ci: ignore .opencode directory
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opencode
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.gitignore
vendored
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.gitignore
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.idea/
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.DS_Store
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Thumbs.db
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.opencode/
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5
WIKI/.gitattributes
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WIKI/.gitattributes
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# Treat markdown files as text with LF normalization
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*.md text eol=lf
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# Ensure JSON files are treated as text
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*.json text
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Agents and Keys
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This project supports running multiple agent identities (private keys) to place orders on Hyperliquid. Agents are lightweight keys authorized on-chain by your main wallet.
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Agent storage and environment
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- For security, agent private keys should be stored as environment variables and not checked into source control.
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- Supported patterns:
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- `AGENT_PRIVATE_KEY` (single default agent)
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- `<NAME>_AGENT_PK` or `<NAME>_AGENT_PRIVATE_KEY` (per-agent keys)
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Discovering agents
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- `trade_executor.py` scans environment variables for agent keys and loads them into `Exchange` objects so each agent can sign orders independently.
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Creating and authorizing agents
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- Use `create_agent.py` with your `MAIN_WALLET_PRIVATE_KEY` to authorize a new agent name. The script will attempt to call `exchange.approve_agent(agent_name)` and print the returned agent private key.
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Security notes
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- Never commit private keys to Git. Keep them in a secure secrets store or local `.env` file excluded from version control.
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- Rotate keys if they are ever exposed and re-authorize agents using your main wallet.
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Example `.env` snippet
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MAIN_WALLET_PRIVATE_KEY=<your-main-wallet-private-key>
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MAIN_WALLET_ADDRESS=<your-main-wallet-address>
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AGENT_PRIVATE_KEY=<agent-private-key>
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EXECUTOR_SCALPER_AGENT_PK=<agent-private-key-for-scalper>
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File `agents`
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- This repository may contain a local `agents` file used as a quick snapshot; treat it as insecure and remove it from the repo or add it to `.gitignore` if it contains secrets.
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@ -1,20 +0,0 @@
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Contributing
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Thanks for considering contributing! Please follow these guidelines to make the process smooth.
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How to contribute
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1. Fork the repository and create a feature branch for your change.
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2. Keep changes focused and add tests where appropriate.
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3. Submit a Pull Request with a clear description and the reason for the change.
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Coding standards
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- Keep functions small and well-documented.
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- Use the existing logging utilities for consistent output.
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- Prefer safe, incremental changes for financial code.
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Security and secrets
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- Never commit private keys, API keys, or secrets. Use environment variables or a secrets manager.
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- If you accidentally commit secrets, rotate them immediately.
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31
WIKI/DATA.md
31
WIKI/DATA.md
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Data layout and formats
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This section describes the `_data/` directory and the important files used by the scripts.
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Important files
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- `_data/market_data.db` — SQLite database that stores candle tables. Tables are typically named `<COIN>_<INTERVAL>` (e.g., `BTC_1m`, `ETH_5m`).
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- `_data/coin_precision.json` — Mapping of coin names to their size precision (created by `list_coins.py`).
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- `_data/current_prices.json` — Latest market prices that `market.py` writes.
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- `_data/fetcher_status.json` — Last run metadata from `data_fetcher.py`.
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- `_data/market_cap_data.json` — Market cap summary saved by `market_cap_fetcher.py`.
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- `_data/strategies.json` — Configuration for strategies (enabled flag, parameters).
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- `_data/strategy_status_<name>.json` — Per-strategy runtime status including last signal and price.
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- `_data/executor_managed_positions.json` — Which strategy is currently managing which live position (used by `trade_executor`).
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Candle schema
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Each candle table contains columns similar to:
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- `timestamp_ms` (INTEGER) — milliseconds since epoch
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- `open`, `high`, `low`, `close` (FLOAT)
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- `volume` (FLOAT)
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- `number_of_trades` (INTEGER)
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Trade logs
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- Persistent trade history is stored in `_logs/trade_history.csv` with the following columns: `timestamp_utc`, `strategy`, `coin`, `action`, `price`, `size`, `signal`, `pnl`.
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Backups and maintenance
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- Periodically back up `_data/market_data.db`. The WAL and SHM files are also present when SQLite uses WAL mode.
