feat: implement strategy metadata and dashboard simulation panel
- Added display_name and description to BaseStrategy - Updated MA44 and MA125 strategies with metadata - Added /api/v1/strategies endpoint for dynamic discovery - Added Strategy Simulation panel to dashboard with date picker and tooltips - Implemented JS polling for backtest results in dashboard - Added performance test scripts and DB connection guide - Expanded indicator config to all 15 timeframes
This commit is contained in:
BTC Bot
273
AGENTS.md
273
AGENTS.md
@ -1,223 +1,128 @@
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# AGENTS.md - AI Coding Assistant Guidelines
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## Project Overview
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BTC Accumulation Bot - Data Collection Phase. High-performance async data collection
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system for cbBTC on Hyperliquid with TimescaleDB storage. Python 3.11, asyncio,
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FastAPI, asyncpg, WebSockets.
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BTC Accumulation Bot - Data Collection & Backtesting Phase. High-performance system for
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cbBTC on Hyperliquid with TimescaleDB. Core components: Data Collector (WS),
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Indicator Engine (SMA, etc.), Brain (Decision Logic), and Backtester.
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## Build/Run Commands
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### Docker (Primary deployment - Synology DS218+)
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```bash
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# Build and start all services (timescaledb, data_collector, api_server)
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cd docker && docker-compose up -d --build
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# View logs
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docker-compose logs -f data_collector
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docker-compose logs -f api_server
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# Full deploy (creates dirs, pulls, builds, starts)
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bash scripts/deploy.sh
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cd docker && docker-compose up -d --build # Build and start all services
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docker-compose logs -f data_collector # View logs
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bash scripts/deploy.sh # Full deploy
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```
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### Development
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```bash
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# API server (requires DB running)
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cd src/api && uvicorn server:app --reload --host 0.0.0.0 --port 8000
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# Docs: http://localhost:8000/docs | Dashboard: http://localhost:8000/dashboard
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# Data collector
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cd src/data_collector && python -m data_collector.main
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```
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### Testing
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```bash
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# Run all tests
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pytest
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# Run a specific test file
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pytest tests/data_collector/test_websocket_client.py
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# Run a single test by name
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pytest tests/data_collector/test_websocket_client.py::test_websocket_connection -v
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# Run with coverage
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pytest --cov=src --cov-report=html
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```
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Note: The tests/ directory structure exists but test files have not been written yet.
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When creating tests, use pytest with pytest-asyncio for async test support.
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### Linting & Formatting
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```bash
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# No config files exist for these tools; use these flags:
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flake8 src/ --max-line-length=100 --extend-ignore=E203,W503
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black --check src/ # Check formatting
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black src/ # Auto-format
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mypy src/ --ignore-missing-imports
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pytest # All tests
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pytest tests/data_collector/test_websocket_client.py # Single file
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pytest --cov=src --cov-report=html # With coverage
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```
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## Project Structure
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```
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src/
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├── data_collector/ # WebSocket client, buffer, database
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│ ├── __init__.py
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│ ├── main.py # Entry point, orchestration, signal handling
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│ ├── websocket_client.py # Hyperliquid WS client, Candle dataclass
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│ ├── __init__.py # Package exports (all public classes)
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│ ├── main.py # Entry point, orchestration
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│ ├── websocket_client.py # Hyperliquid WS client
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│ ├── candle_buffer.py # Circular buffer with async flush
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│ ├── database.py # asyncpg/TimescaleDB interface
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│ └── backfill.py # Historical data backfill from REST API
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│ ├── backfill.py # Historical data backfill
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│ ├── custom_timeframe_generator.py # 37m, 148m, 1d aggregation
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│ ├── indicator_engine.py # SMA/EMA computation & storage
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│ ├── brain.py # Strategy evaluation & decision logging
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│ └── backtester.py # Historical replay driver
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└── api/
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├── server.py # FastAPI app, all endpoints
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└── dashboard/static/
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└── index.html # Real-time web dashboard
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config/data_config.yaml # Non-secret operational config
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docker/
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├── docker-compose.yml # 3-service orchestration
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├── Dockerfile.api / .collector # python:3.11-slim based
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└── init-scripts/ # 01-schema.sql, 02-optimization.sql
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scripts/ # deploy.sh, backup.sh, health_check.sh, backfill.sh
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tests/data_collector/ # Test directory (empty - tests not yet written)
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├── server.py # FastAPI app, endpoints for data/backtests
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└── dashboard/static/index.html # Real-time web dashboard
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config/data_config.yaml # Operational config & indicator settings
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docker/ # Docker orchestration & init-scripts
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scripts/ # Deploy, backup, & utility scripts
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```
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||||
|
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## Architecture & Data Flow
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|
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```
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Live: WS -> Buffer -> DB -> CustomTF -> IndicatorEngine -> Brain -> Decisions
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│ │
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Backtest: DB (History) -> Backtester ─────────┴─────────────┘
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```
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- **Stateless Logic**: `IndicatorEngine` and `Brain` are driver-agnostic. They read from DB
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and write to DB, unaware if the trigger is live WS or backtest replay.
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- **Consistency**: Indicators are computed exactly the same way for live and backtest.
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- **Visualization**: Dashboard queries `indicators` and `decisions` tables directly.
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Decisions contain a JSON snapshot of indicators at the moment of decision.
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||||
## Key Dataclasses
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|
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```python
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@dataclass
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class Candle: # Standard OHLCV
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time: datetime; symbol: str; interval: str; ...
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|
||||
@dataclass
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class Decision: # Brain output
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time: datetime; symbol: str; decision_type: str; confidence: float
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indicator_snapshot: Dict; # Values seen by Brain at decision time
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backtest_id: Optional[str] # UUID if backtest, None if live
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```
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## Database Schema (TimescaleDB)
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||||
| Table | Purpose | Key Columns |
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||||
|-------|---------|-------------|
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||||
| `candles` | OHLCV data | `(time, symbol, interval)` UNIQUE |
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||||
| `indicators` | Computed values | `(time, symbol, interval, indicator_name)` UNIQUE |
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| `decisions` | Buy/sell signals | `(time, symbol, interval, backtest_id)` |
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| `backtest_runs` | Run metadata | `(id, strategy, config, results)` |
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- `decisions` table stores `indicator_snapshot` JSONB for exact replay/audit.
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- Compression enabled on all hypertables (7-day policy).
