I built ZcoreAI, a quant stock scanner that applies
Donchian-Weighted regression channels to compute
Z-scores across multiple timeframes simultaneously.
The idea: instead of eyeballing charts, you get a
matrix of Z-score values per ticker per timeframe
in one scan — so you can spot statistically
overbought/oversold signals across your watchlist
in seconds.
How it works:
- Pick timeframes (1m to 1wk)
- Pick tickers (or use the preloaded free watchlist)
- Hit scan — regression channels + Z-scores are
computed client-side via yfinance
Stack: Python, Streamlit, yfinance, NumPy.
Deployed on Render.
Free tier available, no signup required to scan.
Happy to discuss the regression channel methodology
or any feedback on the approach.
Hey HN,
I built ZcoreAI, a quant stock scanner that applies
Donchian-Weighted regression channels to compute
Z-scores across multiple timeframes simultaneously.
The idea: instead of eyeballing charts, you get a
matrix of Z-score values per ticker per timeframe
in one scan — so you can spot statistically
overbought/oversold signals across your watchlist
in seconds.
How it works:
- Pick timeframes (1m to 1wk)
- Pick tickers (or use the preloaded free watchlist)
- Hit scan — regression channels + Z-scores are
computed client-side via yfinance
Stack: Python, Streamlit, yfinance, NumPy.
Deployed on Render.
Free tier available, no signup required to scan.
Happy to discuss the regression channel methodology
or any feedback on the approach.
I built ZcoreAI, a quant stock scanner that applies Donchian-Weighted regression channels to compute Z-scores across multiple timeframes simultaneously.
The idea: instead of eyeballing charts, you get a matrix of Z-score values per ticker per timeframe in one scan — so you can spot statistically overbought/oversold signals across your watchlist in seconds.
How it works: - Pick timeframes (1m to 1wk) - Pick tickers (or use the preloaded free watchlist) - Hit scan — regression channels + Z-scores are computed client-side via yfinance
Stack: Python, Streamlit, yfinance, NumPy. Deployed on Render.
Free tier available, no signup required to scan.
Happy to discuss the regression channel methodology or any feedback on the approach.
Hey HN,
I built ZcoreAI, a quant stock scanner that applies Donchian-Weighted regression channels to compute Z-scores across multiple timeframes simultaneously.
The idea: instead of eyeballing charts, you get a matrix of Z-score values per ticker per timeframe in one scan — so you can spot statistically overbought/oversold signals across your watchlist in seconds.
How it works: - Pick timeframes (1m to 1wk) - Pick tickers (or use the preloaded free watchlist) - Hit scan — regression channels + Z-scores are computed client-side via yfinance
Stack: Python, Streamlit, yfinance, NumPy. Deployed on Render.
Free tier available, no signup required to scan.
Happy to discuss the regression channel methodology or any feedback on the approach.
https://www.zcoreai.com/