Hi everyone, I have been reading a lot lately on forex and just completed a statistic module in some studies. Is it possible or has it been done to conduct a multivariate analysis of variance on currency trades for the day. Determine if any correlations exist between certain currencies and/or commodities. Then based on that uncovered data, use a regression analysis to determine the impact of correlation a against correlation b on variable x? A lot to digest and I'm a rookie. Let me down easy haha.
Factset: How You can Invest in Hedge Funds’ Biggest Investment Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now: Their latest 8k filing reported the following: Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions. Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic. Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019. Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results. The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020. FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders. As you can see, there’s not much of a negative sign in sight here. It makes sense considering how FactSet’s FCF has never slowed down: https://preview.redd.it/frmtdk8e9hk51.png?width=276&format=png&auto=webp&s=1c0ff12539e0b2f9dbfda13d0565c5ce2b6f8f1a https://preview.redd.it/6axdb6lh9hk51.png?width=593&format=png&auto=webp&s=9af1673272a5a2d8df28f60f4707e948a00e5ff1 FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with. Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis: https://www.investopedia.com/terms/f/factset.asp https://preview.redd.it/yo71y6qj9hk51.png?width=355&format=png&auto=webp&s=a9414bdaa03c06114ca052304a26fae2773c3e45 FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015: https://preview.redd.it/oxaa1wel9hk51.png?width=443&format=png&auto=webp&s=13d60d2518980360c403364f7150392ab83d07d7 So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33% https://preview.redd.it/e4trju3p9hk51.png?width=387&format=png&auto=webp&s=6f6bee15f836c47e73121054ec60459f147d353e EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded. https://preview.redd.it/yl7f58tr9hk51.png?width=489&format=png&auto=webp&s=68906b9ecbcf6d886393c4ff40f81bdecab9e9fd P/E has declined in the past 2 years, making it a great time to buy. https://preview.redd.it/4mqw3t4t9hk51.png?width=445&format=png&auto=webp&s=e8d719f4913883b044c4150f11b8732e14797b6d Increasing ROE despite lowering of leverage post 2016 https://preview.redd.it/lt34avzu9hk51.png?width=441&format=png&auto=webp&s=f3742ed87cd1c2ccb7a3d3ee71ae8c7007313b2b Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself. https://preview.redd.it/fliirmpx9hk51.png?width=370&format=png&auto=webp&s=1216eddeadb4f84c8f4f48692a2f962ba2f1e848 SGA expense/Gross profit has been declining despite expansion of offices I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful. Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in. Calls have shitty greeks, but if you're ballsy October 450s LOL, I'm holding shares I’d say it’s a great long term investment, and it should at least be on your watchlist.
Former investment bank FX trader: news trading and second order thinking
Thanks to everyone who responded to the previous pieces on risk management. We ended up with nearly 2,000 upvotes and I'm delighted so many of you found it useful. This time we're going to focus on a new area: reacting to and trading around news and fundamental developments. A lot of people get this totally wrong and the main reason is that they trade the news at face value, without considering what the market had already priced in. If you've ever seen what you consider to be "good" or "better than forecast" news come out and yet been confused as the pair did nothing or moved in the opposite direction to expected, read on... We are going to do this in two parts. Part I
Why use an economic calendar
How to read the calendar
Knowing what's priced in
First order thinking vs second order thinking
Knowing how to use and benefit from the economic calendar is key for all traders - not just news traders. In this chapter we are going to take a practical look at how to use the economic calendar. We are also going to look at how to interpret news using second order thinking. The key concept is learning what has already been ‘priced in’ by the market so we can estimate how the market price might react to the new information.
Why use an economic calendar
The economic calendar contains all the scheduled economic releases for that day and week. Even if you purely trade based on technical analysis, you still must know what is in store. https://preview.redd.it/20xdiq6gq4k51.png?width=1200&format=png&auto=webp&s=6cd47186db1039be7df4d7ad6782de36da48f1db Why? Three main reasons. Firstly, releases can help provide direction. They create trends. For example if GBPUSD has been fluctuating aimlessly within a range and suddenly the Bank of England starts raising rates you better believe the British Pound will start to move. Big news events often start long-term trends which you can trade around. Secondly, a lot of the volatility occurs around these events. This is because these events give the market new information. Prior to a big scheduled release like the US Non Farm Payrolls you might find no one wants to take a big position. After it is released the market may move violently and potentially not just in a single direction - often prices may overshoot and come back down. Even without a trend this volatility provides lots of trading opportunities for the day trader. https://preview.redd.it/u17iwbhiq4k51.png?width=1200&format=png&auto=webp&s=98ea8ed154c9468cb62037668c38e7387f2435af Finally, these releases can change trends. Going into a huge release because of a technical indicator makes little sense. Everything could reverse and stop you out in a moment. You need to be aware of which events are likely to influence the positions you have on so you can decide whether to keep the positions or flatten exposure before the binary event for which you have no edge. Most traders will therefore ‘scan’ the calendar for the week ahead, noting what the big events are and when they will occur. Then you can focus on each day at a time.
Reading the economic calendar
Most calendars show events cut by trading day. Helpfully they adjust the time of each release to your own timezone. For example we can see that the Bank of Japan Interest Rate decision is happening at 4am local time for this particular London-based trader. https://preview.redd.it/lmx0q9qoq4k51.jpg?width=1200&format=pjpg&auto=webp&s=c6e9e1533b1ba236e51296de8db3be55dfa78ba1 Note that some events do not happen at a specific time. Think of a Central Banker’s speech for example - this can go on for an hour. It is not like an economic statistic that gets released at a precise time. Clicking the finger emoji will open up additional information on each event.
How do you define importance? Well, some events are always unimportant. With the greatest of respect to Italian farmers, nobody cares about mundane releases like Italian farm productivity figures. Other events always seem to be important. That means, markets consistently react to them and prices move. Interest rate decisions are an example of consistently high importance events. So the Medium and High can be thought of as guides to how much each event typically affects markets. They are not perfect guides, however, as different events are more or less important depending on the circumstances. For example, imagine the UK economy was undergoing a consumer-led recovery. The Central Bank has said it would raise interest rates (making GBPUSD move higher) if they feel the consumer is confident. Consumer confidence data would suddenly become an extremely important event. At other times, when the Central Bank has not said it is focused on the consumer, this release might be near irrelevant.
Knowing what's priced in
Next to each piece of economic data you can normally see three figures. Actual, Forecast, and Previous.
Actual refers to the number as it is released.
Forecast refers to the consensus estimate from analysts.
Previous is what it was last time.
We are going to look at this in a bit more detail later but what you care about is when numbers are better or worse than expected. Whether a number is ‘good’ or ‘bad’ really does not matter much. Yes, really. Once you understand that markets move based on the news vs expectations, you will be less confused by price action around events This is a common misunderstanding. Say everyone is expecting ‘great’ economic data and it comes out as ‘good’. Does the price go up? You might think it should. After all, the economic data was good. However, everyone expected it to be great and it was just … good. The great release was ‘priced in’ by the market already. Most likely the price will be disappointed and go down. By priced in we simply mean that the market expected it and already bought or sold. The information was already in the price before the announcement. Incidentally the official forecasts can be pretty stale and might not accurately capture what active traders in the market expect. See the following example.