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|
||||
## API Endpoints (src/api/server.py)
|
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|
||||
| Method | Path | Description |
|
||||
|--------|------|-------------|
|
||||
| GET | `/api/v1/candles` | Query raw candles |
|
||||
| GET | `/api/v1/indicators` | Query computed indicators (MA, RSI, etc.) |
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||||
| GET | `/api/v1/decisions` | Query signals (live or backtest) |
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| GET | `/api/v1/backtests` | List historical backtest runs |
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||||
| POST | `/api/v1/backtests` | Trigger a new backtest (async background task) |
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| GET | `/api/v1/stats` | 24h trading stats |
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||||
|
||||
## Code Style Guidelines
|
||||
|
||||
### Imports
|
||||
Group in this order, separated by blank lines:
|
||||
1. Standard library (`import asyncio`, `from datetime import datetime`)
|
||||
2. Third-party (`import websockets`, `import asyncpg`, `from fastapi import FastAPI`)
|
||||
3. Local/relative (`from .websocket_client import Candle`)
|
||||
|
||||
Use relative imports (`.module`) within the `data_collector` package.
|
||||
Use absolute imports for third-party packages.
|
||||
|
||||
### Formatting
|
||||
- Line length: 100 characters max
|
||||
- Indentation: 4 spaces
|
||||
- Strings: double quotes (single only to avoid escaping)
|
||||
- Trailing commas in multi-line collections
|
||||
- Formatter: black
|
||||
|
||||
### Type Hints
|
||||
- Required on all function parameters and return values
|
||||
- `Optional[Type]` for nullable values
|
||||
- `List[Type]`, `Dict[str, Any]` from `typing` module
|
||||
- `@dataclass` for data-holding classes (e.g., `Candle`, `BufferStats`)
|
||||
- Callable types for callbacks: `Callable[[Candle], Awaitable[None]]`
|
||||
|
||||
### Naming Conventions
|
||||
- Classes: `PascalCase` (DataCollector, CandleBuffer)
|
||||
- Functions/variables: `snake_case` (get_candles, buffer_size)
|
||||
- Constants: `UPPER_SNAKE_CASE` (DB_HOST, MAX_BUFFER_SIZE)
|
||||
- Private methods: `_leading_underscore` (_handle_reconnect, _flush_loop)
|
||||
|
||||
### Docstrings
|
||||
- Triple double quotes on all modules, classes, and public methods
|
||||
- Brief one-line description on first line
|
||||
- Optional blank line + detail if needed
|
||||
- No Args/Returns sections (not strict Google-style)
|
||||
```python
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||||
"""Add a candle to the buffer
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||||
Returns True if added, False if buffer full and candle dropped"""
|
||||
```
|
||||
|
||||
### Error Handling
|
||||
- `try/except` with specific exceptions (never bare `except:`)
|
||||
- Log errors with `logger.error()` before re-raising in critical paths
|
||||
- Catch `asyncio.CancelledError` to break loops cleanly
|
||||
- Use `finally` blocks for cleanup (always call `self.stop()`)
|
||||
- Use `@asynccontextmanager` for resource acquisition (DB connections)
|
||||
|
||||
### Async Patterns
|
||||
- `async/await` for all I/O operations
|
||||
- `asyncio.Lock()` for thread-safe buffer access
|
||||
- `asyncio.Event()` for stop/flush coordination
|
||||
- `asyncio.create_task()` for background loops
|
||||
- `asyncio.gather(*tasks, return_exceptions=True)` for parallel cleanup
|
||||
- `asyncio.wait_for(coro, timeout)` for graceful shutdown
|
||||
- `asyncio.run(main())` as the entry point
|
||||
|
||||
### Logging
|
||||
- Module-level: `logger = logging.getLogger(__name__)` in every file
|
||||
- Format: `'%(asctime)s - %(name)s - %(levelname)s - %(message)s'`
|
||||
- Log level from env: `getattr(logging, os.getenv('LOG_LEVEL', 'INFO'))`
|
||||
- Use f-strings in log messages with relevant context
|
||||
- Levels: DEBUG (candle receipt), INFO (lifecycle), WARNING (gaps), ERROR (failures)
|
||||
|
||||
### Database (asyncpg + TimescaleDB)
|
||||
- Connection pool: `asyncpg.create_pool(min_size=1, max_size=N)`
|
||||
- `@asynccontextmanager` wrapper for connection acquisition
|
||||
- Batch inserts with `executemany()`
|
||||
- Upserts with `ON CONFLICT ... DO UPDATE`
|
||||
- Positional params: `$1, $2, ...` (not `%s`)
|
||||
- Use `conn.fetch()`, `conn.fetchrow()`, `conn.fetchval()` for results
|
||||
|
||||
### Configuration
|
||||
- Secrets via environment variables (`os.getenv('DB_PASSWORD')`)
|
||||
- Non-secret config in `config/data_config.yaml`
|
||||
- Constructor defaults fall back to env vars
|
||||
- Never commit `.env` files (contains real credentials)
|
||||
- **Imports**: Stdlib, then Third-party, then Local (relative within package).
|
||||
- **Async**: Use `async/await` for all I/O. Use `asyncpg` pool.
|
||||
- **Typing**: strict type hints required. `Optional[T]`, `List[T]`.
|
||||
- **Logging**: Use `logger = logging.getLogger(__name__)`.
|
||||
- **Config**: Load from `config/data_config.yaml` or env vars.
|
||||
|
||||
## Common Tasks
|
||||
|
||||
### Add New API Endpoint
|
||||
1. Add route in `src/api/server.py` with `@app.get()`/`@app.post()`
|
||||
2. Type-hint query params with `Query()`; return `dict` or raise `HTTPException`
|
||||
3. Use `asyncpg` pool for database queries
|
||||
### Add New Indicator
|
||||
1. Add to `config/data_config.yaml` under `indicators`.
|
||||
2. Update `IndicatorEngine._compute_indicator` in `src/data_collector/indicator_engine.py` if new type (non-SMA).
|
||||
3. No DB schema change needed (rows are generic).
|
||||
|
||||
### Add New Data Source
|
||||
1. Create module in `src/data_collector/` following `websocket_client.py` pattern
|
||||
2. Implement async `connect()`, `disconnect()`, `receive()` methods
|
||||
3. Use callback architecture: `on_data`, `on_error` callables
|
||||
|
||||
### Database Schema Changes
|
||||
1. Update `docker/init-scripts/01-schema.sql`
|
||||
2. Update `DatabaseManager` methods in `src/data_collector/database.py`
|
||||
3. Rebuild: `docker-compose down -v && docker-compose up -d --build`
|
||||
|
||||
### Writing Tests
|
||||
1. Create test files in `tests/data_collector/` (e.g., `test_websocket_client.py`)
|
||||
2. Use `pytest-asyncio` for async tests: `@pytest.mark.asyncio`
|
||||
3. Mock external services (WebSocket, database) with `unittest.mock`
|
||||
4. Descriptive names: `test_websocket_reconnection_with_backoff`
|
||||
|
||||
### Historical Data Backfill
|
||||
The `backfill.py` module downloads historical candle data from Hyperliquid's REST API.
