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onesmith

The Structure of Non-randomness

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I have heard that randomly created synthetic data is able to mimic many features of market data and even be indistinguishable (from real market data). Assuming everyone contributes toward creating the non random structure within the real data ......... What is the markets structure?

 

What is the non randomness that distinguishes real data from random data.

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I have heard that randomly created synthetic data is able to mimic many features of market data and even be indistinguishable (from real market data). Assuming everyone contributes toward creating the non random structure within the real data ......... What is the markets structure?

 

What is the non randomness that distinguishes real data from random data.

 

This seems to be a debate on a few threads here.

 

The number of people in a queue at a shop is random. It goes up, it goes down, it doesn't go below zero, I can't say with certainty what this number will be in 5 minutes or indeed any future time, so in that sense it has randomness. So someone just observing the queue will see random numbers changing with very little pattern. If that's what we look for, that's what we find. But how many people's actions are actually random in that queue?

 

Having the appearance of being like a random walk, does not make it a random walk. If we paid attention, couldn't we see when people are more likely or less likely to join the queue? We obtain very little from assuming it's all random, but we might gain something by thinking that it's not, and that each of these people has some intent, so that for example, we may conclude that as closing time approaches, the number of people in the shop will quite likely quickly move to the queue.

 

Experiments have been done on whether humans can tell random data from real financial data, and it was demonstrated that humans could. I quote from a recent paper which researched this:

 

"We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets “look random”."

 

And these were not necessarily traders taking this test.

Edited by Seeker

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I have heard that randomly created synthetic data is able to mimic many features of market data and even be indistinguishable (from real market data). Assuming everyone contributes toward creating the non random structure within the real data ......... What is the markets structure?

 

What is the non randomness that distinguishes real data from random data.

 

 

From my (limited) experience, random data is largely used to gauge the statistical significance of results.

 

It might also be used for theoretical market participants in building limit order book models.

 

Also, testing an idea against random data to verify results would imply the tester believes markets are not random.

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What is the non randomness that distinguishes real data from random data.

 

In terms of making money, it doesn't make the slightest bit of difference.

 

What is the markets structure?

 

The market can go up, down, or sideways (or, for extremely brief periods of time, it can stand still). If one knows how to tell the difference between up, down, and sideways, he can exploit -- i.e., profit from -- these movements. If he doesn't, or can't, then whatever profits he realizes will be purely accidental, if he profits at all.

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In terms of making money, it doesn't make the slightest bit of difference.

 

That's the real point. Even great trend trading methods can have win rates of 30-40%. Whether the markets are random or not doesn't even matter. The only thing that matters is how much is made on average vs how much is lost on average and the frequency of both over time (and keeping the trade distribution close to that average - no big losses). With a large enough reward to risk, you can make money with 10% accuracy, probably on 'random' price data and real price data. Purely academic..

 

As Steve from Santa Barbara pointed out, randomness itself is hard to model accurately with a computer (so even our estimations of 'random' are not in fact pure randomness).. You can use Brownian motion of bacteria as a random model... even then, you see trends! They call it 'drift'. So even with natural models of randomness you see trends. Now do trends in prices occur more frequently than drifts in brownian motion? I'll leave that to a PhD.. i'm interested in making money. Lets get a thread going about making money.. step 2..

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This seems to be a debate on a few threads here.

 

The number of people in a queue at a shop is random. It goes up, it goes down, it doesn't go below zero, I can't say with certainty what this number will be in 5 minutes or indeed any future time, so in that sense it has randomness. So someone just observing the queue will see random numbers changing with very little pattern. If that's what we look for, that's what we find. But how many people's actions are actually random in that queue?

 

Having the appearance of being like a random walk, does not make it a random walk. If we paid attention, couldn't we see when people are more likely or less likely to join the queue? We obtain very little from assuming it's all random, but we might gain something by thinking that it's not, and that each of these people has some intent, so that for example, we may conclude that as closing time approaches, the number of people in the shop will quite likely quickly move to the queue.