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- Keep JSON config/state files under version control only if they contain no secrets.
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Development and testing
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Code style and conventions
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- Python 3.11+ with typing hints where helpful.
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- Use `logging_utils.setup_logging` for consistent logs across scripts.
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Running tests
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- This repository doesn't currently include a formal test suite. Suggested quick checks:
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- Run `python list_coins.py` to verify connectivity to Hyperliquid Info.
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- Run `python -m pyflakes .` or `python -m pylint` if you have linters installed.
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Adding a new strategy
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1. Create a new script following the pattern in `strategy_template.py`.
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2. Add an entry to `_data/strategies.json` with `enabled: true` and relevant parameters.
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3. Ensure the strategy writes a status JSON file (`_data/strategy_status_<name>.json`) and uses `trade_log.log_trade` to record actions.
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Recommended improvements (low-risk)
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- Add a lightweight unit test suite (pytest) for core functions like timeframe parsing, SQL helpers, and signal calculation.
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- Add CI (GitHub Actions) to run flake/pylint and unit tests on PRs.
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- Move secrets handling to a `.env.example` and document environment variables in `WIKI/SETUP.md`.
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Hyperliquid Trading Toolkit
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This repository contains a collection of utility scripts, data fetchers, resamplers, trading strategies, and a trade executor for working with Hyperliquid trading APIs and crawled data. It is organized to support data collection, transformation, strategy development, and automated execution via agents.
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Key components
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- Data fetching and management: `data_fetcher.py`, `market.py`, `resampler.py`, `market_cap_fetcher.py`, `list_coins.py`
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- Strategies: `strategy_sma_cross.py`, `strategy_template.py`, `strategy_sma_125d.py` (if present)
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- Execution: `trade_executor.py`, `create_agent.py`, `agents` helper
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- Utilities: `logging_utils.py`, `trade_log.py`
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- Data storage: SQLite database in `_data/market_data.db` and JSON files in `_data`
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Intended audience
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- Developers building strategies and automations on Hyperliquid
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- Data engineers collecting and processing market data
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- Operators running the fetchers and executors on a scheduler or as system services
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Project goals
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- Reliable collection of 1m candles and resampling to common timeframes
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- Clean separation between data, strategies, and execution
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- Lightweight logging and traceable trade records
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Where to start
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- Read `WIKI/SETUP.md` to prepare your environment
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- Use `WIKI/SCRIPTS.md` for a description of individual scripts and how to run them
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- Inspect `WIKI/AGENTS.md` to understand agent keys and how to manage them
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Scripts and How to Use Them
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This file documents the main scripts in the repository and their purpose, typical runtime parameters, and key notes.
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list_coins.py
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- Purpose: Fetches asset metadata from Hyperliquid (name and size/precision) and saves `_data/coin_precision.json`.
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- Usage: `python list_coins.py`
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- Notes: Reads `hyperliquid.info.Info` and writes a JSON file. Useful to run before market feeders.
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market.py (MarketDataFeeder)
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- Purpose: Fetches live prices from Hyperliquid and writes `_data/current_prices.json` while printing a live table.
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- Usage: `python market.py --log-level normal`
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- Notes: Expects `_data/coin_precision.json` to exist.
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data_fetcher.py (CandleFetcherDB)
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- Purpose: Fetches historical 1m candles and stores them in `_data/market_data.db` using a table-per-coin naming convention.
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- Usage: `python data_fetcher.py --coins BTC ETH --interval 1m --days 7`
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- Notes: Can be run regularly by a scheduler to keep the DB up to date.
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resampler.py (Resampler)
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- Purpose: Reads 1m candles from SQLite and resamples to configured timeframes (e.g. 5m, 15m, 1h), appending new candles to tables.
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- Usage: `python resampler.py --coins BTC ETH --timeframes 5m 15m 1h --log-level normal`
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market_cap_fetcher.py (MarketCapFetcher)
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- Purpose: Pulls CoinGecko market cap numbers and maintains historical daily tables in the same SQLite DB.
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- Usage: `python market_cap_fetcher.py --coins BTC ETH --log-level normal`
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- Notes: Optional `COINGECKO_API_KEY` in `.env` avoids throttling.
