# Rachnog/Deep-Trading

Algorithmic trading with deep learning experiments

Repository: https://github.com/Rachnog/Deep-Trading
Canonical: https://ross.abutalabs.com/products/deep-trading
Language: OpenEdge ABL
License Family: other
Last push: 2018-08-07T15:24:46+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": 3728, "days_push": 2948, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1463, forks 685 (observed 2026-08-28T04:04:47.810000+00:00)

## What it is
A collection of deep learning experiments for algorithmic trading, focused on time series forecasting with neural networks. It serves as an educational codebase demonstrating how to apply deep learning models to financial market prediction.

## Use cases
- learn how to forecast stock prices with deep learning
- experiment with neural networks on financial time series
- build a baseline trading strategy with LSTMs
- study examples of time series prediction in Python
- prototype algorithmic trading models

## When to choose
- you want educational example code for deep learning on financial time series
- you need a starting point for forecasting experiments with neural networks

## When to avoid
- you need production-ready trading software
- you require a maintained library with support and license
- you want guaranteed profitable trading strategies

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: machine-learning, deep-learning, trading
- domain: machine-learning, fintech, time-series
- platform: python
- tags: algorithmic-trading, time-series-forecasting, experiments, educational

## Member repositories
- Rachnog/Deep-Trading (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:47.810000+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 2026-08-30T04:35:11.483563+00:00, confidence not recorded.
  - readme: https://github.com/Rachnog/Deep-Trading (fetched 2026-08-28T04:04:47.810000+00:00, sha 6c11a189ff7b)
- Data as of 2026-08-30T08:39:29.467469+00:00.