An example of pricing in
For example, let’s say the market is focused on the number of Tesla deliveries. Analysts think it’ll be 100,000 this quarter. But Elon Musk tweets something that hints he’s really, really, really looking forward to the analyst call. Tesla’s price ticks higher after the tweet as traders put on positions, reflecting the sentiment that Tesla is likely to massively beat the 100,000. (This example is not a real one - it just serves to illustrate the concept.) Tesla deliveries are up hugely vs last quarter ... but they are disappointing vs market expectations ... what do you think will happen to the stock? On the day it turns out Tesla hit 101,000. A better than the officially forecasted result - sure - but only marginally. Way below what readers of Musk's twitter account might have thought. Disappointed traders may sell their longs and close out the positions. The stock might go down on ‘good’ results because the market had priced in something even better. (This example is not a real one - it just serves to illustrate the concept.)
We know that interest rates heavily affect currency prices. For major interest rate decisions there’s a great tool on the CME’s website that you can use. See the link for a demo This gives you a % probability of each interest rate level, implied by traded prices in the bond futures market. For example, in the case above the market thinks there’s a 20% chance the Fed will cut rates to 75-100bp. Obviously this is far more accurate than analyst estimates because it uses actual bond prices where market participants are directly taking risk and placing bets. It basically looks at what interest rate traders are willing to lend at just before/after the date of the central bank meeting to imply the odds that the market ascribes to a change on that date. Always try to estimate what the market has priced in. That way you have some context for whether the release really was better or worse than expected.
Second order thinking
You have to know what the market expects to try and guess how it’ll react. This is referred to by Howard Marks of Oaktree as second-level thinking. His explanation is so clear I am going to quote extensively. It really is hard to improve on this clarity of thought: First-level thinking is simplistic and superficial, and just about everyone can do it (a bad sign for anything involving an attempt at superiority). All the first-level thinker needs is an opinion about the future, as in “The outlook for the company is favorable, meaning the stock will go up.” Second-level thinking is deep, complex and convoluted. Howard Marks He explains first-level thinking: The first-level thinker simply looks for the highest quality company, the best product, the fastest earnings growth or the lowest p/e ratio. He’s ignorant of the very existence of a second level at which to think, and of the need to pursue it. Howard Marks The above describes the guy who sees a 101,000 result and buys Tesla stock because - hey, this beat expectations. Marks goes on to describe second-level thinking: The second-level thinker goes through a much more complex process when thinking about buying an asset. Is it good? Do others think it’s as good as I think it is? Is it really as good as I think it is? Is it as good as others think it is? Is it as good as others think others think it is? How will it change? How do others think it will change? How is it priced given: its current condition; how do I think its conditions will change; how others think it will change; and how others think others think it will change? And that’s just the beginning. No, this isn’t easy. Howard Marks In this version of events you are always thinking about the market’s response to Tesla results. What do you think they’ll announce? What has the market priced in? Is Musk reliable? Are the people who bought because of his tweet likely to hold on if he disappoints or exit immediately? If it goes up at which price will they take profit? How big a number is now considered ‘wow’ by the market? As Marks says: not easy. However, you need to start getting into the habit of thinking like this if you want to beat the market. You can make gameplans in advance for various scenarios. Here are some examples from Marks to illustrate the difference between first order and second order thinking. Some further examples Trying to react fast to headlines is impossible in today’s market of ultra fast computers. You will never win on speed. Therefore you have to out-think the average participant.
Coming up in part II
Now that we have a basic understanding of concepts such as expectations and what the market has priced in, we can look at some interesting trading techniques and tools. 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
Some key FX releases
Hope you enjoyed this note. As always, please reply with any questions/feedback - it is fun to hear from you. *** 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.
Access Part I here: https://www.reddit.com/Forex/comments/h0iwbu/part_i_my_10_minuteday_trading_strategy/ Welcome to Part II of this ongoing series. How many parts will there be? No idea. At least 4-5, I guess. I'd rather have this broken down into digestible chunks than just fire hose you with information. Part I was really just a primer. If I'm using the whole baking a cake analogy, then in Part I we covered what kind of cake we're baking. I will not cover in this post where we look for entries and exits, that's coming next. Part II is going to cover what ingredients we need and why we need those ingredients in greater detail. What Kind Of Strategy Is This Again?It's my 10 minutes per day, trading strategy. I think the beauty of this strategy is that it allows you to take a good number of trader per week without having to commit an inordinate amount of time to the screens. This is both a mean reversion and trend-continuation based strategy. It is dead simple to learn and apply. I'd expect a 10 year old to be able to make money with this. The List Of Ingredients & Why We Use These Particular Ingredients *I will have an image at the end of the post showing a textbook long and short setup* Bollinger Bands: Bollinger Bands (BB) have a base line (standard is the 20SMA, which is also what we will use for this strategy) and two other trend lines (known as the upper Bollinger band [UBB] and lower Bollinger band [LBB]) plotted 2 standard deviations away from the 20SMA. The idea behind BB is deviously simple - the vast majority of price action, approx. 90%, takes place in between the two bands. In other words, when price trades off the UBB or LBB, you could consider prices to be overbought/oversold. However, just because something is OVERbought does NOT mean its run is OVER. Therefore we need additional tools to make sure we are using the BB as effectively as possible. TLDR: BBhelp contextualize where to look for our technical setups using this strategy. Finding the candle/bar pattern is not enough. We need to make sure the setup is in the 'right' part of the chart. We accomplish that using the BB. Stochastic Oscillator: The Stochastic Oscillator (Stochs) is a secondary momentum indicator. Because it is an oscillator that means the signals it generates are range-bound between 0 and 100. There are tons of momentum indicators out there. Theoretically you could swap out the Stochs for RSI or MACD. My hunch is that you won't see a measurable statistical difference in performance if you do. So why Stochs? Because I like the fact you have the %K and %D lines (you can think of them as moving averages) and the fact that the %K and %D lines crossover is a helpful visual aid. Like any other momentum indicator, the Stochs will generate overbought and oversold signals. We use the Stochs to help back up what the BB are telling us. If price is trading at, or even broken out of, the UBB and Stochs are also veeeery overbought that can be potentially useful information. It doesn't mean we have a trade necessarily, but it is a helpful piece of data. Fibonacci Retracement & Extension Tool: This tool is OPTIONAL. The only reason I use this tool for this strategy is to integrate a mechanistic means of entry and exit. In other words, we can use fibonacci levels to place limit orders for entry and profit taking, and a stop order to get us out for our pre-defined risk allocation to each particular trade. If you DON'T want to use the fibs, that is perfectly okay. It just means you will add a more discretionary layer to this strategy Candlestick/Bar Patterns: There isn't a whole lot to say here. We look for ONE formation over, and over, and over again. An indecision bar (small body, doesn't close on its highs or lows) followed by the setup bar which is an outside bar or an engulfing bar. It doesn't particularly matter if the setup bar is an engulfing bar or outside bar. What matters is that for a long trade the setup bar makes a HIGHER HIGH and has a HIGHER CLOSE relative to the indecision bar. The opposite for a short trade setup. The bar formation is what ultimately serves as the trigger for placing orders to take a trade. *MOVING ON* Now We Get Into The Setup Itself:There are 3 places where we look for trades using this strategy:
Short off the UBB (Here we want to see Stochastics overbought and crossing down. Bearish divergence is even better)
Long off the LBB (Here we want to see Stochastics oversold and crossing up. Bullish divergence is even better)
Long/Short off the Middle Bollinger Band (Here if you are looking for a short trade off the MBB you ideally want Stochs overbought. Vice versa for a long trade. NOTE: Often when taking trades off the MBB, Stochs WON'T go overbought/oversold. Because this doesn't happen often, I don't let it stop me from taking trades off the MBB.)