|
||||
|
||||
**API Limitations:**
|
||||
- Max 5000 candles per coin/interval combination
|
||||
- 500 candles per response (requires pagination)
|
||||
- Available intervals: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 8h, 12h, 1d, 3d, 1w, 1M
|
||||
|
||||
**Usage - Python Module:**
|
||||
```python
|
||||
from data_collector.backfill import HyperliquidBackfill
|
||||
from data_collector.database import DatabaseManager
|
||||
|
||||
async with HyperliquidBackfill(db, coin="BTC", intervals=["1m", "1h"]) as backfill:
|
||||
# Backfill last 7 days for all configured intervals
|
||||
results = await backfill.backfill_all_intervals(days_back=7)
|
||||
|
||||
# Or backfill specific interval
|
||||
count = await backfill.backfill_interval("1m", days_back=3)
|
||||
```
|
||||
|
||||
**Usage - CLI:**
|
||||
### Run Backtest
|
||||
```bash
|
||||
# Backfill 7 days of 1m candles for BTC
|
||||
cd src/data_collector && python -m data_collector.backfill --coin BTC --days 7 --intervals 1m
|
||||
# CLI
|
||||
python -m data_collector.backtester --symbol BTC --intervals 37m --start 2025-01-01
|
||||
|
||||
# Backfill multiple intervals
|
||||
python -m data_collector.backfill --coin BTC --days 30 --intervals 1m 5m 1h
|
||||
|
||||
# Backfill MAXIMUM available data (5000 candles per interval)
|
||||
python -m data_collector.backfill --coin BTC --days max --intervals 1m 1h 1d
|
||||
|
||||
# Or use the convenience script
|
||||
bash scripts/backfill.sh BTC 7 "1m 5m 1h"
|
||||
bash scripts/backfill.sh BTC max "1m 1h 1d" # Maximum data
|
||||
# API
|
||||
curl -X POST http://localhost:8000/api/v1/backtests \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"symbol": "BTC", "intervals": ["37m"], "start_date": "2025-01-01"}'
|
||||
```
|
||||
|
||||
**Data Coverage by Interval:**
|
||||
- 1m candles: ~3.5 days (5000 candles)
|
||||
- 1h candles: ~7 months (5000 candles)
|
||||
- 1d candles: ~13.7 years (5000 candles)
|
||||
|
||||
340
IMPLEMENTATION_SUMMARY.md
Normal file
340
IMPLEMENTATION_SUMMARY.md
Normal file
@ -0,0 +1,340 @@
|
||||
# BTC Accumulation Bot - Implementation Summary
|
||||
|
||||
## Current Status: ✅ CORE SYSTEM COMPLETE
|
||||
|
||||
### What We've Built
|
||||
|
||||
This is a **production-ready cryptocurrency trading data collection and backtesting system** for cbBTC on Hyperliquid exchange.
|
||||
|
||||
---
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
```
|
||||
Live Data Flow:
|
||||
Hyperliquid WS → WebSocket Client → Candle Buffer → TimescaleDB
|
||||
↓
|
||||
Custom TF Generator → Indicator Engine (MA44, MA125) → Brain → Decisions
|
||||
↓
|
||||
Backtest Flow:
|
||||
Historical DB Data → Backtester → Same Indicator Engine/Brain → Simulated Trades
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Core Components (All Implemented)
|
||||
|
||||
### 1. Data Collection Pipeline
|
||||
|
||||
**WebSocket Client** (`websocket_client.py`)
|
||||
- Connects to Hyperliquid WebSocket API
|
||||
- Automatic reconnection with exponential backoff (1s to 15min)
|
||||
- Handles multiple candle formats
|
||||
- Health monitoring and connection metrics
|
||||
|
||||
**Candle Buffer** (`candle_buffer.py`)
|
||||
- Thread-safe circular buffer (1000 max size)
|
||||
- Async batch flushing (30s interval or 100 candles)
|
||||
- Gap detection in candle sequences
|
||||
- Statistics tracking for monitoring
|
||||
|
||||
**Database Manager** (`database.py`)
|
||||
- asyncpg connection pool (min: 2, max: 20)
|
||||
- Batch inserts with conflict resolution
|
||||
- Gap detection using window functions
|
||||
- Health statistics queries
|
||||
|
||||
### 2. Data Processing
|
||||
|
||||
**Custom Timeframe Generator** (`custom_timeframe_generator.py`)
|
||||
- Standard intervals: 3m, 5m, 15m, 30m, 1h, 2h, 4h, 8h, 12h, 1d, 3d, 1w, 1M
|
||||
- Custom intervals: **37m**, **148m** (Fibonacci-based)
|
||||
- Real-time aggregation from 1m candles
|
||||
- Historical backfill capability
|
||||
- Integrity verification
|
||||
|
||||
**Indicator Engine** (`indicator_engine.py`)
|
||||
- Stateless design (same code for live and backtest)
|
||||
- Configurable indicators via dataclasses
|
||||
- Currently implemented: **SMA (Simple Moving Average)**
|
||||
- Indicators configured:
|
||||
- MA44 (44-period SMA) on 37m, 148m, 1d
|
||||
- MA125 (125-period SMA) on 37m, 148m, 1d
|
||||
- Batch computation for historical data
|
||||
|
||||
### 3. Decision Making
|
||||
|
||||
**Brain** (`brain.py`)
|
||||
- Strategy-based evaluation system
|
||||
- Pluggable strategy loader
|
||||
- Decision logging with full indicator snapshots (JSONB)
|
||||
- Supports live trading and backtesting
|
||||
- Current strategies:
|
||||
- MA44Strategy: Long when Price > MA44, Short when Price < MA44
|
||||
- MA125Strategy: Long when Price > MA125, Short when Price < MA125
|
||||
|
||||
**Strategy System** (`strategies/`)
|
||||
- Base class with abstract interface
|
||||
- Signal types: OPEN_LONG, OPEN_SHORT, CLOSE_LONG, CLOSE_SHORT, HOLD
|
||||
- Confidence scoring (0.0 - 1.0)
|
||||
- Position-aware decision making
|
||||
|
||||
### 4. Backtesting & Simulation
|
||||
|
||||
**Simulator** (`simulator.py`)
|
||||
- Account management with leverage support
|
||||
- Trade tracking with P&L calculation
|
||||
- Fee calculation (maker/taker)
|
||||
- Equity mark-to-market updates
|
||||
- Statistics generation (win rate, total P&L, etc.)
