15 Minute Stochastic Forex Scalping Trading Strategy

Shorting Noobs - Common Trend Following Mistakes I'm Trading Against.

Shorting Noobs - Common Trend Following Mistakes I'm Trading Against.
Part [1] [2] [3]

Not much in terms of adjustments to add from previous post. I'm going to implement all risk adjustments at the weekend. In the meantime I've used some manual hedging to prevent from over exposure.
In this post I'll talk more about the ideal trades I am looking for. The mistakes people make at these areas, and how to build forward looking trade plans so you are less likely to find yourself caught in one of these market traps. I do consider these to be traps. I think price routinely moves in ways that induce market participants to take losing positions. I think this is done in algorithmic fashion and this means it leaves clues in forms of recurring ways laying traps.
This is just an opinion. I don't know.

First we will examine the classic structure of a trend. All examples will use a downtrend.

Basic Recurring Trend Structure:

Basic Trend Structure

Most of you will have seen this before, and probably recognise it as Elliot Wave theory (EWT). Whether or not you think EWT is valid or not, there are some things I think all of us can agree on. That is for the market to be in a downtrend, it has to keep making new lows. If it doesn't, it's not in a downtrend anymore. You'll also probably agree there are retraces in moves. That not often are new lows consistently made without any retrace. In a broad sense, this is all EWT is describing, which makes it noteworthy in good trending conditions.

Here are the points where most mistakes are made by traders in EWT cycles.


Trend Best/Worst Entries

All areas marked off in orange are places where it's easy to make mistakes.

Looking closer, this is what the more detailed price action on these sorts of moves tend to look like on lower timeframes.


Detailed Best/Worst Entries

Brown boxes are where buying mistakes are made. Purple circles are where selling mistakes are made.
We'll look a bit closer at what the specific mistakes are usually based on (conventional technical analysis theories) soon. First here is an example of this on a real AUDUSD chart.


AUDUSD Example Chart
This is a 45 minute chart, so the swings are not as detailed as the ones I've drawn (mine are more like 15 min), but you should be able to see how this concept can be transferred over onto a real chart. All of us who have been trading for a while know there are times we have made these mistakes. Everyone has ended up selling the bottom pip, or getting stopped out right before it reversed. Many of these times (in a trending market) fit into these areas.
This is not just curve fitting. Using rules to help to describe these conditions to pick the best trades and trading against the trades strategy providers offer, I picked up these trades. This was not perfect, what I'm doing needs a lot of work.

AUDUSD Trade

Here we can see a couple few of the mistakes. The green lines are profits and orange lines are losses. Here shorting these mistakes has done quite well on the spike out low. It's hard to see, but it also got a lot of good buys at the low. There are some losses at the high, but there is a far larger position accumulated around the mistake level.

AUDUSD Result
See previous analysis on these trades in [2] [3]
A big trend leg followed this build up of positions and hit take profits where stops were set under the low. This is where people start to sell, but this is also usually a mistake to sell immediately after this breakout.

The types of mistakes made are due to a handful of concepts. Here I've numbered them.

Mistake Types
Rules/Rational people have in mind making these mistakes.

1 - Breakout/new high relative to recent leg / stops above previous high on sell/ previous low on buy.
2 - Single candle price action mistake.
3 - Breakout trading rushing in / stops go under recent supports/ over recent resistance.
4 - Break and retest.
5 - Deep correction.

Everything listed above has the potential to be very useful and valid in a technical analysis based strategy. However, in some contexts they are literally the very worst thing you can do. That's the thing about trading, you can do the same thing at different times and get wildly different results. What I'm trying to do here is not find people who lose every trade (I want them to win overall, actually. So I can keep copying them). I just want to work out ways to bet against mistakes they are likely to make. I think people will make these mistakes more reliably than an automated system will pick up trades.
I should add that most of these areas the mistakes happen at will be hit with a lot of velocity. This I think is what triggers the mistakes from so many impulsive traders. The market will amble along in a slow non-threatening / uninteresting sort of way, then suddenly all in a rush make these moves that imply something BIG is happening in a certain direction, when actually it's just about to move against these very positions if you take them. Velocity is one of my key filters.

