# huseinzol05/Stock-Prediction-Models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

Repository: https://github.com/huseinzol05/Stock-Prediction-Models
Canonical: https://ross.abutalabs.com/products/stock-prediction-models
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: lstm, lstm-sequence, evolution-strategies, stock-prediction-models, seq2seq, trading-bot, stock-market, stock-price-prediction, stock-price-forecasting, deep-learning-stock, deep-learning, monte-carlo, strategy-agent, learning-agents, monte-carlo-markov-chain
Archived: true
Last push: 2023-04-16T22:03:47+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3180, "days_push": 1235, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9480, forks 3046 (observed 2026-08-28T04:10:31.336691+00:00)

## What it is
A collection of Jupyter Notebook implementations of machine learning and deep learning models for stock price forecasting, including 30+ forecasting models (LSTM, GRU, Seq2seq, Transformers) and 23 trading agents (Q-learning, actor-critic, evolution strategies). It also includes trading simulations and data exploration studies.

## Use cases
- predict stock prices with LSTM or seq2seq models
- learn reinforcement learning trading agents
- backtest trading strategies in simulation
- compare deep learning models for time series forecasting
- study Q-learning and actor-critic for trading
- forecast t+N future stock values
- learn Monte Carlo simulation for stock markets

## When to choose
- you want reference implementations of many forecasting and trading-agent models in one place
- you are learning ML/DL applied to financial time series
- you want notebook-style code to experiment with and modify

## When to avoid
- you need production-ready, maintained trading software
- you expect guaranteed profitable trading signals
- you need a supported library with stable APIs and package releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, reinforcement-learning, data-science, simulation
- domain: fintech, machine-learning, deep-learning, data-science, time-series
- platform: python
- tags: stock-forecasting, trading-bots, lstm, seq2seq, reinforcement-learning-agents, monte-carlo, jupyter-notebooks, time-series-forecasting

## Member repositories
- huseinzol05/Stock-Prediction-Models (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:31.336691+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 2026-08-29T17:22:03.815698+00:00, confidence not recorded.
  - readme: https://github.com/huseinzol05/Stock-Prediction-Models (fetched 2026-08-28T04:10:31.336691+00:00, sha 483724647f5b)
- Data as of 2026-08-30T08:39:29.467469+00:00.