means that for every Rs.100 deposited in a bank, it has to invest Rs.18.50 in any of the asset classes approved by RBI.
A low SLR means higher levels of loans to the private sector. This boosts investment and acts as a positive sentiment for the market. Conversely a high SLR means tighter levels of credit and can cause a negative effect on the market.
Essentially, the RBI uses the SLR to control ease of credit in the economy. It also ensures that the banks maintain a certain level of funds to meet depositor’s demands instead of over liquidation.
- Bank Rate
Bank rate is a rate at which the Reserve Bank of India provides the loan to commercial banks without keeping any security. There is no agreement on repurchase that will be drawn up or agreed upon with no collateral as well. This is different from repo rate as loans taken with repo rate are taken on the basis of securities. Bank rate hence is higher than the repo rate.
Currently the bank rate is 4.25%. Since bank rate is essentially a loan interest rate like repo rate, it affects the market in similar ways.
- Marginal Cost of Funds based Lending Rate (MCLR)
This is the minimum rate below which the banks are not allowed to lend. Raising this rate, makes loans more expensive, drying up liquidity, affecting the market in a negative way. Similarly, lower MCLR rates will bring in high liquidity, affecting the market in a positive way.
MCLR is a varying lending rate instead of a single rate according to the kind of loans. Currently, the MCLR rate is between 6.65% - 7.15%
- Marginal Standing facility
Marginal Standing Facility is the interest rate at which a depository institution (generally banks) lends or borrows funds with another depository institution in the overnight market. Overnight market is the part of financial market which offers the shortest term loans. These loans have to be repaid the next day.
MSF can be used by a bank after it exhausts its eligible security holdings for borrowing under other options like the Liquidity adjustment facilities.
The MSF would be a penal rate for banks and the banks can borrow funds by pledging government securities within the limits of the statutory liquidity ratio.
The current rate stands at 4.25%. The effect it has on the market is synonymous with the other lending rates such as repo rate & bank rate.
- Loan to value ratio
The loan-to-value (LTV) ratio is an assessment of lending risk that financial institutions and other lenders examine before approving a mortgage. Typically, loan assessments with high LTV ratios are considered higher risk loans.
Basically, if a companies preferred form of collateral rises in value and leads the market (growing faster than the market), then the company will see the loans that it signed with higher LTV suddenly reduce (but the interest rate remains the same).
Let’s consider an example of gold as a collateral. Consider a loan was approved with gold as collateral. The market price for gold is Rs 2000/g, and for each g, a loan of Rs 1500 was given. (The numbers are simplified for understanding). This would put LTV of the loan at 1500/2000 = 0.75. Since it is a substantial LTV, say the company priced the loan at 20% interest rate.
Now the next year, the price of gold rose to Rs 3000/kg. This would mean that the LTV of the current loan has changed to 0.5 but the company is not obligated to change the interest rate. This means that even if the company sees a lot of defaults, it is fairly protected by the unexpected surge in the underlying asset. Moreover, since the underlying asset is more valuable, default rates for the loans goes down as people are more protective of the collateral they have placed.
The same scenario for gold is happening right now and is the reason for gold backed loan providers like MUTHOOT to hit ATHs as gold is leading the economy right now. Also, these in these scenarios, it also enables companies to offer additional loan on same gold for those who are interested Instead of keeping the loan amount same most of the gold loan companies.
Based on above, we can see that as RBI changes LTV for certain assets, we are in a position to identify potential institutions that could get a good Quarterly result and try to enter it early.
Conclusion
The above rates contain the ways in the Central Bank manages the monetary policy, growth and inflation in the country.
Its impact on Stock market is often seen when these rates are changed, they act as triggers for the intraday positions on that day. But overall, the outlook is always maintained on how the RBI sees the country is doing, and knee jerk reactions are limited to intraday positions. The long term stance is always well within the limits of the outlook the big players in the market are expecting.
The important thing to keep in mind is that the problems facing the economy needn’t be uni-dimensional. Problems with inflation, growth, liquidity, currency depreciation all can come together, for which the RBI will have to play a balancing role with all it powers to change these rates and the forex reserve. So the effect on the market needs to be given more thought than simply extrapolated as ‘rates go low, markets go up’.
But understanding these individual effects of these rates allows you to start putting together the puzzle of how and where the market and the economy could go.