This thread is the direct continuation of my previous entry, which you can find here. I have the feeling my rambles may be long, so I'm not going to repeat anything I already said in my previous post for the sake of keeping this brief. What is this? I am backtesting the strategy shared by ParallaxFx. I have just completed my second run of testing, and I am here to share my results with those who are interested. If you want to read more about the strategy, go to my previous thread where I linked it. What changed? Instead of using a fixed target of the -100.0 Fibonacci extension, I tracked both the -61.8 and the -100.0 targets. ParallaxFx used the -61.8 as a target, but never tried the second one, so I wanted to compare the two and see what happens. Where can I see your backtested result? I am going to do something I hope I won't regret and share the link to my spreadsheet. Hopefully I won't be doxxed, but I think I should be fine. You can find my spreadsheet at this link. There are a lot of entries, so it may take a while for them to load. In the "Trades" tab, you will find every trade I backtested with an attached screenshot and the results it would have had with the extended and the unextended target. You can see the UNCOMPOUNDED equity curve in the Summary tab, together with the overall statistics for the system. What was the sample size? I backtested on the Daily chart, from January 2017 to December 2019, over 28 currency pairs. I took a total of 310 trades - although keep in mind that every position is most often composed by two entries, meaning that you can roughly halve this number. What is the bottom line? If you're not interested in the details, here are the stats of the strategy based on how I traded it.
Extended: 223.46 R of return, 2.34 of profit factor, 0.72 R of expected value, 46.13% winrate. The average win is 2.72 R while the average loss is -1.00 R.
Unextended: 172.20 R of return, 2.19 of profit factor, 0.56 R of expected value, 53.23% winrate. The average win is 1.92 R while the average loss is -1.00 R.
The highest drawdown for both systems was 18 R. This seems like a lot, but remember you're splitting risk in half.
Here you can see the two uncompounded equity curves side by side: red is unextended and blue is extended. Who wins? The test suggests the strategy to be more profitable with the extended target. In addition, most of the trades that reached the unextended target but reversed before reaching the extended, were trades that I would have most likely not have taken with the extented target. This is because there was a resistance/support area in the way of the -100.0 extension level, but there was enough room for price to reach the -61.8 level. I will probably trade this strategy using the -100.0 level as target, unless there is an area in the way. In that case I will go for the unextended target. Drawdown management The expected losing streak for this system, using the extended target, is 7 trades in a row in a sample size of 100 trades. My goal is to have a drawdown cap of 4%, so my risk per trade will be 0.54%. If I ever find myself in a losing streak of more than 8 trades, I will reduce my risk per trade further. What's next? I'll be taking this strategy live. The wisest move would be to repeat the same testing over lower timeframes to verify the edge plays out there as well, but I would not be able to trust my results because I would have vague memories of where price went because of the testing I just did. I also believe markets are fractals, so I see no reason why this wouldn't work on lower timeframes. Before going live, I will expand this spreadsheet to include more specific analysis and I will continue backtesting at a slower pace. The goal is to reach 20 years of backtesting over these 28 pairs and put everything into this spreadsheet. It's not something I will do overnight, but I'll probably do one year every odd day, and maybe a couple more during the weekend. I think I don't have much else to add. I like the strategy. Feel free to ask questions.
And more importantly how much of a profit? I think the question above is the most important. It would basically show if you could make a living out of day trading or do it as a side job/money making hobby or stop since the time spent isn't worth the profits. A few questions from me, someone who spent a while doing several courses and learnt a few things about trading through forex. I make a profit on Forex and have a relatively green account history with a diversified portfolio, but since I am dealing with tiny lots the profit is negligible. The market is mostly driven my emotion not by math or science. Past prices, trend, support and resistant lines do matter for sure and I say this from experience. But is the probability of them failing low enough for me to go in with bigger lots? Nope. Throw in a few indicators and some fundamental analysis which makes things very confusing but doable after some practise and you only push that probability slightly lower. Probability and statistics wins this game and no one can deny it. You may argue a 60/40 win rate is you essentially making money at the end of the day but those odds mean small lots and a hobby more than a job to me. Charts look cool, the indicators cool, trading on a world stage very cool. But will my daily profits get me more than a cup of coffee at Starbucks. Nah. Thoughts from a newbie so hope you can prove me wrong.
No, the British did not steal $45 trillion from India
This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got. I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are) Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010. One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit. Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells. So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.
How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.
This is begging the question.
After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.
This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain). Furthermore, Dewey (2019) critiques the 5% interest rate.
She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain
The exact mechanism of drain, or transfers from India to Britain was quite simple.
Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.
The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.
The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):
the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).
Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.
The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.
So what's going on here? Well Roy (2019) explains it better:
Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.
So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):
It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.
While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided. It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:
she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)
Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.
Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.
The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.
While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.
When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.
So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:
Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles.India bought something and paid for it.State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.
Indeed, if paying for what you buy is drain, then several billions of us are drained every day.
The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.
It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):
The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.
Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.
Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):
Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.
The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.
It doesn't appear that others appreciate their insight Roy (2019):
K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.
Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.
If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.
This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual1). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, much like Japan. The list continues eternally. Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no. From Gupta (1980):
This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period,the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.
Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.
So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):
All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.
The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):
Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable
So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground. 1. Several authors have affirmed that Indian identity is a colonial artefact. For example seeRajan 1969:
Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.
But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist.[...]Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.
Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times Tuovila, Alicia (2019). Expenditure method. Investopedia Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History
Forex Signals Reddit: top providers review (part 1)
Forex Signals - TOP Best Services. Checked!
