Which Forex Pairs Move the Most ? The Most and Least ...

RBI & how its policies can start to affect the market

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!

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.
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Former investment bank FX trader: Risk management part II

Former investment bank FX trader: Risk management part II
Firstly, thanks for the overwhelming comments and feedback. Genuinely really appreciated. I am pleased 500+ of you find it useful.
If you didn't read the first post you can do so here: risk management part I. You'll need to do so in order to make sense of the topic.
As ever please comment/reply below with questions or feedback and I'll do my best to get back to you.
Part II
  • Letting stops breathe
  • When to change a stop
  • Entering and exiting winning positions
  • Risk:reward ratios
  • Risk-adjusted returns

Letting stops breathe

We talked earlier about giving a position enough room to breathe so it is not stopped out in day-to-day noise.
Let’s consider the chart below and imagine you had a trailing stop. It would be super painful to miss out on the wider move just because you left a stop that was too tight.

Imagine being long and stopped out on a meaningless retracement ... ouch!
One simple technique is simply to look at your chosen chart - let’s say daily bars. And then look at previous trends and use the measuring tool. Those generally look something like this and then you just click and drag to measure.
For example if we wanted to bet on a downtrend on the chart above we might look at the biggest retracement on the previous uptrend. That max drawdown was about 100 pips or just under 1%. So you’d want your stop to be able to withstand at least that.
If market conditions have changed - for example if CVIX has risen - and daily ranges are now higher you should incorporate that. If you know a big event is coming up you might think about that, too. The human brain is a remarkable tool and the power of the eye-ball method is not to be dismissed. This is how most discretionary traders do it.
There are also more analytical approaches.
Some look at the Average True Range (ATR). This attempts to capture the volatility of a pair, typically averaged over a number of sessions. It looks at three separate measures and takes the largest reading. Think of this as a moving average of how much a pair moves.
For example, below shows the daily move in EURUSD was around 60 pips before spiking to 140 pips in March. Conditions were clearly far more volatile in March. Accordingly, you would need to leave your stop further away in March and take a correspondingly smaller position size.

ATR is available on pretty much all charting systems
Professional traders tend to use standard deviation as a measure of volatility instead of ATR. There are advantages and disadvantages to both. Averages are useful but can be misleading when regimes switch (see above chart).
Once you have chosen a measure of volatility, stop distance can then be back-tested and optimised. For example does 2x ATR work best or 5x ATR for a given style and time horizon?
Discretionary traders may still eye-ball the ATR or standard deviation to get a feeling for how it has changed over time and what ‘normal’ feels like for a chosen study period - daily, weekly, monthly etc.

Reasons to change a stop

As a general rule you should be disciplined and not change your stops. Remember - losers average losers. This is really hard at first and we’re going to look at that in more detail later.
There are some good reasons to modify stops but they are rare.
One reason is if another risk management process demands you stop trading and close positions. We’ll look at this later. In that case just close out your positions at market and take the loss/gains as they are.
Another is event risk. If you have some big upcoming data like Non Farm Payrolls that you know can move the market +/- 150 pips and you have no edge going into the release then many traders will take off or scale down their positions. They’ll go back into the positions when the data is out and the market has quietened down after fifteen minutes or so. This is a matter of some debate - many traders consider it a coin toss and argue you win some and lose some and it all averages out.
Trailing stops can also be used to ‘lock in’ profits. We looked at those before. As the trade moves in your favour (say up if you are long) the stop loss ratchets with it. This means you may well end up ‘stopping out’ at a profit - as per the below example.

The mighty trailing stop loss order
It is perfectly reasonable to have your stop loss move in the direction of PNL. This is not exposing you to more risk than you originally were comfortable with. It is taking less and less risk as the trade moves in your favour. Trend-followers in particular love trailing stops.
One final question traders ask is what they should do if they get stopped out but still like the trade. Should they try the same trade again a day later for the same reasons? Nope. Look for a different trade rather than getting emotionally wed to the original idea.
Let’s say a particular stock looked cheap based on valuation metrics yesterday, you bought, it went down and you got stopped out. Well, it is going to look even better on those same metrics today. Maybe the market just doesn’t respect value at the moment and is driven by momentum. Wait it out.
Otherwise, why even have a stop in the first place?

Entering and exiting winning positions

Take profits are the opposite of stop losses. They are also resting orders, left with the broker, to automatically close your position if it reaches a certain price.
Imagine I’m long EURUSD at 1.1250. If it hits a previous high of 1.1400 (150 pips higher) I will leave a sell order to take profit and close the position.
The rookie mistake on take profits is to take profit too early. One should start from the assumption that you will win on no more than half of your trades. Therefore you will need to ensure that you win more on the ones that work than you lose on those that don’t.

Sad to say but incredibly common: retail traders often take profits way too early
This is going to be the exact opposite of what your emotions want you to do. We are going to look at that in the Psychology of Trading chapter.
Remember: let winners run. Just like stops you need to know in advance the level where you will close out at a profit. Then let the trade happen. Don’t override yourself and let emotions force you to take a small profit. A classic mistake to avoid.
The trader puts on a trade and it almost stops out before rebounding. As soon as it is slightly in the money they spook and cut out, instead of letting it run to their original take profit. Do not do this.

Entering positions with limit orders

That covers exiting a position but how about getting into one?
Take profits can also be left speculatively to enter a position. Sometimes referred to as “bids” (buy orders) or “offers” (sell orders). Imagine the price is 1.1250 and the recent low is 1.1205.
You might wish to leave a bid around 1.2010 to enter a long position, if the market reaches that price. This way you don’t need to sit at the computer and wait.
Again, typically traders will use tech analysis to identify attractive levels. Again - other traders will cluster with your orders. Just like the stop loss we need to bake that in.
So this time if we know everyone is going to buy around the recent low of 1.1205 we might leave the take profit bit a little bit above there at 1.1210 to ensure it gets done. Sure it costs 5 more pips but how mad would you be if the low was 1.1207 and then it rallied a hundred points and you didn’t have the trade on?!
There are two more methods that traders often use for entering a position.
Scaling in is one such technique. Let’s imagine that you think we are in a long-term bulltrend for AUDUSD but experiencing a brief retracement. You want to take a total position of 500,000 AUD and don’t have a strong view on the current price action.
You might therefore leave a series of five bids of 100,000. As the price moves lower each one gets hit. The nice thing about scaling in is it reduces pressure on you to pick the perfect level. Of course the risk is that not all your orders get hit before the price moves higher and you have to trade at-market.
Pyramiding is the second technique. Pyramiding is for take profits what a trailing stop loss is to regular stops. It is especially common for momentum traders.

Pyramiding into a position means buying more as it goes in your favour
Again let’s imagine we’re bullish AUDUSD and want to take a position of 500,000 AUD.
Here we add 100,000 when our first signal is reached. Then we add subsequent clips of 100,000 when the trade moves in our favour. We are waiting for confirmation that the move is correct.
Obviously this is quite nice as we humans love trading when it goes in our direction. However, the drawback is obvious: we haven’t had the full amount of risk on from the start of the trend.
You can see the attractions and drawbacks of both approaches. It is best to experiment and choose techniques that work for your own personal psychology as these will be the easiest for you to stick with and build a disciplined process around.

Risk:reward and win ratios

Be extremely skeptical of people who claim to win on 80% of trades. Most traders will win on roughly 50% of trades and lose on 50% of trades. This is why risk management is so important!
Once you start keeping a trading journal you’ll be able to see how the win/loss ratio looks for you. Until then, assume you’re typical and that every other trade will lose money.
If that is the case then you need to be sure you make more on the wins than you lose on the losses. You can see the effect of this below.

A combination of win % and risk:reward ratio determine if you are profitable
A typical rule of thumb is that a ratio of 1:3 works well for most traders.
That is, if you are prepared to risk 100 pips on your stop you should be setting a take profit at a level that would return you 300 pips.
One needn’t be religious about these numbers - 11 pips and 28 pips would be perfectly fine - but they are a guideline.
Again - you should still use technical analysis to find meaningful chart levels for both the stop and take profit. Don’t just blindly take your stop distance and do 3x the pips on the other side as your take profit. Use the ratio to set approximate targets and then look for a relevant resistance or support level in that kind of region.

Risk-adjusted returns

Not all returns are equal. Suppose you are examining the track record of two traders. Now, both have produced a return of 14% over the year. Not bad!
The first trader, however, made hundreds of small bets throughout the year and his cumulative PNL looked like the left image below.
The second trader made just one bet — he sold CADJPY at the start of the year — and his PNL looked like the right image below with lots of large drawdowns and volatility.
Would you rather have the first trading record or the second?
If you were investing money and betting on who would do well next year which would you choose? Of course all sensible people would choose the first trader. Yet if you look only at returns one cannot distinguish between the two. Both are up 14% at that point in time. This is where the Sharpe ratio helps .
A high Sharpe ratio indicates that a portfolio has better risk-adjusted performance. One cannot sensibly compare returns without considering the risk taken to earn that return.
If I can earn 80% of the return of another investor at only 50% of the risk then a rational investor should simply leverage me at 2x and enjoy 160% of the return at the same level of risk.
This is very important in the context of Execution Advisor algorithms (EAs) that are popular in the retail community. You must evaluate historic performance by its risk-adjusted return — not just the nominal return. Incidentally look at the Sharpe ratio of ones that have been live for a year or more ...
Otherwise an EA developer could produce two EAs: the first simply buys at 1000:1 leverage on January 1st ; and the second sells in the same manner. At the end of the year, one of them will be discarded and the other will look incredible. Its risk-adjusted return, however, would be abysmal and the odds of repeated success are similarly poor.

