Volume Spread Analysis with Python | Algorithmic Trading Indicator
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- Опубликовано: 3 июл 2024
- 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.
Patreon: / neurotrader
Code: github.com/neurotrader888/VSA...
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.
Very cool analysis! I might add this indicator to my ML predictive model.
I love the fact you explain everything in detail , Really disserve the Sub, keep it up
Good job, getting rid of any form of serial correlation, when doing the scatterplot, thereby avoiding spurious findings!
Great video
Thank you ✌️
thank you 🤩🤩
You should look into an algorithm called sax, symbolic aggregate approximation
Awesome
Great video , what do you have planned for this channel in the future ?
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
I run my stuff on a AWS ec2 and linode. Linode is generally cheaper. I've never had an issue with either.
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.
I've done a little bit of work with order flow, but nothing rigorous. I'll check those books out, thanks!
Mr. neurotrader, how can i use this in tradingview?
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🎉.
I'm not familiar. I'll take a look. If the pinescript is open source and it is interesting enough I may make a video.
Would you mind if I created a TradingView indicator based on this? Attribution would be provided of course.
thanks! how i use this code?
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.
I'm intrigued. We should open a forum. Did you conduct other tests other than out of sample and how much out of sample data did you use to validate the model?
@@MarcoCosta-ud4uu you (or neurotrader) could create a discord channel to discuss and share further finding