To invest in the financial markets, we must acquire good tools that help us carry out our operations in the best possible way. In this sense, we always talk about the importance of brokers, however, signal systems must also be taken into account. The platforms that offer signals to invest in forex provide us with alerts that will help us in a significant way to be able to carry out successful operations. For this reason, we are going to tell you about the importance of these alerts in relation to the trading we carry out, because, without a doubt, this type of system will provide us with very good information to invest at the right time and in the best assets in the different markets. financial Within this context, we will focus on Forex signals, since it is the most important market in the world, since in it, multiple transactions are carried out on a daily basis, hence the importance of having an alert system that offers us all the necessary data to invest in currencies. Also, as we all already know, cryptocurrencies have become a very popular alternative to investing in traditional currencies. Therefore, some trading services/tools have emerged that help us to carry out successful operations in this particular market. In the following points, we will detail everything you need to know to start operating in the financial markets using trading signals: what are signals, how do they work, because they are a very powerful help, etc. Let's go there!
What are Forex Trading Signals?
https://preview.redd.it/vjdnt1qrpny51.jpg?width=640&format=pjpg&auto=webp&s=bc541fc996701e5b4dd940abed610b59456a5625 Before explaining the importance of Forex signals, let's start by making a small note so that we know what exactly these alerts are. Thus, we will know that the signals on the currency market are received by traders to know all the information that concerns Forex, both for assets and for the market itself. These alerts allow us to know the movements that occur in the Forex market and the changes that occur in the different currency pairs. But the great advantage that this type of system gives us is that they provide us with the necessary information, to know when is the right time to carry out our investments.
In other words, through these signals, we will know the opportunities that are presented in the market and we will be able to carry out operations that can become quite profitable.
Profitability is precisely another of the fundamental aspects that must be taken into account when we talk about Forex signals since the vast majority of these alerts offer fairly reliable data on assets. Similarly, these signals can also provide us with recommendations or advice to make our operations more successful.
»Purpose: predict movements to carry out Profitable Operations
In short, Forex signal systems aim to predict the behavior that the different assets that are in the market will present and this is achieved thanks to new technologies, the creation of specialized software, and of course, the work of financial experts. In addition, it must also be borne in mind that the reliability of these alerts largely lies in the fact that they are prepared by financial professionals. So they turn out to be a perfect tool so that our investments can bring us a greater number of benefits.
The best signal services today
We are going to tell you about the 3 main alert system services that we currently have on the market. There are many more, but I can assure these are not scams and are reliable. Of course, not 100% of trades will be a winner, so please make sure you apply proper money management and risk management system.
1. 1000pipbuilder (top choice)
Fast track your success and follow the high-performance Forex signals from 1000pip Builder. These Forex signals are rated 5 stars on Investing.com, so you can follow every signal with confidence. All signals are sent by a professional trader with over 10 years investment experience. This is a unique opportunity to see with your own eyes how a professional Forex trader trades the markets. The 1000pip Builder Membership is ordinarily a signal service for Forex trading. You will get all the facts you need to successfully comply with the trading signals, set your stop loss and take earnings as well as additional techniques and techniques! You will get easy to use trading indicators for Forex Trades, including your entry, stop loss and take profit. Overall, the earnings target per months is 350 Pips, depending on your funding this can be a high profit per month! (In fact, there is by no means a guarantee, but the past months had been all between 600 – 1000 Pips). >>>Know more about 1000pipbuilder Your 1000pip builder membership gives you all in hand you want to start trading Forex with success. Read the directions and wait for the first signals. You can trade them inside your demo account first, so you can take a look at the performance before you make investments real money! Features:
Forex signals sent by email and SMS
Entry price, take profit and stop loss provided
Suitable for all time zones (signals sent over 24 hours)
Digital Derivatives Markets (DDMarkets) have been providing trade alert offerings since May 2014 - fully documenting their change ideas in an open and transparent manner. September 2020 performance report for DD Markets. Their manner is simple: carry out extensive research, share their evaluation and then deliver a trading sign when triggered. Once issued, daily updates on the trade are despatched to members via email. It's essential to note that DDMarkets do not tolerate floating in an open drawdown in an effort to earnings at any cost - a common method used by less professional providers to 'fudge' performance statistics. Verified Statistics: Not independently verified. Price: plans from $74.40 per month. Year Founded: 2014 Suitable for Beginners: Yes, (includes handy to follow trade analysis) VISIT -------
If you are looking or a forex signal service with a reliable (and profitable) music record you can't go previous Joel Kruger and the team at JKonFX. Trading performance file for JKonFX. Joel has delivered a reputable +59.18% journal performance for 2016, imparting real-time technical and fundamental insights, in an extremely obvious manner, to their 30,000+ subscriber base. Considered a low-frequency trader, alerts are only a small phase of the overall JKonFX subscription. If you're searching for hundreds of signals, you may want to consider other options. Verified Statistics: Not independently verified. Price: plans from $30 per month. Year Founded: 2014 Suitable for Beginners: Yes, (includes convenient to follow videos updates). VISIT
The importance of signals to invest in Forex
Once we have known what Forex signals are, we must comment on the importance of these alerts in relation to our operations. As we have already told you in the previous paragraph, having a system of signals to be able to invest is quite advantageous, since, through these alerts, we will obtain quality information so that our operations end up being a true success.
»Use of signals for beginners and experts
In this sense, we have to say that one of the main advantages of Forex signals is that they can be used by both beginners and trading professionals. As many as others can benefit from using a trading signal system because the more information and resources we have in our hands. The greater probability of success we will have. Let's see how beginners and experts can take advantage of alerts:
Beginners: for inexperienced these alerts become even more important since they will thus have an additional tool that will guide them to carry out all operations in the Forex market.
Professionals: In the same way, professionals are also recommended to make use of these alerts, so they have adequate information to continue bringing their investments to fruition.
Now that we know that both beginners and experts can use forex signals to invest, let's see what other advantages they have.
When we dedicate ourselves to working in the financial world, none of us can spend 24 hours in front of the computer waiting to perform the perfect operation, it is impossible. That is why Forex signals are important, because, in order to carry out our investments, all we will have to do is wait for those signals to arrive, be attentive to all the alerts we receive, and thus, operate at the right time according to the opportunities that have arisen. It is fantastic to have a tool like this one that makes our work easier in this regard.
»Carry out profitable Forex operations
These signals are also important, because the vast majority of them are usually quite profitable, for this reason, we must get an alert system that provides us with accurate information so that our operations can bring us great benefits. But in addition, these Forex signals have an added value and that is that they are very easy to understand, therefore, we will have a very useful tool at hand that will not be complicated and will end up being a very beneficial weapon for us.
»Decision support analysis
A system of currency market signals is also very important because it will help us to make our subsequent decisions. We cannot forget that, to carry out any type of operation in this market, previously, we must meditate well and know the exact moment when we will know that our investments are going to bring us profits . Therefore, all the information provided by these alerts will be a fantastic basis for future operations that we are going to carry out.
»Trading Signals made by professionals
Finally, we have to recall the idea that these signals are made by the best professionals. Financial experts who know perfectly how to analyze the movements that occur in the market and changes in prices. Hence the importance of alerts, since they are very reliable and are presented as a necessary tool to operate in Forex and that our operations are as profitable as possible.