 

Experiments have been done on whether humans can tell random data from real financial data, and it was demonstrated that humans could. I quote from a recent paper which researched this:

 

"We find overwhelming statistical evidence (p-values no greater than 0.5%) that subjects can consistently distinguish between the two types of time series, thereby refuting the widespread belief that financial markets “look random”."

 

And these were not necessarily traders taking this test.

 

Other than the players spoofing orders to hide what might otherwise be viable info there shouldn't be many random orders in the queue. Is the queue a viable source of the non random footprint winning and losing traders leave in the market?

 

I'm curious about that study. What characteristics of the real data enabled determining it wasn't random?

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From my (limited) experience, random data is largely used to gauge the statistical significance of results.

 

It might also be used for theoretical market participants in building limit order book models.

 

Also, testing an idea against random data to verify results would imply the tester believes markets are not random.

I suspect there's a lot of info to be gleaned from what appears as randomness. I'm particularly interested in randomness from the point of view of determing when and how it might deliberately or randomly conceal otherwise non-random aspects of the data. My focus is the non-random aspects and integrating the effects of randomness such as this afternoons Boston Marathon incident interupting an otherwise logical buying opportunity.

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In terms of making money, it doesn't make the slightest bit of difference.

 

The market can go up, down, or sideways (or, for extremely brief periods of time, it can stand still). If one knows how to tell the difference between up, down, and sideways, he can exploit -- i.e., profit from -- these movements. If he doesn't, or can't, then whatever profits he realizes will be purely accidental, if he profits at all.

 

 

From the point of view of differentiating real data from synthetically created random data the utility is limited to what I learn about real data. Within the realm of just the real data it is worthwhile distinguishing between random and non-random. For instance up is not always up. Just because direction appears obvious does not make it the true direction. I have observed losers buying dips and winners capitalizing on that. Correlating the actions of winners to anything that repeats is the essence of trading. Along with understanding the basis (losers leaving non-random footprints) or random lack of a basis for moves that fail.

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Other than the players spoofing orders to hide what might otherwise be viable info there shouldn't be many random orders in the queue. Is the queue a viable source of the non random footprint winning and losing traders leave in the market?

 

I'm curious about that study. What characteristics of the real data enabled determining it wasn't random?

 

It's not known what characteristic enabled people to determine which was real and which was random - just that they could. It was a test only to see if humans could detect the difference. I think you can still see what the test was like here:

 

ARORA

 

Random is a tricky word. Anything you do or write is somewhat random to ME. It's not random to you, because you choose it, but it is to everyone else. I say somewhat because you are still using words and sentences, there is still structure and I am familiar with it, so it's not entirely random. So when you're asking if things are random in the market, the question is 'to whom'? And how random? The markets are random to all participants, but they are not entirely random. At least that is how I see it.

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That's the real point. Even great trend trading methods can have win rates of 30-40%. Whether the markets are random or not doesn't even matter. The only thing that matters is how much is made on average vs how much is lost on average and the frequency of both over time (and keeping the trade distribution close to that average - no big losses). With a large enough reward to risk, you can make money with 10% accuracy, probably on 'random' price data and real price data. Purely academic..

 

As Steve from Santa Barbara pointed out, randomness itself is hard to model accurately with a computer (so even our estimations of 'random' are not in fact pure randomness).. You can use Brownian motion of bacteria as a random model... even then, you see trends! They call it 'drift'. So even with natural models of randomness you see trends. Now do trends in prices occur more frequently than drifts in brownian motion? I'll leave that to a PhD.. i'm interested in making money. Lets get a thread going about making money.. step 2..

 

Risk/Reward is important but discernible risk/reward removes your entry from the random category. The footprint of your having taken the time to learn to calculate r/r forms part of the non randomness that's the essence of why markets are tradable. This isn't academic. Brownian drift of markets tends toward deterministic models dissimilar to random models. Not all buyers and sellers act with the same (random) level of disregard for stucture. Winners and losers both leave footprints. Order and structure that's non-random and discernable. From that point of view studying THE STRUCTURE OF NON RANDOMNESS cuts to the essence of what it takes to make money.