|
||||
|
||||
**Backtester** (`backtester.py`)
|
||||
- Historical replay through same IndicatorEngine/Brain
|
||||
- UUID-based run tracking
|
||||
- Progress logging
|
||||
- Results storage in database
|
||||
- CLI interface for running backtests
|
||||
|
||||
### 5. API & Dashboard
|
||||
|
||||
**FastAPI Server** (`api/server.py`)
|
||||
Endpoints:
|
||||
- `GET /` - API status
|
||||
- `GET /api/v1/candles` - Query candle data
|
||||
- `GET /api/v1/candles/latest` - Latest candle
|
||||
- `GET /api/v1/indicators` - Query indicator values
|
||||
- `GET /api/v1/decisions` - Query brain decisions
|
||||
- `GET /api/v1/backtests` - List backtest runs
|
||||
- `POST /api/v1/backtests` - Trigger new backtest (async)
|
||||
- `GET /api/v1/stats` - 24h trading statistics
|
||||
- `GET /api/v1/health` - System health check
|
||||
- `GET /api/v1/ta` - Technical analysis endpoint
|
||||
- `GET /api/v1/export/csv` - Export data to CSV
|
||||
- `GET /dashboard` - Static dashboard
|
||||
|
||||
**Dashboard**
|
||||
- Real-time web interface
|
||||
- Connects to API endpoints
|
||||
- Visualizes candles, indicators, and decisions
|
||||
|
||||
---
|
||||
|
||||
## Database Schema (TimescaleDB)
|
||||
|
||||
### Tables
|
||||
|
||||
**candles** - OHLCV data
|
||||
- `(time, symbol, interval)` UNIQUE
|
||||
- Compression enabled (7-day policy)
|
||||
- Supports conflict resolution
|
||||
|
||||
**indicators** - Computed technical indicators
|
||||
- `(time, symbol, interval, indicator_name)` UNIQUE
|
||||
- Stores value and parameters (JSONB)
|
||||
|
||||
**decisions** - Trading signals
|
||||
- `(time, symbol, interval, backtest_id)`
|
||||
- Stores indicator_snapshot (JSONB) for audit
|
||||
- Distinguishes live vs backtest decisions
|
||||
|
||||
**backtest_runs** - Backtest metadata
|
||||
- `(id, strategy, config, results)`
|
||||
- JSON config and results storage
|
||||
|
||||
**data_quality** - Quality issues log
|
||||
- Tracks gaps, anomalies, validation failures
|
||||
|
||||
---
|
||||
|
||||
## Key Features
|
||||
|
||||
### ✅ Implemented
|
||||
|
||||
1. **Real-time Data Collection**
|
||||
- Live WebSocket connection to Hyperliquid
|
||||
- Automatic reconnection on failure
|
||||
- Buffering and batch database writes
|
||||
|
||||
2. **Custom Timeframes**
|
||||
- 37-minute candles (Fibonacci sequence)
|
||||
- 148-minute candles (4 × 37m)
|
||||
- Real-time aggregation from 1m data
|
||||
|
||||
3. **Technical Indicators**
|
||||
- Moving Average 44 (MA44)
|
||||
- Moving Average 125 (MA125)
|
||||
- Computed on all timeframes
|
||||
|
||||
4. **Strategy Engine**
|
||||
- Pluggable strategy system
|
||||
- Position-aware decisions
|
||||
- Confidence scoring
|
||||
|
||||
5. **Backtesting Framework**
|
||||
- Historical replay capability
|
||||
- Same code path as live trading
|
||||
- P&L tracking and statistics
|
||||
|
||||
6. **REST API**
|
||||
- Full CRUD for all data types
|
||||
- Async backtest execution
|
||||
- CSV export functionality
|
||||
|
||||
7. **Docker Deployment**
|
||||
- Docker Compose configuration
|
||||
- Optimized for Synology DS218+
|
||||
- Persistent volumes for data
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Indicators (Hardcoded in main.py)
|
||||
```python
|
||||
indicator_configs = [
|
||||
IndicatorConfig("ma44", "sma", 44, ["37m", "148m", "1d"]),
|
||||
IndicatorConfig("ma125", "sma", 125, ["37m", "148m", "1d"])
|
||||
]
|
||||
```
|
||||
|
||||
### Database Connection
|
||||
Environment variables:
|
||||
- `DB_HOST` (default: localhost)
|
||||
- `DB_PORT` (default: 5432)
|
||||
- `DB_NAME` (default: btc_data)
|
||||
- `DB_USER` (default: btc_bot)
|
||||
- `DB_PASSWORD` (default: empty)
|
||||
|
||||
### Buffer Settings
|
||||
- Max size: 1000 candles
|
||||
- Flush interval: 30 seconds
|
||||
- Batch size: 100 candles
|
||||
|
||||
---
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Run Data Collector
|
||||
```bash
|
||||
cd src/data_collector && python -m data_collector.main
|
||||
```
|
||||
|
||||
### Run API Server
|
||||
```bash
|
||||
cd src/api && uvicorn server:app --reload --host 0.0.0.0 --port 8000
|
||||
```
|
||||
|
||||
### Run Backtest (CLI)
|
||||
```bash
|
||||
python -m data_collector.backtester \
|
||||
--symbol BTC \
|
||||
--intervals 37m \
|
||||
--start 2025-01-01
|
||||
```
|
||||
|
||||
### Run Backtest (API)
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/api/v1/backtests \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"symbol": "BTC", "intervals": ["37m"], "start_date": "2025-01-01"}'
|
||||
```
|
||||
|
||||
### Query Candles
|
||||
```bash
|
||||
curl "http://localhost:8000/api/v1/candles?symbol=BTC&interval=37m&limit=100"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Code Quality
|
||||
|
||||
### Strengths
|
||||
- ✅ Full async/await implementation
|
||||
- ✅ Comprehensive type hints
|
||||
- ✅ Proper error handling and logging
|
||||
- ✅ Stateless design for core logic
|
||||
- ✅ Clean separation of concerns
|
||||
- ✅ Follows AGENTS.md style guidelines
|
||||
- ✅ Docker-ready deployment
|
||||
|
||||
### Testing
|
||||
- ⚠️ No unit tests implemented yet
|
||||
- ⚠️ No integration tests
|
||||
- Recommendation: Add pytest suite
|
||||
|
||||
---
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Data Collection
|
||||
- Handles 1 candle/minute per symbol
|
||||
- Buffer prevents database overload
|
||||
- Batch inserts reduce DB round trips
|
||||
|
||||
### Memory Usage
|
||||
- Fixed buffer size (1000 candles)
|
||||
- Connection pooling (max 20)
|
||||
- Optimized for low-memory devices (Synology DS218+)
|
||||
|
||||
### Database
|
||||
- TimescaleDB compression (7-day policy)
|
||||
- Proper indexing on all tables
|
||||
- Window functions for efficient queries
|
||||
|
||||
---
|
||||
|
||||
## Missing Features (Future Work)
|
||||
|
||||
### High Priority
|
||||
1. **Unit Tests** - pytest suite for all components
|
||||
2. **Integration Tests** - End-to-end testing
|
||||
3. **Alerting System** - Telegram/Discord notifications
|
||||
4. **Configuration File** - YAML-based config (currently hardcoded)
|
||||
|
||||
### Medium Priority
|
||||
5. **Additional Indicators** - RSI, MACD, Bollinger Bands
|
||||
6. **Advanced Strategies** - Multi-indicator strategies
|
||||
7. **Performance Metrics** - Sharpe ratio, max drawdown
|
||||
8. **Data Validation** - Enhanced quality checks
|
||||
|
||||
### Low Priority
|
||||
9. **WebSocket API** - Real-time streaming
|
||||
10. **Multi-symbol Support** - Beyond BTC
|
||||
11. **Machine Learning** - Pattern recognition
|
||||
12. **Paper Trading** - Test mode before live
|
||||
|
||||
---
|
||||
|
||||
## Production Readiness
|
||||
|
||||
### ✅ Ready for Production
|
||||
- Core data collection pipeline
|
||||
- Database schema and operations
|
||||
- REST API endpoints
|
||||
- Backtesting framework
|
||||
- Docker deployment
|
||||
|
||||
### ⚠️ Needs Attention
|
||||
- No automated testing
|
||||
- No monitoring/alerting
|
||||
- Basic strategies only
|
||||
- No backup/restore scripts
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**The BTC Accumulation Bot is a fully functional, production-ready system for collecting and analyzing cryptocurrency trading data.**
|
||||
|
||||
It successfully implements:
|
||||
- Live data collection from Hyperliquid
|
||||
- Custom timeframe generation (37m, 148m)
|
||||
- Technical indicator computation (MA44, MA125)
|
||||
- Strategy-based decision making
|
||||
- Historical backtesting with P&L tracking
|
||||
- REST API with dashboard
|
||||
- Docker deployment
|
||||
|
||||
The system is architected for consistency between live trading and backtesting, ensuring that strategies tested historically will behave identically in production.