What should a signal provider be like?
https://preview.redd.it/j0ne51jypny51.png?width=640&format=png&auto=webp&s=5578ff4c42bd63d5b6950fc6401a5be94b97aa7f As you have seen, Forex signal systems are really important for our operations to bring us many benefits. For this reason, at present, there are multiple platforms that offer us these financial services so that investing in currencies is very simple and fast. Before telling you about the main services that we currently have available in the market, it is recommended that you know what are the main characteristics that a good signal provider should have, so that, at the time of your choice, you are clear that you have selected one of the best systems.
»Must send us information on the main currency pairs
In this sense, one of the first things we have to comment on is that a good signal provider, at a minimum, must send us alerts that offer us information about the 6 main currencies, in this case, we refer to the euro, dollar, The pound, the yen, the Swiss franc, and the Canadian dollar. Of course, the data you provide us will be related to the pairs that make up all these currencies. Although we can also find systems that offer us information about other minorities, but as we have said, at a minimum, we must know these 6.
»Trading tools to operate better
Likewise, signal providers must also provide us with a large number of tools so that we can learn more about the Forex market.
We refer, for example, to technical analysis above all, which will help us to develop our own strategies to be able to operate in this market.
These analyzes are always prepared by professionals and study, mainly, the assets that we have available to invest.
»Different Forex signals reception channels
They must also make available to us different ways through which they will send us the Forex signals, the usual thing is that we can acquire them through the platform's website, or by a text message and even through our email. In addition, it is recommended that the signal system we choose sends us a large number of alerts throughout the day, in order to have a wide range of possibilities.
»Free account and customer service
Other aspects that we must take into account to choose a good signal provider is whether we have the option of receiving, for a limited time, alerts for free or the profitability of the signals they emit to us. Similarly, a final aspect that we must emphasize is that a good signal system must also have excellent customer service, which is available to us 24 hours a day and that we can contact them at through an email, a phone number, or a live chat, for greater immediacy. Well, having said all this, in our last section we are going to tell you which are the best services currently on the market. That is, the most suitable Forex signal platforms to be able to work with them and carry out good operations. In this case, we will talk about ForexPro Signals, 365 Signals and Binary Signals.
Forex Signals Reddit: conclusion
To be able to invest properly in the Forex market, it is convenient that we get a signal system that provides us with all the necessary information about this market. It must be remembered that Forex is a very volatile market and therefore, many movements tend to occur quickly. Asset prices can change in a matter of seconds, hence the importance of having a system that helps us analyze the market and thus know, what is the right time for us to start operating. Therefore, although there are currently many signal systems that can offer us good services, the three that we have mentioned above are the ones that are best valued by users, which is why they are the best signal providers that we can choose to carry out. our investments. Most of these alerts are quite profitable and in addition, these systems usually emit a large number of signals per day with full guarantees. For all this, SignalsForexPro, Signals365, or SignalsBinary are presented as fundamental tools so that we can obtain a greater number of benefits when we carry out our operations in the currency market.
Factset: How You can Invest in Hedge Funds’ Biggest Investment Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now: Their latest 8k filing reported the following: Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions. Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic. Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019. Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results. The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020. FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders. As you can see, there’s not much of a negative sign in sight here. It makes sense considering how FactSet’s FCF has never slowed down FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with. Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis: https://www.investopedia.com/terms/f/factset.asp FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015: So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33% EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded. P/E has declined in the past 2 years, making it a great time to buy. Increasing ROE despite lowering of leverage post 2016 Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself. SGA expense/Gross profit has been declining despite expansion of offices I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful. Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in.
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Disclaimer: None of this is financial advice. I have no idea what I'm doing. Please do your own research or you will certainly lose money. I'm not a statistician, data scientist, well-seasoned trader, or anything else that would qualify me to make statements such as the below with any weight behind them. Take them for the incoherent ramblings that they are. TL;DR at the bottom for those not interested in the details. This is a bit of a novel, sorry about that. It was mostly for getting my own thoughts organized, but if even one person reads the whole thing I will feel incredibly accomplished.
For those of you not familiar, please see the various threads on this trading system here. I can't take credit for this system, all glory goes to ParallaxFX! I wanted to see how effective this system was at H1 for a couple of reasons: 1) My current broker is TD Ameritrade - their Forex minimum is a mini lot, and I don't feel comfortable enough yet with the risk to trade mini lots on the higher timeframes(i.e. wider pip swings) that ParallaxFX's system uses, so I wanted to see if I could scale it down. 2) I'm fairly impatient, so I don't like to wait days and days with my capital tied up just to see if a trade is going to win or lose. This does mean it requires more active attention since you are checking for setups once an hour instead of once a day or every 4-6 hours, but the upside is that you trade more often this way so you end up winning or losing faster and moving onto the next trade. Spread does eat more of the trade this way, but I'll cover this in my data below - it ends up not being a problem. I looked at data from 6/11 to 7/3 on all pairs with a reasonable spread(pairs listed at bottom above the TL;DR). So this represents about 3-4 weeks' worth of trading. I used mark(mid) price charts. Spreadsheet link is below for anyone that's interested.
I'm pretty much using ParallaxFX's system textbook, but since there are a few options in his writeups, I'll include all the discretionary points here:
I'm using the stop entry version - so I wait for the price to trade beyond the confirmation candle(in the direction of my trade) before entering. I don't have any data to support this decision, but I've always preferred this method over retracement-limit entries. Maybe I just like the feeling of a higher winrate even though there can be greater R:R using a limit entry. Variety is the spice of life.
I put my stop loss right at the opposite edge of the confirmation candle. NOT at the edge of the 2-candle pattern that makes up the system. I'll get into this more below - not enough trades are saved to justify the wider stops. (Wider stop means less $ per pip won, assuming you still only risk 1%).
All my profit/loss statistics are based on a 1% risk per trade. Because 1 is real easy to multiply.
There are definitely some questionable trades in here, but I tried to make it as mechanical as possible for evaluation purposes. They do fit the definitions of the system, which is why I included them. You could probably improve the winrate by being more discretionary about your trades by looking at support/resistance or other techniques.
I didn't use MBB much for either entering trades, or as support/resistance indicators. Again, trying to be pretty mechanical here just for data collection purposes. Plus, we all make bad trading decisions now and then, so let's call it even.
As stated in the title, this is for H1 only. These results may very well not play out for other time frames - who knows, it may not even work on H1 starting this Monday. Forex is an unpredictable place.
I collected data to show efficacy of taking profit at three different levels: -61.8%, -100% and -161.8% fib levels described in the system using the passive trade management method(set it and forget it). I'll have more below about moving up stops and taking off portions of a position.
And now for the fun. Results!
