How To Use Forex Correlations - FXStreet

Day #2 of my Forex Journey

Real quick before I get into my next steps of my FX Journey, id like to say thank you to all the people who commented on my last post! All of the tips I got were really eye-opening and introduced me to different parts of FX trading that I didn't even know existed. So thank you so much, and I hope to get more interesting feedback from you guys in the future! Also Im going to probably change my writing frequency from daily to biweekly. I think writing about every little trade is not going to be as beneficial to me as writing about my overall progress at certain points throughout the week.
I started this trading day out by learning up on order flow. A whole bunch of you guys suggested really interesting youtubers to watch, and I started with Mr. pip's series on order flow. After I finished up watching a few of his videos, I started to tweak my trading plan so that I could get in some chart time. I changed currency pair from EUUSD to the AUD/USD, the time frame from the 4 hour to the 1 hour, and my indicators from RSI, Stochastic, 2 SMAs and ADX to ATR, RSI, and Ichimoku Kinko Hyo. I also added a little fundamental analysis in my trading plan because I think that I am being far too reliant on my indicators. I planned to check the economic calendar and determine the general trend of the currency pairs that are strongly correlated to the AUD/USD before I began my chart analysis. In addition to all of my analysis, I tried to practice using the techniques I learned in Mr. Pip's videos and analyze the order flow of the chart. Even if my analysis of order flow is wrong, as long as I am getting practice I am learning.
Eventhough I planned to use today to back-test indicators and find a solid new plan, I did not have enough time. I ended up getting on my demo account really late in the day, and started to force myself to enter a trade. Destructive habits like this could lead into some massive issues when I eventually get into live trading. To combat this harmful attitude specifically, I will restrict myself to trading on certain parts of the day (for example session overlaps, news releases, and earlier in the day). Despite this mistake I still continued with my trading strategy. I calculated all the currency correlations for AUS/USD using the past weeks economic data, and set my indicators in place. After checking the overall trend of the most strongly correlated pairs (Positive: EUUSD, GPB/USD, Negative: USD/CAD, USD/JPY) I started to analyze the order flow. All the correlated currencies, except for EUUSD, indicated that the AUD/USD would fall, while my order flow analysis indicated the opposite. Seeing as though I am extremely new to order flow, I dismissed this analysis, and ended up forcing a trade on the AUD/USD going short when my indicators seemed to line up correctly. I learned from last time that I should not alter or close my trade purely based on emotion, and to just wait till the market hits my stop loss or take profit. I included a trailing stop loss of 60 pips this time, but I have no evidence to base that number range on. The trade is currently open and I am down about 30 pips.
Although I am not labeling this trade as a loser yet, I can definitely see a lot of holes in my trading strategy. The most obvious mistake in my eyes right now is my use of indicators. Currently all my trades are purely based on what my indicators say, and since I do not have any back-tested data to support the credibility of my indicators, it feels a lot like strategic gambling. Another issue is that I feel far too reliant on indicators alone. I think that if I can find ways to include various types of analysis efficiently and evenly in my trading plan I will become a much more skillful and well-rounded trader. In order to combat these two issues I will begin forming various types of trading strategies this weekend and back-test them all extensively. I also plan on researching more on price action, order flow, and Naked Forex.
Once again any and all feedback is welcome. I am just beginning Forex, but it had been a huge passion of mine and I don't plan on stopping anytime soon.
submitted by Aman-1127 to Forex [link] [comments]

H1 Backtest of ParallaxFX's BBStoch system

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

Background

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

System Details

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

And now for the fun. Results!

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):
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.
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:
From a literal standpoint, what I see behind this behavior is that 44 of the 69 breakeven trades(65%!) ended up being profitable to -100% after retracing deeply(but not to the original SL level), which greatly helped offset the purely losing trades better than the partial profit taken at -61.8%. And 36 went all the way back to -161.8% after a deep retracement without hitting the original SL. Anyone have any insight into this? Is this a problem with just not enough data? It seems like enough trades that a pattern should emerge, but again I'm no expert.
I also briefly looked at moving stops to other lower levels (78.6%, 61.8%, 50%, 38.2%, 23.6%), but that didn't improve things any. No hard data to share as I only took a quick look - and I still might have done something wrong overall.
The data is there to infer other strategies if anyone would like to dig in deep(more explanation on the spreadsheet below). I didn't do other combinations because the formulas got pretty complicated and I had already answered all the questions I was looking to answer.

2-Candle vs Confirmation Candle Stops

Another interesting point is that the original system has the SL level(for stop entries) just at the outer edge of the 2-candle pattern that makes up the system. Out of pure laziness, I set up my stops just based on the confirmation candle. And as it turns out, that is much a much better way to go about it.
Of the 60 purely losing trades, only 9 of them(15%) would go on to be winners with stops on the 2-candle formation. Certainly not enough to justify the extra loss and/or reduced profits you are exposing yourself to in every single other trade by setting a wider SL.
Oddly, in every single scenario where the wider stop did save the trade, it ended up going all the way to the -161.8% profit level. Still, not nearly worth it.

Correlated Trades

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

Other Technical Details

Raw Data

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

Insanely detailed spreadsheet notes

For you real nerds out there. Here's an explanation of what each column means:

Pairs

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

TL;DR

Based on the reasonable rules I discovered in this backtest:

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!

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.
submitted by ForexBorex to Forex [link] [comments]

I've reproduced 130+ research papers about "predicting the stock market", coded them from scratch and recorded the results. Here's what I've learnt.

ok, so firstly,
all of the papers I found through Google search and Google scholar. Google scholar doesn't actually have every research paper so you need to use both together to find them all. They were all found by using phrases like "predict stock market" or "predict forex" or "predict bitcoin" and terms related to those.

Next,
I only tested papers written in the past 8 years or so, I think anything older is just going to be heavily Alpha-mined so we can probably just ignore those ones altogether.

