# LastAncientOne/Stock_Analysis_For_Quant

Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

Repository: https://github.com/LastAncientOne/Stock_Analysis_For_Quant
Canonical: https://ross.abutalabs.com/products/stock_analysis_for_quant
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: stock-market, stock-analysis, trading-strategies, technical-analysis, technical-indicators, stock-prediction, signals, quantitative-finance, quantitative-trading, quantitative-analysis, financial-analysis, financial-data, financial-engineering, excel, r, python3, powerbi, tableau, vba
Last push: 2025-05-04T18:46:47+00:00

## Health v2 (maintenance only)
Score: 41/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 19, release rhythm 35, longevity 100
- inputs: {"age_days": 2741, "days_push": 486, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2053, forks 515 (observed 2026-08-28T04:06:09.737921+00:00)

## What it is
A collection of stock analysis examples and tutorials implemented across Excel, Matlab, Power BI, Python, R, and Tableau. It covers technical, fundamental, and quantitative analysis, candlestick patterns, and trading strategy development.

## Use cases
- analyze stock prices with python
- learn technical analysis indicators
- backtest trading strategies
- detect candlestick patterns
- build stock dashboards in power bi or tableau
- learn quantitative finance with r
- stock price prediction examples

## When to choose
- learning stock analysis techniques across multiple tools and languages
- finding reference implementations of technical indicators and trading strategies
- exploring quantitative finance concepts with worked examples

## When to avoid
- needing production-ready trading software or a maintained library
- requiring real-time market data feeds or broker integration
- expecting a single installable package rather than notebooks and spreadsheets

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-visualization, analytics, machine-learning, trading
- domain: fintech, data-science, tutorials, analytics
- platform: python, cross-platform
- tags: stock-analysis, quantitative-finance, technical-analysis, trading-strategies, excel, matlab, powerbi, tableau, jupyter-notebook, financial-data

## Member repositories
- LastAncientOne/Stock_Analysis_For_Quant (main) score 41

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:09.737921+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:57:18.931928+00:00, confidence not recorded.
  - readme: https://github.com/LastAncientOne/Stock_Analysis_For_Quant (fetched 2026-08-28T04:06:09.737921+00:00, sha ee7ce4637adf)
- Data as of 2026-08-30T08:39:29.467469+00:00.