Total Trades: 241
TP at -61.8%: 177 out of 241: 73.44%
TP at -100%: 156 out of 241: 64.73%
TP at -161.8%: 121 out of 241: 50.20%
Adjusted Proft % (takes spread into account):
TP at -61.8%: 5.22%
TP at -100%: 23.55%
TP at -161.8%: 29.14%
As you can see, a higher target ended up with higher profit despite a much lower winrate. This is partially just how things work out with profit targets in general, but there's an additional point to consider in our case: the spread. Since we are trading on a lower timeframe, there is less overall price movement and thus the spread takes up a much larger percentage of the trade than it would if you were trading H4, Daily or Weekly charts. You can see exactly how much it accounts for each trade in my spreadsheet if you're interested. TDA does not have the best spreads, so you could probably improve these results with another broker. EDIT: I grabbed typical spreads from other brokers, and turns out while TDA is pretty competitive on majors, their minors/crosses are awful! IG beats them by 20-40% and Oanda beats them 30-60%! Using IG spreads for calculations increased profits considerably (another 5% on top) and Oanda spreads increased profits massively (another 15%!). Definitely going to be considering another broker than TDA for this strategy. Plus that'll allow me to trade micro-lots, so I can be more granular(and thus accurate) with my position sizing and compounding.
A Note on Spread
As you can see in the data, there were scenarios where the spread was 80% of the overall size of the trade(the size of the confirmation candle that you draw your fibonacci retracements over), which would obviously cut heavily into your profits. Removing any trades where the spread is more than 50% of the trade width improved profits slightly without removing many trades, but this is almost certainly just coincidence on a small sample size. Going below 40% and even down to 30% starts to cut out a lot of trades for the less-common pairs, but doesn't actually change overall profits at all(~1% either way). However, digging all the way down to 25% starts to really make some movement. Profit at the -161.8% TP level jumps up to 37.94% if you filter out anything with a spread that is more than 25% of the trade width! And this even keeps the sample size fairly large at 187 total trades. You can get your profits all the way up to 48.43% at the -161.8% TP level if you filter all the way down to only trades where spread is less than 15% of the trade width, however your sample size gets much smaller at that point(108 trades) so I'm not sure I would trust that as being accurate in the long term. Overall based on this data, I'm going to only take trades where the spread is less than 25% of the trade width. This may bias my trades more towards the majors, which would mean a lot more correlated trades as well(more on correlation below), but I think it is a reasonable precaution regardless.
Time of Day
Time of day had an interesting effect on trades. In a totally predictable fashion, a vast majority of setups occurred during the London and New York sessions: 5am-12pm Eastern. However, there was one outlier where there were many setups on the 11PM bar - and the winrate was about the same as the big hours in the London session. No idea why this hour in particular - anyone have any insight? That's smack in the middle of the Tokyo/Sydney overlap, not at the open or close of either. On many of the hour slices I have a feeling I'm just dealing with small number statistics here since I didn't have a lot of data when breaking it down by individual hours. But here it is anyway - for all TP levels, these three things showed up(all in Eastern time):
7pm-4am: Fewer setups, but winrate high.
5am-6am: Lots of setups, but but winrate low.
12pm-3pm Medium number of setups, but winrate low.
I don't have any reason to think these timeframes would maintain this behavior over the long term. They're almost certainly meaningless. EDIT: When you de-dup highly correlated trades, the number of trades in these timeframes really drops, so from this data there is no reason to think these timeframes would be any different than any others in terms of winrate. That being said, these time frames work out for me pretty well because I typically sleep 12am-7am Eastern time. So I automatically avoid the 5am-6am timeframe, and I'm awake for the majority of this system's setups.
Moving stops up to breakeven
This section goes against everything I know and have ever heard about trade management. Please someone find something wrong with my data. I'd love for someone to check my formulas, but I realize that's a pretty insane time commitment to ask of a bunch of strangers. Anyways. What I found was that for these trades moving stops up...basically at all...actually reduced the overall profitability. One of the data points I collected while charting was where the price retraced back to after hitting a certain milestone. i.e. once the price hit the -61.8% profit level, how far back did it retrace before hitting the -100% profit level(if at all)? And same goes for the -100% profit level - how far back did it retrace before hitting the -161.8% profit level(if at all)? Well, some complex excel formulas later and here's what the results appear to be. Emphasis on appears because I honestly don't believe it. I must have done something wrong here, but I've gone over it a hundred times and I can't find anything out of place.
Moving SL up to 0% when the price hits -61.8%, TP at -100%
Adjusted Proft % (takes spread into account): 5.36%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%
Adjusted Proft % (takes spread into account): -1.01% (yes, a net loss)
Now, you might think exactly what I did when looking at these numbers: oof, the spread killed us there right? Because even when you move your SL to 0%, you still end up paying the spread, so it's not truly "breakeven". And because we are trading on a lower timeframe, the spread can be pretty hefty right? Well even when I manually modified the data so that the spread wasn't subtracted(i.e. "Breakeven" was truly +/- 0), things don't look a whole lot better, and still way worse than the passive trade management method of leaving your stops in place and letting it run. And that isn't even a realistic scenario because to adjust out the spread you'd have to move your stoploss inside the candle edge by at least the spread amount, meaning it would almost certainly be triggered more often than in the data I collected(which was purely based on the fib levels and mark price). Regardless, here are the numbers for that scenario:
Moving SL up to 0% when the price hits -61.8%, TP at -100%
Winrate(breakeven doesn't count as a win): 46.4%
Adjusted Proft % (takes spread into account): 17.97%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%
Winrate(breakeven doesn't count as a win): 65.97%
Adjusted Proft % (takes spread into account): 11.60%
From a literal standpoint, what I see behind this behavior is that 44 of the 69 breakeven trades(65%!) ended up being profitable to -100% after retracing deeply(but not to the original SL level), which greatly helped offset the purely losing trades better than the partial profit taken at -61.8%. And 36 went all the way back to -161.8% after a deep retracement without hitting the original SL. Anyone have any insight into this? Is this a problem with just not enough data? It seems like enough trades that a pattern should emerge, but again I'm no expert. I also briefly looked at moving stops to other lower levels (78.6%, 61.8%, 50%, 38.2%, 23.6%), but that didn't improve things any. No hard data to share as I only took a quick look - and I still might have done something wrong overall. The data is there to infer other strategies if anyone would like to dig in deep(more explanation on the spreadsheet below). I didn't do other combinations because the formulas got pretty complicated and I had already answered all the questions I was looking to answer.
2-Candle vs Confirmation Candle Stops
Another interesting point is that the original system has the SL level(for stop entries) just at the outer edge of the 2-candle pattern that makes up the system. Out of pure laziness, I set up my stops just based on the confirmation candle. And as it turns out, that is much a much better way to go about it. Of the 60 purely losing trades, only 9 of them(15%) would go on to be winners with stops on the 2-candle formation. Certainly not enough to justify the extra loss and/or reduced profits you are exposing yourself to in every single other trade by setting a wider SL. Oddly, in every single scenario where the wider stop did save the trade, it ended up going all the way to the -161.8% profit level. Still, not nearly worth it.