Then,
Anything where it's slightly ambiguous with methodology, I tried every possible permutation to try and capture what the authors may have meant. For example, one paper adds engineered features to the price then says "then we ran the data through our model" - it's not clear if it means the original data or the engineered data, so I tried both ways. This happens more than you'd think!

THEN,
Anything that didn't work, I tried my own ideas with the data they were using or substituted one of their models with others that I knew of.

Now before we go any further, I should caveat that I was a profitable trader at multiple Tier-1 US banks so I can say with confidence that I made a decent attempt of building whatever the author was trying to get at.

Oh, and one more thing. All of this work took about 7 months in total.

Right, let's jump in.

So with the papers, I found as many as I could, then I read through them and put them in categories and then tested each category at a time because a lot of papers were kinda saying the same things.
Here are the categories:
Results:
Literally every single paper was either p-hacked, overfit, or a subsample of favourable data was selected (I guess ultimately they're all the same thing but still) OR a few may have had a smidge of Alpha but as soon as you add transaction costs it all disappears.
Every author that's been publicly challenged about the results of their paper says it's stopped working due to "Alpha decay" because they made their methodology public. The easiest way to test whether it was truly Alpha decay or just overfitting by the authors is just to reproduce the paper then go further back in time instead of further forwards. For the papers that I could reproduce, all of them failed regardless of whether you go back or forwards. :)

Now, results from the two most popular categories were:

The most frustrating paper:
I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think).

The two positive take-aways that I did find from all of this research are:
  1. Almost every instrument is mean-reverting on short timelines and trending on longer timelines. This has held true across most of the data that I tested. Putting this information into a strategy would be rather easy and straightforward (although you have no guarantee that it'll continue to work in future).
  2. When we were in the depths of the great recession, almost every signal was bearish (seeking alpha contributors, news, google trends). If this holds in the next recession, just using this data alone would give you a strategy that vastly outperforms the index across long time periods.
Hopefully if anyone is getting into this space this will save you an absolute tonne of time and effort.
So in conclusion, if you're building trading strategies. Simple is good :)

Also one other thing I'd like to add, even the Godfather of value investing, the late Benjamin Graham (Warren Buffet's mentor) used to test his strategies (even though he'd be trading manually) so literally every investor needs to backtest regardless of if you're day-trading or long-term investing or building trading algorithms.
submitted by chiefkul to StockMarket [link] [comments]

Looking for someone to collaborate with in exploring some of the fundamental questions in algo trading in relation to quantitative analysis and the Forex market specifically.

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.
Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.
Can quantitative analysis of the Forex market give an edge to a retail trader?
Can a retail trader utilize said edge to make consistent profits, within the market?
Are these profits enough to make a full time living on?
But before we answer these questions, there are even more fundamental questions that need to be answered.
To what degree if any is back-testing useful in generating successful algo strategies?
Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?
What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?
What indicators if any are most successful in helping generate profitable strategies?
What data horizons are best suited to generate most successful strategies?
What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?
What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....
And many many more fundamental questions....
As you can see answering these questions will be no easy or fast task, there is a lot of research and data mining that will have to be done. I like to approach things from a purely scientific method, make no assumptions about anything and use a rigorous approach when testing, validating any and all conclusions. I like to see real data and correlations that are actually there before I start making assumptions.
The reason I am searching for these answers is because, they are simply not available out on the internet. I have read many research papers on-line, and articles on this or that about various topics related to Forex and quantitative analysis, but whatever information there is, its very sparse or very vague (and there is no shortage of disinformation out there). So, I have no choice but to answer these questions myself.
I have and will be spending considerable time on the endeavour, but I am also not delusional, there is only so much 1 man can do and achieve with the resources at his disposal. And at the end of the whole thing, I can at least say I gave it a good try. And along the way learn some very interesting things (already had a few eureka moments).
Mo workflow so far has consisted of using a specific (free) software package that generate strategies. You can either use it to auto generate strategies or create very specific rules yourself and create the strategies from scratch. I am not a coder so I find this tool quite useful. I mainly use this tool to do lots of hypothesis testing as I am capable of checking for any possible correlations in the markets very fast, and then test for the significance if any of said correlations.
Anyways who I am looking for? Well if you are the type of person that has free time on their hands, is keen on the scientific method and rigorous testing and retesting of various hypothesis, hit me up. You don't need to be a coder or have a PHD in statistics. Just someone who is interested in answering the same questions I am.
Whats the end goal? I want to answer enough of these questions with enough certainty, whereby I can generate profitable algo strategies consistently. OR, maybe the answer is that It cant be done by small fry such as a retail trader. And that answer would be just as satisfactory, because It could save me a lot more time and money down the road, because I could close off this particular road and look elsewhere to make money.
submitted by no_witty_username to Forex [link] [comments]

I've reproduced 130+ research papers about "predicting bitcoin", coded them from scratch and recorded the results. Here's what I've learnt.

ok, so firstly,
all of the papers I found through Google search and Google scholar. Google scholar doesn't actually have every research paper so you need to use both together to find them all. They were all found by using phrases like "predict bitcoin" or "predict stock market" or "predict forex" and terms related to those.

Next,
I only tested papers written in the past 8 years or so, I think anything older is just going to be heavily Alpha-mined so we can probably just ignore those ones altogether.

Then,
Anything where it's slightly ambiguous with methodology, I tried every possible permutation to try and capture what the authors may have meant. For example, one paper adds engineered features to the price then says "then we ran the data through our model" - it's not clear if it means the original data or the engineered data, so I tried both ways. This happens more than you'd think!

THEN,
Anything that didn't work, I tried my own ideas with the data they were using or substituted one of their models with others that I knew of.

Now before we go any further, I should caveat that I was a profitable trader at multiple Tier-1 US banks so I can say with confidence that I made a decent attempt of building whatever the author was trying to get at.

Oh, and one more thing. All of this work took about 7 months in total.

