macd sample 5 digit.mq4Forex – (Tested with over ...

3 years, 28 pairs and 310 trades later

This thread is the direct continuation of my previous entry, which you can find here. I have the feeling my rambles may be long, so I'm not going to repeat anything I already said in my previous post for the sake of keeping this brief.
What is this?
I am backtesting the strategy shared by ParallaxFx. I have just completed my second run of testing, and I am here to share my results with those who are interested. If you want to read more about the strategy, go to my previous thread where I linked it.
What changed?
Instead of using a fixed target of the -100.0 Fibonacci extension, I tracked both the -61.8 and the -100.0 targets. ParallaxFx used the -61.8 as a target, but never tried the second one, so I wanted to compare the two and see what happens.
Where can I see your backtested result?
I am going to do something I hope I won't regret and share the link to my spreadsheet. Hopefully I won't be doxxed, but I think I should be fine. You can find my spreadsheet at this link. There are a lot of entries, so it may take a while for them to load. In the "Trades" tab, you will find every trade I backtested with an attached screenshot and the results it would have had with the extended and the unextended target. You can see the UNCOMPOUNDED equity curve in the Summary tab, together with the overall statistics for the system.
What was the sample size?
I backtested on the Daily chart, from January 2017 to December 2019, over 28 currency pairs. I took a total of 310 trades - although keep in mind that every position is most often composed by two entries, meaning that you can roughly halve this number.
What is the bottom line?
If you're not interested in the details, here are the stats of the strategy based on how I traded it.
Here you can see the two uncompounded equity curves side by side: red is unextended and blue is extended.
Who wins?
The test suggests the strategy to be more profitable with the extended target. In addition, most of the trades that reached the unextended target but reversed before reaching the extended, were trades that I would have most likely not have taken with the extented target. This is because there was a resistance/support area in the way of the -100.0 extension level, but there was enough room for price to reach the -61.8 level.
I will probably trade this strategy using the -100.0 level as target, unless there is an area in the way. In that case I will go for the unextended target.
Drawdown management
The expected losing streak for this system, using the extended target, is 7 trades in a row in a sample size of 100 trades. My goal is to have a drawdown cap of 4%, so my risk per trade will be 0.54%. If I ever find myself in a losing streak of more than 8 trades, I will reduce my risk per trade further.
What's next?
I'll be taking this strategy live. The wisest move would be to repeat the same testing over lower timeframes to verify the edge plays out there as well, but I would not be able to trust my results because I would have vague memories of where price went because of the testing I just did. I also believe markets are fractals, so I see no reason why this wouldn't work on lower timeframes.
Before going live, I will expand this spreadsheet to include more specific analysis and I will continue backtesting at a slower pace. The goal is to reach 20 years of backtesting over these 28 pairs and put everything into this spreadsheet. It's not something I will do overnight, but I'll probably do one year every odd day, and maybe a couple more during the weekend.
I think I don't have much else to add. I like the strategy. Feel free to ask questions.
submitted by Vanguer to Forex [link] [comments]

2.5 years and 145 backtested trades later

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

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]

Good backtest, bad forward test?

FOREX, DAILY
Backtesting my system last 7 years, 2000 trades. Data was split 75:25 in sample and out of sample, both profitable.
However, when forward testing the algo entered drawdown and is still on its way down.
What could be the reasons why?
submitted by BowsMind to algotrading [link] [comments]

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submitted by dev_lurve to HireaWriter [link] [comments]

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submitted by dev_lurve to forhire [link] [comments]

How do I perform more accurate backtests?

I use the backtrader package in Python alongside TA-Lib. Through playing around with some ideas, I've generated some truly ridiculous performing algos that get upwards of 1,000% - 2,000% gains in 3 months.
Here's an example of performance
Cumulative performance of 300 random 3-month time periods
Here is a histogram of that data where X is the ending portfolio value
However, I've written a Python script hooked up to a CRON job that runs every four hours checking the market for the right conditions on an Oanda Demo Account. I've let it run for about 3 months now, and the portfolio is slowly bleeding out.
It could be an infinite number of things from order execution to hyperparameter tuning, but I don't want to wait another 3 months on a forward test to find out.
Is there a way that I can optimize my development process so that I come to a more confident conclusion on a trading algo before I hook it up to a forward-walking test and waste time twiddling my thumbs to see how it performs?
Any tips would be helpful :)
submitted by FR_STARMER to algotrading [link] [comments]

How do I go about creating an algo?

