# deependersingla/deep_trader

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Repository: https://github.com/deependersingla/deep_trader
Canonical: https://ross.abutalabs.com/products/deep_trader
Language: Python
License Family: other
Last push: 2018-01-22T14:35:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3735, "days_push": 3145, "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 1499, forks 496 (observed 2026-08-28T04:04:54.090924+00:00)

## What it is
A Python research project applying deep reinforcement learning (DQN and policy gradient methods) to stock market trading, exploring whether an agent can learn to read tape. It uses TensorFlow to train trading agents on historical market data.

## Use cases
- train a reinforcement learning agent to trade stocks
- experiment with DQN on financial time series
- learn how policy gradient methods apply to trading
- reproduce reinforcement learning trading research
- explore whether RL can learn tape reading

## When to choose
- you want a reference implementation of DQN or policy gradients for trading research
- you are studying reinforcement learning applied to market data
- you want a small educational codebase to extend with your own RL experiments

## When to avoid
- you need production-ready or maintained trading software
- you require a supported license for commercial use
- you expect active development or author support
- you need a robust backtesting framework with modern tooling

## Facets
- artifact type: application
- maturity: abandoned
- function: reinforcement-learning, machine-learning, trading
- domain: reinforcement-learning, fintech, machine-learning
- platform: python
- tags: dqn, policy-gradient, tensorflow, stock-trading, tape-reading, research-project

## Member repositories
- deependersingla/deep_trader (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:54.090924+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 2026-08-30T04:33:02.457797+00:00, confidence not recorded.
  - readme: https://github.com/deependersingla/deep_trader (fetched 2026-08-28T04:04:54.090924+00:00, sha c6b04f1920ad)
- Data as of 2026-08-30T08:39:29.467469+00:00.