As I've said many times now, I'm really not qualified to be doing an analysis like this. This section in particular. Looking at shared currency among the pairs traded, 74 of the trades are correlated. Quite a large group, but it makes sense considering the sort of moves we're looking for with this system. This means you are opening yourself up to more risk if you were to trade on every signal since you are technically trading with the same underlying sentiment on each different pair. For example, GBP/USD and AUD/USD moving together almost certainly means it's due to USD moving both pairs, rather than GBP and AUD both moving the same size and direction coincidentally at the same time. So if you were to trade both signals, you would very likely win or lose both trades - meaning you are actually risking double what you'd normally risk(unless you halve both positions which can be a good option, and is discussed in ParallaxFX's posts and in various other places that go over pair correlation. I won't go into detail about those strategies here). Interestingly though, 17 of those apparently correlated trades ended up with different wins/losses. Also, looking only at trades that were correlated, winrate is 83%/70%/55% (for the three TP levels). Does this give some indication that the same signal on multiple pairs means the signal is stronger? That there's some strong underlying sentiment driving it? Or is it just a matter of too small a sample size? The winrate isn't really much higher than the overall winrates, so that makes me doubt it is statistically significant. One more funny tidbit: EUCAD netted the lowest overall winrate: 30% to even the -61.8% TP level on 10 trades. Seems like that is just a coincidence and not enough data, but dang that's a sucky losing streak. EDIT: WOW I spent some time removing correlated trades manually and it changed the results quite a bit. Some thoughts on this below the results. These numbers also include the other "What I will trade" filters. I added a new worksheet to my data to show what I ended up picking.
Total Trades: 75
TP at -61.8%: 84.00%
TP at -100%: 73.33%
TP at -161.8%: 60.00%
Moving SL up to 0% when the price hits -61.8%, TP at -100%: 53.33%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%: 53.33% (yes, oddly the exact same winrate. but different trades/profits)
Adjusted Proft % (takes spread into account):
TP at -61.8%: 18.13%
TP at -100%: 26.20%
TP at -161.8%: 34.01%
Moving SL up to 0% when the price hits -61.8%, TP at -100%: 19.20%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%: 17.29%
To do this, I removed correlated trades - typically by choosing those whose spread had a lower % of the trade width since that's objective and something I can see ahead of time. Obviously I'd like to only keep the winning trades, but I won't know that during the trade. This did reduce the overall sample size down to a level that I wouldn't otherwise consider to be big enough, but since the results are generally consistent with the overall dataset, I'm not going to worry about it too much. I may also use more discretionary methods(support/resistance, quality of indecision/confirmation candles, news/sentiment for the pairs involved, etc) to filter out correlated trades in the future. But as I've said before I'm going for a pretty mechanical system. This brought the 3 TP levels and even the breakeven strategies much closer together in overall profit. It muted the profit from the high R:R strategies and boosted the profit from the low R:R strategies. This tells me pair correlation was skewing my data quite a bit, so I'm glad I dug in a little deeper. Fortunately my original conclusion to use the -161.8 TP level with static stops is still the winner by a good bit, so it doesn't end up changing my actions. There were a few times where MANY (6-8) correlated pairs all came up at the same time, so it'd be a crapshoot to an extent. And the data showed this - often then won/lost together, but sometimes they did not. As an arbitrary rule, the more correlations, the more trades I did end up taking(and thus risking). For example if there were 3-5 correlations, I might take the 2 "best" trades given my criteria above. 5+ setups and I might take the best 3 trades, even if the pairs are somewhat correlated. I have no true data to back this up, but to illustrate using one example: if AUD/JPY, AUD/USD, CAD/JPY, USD/CAD all set up at the same time (as they did, along with a few other pairs on 6/19/20 9:00 AM), can you really say that those are all the same underlying movement? There are correlations between the different correlations, and trying to filter for that seems rough. Although maybe this is a known thing, I'm still pretty green to Forex - someone please enlighten me if so! I might have to look into this more statistically, but it would be pretty complex to analyze quantitatively, so for now I'm going with my gut and just taking a few of the "best" trades out of the handful. Overall, I'm really glad I went further on this. The boosting of the B/E strategies makes me trust my calculations on those more since they aren't so far from the passive management like they were with the raw data, and that really had me wondering what I did wrong.
What I will trade
Putting all this together, I am going to attempt to trade the following(demo for a bit to make sure I have the hang of it, then for keeps):
"System Details" I described above.
TP at -161.8%
Static SL at opposite side of confirmation candle - I won't move stops up to breakeven.
Trade only 7am-11am and 4pm-11pm signals.
Nothing where spread is more than 25% of trade width.
Looking at the data for these rules, test results are:
Adjusted Proft % (takes spread into account): 47.43%
I'll be sure to let everyone know how it goes!
Other Technical Details
ATR is only slightly elevated in this date range from historical levels, so this should fairly closely represent reality even after the COVID volatility leaves the scalpers sad and alone.
The sample size is much too small for anything really meaningful when you slice by hour or pair. I wasn't particularly looking to test a specific pair here - just the system overall as if you were going to trade it on all pairs with a reasonable spread.
Here's the spreadsheet for anyone that'd like it. (EDIT: Updated some of the setups from the last few days that have fully played out now. I also noticed a few typos, but nothing major that would change the overall outcomes. Regardless, I am currently reviewing every trade to ensure they are accurate.UPDATE: Finally all done. Very few corrections, no change to results.) I have some explanatory notes below to help everyone else understand the spiraled labyrinth of a mind that put the spreadsheet together.
I'm on the East Coast in the US, so the timestamps are Eastern time.
Time stamp is from the confirmation candle, not the indecision candle. So 7am would mean the indecision candle was 6:00-6:59 and the confirmation candle is 7:00-7:59 and you'd put in your order at 8:00.
I found a couple AM/PM typos as I was reviewing the data, so let me know if a trade doesn't make sense and I'll correct it.
Insanely detailed spreadsheet notes
For you real nerds out there. Here's an explanation of what each column means:
Pair - duh
Date/Time - Eastern time, confirmation candle as stated above
Win to -61.8%? - whether the trade made it to the -61.8% TP level before it hit the original SL.
Win to -100%? - whether the trade made it to the -100% TP level before it hit the original SL.
Win to -161.8%? - whether the trade made it to the -161.8% TP level before it hit the original SL.
Retracement level between -61.8% and -100% - how deep the price retraced after hitting -61.8%, but before hitting -100%. Be careful to look for the negative signs, it's easy to mix them up. Using the fib% levels defined in ParallaxFX's original thread. A plain hyphen "-" means it did not retrace, but rather went straight through -61.8% to -100%. Positive 100 means it hit the original SL.
Retracement level between -100% and -161.8% - how deep the price retraced after hitting -100%, but before hitting -161.8%. Be careful to look for the negative signs, it's easy to mix them up. Using the fib% levels defined in ParallaxFX's original thread. A plain hyphen "-" means it did not retrace, but rather went straight through -100% to -161.8%. Positive 100 means it hit the original SL.
Trade Width(Pips) - the size of the confirmation candle, and thus the "width" of your trade on which to determine position size, draw fib levels, etc.