 

I'm not interested in swaying opinions or convincing anyone of anything. I prefer keeping my beliefs and opinions to myself. I'm interested in hearing everyone elses thoughts and learning something here. Please use this exception to my more normal not sharing as an opportunity to throw your best at me and convince me what matters in your world is the really important thing I should be thinking about. Particularly within the realm of the existence or non existence of the non random structure I am claiming is the basis and driving force behind all markets.

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From the point of view of differentiating real data from synthetically created random data the utility is limited to what I learn about real data. Within the realm of just the real data it is worthwhile distinguishing between random and non-random. For instance up is not always up. Just because direction appears obvious does not make it the true direction. I have observed losers buying dips and winners capitalizing on that. Correlating the actions of winners to anything that repeats is the essence of trading. Along with understanding the basis (losers leaving non-random footprints) or random lack of a basis for moves that fail.

 

You're thinking about this too much. Instead of trading what's in front of you, you're wondering whether or not it's real.

 

Just trade it, and you'll do better than most. Or overthink it and risk doing worse.

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I'm not interested in swaying opinions or convincing anyone of anything. I prefer keeping my beliefs and opinions to myself. I'm interested in hearing everyone elses thoughts and learning something here. Please use this exception to my more normal not sharing as an opportunity to throw your best at me and convince me what matters in your world is the really important thing I should be thinking about. Particularly within the realm of the existence or non existence of the non random structure I am claiming is the basis and driving force behind all markets.

 

Not answers, but various thoughts . . .

 

  • If you muddle up the data in a time series (monte carlo), then the way that each member of the series relates to all other members is random, but . . . the time series will still have the same outcome, or sum to the same total. The process is random; the outcome is fixed.
     
  • Suppose you have a known price now, and you think you can predict a future price. Assuming you can, then that still isn't the same as saying that you can predict the path involved in getting there. That would involve a hell of a lot more prediction. Which is one reason that people doing fancy things with trailing stops etc confounds me . . .
     
  • A lot of the references to 'non-random' structures appear to be rooted purely in price, and how it relates to itself visually within a chart. Is it any wonder if such a limited view of structure should turn out to hide all manner of randomness?
     
  • What about Mandelbrot - tiny differences in even the simplest and most benign systems can have diverse outcomes over many iterations . . . and a fat-finger error from a one contract retail trader can trigger a market crash . . . Does anyone actually believe this?
     
  • Talk of footprints - just because an order is not randomly placed doesn't mean that the individual placing it has any kind of directional motive. If someone is operating within a completely different structure of behaviour to yourself, then are their actions really in any worthwhile sense any different to those of someone/thing behaving randomly?
     
  • Non-random behaviour is predictable if you know the cause. Random behaviour is predictable if you know that there is no cause . . .

 

BlueHorseshoe

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all this talk about randomness poped into my head when reading this news/infovertisement

 

What to do when everyone is right

 

.............

 

What does your taking time to draw attention to that link return for you? How does it benefit you? On an objective scale I'm not aware of an ojective value other than if you choose introspection based upon my questioning what you're attracting to yourself. Thanks for your post. I've been bored by the direction those topics on randomness took as much as anyone but they created my obsession with non randomness. I'm more concerned about people finding this interesting than boring.

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You're thinking about this too much. Instead of trading what's in front of you, you're wondering whether or not it's real.

 

Just trade it, and you'll do better than most. Or overthink it and risk doing worse.

 

 

First ten bagger charting daily bars by hand one bar at a time forty years ago

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It's not known what characteristic enabled people to determine which was real and which was random - just that they could. It was a test only to see if humans could detect the difference. I think you can still see what the test was like here:

 

ARORA

 

Random is a tricky word. Anything you do or write is somewhat random to ME. It's not random to you, because you choose it, but it is to everyone else. I say somewhat because you are still using words and sentences, there is still structure and I am familiar with it, so it's not entirely random. So when you're asking if things are random in the market, the question is 'to whom'? And how random? The markets are random to all participants, but they are not entirely random. At least that is how I see it.