Sharpe ratio

The Sharpe ratio works like this:
  • It takes the average returns of your strategy;
  • It deducts from these the risk-free rate of return i.e. the rate anyone could have got by investing in US government bonds with very little risk;
  • It then divides this total return by its own volatility - the more smooth the return the higher and better the Sharpe, the more volatile the lower and worse the Sharpe.
For example, say the return last year was 15% with a volatility of 10% and US bonds are trading at 2%. That gives (15-2)/10 or a Sharpe ratio of 1.3. As a rule of thumb a Sharpe ratio of above 0.5 would be considered decent for a discretionary retail trader. Above 1 is excellent.
You don’t really need to know how to calculate Sharpe ratios. Good trading software will do this for you. It will either be available in the system by default or you can add a plug-in.

VAR

VAR is another useful measure to help with drawdowns. It stands for Value at Risk. Normally people will use 99% VAR (conservative) or 95% VAR (aggressive). Let’s say you’re long EURUSD and using 95% VAR. The system will look at the historic movement of EURUSD. It might spit out a number of -1.2%.

A 5% VAR of -1.2% tells you you should expect to lose 1.2% on 5% of days, whilst 95% of days should be better than that
This means it is expected that on 5 days out of 100 (hence the 95%) the portfolio will lose 1.2% or more. This can help you manage your capital by taking appropriately sized positions. Typically you would look at VAR across your portfolio of trades rather than trade by trade.
Sharpe ratios and VAR don’t give you the whole picture, though. Legendary fund manager, Howard Marks of Oaktree, notes that, while tools like VAR and Sharpe ratios are helpful and absolutely necessary, the best investors will also overlay their own judgment.
Investors can calculate risk metrics like VaR and Sharpe ratios (we use them at Oaktree; they’re the best tools we have), but they shouldn’t put too much faith in them. The bottom line for me is that risk management should be the responsibility of every participant in the investment process, applying experience, judgment and knowledge of the underlying investments.Howard Marks of Oaktree Capital
What he’s saying is don’t misplace your common sense. Do use these tools as they are helpful. However, you cannot fully rely on them. Both assume a normal distribution of returns. Whereas in real life you get “black swans” - events that should supposedly happen only once every thousand years but which actually seem to happen fairly often.
These outlier events are often referred to as “tail risk”. Don’t make the mistake of saying “well, the model said…” - overlay what the model is telling you with your own common sense and good judgment.

Coming up in part III

Available here
Squeezes and other risks
Market positioning
Bet correlation
Crap trades, timeouts and monthly limits

***
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.
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Former investment bank FX trader: Risk management part 3/3

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
  • Market positioning
  • Bet correlation
  • 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.

Events

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.

Squeezes

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.

Asymmetric losses

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.

Market positioning

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.

Bet correlation

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
  • Introduction
  • Why use the economic calendar
  • Reading the economic calendar
  • Knowing what's priced in
  • Surveys
  • Interest rates
  • 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
  • Reversals
  • 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.
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Former investment bank FX trader: News trading and second order thinking part 2/2

Former investment bank FX trader: News trading and second order thinking part 2/2
Thanks for all the upvotes and comments on the previous pieces:
From the first half of the news trading note we learned some ways to estimate what is priced in by the market. We learned that we are trading any gap in market expectations rather than the result itself. A good result when the market expected a fantastic result is disappointing! We also looked at second order thinking. After all that, I hope the reaction of prices to events is starting to make more sense to you.

Before you understand the core concepts of pricing in and second order thinking, price reactions to events can seem mystifying at times
We'll add one thought-provoking quote. Keynes (that rare economist who also managed institutional money) offered this analogy. He compared selecting investments to a beauty contest in which newspaper readers would write in with their votes and win a prize if their votes most closely matched the six most popularly selected women across all readers:
It is not a case of choosing those (faces) which, to the best of one’s judgment, are really the prettiest, nor even those which average opinions genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be.
Trading is no different. You are trying to anticipate how other traders will react to news and how that will move prices. Perhaps you disagree with their reaction. Still, if you can anticipate what it will be you would be sensible to act upon it. Don't forget: meanwhile they are also trying to anticipate what you and everyone else will do.

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 trimming position effect
  • Reversals
  • Some key FX releases

Preparing for quantitative and qualitative releases

The majority of releases are quantitative. All that means is there’s some number. Like unemployment figures or GDP.
Historic results provide interesting context. We are looking below the Australian unemployment rate which is released monthly. If you plot it out a few years back you can spot a clear trend, which got massively reversed. Knowing this trend gives you additional information when the figure is released. In the same way prices can trend so do economic data.

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This makes sense: if for example things are getting steadily better in the economy you’d expect to see unemployment steadily going down.
Knowing the trend and how much noise there is in the data gives you an informational edge over lazy traders.
For example, when we see the spike above 6% on the above you’d instantly know it was crazy and a huge trading opportunity since a) the fluctuations month on month are normally tiny and b) it is a huge reversal of the long-term trend.
Would all the other AUDUSD traders know and react proportionately? If not and yet they still trade, their laziness may be an opportunity for more informed traders to make some money.
Tradingeconomics.com offers really high quality analysis. You can see all the major indicators for each country. Clicking them brings up their history as well as an explanation of what they show.
For example, here’s German Consumer Confidence.

Helpful context
There are also qualitative events. Normally these are speeches by Central Bankers.
There are whole blogs dedicated to closely reading such texts and looking for subtle changes in direction or opinion on the economy. Stuff like how often does the phrase "in a good place" come up when the Chair of the Fed speaks. It is pretty dry stuff. Yet these are leading indicators of how each member may vote to set interest rates. Ed Yardeni is the go-to guy on central banks.

Data surprise index

The other thing you might look at is something investment banks produce for their customers. A data surprise index. I am not sure if these are available in retail land - there's no reason they shouldn't be but the economic calendars online are very basic.
You’ll remember we talked about data not being good or bad of itself but good or bad relative to what was expected. These indices measure this difference.
If results are consistently better than analysts expect then you’ll see a positive number. If they are consistently worse than analysts expect a negative number. You can see they tend to swing from positive to negative.

Mean reversion at its best! Data surprise indices measure how much better or worse data came in vs forecast
There are many theories for this but in general people consider that analysts herd around the consensus. They are scared to be outliers and look ‘wrong’ or ‘stupid’ so they instead place estimates close to the pack of their peers.
When economic conditions change they may therefore be slow to update. When they are wrong consistently - say too bearish - they eventually flip the other way and become too bullish.
These charts can be interesting to give you an idea of how the recent data releases have been versus market expectations. You may try to spot the turning points in macroeconomic data that drive long term currency prices and trends.

Using recent events to predict future reactions

The market reaction function is the most important thing on an economic calendar in many ways. It means: what will happen to the price if the data is better or worse than the market expects?
That seems easy to answer but it is not.
Consider the example of consumer confidence we had earlier.
  • Many times the market will shrug and ignore it.
  • But when the economic recovery is predicated on a strong consumer it may move markets a lot.
Or consider the S&P index of US stocks (Wall Street).
  • If you get good economic data that beats analyst estimates surely it should go up? Well, sometimes that is certainly the case.
  • But good economic data might result in the US Central Bank raising interest rates. Raising interest rates will generally make the stock market go down!
So better than expected data could make the S&P go up (“the economy is great”) or down (“the Fed is more likely to raise rates”). It depends. The market can interpret the same data totally differently at different times.
One clue is to look at what happened to the price of risk assets at the last event.
For example, let’s say we looked at unemployment and it came in a lot worse than forecast last month. What happened to the S&P back then?

2% drop last time on a 'worse than expected' number ... so it it is 'better than expected' best guess is we rally 2% higher
So this tells us that - at least for our most recent event - the S&P moved 2% lower on a far worse than expected number. This gives us some guidance as to what it might do next time and the direction. Bad number = lower S&P. For a huge surprise 2% is the size of move we’d expect.
Again - this is a real limitation of online calendars. They should show next to the historic results (expected/actual) the reaction of various instruments.

Buy the rumour, sell the fact

A final example of an unpredictable reaction relates to the old rule of ‘Buy the rumour, sell the fact.’ This captures the tendency for markets to anticipate events and then reverse when they occur.

Buy the rumour, sell the fact
In short: people take profit and close their positions when what they expected to happen is confirmed.
So we have to decide which driver is most important to the market at any point in time. You obviously cannot ask every participant. The best way to do it is to look at what happened recently. Look at the price action during recent releases and you will get a feel for how much the market moves and in which direction.

Trimming or taking off positions

One thing to note is that events sometimes give smart participants information about positioning. This is because many traders take off or reduce positions ahead of big news events for risk management purposes.
Imagine we see GBPUSD rises in the hour before GDP release. That probably indicates the market is short and has taken off / flattened its positions.

The price action before an event can tell you about speculative positioning
If GDP is merely in line with expectations those same people are likely to add back their positions. They avoided a potential banana skin. This is why sometimes the market moves on an event that seemingly was bang on consensus.
But you have learned something. The speculative market is short and may prove vulnerable to a squeeze.