Been learning forex trading now for about 2 years just reading and practising on demo, in the past 6 months however I've taken a more data driven approach and now I've just completed my first week of live trading (a small amount, nothing too big, just 200). the plan I guess is to reflect on the trades I've taken every week and use this medium as a way of peer reviewing the things I do.
Took 3 trades this week,
21/10/2020 - EURAUD and EURNZD sell
After identifying these pairs as sells, I viewed the potential sell move that could take place as a broader sell off of the euro rather than individual rising of the AUD or the NZD. For that reason, I decided to split my normal risk per trade amount (2.5%) in half and treat this as one euro sell trade. I closed my trades when they hit the previous days low, however as you can see, price continued moving down for a deeper sell off which is a lilbit depressing to see, knowing I could have made more if I had held, but meh, still bagged 3 times my risk so can't be too sad for the first trades of the first week.
Outcome (In RR):
EURAUD - 2.9
EURNZD -3.35
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23/10/2020 - AUDNZD Buy
I was late on my analysis on this trade because I woke up late, however I was still able to fill the limit order after price returned back to my buy zone. The trade looked good going in, however, the moves that I trade on tend to be higher momentum moves, and a few hours into the trade I could tell that there just wasnt any steam, so I moved the stop closer to the entry (but not breakeven, to give it room to breath), anyway, the market moved against me and took me out for 0.5R, saved 50% from the initial risk size so not bad.
Outcome (In RR)
-0.5
From demo trading this strategy, I know that sometimes the trades can run for a long time. I was curious to know how those of you that have long term swing moves manage the risk and lock in profits but also give the trades room to breath, I've been messing around with using donchians as a trailing stop method however Im eager to know of other methods.
p.s lemme know if Im breaking any rules by posting this here, new to reddit and all, also, if there is any interest in it, I can post my reasoning for entering these trades if requested.
p.p.s the screenshots of the trades were moderated out, but I've reposted them on my profile

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Disclaimer: None of this is financial advice. I have no idea what I'm doing. Please do your own research or you will certainly lose money. I'm not a statistician, data scientist, well-seasoned trader, or anything else that would qualify me to make statements such as the below with any weight behind them. Take them for the incoherent ramblings that they are.
TL;DR at the bottom for those not interested in the details.
This is a bit of a novel, sorry about that. It was mostly for getting my own thoughts organized, but if even one person reads the whole thing I will feel incredibly accomplished.

Background

For those of you not familiar, please see the various threads on this trading system here. I can't take credit for this system, all glory goes to ParallaxFX!
I wanted to see how effective this system was at H1 for a couple of reasons: 1) My current broker is TD Ameritrade - their Forex minimum is a mini lot, and I don't feel comfortable enough yet with the risk to trade mini lots on the higher timeframes(i.e. wider pip swings) that ParallaxFX's system uses, so I wanted to see if I could scale it down. 2) I'm fairly impatient, so I don't like to wait days and days with my capital tied up just to see if a trade is going to win or lose.
This does mean it requires more active attention since you are checking for setups once an hour instead of once a day or every 4-6 hours, but the upside is that you trade more often this way so you end up winning or losing faster and moving onto the next trade. Spread does eat more of the trade this way, but I'll cover this in my data below - it ends up not being a problem.
I looked at data from 6/11 to 7/3 on all pairs with a reasonable spread(pairs listed at bottom above the TL;DR). So this represents about 3-4 weeks' worth of trading. I used mark(mid) price charts. Spreadsheet link is below for anyone that's interested.

System Details

I'm pretty much using ParallaxFX's system textbook, but since there are a few options in his writeups, I'll include all the discretionary points here:

• 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
• Raw Winrates:
  • 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%
  • Winrate: 46.4%
  • 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%
  • Winrate: 65.97%
  • 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.

Correlated Trades

As I've said many times now, I'm really not qualified to be doing an analysis like this. This section in particular.
Looking at shared currency among the pairs traded, 74 of the trades are correlated. Quite a large group, but it makes sense considering the sort of moves we're looking for with this system.
This means you are opening yourself up to more risk if you were to trade on every signal since you are technically trading with the same underlying sentiment on each different pair. For example, GBP/USD and AUD/USD moving together almost certainly means it's due to USD moving both pairs, rather than GBP and AUD both moving the same size and direction coincidentally at the same time. So if you were to trade both signals, you would very likely win or lose both trades - meaning you are actually risking double what you'd normally risk(unless you halve both positions which can be a good option, and is discussed in ParallaxFX's posts and in various other places that go over pair correlation. I won't go into detail about those strategies here).
Interestingly though, 17 of those apparently correlated trades ended up with different wins/losses.
Also, looking only at trades that were correlated, winrate is 83%/70%/55% (for the three TP levels).
Does this give some indication that the same signal on multiple pairs means the signal is stronger? That there's some strong underlying sentiment driving it? Or is it just a matter of too small a sample size? The winrate isn't really much higher than the overall winrates, so that makes me doubt it is statistically significant.
One more funny tidbit: EUCAD netted the lowest overall winrate: 30% to even the -61.8% TP level on 10 trades. Seems like that is just a coincidence and not enough data, but dang that's a sucky losing streak.
EDIT: WOW I spent some time removing correlated trades manually and it changed the results quite a bit. Some thoughts on this below the results. These numbers also include the other "What I will trade" filters. I added a new worksheet to my data to show what I ended up picking.