Right, let's jump in.

So with the papers, I found as many as I could, then I read through them and put them in categories and then tested each category at a time because a lot of papers were kinda saying the same things.

Here are the categories:

Results:
Literally every single paper was either p-hacked, overfit, or a subsample of favourable data was selected (I guess ultimately they're all the same thing but still) OR a few may have had a smidge of Alpha but as soon as you add transaction costs it all disappears.

Every author that's been publicly challenged about the results of their paper says it's stopped working due to "Alpha decay" because they made their methodology public. The easiest way to test whether it was truly Alpha decay or just overfitting by the authors is just to reproduce the paper then go further back in time instead of further forwards. For the papers that I could reproduce, all of them failed regardless of whether you go back or forwards. :)

Now, results from the two most popular categories were:

The most frustrating paper:
I have true hate for the authors of this paper: "A deep learning framework for financial time series using stacked autoencoders and long-short term memory". Probably the most complex AND vague in terms of methodology and after weeks trying to reproduce their results (and failing) I figured out that they were leaking future data into their training set (this also happens more than you'd think).

The two positive take-aways that I did find from all of this research are:
  1. Almost every instrument is mean-reverting on short timelines and trending on longer timelines. This has held true across most of the data that I tested. Putting this information into a strategy would be rather easy and straightforward (although you have no guarantee that it'll continue to work in future).
  2. When we were in the depths of the great recession, almost every signal was bearish (seeking alpha contributors, news, google trends). If this holds in the next recession, just using this data alone would give you a strategy that vastly outperforms the index across long time periods.

Hopefully if anyone is getting into this space this will save you an absolute tonne of time and effort.

So in conclusion, if you're building trading strategies, simple is good :)

Also one other thing I'd like to add, even the Godfather of value investing, the late Benjamin Graham (Warren Buffet's mentor) used to test his strategies (even though he'd be trading manually) so literally every investor needs to backtest regardless of if you're day-trading or long-term investing or building trading algorithms.


EDIT: in case anyone wants to read more from me I occasionally write on medium (even though I'm not a good writer)
submitted by chiefkul to CryptoCurrency [link] [comments]

Looking for someone to collaborate with in exploring some of the fundamental questions in algo trading in relation to quantitative analysis and the Forex market specifically.

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.
Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.
Can quantitative analysis of the Forex market give an edge to a retail trader?
Can a retail trader utilize said edge to make consistent profits, within the market?
Are these profits enough to make a full time living on?
But before we answer these questions, there are even more fundamental questions that need to be answered.
To what degree if any is back-testing useful in generating successful algo strategies?
Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?
What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?
What indicators if any are most successful in helping generate profitable strategies?
What data horizons are best suited to generate most successful strategies?
What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?
What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....
And many many more fundamental questions....
As you can see answering these questions will be no easy or fast task, there is a lot of research and data mining that will have to be done. I like to approach things from a purely scientific method, make no assumptions about anything and use a rigorous approach when testing, validating any and all conclusions. I like to see real data and correlations that are actually there before I start making assumptions.
The reason I am searching for these answers is because, they are simply not available out on the internet. I have read many research papers on-line, and articles on this or that about various topics related to Forex and quantitative analysis, but whatever information there is, its very sparse or very vague (and there is no shortage of disinformation out there). So, I have no choice but to answer these questions myself.
I have and will be spending considerable time on the endeavour, but I am also not delusional, there is only so much 1 man can do and achieve with the resources at his disposal. And at the end of the whole thing, I can at least say I gave it a good try. And along the way learn some very interesting things (already had a few eureka moments).
Mo workflow so far has consisted of using a specific (free) software package that generate strategies. You can either use it to auto generate strategies or create very specific rules yourself and create the strategies from scratch. I am not a coder so I find this tool quite useful. I mainly use this tool to do lots of hypothesis testing as I am capable of checking for any possible correlations in the markets very fast, and then test for the significance if any of said correlations.
Anyways who I am looking for? Well if you are the type of person that has free time on their hands, is keen on the scientific method and rigorous testing and retesting of various hypothesis, hit me up. You don't need to be a coder or have a PHD in statistics. Just someone who is interested in answering the same questions I am.
Whats the end goal? I want to answer enough of these questions with enough certainty, whereby I can generate profitable algo strategies consistently. OR, maybe the answer is that It cant be done by small fry such as a retail trader. And that answer would be just as satisfactory, because It could save me a lot more time and money down the road, because I could close off this particular road and look elsewhere to make money.
submitted by no_witty_username to algotrading [link] [comments]

You should see Glam and Gore for scary tutorials on makeup on Youtube

You should see Glam and Gore for scary tutorials on makeup on Youtube

https://preview.redd.it/tbbwybr72fv31.jpg?width=1920&format=pjpg&auto=webp&s=24fafceff269e3d5946ed572ec5385f16d03606c
If you are not familiar with the iconic makeup artist Mickey of Los Angeles, it is time to get acquainted with you and your Youtube channel, Glam and Gore. This groundbreaking 28-year-old is known for making things "pretty ugly"; Mykie combines her passions and talents to create Instagram-worthy makeup photos and cool special effects, perfect for true-to-life costumes and horror movies. Mikey has developed a passion and can use cosmetics during her graduate studies in cinema and has gained professional experience through her own research and desire to work in local haunted homes. Since joining YouTube in 2014 on Mykie since then, sincere loyalty has gained 3.1 million subscribers. But why is it worth looking at? Mykie's textbooks combine comedy and education perfectly. If you want to master this killer cat look by creating the most realistic zombie costume on the planet, or try wearing a wig, Glam and Gore has something for you. In addition, the latest changes to the Youtube algorithm have had a negative impact on your views and future revenue, meaning you can now use support as never before. Because of these updates, subscribers were not notified of new messages or were unable to view new videos. Also, old Mykie content will suddenly go to YouTube, your forex tutorials are considered "too open". So where can you cross the line between "mature content" and "creative licensing?" Is it true that man is now manifesting himself? let's just say no, the Mykie channel is dedicated to makeup tutorials - there's nothing wrong with that, other makeup channels haven't had such a negative impact, Mikey deserves credit for your talent, and that's why. Your relationship.