Two kinds of reversals

Fairly often you’ll see the market move in one direction on a release then turn around and go the other way.
These are known as reversals. Traders will often ‘fade’ a move, meaning bet against it and expect it to reverse.

Logical reversals

Sometimes this happens when the data looks good at first glance but the details don’t support it.
For example, say the headline is very bullish on German manufacturing numbers but then a minute later it becomes clear the company who releases the data has changed methodology or believes the number is driven by a one-off event. Or maybe the headline number is positive but buried in the detail there is a very negative revision to previous numbers.
Fading the initial spike is one way to trade news. Try looking at what the price action is one minute after the event and thirty minutes afterwards on historic releases.

Crazy reversals


Some reversals don't make sense
Sometimes a reversal happens for seemingly no fundamental reason. Say you get clearly positive news that is better than anyone expects. There are no caveats to the positive number. Yet the price briefly spikes up and then falls hard. What on earth?
This is a pure supply and demand thing. Even on bullish news the market cannot sustain a rally. The market is telling you it wants to sell this asset. Try not to get in its way.

Some key releases

As we have already discussed, different releases are important at different times. However, we’ll look at some consistently important ones in this final section.

Interest rates decisions

These can sometimes be unscheduled. However, normally the decisions are announced monthly. The exact process varies for each central bank. Typically there’s a headline decision e.g. maintain 0.75% rate.
You may also see “minutes” of the meeting in which the decision was reached and a vote tally e.g. 7 for maintain, 2 for lower rates. These are always top-tier data releases and have capacity to move the currency a lot.
A hawkish central bank (higher rates) will tend to move a currency higher whilst a dovish central bank (lower rates) will tend to move a currency lower.
A central banker speaking is always a big event

Non farm payrolls

These are released once per month. This is another top-tier release that will move all USD pairs as well as equities.
There are three numbers:
  • The headline number of jobs created (bigger is better)
  • The unemployment rate (smaller is better)
  • Average hourly earnings (depends)
Bear in mind these headline numbers are often off by around 75,000. If a report comes in +/- 25,000 of the forecast, that is probably a non event.
In general a positive response should move the USD higher but check recent price action.
Other countries each have their own unemployment data releases but this is the single most important release.

Surveys

There are various types of surveys: consumer confidence; house price expectations; purchasing managers index etc.
Each one basically asks a group of people if they expect to make more purchases or activity in their area of expertise to rise. There are so many we won’t go into each one here.
A really useful tool is the tradingeconomics.com economic indicators for each country. You can see all the major indicators and an explanation of each plus the historic results.

GDP

Gross Domestic Product is another big release. It is a measure of how much a country’s economy is growing.
In general the market focuses more on ‘advance’ GDP forecasts more than ‘final’ numbers, which are often released at the same time.
This is because the final figures are accurate but by the time they come around the market has already seen all the inputs. The advance figure tends to be less accurate but incorporates new information that the market may not have known before the release.
In general a strong GDP number is good for the domestic currency.

Inflation

Countries tend to release measures of inflation (increase in prices) each month. These releases are important mainly because they may influence the future decisions of the central bank, when setting the interest rate.
See the FX fundamentals section for more details.

Industrial data

Things like factory orders or or inventory levels. These can provide a leading indicator of the strength of the economy.
These numbers can be extremely volatile. This is because a one-off large order can drive the numbers well outside usual levels.
Pay careful attention to previous releases so you have a sense of how noisy each release is and what kind of moves might be expected.

Comments

Often there is really good stuff in the comments/replies. Check out 'squitstoomuch' for some excellent observations on why some news sources are noisy but early (think: Twitter, ZeroHedge). The Softbank story is a good recent example: was in ZeroHedge a day before the FT but the market moved on the FT. Also an interesting comment on mistakes, which definitely happen on breaking news, and can cause massive reversals.

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How to Trade the Fisher Transform Indicator

One of the good things about trading is that everybody can have their own unique style. albeit two different trading styles conflict, it doesn’t mean that one strategy is true and one is wrong.
With thousands upon thousands of stocks to settle on from, there’s always an abundance of effective ways to trade.
Technical analysis is usually lumped together into one specific style, but not all indicators point within the same direction.
We’re all conversant in commonly used technical concepts like support and resistance and moving averages, alongside more refined tools like MACD and RSI.
No single indicator may be a golden goose for trading profits, but when utilized in the right situations, you'll spot opportunities before the bulk of the gang .
One technical trading indicator that tends to fly under the radar is that the Fisher Transform Indicator.
Despite its lack of recognition , the Fisher Transform Indicator may be a useful gizmo to feature to your trading arsenal since it’s fairly easy to read and influence .
What is the Fisher Transform Indicator?
One of the best struggles in marketing research is the way to affect such a lot of random data.
The distribution of stock prices makes it difficult to locate trends and patterns, which is why technical analysis exists within the first place.
Hey, if the trends were easy to identify , everyone would get rich trading stocks and therefore the advantage provided by technical analysis would be whittled away.
But since technical trends are difficult to identify with an untrained eye, we believe trading tools just like the RSI and MACD to form informed decisions.
The Fisher Transform Indicator was developed by John F. Ehlers, who’s authored market books like Rocket Science For Traders.
Visit Equiti Forex
The Fisher Transform Indicator attempts to bring order to chaos by normalizing the distribution of stock prices over various timeframes.
Instead of messy, random prices, the Fisher Transform Indicators puts prices into a Gaussian Gaussian distribution . you would possibly know such a distribution by its more commonly used name – the bell curve.
Bell curves usually want to measure school grades, but during this instance, it’s wont to more neatly smooth prices along a selected timeline.
Think of stock prices like players on a five – if you organize everyone during a pattern by height, you’ll have a way better understanding of the makeup of the team.
So what does the Fisher Transform Indicator look for? Extreme market conditions.
Unlike other trading signals where many false positives are delivered on a day to day , this indicator is meant to pop only during rare market moments.
By utilizing a normal distribution , much of the noise made by stock prices is ironed away.
Despite the complex mathematics, Fisher Transform tends to offer clear overbought and oversold signals since the extremes of the indicator are rarely reached.
How Can Traders Utilize the Fisher Transform Indicator?
One of the advantages of the Fisher Transform Indicator is its role as a number one indicator, not a lagging indicator.
Lagging indicators tend to inform us of information we already know. a number one indicator is best at remarking potential trend reversals before they occur, not as they’re occurring or after the very fact .
There are two main ways to trade the Fisher Transform Indicator – a sign reversal or the reaching of a particular threshold.
For a sign reversal, you’re simply trying to find the indicator to vary course.
If the Fisher Transform indicator had been during a prolonged upswing but suddenly turned down, it might be foreshadowing a trend reversal within the stock price.
On the opposite hand, the Fisher Transform Indicator might be used as a “breach” indicator for identifying trade opportunities that support certain levels.
A signal line often accompanies the Fisher Transform Indicator, which may be wont to spot opportunities in not just stocks, but assets like commodities and forex also .
Examples
Alphabet (NASDAQ: GOOGL)
Google has been one among tech’s best stay-at-home plays during the coronavirus pandemic, but you wouldn’t have thought that back in late March when shares cratered down near the $1000 mark.
A bounce eventually came, but the stock didn’t rebound quickly.
However, the Fisher Transform Indicator provided a playbook for the stock beginning in February.
The extreme boundary was reached around the same time because the market was high, offering a sell signal before the top of the month. because the shares fell, the Fisher Transform Indicator moved right down to the boundary and bottomed before the stock.
Buying when the indicator eclipsed the signal line in mid-April would have allowed you to catch most of the rebound.
Nikola Corporation (NASDAQ: NKLA)
Before becoming marred in controversy, Nikola Corporation was the most well liked stock of summer 2020.
The obscure car maker was toiling within the $10-12 range before exploding higher in June.
And I don’t mean just a fast double or triple up – Nikola reached a high of $93 before the music stopped.
When a stock goes parabolic, one among the toughest things to work out is when to require profits and bail.
Nikola was a cautionary tale since the corporate seemed pretty shady from the beginning , but traders using the Fisher Transform Indicator got a sign that the highest was in before the stock began its quick descent backtrack .
The June high coincided with the Fisher Transform Indicator reaching its highest level since December of 2019, a sign that sounded the alarm for observant traders.
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Has Forex market always been such unstable or is it just since the stock market crash?