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strategy_sma_cross.py (SmaCrossStrategy)
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- Purpose: Run an SMA-based trading strategy. Reads candles from `_data/market_data.db` and writes status to `_data/strategy_status_<name>.json`.
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- Usage: `python strategy_sma_cross.py --name sma_cross_1 --params '{"coin":"BTC","timeframe":"1m","fast":5,"slow":20}' --log-level normal`
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trade_executor.py (TradeExecutor)
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- Purpose: Orchestrates agent-based order execution using agent private keys found in environment variables. Uses `_data/strategies.json` to determine active strategies.
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- Usage: `python trade_executor.py --log-level normal`
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- Notes: Requires `MAIN_WALLET_ADDRESS` and agent keys. See `create_agent.py` to authorize agents on-chain.
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create_agent.py
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- Purpose: Authorizes a new on-chain agent using your main wallet (requires `MAIN_WALLET_PRIVATE_KEY`).
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- Usage: `python create_agent.py`
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- Notes: Prints the new agent private key to stdout — save it securely.
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trade_log.py
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- Purpose: Provides a thread-safe CSV trade history logger. Used by the executor and strategies to record actions.
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Other utility scripts
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- import_csv.py, fix_timestamps.py, list_coins.py, etc. — see file headers for details.
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Setup and Installation
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Prerequisites
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- Python 3.11+ (project uses modern dependencies)
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- Git (optional)
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- A Hyperliquid account and an activated main wallet if you want to authorize agents and trade
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Virtual environment
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1. Create a virtual environment:
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python -m venv .venv
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2. Activate the virtual environment (PowerShell on Windows):
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.\.venv\Scripts\Activate.ps1
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3. Upgrade pip and install dependencies:
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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Configuration
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- Copy `.env.example` to `.env` and set the following variables as required:
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- MAIN_WALLET_PRIVATE_KEY (used by `create_agent.py` to authorize agents)
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- MAIN_WALLET_ADDRESS (used by `trade_executor.py`)
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- AGENT_PRIVATE_KEY or per-agent keys like `EXECUTOR_SCALPER_AGENT_PK`
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- Optional: COINGECKO_API_KEY for `market_cap_fetcher.py` to avoid rate limits
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Data directory
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- The project writes and reads data from the `_data/` folder. Ensure the directory exists and is writable by the user running the scripts.
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Quick test
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After installing packages, run `list_coins.py` in a dry run to verify connectivity to the Hyperliquid info API:
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python list_coins.py
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If you encounter import errors, ensure the virtual environment is active and the `requirements.txt` dependencies are installed.
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Project Wiki Summary
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This directory contains human-friendly documentation for the project. Files:
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- `OVERVIEW.md` — High-level overview and where to start
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- `SETUP.md` — Environment setup and quick test steps
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- `SCRIPTS.md` — Per-script documentation and usage examples
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- `AGENTS.md` — How agents work and secure handling of keys
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- `DATA.md` — Data folder layout and schema notes
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- `DEVELOPMENT.md` — Developer guidance and recommended improvements
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- `CONTRIBUTING.md` — How to contribute safely
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- `TROUBLESHOOTING.md` — Common problems and solutions
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Notes:
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- These pages were generated from repository source files and common patterns in trading/data projects. Validate any sensitive information (agent keys) and remove them from the repository when sharing.
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Troubleshooting common issues
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1. Import errors
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- Ensure the virtual environment is active.
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- Run `pip install -r requirements.txt`.
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2. Agent authorization failures
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- Ensure your main wallet is activated on Hyperliquid and has funds.
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- The `create_agent.py` script will print helpful messages if the vault (main wallet) cannot act.
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3. SQLite locked errors
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- Increase the SQLite timeout when opening connections (this project uses a 10s timeout in fetcher). Close other programs that may hold the DB open.
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4. Missing coin precision file
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- Run `python list_coins.py` to regenerate `_data/coin_precision.json`.
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5. Rate limits from CoinGecko
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- Set `COINGECKO_API_KEY` in your `.env` file and ensure the fetcher respects backoff.
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6. Agent keys in `agents` file or other local files
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- Treat any `agents` file with private keys as compromised; rotate keys and remove the file from the repository.
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