I trade the daily chart and base my trades purely on technical indicator confluence, however every algo I’ve made has been curve fitted to the 3 years sample data and fails elsewhere, realised from forward testing and OOS testing.
I backtest, view results, add an indicator that improves the results and so on. I use indicators with the least parameters and different maths.
How can I create an algo that is generalised enough for forex not a specific pair or period of time.
submitted by BowsMind to Forex [link] [comments]

MAME 0.215

MAME 0.215

A wild MAME 0.215 appears! Yes, another month has gone by, and it’s time to check out what’s new. On the arcade side, Taito’s incredibly rare 4-screen top-down racer Super Dead Heat is now playable! Joining its ranks are other rarities, such as the European release of Capcom‘s 19XX: The War Against Destiny, and a bootleg of Jaleco’s P-47 – The Freedom Fighter using a different sound system. We’ve got three newly supported Game & Watch titles: Lion, Manhole, and Spitball Sparky, as well as the crystal screen version of Super Mario Bros. Two new JAKKS Pacific TV games, Capcom 3-in-1 and Disney Princesses, have also been added.
Other improvements include several more protection microcontrollers dumped and emulated, the NCR Decision Mate V working (now including hard disk controllers), graphics fixes for the 68k-based SNK and Alpha Denshi games, and some graphical updates to the Super A'Can driver.
We’ve updated bgfx, adding preliminary Vulkan support. There are some issues we’re aware of, so if you run into issues, check our GitHub issues page to see if it’s already known, and report it if it isn’t. We’ve also improved support for building and running on Linux systems without X11.
You can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

MAME 0.215

MAME 0.215

A wild MAME 0.215 appears! Yes, another month has gone by, and it’s time to check out what’s new. On the arcade side, Taito’s incredibly rare 4-screen top-down racer Super Dead Heat is now playable! Joining its ranks are other rarities, such as the European release of Capcom‘s 19XX: The War Against Destiny, and a bootleg of Jaleco’s P-47 – The Freedom Fighter using a different sound system. We’ve got three newly supported Game & Watch titles: Lion, Manhole, and Spitball Sparky, as well as the crystal screen version of Super Mario Bros. Two new JAKKS Pacific TV games, Capcom 3-in-1 and Disney Princesses, have also been added.
Other improvements include several more protection microcontrollers dumped and emulated, the NCR Decision Mate V working (now including hard disk controllers), graphics fixes for the 68k-based SNK and Alpha Denshi games, and some graphical updates to the Super A'Can driver.
We’ve updated bgfx, adding preliminary Vulkan support. There are some issues we’re aware of, so if you run into issues, check our GitHub issues page to see if it’s already known, and report it if it isn’t. We’ve also improved support for building and running on Linux systems without X11.
You can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to MAME [link] [comments]

Strat for 50 - 100% a Year - Common Points, Example of Setup 3 and First Weeks Results.

Strat for 50 - 100% a Year - Common Points, Example of Setup 3 and First Weeks Results.
Part 1
Part 2

We're going to start this post with dealing with common heckles. Some people have heckled me already in this posting series. I know from having done things like this publicly a few times before there are catchphrase heckles to be dealt with, and we'll do this one and for all here. If I've linked you here, you've done a FMH (Frequently Made Heckle).
If you're not a heckler, you can skip the line break for the strategy stuff, but this section may still be interesting for you.
FMH 1 : Elliot wave does not work all the time.

I know. The clock in my living-room does not work all the time. If it tells me it's 2am and I look out and it's broad day light, I use some discerning judgement based on my experience of looking out of a window, and I suspect it may be incorrect. If it tells me it's 8.30am and I look out and see little kids with school bags walking past the window, I suspect the clock may have a point.

When I write all the rules and exceptions in my posts, I am not doing this to make the posts longer. These are rules and exceptions designed to describe situations when it probably is happening. Of course it does not "Always work". I am not say it does. Your assumption I have not thought through the same extraordinary simplistic, "But, what if ...." questions is either you under estimating me, over estimating you, or both.

FMH 2 : Fibs levels do not work, studies show it is as good as random.

Two points. Firstly, I've read some of these studies. These hypothetical things done by people who have never traded in the market and want to produce intellectual ideas about it. While reading through the method of the experiment it's apparent to me it won't work. I could save them some time if they call me and tell me their hypothesis;

"Nope. You'll lose about 20% a year doing that. Good general idea. Okay starting point, but you get fucked here, here and here. Work on that".
I will not value the opinion of someone paid to write papers on fibs over my experience being paid to trade them. I will not go out my way to try to get you to value my opinion. I've learned people will either test things I say and know the truth of them for their selves with me posting the amount of interesting evidence/results that I do, and others would not test it if I posted a million examples.