Starting with each Crown set video, with a Russian accent: "Hi, Zombies!" This is a great example of Mike's unpretentious nature being your true self. She also has a delicious Alaska CLI waterfront called Ripley (if you see "pun intended" you will get a really good idea of ​​what I am taking). Ripley is as theatrical as her mother-dog, and very loud in Mykie-Videos. Mikey also talks openly about his personal health and fitness journey and has recently challenged himself to join a gym and use a personal trainer. What is your ultimate goal? Be stronger and feel better. She also wants to have tons of muscle and tries to contradict the pre-conceived notion that one cannot be "feminine" and "beautiful" to have muscle. Mykie also understands that a career in makeup, especially FX, is expensive. She honestly seeks to offer her audience tangible and effective alternatives to inspire success and more creativity. She is also a social media icon: her tweets are pure fire. The story of Disney glamor and Princess of the mountains. This series was one of the first to bring Mickey to Internet fame, and is one of my personal favorites. This series is absolutely reasonable. She does lessons with the original Disney princesses we know and love, and creates bloody alternatives. For example, there is a "fascinating" mermaid, a "suffocated" Rapunzel and a smashed "Cinderella." Lip Challenge "Among the other insane issues on the Internet, such as Chanenge Mannequin, Chanelnge Cinnamon and Harlem Shake, the Kylie Jenner Challenge is perhaps one of the most well-known. What is the main idea of ​​this video? Satire. She is the winner of the 2015 NYX Face Awards. If you want to try Mykie talent, look no further than your video app! Recorded in dummy style, Mickey captures a nasty paranormal creature. Awesome and intriguing, Mykie is open to your creative process and offers beautiful editing videos to show off your featured exterior. Tinder takes my makeup. To jump on the trend bar, Mickey asks her unsuspecting prospect Tinder random questions that correlate with a certain makeup. Composed of a trilogy, your humor is hilarious. It also means that you agree with your good friend's brother ... conceding! She is launching her own line of wigs from Bellamy! Keep up the wigs, because there's nothing better than supporting #GirlBoss, celebrate entrepreneurship. Mike enjoys lifelong wigs that not only make you shine through your personal creativity, but also complement your look. In the recently released video for September. 30, 2018, Mykie has announced the launch of its line of wigs: a very personal and unique cosmetic collaboration, unlike any other. The line consists of three wigs: a long silvery-bright wig called "Reagan" in honor of the exorcist girl; Rose Gold / Pink forehead called "Claris", hthere is a future detective in the silence of the lambs; and a theatrically long wig, known as "Carrie," consisting of blood-red curls and dark roots. Mykie promises quality and satisfaction at $ 100. Glam and Gore x Bellamy kicked off in October. 15, 2018 - It's Halloween Time!
submitted by 10k_Tye to u/10k_Tye [link] [comments]

New stars: looking at possible OMG staking revenues with regards to # of transactions instead of volume.

We have an expression in Sweden which translated directly to English becomes “Aim for the stars and you might end up in the tree tops.”. This is my attempt at giving us some new stars to take aim for.
 
I will start by saying that I’m 100 % in OMG and that this is not a technical analysis of the future price movement, nor is it an analysis of OmiseGO’s potential of delivering what they have promised. This is simply a new way of looking at the future rewards to the stakers of OMG.
 
Secondly, this is not a finished product and the approximations that I’ve done can surely be improved by people with more knowledge than me about the subjects. I welcome everyone to correct my mistakes and/or add their knowledge.
 
The reason that I’ve spent the time to research this subject is that I believe that most speculators in this forum are looking at the wrong parameters when trying to calculate future staking returns. They are looking at volume and speculating on which percentage that OMG will take of that volume; I think that we instead should be looking at the number of transactions. The cost of a simple transaction of value from one part to another on a blockchain (Ethereum or OMG) is, as far as I know, not dependent on the size of the value. I believe that the OMG blockchain will have a dynamic fee structure where buying an apple or buying a car will cost the same amount in transaction fees (although payment companies on top of the OMG blockchain might take a fixed percentage for their services, but that won’t affect us OMG stakers).
 
If my assumption above is correct, then we need to figure out how many transactions that OMG might process. So, how many transactions are we talking here? Well, I’ve started now by looking at four different areas of value transactions:
 
  1. Every day purchases
  2. Stock exchange
  3. Money transfers
  4. Forex market
 
 
Every day purchases
 
These are transactions where people use cash or card to purchase some item (buying fruit or a computer). Now, the exact number is impossible to find but we can get some good approximations. According to the 2016 Nilson report there was 227 billion card transactions during 2015 worldwide. [1] But, as we all know, OmiseGO’s plan stretches also to the unbanked and the cash payments. According to Raconteur 85 % of the worlds transactions are still done in cash. [2] Simple math tells us then that
 
A*0.15 = 227 billion -> A = (227 billion)/0.15 = 1.51 trillion payment transactions every year.
 
[This is of course not 100 % accurate since in the parts of the world where there is the least amount of card transactions there is also probably a lot fewer transactions overall. However, I think that we at least are at the right order of magnitude.]
 