Started trading forex during lockdown. I used simple SMA retest strategy in forex. Before that I had 6 months experience in stock trading
I blew my account today. Stress hormone levels have never been this high for months. So I have to ask this question before considering re-entering the market:
Has the price always been this jerky? Or the pandemic has made it more crazy?
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Top 5 Most Famous Traders Of All Time

In all industries there are people credited to being the simplest .
In design, the late Steve Jobs is credited to being the simplest in his industry. In boxing, Muhammad Ali was credited to being the simplest boxer of all time.
In U.S. politics, there's a consensus that Lincoln was the nation’s greatest President by every measure applied.
In the trading world, a variety of traders are known worldwide for his or her skills. From Jesse Livermore to George Soros, we are sharing these tales of past and present traders who had to claw their thanks to the highest .
Here, we'll check out the five most famous traders of all time and canopy a touch bit about each trader and why they became so famous.
Jesse Livermore
Jesse Livermore jumped into the stock exchange with incredible calculations at the age of 15, amassed huge profits, then lost all of them , then mastered two massive crises and came out the opposite side while following his own rules, earning him the nickname “The Great Bear of Wall Street.”
Livermore was born in 1877 in Shrewsbury, Massachusetts.
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He is remembered for his incredible risk taking, his gregarious method of reading the potential moves within the stock exchange , derivatives and commodities, and for sustaining vast losses also as rising to fortune.
He began his career having run far away from home by carriage to flee a lifetime of farming that his father had planned for him, instead choosing city life and finding work posting stock quotes at Paine Webber, a Boston stockbroker.
Livermore bought his first share at 15 and earned a profit of $3.12 from $5 after teaching himself about trends.
George Soros
George Soros has a fantastic backstory.
Born in Hungary in 1930 to Jewish parents, Soros survived the Holocaust and later fled the country when the Communists took power. He went on to become one among the richest men and one among the foremost famous philanthropists within the world.
Most day traders know him for his long and prolific career as a trader who famously “broke the Bank of England” in 1992. Soros made an enormous bet against British Pound, which earned him $1 billion in profit in only 24 hours.
Along with other currency speculators, he placed a bet against the bank’s ability to carry the road on the pound. He borrowed pounds, then sold them, helping to down the worth of the currency on forex markets and ultimately forcing the united kingdom to crash out of the ecu rate of exchange Mechanism.
It was perhaps the quickest billion dollars anyone has ever made and one among the foremost famous trades ever taken, which later became referred to as “breaking the Bank of England”.
Soros is believed to have netted a complete of about $44 billion through financial speculation. And he has used his fortune to find thousands of human rights, democracy, health, and education projects.
Richard Dennis
There are only a couple of traders which will take a little amount of cash and switch it into millions and Richard Dennis was one among them.
Known as the “Prince of the Pit”, Dennis is claimed to have borrowed $1,600 when he was around 23 years old and turned it into $200 million in about 10 years trading commodities. Even more interesting to notice , he only traded $400 of the $1,600.
Not only did he achieve great success as a commodities trader, he also went on to launch the famous “Turtle Traders Group”. Using mini contracts, Dennis began to trade his own account at the Mid America commodities exchange .
He made a profit of $100,000 in 1973. The subsequent year, he capitalized on a runway soybean market to earn $500,000 in profits. He became an impressive millionaire at the top of the year.
However, he incurred massive losses within the Black Monday stock exchange crash in 1987 and therefore the dot-com bubble burst in 2000.
While he's famous for creating and losing tons of cash , Dennis is additionally famous for something else – an experiment. He and his friend William Eckhardt recruited and trained traders, a couple of men and ladies, the way to trade futures. These so-called Turtle Traders went on to form profits of $175 million in 4 years, consistent with a former student.
Paul Tudor Jones
Paul Tudor Jones thrust into the limelight within the 80s when he successfully predicted the 1987 stock exchange , as shown within the riveting one hour documentary called “Trader”.
The legendary trader was born in Memphis, Tennessee in 1954. His father ran a financial and legal trade newspaper. While he was in college, he want to write articles for the newspaper under the pseudonym, “Eagle Jones”.
Jones began his journey within the finance business by trading cotton. He started trading on his own following 4 years of non-trading experience, made profits from his trades but got bored, and later hired people to trade for him so he would not get bored.
But the trade that shot him to fame came on Black Monday in 1987, when he made an estimated $100 million whilst the Dow Jones Industrial Average plunged 22%.
He became a pioneer within the area of worldwide macro investing and was an enormous player within the meteoric growth of the hedge fund industry. He's also known for depending on currencies and interest rates.
He founded his hedge fund, Tudor Investment Corp, in 1980. The fund currently has around $21 billion in assets under management and he himself has an estimated net worth of nearly $5.8 Billion.
John Paulson
Super-trader John Paulson built a private fortune worth $4.4 billion from managing other people’s money. Born in 1955, Paulson made his name and far of his money betting a huge amount of money against the U.S. housing market during the worldwide financial crisis of 2007–2008.
Paulson bought insurance against defaults by subprime mortgages before the market collapse in 2007. He netted an estimated $20 billion on the collapse of the subprime mortgage market, dubbed the best trade ever.
However, his diary since that bet has been patchy at the best . Within the years following the financial crisis, Paulson struggled to match this success.
Failed bets on gold, healthcare and pharmaceutical stocks caused investors to escape his hedge fund Paulson & Co, cutting its assets under management to $10 billion as of January 2020 from a high of $36 billion in 2011.
Earlier this year, Paulson announced the fund would stop managing money for outdoor clients and switch it into a family office. He launched the fund in 1994.
submitted by equiti-me to u/equiti-me [link] [comments]

2.5 years and 145 backtested trades later

I have a habit of backtesting every strategy I find as long as it makes sense. I find it fun, and even if the strategy ends up being underperforming, it gives me a good excuse to gain valuable chart experience that would normally take years to gather. After I backtest something, I compare it to my current methodology, and usually conclude that mine is better either because it has a better performance or the new method requires too much time to manage (Spoiler: until now, I like this better)
During the last two days, I have worked on backtesting ParallaxFx strategy, as it seemed promising and it seemed to fit my personality (a lazy fuck who will happily halve his yearly return if it means he can spend 10% less time in front of the screens). My backtesting is preliminary, and I didn't delve very deep in the data gathering. I usually track all sort of stuff, but for this first pass, I sticked to the main indicators of performance over a restricted sample size of markets.
Before I share my results with you, I always feel the need to make a preface that I know most people will ignore.
Strategy
I am not going to go into the strategy in this thread. If you haven't read the series of threads by the guy who shared it, go here.
As suggested by my mentioned personality type, I went with the passive management options of ParallaxFx's strategy. After a valid setup forms, I place two orders of half my risk. I add or remove 1 pip from each level to account for spread.
Sample
I tested this strategy over the seven major currency pairs: AUDUSD, USDCAD, NZDUSD, GBPUSD, USDJPY, EURUSD, USDCHF. The time period started on January 1th 2018 and ended on July 1th 2020, so a 2.5 years backtest. I tested over the D1 timeframe, and I plan on testing other timeframes.
My "protocol" for backtesting is that, if I like what I see during this phase, I will move to the second phase where I'll backtest over 5 years and 28 currency pairs.
Units of measure
I used R multiples to track my performance. If you don't know what they are, I'm too sleepy to explain right now. This article explains what they are. The gist is that the results you'll see do not take into consideration compounding and they normalize volatility (something pips don't do, and why pips are in my opinion a terrible unit of measure for performance) as well as percentage risk (you can attach variable risk profiles on your R values to optimize position sizing in order to maximize returns and minimize drawdowns, but I won't get into that).
Results
I am not going to link the spreadsheet directly, because it is in my GDrive folder and that would allow you to see my personal information. I will attach screenshots of both the results and the list of trades. In the latter, I have included the day of entry for each trade, so if you're up to the task, you can cross-reference all the trades I have placed to make sure I am not making things up.
Overall results: R Curve and Segmented performance.
List of trades: 1, 2, 3, 4, 5, 6, 7. Something to note: I treated every half position as an individual trade for the sake of simplicity. It should not mess with the results, but it simply means you will see huge streaks of wins and losses. This does not matter because I'm half risk in each of them, so a winstreak of 6 trades is just a winstreak of 3 trades.
For reference:
Thoughts
Nice. I'll keep testing. As of now it is vastly better than my current strategy.
submitted by Vanguer to Forex [link] [comments]

When will we bottom out?