Point two. Not perfect does not mean not practical. Fib levels do not react absolutely perfectly. I suspect the reason for this is so many people use them to put stops behind these days. In days gone by, they were probably more accurate, but as stop clusters became more predictable and concentrated this change. Game theory sort of stuff. Read more about my thoughts on this here.

The thing is, for those who pay enough time and attention, there are patterns of when the fibs either do work very well, or "do not work" in the exact same way over and over again. If they do "not work" in the same way over and over, that's the same as working to me. I am looking for patterns to trade for profit. Not to compile a pretty chart of data points as to if price turned specifically on the 61.8 over a million samples.

FMH 3 - "Everything you're saying is wrong", "You're an idiot", "I am non-specifically and non-constructively disagreeing" (Yeah, people drop that last one, verbatim, all the time)

Pics, or it didn't happen. I am willing to "get up here" so to speak and succeed or fail in front of everyone. I'm posting what I do, and explaining all my rational. Results are being tracked. Time and continuity will display my outcomes. Is there a way you suggest you can provide stronger proof I am wrong that I am proposing to prove I am right?

If you're just saying you think I am stupid, because you know the market so much better than me my standard reply is as follows;

" If you'd like to propose, explain and track a strategy you think will outperform this we can both keep our records and that will best determine who's opinions have profitability. It seems something that would be good for the community. "

Pics or it didn't happen. Only analysts and economists are paid for opinions. My job demands a far more practical approach.

FMH 4 - "What REALLY happened with (insert news related thing) this and your guesses were just lucky".

If I said it would happen yesterday, then set trades for it happening and profited from them today; it does not matter to me the reason you give me for it tomorrow. If you choose to view the market as being like this, you may. If it ever does start to become more relevant to me making profits or not, I will pay attention to other things. Right now, I do not follow them closely and that has never mattered. Either I am consistently lucky, physic or right. Pick the flavor for you.

I will not engage in conversation on any of these points coming from a closed minded perspective. By which I mean you only commenting to tell me why you're right. If you feel someone has to add balance with these comments, go ahead. I encourage people to be scientific in their approach and having different viewpoints helps with this. Do your own experiments.

I will answer honest questions, and will gladly engage people who disagree with me and do so from the perspective of personal study. Usually we can both learn and teach if both of us have firsthand knowledge. This is rare, but enjoyable.

==================================================================================================

On to GBPUSD. As I said may be possible in the previous post, the trade for the bigger run up post Chicago was missed. This can happen. It's better to miss bad opportunities than squander good money on bad ones, and at the time I had the option of entering, there was no way to tell the difference between these - so I did nothing.

Later in the day price continued to be consistent with the formations of a spike pattern. Here I engaged the market.

GBPUSD 1 Minute

My entering pattern was to first open two small trades with a 13 pip stop. This was an emergency stop, I always planned to tighten it up (it'd only hit in the event of an immediate capitulation). The risk here was about 0.15%. When the market moved a bit lower, I entered more positions and having more data felt better about where to place stops. All stops went to 6 pips or less (bigger position, same starting risk).

As price reached the best level, I opened my largest trade. Stop went from 3 - 6 pips with big stops being 2 pips. Effective stop something like 3-4 pips. Targets hit for 10 - 12 pips, giving an effective pay off on risk just short of 1:3. I do not use aggressive position sizing in this part of the trade (usually it already carries made profits), so the net risk was low. Around 0.25%. Net gain in positions was 0.6%.

GBPUSD 1 min
From left to right the positions get bigger. Notice also the biggest position (low) takes profit a good bit before where I forecast the high (bulk close). This trade hitting should give assurance of breakeven on this trade, so the risk on capital is gone on this trade on a double top move, then profits accumulated in the breakout.

Results for the day;


https://preview.redd.it/96zq7cy1j9i31.png?width=821&format=png&auto=webp&s=d324348621a5c0931a90de207fe8aebb116f934d


Current Gain = 0.65%
Max risk exposure possible - 0.4%
Max real equity drawdown - < 0.2%

Due to not being entirely available for trading today this was a big under-performance of what the strategy could have achieved. It's been a decent example day to show the logistics of how the trades can form. To make 2 - 3% today with the same draw-down was possible.
submitted by whatthefx to Forex [link] [comments]

What factors predict the success of a Steam game? (An analysis)

What factors predict the success of a Steam game?