|
||||
|
||||
**Current State: Ready for live deployment and iterative enhancement.**
|
||||
@ -32,6 +32,15 @@ data_collection:
|
||||
# Intervals to collect (1m is base, others computed)
|
||||
intervals:
|
||||
- "1m" # Base collection
|
||||
indicators:
|
||||
ma44:
|
||||
type: "sma"
|
||||
period: 44
|
||||
intervals: ["1d"]
|
||||
ma125:
|
||||
type: "sma"
|
||||
period: 125
|
||||
intervals: ["1d"]
|
||||
|
||||
# WebSocket settings
|
||||
websocket:
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
# Update docker-compose.yml to mount source code as volume
|
||||
version: '3.8'
|
||||
|
||||
services:
|
||||
@ -45,6 +46,7 @@ services:
|
||||
- DB_PASSWORD=${DB_PASSWORD}
|
||||
- LOG_LEVEL=INFO
|
||||
volumes:
|
||||
- ../src:/app/src
|
||||
- /volume1/btc_bot/logs:/app/logs
|
||||
- ../config:/app/config:ro
|
||||
depends_on:
|
||||
@ -72,6 +74,7 @@ services:
|
||||
- DB_USER=btc_bot
|
||||
- DB_PASSWORD=${DB_PASSWORD}
|
||||
volumes:
|
||||
- ../src:/app/src
|
||||
- /volume1/btc_bot/exports:/app/exports
|
||||
- ../config:/app/config:ro
|
||||
depends_on:
|
||||
@ -80,4 +83,4 @@ services:
|
||||
deploy:
|
||||
resources:
|
||||
limits:
|
||||
memory: 512M
|
||||
memory: 512M
|
||||
|
||||
@ -51,7 +51,11 @@ SELECT create_hypertable('indicators', 'time',
|
||||
if_not_exists => TRUE
|
||||
);
|
||||
|
||||
-- 7. Create index for indicators
|
||||
-- 7. Create unique constraint + index for indicators (required for upserts)
|
||||
ALTER TABLE indicators
|
||||
ADD CONSTRAINT indicators_unique
|
||||
UNIQUE (time, symbol, interval, indicator_name);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_indicators_lookup
|
||||
ON indicators (symbol, interval, indicator_name, time DESC);
|
||||
|
||||
@ -135,5 +139,61 @@ SELECT
|
||||
FROM candles
|
||||
GROUP BY symbol;
|
||||
|
||||
-- 16. Create decisions table (brain outputs - buy/sell/hold with full context)
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
time TIMESTAMPTZ NOT NULL,
|
||||
symbol TEXT NOT NULL,
|
||||
interval TEXT NOT NULL,
|
||||
decision_type TEXT NOT NULL,
|
||||
strategy TEXT NOT NULL,
|
||||
confidence DECIMAL(5,4),
|
||||
price_at_decision DECIMAL(18,8),
|
||||
indicator_snapshot JSONB NOT NULL,
|
||||
candle_snapshot JSONB NOT NULL,
|
||||
reasoning TEXT,
|
||||
backtest_id TEXT,
|
||||
executed BOOLEAN DEFAULT FALSE,
|
||||
execution_price DECIMAL(18,8),
|
||||
execution_time TIMESTAMPTZ,
|
||||
created_at TIMESTAMPTZ DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- 17. Convert decisions to hypertable
|
||||
SELECT create_hypertable('decisions', 'time',
|
||||
chunk_time_interval => INTERVAL '7 days',
|
||||
if_not_exists => TRUE
|
||||
);
|
||||
|
||||
-- 18. Indexes for decisions - separate live from backtest queries
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_live
|
||||
ON decisions (symbol, interval, time DESC) WHERE backtest_id IS NULL;
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_backtest
|
||||
ON decisions (backtest_id, symbol, time DESC) WHERE backtest_id IS NOT NULL;
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type
|
||||
ON decisions (symbol, decision_type, time DESC);
|
||||
|
||||
-- 19. Create backtest_runs metadata table
|
||||
CREATE TABLE IF NOT EXISTS backtest_runs (
|
||||
id TEXT PRIMARY KEY,
|
||||
strategy TEXT NOT NULL,
|
||||
symbol TEXT NOT NULL DEFAULT 'BTC',
|
||||
start_time TIMESTAMPTZ NOT NULL,
|
||||
end_time TIMESTAMPTZ NOT NULL,
|
||||
intervals TEXT[] NOT NULL,
|
||||
config JSONB,
|
||||
results JSONB,
|
||||
created_at TIMESTAMPTZ DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- 20. Compression for decisions
|
||||
ALTER TABLE decisions SET (
|
||||
timescaledb.compress,
|
||||
timescaledb.compress_segmentby = 'symbol,interval,strategy'
|
||||
);
|
||||
|
||||
SELECT add_compression_policy('decisions', INTERVAL '7 days', if_not_exists => TRUE);
|
||||
|
||||
-- Success message
|
||||
SELECT 'Database schema initialized successfully' as status;
|
||||
673
docs/synology_db_connection_guide.md
Normal file
673
docs/synology_db_connection_guide.md
Normal file
@ -0,0 +1,673 @@
|
||||
# Guide: Connecting to Synology PostgreSQL from Local PC
|
||||
|
||||
This guide explains how to connect to your Synology's TimescaleDB from your local PC to work with historical 1m candle data.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
### 1. Install PostgreSQL Client on Your PC
|
||||
|
||||
**Windows:**
|
||||
```bash
|
||||
# Download from: https://www.postgresql.org/download/windows/
|
||||
# Or use chocolatey:
|
||||
choco install postgresql
|
||||
```
|
||||
|
||||
**Mac:**
|
||||
```bash
|
||||
brew install postgresql
|
||||
# Or download Postgres.app from postgresapp.com
|
||||
```
|
||||
|
||||
**Linux:**
|
||||
```bash
|
||||
# Ubuntu/Debian
|
||||
sudo apt-get install postgresql-client
|
||||
|
||||
# Or use Docker:
|
||||
docker run -it --rm postgres:15 psql --version
|
||||
```
|
||||
|
||||
### 2. Install Python Dependencies
|
||||
|
||||
```bash
|
||||
pip install asyncpg pandas numpy
|
||||
# Or use requirements.txt from the project
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Step 1: Configure Synology for Remote Access
|
||||
|
||||
### Open PostgreSQL Port
|
||||
|
||||
1. **SSH into your Synology:**
|
||||
```bash
|
||||
ssh admin@YOUR_SYNOLOGY_IP
|
||||
```
|
||||
|
||||
2. **Edit PostgreSQL configuration:**
|
||||
```bash
|
||||
# Find postgresql.conf (usually in /var/lib/postgresql/data/)
|
||||
sudo vim /var/lib/postgresql/data/postgresql.conf
|
||||
|
||||
# Change:
|
||||
# listen_addresses = 'localhost'
|
||||
# To:
|
||||
listen_addresses = '*'
|
||||
```
|
||||
|
||||
3. **Edit pg_hba.conf to allow remote connections:**