PART 2 : https://www.reddit.com/wallstreetbets/comments/g0sd44/what_is_the_bottom/
PART 3: https://www.reddit.com/wallstreetbets/comments/g2enz2/why_the_printer_must_continue/
Edit: By popular demand, the too long didn't read is now at the top
TL;DR
SPY 220p 11/20
This will likely be a multi-part series. It should be noted that I am no expert by any means, I'm actually quite new to this, it is just an elementary analysis of patterns in price and time. I am not a financial advisor, and this is not advice for a person to enter trades upon.
The fundamental divide in trading revolves around the question of market structure. Many feel that the market operates totally randomly and its’ behavior cannot be predicted. For the purposes of this DD, we will assume that the market has a structure, but that that structure is not perfect. That market structure naturally generates chart patterns as the market records prices in time. We will analyze an instrument, an exchange traded fund, which represents an index, as opposed to a particular stock. The price patterns of the various stocks in an index are effectively smoothed out. In doing so, a more technical picture arises. Perhaps the most popular of these is the SPDR S&P Standard and Poor 500 Exchange Traded Fund ($SPY).
In trading, little to no concern is given about value of underlying asset. We concerned primarily about liquidity and trading ranges, which are the amount of value fluctuating on a short-term basis, as measured by volatility-implied trading ranges. Fundamental analysis plays a role, however markets often do not react to real-world factors in a logical fashion. Therefore, fundamental analysis is more appropriate for long-term investing.
The fundamental derivatives of a chart are time (x-axis) and price (y-axis). The primary technical indicator is price, as everything else is lagging in the past. Price represents current asking price and incorrectly implementing positions based on price is one of the biggest trading errors.
Markets ordinarily have noise, their tendency to back-and-fill, which must be filtered out for true pattern recognition. That noise does have a utility, however, in allowing traders second chances to enter favorable positions at slightly less favorable entry points. When you have any market with enough liquidity for historical data to record a pattern, then a structure can be divined. The market probes prices as part of an ongoing price-discovery process. Market technicians must sometimes look outside of the technical realm and use visual inspection to ascertain the relevance of certain patterns, using a qualitative eye that recognizes the underlying quantitative nature
Markets rise slower than they correct, however they rise much more than they fall. In the same vein, instruments can only fall to having no worth, whereas they could theoretically grow infinitely and have continued to grow over time. Money in a fiat system is illusory. It is a fundamentally synthetic instrument which has no intrinsic value. Hence, the recent seemingly illogical fluctuations in the market.
According to trade theory, the unending purpose of a market is to create and break price ranges according to the laws of supply and demand. We must determine when to trade based on each market inflection point as defined in price and in time as opposed to abandoning the trend (as the contrarian trading in this sub often does). Time and Price symmetry must be used to be in accordance with the trend. When coupled with a favorable risk to reward ratio, the ability to stay in the market for most of the defined time period, and adherence to risk management rules; the trader has a solid methodology for achieving considerable gains.
We will engage in a longer term market-oriented analysis to avoid any time-focused pressure. The market is technically open 24-hours a day, so trading may be done when the individual is ready, without any pressing need to be constantly alert. Let alone, we can safely project months in advance with relatively high accuracy.
Some important terms to keep in mind:
§ Discrete – terminal points at the extremes of ranges
§ Secondary Discrete – quantified retracement or correction between two discrete
§ Longs (asset appreciation) and shorts (asset depreciation)
- Technical indicators are often considered self-fulfilling prophecies due to mass-market psychology gravitating towards certain common numbers yielded from them. That means a trader must be especially aware of these numbers as they can prognosticate market movements. Often, they are meaningless in the larger picture of things.
§ Volume – derived from the market itself, it is mostly irrelevant. The major problem with volume is that the US market open causes tremendous volume surges eradicating any intrinsic volume analysis. At major highs and lows, the market is typically anemic. Most traders are not active at terminal discretes because of levels of fear. Allows us confidence in time and price symmetry market inflection points, if we observe low volume at a foretold range of values. We can rationalize that an absolute discrete is usually only discovered and anticipated by very few traders. As the general market realizes it, a herd mentality will push the market in the direction favorable to defending it. Volume is also useful for swing trading, as chances for swing’s validity increases if an increase in volume is seen on and after the swing’s activation.
Therefore, due to the relatively high volume on the 23rd of March, we can safely determine that a low WAS NOT reached.
§ VIX – Volatility Index, this technical indicator indicates level of fear by the amount of options-based “insurance” in portfolios. A low VIX environment, less than 20 for the S&P index, indicates a stable market with a possible uptrend. A high VIX, over 20, indicates a possible downtrend. However, it is equally important to see how VIX is changing over time, if it is decreasing or increasing, as that indicates increasing or decreasing fear. Low volatility allows high leverage without risk or rest. Occasionally, markets do rise with high VIX.
As VIX is unusually high, in the forties, we can be confident that a downtrend is imminent.
– Trend definition is highly powerful, cannot be understated. Knowledge of trend logic is enough to be a profitable trader, yet defining a trend is an arduous process. Multiple trends coexist across multiple time frames and across multiple market sectors. Like time structure, it makes the underlying price of the instrument irrelevant. Trend definitions cannot determine the validity of newly formed discretes. Trend becomes apparent when trades based in counter-trend inflection points continue to fail.
Downtrends are defined as an instrument making lower lows and lower highs that are recurrent, additive, qualified swing setups. Downtrends for all instruments are similar, except forex. They are fast and complete much quicker than uptrends. An average downtrend is 18 months, something which we will return to. An uptrend inception occurs when an instrument reaches a point where it fails to make a new low, then that low will be tested. After that, the instrument will either have a deep range retracement or it may take out the low slightly, resulting in a double-bottom. A swing must eventually form.
A simple way to roughly determine trend is to attempt to draw a line from three tops going upwards (uptrend) or a line from three bottoms going downwards (downtrend). It is not possible to correctly draw an uptrend line on the SPY chart, but it is possible to correctly draw a downtrend – indicating that the overall trend is downwards.
Now that we have determined that the overall trend is downwards, the next issue is the question of when SPY will bottom out.
Time is the movement from the past through the present into the future. It is a measurement in quantified intervals. In many ways, our perception of it is a human construct. It is more powerful than price as time may be utilized for a trade regardless of the market inflection point’s price. Were it possible to perfectly understand time, price would be totally irrelevant due to the predictive certainty time affords. Time structure is easier to learn than price, but much more difficult to apply with any accuracy. It is the hardest aspect of trading to learn, but also the most rewarding.
Humans do not have the ability to recognize every time window, however the ability to define market inflection points in terms of time is the single most powerful trading edge. Regardless, price should not be abandoned for time alone. Time structure analysis It is inherently flawed, as such the markets have a fail-safe, which is Price Structure. Even though Time is much more powerful, Price Structure should never be completely ignored. Time is the qualifier for Price and vice versa. Time can fail by tricking traders into counter-trend trading.
Time is a predestined trade quantifier, a filter to slow trades down, as it allows a trader to specifically focus on specific time windows and rest at others. It allows for quantitative measurements to reach deterministic values and is the primary qualifier for trends. Time structure should be utilized before price structure, and it is the primary trade criterion which requires support from price. We can see price structure on a chart, as areas of mathematical support or resistance, but we cannot see time structure.
Time may be used to tell us an exact point in the future where the market will inflect, after Price Theory has been fulfilled. In the present, price objectives based on price theory added to possible future times for market inflection points give us the exact time of market inflection points and price.
Time Structure is repetitions of time or inherent cycles of time, occurring in a methodical way to provide time windows which may be utilized for inflection points. They are not easily recognized and not easily defined by a price chart as measuring and observing time is very exact. Time structure is not a science, yet it does require precise measurements. Nothing is certain or definite. The critical question must be if a particular approach to time structure is currently lucrative or not.
We will complete our analysis of time by measuring it in intervals of 180 bars. Our goal is to determine time windows, when the market will react and when we should pay the most attention. By using time repetitions, the fact that market inflection points occurred at some point in the past and should, therefore, reoccur at some point in the future, we should obtain confidence as to when SPY will reach a market inflection point. Time repetitions are essentially the market’s memory. However, simply measuring the time between two points then trying to extrapolate into the future does not work. Measuring time is not the same as defining time repetitions. We will evaluate past sessions for market inflection points, whether discretes, qualified swings, or intra-range. Then records the times that the market has made highs or lows in a comparable time period to the future one seeks to trade in.
What follows is a time Histogram – A grouping of times which appear close together, then segregated based on that closeness. Time is aligned into combined histogram of repetitions and cycles, however cycles are irrelevant on a daily basis. If trading on an hourly basis, do not use hours.
Yearly Lows: 12/31/2000, 9/21/2001, 10/9/2002, 3/11/2003, 8/2/2004, 4/15/2005, 6/12/2006, 3/5/2007, 11/17/2008, 3/9/2009, 7/2/10, 10/3/11, 1/1/12, 1/1/13, 2/3/14, 9/28/15, 2/8/16, 1/3/17, 12/24/18, 6/3/19
Months: 1, 1, 1, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 9, 9, 10, 10, 11, 12, 12
Days: 1, 1, 2, 2, 3, 3, 3, 3, 5, 8, 9, 9, 11, 12, 15, 17, 21, 24, 28, 31
Monthly Lows: 3/23, 2/28, 1/27, 12/3, 11/1, 10/2, 9/3, 8/5, 7/1, 6/3, 5/31, 4/1
Days: 1, 1, 1, 2, 3, 3, 3, 5, 23, 27, 27, 31
Weighted Times are repetitions which appears multiple times within the same list, observed and accentuated once divided into relevant sections of the histogram. They are important in the presently defined trading time period and are similar to a mathematical mode with respect to a series. Phased times are essentially periodical patterns in histograms, though they do not guarantee inflection points*.*
We see that SPY tends to have its lows between three major month clusters: 1-4, primarily March (which has actually occurred already this year), 6-9, averaged out to July, and 10-12, averaged out to November. Following the same methodology, we get the third and tenth days of the month as the likeliest days. However, evaluating the monthly lows for the past year, the end of the month has replaced the average of the tenth. Therefore, we have four primary dates for our histogram.
7/3/20, 7/27/20, and 11/3/20, 11/27/20 .
How do we narrow this group down with any accuracy? Let us average the days together to work with two dates - 7/15/20 and 11/15/20.
The 8.6-Year Armstrong-Princeton Global Economic Confidence model – states that 2.15 year intervals occur between corrections, relevant highs and lows. 2.15 years from the all-time peak discrete is April 14th of 2022. However, we can time-shift to other peaks and troughs to determine a date for this year. If we consider 1/28/2018 as a localized high and apply this model, we get 3/23/20 as a low - strikingly accurate. I have chosen the next localized high, 9/21/2018 to apply the model to. We achieve a date of 11/14/2020.
The average bear market is eighteen months long, giving us a date of August 19th, 2021 for the end of the bear market - roughly speaking.
Therefore, our timeline looks like:
As we move forward in time, our predictions may be less accurate. It is important to keep in mind that this analysis will likely change and become more accurate as we factor in Terry Laundry’s T-Theory, the Bradley Cycle, a more sophisticated analysis of Bull and Bear Market Cycles, the Fundamental Investor Cyclic Approach, and Seasons and Half-Seasons.
I have also assumed that the audience believes in these models, which is not necessary. Anyone with free time may construct histograms and view these time models, determining for themselves what is accurate and what is not. Take a look at 1/28/2008, that localized high, and 2.15 years (1/4th of the sinusoidal wave of the model) later.
The question now is, what prices will SPY reach on 11/14? Where will we be at 7/28? What will happen on 4/14/22?
submitted by aibnsamin1 to wallstreetbets [link] [comments]

