Auto-detect Support and Resistance in Python

[@k2icc]

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]

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!

[@jorgeplaza7938]

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.

[@daymaker_trading]

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

[@paulbrown5839]

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.

[@ThinAirElon]

High Quality Channel on Algo

[@ChadThackray]

Thanks!

[@TavoPlus]

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

[@UnbenutzerKanalname]

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

[@brich8110]

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

[@GlebKretinin]

Great job Chad! Thank you for sharing!

[@eige2992]

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?

[@karlbooklover]

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

[@kurdmanpar5706]

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.

[@mehranhub8312]

you are amazing man please keep going on your educations😍

[@anilmm2005]

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

[@SkyRiderJavelin]

Brilliant work Chad

[@2907866]

Great information and tutorial!

[@TwelfthRoot2]

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

[@rabbitgomes5817]

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]

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

[@rowanans]

very beautiful, Chad!

[@segunda_parte]

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?

[@tilttrader]

awesome video.

[@davidberry8511]

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?

[@dataml-trading4085]

Very helpful man !

[@valueray]

where is the jupyter?

[@misaghesh4963]

Thank you for your good video

[@mohitgarg4168]

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

[@zacharyperryman8663]

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

[@rickymacharm3437]

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

[@ChadThackray]

This is a good idea!

[@BeyondTheHodl]

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?

[@akbarboghani1]

Thanks Chad.

[@kailee-st2mf]

good job

[@maverick456-33]

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

[@ChadThackray]

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

[@SY41LENDRA]

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

[@orderflow2322]

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

[@paulbrown5839]

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]

What qre these rules?

[@paulbrown5839]

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

[@rezasadeghi2520]

thank you :)

[@siddharththakur5978]

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

[@dimayasnohurskyi2805]

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

[@ChadThackray]

Not yet!

[@monkeyloopingtrading2013]

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]

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]

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

[@DGM-DailyGameMoments]

Find it difficult to comprehend the logic and codes

[@bikramssharemarkettradingc7366]

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

[@FilSerge]

There's no ML, just pure statistics.