ARES is a structured trading journal and review framework for documenting thesis, execution, risk, behavior, and post-trade performance. It is designed for local, privacy-first trade review rather than public performance marketing. The project emphasizes process quality: why a trade was considered, what invalidates the thesis, what risk was accepted, and what can be learned after the outcome. Public examples use synthetic data only.
Trading systems need more than entries and exits. They need review loops that capture context, risk state, decision quality, and rule adherence. ARES turns post-trade review into structured data so patterns can be audited without exposing private account details.
- Trade ledger schema for thesis, execution, risk, and review fields
- Thesis and invalidation logging
- Risk-state and emotional-state fields
- Behavioral gate and rule-adherence review
- Post-trade summary metrics
- CSV/JSON export for local analysis
- Privacy-first workflow for sensitive trade records
graph TD
A[Trade Entry] --> B[Journal Schema]
B --> C[Risk / Behavior Review]
C --> D[Metrics Summary]
D --> E[Export]
E --> F[Post-Trade Notes]
ares/
src/ares/
journal.py
schema.py
metrics.py
review.py
export.py
examples/
sample_trade_log.csv
demo_review.py
docs/
journal_schema.md
review_methodology.md
tests/
test_schema.py
- Python 3.10+
- CSV and JSON
- dataclasses and typed schemas
- pytest
git clone https://github.com/shawsignaldev/ares.git
cd ares
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e .
python examples/demo_review.pyWindows:
git clone https://github.com/shawsignaldev/ares.git
cd ares
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
pip install -e .
python examples\demo_review.py| Metric | Example |
|---|---|
| Trades reviewed | 3 |
| Rule adherence rate | 66.67% |
| Average emotional state | 4.67 |
| Review flag | needs_process_review |
Version 0.1 public trade-review release with typed records, CSV loading, process metrics, JSON export, examples, tests, privacy model, and analytics specification.
- Add richer journal import/export adapters
- Add local-only dashboards for review summaries
- Add typed validation for additional strategy-specific fields
- Add privacy filters for safe report generation
This repository is for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security. Trading involves risk, including possible loss of capital.