# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Development commands

- **Install (dev):** `pip install -e ".[dev]"` or `conda env create -f environment.yml && conda activate coinhunter`
- **Run CLI locally:** `python -m coinhunter --help`
- **Run tests:** `pytest` or `python -m pytest tests/`
- **Run single test file:** `pytest tests/test_cli.py -v`
- **Lint:** `ruff check src tests`
- **Format:** `ruff format src tests`
- **Type-check:** `mypy src`

## Architecture

CoinHunter is a **lightweight data-layer CLI** designed to pair with the `coinbuddy` AI Skill for crypto trading on Binance. The philosophy is **layered screening**: the CLI handles cheap rule-based filtering and monitoring, while the AI Skill handles expensive deep analysis on a small set of curated candidates.

### CLI layer (data + execution)

- **`src/coinhunter/cli.py`** — Single entrypoint (`main()`). Uses `argparse` to parse commands and directly dispatches to service functions. Core commands: `init`, `config`, `account`, `market`, `buy`, `sell`, `portfolio`, `scan`, `analyze`, `watch`, `upgrade`, `catlog`, `completion`.
- **`src/coinhunter/services/`** — Domain logic:
  - `account_service.py` — balances, positions
  - `market_service.py` — tickers, klines, scan universe, symbol normalization
  - `signal_service.py` — shared market signal scoring (rule-based, zero token cost)
  - `portfolio_service.py` — held-position review (`analyze_portfolio`) and lightweight anomaly monitoring (`watch_portfolio`)
  - `trade_service.py` — spot order execution only
  - `opportunity_service.py` — market scanning (`scan_opportunities`) returning top-N candidates
  - `analyze_service.py` — multi-timeframe deep technical analysis for AI consumption
- **`src/coinhunter/binance/spot_client.py`** — Thin wrapper around `binance.spot.Spot`. Normalizes request errors into `RuntimeError`.
- **`src/coinhunter/config.py`** — `load_config()`, `get_binance_credentials()`, `ensure_init_files()`.
- **`src/coinhunter/runtime.py`** — `RuntimePaths`, `get_runtime_paths()`, `print_json()`, TUI rendering.
- **`src/coinhunter/audit.py`** — Writes JSONL audit events to dated files.

### AI layer (decision)

- **`coinbuddy` Skill** — Lives at `~/.claude/skills/coinbuddy/SKILL.md`. Governs how the AI interacts with the CLI:
  - **Discovery flow:** `scan` → `analyze` → AI synthesis → user confirm → `trade`
  - **Portfolio flow:** `watch` → flag NEED_REVIEW → `analyze` → AI synthesis → user confirm → `trade`
  - The Skill always uses `--agent` for structured JSON consumption.

## Runtime and environment

User data lives in `~/.coinhunter/` by default (override with `COINHUNTER_HOME`):

- `config.toml` — runtime, binance, trading, signal, opportunity, portfolio, and watch settings
- `.env` — `BINANCE_API_KEY` and `BINANCE_API_SECRET`
- `logs/audit_YYYYMMDD.jsonl` — structured audit log
- `logs/dry-run/audit_YYYYMMDD.jsonl` — dry-run audit log

Run `coinhunter init` to generate the config and env templates.

## Key conventions

- **Symbol normalization:** `market_service.normalize_symbol()` strips `/`, `-`, `_`, and uppercases the symbol. CLI inputs like `ETH/USDT`, `eth-usdt`, and `ETHUSDT` are all normalized to `ETHUSDT`.
- **Dry-run behavior:** Trade commands support `--dry-run`. If omitted, the default falls back to `trading.dry_run_default` in `config.toml`.
- **Client injection:** Service functions accept `spot_client` as a keyword argument for easy unit testing with mocks.
- **Error handling:** `spot_client.py` catches `requests.exceptions.SSLError` and `RequestException` and re-raises as human-readable `RuntimeError`. The CLI catches all exceptions in `main()` and prints `error: {message}` to stderr with exit code 1.
- **Ticker API fallback:** `spot_client.ticker_stats()` uses `rolling_window_ticker` for symbol-specific queries and `ticker_24hr` for full-market scans (no symbols).
- **Output modes:** All commands support `--agent` for JSON output and `--doc` to print the command's output schema.
- **Watch rules:** `portfolio_service.watch_portfolio()` monitors held positions for anomalies (1h/24h drawdowns, spikes, concentration risk, technical score deterioration). This is pure rule-based and costs zero tokens.
- **Analyze design:** `analyze_service.analyze_symbols()` fetches multi-timeframe klines (1h, 4h, 1d) and produces an AI-friendly output with `summary`, `timeframes`, `key_levels`, `alerts`, and `signal_score`. It is designed for LLM consumption.

## CLI command reference

| Command | Purpose | Token cost |
|---------|---------|-----------|
| `coin scan` | Rule-based market scan, returns top-N candidates | 0 |
| `coin analyze <sym>` | Multi-timeframe deep technical analysis | 0 |
| `coin watch` | Portfolio anomaly monitoring | 0 |
| `coin portfolio` | Full portfolio scoring | 0 |
| `coin account` | Balances | 0 |
| `coin buy/sell` | Trade execution | 0 |

## Testing

Tests live in `tests/` and use `unittest.TestCase` with `unittest.mock.patch`. The test suite covers CLI parser smoke tests, config loading, service logic with mocked clients, and trade execution paths.

## Notes

- `AGENTS.md` in this repo is stale and describes a prior V1 architecture (commands/, smart executor, precheck, review engine). Do not rely on it.
- Removed in the V2 simplification: `backtest`, `strategy`, `opportunity dataset/evaluate/optimize`, `research_service` (CoinGecko). These were over-engineered for the AI-assisted trading flow and have been archived out of the core codebase.