|
||||
```bash
|
||||
sudo vim /var/lib/postgresql/data/pg_hba.conf
|
||||
|
||||
# Add at the end:
|
||||
host all all 0.0.0.0/0 md5
|
||||
# Or for specific IP:
|
||||
host all all YOUR_PC_IP/32 md5
|
||||
```
|
||||
|
||||
4. **Restart PostgreSQL:**
|
||||
```bash
|
||||
sudo systemctl restart postgresql
|
||||
# Or if using Docker:
|
||||
cd ~/btc_bot/docker && docker-compose restart timescaledb
|
||||
```
|
||||
|
||||
### Configure Synology Firewall
|
||||
|
||||
1. Open **Control Panel** → **Security** → **Firewall**
|
||||
2. Click **Edit Rules**
|
||||
3. Create new rule:
|
||||
- **Ports:** Custom → TCP → 5433 (or your PostgreSQL port)
|
||||
- **Source IP:** Your PC's IP address (or allow all)
|
||||
- **Action:** Allow
|
||||
4. Apply the rule
|
||||
|
||||
## Step 2: Test Connection
|
||||
|
||||
### Using psql CLI
|
||||
|
||||
```bash
|
||||
# Replace with your Synology IP
|
||||
export DB_HOST=192.168.1.100 # Your Synology IP
|
||||
export DB_PORT=5433
|
||||
export DB_NAME=btc_data
|
||||
export DB_USER=btc_bot
|
||||
export DB_PASSWORD=your_password
|
||||
|
||||
# Test connection
|
||||
psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "SELECT version();"
|
||||
```
|
||||
|
||||
### Using Python
|
||||
|
||||
Create `test_connection.py`:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
import asyncpg
|
||||
|
||||
async def test():
|
||||
conn = await asyncpg.connect(
|
||||
host='192.168.1.100', # Your Synology IP
|
||||
port=5433,
|
||||
database='btc_data',
|
||||
user='btc_bot',
|
||||
password='your_password'
|
||||
)
|
||||
|
||||
version = await conn.fetchval('SELECT version()')
|
||||
print(f"Connected! PostgreSQL version: {version}")
|
||||
|
||||
# Test candle count
|
||||
count = await conn.fetchval(
|
||||
"SELECT COUNT(*) FROM candles WHERE interval = '1m'"
|
||||
)
|
||||
print(f"Total 1m candles: {count:,}")
|
||||
|
||||
await conn.close()
|
||||
|
||||
asyncio.run(test())
|
||||
```
|
||||
|
||||
Run it:
|
||||
```bash
|
||||
python test_connection.py
|
||||
```
|
||||
|
||||
## Step 3: Export Historical 1m Data
|
||||
|
||||
### Option A: Using psql (Quick Export)
|
||||
|
||||
```bash
|
||||
psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "
|
||||
COPY (
|
||||
SELECT time, symbol, interval, open, high, low, close, volume
|
||||
FROM candles
|
||||
WHERE symbol = 'BTC'
|
||||
AND interval = '1m'
|
||||
AND time >= '2025-01-01'
|
||||
ORDER BY time
|
||||
) TO STDOUT WITH CSV HEADER;
|
||||
" > btc_1m_candles.csv
|
||||
```
|
||||
|
||||
### Option B: Using Python (With Progress Bar)
|
||||
|
||||
Create `export_candles.py`:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
import asyncpg
|
||||
import csv
|
||||
from datetime import datetime, timezone
|
||||
from tqdm import tqdm
|
||||
|
||||
async def export_candles(
|
||||
host: str,
|
||||
port: int,
|
||||
database: str,
|
||||
user: str,
|
||||
password: str,
|
||||
symbol: str = 'BTC',
|
||||
interval: str = '1m',
|
||||
start_date: str = '2025-01-01',
|
||||
output_file: str = 'candles.csv'
|
||||
):
|
||||
"""Export candles to CSV with progress bar"""
|
||||
|
||||
conn = await asyncpg.connect(
|
||||
host=host, port=port, database=database,
|
||||
user=user, password=password
|
||||
)
|
||||
|
||||
try:
|
||||
# Get total count
|
||||
total = await conn.fetchval("""
|
||||
SELECT COUNT(*) FROM candles
|
||||
WHERE symbol = $1 AND interval = $2 AND time >= $3
|
||||
""", symbol, interval, datetime.fromisoformat(start_date).replace(tzinfo=timezone.utc))
|
||||
|
||||
print(f"Exporting {total:,} candles...")
|
||||
|
||||
# Export in batches
|
||||
with open(output_file, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['time', 'symbol', 'interval', 'open', 'high', 'low', 'close', 'volume'])
|
||||
|
||||
async with conn.transaction():
|
||||
cursor = await conn.cursor(
|
||||
"SELECT time, symbol, interval, open, high, low, close, volume "
|
||||
"FROM candles WHERE symbol = $1 AND interval = $2 AND time >= $3 "
|
||||
"ORDER BY time",
|
||||
symbol, interval, datetime.fromisoformat(start_date).replace(tzinfo=timezone.utc)
|
||||
)
|
||||
|
||||
with tqdm(total=total, unit='candles') as pbar:
|
||||
while True:
|
||||
rows = await cursor.fetch(1000)
|
||||
if not rows:
|
||||
break
|
||||
for row in rows:
|
||||
writer.writerow(row)
|
||||
pbar.update(len(rows))
|
||||
|
||||
print(f"✓ Exported to {output_file}")
|
||||
print(f" Total rows: {total:,}")
|
||||
|
||||
finally:
|
||||
await conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(export_candles(
|
||||
host='192.168.1.100', # Change to your Synology IP
|
||||
port=5433,
|
||||
database='btc_data',
|
||||
user='btc_bot',
|
||||
password='your_password',
|
||||
symbol='BTC',
|
||||
interval='1m',
|
||||
start_date='2025-01-01',
|
||||
output_file='btc_1m_candles.csv'
|
||||
))
|
||||
```
|
||||
|
||||
Run:
|
||||
```bash
|
||||
pip install tqdm
|
||||
python export_candles.py
|
||||
```
|
||||
|
||||
## Step 4: Calculate Indicators on PC
|
||||
|
||||
### Using pandas-ta (Recommended)
|
||||
|
||||
Create `calculate_indicators.py`:
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import pandas_ta as ta
|
||||
from datetime import datetime
|
||||
|
||||
def calculate_indicators(input_file: str, output_file: str):
|
||||
"""Calculate technical indicators from candle data"""
|
||||
|
||||
# Read candles
|
||||
print(f"Reading {input_file}...")
|
||||
df = pd.read_csv(input_file)
|
||||
df['time'] = pd.to_datetime(df['time'])
|
||||
df = df.sort_values('time')
|
||||
|
||||
print(f"Loaded {len(df):,} candles")
|
||||
|
||||
# Calculate indicators
|
||||
print("Calculating indicators...")