 
Stock exchange
 
All the world’s stock exchanges could be rebuilt on top of the OMG blockchain in a more efficient way than before. So, how many transactions is that? This is a hard number to get ahold of and my estimate will probably end up being too low since many transactions never get registered outside of the trading houses. For example: a trading house can make 1 trade on the exchange where it buys 10,000.00 off Stock A, but then it sells it too 100 of their own customers. This last data is the hardest to get ahold of even if the trade data from the exchanges are almost impossible to find as well. According to Nasdaq there is an average of 10.5 million transactions per day on their exchange. [3] Now, The Money Project has published an infographic showing the worlds largest stock exchanges in relative volume to each other and the whole world market; it says that Nasdaq has a 10.79 % share of a global market of $69 trillion. [4] If we assume that there is a linear correlation between volume and number transactions for all the worlds stock exchanges, then we can assume that Nasdaq’s volume percentage of 10.78 is also it’s transactions percentage in relation to the worlds stock markets. This means that the global number of stock transactions is annually
 
B = (252*10.5 million)/0.1078 = $24.5 billion. [The number 252 in the equation is the average number of trading days per year.]
 
 
Money transfer
 
Pure money transfer transactions. One of the leading actors on that market is Western Union which, according to Forbes holds a 15 % market share with 231 million transactions per year. [5] This leads us to the approximation that the total market sees
 
C = (231*10 million)/0.15 = 1.54 billion transactions every year.
 
 
Forex market
 
Now, the forex market is definitely the hardest one to find information about. I posted in /Forex and they were helpful but told me that exact figures would be impossible to find. [6] However, much in the same manner as with the stock markets I was able to reach an approximation which others will have to judge if it’s fair or not. According to the CEO of LMAX exchange they print 1.5 million trades per day [7] which I will use as 1 trade = 1 transaction (same with the stock market) which gives them
 
252*1.5 million = 378 million transactions per year.
 
The website Leaprate.com has reported that LMAX has an average monthly volume of $175 billion [8] which ends up being $2.1 trillion per year. Let’s say that a linear correlation between number of trades and volume exists over all exchanges on the global forex market, again as we did with the stock market. This means that
 
378 million / ”Number of forex transactions globally” = $2.1 trillion/”$Global forex annual volume”.
 
The number of transactions globally is therefore
 
D = 378 million/($2.1 trillion/”$Global forex annual volume”)
 
with the variable being the annual total volume on the global forex market. According to Businessinsider.com the daily global volume is on average $5.1 trillion [9] which means that the annual volume is 252*$5.1 trillion = $1290 trillion which gives us the number of transactions annually on the global Forex market to be
 
D = 231 billion.
 
 
Total number of transactions and what that means in regard to staking
 
The total number of transactions E = A + B + C + D = 1.51 trillion + 24.5 billion + 1.54 billion + 231 billion = 1.77 trillion transactions every year.
 
If anyone wants to do their own calculations and only include certain percentages of the different markets then of course it is easy to add these percentages before their respected market in the equation above. I however, will calculate staking returns based on the premise that 100 % of these markets will be built and thrive on top of the OMG blockchain.
 
The equation for the returns of every OMG staked is pretty easy:
 
$/omg/year = Number of transaction per year*transaction fee / Number of coins staked .
 
The question is of course: what will the transaction fee be? No one can say, but I will use the transaction fee for the Ethereum network as a benchmark. The transaction fees on the Ethereum network varies but a recent low average has been around $0.2 per transaction. [10] Number of staked coins has also been up for debate at various times; 60 % has been thrown around and I’ll use that.
 
Case 1: OMG blockchain charge as much as the Ethereum blockchain does right now
 
$/omg/year = 1.77 trillion*0.2 / 0.6*140 million = $4,214.29
 
Case 2: OMG blockchain charge 10 % of what Ethereum blockchain does right now
 
$/omg/year = 1.77 trillion*0.1*0.2 / 0.6*140 million = $421.43
 
Case 3: OMG blockchain charge 1 % of what Ethereum blockchain does right now
 
$/omg/year = 1.77 trillion*0.01*0.2 / 0.6*140 million = $42.14
 
Again, I would like to invite anyone here to help me and point out any faults in my calculations, approximations and/or assumptions. However, could you also do the new calculations and add your proposed staking returns? I am a full-time student so hopefully the edits can happen in the comments instead of by me. Thanks!
 
References
 
[1] https://www.nilsonreport.com/publication_special_feature_article.php
[2] https://www.raconteur.net/technology/the-decline-of-cash
[3] http://www.nasdaqtrader.com/Trader.aspx?id=DailyMarketSummary
[4] http://money.visualcapitalist.com/all-of-the-worlds-stock-exchanges-by-size/
[5] https://www.forbes.com/sites/hilarykrame2013/05/10/wu-stock-report/#45eefb5b7771
[6] https://www.reddit.com/Forex/comments/7exyzn/does_statistics_exist_regarding_number_of_trades/
[7] https://www.lmax.com/blog/business-and-technology/2013/07/05/average-trade-size-declines-spot-fx/
[8] https://www.leaprate.com/forex/institutional/lmax-revenues-20-2016-27-7-million-monthly-volumes-175-billion/
[9] http://www.businessinsider.com/heres-how-much-currency-is-traded-every-day-2016-9?r=US&IR=T&IR=T
[10] https://bitinfocharts.com/comparison/ethereum-transactionfees.html#3m
submitted by jeneman to omise_go [link] [comments]

TradeRiser Can Answer Simple and Complex Trading Questions

TradeRiser Can Answer Simple and Complex Trading Questions
Artificial Intelligence as the term is most often used today is simply put the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format. Because data is encrypted and distributed across many different computers, it enables the creation of tamper-proof, highly robust databases which can be read and updated only by those with permission.
https://preview.redd.it/lcgl0t05hid11.png?width=999&format=png&auto=webp&s=770bc5060c1d3852385a4cceb3603668b7027302

What is TradeRiser

TradeRiser is an artificially intelligent Research Assistant, that can answer simple and complex trading questions.