 

When I go to that website and attempt to train as a guest it returns a box with nothing but a red x in the upper left corner. It's either on their end or javascript or anti-virus at my end. I have other priorities but I'm grateful for your contributions thus far because they have helped my focus. Thanks.

 

When setting upper and lower limits on a simple random number generator it taints the results. Constraints within the more sophisticated random number generators likewise contribute non-random artifacts to the random data that mimics market data. The best of the best strategies should fail when trading on random data. But imagine an algorithm generating random data. What if it's constraints are based upon the non random structure of real market data? Would the human eye be able to tell the differencer between that flavor of random data and real data? Would other strats be able to distinguish a difference in the data? Would the results of other strats improve when they were trading on this random data? Would it really be random data?

 

Is real data generated by (non random) strats?

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Not answers, but various thoughts . . .

 

  • If you muddle up the data in a time series (monte carlo), then the way that each member of the series relates to all other members is random, but . . . the time series will still have the same outcome, or sum to the same total. The process is random; the outcome is fixed.
    In life, order matters, (think PEMDAS). Monte carlo simulation is data in a vacuum.
     
  • Suppose you have a known price now, and you think you can predict a future price. Assuming you can, then that still isn't the same as saying that you can predict the path involved in getting there. That would involve a hell of a lot more prediction. Which is one reason that people doing fancy things with trailing stops etc confounds me . . .
     
  • A lot of the references to 'non-random' structures appear to be rooted purely in price, and how it relates to itself visually within a chart. Is it any wonder if such a limited view of structure should turn out to hide all manner of randomness?
    Almost all studies of randomness in markets are defined with historic price data, entirely ignoring the other side of the market, liquidity.
     
  • What about Mandelbrot - tiny differences in even the simplest and most benign systems can have diverse outcomes over many iterations . . . and a fat-finger error from a one contract retail trader can trigger a market crash . . . Does anyone actually believe this?
    I think you are referring to the "butterfly effect"? That was Lorenz. Also, perception has leverage, If one extra contract changes the perception of someone with 10k contracts, that single lot contains a lot of leverage.
     
  • Talk of footprints - just because an order is not randomly placed doesn't mean that the individual placing it has any kind of directional motive. If someone is operating within a completely different structure of behaviour to yourself, then are their actions really in any worthwhile sense any different to those of someone/thing behaving randomly?
     
    Markets are a function of all participants, which is independent of their motives.
     
  • Non-random behaviour is predictable if you know the cause. Random behaviour is predictable if you know that there is no cause . . .
     
    Non-random behavior is predictable regardless of the cause. Random behavior is only predictable in the sense that it will continue to be random.

 

BlueHorseshoe

 

My thoughts on your thoughts.

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What does your taking time to draw attention to that link return for you? How does it benefit you? On an objective scale I'm not aware of an ojective value other than if you choose introspection based upon my questioning what you're attracting to yourself. Thanks for your post. I've been bored by the direction those topics on randomness took as much as anyone but they created my obsession with non randomness. I'm more concerned about people finding this interesting than boring.

 

the answer to your question is largely this article reminded of the same circular boring discussions.

yeah I probably should have put it more into the other thread about randomness as everyone has an opinion on it, they all think they are right and most of the talk is rubbish. On re reading your original question - wrong thread - sorry....

 

.......

so as to your original question...

 

"Assuming everyone contributes toward creating the non random structure within the real data ......... What is the markets structure?

 

What is the non randomness that distinguishes(differentiates) real and random data?

 

It has to be human behavior and in real live data you are actually trying to pick up patterns of human behavior.

Real data does not rely on being totally random, we have biases and context, rational and irrational thought, real life news, opinions etc; etc; If you think markets bounce off support because of magic or that its random then that’s being naive. Orders in markets are based on the past for various reasons and the expectations of the future – they are not random. The patterns we choose to see might be random, but the price levels of orders are not.

I dont think it makes any difference - if the market data is random or non random we are all attempting to place it into some sort of discernible structure from which we can make sense of it, analyses it and profit from it; Which is why money management and letting a position run is important.

