Forex fortune factory reviews

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]

E-F 1k-5k

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fightingillini fightoffyourdemons fightscenes FightTraining figuredrawing fijerk filecoin filemaker filesfm filesystems filipinabargirls filipinas Filipino FillsYourNiche filmclass FilmClubPH Filme FilmFaces filmfashion Filmmaker4Filmmaker filmphotos filmreroll filmscoring FilmsExplained filmstruck filmstudies FilmTalk FilmTheorists FilmTVBudgeting filosofia_en_espanol filth FIN_EL_MEME FinalDestinationShit FinalFantasy14 FinalFantasy7 FinalFantasyExplorers FinalFantasyIX FinalFantasyVIII FinallyDeclassified FinalMouse FinalPunch financaspessoaispt FinanceNews finances financestudents financial FinancialAdvice financialaid FinancialPorn FindABand findanentwifefriend finddit FindExchange FindingDitto FindingFinna FindItOnAmazon FindMeADistro FindMeFood findswingerssexdating findthisshirt FineArt FineArtPhoto Fingerboards fingerstyleguitar fingols FinishTheJoke Finside Fios Fire firealarms firearmdealscanada firearts Firebase firebrigade fireemblemcasual FireEmblemCats FireEmblemFanArt 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Apple Card Review: A (Mostly) Rewarding Way to Pay

I spent a few days hopping around New York City last week, trying to keep my wallet in my pocket. It wasn’t that I was on a tight budget. But I was testing the Apple Card, the new credit card from Apple, and according to its reward program, that’s the most lucrative way to shop.
Introduced back in March, the Apple Card is now generally available to anyone with an Apple mobile device who wants to apply.
If you use the Apple Card via the wireless, contactless Apple Pay system that is becoming increasingly popular with iPhone owners and businesses alike, you get a fairly generous return on every purchase of 2% cash back, no strings attached. That’s a bonus which lines up with the best credit cards around, from major issuers like JP Morgan Chase and Bank of America.
So when I grabbed a cup of coffee and a cookie at a cute bakery on the Upper West Side, for example, paying with the Apple Card through my iPhone earned me an almost immediate refund of 11 cents on my $5.63 purchase. (The cookie was good, too.) Later, after traipsing around on a hot summer day, I picked up a $2.87 bottle of water at CVS, also using wireless Apple Pay. Along with the hydration, I scored 6 cents cash back.
A big difference between this credit card and its competition is that unlike other rebate cards, the Apple Card’s cash reward appears almost immediately after the purchase is processed. To access these funds, you simply open the Wallet app on your iPhone, which is the home of Apple Card itself, showing your current balance, recent transactions, and other info updated in almost real time. The Wallet app also displays your “Daily Cash Balance.” These funds can be spent like a debit card on purchases using the digital Apple Pay Cash card, sent to a friend via Apple Pay, or even used to partially pay off the balance on your Apple Card.
There’s another, better benefit to using the Apple Card: Paying for purchases from Apple using the digital credit card earns 3% cash back. For example, my family’s $5 per month _New York Times_cooking app subscription now brings back 15 cents each month. And the $120 a year I pay for a family iCloud storage plan earns $3.60 in rewards. And if I decide finally to upgrade my aging MacBook Pro with the rumored 16-inch model coming later this year (please revamp the keyboard, Apple!), the cash back perk will be even more substantial—$90 on a $3,000 purchase, for example. There’s no other way to get such high rebates on purchases directly from Apple (though some cards affiliated with retailers like Target and Amazon will give 5%, if you’re buying Apple hardware sold at those outlets).