Let's talk about the end of the two leg correction, this is one of the places I think most of the money is made and lost. At this point in an EWT cycle, the market is gearing up to enter it's strongest move. The best move to trade, and the smart market is going to need to get people on the wrong side. This is usually achieved with three things. One, the market makes it's first false reversal from a 50% retrace, and then moves with a lot of velocity into the 61.8% fib (briefly described in [2]). Then there's a second false breakout with price trading a little over the 61.8%, followed by a price crash into new lows.
The interesting thing about this move is if you speak to anyone with any sort of interest in EWT, they will tell you this move often completes with a news spike. There is positive news, the market moves quickly in the direction it "should" and then quickly makes a rapid reversal. Sometimes the move on the news makes absolutely no sense what-so-ever fundamentally. but does strike these areas.
Here is the Brexit chart.
Brexit trend continuation from 61.8% spike out pattern

Let's go further back.

Scotland Vote High
Here is where GBP made it's high point. This was after the fantastic fundamental news (apparently) that Scotland was staying in the UK. Price shot up, then began to heavily downtrend. I've marked in the start to the previous swing with a line, if you check these fibs you'll see it fits with the mistake. We are now in the part of cycle where GBP is aggressive pulling away from the range where the false reversals happened. This is punishing those who bought in this range, and we should expect to see it end in a violent spike down. Remember the people who thought buying Sterling after Brexit was free money? Nah uh.
This happens a lot. When it happens I see people trying to explain it with all sorts of theories. Usually involving the saying "priced in already". People often refer to these in aloof and vague ways, as if there was no way we could have ever known, and it's certainly not worth trying to forecast these sorts of things ... but next time you see this, have a quick check and see if we happened to be in a correction that spiked out a 50% high and reversed around 61.8%. It is wiser to look at what happened than take wild guesses as to why. I am not saying that it always it, nor am I saying it works like magic. I'm just saying it can be quantified. When someone says, "Well you see it was not what was said, or the number, but what was inferred ...", really means nothing. It's an opinion. We're better to look for things we can test, in my view at least.

So, let me talk you through the mental mindset of people when they're making these mistakes. I'm going to use this big Sterling chart, so this will also be a bit of a price forecast.


Mistakes Thinking Patterns.
1 - Price has been going up strongly, it's retraced and there is a single candle PA buy signal. Sets people up to take a horrible trade.
2 - Price has been falling hard and made yet another breakout, it's an easy sell ...
3 - This has fell too far, it's a reversal now. Look how strong it is.
4 - This is a strong breakout and this must be the start of a bigger move.
5 - Wow, it's broke the lows and look how hard it's falling, big sell time.

I think we will see stage 5 complete around 1.190. I think we may be due a fast move into this. Maybe in the coming week or two. It would be typical of the spike nature of end of this sort of move that this will be a single candle of over 150 pips that fills this. Being and holding GBPUSD shorts targeting 1.196 seems a great idea to me.

These five mistakes, made at these handful of areas are the ones I wanting to trade against, and if you'd like to be a profitable trader, are the places you should be looking for entries.
submitted by whatthefx to Forex [link] [comments]

