# LastAncientOne/Deep_Learning_Machine_Learning_Stock

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Repository: https://github.com/LastAncientOne/Deep_Learning_Machine_Learning_Stock
Canonical: https://ross.abutalabs.com/products/deep_learning_machine_learning_stock
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, machine-learning, stock-price-prediction, features-extraction, financial-engineering, prediction, feature-engineering, feature-extraction, feature-selection, stock-data, stock-trading, stock-analysis, stock-prices, stock-market, stock-prediction, algorithms, data-science, trading, technical-analysis, neural-network
Last push: 2024-03-01T00:12:23+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2895, "days_push": 916, "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 1785, forks 365 (observed 2026-08-28T04:05:35.992475+00:00)

## What it is
A collection of Jupyter Notebook tutorials and studies applying machine learning and deep learning algorithms to stock price prediction. It covers feature engineering, technical and fundamental analysis, and both long-term and short-term trading strategies.

## Use cases
- predict stock prices with machine learning
- learn deep learning for stock trading
- feature engineering for financial data
- apply neural networks to stock market data
- study technical analysis with Python
- build stock prediction models in Jupyter notebooks

## When to choose
- you want educational notebooks on ML/DL applied to stock prediction
- you are learning feature engineering and selection for financial time series
- you want to experiment with different algorithms on stock data

## When to avoid
- you need production-ready trading software or a backtesting framework
- you expect guaranteed profitable trading signals
- you need maintained, tested library code rather than study notebooks

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science, trading
- domain: fintech, machine-learning, data-science
- platform: python, cross-platform
- tags: stock-prediction, jupyter-notebooks, technical-analysis, feature-engineering, financial-machine-learning, educational, trading

## Member repositories
- LastAncientOne/Deep_Learning_Machine_Learning_Stock (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:35.992475+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 2026-08-30T03:24:11.476577+00:00, confidence not recorded.
  - readme: https://github.com/LastAncientOne/Deep_Learning_Machine_Learning_Stock (fetched 2026-08-28T04:05:35.992475+00:00, sha c1d2cdc51216)
- Data as of 2026-08-30T08:39:29.467469+00:00.