Taking a swipe at other cards

When using the Apple Card at establishments that aren’t set up for app-enabled, contactless payments, things get markedly less magical. To start, you have to pull the (admittedly cool looking white, titanium) Apple Card out of your wallet—and that can be a drag. Then, the rebates drop to just 1%, lagging competing cards.
A fair counterpoint, however, to the meager 1% cash back on physical card swipes is that Apple also forgoes fees that other cards charge. Apple Card has no annual, over-limit, late, or foreign exchange fees. And that’s great, because those can add up. For instance, imagine if I spent $1,000 over the course of a month on a competing card to get $20 cash back, instead of the $10 I’d get from swiping my Apple Card. Every other credit card I know of charges late fees—and one $35 late fee would quickly wipe out that $20 cash back reward, and then some. Foreign exchange fees can also add up quickly (though there are other credit cards, particularly those affiliated with airline rewards programs, that also forgo forex fees).
Assessing whether the Apple Card makes financial sense for you, therefore, requires making assumptions about how much you spend with Apple (including all your iTunes purchases and subscriptions), how often you’re able to use mobile payments, and how often you typically trigger the fees that Apple doesn’t charge.
For me, it certainly makes sense for all my Apple purchases and when I’m paying via mobile. But Apple also just added Uber as 3% rebate partner—a perk for its cardholders—and future partnerships like this could make the Apple Card more attractive at more businesses.
Even when you’re not rebate hunting or avoiding fees, the Apple Card feels like a futuristic, if long overdue upgrade to spending on plastic in the 21st century.
The application process, within the Wallet app on an iPhone or iPad, takes just a few minutes and, if you’re approved, the card is added as an option in Apple Pay immediately. The white, titanium physical card is optional, but came via FedEx within a few days after I requested one. Activating the card was as simple as holding it near my phone with the Wallet app open.
Every transaction quickly appears listed in the Wallet app on my iPhone, with a categorization (like “transportation” or “food and drink”) along with the rate of cash back I received (3% for spending with Apple, 2% for mobile payments, and 1% for everything else). Tap on any transaction, and Apple shows on a map exactly where you made the purchase. For some stores, like that CVS where I got the water, there’s even a deeper link, with all kinds of info about the business, like the phone number, hours of operation, and customer reviews. Apps for my other credit and bank cards aren’t nearly so nimble.
With a couple of teenagers out in the wild using our family credit card, it can be hard to identify who spent what where, with the typically meager information provided by the credit card company, so the geo-location info is fantastic. Of course, I can’t yet opt to switch the whole family to the Apple card—there’s no option yet to add additional cardholders to my account (a feature available with every other card I’m aware of).
Another potential perk: Apple has committed to not share cardholders’ spending data with marketers, a promise partner Goldman Sachs has also agreed to.
But there is a downside to that privacy policy. As a result of refusing to share data, information that goes into the Apple Card doesn’t come out. That means there’s no way to see it on the web or share it with other financial apps, like Mint or Personal Capital, that can help you budget and track spending across multiple bank and credit card accounts. There also doesn’t appear to be any way to generate an annual report, a helpful tool for tax preparation, though Apple could always add that feature later.
Another thing that could be added to the Apple Card later is discoloration, apparently. A close reading of the card’s care instructions has prompted concern that its white, titanium material may lose its luster when housed in leather wallets, or after rubbing against other cards. But after my initial week of Apple Card use—mostly through the app, which provides the best incentives—I can report that my “plastic” remains pristine.
With version 1.0 of the Apple Card, it’s a little hard to square the product with Apple CEO Tim Cook’s assertion of “the most significant change in the credit card experience in 50 years.” But for people who spend a lot with Apple, it’s a solid addition to your wallet—at least your mobile one.

More must-read stories from Fortune:

—[A rare tech company where women dominate
](https://fortune.com/2019/08/21/thirdlove-tech-diversity-women/)—Walmart CEO: VR training helped save lives in the El Paso shooting
—Can Apple afford to make its streaming video service free?
—How to compete with technology in the [age of automation
](https://fortune.com/2019/08/18/job-replaced-by-automation-artificial-intelligence-ai/)—[Disney’s streaming service](https://fortune.com/2019/08/19/disney-streaming-service/) won’t be available on the most popular streaming devices
Catch up with Data Sheet, _Fortune_‘s daily digest on the business of tech.
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