• Total Trades: 75
• Raw Winrates:
  • 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:

• Winrate: 58.19%
• 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.

Raw Data

Here's the spreadsheet for anyone that'd like it. (EDIT: Updated some of the setups from the last few days that have fully played out now. I also noticed a few typos, but nothing major that would change the overall outcomes. Regardless, I am currently reviewing every trade to ensure they are accurate.UPDATE: Finally all done. Very few corrections, no change to results.)
I have some explanatory notes below to help everyone else understand the spiraled labyrinth of a mind that put the spreadsheet together.

• 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).

Pairs

1. AUD/CAD
2. AUD/CHF
3. AUD/JPY
4. AUD/NZD
5. AUD/USD
6. CAD/CHF
7. CAD/JPY
8. CHF/JPY
9. EUAUD
10. EUCAD
11. EUCHF
12. EUGBP
13. EUJPY
14. EUNZD
15. EUUSD
16. GBP/AUD
17. GBP/CAD
18. GBP/CHF
19. GBP/JPY
20. GBP/NZD
21. GBP/USD
22. NZD/CAD
23. NZD/CHF
24. NZD/JPY
25. NZD/USD
26. USD/CAD
27. USD/CHF
28. USD/JPY

TL;DR

Based on the reasonable rules I discovered in this backtest:

• Date range: 6/11-7/3
• Winrate: 58.19%
• 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!

• 86 Trades
  • Date range: 7/9-7/30
  • Winrate: 52.32%
  • 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.

After a few...especially trying...interactions with unhappy ban recipients today, I thought it would be fun to share a little info on what moderators do to keep this place clean. :)
The forex industry is full of shady characters. Any industry sitting on the intersection of financial independence, work, and money, is bound to attract them. There are many reasons for this; the lower barrier to entry compared to other markets, the lack of public knowledge on the subject, and greedy human nature to name a few.
Moderating a subreddit dedicated to forex (or anything trading realted for that matter,) presents extra challenges beyond your regular sub. Marketers and scammers are super motivated, and MLM / referral marketing is extremely popular right now, which can turn everyday regular users into sources of spam.
How we currently tackle this problem involves technology (scripts, bots, and automod,) a mod review workflow, and some smart sleuthing when needed.
The mod team and our scripts aren't perfect though... but the few false positives we get are a very, very small fraction of all mod actions taken (~1%.) Unfortunately, that means some otherwise sincere members get handled roughly, and that can really suck.. I wish there was a better way, but the alternative is this place becomes a wild west and starts looking like your gmail spam folder.
That said, here's my personal stats for JUST the last 24 hours:

• Bans: 14 [edit: 16 now before day's end, two more responding to a 'where can I learn how to trade' post.]
• All mod actions (including bans, post and comment removal, etc..): 63
• Ban appeals: 2

And I'm just one of the mods. . .
So what scammer and marketing trends are we seeing lately?

• Content marketing - Infographics with instagram handles watermarked in them, or a blog-like post with a embedded links to their own site.
• Personal/direct selling - trying to move the conversation out of public view, usually by taking things to DM, or promoting a 3rd party chatroom where the rules here no longer apply.
• Shills - Fake accounts used to boost the credit of another user, or service. It's no coincidence that a user asking about 'ULRA PRO SIGNALZ' will quickly have 5+ replies by low karma, new users, saying how great the service is. [edit: here's an example I just caught..]
• Fake P/L Porn - We see this quite often. It's easy to fake MT4 account statements and MT4 Mobile screenshots, and new users can't tell the difference so these posts will get a lot of undeserved attention. When people ask how OP made such mad cash, a sales pitch is usually coming right up.

