Auto-detect Support and Resistance in Python

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  • Опубликовано: 20 июн 2024
  • We use some simple machine learning techniques like Kernel Density estimators to automatically detect lines of support and resistance on a candlestick chart in python.
    00:00 - Intro
    00:40 - Demo and Methodology Overview
    02:59 - Extracting turning points
    08:09 - Clustering our support lines
    13:42 - Automated Bandwidth selection

Комментарии • 52

  • @GlebKretinin
    @GlebKretinin Год назад

    Great job Chad! Thank you for sharing!

  • @daymaker_bybit
    @daymaker_bybit Год назад +2

    Wow, this is gold! Thanks so much for sharing your knowledge ❤

  • @paulbrown5839
    @paulbrown5839 Год назад

    Very solid piece of work this Chad. I've just been through it again and will take some of your innovative ideas from it. Cheers.

  • @k2icc
    @k2icc Год назад +4

    Definitely a great job there and a very nice tutorial. I love technical indicators, but Python has given me a lot when it comes to manipulate or handle data for a visual presentation.

  • @whatsthememe2913
    @whatsthememe2913 Год назад

    This exactly what I was looking for! Chad you’re a lifesaver. Next step would be to create a strategy on this information in python!

  • @2907866
    @2907866 Год назад

    Great information and tutorial!

  • @TavoPlus
    @TavoPlus Год назад

    Excellent, I tried and personalized your code. Thank you, it helped me a lot!

  • @UnbenutzerKanalname
    @UnbenutzerKanalname Год назад

    i was trying to use a simple histogram for the min/max values, but the kernel is a great idea. thanks!

  • @jorgeplaza7938
    @jorgeplaza7938 11 месяцев назад

    Thanks for the video! Tried many aproaches to this matter and certainly yours is the best. KDE is a very interesting concept I now fully understand.

  • @SkyRiderJavelin
    @SkyRiderJavelin Год назад

    Brilliant work Chad

  • @karlbooklover
    @karlbooklover Год назад

    was just thinking about codifying patterns into vectorbt, this should work to implement as a strategy, thanks! :)

  • @naivetrader
    @naivetrader 10 месяцев назад

    awesome video.

  • @dataml-trading4085
    @dataml-trading4085 Год назад

    Very helpful man !

  • @rowanans
    @rowanans Год назад

    very beautiful, Chad!

  • @mehranhub8312
    @mehranhub8312 Год назад

    you are amazing man
    please keep going on your educations😍

  • @eige2992
    @eige2992 Год назад +1

    hello, great concept! i was in a confusion when I had to decide how to group the extrema values, but the concept of KDE in your video was very helpful. I guess you could try to find the peaks and trough separately instead of combining them together and running separate KDE algorithms to both of those values to differentiate support and resistance lines?

  • @misaghesh4963
    @misaghesh4963 Год назад

    Thank you for your good video

  • @kurdmanpar5706
    @kurdmanpar5706 Год назад

    Thank you for your good video. If this project is more complete to clearly define the lines of resistance and support, it will be very valuable. sincerely.

  • @BeyondTheHodl
    @BeyondTheHodl Год назад

    great video! Just wondering once you get the horizontal lines like you did at 13:38 how would I go about creating code so that the horizontal line stops once price hits it/crosses it?

  • @davidberry8511
    @davidberry8511 Год назад +1

    Great video, support and resistance is a big topic to code since it is so subjective. Where can I download the your code fom this lesson?

  • @anilmm2005
    @anilmm2005 Год назад +3

    Thanks lot Chad can you confirm how to get the code shown in the video?

  • @brich8110
    @brich8110 Год назад +1

    Really really helpfull video, also what a great idea that you can try is to use some AI to learn with a volume indicator (for example) what support and res are more likely to be "respected". Thansk alot for the vid
    (Pretty sure you can do it with scikit

  • @mohitgarg4168
    @mohitgarg4168 Год назад

    Fantastic work Chad..!! Can you please share a link to download this Jupyter notebook.

  • @TwelfthRoot2
    @TwelfthRoot2 Год назад

    i think weighting the S/R by volume could be helpful. great video.

  • @kailee-st2mf
    @kailee-st2mf 7 месяцев назад

    good job

  • @segunda_parte
    @segunda_parte 3 месяца назад

    Thank you very much for the video. How can I make the volume be taken into account to give more relevance to the candles that have greater volume?