|
||||
|
||||
# Moving Averages
|
||||
df['ma44'] = ta.sma(df['close'], length=44)
|
||||
df['ma125'] = ta.sma(df['close'], length=125)
|
||||
df['ma200'] = ta.sma(df['close'], length=200)
|
||||
|
||||
# RSI
|
||||
df['rsi'] = ta.rsi(df['close'], length=14)
|
||||
|
||||
# Bollinger Bands
|
||||
bb = ta.bbands(df['close'], length=20, std=2)
|
||||
df['bb_upper'] = bb['BBU_20_2.0']
|
||||
df['bb_middle'] = bb['BBM_20_2.0']
|
||||
df['bb_lower'] = bb['BBL_20_2.0']
|
||||
|
||||
# MACD
|
||||
macd = ta.macd(df['close'], fast=12, slow=26, signal=9)
|
||||
df['macd'] = macd['MACD_12_26_9']
|
||||
df['macd_signal'] = macd['MACDs_12_26_9']
|
||||
df['macd_histogram'] = macd['MACDh_12_26_9']
|
||||
|
||||
# Save
|
||||
print(f"Saving to {output_file}...")
|
||||
df.to_csv(output_file, index=False)
|
||||
|
||||
print(f"✓ Calculated {len(df.columns) - 8} indicators")
|
||||
print(f" Output: {output_file}")
|
||||
|
||||
return df
|
||||
|
||||
if __name__ == "__main__":
|
||||
df = calculate_indicators(
|
||||
input_file='btc_1m_candles.csv',
|
||||
output_file='btc_1m_with_indicators.csv'
|
||||
)
|
||||
|
||||
# Show sample
|
||||
print("\nSample data:")
|
||||
print(df[['time', 'close', 'ma44', 'ma125', 'rsi']].tail(10))
|
||||
```
|
||||
|
||||
Install dependencies:
|
||||
```bash
|
||||
pip install pandas pandas-ta
|
||||
```
|
||||
|
||||
Run:
|
||||
```bash
|
||||
python calculate_indicators.py
|
||||
```
|
||||
|
||||
### Performance Tips
|
||||
|
||||
- **Chunk processing** for very large files (> 1GB):
|
||||
```python
|
||||
# Process 100k rows at a time
|
||||
chunksize = 100000
|
||||
for chunk in pd.read_csv(input_file, chunksize=chunksize):
|
||||
# Calculate indicators for chunk
|
||||
pass
|
||||
```
|
||||
|
||||
- **Use multiple cores:**
|
||||
```python
|
||||
from multiprocessing import Pool
|
||||
# Parallelize indicator calculation
|
||||
```
|
||||
|
||||
## Step 5: Import Indicators Back to Synology
|
||||
|
||||
### Option A: Direct SQL Insert (Fastest)
|
||||
|
||||
Create `import_indicators.py`:
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
import asyncpg
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from tqdm import tqdm
|
||||
|
||||
async def import_indicators(
|
||||
host: str,
|
||||
port: int,
|
||||
database: str,
|
||||
user: str,
|
||||
password: str,
|
||||
input_file: str,
|
||||
batch_size: int = 1000
|
||||
):
|
||||
"""Import calculated indicators to Synology database"""
|
||||
|
||||
# Read calculated indicators
|
||||
print(f"Reading {input_file}...")
|
||||
df = pd.read_csv(input_file)
|
||||
print(f"Loaded {len(df):,} rows with {len(df.columns)} columns")
|
||||
|
||||
# Connect to database
|
||||
conn = await asyncpg.connect(
|
||||
host=host, port=port, database=database,
|
||||
user=user, password=password
|
||||
)
|
||||
|
||||
try:
|
||||
# Get indicator columns (exclude candle data)
|
||||
indicator_cols = [c for c in df.columns if c not in
|
||||
['time', 'symbol', 'interval', 'open', 'high', 'low', 'close', 'volume']]
|
||||
|
||||
print(f"Importing {len(indicator_cols)} indicators: {indicator_cols}")
|
||||
|
||||
# Prepare data
|
||||
symbol = df['symbol'].iloc[0]
|
||||
interval = df['interval'].iloc[0]
|
||||
|
||||
total_inserted = 0
|
||||
|
||||
with tqdm(total=len(df) * len(indicator_cols), unit='indicators') as pbar:
|
||||
for col in indicator_cols:
|
||||
# Prepare batch
|
||||
values = []
|
||||
for _, row in df.iterrows():
|
||||
if pd.notna(row[col]): # Skip NaN values
|
||||
values.append((
|
||||
row['time'],
|
||||
symbol,
|
||||
interval,
|
||||
col, # indicator_name
|
||||
float(row[col]),
|
||||
{} # parameters (JSONB)
|
||||
))
|
||||
|
||||
# Insert in batches
|
||||
for i in range(0, len(values), batch_size):
|
||||
batch = values[i:i + batch_size]
|
||||
await conn.executemany(
|
||||
"""
|
||||
INSERT INTO indicators (time, symbol, interval, indicator_name, value, parameters)
|
||||
VALUES ($1, $2, $3, $4, $5, $6)
|
||||
ON CONFLICT (time, symbol, interval, indicator_name) DO UPDATE SET
|
||||
value = EXCLUDED.value,
|
||||
updated_at = NOW()
|
||||
""",
|
||||
batch
|
||||
)
|
||||
total_inserted += len(batch)
|
||||
pbar.update(len(batch))
|
||||
|
||||
print(f"\n✓ Imported {total_inserted:,} indicator values")
|
||||
|
||||
finally:
|
||||
await conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(import_indicators(
|
||||
host='192.168.1.100',
|
||||
port=5433,
|
||||
database='btc_data',
|
||||
user='btc_bot',
|
||||
password='your_password',
|
||||
input_file='btc_1m_with_indicators.csv',
|
||||
batch_size=1000
|
||||
))
|
||||
```
|
||||
|
||||
Run:
|
||||
```bash
|
||||
python import_indicators.py
|
||||
```
|
||||
|
||||
### Option B: Using psql COPY (For Large Files)
|
||||
|
||||
```bash
|
||||
# Convert to format for COPY command
|
||||
python -c "
|
||||
import pandas as pd
|
||||
df = pd.read_csv('btc_1m_with_indicators.csv')
|
||||
# Transform to long format for indicators table
|
||||
indicators = []
|
||||
for col in ['ma44', 'ma125', 'rsi', 'bb_upper', 'bb_lower']:
|
||||
temp = df[['time', 'symbol', 'interval', col]].copy()
|
||||
temp['indicator_name'] = col
|
||||
temp = temp.rename(columns={col: 'value'})
|
||||
indicators.append(temp)
|
||||
result = pd.concat(indicators)
|
||||
result = result[result['value'].notna()]
|
||||
result.to_csv('indicators_for_import.csv', index=False)
|
||||
"
|
||||
|
||||
# Upload and import on Synology
|
||||
psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "
|
||||
COPY indicators (time, symbol, interval, indicator_name, value)
|
||||
FROM '/path/to/indicators_for_import.csv'
|
||||
DELIMITER ',' CSV HEADER;
|
||||
"
|
||||
```
|
||||
|
||||
## Complete Workflow Script
|
||||
|
||||
Create `sync_indicators.py` for one-command workflow:
|
||||
|
||||
```python
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Complete workflow: Export candles → Calculate indicators → Import to Synology
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import asyncpg
|
||||
import pandas as pd
|
||||
import pandas_ta as ta
|
||||
from datetime import datetime, timezone
|
||||
from tqdm import tqdm
|
||||
|
||||
class IndicatorSync:
|
||||
def __init__(self, host: str, port: int, database: str, user: str, password: str):
|
||||
self.db_config = {
|
||||
'host': host, 'port': port, 'database': database,
|
||||
'user': user, 'password': password
|
||||
}
|
||||
|
||||
async def export_and_calculate(
|
||||
self,
|
||||
symbol: str = 'BTC',
|
||||
interval: str = '1m',
|
||||
start_date: str = '2025-01-01',
|
||||
indicators_config: dict = None
|
||||
):
|
||||
"""Main workflow"""
|
||||
|
||||
print("="*70)
|
||||
print(f"INDICATOR SYNC: {symbol}/{interval}")
|
||||
print(f"Period: {start_date} to now")
|
||||
print("="*70)
|
||||
|
||||
# Connect to database
|
||||
conn = await asyncpg.connect(**self.db_config)
|
||||
|
||||
try:
|
||||
# Step 1: Export candles
|
||||
print("\n📥 Step 1: Exporting candles...")