Honestly, it can be really frustrating at times.. luckily the scripts we have in place make weeding out ~80% of these jokers quite easy and quick. Heck, we had one scammer who blew through 12+ accounts over the last few days trying to scam people but none of their posts ever saw the light of day thanks to the spam triggers I've written.
What motivates the mod team to keep this place clean? That's an easy answer: The majority of users here are new to trading. Making sure they aren't food for the wolves is important.
But even with all the measures we take, some bad actors still get through.
So here's where you can help: Use the report button! Anytime you see something that you think fits the descriptions listed above, or violates our sidebar rules, just report it. Even if you're not 100% sure, don't be afraid to use the report tool.. The worst thing that can happen is the mod team reviews and approves it, but the best outcome is you directly help keep this place clean and humming! :)
And the mod team is always looking to improve where it can: I've already talked about what we do to scrub away bad actors, but one place we could do better is education. The plan is to rewrite a good portion of the wiki to include the following sections:

• Spotting scams and scammers
• How to properly compare brokers and regulatory bodies
• The real reason why your old high school friend wants you to sign up to IML, and 10 ways to politely tell him to pound sand
• No, that hot instagram model won't sleep with you if you buy her online course
• Why all signal services are trash and can die in a fire

(Titles above are a work in progress ;P)
Are you a good writer and want to help out with this? Think you can write up a killer wiki article on spotting scam artists? Message the mods and let us know!
Finally, a reminder, we are still interested in taking on more moderators and will be revisiting that very shortly. If you'd be interested, read through this post and reply accordingly: https://www.reddit.com/Forex/comments/h7ok6k/seeking_more_mods_recruitment_thread/

This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.

How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.

This is begging the question.

After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.

This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.

She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain

Moving on:

The exact mechanism of drain, or transfers from India to Britain was quite simple.

Convenient.

Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.

- Chandra et al. (1989)

The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.

The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):

the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).

Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.

The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.

So what's going on here? Well Roy (2019) explains it better:

Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.

So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):

It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.

While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:

she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

​

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.

Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.

The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.

While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.

When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.

So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:

Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.

Indeed, if paying for what you buy is drain, then several billions of us are drained every day.

The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.

It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):

The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

​

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.

Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):

Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

​

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.

It doesn't appear that others appreciate their insight Roy (2019):

K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

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Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.

Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).

If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.

This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual¹). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):

This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.

A view echoed in Raychaudhuri (1983):

Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.

So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):

All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.

The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):

Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable

So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
^{1. Several authors have affirmed that Indian identity is a colonial artefact. For example see Rajan 1969:}

^{Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.}

^{or see Bryant 2000:}

^{But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist. [...] Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.}

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
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