Market Problem

  • Motivation - Simplifying financial data analytics
  • Disrupting Human Intensive Research
  • Fewer Ideas Are Tested
  • Time-Consuming
  • Inefficiency
  • Information Overload
  • News and Events - Unstructured Data

TradeRiser Solution

  • TradeRiser's Research Assistant can immediately answer trading questions that a trader or investor has about the financial markets.
  • TradeRiser’s token mechanism will keep track and compensate financial analysts for their datasets of questions, data validation, accuracy checking, suggestions and example report creation.
  • The financial analysts can contribute in these ways to help train TradeRiser's machine learning Research Assistant, and be compensated accordingly.
  • XTI is the underlying mechanism used to facilitate this ecosystem, and provides XTI holders with direct participation in advancing our “single source of truth” questioning and answering system.

The Blockchain Features

  • Financial analysts are like freelancers or contractors, the blockchain allows TradeRiser to create smart contracts with the financial analysts for various pieces of work.
  • TradeRaiser commercial transactions and agreements will be executed automatically, it will enforce the obligations that the financial analysts have in a contract
  • It provides an automated collaborative approach for data gathering using a large diverse pool of financial analysts. The smart contract allows for the different stages of work carried out by çto be rewarded.

TradeRiser Ecosystem

https://preview.redd.it/5joo1336hid11.jpg?width=1349&format=pjpg&auto=webp&s=06df4aaf508337f91e57869423df76c5b00ffdcd

The Platform

TradeRiser has built an alpha/private beta version of the Research Assistant which focuses on:-
  • Forex
  • Commodities
  • Indices
The Platform will allow users to ask questions surrounding:-
  • The economic calendar events
  • Technical analysis
  • Correlation
  • Performance
Note:- The Current Version is alpha/private beta.
TradeRiser intends to transform this into powerful fully fledged Research Assistant that will accompany all corners of the trading and investing space. So far it has been seen by major investment banks and technology vendors, and has received a lot of positive feedback.
https://preview.redd.it/5j1p27s6hid11.jpg?width=1909&format=pjpg&auto=webp&s=f40fee3e9330b9206bf2a016a83f1c6c71f27552
https://preview.redd.it/mz6nteg7hid11.jpg?width=1918&format=pjpg&auto=webp&s=383b4812ef4aab510be15adb9997aff55233b5a7

TradeRiser Platform Features

  • Community Edition
  • Research Marketplace
  • Enterprise Edition

TradeRiser Tokens & Its Uses

A token based economy called XTI will be introduced, to incentivize researchers, for their data and contributions to the platform.
It'll be used for:-
  • Community Credibility
  • Payment Issues
  • Managing the Monetary Supply

Finally

Blockchain is best used in a distributed system where nodes are not necessarily trustworthy. Artificial Intelligence gains little from being distributed. It is best used on problems which require noticing patterns inferring rules of behavior, predicting eventual outcomes, determining underlying causes. The one use where these could come together would be in distributing the data a blockchain needs to contain.
✅Website: https://www.traderiser.com/ ✅Whitepaper: https://www.traderiser.com/sites/default/files/TradeRiser_WhitePaper.pdf
submitted by Ahmedgalal81 to CryptocurrencyICOs [link] [comments]

Cryptocurrencies, Measuring Inflation by Value "siphoning" not by Supply

This is a follow up to this post:
I have been thinking about inflation and market cap a little bit more. In that post I have debunked the hyperinflation claims nonsense, but it got me thinking about the nature of inflation as probabilistic in nature.
I think the supply size doesn’t even matter at all, all of it is market based, and I tend to view inflation now as rather a vacuum of value or a transfer of value rather than a supply increase.
Simply put the supply is just an arbitrary number it has no value, it only has value in the context of a price. Thus the value of an asset is it’s market cap.

MarketCap based valuation & inflation

The market cap is the ultimate value of an asset, relative to a major currency. This still opens up new cans of worms since it’s all relativistic, to get an absolute value you might want to measure it compared to a basket of major currencies like the Dollar Index, and so on.
But the market cap gives the best valuation of an asset, so let’s just use USD as the benchmark.
The market cap is market based so as the market tends towards efficiency, the price will reflect eventually it’s true value.
However the market cap is still an uncertain valuation technique since it assumes that the hard cap is sacrosanct, which I don’t think it is (derivatives, and forks could extend it, not to mention fractional reserving on exchanges). By the way this applies to fiat currencies too.
As demonstrated in the previous post the 21m hard limit of Bitcoin can be extended to 27m assuming the forks are derivatives of the original coins, which is a stretch but currently it does influence the price of eachother (high correlation) so for now it’s true, later it might not be (the forks might decouple).
So we see that the M1,M2,M3 sort of supply classification is reasonable for cryptocurrencies too, the M1 would be the immediately usable blockchain coins out of which M0 would be personally held ones and M1 the ones on exchanges, the M2 would be the “investments” which are the forks too, as speculators put money in there in hopes of them going up in price and of course the savings and lending schemes in many Bitcoin based businesses, and the M3 would be the derivative tokens (like Ethereum’s tokens and Bitcoin’s colored coins) and leverage (many exchanges are allegedly operating on fractional reserves) and so forth.
Now the thing I observed and this made me think about it, is that many new cryptocurrencies are essentially worthless. So what stops people from creating millions of new ones a day? Nothing since it cost’s almost nothing except the minimal electricity and mining operation setup. So perhaps a couple of dollars of hosting and electricity costs to setup a new one, not totally free, but anyone can essentially create millions of new ones.
So what stops the hyperinflation? As pointed out in the other post that the inflation is not in the supply but in the market cap.
So if you create “XYZ coin” with 1 trillion supply but a market cap of only 1$ and a price of 1E-12 . You haven’t created an inflation of 4,762,004.76% to Bitcoin for example.
You have only created an inflation of 1$ worth, that is “stealing” away 1$ worth of value from Bitcoin as you transferred that value from Bitcoin into your worthless coin. You have only created an inflation of 0.00000000063464788980% to Bitcoin.
Also the value doesn’t come out of thin air, you didn’t just print up that 1$ out of nowhere, you had to put in at least that amount of effort into coding your new currency and setting up the web servers and so on.
So inflation is not supply based, it’s value based. And this holds true for fiat as well.