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    3. Models and black swans - discussion (3 points, 0 comments)
    4. Normal distribution assumption for options pricing (2 points, 3 comments)
    5. Technical analysis for cryptocurrency market? (2 points, 4 comments)
    6. A counter argument to models by Nassim Taleb (1 point, 0 comments)
    7. Are we demandas to use the sample for part 1? (1 point, 1 comment)
    8. Benchmark for "trusting" your trading algorithm (1 point, 5 comments)
    9. Don't these two statements on the project description contradict each other? (1 point, 2 comments)
    10. Forgot my TA (1 point, 6 comments)
  39. 24 points, 11 submissions: nurobezede
    1. Best way to obtain survivor bias free stock data (8 points, 1 comment)
    2. Please confirm Midterm is from October 13-16 online with proctortrack. (5 points, 2 comments)
    3. Are these DTlearner Corr values good? (2 points, 6 comments)
    4. Testing gen_data.py (2 points, 3 comments)
    5. BagLearner of Baglearners says 'Object is not callable' (1 point, 8 comments)
    6. DTlearner training RMSE none zero but almost there (1 point, 2 comments)
    7. How to submit analysis using git and confirm it? (1 point, 2 comments)
    8. Passing kwargs to learners in a BagLearner (1 point, 5 comments)
    9. Sampling for bagging tree (1 point, 8 comments)
    10. code failing the 18th test with grade_learners.py (1 point, 6 comments)
  40. 24 points, 4 submissions: AeroZach
    1. questions about how to build a machine learning system that's going to work well in a real market (12 points, 6 comments)
    2. Survivor Bias Free Data (7 points, 5 comments)
    3. Genetic Algorithms for Feature selection (3 points, 5 comments)
    4. How far back can you train? (2 points, 2 comments)
  41. 23 points, 9 submissions: vsrinath6
    1. Participation check #3 - Haven't seen it yet (5 points, 5 comments)
    2. What are the tasks for this week? (5 points, 12 comments)
    3. No projects until after the mid-term? (4 points, 5 comments)
    4. Format / Syllabus for the exams (2 points, 3 comments)
    5. Has there been a Participation check #4? (2 points, 8 comments)
    6. Project 3 not visible on T-Square (2 points, 3 comments)
    7. Assess learners - do we need to check is method implemented for BagLearner? (1 point, 4 comments)
    8. Correct number of days reported in the dataframe (should be the number of trading days between the start date and end date, inclusive). (1 point, 0 comments)
    9. RuntimeError: Invalid DISPLAY variable (1 point, 2 comments)
  42. 23 points, 8 submissions: nick_algorithm
    1. Help with getting Average Daily Return Right (6 points, 7 comments)
    2. Hint for args argument in scipy minimize (5 points, 2 comments)
    3. How do you make money off of highly volatile (high SDDR) stocks? (4 points, 5 comments)
    4. Can We Use Code Obtained from Class To Make Money without Fear of Being Sued (3 points, 6 comments)
    5. Is the Std for Bollinger Bands calculated over the same timespan of the Moving Average? (2 points, 2 comments)
    6. Can't run grade_learners.py but I'm not doing anything different from the last assignment (?) (1 point, 5 comments)
    7. How to determine value at terminal node of tree? (1 point, 1 comment)
    8. Is there a way to get Reddit announcements piped to email (or have a subsequent T-Square announcement published simultaneously) (1 point, 2 comments)
  43. 23 points, 1 submission: gong6
    1. Is manual strategy ready? (23 points, 6 comments)
  44. 21 points, 6 submissions: amchang87
    1. Reason for public reddit? (6 points, 4 comments)
    2. Manual Strategy - 21 day holding Period (4 points, 12 comments)
    3. Sharpe Ratio (4 points, 6 comments)
    4. Manual Strategy - No Position? (3 points, 3 comments)
    5. ML / Manual Trader Performance (2 points, 0 comments)
    6. T-Square Submission Missing? (2 points, 3 comments)
  45. 21 points, 6 submissions: fall2017_ml4t_cs_god
    1. PSA: When typing in code, please use 'formatting help' to see how to make the code read cleaner. (8 points, 2 comments)
    2. Why do Bollinger Bands use 2 standard deviations? (5 points, 20 comments)
    3. How do I log into the [email protected]? (3 points, 1 comment)
    4. Is midterm 2 cumulative? (2 points, 3 comments)
    5. Where can we learn about options? (2 points, 2 comments)
    6. How do you calculate the analysis statistics for bps and manual strategy? (1 point, 1 comment)
  46. 21 points, 5 submissions: Jmitchell83
    1. Manual Strategy Grades (12 points, 9 comments)
    2. two-factor (3 points, 6 comments)
    3. Free to use volume? (2 points, 1 comment)
    4. Is MC1-Project-1 different than assess_portfolio? (2 points, 2 comments)
    5. Online Participation Checks (2 points, 4 comments)
  47. 21 points, 5 submissions: Sergei_B
    1. Do we need to worry about missing data for Asset Portfolio? (14 points, 13 comments)
    2. How do you get data from yahoo in panda? the sample old code is below: (2 points, 3 comments)
    3. How to fix import pandas as pd ImportError: No module named pandas? (2 points, 4 comments)
    4. Python Practice exam Question 48 (2 points, 2 comments)
    5. Mac: "virtualenv : command not found" (1 point, 2 comments)
  48. 21 points, 3 submissions: mharrow3
    1. First time reddit user .. (17 points, 37 comments)
    2. Course errors/types (2 points, 2 comments)
    3. Install course software on macOS using Vagrant .. (2 points, 0 comments)
  49. 20 points, 9 submissions: iceguyvn
    1. Manual strategy implementation for future projects (4 points, 15 comments)
    2. Help with correlation calculation (3 points, 15 comments)
    3. Help! maximum recursion depth exceeded (3 points, 10 comments)
    4. Help: how to index by date? (2 points, 4 comments)
    5. How to attach a 1D array to a 2D array? (2 points, 2 comments)
    6. How to set a single cell in a 2D DataFrame? (2 points, 4 comments)
    7. Next assignment after marketsim? (2 points, 4 comments)
    8. Pythonic way to detect the first row? (1 point, 6 comments)
    9. Questions regarding seed (1 point, 1 comment)
  50. 20 points, 3 submissions: JetsonDavis
    1. Push back assignment 3? (10 points, 14 comments)
    2. Final project (9 points, 3 comments)
    3. Numpy versions (1 point, 2 comments)
  51. 20 points, 2 submissions: pharmerino
    1. assess_portfolio test cases (16 points, 88 comments)
    2. ML4T Assignments (4 points, 6 comments)