The only real value true (or as close to) random data has is its ability to strengthen our models of the market for robustness of that model. Most models of the market still wont make any real money, even those that make money in testing real and in random data will be likely to make less than estimated in real life, but they might give us a better expectation of what to expect should that model be applied without the added subjective input of a trader on a daily basis.

 

Maths and statistics ….. ignore it at your peril, but this is not everything. (no matter how many people try and tell you their opinion is right – relates to the article – Darwins ideas don’t set hard and fast rules but context determines a lot, ignore the predictions, look at- but -question the data and learn to anticipate what others will do)

Even the great modellers and systematic traders of the market constantly update their models or use multiple ones as the market always changes.

 

The markets structure is different therefore can be whatever it is we want to box it into – the way we see it ebb and flow and how we can then profit from that - be it that we see patterns that might be entirely random but we are still able to apply some money management, and extra context for that pattern – that’s all we need to care about IMHO and if we want to understand markets then understanding human behaviour helps take some of the randomness out of the data.

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In life, order matters, (think PEMDAS). Monte carlo simulation is data in a vacuum.

 

I think outcome is far more important than process. I would rather go through hell and high water for a better outcome than have an easy ride to a mediocre result.

 

Almost all studies of randomness in markets are defined with historic price data, entirely ignoring the other side of the market, liquidity.

 

I've spent a little time looking for structure in the order book without significant success, but I have nothing authoritative to say on the topic.

 

I think you are referring to the "butterfly effect"? That was Lorenz.

 

Well, the Noah and Joseph effect also . . . Lorenz, Mandelbrot, James Yorke, Robert May . . . I read an article in Focus magazine :)

 

Also, perception has leverage, If one extra contract changes the perception of someone with 10k contracts, that single lot contains a lot of leverage.

 

The amount of leverage it has is random, though, isn't it? Could be 2 5k participants, or a 40k trader.

 

Markets are a function of all participants, which is independent of their motives.

 

I am not totally sure that I understand this.

 

Non-random behavior is predictable regardless of the cause.

 

So . . . I washed my hair yesterday, and I washed it the day before. My behaviour is rule governed, not random. Will I wash my hair today?

 

You need to know the cause, the rule that triggers the behaviour. Otherwise, you either have to assume that a pattern will repeat (washes hair every day) in a linear fashion, or that the bahaviour is random.

 

Random behavior is only predictable in the sense that it will continue to be random. .

 

And therefore follow certain probability distributions that are easily modelled?

 

 

That's enough over-thinking for me for one day ;)

 