  • @rezasadeghi2520
    @rezasadeghi2520 Год назад

    thank you :)

  • @ThinAirElon
    @ThinAirElon Год назад +1

    High Quality Channel on Algo

  • @akbarboghani1
    @akbarboghani1 Год назад

    Thanks Chad.

  • @rabbitgomes5817
    @rabbitgomes5817 7 месяцев назад +1

    hey Chad! nice video, and awesome approach with signal processing and kde! Can you supply the code behind the "draw_candle_chart" function? I believe thats in the top of the notebook, but can't see it clearly on the video.

    • @ChadThackray
      @ChadThackray  7 месяцев назад +1

      If you lookup "How to Create a Candlestick Chart Using Matplotlib in Python" that's the code I used.
      At some point I'm planning on going through and linking all the code from my videos, but will take a few months

  • @rickymacharm3437
    @rickymacharm3437 Год назад

    lovely video. for the interval could you test with ATR or standard deviation so it works with the present volatility of the asset

  • @maverick456-33
    @maverick456-33 Год назад

    Very Nice Tutorial which can be applied for Pivot and Fibonacci ! Is it possible for Triangle patterns as well with this ?

    • @ChadThackray
      @ChadThackray  Год назад +1

      With a bit of tweaking maybe.
      I'm going to try mess around with some other ML techniques to see if I get make it more reliable and recognise other patterns as well

  • @orderflow2322
    @orderflow2322 Год назад

    it would be cool to see how to determine the flat algorithmically on python

  • @dimayasnohurskyi2805
    @dimayasnohurskyi2805 Год назад

    Do you have something similar for another TA stuffs, like triangles and etc?

  • @valueray
    @valueray 11 месяцев назад +2

    where is the jupyter?

  • @zacharyperryman8663
    @zacharyperryman8663 Месяц назад

    Do you ever post your code so that followers can mess around on their own? Thanks!

  • @SY41LENDRA
    @SY41LENDRA Год назад

    i dont undesrtand about phyton coding, can you tell me what is the logic how to draw horisontal lines support and resistant in candlestick chart, i saw your video 14 minutes, i dont get the points

  • @siddharththakur5978
    @siddharththakur5978 10 месяцев назад

    Why aren't we using moving average instead of kernel density?

  • @monkeyloopingtrading2013
    @monkeyloopingtrading2013 Год назад

    thank you ! i like it! but please help me. Why this error?
    NameError Traceback (most recent call last)
    Input In [131], in ()
    ----> 1 draw_candle_chart(sample_df, price_range[peaks])
    NameError: name 'draw_candle_chart' is not defined.
    i'm so frustrated🙃😊

    • @ChadThackray
      @ChadThackray  Год назад +2

      You'll need to define a function to draw a candle chart, it's not built in. I copy-pasted the one from a website called statology and it worked pretty well

    • @CptnYarface
      @CptnYarface 9 дней назад

      He used a function called 'draw_candle_chart' to make the final plots with the support levels included. Statology has an example of how a candlestick chart can be made, but to overlay the support levels and create the "error bars," you will have to adapt the code yourself and create your own plotting function.. or maybe ChatGPT can help. I use mpf.plot(df, type='candle') to make candle charts, where df is a DataFrame with 'Open', 'High', 'Low', and 'Close' prices for the stock. Would have been nice to see the code for his function, for sure, but it will be an adventure trying to figure it out, at least lol

  • @paulbrown5839
    @paulbrown5839 Год назад +1

    What we want is the strongest support and resistance levels. There are rules about which level is strong, and which is weak. Apply those to filter the levels down to the best ones.

    • @FilSerge
      @FilSerge Год назад

      What qre these rules?

    • @paulbrown5839
      @paulbrown5839 Год назад

      ​@@FilSerge You can find them on YT..for example, a level near a round number is good, eg. 1.0850.

  • @bikramssharemarkettradingc7366

    Your video is great, but there is. Some theory part. Not very easy for persons new to machine learning

    • @FilSerge
      @FilSerge Год назад

      There's no ML, just pure statistics.

  • @DGM-DailyGameMoments
    @DGM-DailyGameMoments День назад

    Find it difficult to comprehend the logic and codes