I've seen quite a few discussions, comments and questions on /gamedev about what determines a game's success. How much does quality matter? Is establishing market awareness before launch the only thing that matters? Does a demo help or hurt? If your game has a poor launch, how likely is it to recover? Is it possible to roughly predict the sales of a game before launch?
In preparation for my game's launch, I spent a lot of time monitoring upcoming releases trying to find the answer to these questions. I compiled a spreadsheet, noted followers, whether it was Early Access or not, and saw how many reviews it received in the first week, month and quarter.
I'm sharing this data now in the hopes that it helps other developers understand and predict their games' sales.
First some notes on the data:
Game Price Launch Discount Week Guess Week actual 3 Month 3 Month/week Followers Early Access Demo Review Score
Pit of Doom 9.99 0 7 27 43 1.592592593 295 Y N 0.8
Citrouille 9.99 0.2 16 8 12 1.5 226 N N
Corspe Party: Book 14.99 0.1 32 40 79 1.975 1015 N N 0.95
Call of Cthulhu 44.99 0 800 875 1595 1.822857143 26600 N N 0.74
On Space 0.99 0.4 0 0 0 4 N N
Orphan 14.99 0 50 0 8 732 N N
Black Bird 19.99 0 20 13 34 2.615384615 227 N N
Gloom 6.99 0 20 8 17 2.125 159 N N
Gilded Rails 5.99 0.35 2 3 7 2.333333333 11 N Y
The Quiet Man 14.99 0.1 120 207 296 1.429951691 5596 N N 0.31
KartKraft 19.99 0.1 150 90 223 2.477777778 7691 Y N 0.84
The Other Half 7.99 0 2 3 27 9 91 N Y 0.86
Parabolus 14.99 0.15 0 0 0 16 N Y
Yet Another Tower Defense 1.99 0.4 20 22 38 1.727272727 396 N N 0.65
Galaxy Squad 9.99 0.25 8 42 5.25 3741 Y N 0.87
Swords and Soldiers 2 14.99 0.1 65 36 63 1.75 1742 N N 0.84
SpitKiss 2.99 0 3 1 2 2 63 N N
Holy Potatoes 14.99 0 24 11 22 2 617 N N 0.7
Kursk 29.99 0.15 90 62 98 1.580645161 2394 N N 0.57
SimpleRockets 2 14.99 0.15 90 142 272 1.915492958 3441 Y N 0.85
Egress 14.99 0.15 160 44 75 1.704545455 7304 Y N 0.67
Kynseed 9.99 0 600 128 237 1.8515625 12984 Y N 0.86
11-11 Memories 29.99 0 30 10 69 6.9 767 N N 0.96
Rage in Peace 12.99 0.1 15 10 42 4.2 377 N N 0.85
One Hour One Life 19.99 0 12 153 708 4.62745098 573 N N 0.81
Optica 9.99 0 0 2 3 1.5 18 N N
Cybarian 5.99 0.15 8 4 18 4.5 225 N N
Zeon 25 3.99 0.3 3 11 12 1.090909091 82 Y N
Of Gods and Men 7.99 0.4 3 10 18 1.8 111 N Y
Welcome to Princeland 4.99 0.1 1 15 55 3.666666667 30 N N 0.85
Zero Caliber VR 24.99 0.1 100 169 420 2.485207101 5569 Y N 0.73
HellSign 14.99 0 100 131 334 2.549618321 3360 Y N 0.85
Thief Simulator 19.99 0.15 400 622 1867 3.001607717 10670 N N 0.81
Last Stanza 7.99 0.1 8 2 4 2 228 N Y
Evil Bank Manager 11.99 0.1 106 460 4.339622642 8147 Y N 0.78
Oppai Puzzle 0.99 0.3 36 93 2.583333333 54 N N 0.92
Hexen Hegemony 9.99 0.15 3 1 5 5 55 Y N
Blokin 2.99 0 0 0 0 0 10 N N
Light Fairytale Ep 1 9.99 0.1 80 23 54 2.347826087 4694 Y N 0.89
The Last Sphinx 2.99 0.1 0 0 1 0 17 N N
Glassteroids 9.99 0.2 0 0 0 0 5 Y N
Hitman 2 59.99 0 2000 2653 3677 1.385978138 52226 N N 0.88
Golf Peaks 4.99 0.1 1 8 25 3.125 46 N N 1
Sipho 13.99 0 24 5 14 2.8 665 Y N
Distraint 2 8.99 0.1 40 104 321 3.086538462 1799 N N 0.97
Healing Harem 12.99 0.1 24 10 15 1.5 605 N N
Spark Five 2.99 0.3 0 0 0 0 7 N N
Bad Dream: Fever 9.99 0.2 30 78 134 1.717948718 907 N N 0.72
Underworld Ascendant 29.99 0.15 200 216 288 1.333333333 8870 N N 0.34