Top Commenters

  1. tuckerbalch (2296 points, 1185 comments)
  2. davebyrd (1033 points, 466 comments)
  3. yokh_cs7646 (320 points, 177 comments)
  4. rgraziano3 (266 points, 147 comments)
  5. j0shj0nes (264 points, 148 comments)
  6. i__want__piazza (236 points, 127 comments)
  7. swamijay (227 points, 116 comments)
  8. _ant0n_ (205 points, 149 comments)
  9. ml4tstudent (204 points, 117 comments)
  10. gatechben (179 points, 107 comments)
  11. BNielson (176 points, 108 comments)
  12. jameschanx (176 points, 94 comments)
  13. Artmageddon (167 points, 83 comments)
  14. htrajan (162 points, 81 comments)
  15. boyko11 (154 points, 99 comments)
  16. alyssa_p_hacker (146 points, 80 comments)
  17. log_base_pi (141 points, 80 comments)
  18. Ran__Ran (139 points, 99 comments)
  19. johnsmarion (136 points, 86 comments)
  20. jgorman30_gatech (135 points, 102 comments)
  21. dyllll (125 points, 91 comments)
  22. MikeLachmayr (123 points, 95 comments)
  23. awhoof (113 points, 72 comments)
  24. SharjeelHanif (106 points, 59 comments)
  25. larrva (101 points, 69 comments)
  26. augustinius (100 points, 52 comments)
  27. oimesbcs (99 points, 67 comments)
  28. vansh21k (98 points, 62 comments)
  29. W1redgh0st (97 points, 70 comments)
  30. ybai67 (96 points, 41 comments)
  31. JuanCarlosKuriPinto (95 points, 54 comments)
  32. acschwabe (93 points, 58 comments)
  33. pharmerino (92 points, 47 comments)
  34. jgeiger (91 points, 28 comments)
  35. Zapurza (88 points, 70 comments)
  36. jyoms (87 points, 55 comments)
  37. omscs_zenan (87 points, 44 comments)
  38. nurobezede (85 points, 64 comments)
  39. BelaZhu (83 points, 50 comments)
  40. jason_gt (82 points, 36 comments)
  41. shuang379 (81 points, 64 comments)
  42. ggatech (81 points, 51 comments)
  43. nitinkodial_gatech (78 points, 59 comments)
  44. harshsikka123 (77 points, 55 comments)
  45. bkeenan7 (76 points, 49 comments)
  46. moxyll (76 points, 32 comments)
  47. nelsongcg (75 points, 53 comments)
  48. nickzelei (75 points, 41 comments)
  49. hunter2omscs (74 points, 29 comments)
  50. pointblank41 (73 points, 36 comments)
  51. zheweisun (66 points, 48 comments)
  52. bs_123 (66 points, 36 comments)
  53. storytimeuva (66 points, 36 comments)
  54. sva6 (66 points, 31 comments)
  55. bhrolenok (66 points, 27 comments)
  56. lingkaizuo (63 points, 46 comments)
  57. Marvel_this (62 points, 36 comments)
  58. agifft3_omscs (62 points, 35 comments)
  59. ssung40 (61 points, 47 comments)
  60. amchang87 (61 points, 32 comments)
  61. joshuak_gatech (61 points, 30 comments)
  62. fall2017_ml4t_cs_god (60 points, 50 comments)
  63. ccrouch8 (60 points, 45 comments)
  64. nick_algorithm (60 points, 29 comments)
  65. JetsonDavis (59 points, 35 comments)
  66. yjacket103 (58 points, 36 comments)
  67. hilo260 (58 points, 29 comments)
  68. coolwhip1234 (58 points, 15 comments)
  69. chvbs2000 (57 points, 49 comments)
  70. suman_paul (57 points, 29 comments)
  71. masterm (57 points, 23 comments)
  72. RolfKwakkelaar (55 points, 32 comments)
  73. rpb3 (55 points, 23 comments)
  74. venkatesh8 (54 points, 30 comments)
  75. omscs_avik (53 points, 37 comments)
  76. bman8810 (52 points, 31 comments)
  77. snladak (51 points, 31 comments)
  78. dfihn3 (50 points, 43 comments)
  79. mlcrypto (50 points, 32 comments)
  80. omscs-student (49 points, 26 comments)
  81. NellVega (48 points, 32 comments)
  82. booglespace (48 points, 23 comments)
  83. ccortner3 (48 points, 23 comments)
  84. caa5042 (47 points, 34 comments)
  85. gcalma3 (47 points, 25 comments)
  86. krushnatmore (44 points, 32 comments)
  87. sn_48 (43 points, 22 comments)
  88. thenewprofessional (43 points, 16 comments)
  89. urider (42 points, 33 comments)
  90. gatech-raleighite (42 points, 30 comments)
  91. chrisong2017 (41 points, 26 comments)
  92. ProudRamblinWreck (41 points, 24 comments)
  93. kramey8 (41 points, 24 comments)
  94. coderafk (40 points, 28 comments)
  95. niufen (40 points, 23 comments)
  96. tholladay3 (40 points, 23 comments)
  97. SaberCrunch (40 points, 22 comments)
  98. gnr11 (40 points, 21 comments)
  99. nadav3 (40 points, 18 comments)
  100. gt7431a (40 points, 16 comments)

