Volume Spread Analysis with Python | Algorithmic Trading Indicator

Indicator inspired from Volume Spread Analysis. This indicator models the relationship between the candle range and volume with a linear regression. Then it predicts the current candle's range with the current volume. If the actual range is higher than expected the indicator will output a positive value. If the actual range is lower than expected the indicator will output a negative value. Extreme values output by this indicator mark points of interest where trade entries/exits could be considered.

The content covered on this channel is NOT to be considered as any financial or investment advice. Past results are not necessarily indicative of future results. This content is purely for education/entertainment.

[ziad]

I love the fact you explain everything in detail, Really disserve the Sub, keep it up

[Sarunas-lv]

Very cool analysis! I might add this indicator to my ML predictive model.

[FerranFont]

Thanks for the amazing info you share. It's 20 y trading volume and price action. Won the Bookmap-willmot in 2014, Spanish Rankia in 2023 and2024. Trying to help, I think regarding scatter chart, we need to make volume relative to time (previous n candles) not absolute. Another advice is that active-initiative volume follows pasive-responsive price action, shold consider to concatetate both. Finally, use this set up in anyother set up mentioned in you channel, for instance, as a trigger entry for instance in a Bull Flag. Hope to help !

[BAWSMAAS]

Would you mind if I created a TradingView indicator based on this? Attribution would be provided of course.

[SliverHell]

Good job, getting rid of any form of serial correlation, when doing the scatterplot, thereby avoiding spurious findings!

[jamesspencer]

Have you tried implementing and testing Order Flow Analysis and Auction Theory methods? I've heard that the books "Markets in Profile" and "Mind Over Markets" by James Dalton are good for it.

[mdhcclothing]

This is great if your eyes aren’t trained to see it or if you’re in a very choppy market

[randomdude]

Great video, what do you have planned for this channel in the future ?

[labangithiaka]

I subscribed due to the nice explanation. Ok I have a question, in trading view have you checked out the lorentzian classification model and how can I mimic the same in python in real time and backtesting. If you can do a video of it I would be very grateful🎉.

[randomuser-xe]

great idea and great analysis. I assume you have live systems, can I ask you where do you run them on and how? I briefly ran a system on python anrywhere which offers a free plan and decent cpu time using binance apis, but it wasn't realiable as after some days you manually have to load everything again (I assume they reset something to prevent ghosts programs in the free tiers from running forver). I was wondering given your experience if there are services that serves us better than others

[sadface]

You should look into an algorithm called sax, symbolic aggregate approximation

[donyxd]

Mr. neurotrader, how can i use this in tradingview?

[AlgoTrader]

Can I reach out to you? I would love to share my findings and receive your feedback. I've trained a model that has produced incredibly promising results (out-of-sample performance, profit factor > 3.6, 70% win rate, with a risk/reward ratio of 2/3, and more). I trained the model using the top 50 cryptocurrencies (by volume on Binance) across various time frames, as well as daily stock data from S&P 500 stocks. Although the model's main purpose is crypto trading, I have also backtested it on some stocks (low time frame), and it has shown successful outcomes.