BlueHorseshoe

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    • Brexit Aftermath: The Market Reaction Of Bitcoin, Gold And Pound Sterling To Headline News In The EURO Zone   After the UK made it public to exit from the EURO bloc, the market cap for Bitcoin and Gold has increased almost by $133 billion and $1 trillion. Is this the Brexit aftermath?   As it is, the end may be near for Brexit. In the recent declaration an accord is reached between the British government and the EU, everyone is on the lookout for the final date Brexit will conclude. And based on this scenario, an analysis is drawn on the aftermath of this separation in the politics of the EURO bloc and the effect on the price of Bitcoin, Gold and pound sterling.   Bitcoin: Since the start of Brexit, Bitcoin’s market cap had spiked higher and recovered about $10 billion worth. Before Brexit, the cryptocurrency of the first choice had been stable in price after crashing to a market cap of about $2.9 billion low around January 2015. However, after the crash, the cryptocurrency had spiked to about 300% within 18 months while the next super halving of the project is expected on the network from 25 to 12.5 fresh Bitcoin’s per 10 minutes.   As of mid-2016, the most liquid GBP market was the London based Coinfloor exchange. The exchange did around 772 Bitcoins’ worth of volume that day, valued back then at around $4.9 million, with data from the technical back end at the Trading view.   The Pound Sterling: The British national currency had crashed by almost 20% on the night of the vote after hitting a momentary high of about $1.5 versus the USD for about 8 months. Since crashing to a low of about $1.2 as at March 2017, the Pound sterling had rallied 6% within a 4-week time frame, after the UK parliament decided to vote and activate the Article 50 while then the Brexit journey began for the UK taking it two years to discuss its planned exit from the EURO bloc.     Gold: The safe-haven asset also spiked higher around the same time frame from mid-March to mid-April 2017 with its price rising about 7% versus the USD. Nevertheless, this scenario didn’t play out on Bitcoin as in March 2017, beginning with its price at $1000, Bitcoin had surged to hit an all-time value of about $1300, as a result of markets expectation for a Bitcoin ETF being endorsed. However, after its nullification was declared on 10th March 2017, the cryptocurrency fell to a low of about $888 which occurred concurrently with the UK’s law passage for its exit from EURO bloc. Ever since then as the UK’s Brexit discussions with the EU raged on, so did the Pound against the US-dollar and Bitcoin gained more to its price.   Bitcoin, Gold, and Pound Sterling Reactions to Brexit During this timeframe transversing Brexit discussion and its process, the Pound lost the majority of its 15% gains recovered, to tumble from a high of $1.43 to hit $1.20 on 3 September. In a similar multi-day timeframe, gold broke out of its basic $1400 resistance level to rally 15% versus the US-dollar. While Bitcoin gained higher, then again, stayed on the level around $8,000—yet the genuine story of those 17 months incorporates the cryptocurrency crashing towards $3,000 (December 2018) preceding the move spiking to a high of almost $14,000 in June this year.   Source: https://learn2.trade     
    • Despite Running To The Highest Close In Six Months, GBPUSD May Fail To Reverse   GBPUSD Price Analysis – October 20 The GBPUSD had closed on Friday above its opening price after recovering from early selling pressure and trending higher for the 4th day consecutively in a row. After failing to reverse from its highs, the FX pair is unstable and due to weekend UK parliament vote on Brexit, with this scenario, the pair is likely to gap while it reopens on Monday morning in Asia (Sunday evening in the US).   Key Levels Resistance Levels: 1.3301, 1.3185, 1.2988   Support Levels: 1.2582, 1.2204, 1.1958   GBPUSD Long term Trend: Bullish On the daily picture, the bulls took charge in the previous session and exited the day above its opening price, however, the pair failed to move past the prior’s day’s trading range and the price likewise failed to reverse below the previous day’s range.   The GBPUSD had rallied upwards to as high as the level at 1.2988 last week, before forming a temporary top there. In the case of a reverse, the fall may be contained by the level at 1.2582 resistance turned support to bring rise resumption.     GBPUSD Short term Trend: Bullish An impermanent top is structured on the level at 1.2988 and intraday bias in GBPUSD stays on the upside. A few consolidations may be seen. Be that as it may, any pullback ought to be contained above the level at 1.2582 support to bring rise resumption.   Meanwhile, on the upside, a break of the level at 1.2988 will stretch out the recovery from the level at 1.1958 to 1.2582 from 1.2204 at 1.3185 next. Without bias analysis, the outlook is bullish and displaying an intact uptrend in the short and long-term.   Source: https://learn2.trade 
