AlphaGPT

SkillAI & models

"Route AlphaGPT data ingestion, factor mining, live strategy, and

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the AlphaGPT skill

What this skill tells your AI

The instructions your AI receives, as published by vectorspacelab/arex-skill in skills/repositories/repo-skills/alpha-gpt/SKILL.md and read by ahel’s review.

Use this repo skill for the AlphaGPT crypto-quant system. It helps future agents work with the repository's four main workflows without reopening the source tree for basic navigation:

  • data ingestion from Birdeye/DexScreener into Postgres/Timescale
  • factor-token mining, StackVM formula execution, and training artifacts
  • live strategy, risk, portfolio state, and Solana/Jupiter execution
  • Streamlit dashboard monitoring and local fixture generation

Start here

  1. Read references/repo-provenance.md when you need to check whether this skill still matches the current AlphaGPT checkout or before a refresh.
  2. Read references/install-and-operations.md for the public install baseline, dependency pin note, environment expectations, and safe import check.
  3. Read references/troubleshooting.md for cross-cutting import, dependency, credential, DB, and live-operation failures.
  4. Read references/experiments-and-exclusions.md for optional research scripts and explicit exclusions.
  5. Run scripts/alpha_gpt_env_check.py when you want an offline, no-network preflight across env vars, files, and imports.

Route map

  • data-pipeline: OHLCV ingestion, provider config, DDL preview, and safe preflight checks.
  • factor-mining: feature engineering, formula grammar, StackVM execution, backtest scoring, and training artifacts.
  • live-strategy: strategy runner, portfolio state, risk controls, and guarded Solana/Jupiter execution.
  • dashboard-ops: Streamlit dashboard, read-only state inspection, and local fixture generation.

Public baseline

  • Install from the repository root with python -m pip install -r requirements.txt.
  • The source tree is not packaged as a pyproject.toml/setup.py distribution, so use the checkout itself as the import root when validating a local clone.
  • If execution.trader fails on the newest Solana wheel because TokenAccountOpts is missing, consult the live-strategy troubleshooting notes for the verified solana==0.36.12 and websockets==15.0.1 compatibility pin.
  • Core runtime facts were verified from the current source: source import roots are data_pipeline, model_core, execution, strategy_manager, and dashboard.

Minimal import check

From a checkout that is on PYTHONPATH or otherwise visible to the environment, a safe smoke check is:

python -I -c "import data_pipeline, model_core, execution, strategy_manager, dashboard"

If you need a deeper no-network preflight, use the bundled env checker instead of touching databases, RPC endpoints, or wallets.

What this skill does not do automatically

  • It does not start the live strategy runner.
  • It does not fetch Birdeye data or write Postgres rows as a default check.
  • It does not submit Solana transactions.
  • It does not launch Streamlit for you.
  • It does not install optional research dependencies from requirements-optional.txt unless you explicitly choose the optional research workflows.

Safe reading order for common tasks

  • "Why is no market data showing up?" -> data-pipeline troubleshooting.
  • "How do I inspect or validate a formula?" -> factor-mining references and smoke script.
  • "How do I configure the bot safely?" -> live-strategy reference and offline config checker.
  • "Why is the dashboard empty?" -> dashboard-ops reference and fixture generator.

Notes for later refreshes

  • Read references/repo-provenance.md and compare the current checkout before deciding whether to refresh this skill.
  • If the repository adds package metadata, new workflows, or additional service/backends, route those through a refresh rather than widening the existing instructions silently.

Signals

GitHub stars
266
Forks
21
Last commit
Sep 2026

ahel review

  • K1binfo
    installs-packages
  • K6low
    bundled executables the agent is told to run
  • K1binfo
    installs-packages (in references/install-and-operations.md)
  • K1binfo
    installs-packages (in references/troubleshooting.md)

Automated review, not a security audit. Ruleset v1+k2.

Advanced
Catalog kind
skill
Gateway key
alpha-gpt
Source
github.com/vectorspacelab/arex-skill