How To Make Money Trading Reddit

How To Make Money Trading Reddit

MAKE MONEY WITH TRADING (Forex, Stocks, Binary Options)

https://preview.redd.it/onvu1owbn2v51.jpg?width=640&format=pjpg&auto=webp&s=63508b4c3653556bc53e4ef2df86a29df5e5dd0b
Trading consists of buying and selling assets, such as stocks, futures, currencies or derivatives, in a financial market. To trade, so that we obtain benefits, we will have to speculate with the movements in the price of the assets. This is the first step to making money from trading.
The word trading is usually associated with short-term investments, that is, short operations that seek benefits limited to a small time frame.
In other words, trading and investing are the same, only the time frame changes.
So if you hear terms like "stock trading" or "stock trading" it is the same thing, only they usually refer to different time frames.
The person who invests or trades is called a trader. A trader then is someone who invests in the financial markets.
Generally, the term trader is usually added to the asset that operates. For example, stock trader, futures trader, forex trader, in short, the asset that operates.
As you can see I am adding several concepts so that we all start from the same base.
So, trading is basically buying and selling assets, trying to buy at the lowest possible price and sell as high as possible. As simple as that.
I want you to understand something, the bases are 70% of your trading. It is amazing to see how advanced traders forget the basics before trading.
By advanced trader I mean someone who already knows how to trade but that doesn't necessarily make him a winning trader. In most cases they apply complicated strategies and forget something as simple as the bases.
How much can a trader earn? You put the roof on it, there is no limit. I recommend you measure your progress in percentages and not in nominals. It is best to verify your progress.
Is it necessary to be in a Trading Academy? Like everything, there are some who like to be social and others who prefer to work in a self-taught way. In trading, it is the same. If you need the constant support of people to not be demotivated, then a Trading Academy is a good option. Now, if you are an already motivated person who only needs to clear up doubts, then the best thing is a mentor, consulting professional, or a trading teacher who clears your doubts.
The foundations for making money trading have to be solid if we want to make profits consistently. So today I want to emphasize that, the foundations of being a successful trader. Let us begin!

How to Make Money Trading Reddit - Key Steps

https://preview.redd.it/la3o4919o2v51.jpg?width=640&format=pjpg&auto=webp&s=02e5635985796aa609c9ed4848285b4ce69f1196
1) Buy Supports (and resistances)
Buying in supports is buying in a key area where the price exerts a certain friction preventing the price from continuing to advance, for whatever reason.
A support is nothing more than an area where the asset finds the confidence of investors, it is the level where they estimate that it is a good purchase price for them, and that is why they buy the asset in question, in such a way that the asset finds help in that level.
Most trading systems, at least the ones I know of which are a few, are based on this principle but what happens, they camouflage it with flourishes.
Instead of saying, to the purchase in supports, they add colored mirrors so that it does not look so simple.
I'm not saying that details are not good, but exaggeration of details can lead to confusion and later paralysis.
Systems must necessarily be simple.
Buying in stands not only improves your overall entry, but it drastically lowers your risks. The further we move away from a support, the more the risk increases.
Many times we end up buying halfway because the price "escaped" us and we think that we will not have another equal opportunity. The reality is that the market always provides opportunities for those who know how to wait.
There is a saying that the beginning trader has fun in the market, the professional trader gets bored.
This does not mean that the professional trader does things reluctantly, or that he does not like to invest. It means that the professional trader waits crouched, calm, for that opportunity that he is looking for appears, that entry into support that reduces his risk. While the novice trader enters and exits the market euphoric.
A professional trader can be in front of the screen all day and not make a single trade. The novice trader, on the other hand, if he spends more than 5 minutes without trading, he already feels bad, anxious and thinks that he is losing opportunities.
Without further ado, enter supports.
2) Execute stop loss
Holding losses is the biggest mistake of traders. Who in the beginning has not moved the stop loss because the operation moved against him?
It's a very common mistake. We enter the market, we put the stop, the operation turns against us and instead of executing the stop, we RUN IT!
We are camicaces.
The typical phrase "I'm waiting to recover" has burned entire wallets.
The market fell 40% and instead of leaving, they began to pray.
The great advantage of small portfolios, that is, investors with little capital, is flexibility and speed of reaction.
By running the stop loss you are losing the only advantage you have with respect to professionals and large investors. Because they sure have more capital and have wider margins.
Please don't take losses, don't run the stop loss.
If you miss the stop, distance yourself from the market and analyze why that happened to you for the next better place your stop.
3) Sell in resistonce
I want you to remember something. Until you sell, the profits are not yours.
Until you sell, you have no money.
Until you sell, you cannot say that the operation was successful.
Many traders are very good at finding entries. They perfectly see the supports and manage to enter at the best prices. But what happens to them, they don't sell.
It hits a key resistance, where price clearly can't break through and what they do, they hold out in case it breaks.
The worst, the price does not break or make an upthrust (which would be a kind of professional feint), it returns to support, it bounces, it goes back to resistance and what we do ... we wait again to see if it breaks, because now it is the correct.
And there is a worse case. It reaches resistance and we want to apply the phrase "let the profits run", so what do we do, we adjust the stop loss near the resistance in case the price breaks and continues.
The price tests the resistance, falls, touches our stop and we run it in case the price returns to the path. Instead of applying the phrase “let the profits run” we apply the phrase “let the losses run”.
An old master used to say, when the price reaches resistance, I collect my winnings and go on vacation.
It seems silly but it is a way of telling our brain, if you do things well you have a prize.
Sell ​​in resistance, the market always gives new opportunities.
4) The Trend is your friend
No better elaborated phrase. The trend is your friend. And as we all know, almost no one pays attention to their friends. We ask them for advice and if they don't say what we want to hear, we won't.
If the price goes up, where do you have to invest?
"It is not that the price was stretched too much and surely now a correction is coming, so I invest against it."
You are seeing that the trend is upward in an annual, monthly, weekly, daily, hourly and minute time frame, but just in case you invest against it.
Please, the trend is your friend, if it tells you that the price is going up, it is because it is going up.
I invested in favor of the trend. You do not want to beat the market because I assure you that it breaks your arm in a blink of an eye.
5) Statistical advantage
In the financial markets there are no certainties, only probabilities and whoever tells you otherwise is surely not winning in silver.
What we are looking for are windows of statistical opportunities. In other words, we try to turn the odds in our favor.
That is why it is always important to ask yourself the question, what is more likely, that the price will go up or down?
This is because many times we operate and do not realize that the odds are against us.
We can never be 100% certain, but just putting the odds in our favor by making concrete decisions based on logic and not on emotions can earn us a lot of money.
6) Consistency
You often see many traders showing one or two of their most successful trades and the occasional loss. This is good for teaching purposes, and it is useful for transmitting teachings.
But if you want to become a professional trader you need consistency. And consistency does not speak of an isolated operation, it speaks of sustained profits over time.
And when I say time I speak of years. Not a month, not a week, not a semester. 3 years, 5 years, 10 years, 20 years.
To give you an idea, ultra-professional traders fight to see who is more consistent.
In other words, the first question they ask themselves is how many years have you been winning?
A trader who every year earns a tight, modest percentage, reasonable to say the least, but consistently, is a much better professional than one who doubles the capital one year and the other is -90.
Consistency is highly treasured as it allows for simulations, strategizing, and even projections.
7) Trading plan
The number of traders who invest without having a trading plan is impressive. Something so important, so simple to make, so useful and very few use it.
A trading plan allows you to analyze your operations, see what you are doing, and then improve.
When we don't have a trading plan, what we did last week goes completely unnoticed because we can't internalize the teaching.
And when I speak of teachings, they can be gains or losses.
A loss allows us to adjust the plan but a success also.
In fact, when we have several successful operations, there is nothing better than taking their teachings and replicating them.
The trading plan is the only tool that allows us to do this, learn, improve and be the most objective possible, leaving aside emotions.

Forex trading Reddit

https://preview.redd.it/ljyjklqgo2v51.jpg?width=640&format=pjpg&auto=webp&s=c50d6af6b81521fbbfe25938c98971e1592de261
When it comes to the currency market, one of the most popular trading markets is Forex. It represents the world's largest decentralized currency market. So we will answer how to make money from forex trading.
With only having a computer, tablet or mobile phone, and an excellent internet connection service, you will be able to operate from anywhere in the world in the Forex market. It has the great strength of being flexible and adaptable to all types of investors.
Select a prominent broker or intermediary agent, one that is recognized and very professional. Conduct negotiation trials with him, so that you get to know each other and do not put your capital at risk.
Develop together the work style that most identifies you and decide to earn money by trading, enriching yourself with all the possible knowledge and strategies.
Acquire strengths in detecting the ideal moment to carry out operations. You will achieve this by studying and understanding the graphs and trends of transactions, detecting that unique pattern that tells you when is the right time to proceed.
Do not hesitate, it is possible to earn a lot of money with trading! But, make sure, above all things, train yourself with a duly accredited professional, in guarantee of acquiring quality theoretical knowledge, imperative to understand the movement of the market.