MarketCap Misleading

Now in cryptocurrencies market cap is misleading sometimes. Some exchanges have a minimum price of 1 satoshi. So of course if XYZ coin will have an 1 tn supply it may look like it’s worth a lot , in this case 10,000 $, but we know your XYZ coins is not worth that, so it’s misleading.
Now of course the orders won’t be executed at 1 satoshi, there is no way to create value out of nothing, most of these worthless coins won’t even have a Bid wall, only desperate people trying to sell all of it at 1 satoshi but no buyers, thus the coin may be worth 0 or between 0 and 1 satoshi. If the resolution of the price would be higher ,I am sure the market would find a suitable price for it in that range.
Either way you won’t be able to steal value out of the coin. Printing money simply doesn’t create wealth.

Volume & Probability based inflation

So then what to do with these situations? And by the way fiat currency can be mispriced like this due to capital controls and other regulatory barriers. So simply multiplying the M3 of the EUR with it’s EUUSD price won’t give it’s value.
So I thought an even more accurate way to look at inflation is the volume itself. I mean the dormant coins have no inflationary effect.
For instance Satoshi lost 1 million coins allegedly, now we can’t know for sure,but given that the coins haven’t been moved in almost a decade the probability is high.
So we can just assign a probability to that. Say 90% probability that the coins will never be moved. Okay then that is a -900,000 supply for Bitcoin. Similarly many people lost their wallets and will continue to do so, so the usable supply is shrinking.
So the best metric is to look at the money velocity or the transaction volume, say in a year. That can be a good metric. Some research is needed on this.
Now this gets complicated because we also need to add in the transaction volume on the chain and the transaction volume off chain, like exchanges. As 3rd party middleman do a lot of inhouse micro-transactions.
But basically the big picture can be seen on the transaction volume.
So the most important metric of Bitcoin is the transaction value in BTC, which can be seen here:
As you can see it’s pretty stagnant and stable, I my opinion this is the true metric of inflation of Bitcoin, because this reflects how much coins are active and dormant.
By definition dormant coins are not sold, this not increasing the coins in circulation, so they can’t siphon off value from Bitcoin by increasing it’s tradeable supply.
Thus Bitcoin has a maximum 0.85% “siphon” rate/ day, correlated with it’s true inflation which could be estimated by observing this, removing duplicate transactions and estimating the sell activity on exchanges to be more precise.

M3 & Conclusion

So it’s kind of ironic but I think the FED is right when they say that M3 doesn’t affect inflation that much. They have removed the M3 data in 2006 from their website which I don’t like due to transparency reasons, but they are right in saying that M3 doesn’t affect inflation that much.
I am not saying Keynesian economics gives a perfect answer I am just saying that hard limits aren’t entirely correct either.
Besides the USD M3 can be easily reconstructed and estimated and it really shows not much impact on inflation.
So this concept can be extended to fiat currency too, we know the volumes of Forex markets, at least at the central hubs which are reported by the World Bank periodically, so we could estimate the value of fiat currencies based on their transaction volumes on Forex markets.
This should be a much better approach than silly CPI indices.
submitted by alexander7k to Anarcho_Capitalism [link] [comments]

Technical Analysis Weekly Review: 6. A Trading Plan, Part 1

Technical Analysis Weekly Review by ClydeMachine

Previous Week's Post:
5. Momentum & Volatility
This Week:
6. A Trading Plan, Part 1
Next Week's Post:
7. A Trading Plan, Part 2

6. A Trading Plan TL;DR


6. A Trading Plan

So you've been following TAWR for the last month - what does your trading plan look like? If you haven't started one yet, that's okay - that's what we start to cover in this week's post. First, you need to do a little soul searching.

Is this the right market for you to trade in?

Unlike other markets, the Bitcoin market does not close, not even on weekends. (International exchanges are for the most part open 6 days out of 7. BTC is around the clock.) This means there is constantly something happening, something to be watching for. Obviously you needn't be watching charts all the time and losing sleep and cuddle time because of a possible overseas news bit making waves - but this does open the market up for a lot of activity and this can be a serious stressor. If this will be too much for you, don't worry! This isn't the only market you can trade in. If this is a serious concern for you, consider other markets on the Forex. There are plenty of currency pairs to trade in that aren't nearly as crazy as those involving XBTs.
...If you're still here and not looking up USD/CHF market behaviour, that must mean you like rollercoasters.

Type of Trader: Being Honest With Yourself

Are you a swing trader? Long-term buy-and-holder looking to make a little extra in the short-term? Just curious what it's like to do what a daytrader does? Answering the question of "what type of trader are you" is important when setting up a trading plan, because certain indicators are better suited to different styles of trading. Your trading style will not necessarily reflect mine. Yours will likely differ a lot from mine and everyone else' - but as long as you can make decisions based off of that plan, and they make you money when followed, it is a good trading plan.
Ultimately, the goal of answering that question isn't to give yourself a label, it's to find a set of technical rules that you can follow that 1) make you money, and 2) that you can actually act on. Trader indecisiveness is a serious problem when on the (digital) trading floor. If you have a killer plan that seems like it'll work well for you based on the backtesting, but you find that you can't actually decide when to enter and exit a position because it's reacting very sensitive to market movements, that's trader indecisiveness. Suppose it's not reactive enough and you miss entry points every time they pass? That's also trader indecision. If you can take action based on the indicators, and make money as a result, that's a good plan. If not, go ahead and make revisions to the plan. Identify what's causing your money to disappear into fees and other traders' pockets, and make changes to keep that from happening!
I mentioned backtesting. That's important because whenever you come up with (or change) a trading plan, you need to...

PAPER TRADE FIRST.