Top Submissions

  1. [Project Questions] Unit Tests for assess_portfolio assignment by reyallan (58 points, 52 comments)
  2. [Project Questions] Unit Tests for optimize_something assignment by agifft3_omscs (53 points, 94 comments)
  3. Proper git workflow by jan-laszlo (43 points, 19 comments)
  4. Exam 2 Information by yokh_cs7646 (39 points, 40 comments)
  5. A little more on Pandas indexing/slicing ([] vs ix vs iloc vs loc) and numpy shapes by davebyrd (37 points, 10 comments)
  6. Project 1 Megathread (assess_portfolio) by davebyrd (34 points, 466 comments)
  7. defeat_learner test case by swamijay (34 points, 38 comments)
  8. Project 2 Megathread (optimize_something) by tuckerbalch (33 points, 475 comments)
  9. project 3 megathread (assess_learners) by tuckerbalch (27 points, 1130 comments)
  10. Deadline extension? by johannes_92 (26 points, 40 comments)

Top Comments

  1. 34 points: jgeiger's comment in QLearning Robot project megathread
  2. 31 points: coolwhip1234's comment in QLearning Robot project megathread
  3. 30 points: tuckerbalch's comment in Why Professor is usually late for class?
  4. 23 points: davebyrd's comment in Deadline extension?
  5. 20 points: jason_gt's comment in What would be a good quiz question regarding The Big Short?
  6. 19 points: yokh_cs7646's comment in For online students: Participation check #2
  7. 17 points: i__want__piazza's comment in project 3 megathread (assess_learners)
  8. 17 points: nathakhanh2's comment in Project 2 Megathread (optimize_something)
  9. 17 points: pharmerino's comment in Midterm study Megathread
  10. 17 points: tuckerbalch's comment in Midterm grades posted to T-Square
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