    • Date : 21st October 2019. MACRO EVENTS & NEWS OF 21st October 2019.The week ahead will definitely not be a quite one, with high anxiety on Brexit, the last ECB policy meeting before Mario draghi hand over the ECB presidency to Christine Lagarde and few significant US data prior FED on October 30.Monday – 21 October 2019   Producer Price Index (EUR, GMT 06:00) – The German PPI is expected to drop to -0.2% for September. As expected readings would result in a y/y loss of 0.3% for headline PPI, versus a 0.3% pace for August. Tuesday – 22 October 2019   Retail Sales (CAD, GMT 12:30) – Canadian sales are expected to have increased by 0.6% m/m in August compared to 0.4% m/m in July, with the ex-autos component down -0.3%. Existing Home Sales (USD, GMT 14:00) – Home sales have regained their status as an important indicator after the financial crisis and can have a strong effect on the markets. The release is expected to record a slight -0.2% pull-back in September to a 5.480 mln pace, after a bounce to 5.490 mln in August. In Q2, we saw an average sales pace of 5.287 mln, and we expect a better 5.463 mln pace in Q3. Thursday – 24 October 2019   Services and Manufacturing PMI (EUR, GMT 08:30-09:00) – September PMIs showed a marked contraction in manufacturing activity and a sharp slowdown in services sector growth. This picture is likely to be seen again in the preliminary readings for October, as German Manufacturing PMI has been forecast at 40 and composite at 49.2, which it is still below neutral. Meanwhile, Services PMI is expected to fall to 51.2. The overall Markit for Eurozone is seen at 49.4, signalling stagnation and highlighting the risk that the weakness in manufacturing sectors is spreading. Interest Rate Decision, Monetary Policy Statement and Press Conference (EUR, GMT 11:45 & 12:30) – The ECB is widely expected to keep policy settings on hold after Draghi’s parting shot at the last meeting. The outgoing president pushed through another deposit rate cut and an open ended asset purchase program against the opposition of some of the more senior national central bank heads and incoming president Lagarde will face the task of uniting the board and dealing with growing demands for a comprehensive revision of the ECB’s policy setting framework and in particular the inflation target. Draghi’s last press conference meanwhile will likely focus heavily on calls for fiscal measures to boost the economy in a challenging international environment. Durable Goods (USD, GMT 12:30) – Durable goods orders are expected to fall -1.8% in September, after gains of 0.2% in August, thanks to an expected transportation orders drop. Boeing orders rose to a still-lean 25 from 18 in August. Services and Manufacturing PMI (USD, GMT 13:45) – Preliminary Manufacturing are expected to slip in October, to 50.1 from 51.1, while Services PMIs are likely to rise to 51.3 from 50.9, indicating a slowdown in the sector that has been hit by global trade tensions. Friday – 25 October 2019   German IFO (EUR, GMT 08:00) – In September, the German IFO business confidence came in slightly higher than expected at 94.6. In October, however, the overall business climate reading is seen slightly lower at 94.4. The more forward looking expectations reading is anticipated at 91.8 from 90.8. Always trade with strict risk management. Your capital is the single most important aspect of your trading business.Please note that times displayed based on local time zone and are from time of writing this report.Click HERE to access the full HotForex Economic calendar.Want to learn to trade and analyse the markets? Join our webinars and get analysis and trading ideas combined with better understanding on how markets work. Click HERE to register for FREE!Click HERE to READ more Market news. Andria Pichidi Market Analyst HotForex Disclaimer: This material is provided as a general marketing communication for information purposes only and does not constitute an independent investment research. Nothing in this communication contains, or should be considered as containing, an investment advice or an investment recommendation or a solicitation for the purpose of buying or selling of any financial instrument. All information provided is gathered from reputable sources and any information containing an indication of past performance is not a guarantee or reliable indicator of future performance. Users acknowledge that any investment in FX and CFDs products is characterized by a certain degree of uncertainty and that any investment of this nature involves a high level of risk for which the users are solely responsible and liable. We assume no liability for any loss arising from any investment made based on the information provided in this communication. This communication must not be reproduced or further distributed without our prior written permission.
    • Perfect Trend Lines, PTL, is a short-term trend trading indicator. The lines showing the trend in this indicator is not straight lines like normal trend lines. PTL indicator calculation is simple. First take the 7 bar high and low, then the 3 bar high and low. If the close price is above the 7 bar high and 3 bar high, then an uptrend is identified. When the close price is below the 7 bar low and 3 bar low then a downtrend is identified. These bars are considered as strong trend bars. The magenta line is the 7 bar high or low depending on the trend. The cyan line is the 3 bar high or low depending on trend direction. When price is trading between these 2 lines trend strength is weak.   A magenta diamond shape appears when sell signal is generated. Cyan diamond shape appears for a buy signal. The magenta line can be used as stop loss. The cyan line provides a tighter stop loss level. Strong downtrend bars are marked by a magenta dot at the bar high and strong uptrend bars are marked by a cyan dot at the bottom of the bar.   PTL.zip
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