How to Make Money Trading Reddit - Final Words

Trading is an “investment vehicle” that can serve your objectives of having financial peace of mind as long as it is part of a broad economic and financial planning in the short, medium and long term. If not, trading can become a fast track to lose your money, if you lack the necessary knowledge, experience and training. Follow the following formula to Make Money in Trading Consistently:

Profitability = (Knowledge + experience) x emotional and mental management

submitted by kayakero to makemoneyforexreddit [link] [comments]

[META] Recent scam/spam trends.. Or, a peak inside what it's like to moderate /r/forex

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:
And I'm just one of the mods. . .
So what scammer and marketing trends are we seeing lately?
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:
(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/
submitted by finance_student to Forex [link] [comments]

Trading economic news

The majority of this sub is focused on technical analysis. I regularly ridicule such "tea leaf readers" and advocate for trading based on fundamentals and economic news instead, so I figured I should take the time to write up something on how exactly you can trade economic news releases.
This post is long as balls so I won't be upset if you get bored and go back to your drooping dick patterns or whatever.

How economic news is released

First, it helps to know how economic news is compiled and released. Let's take Initial Jobless Claims, the number of initial claims for unemployment benefits around the United States from Sunday through Saturday. Initial in this context means the first claim for benefits made by an individual during a particular stretch of unemployment. The Initial Jobless Claims figure appears in the Department of Labor's Unemployment Insurance Weekly Claims Report, which compiles information from all of the per-state departments that report to the DOL during the week. A typical number is between 100k and 250k and it can vary quite significantly week-to-week.
The Unemployment Insurance Weekly Claims Report contains data that lags 5 days behind. For example, the Report issued on Thursday March 26th 2020 contained data about the week ending on Saturday March 21st 2020.
In the days leading up to the Report, financial companies will survey economists and run complicated mathematical models to forecast the upcoming Initial Jobless Claims figure. The results of surveyed experts is called the "consensus"; specific companies, experts, and websites will also provide their own forecasts. Different companies will release different consensuses. Usually they are pretty close (within 2-3k), but for last week's record-high Initial Jobless Claims the reported consensuses varied by up to 1M! In other words, there was essentially no consensus.
The Unemployment Insurance Weekly Claims Report is released each Thursday morning at exactly 8:30 AM ET. (On Thanksgiving the Report is released on Wednesday instead.) Media representatives gather at the Frances Perkins Building in Washington DC and are admitted to the "lockup" at 8:00 AM ET. In order to be admitted to the lockup you have to be a credentialed member of a media organization that has signed the DOL lockup agreement. The lockup room is small so there is a limited number of spots.
No phones are allowed. Reporters bring their laptops and connect to a local network; there is a master switch on the wall that prevents/enables Internet connectivity on this network. Once the doors are closed the Unemployment Insurance Weekly Claims Report is distributed, with a heading that announces it is "embargoed" (not to be released) prior to 8:30 AM. Reporters type up their analyses of the report, including extracting key figures like Initial Jobless Claims. They load their write-ups into their companies' software, which prepares to send it out as soon as Internet is enabled. At 8:30 AM the DOL representative in the room flips the wall switch and all of the laptops are connected to the Internet, releasing their write-ups to their companies and on to their companies' partners.
Many of those media companies have externally accessible APIs for distributing news. Media aggregators and squawk services (like RanSquawk and TradeTheNews) subscribe to all of these different APIs and then redistribute the key economic figures from the Report to their own subscribers within one second after Internet is enabled in the DOL lockup.
Some squawk services are text-based while others are audio-based. FinancialJuice.com provides a free audio squawk service; internally they have a paid subscription to a professional squawk service and they simply read out the latest headlines to their own listeners, subsidized by ads on the site. I've been using it for 4 months now and have been pretty happy. It usually lags behind the official release times by 1-2 seconds and occasionally they verbally flub the numbers or stutter and have to repeat, but you can't beat the price!
Important - I’m not affiliated with FinancialJuice and I’m not advocating that you use them over any other squawk. If you use them and they misspeak a number and you lose all your money don’t blame me. If anybody has any other free alternatives please share them!

How the news affects forex markets

Institutional forex traders subscribe to these squawk services and use custom software to consume the emerging data programmatically and then automatically initiate trades based on the perceived change to the fundamentals that the figures represent.
It's important to note that every institution will have "priced in" their own forecasted figures well in advance of an actual news release. Forecasts and consensuses all come out at different times in the days leading up to a news release, so by the time the news drops everybody is really only looking for an unexpected result. You can't really know what any given institution expects the value to be, but unless someone has inside information you can pretty much assume that the market has collectively priced in the experts' consensus. When the news comes out, institutions will trade based on the difference between the actual and their forecast.
Sometimes the news reflects a real change to the fundamentals with an economic effect that will change the demand for a currency, like an interest rate decision. However, in the case of the Initial Jobless Claims figure, which is a backwards-looking metric, trading is really just self-fulfilling speculation that market participants will buy dollars when unemployment is low and sell dollars when unemployment is high. Generally speaking, news that reflects a real economic shift has a bigger effect than news that only matters to speculators.
Massive and extremely fast news-based trades happen within tenths of a second on the ECNs on which institutional traders are participants. Over the next few seconds the resulting price changes trickle down to retail traders. Some economic news, like Non Farm Payroll Employment, has an effect that can last minutes to hours as "slow money" follows behind on the trend created by the "fast money". Other news, like Initial Jobless Claims, has a short impact that trails off within a couple minutes and is subsequently dwarfed by the usual pseudorandom movements in the market.
The bigger the difference between actual and consensus, the bigger the effect on any given currency pair. Since economic news releases generally relate to a single currency, the biggest and most easily predicted effects are seen on pairs where one currency is directly effected and the other is not affected at all. Personally I trade USD/JPY because the time difference between the US and Japan ensures that no news will be coming out of Japan at the same time that economic news is being released in the US.
Before deciding to trade any particular news release you should measure the historical correlation between the release (specifically, the difference between actual and consensus) and the resulting short-term change in the currency pair. Historical data for various news releases (along with historical consensus data) is readily available. You can pay to get it exported into Excel or whatever, or you can scroll through it for free on websites like TradingEconomics.com.
Let's look at two examples: Initial Jobless Claims and Non Farm Payroll Employment (NFP). I collected historical consensuses and actuals for these releases from January 2018 through the present, measured the "surprise" difference for each, and then correlated that to short-term changes in USD/JPY at the time of release using 5 second candles.
I omitted any releases that occurred simultaneously as another major release. For example, occasionally the monthly Initial Jobless Claims comes out at the exact same time as the monthly Balance of Trade figure, which is a more significant economic indicator and can be expected to dwarf the effect of the Unemployment Insurance Weekly Claims Report.
USD/JPY correlation with Initial Jobless Claims (2018 - present)
USD/JPY correlation with Non Farm Payrolls (2018 - present)
The horizontal axes on these charts is the duration (in seconds) after the news release over which correlation was calculated. The vertical axis is the Pearson correlation coefficient: +1 means that the change in USD/JPY over that duration was perfectly linearly correlated to the "surprise" in the releases; -1 means that the change in USD/JPY was perfectly linearly correlated but in the opposite direction, and 0 means that there is no correlation at all.
For Initial Jobless Claims you can see that for the first 30 seconds USD/JPY is strongly negatively correlated with the difference between consensus and actual jobless claims. That is, fewer-than-forecast jobless claims (fewer newly unemployed people than expected) strengthens the dollar and greater-than-forecast jobless claims (more newly unemployed people than expected) weakens the dollar. Correlation then trails off and changes to a moderate/weak positive correlation. I interpret this as algorithms "buying the dip" and vice versa, but I don't know for sure. From this chart it appears that you could profit by opening a trade for 15 seconds (duration with strongest correlation) that is long USD/JPY when Initial Jobless Claims is lower than the consensus and short USD/JPY when Initial Jobless Claims is higher than expected.
The chart for Non Farm Payroll looks very different. Correlation is positive (higher-than-expected payrolls strengthen the dollar and lower-than-expected payrolls weaken the dollar) and peaks at around 45 seconds, then slowly decreases as time goes on. This implies that price changes due to NFP are quite significant relative to background noise and "stick" even as normal fluctuations pick back up.
I wanted to show an example of what the USD/JPY S5 chart looks like when an "uncontested" (no other major simultaneously news release) Initial Jobless Claims and NFP drops, but unfortunately my broker's charts only go back a week. (I can pull historical data going back years through the API but to make it into a pretty chart would be a bit of work.) If anybody can get a 5-second chart of USD/JPY at March 19, 2020, UTC 12:30 and/or at February 7, 2020, UTC 13:30 let me know and I'll add it here.