If you aren't making money on paper, why would you make money in the market?
To paper trade, take down your actions based on your prospective trading plan, using actual market data. Follow the market and see if your trades would have made money if you had actually executed them on the market. If you're making satisfying gains consistently on those trades based on the rules of your plan, you can have confidence in your trading plan. If you're losing money or just barely breaking even, consider revisions to your trading plan. You can use historical data to check your plan's profitability, since it's readily available. Bitcoincharts.com and Tradingview.com both let you see historical data from the Bitcoin market, for example.
Obviously this will not be terribly useful to you until you've built your plan, but if you've already started to play with some indicators just to get a feel of how they look and react with the data, you'll find those two links somewhat helpful in getting a jump on next week's post.

Stick to the Facts.

Maybe your gut has never done you wrong, but always follow the chart. Befriend the trend. Trust the chart. Facts don't lie. Evidence doesn't lie. Make money by going with the market, not against it, no matter what your emotions or feelings are telling you.
This is something I've been guilty of, because the fact is I love Bitcoin. I really do. I love its functionality, its widespread growth, and the fact that it's techy at its decentralized heart. (That's a paradox, by the way.) But when a trader gets too involved with their chosen security, they believe in it for the wrong reasons. As much as I love Bitcoin, I have to sell it if the price goes into a mad nosedive. If you believe in the long-term success of Bitcoin, cool - know why you believe in it. Otherwise, just trade it and don't get too attached to it.
One of the key differences between Bitcoin and traditional stocks are that stocks are not food or clothes - you can't eat or wear stocks, so selling them is how you make money (locking in profits vs making gains "on paper"). However, Bitcoin actually does have use. It can be spent like any other currency (except faster!) and therefore having a lot of this security actually does give you a function you might not otherwise have. All the same, decide just how close you want to be to Bitcoin. If you believe it'll always and forever have a value, and will increase in value over time no matter what, then go ahead and collect as many as you can afford. If you have your cautious doubts, be aware of the previous point about getting too close to the security, and trade it like any other stock.
It's all about making money, whether you measure your monetary gains in USD or XBTs.
This next segment is right out of Barbara Rockefeller's "Technical Analysis for Dummies, 2nd ed." book, and is always true whether you're into cryptocurrencies or traditional stocks.

Diversify

"Diversification reduces risk. The proof of the concept in financial math won its proponents the Nobel prize, but the old adage has been around for centuries: “Don’t put all your eggs in one basket.” In technical trading, diversification applies in two places:

Deciding on Indicators

Wait til next week and we'll go over those! We'll see which ones fit with faster or slower trading plans (both are useful in Bitcoin) and you get to branch off from there and build your plan accordingly.

Next Week:

I'll welcome redditors to either comment or PM me their trading plans I'll do my best to look them over and offer suggestions or warnings as I see them. Again, I'm no guru or all-knowing being, and I'm not a certified trader or money manager or anything of that nature - but I'll offer the benefit of my research over the last few months regarding the indicators we've covered.
Stay curious, make money, have fun and see you next week.
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Currency Correlation and Deviation in Forex Weekly Forex Forecast 13th-17th April 2020 ForexForecast24 Time Frame Correlation Lesson #4 - Descriptive and Correlational Research Spearman’s Rank Correlation – indicator for MetaTrader 4 What is Correlation, and How to Distinguish It from Causation? Correlation Method

One of the most basic correlation types that any Forex trader should understand is between currencies. Two individual currencies can indeed exhibit a certain correlation due to their specific fundamental characteristics. To explain correlations between individual currencies, let’s first cover how they tend to behave during different market environments. As you already know, there are eight ... Forex trading involves risk. Losses can exceed deposits. Losses can exceed deposits. We recommend that you seek independent advice and ensure you fully understand the risks involved before trading. Hi all, I wanted to share this chart with you - I am hoping it works when I publish it and the arrows stay inline with the text - something very interesting we all know about currencies moving in tandem with each other to some degree different economic events causing them to stop moving together but eventually they will again. As a forex trader, if you check several different currency pairs to ... Positive Correlation-Three of the most traded pairs in the Forex market -GBP/USD, AUD/USD, and EUR/USD are positively correlated with each other, as the counter currency is the US dollar. Therefore any change in the strength of the US dollar directly impacts the pair as a whole. Moreover, the pair NZD/USD also called ‘Kiwi’ is also positively correlated to the above mentioned major pairs. Forex correlations can be an incredibly useful tool for traders and is certainly an interesting subject, if you want to keep up-to-date on the key fundamental issues shaping these correlations ... Forex Signals Market Buzz Professional Calendar Social trading >> TRADE NOW << Correlation indicator . Highly effective and robust indicator for free ; Compare correlations between two or more instruments ; With the indicator, you can spot profitable opportunities in the market ; Useful for all currencies and timeframes ; Compatibility: MetaTrader 4 ; Regularly updated for free ; How to trade ... Psychology students are often asked to conduct correlational research to obtain a specific outcome related to their field of interest. The correlational research design refers to a relationship between two variables that have nothing to do with any extra venous variable. It is a non-experimental ...

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Currency Correlation and Deviation in Forex

Currency Correlation and Deviation in Forex. Strategy to understand correlations and how to compute it manually to get the latest analysis. All opinions, discussions, messages, articles, research, technical analysis, prices and other information contained in this website are provided only as general information on the forex market and ... Activity 2 requires the most time, of about 10-15 minutes. The rest of the activities are fairly quick and require around 4 minutes each. After the actual video lesson, there is a video Teacher ... ForexMT4Indicators.com is a compilation of free download of forex strategies, forex systems, forex mt4 indicators, forex mt5 indicators, technical analysis and fundamental analysis in forex trading. Brief video explaining the correlation method of research. Not spectacular but still good stuff! :) M Within the forex market because of the huge liquidity, it is possible to trade successfully on small time frames. However, trading in the direction of the do... A quick look into the descriptive/correlation method and also evaluating research methods as a whole.

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