Reentry 19.99 0.15 8 24 78 3.25 202 Y N 0.95
Zvezda 5.99 0 2 0 0 0 25 Y Y
Space Gladiator 2.99 0 0 1 2 2 5 N N
Bad North 14.99 0.1 500 360 739 2.052777778 15908 N N 0.8
Sanctus Mortem 9.99 0.15 3 3 3 1 84 N Y
The Occluder 1.99 0.2 1 1 1 1 13 N N
Dark Fantasy: Jigsaw 2.99 0.2 1 9 36 4 32 N N 0.91
Farming Simulator 19 34.99 0 1500 3895 5759 1.478562259 37478 N N 0.76
Don't Forget Our Esports Dream 14.99 0.13 3 16 22 1.375 150 N N 1
Space Toads Mayhem 3.99 0.15 1 2 3 1.5 18 N N
Cattle Call 11.99 0.1 10 19 53 2.789473684 250 Y N 0.71
Ralf 9.99 0.2 0 0 2 0 6 N N
Elite Archery 0.99 0.4 0 2 3 1.5 5 Y N
Evidence of Life 4.99 0 0 2 4 2 10 N N
Trinity VR 4.99 0 2 8 15 1.875 61 N N
Quiet as a Stone 9.99 0.1 1 1 4 4 42 N N
Overdungeon 14.99 0 3 86 572 6.651162791 77 Y N 0.91
Protocol 24.99 0.15 60 41 117 2.853658537 1764 N N 0.68
Scraper: First Strike 29.99 0 3 3 15 5 69 N N
Experiment Gone Rogue 16.99 0 1 1 5 5 27 Y N
Emerald Shores 9.99 0.2 0 1 2 2 12 N N
Age of Civilizations II 4.99 0 600 1109 2733 2.464382326 18568 N N 0.82
Dereliction 4.99 0 0 0 0 #DIV/0! 18 N N
Poopy Philosophy 0.99 0 0 6 10 1.666666667 6 N N
NOCE 17.99 0.1 1 3 4 1.333333333 35 N N
Qu-tros 2.99 0.4 0 3 7 2.333333333 4 N N
Mosaics Galore. Challenging Journey 4.99 0.2 1 1 8 8 14 N N
Zquirrels Jump 2.99 0.4 0 1 4 4 9 N N
Dark Siders III 59.99 0 2400 1721 2708 1.573503777 85498 N N 0.67
R-Type Dimensions Ex 14.99 0.2 10 48 64 1.333333333 278 N N 0.92
Artifact 19.99 0 7000 9700 16584 1.709690722 140000 N N 0.53
Crimson Keep 14.99 0.15 20 5 6 1.2 367 N N
Rival Megagun 14.99 0 35 26 31 1.192307692 818 N N
Santa's Workshop 1.99 0.1 3 1 1 1 8 N N
Hentai Shadow 1.99 0.3 2 12 6 14 N N
Ricky Runner 12.99 0.3 3 6 13 2.166666667 66 Y N 0.87
Pro Fishing Simulator 39.99 0.15 24 20 19 0.95 609 N N 0.22
Broken Reality 14.99 0.1 60 58 138 2.379310345 1313 N Y 0.98
Rapture Rejects 19.99 0 200 82 151 1.841463415 9250 Y N 0.64
Lost Cave 19.99 0 3 8 11 1.375 43 Y N
Epic Battle Fantasy 5 14.99 0 300 395 896 2.26835443 4236 N N 0.97
Ride 3 49.99 0 75 161 371 2.304347826 1951 N N 0.74
Escape Doodland 9.99 0.2 25 16 19 1.1875 1542 N N
Hillbilly Apocalypse 5.99 0.1 0 1 2 2 8 N N
X4 49.99 0 1500 2638 4303 1.63115997 38152 N N 0.7
Splotches 9.99 0.15 0 2 1 0.5 10 N N
Above the Fold 13.99 0.15 5 2 6 3 65 Y N
The Seven Chambers 12.99 0.3 3 0 0 #DIV/0! 55 N N
Terminal Conflict 29.99 0 5 4 11 2.75 125 Y N
Just Cause 4 59.99 0 2400 2083 3500 1.680268843 50000 N N 0.34
Grapple Force Rena 14.99 0 11 12 29 2.416666667 321 N Y
Beholder 2 14.99 0.1 479 950 1.983298539 16000 N N 0.84
Blueprint Word 1.99 0 12 15 1.25 244 N Y
Aeon of Sands 19.99 0.1 20 12 25 2.083333333 320 N N
Oakwood 4.99 0.1 32 68 2.125 70 N N 0.82
Endhall 4.99 0 4 22 42 1.909090909 79 N N 0.84
Dr. Cares - Family Practice 12.99 0.25 6 3 8 2.666666667 39 N N
Treasure Hunter 16.99 0.15 200 196 252 1.285714286 4835 N N 0.6
Forex Trading 1.99 0.4 7 10 14 1.4 209 N N
Ancient Frontier 14.99 0 24 5 16 3.2 389 N N
Fear the Night 14.99 0.25 25 201 440 2.189054726 835 Y N 0.65
Subterraneus 12.99 0.1 4 0 3 #DIV/0! 82 N N
Starcom: Nexus 14.99 0.15 53 119 2.245283019 1140 Y N 0.93
Subject 264 14.99 0.2 25 2 3 1.5 800 N N
Gris 16.9 0 100 1484 4650 3.133423181 5779 N N 0.96
Exiled to the Void 7.99 0.3 9 4 11 2.75 84 Y N
Column Explanations
For the columns that are not self-explanatory:

Question 1: Does Quality Predict Success?

There was a recent blog post stating that the #1 metric for indie games' success is how good it is.
Quality is obviously a subjective metric. The most obvious objective measure of quality for Steam games is their % Favorable Review score. This is the percentage of reviews by purchasers of the game that gave the game a positive rating. I excluded any game that did not have at least 20 user reviews in the first month, which limited the sample size to 56.
The (Pearson) correlation of a game's review score to its number of reviews three months after its release was -0.2. But 0.2 (plus or minus) isn't a very strong correlation at all. More importantly, Pearson correlation can be swayed if the data contains some big outliers. Looking at the actual games, we can see that the difference is an artifact of an outlier. Literally. Valve's Artifact by far had the most reviews after three months and had one of the lowest review scores (53% at the time). Removing this game from the data changed the correlation to essentially zero.
Spearman's Rho, an alternative correlation model that correlates rank position and minimizes the effect of huge outliers produced a similar result.
Conclusion: If there is correlation between a game's quality (as measured by Steam review score) and first quarter sales (as measured by total review count), it is too subtle to be detected in this data.

Question 2: Do Demos, Early Access or Launch Discounts Affect Success/Failure?

Unfortunately, there were so few games that had demos prior to release (10) that only a very strong correlation would really tell us anything. As it happens, there was no meaningful correlation one way or another.
There were more Early Access titles (28), but again the correlation was too small to be meaningful.
More than half the titles had a launch week discount and there was actually a moderate negative correlation of -0.3 between having a launch discount and first week review count. However it appears that this is primarily the result of the tendency of AAA titles (which sell the most copies) to not do launch discounts. Removing the titles that likely grossed over a $1 million in the first week reduced the correlation to basically zero.
Conclusion: Insufficient data. No clear correlation between demos, Early Access or launch discount and review counts: if they help or hurt the effect is not consistent enough to be seen here.

Question 3: Does pre-launch awareness (i.e., Steam followers) predict success?

You can see the number of "followers" for any game on Steam by searching for its automatically-created Community Group. Prior to launch, this is a good rough indicator of market awareness.
The correlation between group followers shortly before launch and review count at 3 months was 0.89. That's a very strong positive correlation. The rank correlation was also high (0.85) suggesting that this wasn't the result of a few highly anticipated games.
Save for a single outlier (discussed later), the ratio of 3 month review counts to pre-launch followers ranged from 0 (for the handful of games that never received any reviews) to 1.8, with a median value of 0.1. If you have 1000 followers just prior to launch, then at the end of the first quarter you should expect "about" 100 reviews.
One thing I noticed was that there were a few games that had follower counts that seemed too high compared to secondary indicators of market awareness, such as discussion forum threads and Twitter engagement. After some investigation I came to the conclusion that pre-launch key activations are treated as followers by Steam. If a game gave away a lot of Steam keys before launch (say as Kickstarter rewards or part of beta testing) this would cause the game to appear to have more followers than it had gained "organically."
Conclusion: Organic followers prior to launch are a strong predictor of a game's eventual success.

