Trader Bot / Strategy Tester

A compact Python library and example bot that implements candle-based trading strategies and a minimal execution/persistence stack. It is intended for learning, experimenting with simple technical rules, and running small strategy simulations. The codebase includes example strategies, a lightweight trader, persistence helpers and a tiny API entrypoint.

Key files

• strategy.py — strategy implementations (e.g. StrategyAllCandles, StrategyHammerCandles)
• models.py — domain models (e.g. Position)
• trader.py — trade execution / simulation logic
• persistence.py — simple save/load helpers
• api.py — optional minimal HTTP interface (if present)
• main.py — example entrypoint to run the bot
• requirements.txt — Python dependencies
• Dockerfile — container image build
• tests/ — unit tests (e.g. tests/test_strategy.py, tests/test_trader.py)

What this project is for

• Learn how simple candle-based strategies can be implemented and tested in Python.
• Provide a minimal simulation/execution loop for developing and validating strategies.
• Demonstrate small, testable components (strategy, trader, persistence, models).

Requirements

• Python 3.8+ (project works on modern Python; CI uses 3.11 in this repo)
• pip (for installing dependencies)

Quickstart (Windows PowerShell)

1. Create and activate a virtual environment (recommended):

python -m venv .venv
.\.venv\Scripts\Activate.ps1

2. Install dependencies:

python -m pip install --upgrade pip
python -m pip install -r requirements.txt

Run the example bot

python main.py

Run the tests

python -m pytest tests/ --disable-warnings -q

Notes about tests

• The tests/ directory contains unit tests that exercise strategies and trader behavior. Running pytest will run the full suite.

Docker

• Build image locally:

docker build -t trader-bot:local .

• Run container (example):

docker run --rm -d --name traderBot trader-bot:local

Development & project structure

• Strategies: check strategy.py for example implementations and to add new rules.
• Models: models.py contains data structures used across components (positions, orders, etc.).
• Trader: trader.py performs simulated execution; adapt it for realistic slippage/fees.
• Persistence: persistence.py currently provides minimal save helpers; replace with a DB or storage layer as needed.

License

• This repository does not currently include a license file.

Contact / help

• For quick experiments, inspect tests/test_strategy.py and main.py to see how strategies are invoked and validated.