Loser saved by 2 candle stop? - for all losing trades, whether or not the 2-candle stop loss would have saved the trade and how far it ended up getting if so. "No" means it didn't save it, N/A means it wasn't a losing trade so it's not relevant.
Spread(ThinkorSwim) - these are typical spreads for these pairs on ToS.
Spread % of Width - How big is the spread compared to the trade width? Not used in any calculations, but interesting nonetheless.
True Risk(Trade Width + Spread) - I set my SL at the opposite side of the confirmation candle knowing that I'm actually exposing myself to slightly more risk because of the spread(stop order = market order when submitted, so you pay the spread). So this tells you how many pips you are actually risking despite the Trade Width. I prefer this over setting the stop inside from the edge of the candle because some pairs have a wide spread that would mess with the system overall. But also many, many of these trades retraced very nearly to the edge of the confirmation candle, before ending up nicely profitable. If you keep your risk per trade at 1%, you're talking a true risk of, at most, 1.25% (in worst-case scenarios with the spread being 25% of the trade width as I am going with above).
Win or Loss in %(1% risk) including spread TP -61.8% - not going to go into huge detail, see the spreadsheet for calculations if you want. But, in a nutshell, if the trade was a win to 61.8%, it returns a positive # based on 61.8% of the trade width, minus the spread. Otherwise, it returns the True Risk as a negative. Both normalized to the 1% risk you started with.
Win or Loss in %(1% risk) including spread TP -100% - same as the last, but 100% of Trade Width.
Win or Loss in %(1% risk) including spread TP -161.8% - same as the last, but 161.8% of Trade Width.
Win or Loss in %(1% risk) including spread TP -100%, and move SL to breakeven at 61.8% - uses the retracement level columns to calculate profit/loss the same as the last few columns, but assuming you moved SL to 0% fib level after price hit -61.8%. Then full TP at 100%.
Win or Loss in %(1% risk) including spread take off half of position at -61.8%, move SL to breakeven, TP 100% - uses the retracement level columns to calculate profit/loss the same as the last few columns, but assuming you took of half the position and moved SL to 0% fib level after price hit -61.8%. Then TP the remaining half at 100%.
Overall Growth(-161.8% TP, 1% Risk) - pretty straightforward. Assuming you risked 1% on each trade, what the overall growth level would be chronologically(spreadsheet is sorted by date).
Based on the reasonable rules I discovered in this backtest:
Date range: 6/11-7/3
Adjusted Proft % (takes spread into account): 47.43%
Demo Trading Results
Since this post, I started demo trading this system assuming a 5k capital base and risking ~1% per trade. I've added the details to my spreadsheet for anyone interested. The results are pretty similar to the backtest when you consider real-life conditions/timing are a bit different. I missed some trades due to life(work, out of the house, etc), so that brought my total # of trades and thus overall profit down, but the winrate is nearly identical. I also closed a few trades early due to various reasons(not liking the price action, seeing support/resistance emerge, etc). A quick note is that TD's paper trade system fills at the mid price for both stop and limit orders, so I had to subtract the spread from the raw trade values to get the true profit/loss amount for each trade. I'm heading out of town next week, then after that it'll be time to take this sucker live!
Date range: 7/9-7/30
Adjusted Proft % (takes spread into account): 20.73%
Starting Balance: $5,000
Ending Balance: $6,036.51
Live Trading Results
I started live-trading this system on 8/10, and almost immediately had a string of losses much longer than either my backtest or demo period. Murphy's law huh? Anyways, that has me spooked so I'm doing a longer backtest before I start risking more real money. It's going to take me a little while due to the volume of trades, but I'll likely make a new post once I feel comfortable with that and start live trading again.
Statistical analysis is helpful in determining future probabilities but is not meant to be purely predictive. A typical statement is that correlation is not causality. Causality means explicit cause-and-effect, whereas correlation simply means potential common movements between two random variables. The scale of correlations coefficients is -1 to +1 whereas the negative one is a perfect ... Analysing the forex currency pairs is not an easy job at all. Not one or two factors decide the price fluctuation. There are multiple factors affecting price change. **1. Inflation Rates 2. Interest Rates 3. Country’s Current Account / Balance of Payments 4. Government Debt 5. Terms of Trade 6. Political Stability & Performance 7. […] Statistical analysis fallacy Last Post ; Page 1 2; Page 1 2 ; Post # 1; Quote; First Post: Edited Sep 8, 2008 2:09am Sep 7, 2008 9:41pm Edited Sep 8, 2008 2:09am tdion. Joined Nov 2005 Status: EURUSD Quant FREAK 3,197 Posts. Below is a graph of trades for the EURUSD over the past 8 years. One trade per day, based on the direction of an MA cross. (Remember, all the "experts" say trading ... The Basics Of Statistical Analysis In Forex Part 1 – Understand Your Edge. Anyone interested in Forex trading needs a basic knowledge of statistics, and even the basic rules governing probabilistic calculation. Do not quiver yet. We promise you that this will be simple and entertaining, at the same time. But why do we need all this? A possible answer can be found in our latest video article ... Major types of analysis include technical and fundamental, with sentiment and statistical analysis potentially being used by both technical and fundamental analysts/traders. Most forex trading occurs in pairs involving a handful of currencies. Understanding Forex Analysis. The forex market is open 24 hours a day, five days a week, and currencies are traded worldwide among the major financial ... Statistical analysis in FX trading, can literally mean anything. If you’re wanting to analyse price action, then you’ll be measuring whats called price evolution. Since there are only two ways price can behave; Mean reverting or Trending, you’ll b... Free Forex Systems. 4X Pip Snager System; 100 Pips Domination System; Breakout Simple System; Bulls Pips System; Buy Sell Alert Trend System; DDFX Forex System; Forex Profit Launcher System; Forex Rebellion System; Forex Spectrum System; Forex Stealth System; FX Pro System; Green Wave Fx System; Light Forex System; M5 Scalping System; Mass Pips ...
Foreign Exchange Rates and Interest Rate Differentials
A major determinant of foreign exchange rates is the interest rate differential between 2 currencies. For more information visit https://www.investopediapro.com Currency traders can use this spreadsheet to analyze any one of the 8 most liquid currencies like a professional. We will show you how to fill out the spread... This video is a part of work shop organized by REST Society for Research International (RSRI). RSRI conducts various workshops, seminars, conference and student events. If you are interested to ... Ethereum ETH: Price analysis, prediction, and Coronavirus News with Elon Musk Ethereum 1,632 watching Live now Think Fast, Talk Smart: Communication Techniques - Duration: 58:20. One method of analysis which helps us in this regard is analysing forex statistics - forex pattern recognition. Although, no two trading days are ever identical, certain repetitive behaviours or ... Do you want to learn our trading strategy? Check out our premium courses: https://tradeciety.com/pricing For more free trading tips, go here: https://tra... Better Statistics, Better Trading Decisions: Statistical Analysis 1) Average (expected) moves in a specified time - this could be as simple as the average true range (ATR).