Question 4: What about price?

The correlation between price and review count at 3 month is 0.36, which is moderate correlation. I'm not sure how useful that data point is: it is somewhat obvious that higher budget games have larger marketing budgets.
There is a correlation between price and review score of -0.41. It seems likely that players do factor price into their reviews and a game priced at $60 has a higher bar to clear to earn a thumbs up review than a game priced at $10.

Question 5: Do first week sales predict first quarter results?

The correlation between number of reviews after 1 week and number of reviews after 3 months was 0.99. The Spearman correlation was 0.97. This is the highest correlation I found in the data.
Excluding games that sold very few copies (fewer than 5 reviews after the first week), most games had around twice as many reviews after 3 months as they did after 1 week. This suggests that games sell about as many copies in their first week as they do in the next 12 weeks combined. The vast majority of games had a tail ratio (ratio of reviews at 3 months to 1 week) of between 1.3 to 3.2.
I have seen a number of questions from developers whose game had a poor launch on Steam and wanted to know what they can do to improve sales. While I'm certain post-launch marketing can have an effect on continuing sales, your first week does seem to set hard bounds on your results.
Conclusion: ALL SIGNS POINT TO YES

Question 6: Does Quality Help with a Game's "Tail"?

As discussed in the last question while first week sales are very strongly correlated with first quarter, there's still quite a wide range of ratios. Defining a game's Tail Ratio as the ratio of reviews after 3 months to after 1 week, the lowest value was 0.95 for "Pro Fishing Simulator" which actually managed to lose 1 review. The highest ratio was 6.9, an extreme outlier that I'll talk about later. It is perhaps not a coincidence that the worst tail had a Steam score of 22% and the best tail had a Steam score of 96%.
The overall correlation between the Tail Ratio and Steam score was 0.42.
Conclusion: Even though there is no clear correlation between quality and overall review count/sales, there is a moderate correlation between a game's review score and its tail. This suggests that "good games" do better in the long run than "bad games," but the effect is small compared to the more important factor of pre-launch awareness.

Question 7: Is it possible to predict a game's success before launch without knowing its wishlists?

While I was compiling the data for each game, sometime prior to its scheduled launch date, I would make a prediction of how many reviews I thought it would receive in its first week and add that prediction to the spreadsheet.
The #1 factor I used in making my prediction was group follower count. In some cases I would adjust my prediction if I thought that value was off, using secondary sources such as Steam forum activity and Twitter engagement.
The correlation between my guess and the actual value was 0.96, which is a very strong correlation. As you can see in the data, the predictions are, for the most part, in the right ballpack with a few cases where I was way off.
Based on my experience, multiplying the group follower count by 0.1 will, in most cases, give you a ballpark sense of the first week quarter review count. If a game doesn't have at least one question in the discussion forum for every 100 followers, that may indicate that there are large number of "inorganic" followers and you may need to adjust your estimate.
Conclusion: Yes, with a few exceptions, using follower data and other indicators you can predict first week results approximately. Given the strong correlation between first week and quarter sales, it should also be possible to have a ballpark idea of first quarter results before launch.

Final Question: What about the outliers you mentioned?

There were a few games in the data that stood out significantly in one way or another.
Outlier #1: Overdungeon. This game had 77 group followers shortly before launch, a fairly small number and based solely on that number I would have expected fewer than a dozen reviews in the first week. It ended up with 86. Not only that, it had a strong tail and finished its first quarter with 572 reviews. This was by a wide margin the highest review count to follower ratio in the sample.
Based on the reviews, it appears to basically be Slay the Spire, but huge in Asia. 90% of the reviews seem to be in Japanese or Chinese. If anyone has some insight to this game's unusual apparent success, I'm very curious.
This seems to be the only clear example in the data of a game with minimal following prior to launch going on to having a solid first quarter.
Outlier #2: 11-11 Memories Retold. This game had 767 group followers shortly before launch, ten times as many as Overdungeon. That's still not a large number for even a small indie title. It had a fair amount going for it, though: it was directed by Yoan Fanise, who co-directed the critally acclaimed Valiant Hearts, a game with a similar theme. It was animated by Aardman Studios of "Wallace and Gromit" fame. Its publisher was Bandai Namco Europe, a not inexperienced publisher. The voice acting was by Sebastian Koch and Elijah Wood. It has dozens of good reviews in both gaming and traditional press. It currently has a 95% positive review rating on Steam.
Despite all that, nobody bought it. 24 hours after it came out it had literally zero reviews on Steam. One week after it came out it had just 10. Three months later it had demonstrated the largest tail in the data, but even then it had only climbed to 69 reviews. Now it's at about 100, an incredible tail ratio, but almost certainly a commercial failure.
This is a solid example that good game + good production values does necessarily equal good sales.