Backtesting

So without too much effort we determined that (1) USD/JPY is strongly negatively correlated with the Initial Jobless Claims figure for the first 15 seconds after the release of the Unemployment Insurance Weekly Claims Report (when no other major news is being released) and also that (2) USD/JPY is strongly positively correlated with the Non Farms Payroll figure for the first 45 seconds after the release of the Employment Situation report.
Before you can assume you can profit off the news you have to backtest and consider three important parameters.
Entry speed: How quickly can you realistically enter the trade? The correlation performed above was measured from the exact moment the news was released, but realistically if you've got your finger on the trigger and your ear to the squawk it will take a few seconds to hit "Buy" or "Sell" and confirm. If 90% of the price move happens in the first second you're SOL. For back-testing purposes I assume a 5 second delay. In practice I use custom software that opens a trade with one click, and I can reliably enter a trade within 2-3 seconds after the news drops, using the FinancialJuice free squawk.
Minimum surprise: Should you trade every release or can you do better by only trading those with a big enough "surprise" factor? Backtesting will tell you whether being more selective is better long-term or not.
Hold time: The optimal time to hold the trade is not necessarily the same as the time of maximum correlation. That's a good starting point but it's not necessarily the best number. Backtesting each possible hold time will let you find the best one.
The spread: When you're only holding a position open for 30 seconds, the spread will kill you. The correlations performed above used the midpoint price, but in reality you have to buy at the ask and sell at the bid. Brokers aren't stupid and the moment volume on the ECN jumps they will widen the spread for their retail customers. The only way to determine if the news-driven price movements reliably overcome the spread is to backtest.
Stops: Personally I don't use stops, neither take-profit nor stop-loss, since I'm automatically closing the trade after a fixed (and very short) amount of time. Additionally, brokers have a minimum stop distance; the profits from scalping the news are so slim that even the nearest stops they allow will generally not get triggered.
I backtested trading these two news releases (since 2018), using a 5 second entry delay, real historical spreads, and no stops, cycling through different "surprise" thresholds and hold times to find the combination that returns the highest net profit. It's important to maximize net profit, not expected value per trade, so you don't over-optimize and reduce the total number of trades taken to one single profitable trade. If you want to get fancy you can set up a custom metric that combines number of trades, expected value, and drawdown into a single score to be maximized.
For the Initial Jobless Claims figure I found that the best combination is to hold trades open for 25 seconds (that is, open at 5 seconds elapsed and hold until 30 seconds elapsed) and only trade when the difference between consensus and actual is 7k or higher. That leads to 30 trades taken since 2018 and an expected return of... drumroll please... -0.0093 yen per unit per trade.
Yep, that's a loss of approx. $8.63 per lot.
Disappointing right? That's the spread and that's why you have to backtest. Even though the release of the Unemployment Insurance Weekly Claims Report has a strong correlation with movement in USD/JPY, it's simply not something that a retail trader can profit from.
Let's turn to the NFP. There I found that the best combination is to hold trades open for 75 seconds (that is, open at 5 seconds elapsed and hold until 80 seconds elapsed) and trade every single NFP (no minimum "surprise" threshold). That leads to 20 trades taken since 2018 and an expected return of... drumroll please... +0.1306 yen per unit per trade.
That's a profit of approx. $121.25 per lot. Not bad for 75 seconds of work! That's a +6% ROI at 50x leverage.

Make it real

If you want to do this for realsies, you need to run these numbers for all of the major economic news releases. Markit Manufacturing PMI, Factory Orders MoM, Trade Balance, PPI MoM, Export and Import Prices, Michigan Consumer Sentiment, Retail Sales MoM, Industrial Production MoM, you get the idea. You keep a list of all of the releases you want to trade, when they are released, and the ideal hold time and "surprise" threshold. A few minutes before the prescribed release time you open up your broker's software, turn on your squawk, maybe jot a few notes about consensuses and model forecasts, and get your finger on the button. At the moment you hear the release you open the trade in the correct direction, hold it (without looking at the chart!) for the required amount of time, then close it and go on with your day.
Some benefits of trading this way: * Most major economic releases come out at either 8:30 AM ET or 10:00 AM ET, and then you're done for the day. * It's easily backtestable. You can look back at the numbers and see exactly what to expect your return to be. * It's fun! Packing your trading into 30 seconds and knowing that institutions are moving billions of dollars around as fast as they can based on the exact same news you just read is thrilling. * You can wow your friends by saying things like "The St. Louis Fed had some interesting remarks on consumer spending in the latest Beige Book." * No crayons involved.
Some downsides: * It's tricky to be fast enough without writing custom software. Some broker software is very slow and requires multiple dialog boxes before a position is opened, which won't cut it. * The profits are very slim, you're not going to impress your instagram followers to join your expensive trade copying service with your 30-second twice-weekly trades. * Any friends you might wow with your boring-ass economic talking points are themselves the most boring people in the world.
I hope you enjoyed this long as fuck post and you give trading economic news a try!
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No, the British did not steal $45 trillion from India

This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.
How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.
This is begging the question.
After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.
This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.
She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain
Moving on:
The exact mechanism of drain, or transfers from India to Britain was quite simple.
Convenient.
Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.
- Chandra et al. (1989)
The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.
The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):
the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).
Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.
The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.
So what's going on here? Well Roy (2019) explains it better:
Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.
So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):
It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.
While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:
she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.
Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.
The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.
While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.
When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.
So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:
Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.
Indeed, if paying for what you buy is drain, then several billions of us are drained every day.
The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.
It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):
The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.
Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):
Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.
It doesn't appear that others appreciate their insight Roy (2019):
K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.
Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).
If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.
This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual1). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):
This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.
A view echoed in Raychaudhuri (1983):
Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.
So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):
All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.
The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):
Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable
So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
1. Several authors have affirmed that Indian identity is a colonial artefact. For example see Rajan 1969:
Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.
or see Bryant 2000:
But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist. [...] Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
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
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Trading a Measured Move SIMPLE FOREX TRADING STRATEGY - STRUCTURE AND MEASURED MOVES Measured Move Price Prediction Chart Pattern - YouTube AAPL Trade: Measured Move Signals Top How to Trade Forex Head and Shoulder Pattern Measured Moves

Learn Forex: Measured Moves in Time & Price As Shown on SPX500 . Measured_Moves_in_FX_body_Picture_1.png, How Measured Moves in Markets Can Show You Profit Target Guidelines . Presented by FXCM ... And if you are trading a bearish Measured Move, you should sell the market when the price action breaks the lower level of the bullish trend line or linear regression channel of the 2 nd wave. Measured Move Stop Loss Order. If the pattern is a bullish Measured Move, you should place a stop loss order below the lowest point of the 2 nd wave on ... In a Forex measured move, the CD leg should be equal to the AB move. Note * From our personal experience we’ve found that the CD leg tends to greater than AB. The Psychology behind the bullish measured move. Trading with the measured move pattern will give us clues into the trend direction and also the trend strength. The strength of the trend stands in the BC retracement. We’re going to ... So, if the measured move target is 100 pips away but there’s key support or resistance 75 pips away, it’s a good idea to watch these levels quite carefully, because as you know too well that because they’re tried and tested areas, they’ll certainly influence price. Why try and squeeze an extra 25 pips when the support and resistance is clearly more influential than a measured move target. Measured moves in pattern FX trading is all about finding the price objectives of the breakout or breakdown move of price when a chart pattern resolves. What are Measured Moves? With regards to chart patterns, a measured move is a method of determining the price objective of a price breakout or breakdown move, following the […] Measured moves in pattern FX trading is all about finding the price objectives of the breakout or breakdown move of price when a chart pattern resolves. The 'measured move' is a common pattern repeatedly found across every market and time frame. Using key price swings, measured moves can be recognised in the market when price makes a 50% - 61.8% retracement followed by a move to the -23.6% target in the same direction as the previous actionary price movement. Forex volatility is the measure of overall price fluctuations over a certain time, how rapidly a market’s prices change in the forex market and it is merely the standard deviation of returns. Which Forex Pairs Move the Most? The most volatile forex pairs (forex pairs that move the most) in the last several years are exotic pairs and then GBP cross pairs such as GBPNZD or GBPCAD etc. The ... Market is trending higher, currently it is facing some resistance. Expect it to break up in the next few days. Short term target $43 @Fibonacci 1.272 level. It is possible that price might continue to climb, could retest $44.79 June 2 2020 ATH. 7. 2. ERD:BTC - Patiently waiting for an opportunity to buy in. ERDBTC, 180. cybernetwork. Head and shoulders registered. Measured move down to 78.6% ... Measured Move Up: Important Bull Market Results. Overall performance rank for up/down breakouts: Not ranked because of the nature of this pattern. Average first leg price rise: 36% in 38 days . Average corrective phase retrace: 48% in 27 days. Average second leg price rise: 31% in 33 days. Percentage meeting price target: 60%. The above numbers are based on over 1,000 perfect trades. See the ...

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Trading a Measured Move

simple forex trading strategy - structure and measured moves My simple forex trading strategy is all about making 50 pips a day. I hope you enjoyed today's Daily Pip Talk! The measured move is a price prediction pattern. This video as well as the more detailed information below this video at FinVids.com explain the upward measu... You will gain clarity on my explanation as to why this move happen the way it did and how you can dominate forex trading head and shoulders measured moves and supply and demand zones. Category ... Learn how to trade a measured move in this free option trading webinar. This is a recording of our live mentoring sessions that members attend at our website... Watch this video now to gain insight what one of the best Pro Traders in the world is trading right now! If you want to learn and earn from the stock market ...

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