Managers face a new challenge–managing a remote forex trader team with more and more Forex companies looking out for outsourcing. Events such as motivational tactics, task control and Communications still require a specialist approach even if this has been simplified using technology and a high-speed Internet.
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The advantages of employing people on a distance are endless and make your competitive edge easy. Your company can help create a talented team of the best specialists in the market because you can not just hire people from everywhere in the world. This is why a remote employment type will continue to gain momentum in the future. It’s all the challenges that value it.
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Remote job is very easy–output, autonomy and flexibility. You influence the manner you operate your team, the smoothness and achievement of the company procedures. And although most individuals still think that remote employees are not as productive as usual in office, the reality of the matter is that most remote employees actually see an increase in productivity equal to a complete additional working day.
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So how do you efficiently handle a distant team? How can you contribute to its growth, commitment and coordination? Below are some tips that we have collected to assist you find the response.
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TIP 1 – Maintain high performance
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If your team scatters throughout the world, productivity can be impacted, unless certain measures are taken to keep it. It is not only difficult to say in a virtual environment how much time your staff spend on assignments and how successful they are, but also it is essential to be evident how the entire team handles the workload.
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Therefore, it is essential that a business process is well thought-out and that several software tools are used. Training tools such as Toggl or Clockify can help you to really understand the processes underway, the amount of time spent on these projects, and the number of breaks taken in real time. This allows executives to identify the weaknesses of each worker and attempt to solve them.
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This doesn’t imply that you need to overuse these applications and develop stringent procedures. If you work remotely, you are likely to choose this due to the flexibility it provides you. Managers need to strike a balance between liberty and coherent timescales for their staff.
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For instance, they must decide which communication instruments they use to discuss issues of great significance–Chat with urgent subjects, Email with stuff to wait and Video Calls. Furthermore, all project information must be available to every member of the team (instruments like Google Drive and DropBox may assist). It must also ensure complete transparency and transparency.
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They also need to bear in mind always that distant workers can all operate in distinct time zones, so that everyone can tune in to a video call, it is essential to discover a time window. Also, when employing persons, you need to ensure that your location time differences are not more than 3 hours from the time zone of your office. Due to time differences, for each conference you always have to have a very clear agenda and must stick to it.
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Finally, you need to measure the yield to always have your hand on the pulse. Try to list all the main points in the work of each team member that indicate the performance. Set clear objectives with measurable outcomes so you can see clearly what is anticipated and how well your staff are responding to these expectations. Hold monthly reviews in the team to see how all people work together and whether everyone handles them properly.
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However, do not be scared to give honest feedback, remember that individuals almost always concentrate more on beneficial than adverse points. This is why executives have to be frank and always attempt to discover something useful at the end.
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TIP 2 – Strengthen communication skills
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For each team, good communication between employees is essential, but for distant teams in particular. The absence of physical attendance and distinct working schedules can all lead your team members to operate as people instead of a united front.
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Remote teams effectively need to interact twice if not three times the ordinary team. And executives must support these communications by creating more possibilities.
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Set some of your team’s prompters. Say you always mark the time places if you are on your schedule for calls. Select and ensure your team uses the primary communication instruments. Implement instruments like Slack, WebEx and Skype and let your staff know that they are available all the time. Agree on the duration of an email reply so that you understand when to expect answers to your message.
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Try as much as necessary to integrate video calls. IMCCAs have found that, when they actively use video conference instruments, 90% of remote personnel feel more linked with their team. Video enables your team to make a name face and bind better. When you see a individual, it’s always simpler to speak to than just by messaging.
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Video calling helps your distant team feel moved and isolated less.
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Share your screen with your team members to facilitate and clarify clarifications. Create distinct channels for sharing of interesting stuff, such as fun stories, suggestions for films or updates on TV shows that you all view.
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Your purpose is to create a virtual environment that fosters and enhances
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communication between your team.
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TIP 3 – Engaging, Inspiring And Motivating
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The main element of effective teamwork is motivation. Entrepreneur says company costs 450 to $550 billion per year in losses of productivity for an unmotivated or de-engaged employee.
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Managers who work with remote teams have to invest in them and work hard every day to increase morality. In distant teams it is even more essential to build a powerful corporate culture than in the physical.
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Begin with transparency, one of the basic elements of a driven team. You need to make sure that all of your team know precisely what the business is doing, its goals and its role. Share corporate news and updates, celebrate your own accomplishments and attempt to make your team feel real.
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Be frank and let them know you as their leader. Discuss yourselves with them and share your private vision. Describe the stuff you handle at the moment, so they know that you do as difficult as you do. Request your advice on various issues and opinions.
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Be in keeping with feedback and with your praise in particular. Don’t just ask them to do a nice job, but to highlight certain accomplishments and duties. In corporate public chats, try also to praise your distant staff for their accomplishments throughout the whole business.
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Create your team’s rituals and traditions. Celebrate the birthdays and unique occasions of your distant staff. Why doesn’t every team member ask for a brief video that you wish to edit later and send to your partner celebrating?
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Improve camaraderie with true private communication through investments at least once a year in corporate retreats where your distant team can meet in reality.
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Find out more here about motivating your team.
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TIP 4 – Select your team with wisely
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Hiring individuals with the correct distance to work is your halfway to achievement. It’s all about everything else. You can also influence your entire team with a lazy or unprofessional distant worker. This is why certain precautions are essential during the recruitment process.
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First and foremost, nobody claims you have to employ the individual from the beginning. Test your skills and abilities for a number of months by employing them part-time and giving them the opportunity to work long term when you’re sure they’re a nice addition to your staff.
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Don’t be stingy with salaries. Just because your employee is working remotely doesn’t mean that they aren’t doing more than your typical office worker does. In most cases, remote workers are actually doing more, so their salaries have to reflect this.
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Make sure your fresh remote staff can function from home. Do you have a good working environment where you can concentrate without distractions? Otherwise, it may be better to propose either paying for co-working space or buying equipment to build that room at home for them.
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Take into consideration that your staff can all come from distinct nations and cultures. Learn how to talk obviously and broadly convey your message. Ask your team in their message to use easy phrases and a neutral tone. Maintain a calendar of all global holidays affecting and planning your team members.
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Make sure fresh staff are quick to retire. Create a guide with all the primary information about your job and the instruments you use. Ask your team members to share their finest advice on remote working. Recall the faster the better they adjust.
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Managing distance teams can be challenging, but actually you can create a skilled, loyal, hard-working team with a few simple rules to assist your company to succeed.

Putting business online is one of the crucial things in today’s world. But just going online isn’t enough to reach your target audience; you also need to preserve all sorts of website expertise. You should also concentrate on sophisticated SEO strategies and properly apply them for better productivity.
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Bitcoin is a cryptocurrency, which can be spent, saved, or invested, and it can be stolen too. Trading with Bitcoins was considered to be risky, but the current trends show that it has become a big hit the binary options sector. This decentralized currency is not regulated by any Government, or by any central authority.
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