Final notes:
The big take-aways from this analysis are:
Thanks for reading!
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Forex Quiz. Below is a small sample of the forex quiz published in Forex for Ambitious Beginners. Fill out the answers in the form on the right side and you'll receive the results within 48 hours. Do you have any questions about the quiz? Don't hesitate to contact us. Question 1 How are currencies being traded? A Through central banks B In pairs This guide is intended to give you quick introduction how to use the Smart Forex Tester. To get you started, we will show you a concrete example of running a test, for which we will use the sample 3 RSI based trading strategy that is provided with the Smart Forex Tester.. The strategy is very simple and was developed for educational purposes. Forex currency trading is a zero sum game and those with a trading plan and the necessary discipline to stick to it will succeed over those that trade without one. If you want to be on the positive side of this game start with your trading plan - it is your most important weapon against your opponents. Recent Posts. Kijun Sen Robot (KSRobot) – (tested with over $1,100,000 profit) September 4, 2020 Farhad3.mq4 free Forex EA – (Tested with over $1,800,000 profit) September 3, 2020 gbp9am Forex EA free download – (Tested with over $1,000,000 profit) September 3, 2020 e.2.16 5min Scalper.mq4 – (tested with over $640,000 (90,000%) profit) September 3, 2020 Simple forex tester free Mt4 or metatrader four is still the most popular forex trading platform around. No matter the discharge of the fifth generation of this platform chain from metaquotes software program corp. The 4th version remains going sturdy with the aid of recording paid downloads even now. Find out if you’ve got what it takes to make big money trading forex! Log In. Home; OUR TESTS; ABOUT; FAQs; CONTACT US; Test Your Trading Skills. Find out if you've got what it takes to make big money trading forex! Get Started. Discover your trader personality. Find out your 3 top trader motivators. Measure your general intellect in trading . Evaluate your technical analysis skills. Tests ... In forex trading, the trade size is in units of the first, or base, currency in the pair. EUR/USD has a margin factor of 3.33% . The margin as well as the p&l are calculated in dollars, the counter currency of the pair. Winning trade. The euro drops against the dollar as political event risk increases and you decide to buy €20,000 at 1.0570 to close your trade with a profit of $160. City ... EUR/GBP is trading at 0.84950 / 0.84960. You decide to buy €20,000 because you think the price of EUR/GBP will go up. EUR/GBP has a margin rate of 3.34%, which means that you only have to deposit 3.34% of the total position value as position margin. Therefore, in this example your position margin ... Learn to trade in Forex with us and monetize your foresight. Here are the basics of Forex trading. Currency Trading: Basics. Traders can work with different currencies. These are valued against one another and are always traded in pairs. For example, the price for GBP USD shows how many US dollars one British pound is worth. Each combination is classified as Major, Minor, or Exotic based on ... Unpredictability of forex markets makes test strategy even more important: as we remember, any testing still can’t give us 100% test coverage. On the other hand, testing multitude of strategy parameters against big amount of market data is already complex and time-consuming task. So, the test strategy is responsible for selecting essential Test Scenarios so that the trustworthy quality level ...

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ICT Forex Trade Example - EurUsd New York Open - YouTube

An example of trade taken on the failure of a trend line on GBP/USD. I saw the big picture and want to highlight the importance of that first. Then it's a ma... Simple forex trading can work in the markets. You don't need to over complicate your trading. Look for the best types of trades and keep it simple... Are you struggling to be consistent with your ... AUDCAD Short from October 2020. This is a quick example of trades I like to take and the way I use multiple timeframes to gain an edge. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. This video shows how simple it is to analyse a Forex chart, with a USDJPY example we execute a top down analysis using strictly tools and price action. This ... Enter our Giveaway: https://www.forexboat.com/forex-giveaway/ Premium Signals Telegram Channel: https://www.forexboat.com/forex-signals/ Free Telegram Channe... Video is intended to be silent. Study this trade example & I will review it here 08/18/20 in detail. There is Risk in Trading.

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