|
||||
candles = await self._export_candles(conn, symbol, interval, start_date)
|
||||
print(f" Exported {len(candles):,} candles")
|
||||
|
||||
# Step 2: Calculate indicators
|
||||
print("\n⚙️ Step 2: Calculating indicators...")
|
||||
indicators_df = self._calculate_indicators(candles, indicators_config)
|
||||
print(f" Calculated {len(indicators_df)} indicator values")
|
||||
|
||||
# Step 3: Import indicators
|
||||
print("\n📤 Step 3: Importing to database...")
|
||||
await self._import_indicators(conn, indicators_df)
|
||||
print(" Import complete!")
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("✅ SYNC COMPLETE")
|
||||
print("="*70)
|
||||
|
||||
finally:
|
||||
await conn.close()
|
||||
|
||||
async def _export_candles(self, conn, symbol, interval, start_date):
|
||||
"""Export candles from database"""
|
||||
rows = await conn.fetch(
|
||||
"SELECT time, symbol, interval, open, high, low, close, volume "
|
||||
"FROM candles WHERE symbol = $1 AND interval = $2 AND time >= $3 "
|
||||
"ORDER BY time",
|
||||
symbol, interval, datetime.fromisoformat(start_date).replace(tzinfo=timezone.utc)
|
||||
)
|
||||
return pd.DataFrame(rows, columns=['time', 'symbol', 'interval', 'open', 'high', 'low', 'close', 'volume'])
|
||||
|
||||
def _calculate_indicators(self, candles_df, config=None):
|
||||
"""Calculate technical indicators"""
|
||||
df = candles_df.copy()
|
||||
|
||||
# Default indicators
|
||||
config = config or {
|
||||
'ma44': lambda d: ta.sma(d['close'], length=44),
|
||||
'ma125': lambda d: ta.sma(d['close'], length=125),
|
||||
'rsi': lambda d: ta.rsi(d['close'], length=14),
|
||||
}
|
||||
|
||||
# Calculate each indicator
|
||||
for name, func in config.items():
|
||||
df[name] = func(df)
|
||||
|
||||
# Transform to long format
|
||||
indicators = []
|
||||
indicator_cols = list(config.keys())
|
||||
|
||||
for col in indicator_cols:
|
||||
temp = df[['time', 'symbol', 'interval', col]].copy()
|
||||
temp['indicator_name'] = col
|
||||
temp = temp.rename(columns={col: 'value'})
|
||||
temp = temp[temp['value'].notna()]
|
||||
indicators.append(temp)
|
||||
|
||||
return pd.concat(indicators, ignore_index=True)
|
||||
|
||||
async def _import_indicators(self, conn, indicators_df):
|
||||
"""Import indicators to database"""
|
||||
# Convert to list of tuples
|
||||
values = [
|
||||
(row['time'], row['symbol'], row['interval'], row['indicator_name'], float(row['value']), {})
|
||||
for _, row in indicators_df.iterrows()
|
||||
]
|
||||
|
||||
# Insert in batches
|
||||
batch_size = 1000
|
||||
for i in tqdm(range(0, len(values), batch_size), desc="Importing"):
|
||||
batch = values[i:i + batch_size]
|
||||
await conn.executemany(
|
||||
"""
|
||||
INSERT INTO indicators (time, symbol, interval, indicator_name, value, parameters)
|
||||
VALUES ($1, $2, $3, $4, $5, $6)
|
||||
ON CONFLICT (time, symbol, interval, indicator_name) DO UPDATE SET
|
||||
value = EXCLUDED.value,
|
||||
updated_at = NOW()
|
||||
""",
|
||||
batch
|
||||
)
|
||||
|
||||
# Usage
|
||||
if __name__ == "__main__":
|
||||
sync = IndicatorSync(
|
||||
host='192.168.1.100', # Your Synology IP
|
||||
port=5433,
|
||||
database='btc_data',
|
||||
user='btc_bot',
|
||||
password='your_password'
|
||||
)
|
||||
|
||||
asyncio.run(sync.export_and_calculate(
|
||||
symbol='BTC',
|
||||
interval='1m',
|
||||
start_date='2025-01-01'
|
||||
))
|
||||
```
|
||||
|
||||
Run complete workflow:
|
||||
```bash
|
||||
python sync_indicators.py
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Connection Refused
|
||||
```
|
||||
Error: connection refused
|
||||
```
|
||||
- Check if PostgreSQL is running: `docker ps | grep timescale`
|
||||
- Verify port is open: `telnet SYNOLOGY_IP 5433`
|
||||
- Check firewall rules on Synology
|
||||
|
||||
### Permission Denied
|
||||
```
|
||||
Error: password authentication failed
|
||||
```
|
||||
- Verify password is correct
|
||||
- Check pg_hba.conf has proper entries
|
||||
- Restart PostgreSQL after config changes
|
||||
|
||||
### Slow Performance
|
||||
- Use batch inserts (1000 rows at a time)
|
||||
- Process large datasets in chunks
|
||||
- Consider using COPY command for very large imports
|
||||
- Close cursor after use
|
||||
|
||||
### Memory Issues
|
||||
- Don't load entire table into memory
|
||||
- Use server-side cursors
|
||||
- Process in smaller date ranges
|
||||
- Use chunksize when reading CSV
|
||||
|
||||
## Security Notes
|
||||
|
||||
⚠️ **Important:**
|
||||
- Use strong password for database user
|
||||
- Limit pg_hba.conf to specific IPs when possible
|
||||
- Use VPN if accessing over internet
|
||||
- Consider SSL connection for remote access
|
||||
- Don't commit passwords to git
|
||||
|
||||
## Next Steps
|
||||
|
||||
Once indicators are calculated and imported:
|
||||
|
||||
1. **Backtests will be fast** - Server just reads pre-calculated values
|
||||
2. **Dashboard will load quickly** - No on-the-fly calculation needed
|
||||
3. **Can add more indicators** - Just re-run sync with new calculations
|
||||
|
||||
## Alternative: SSH Tunnel (More Secure)
|
||||
|
||||
Instead of opening PostgreSQL port, use SSH tunnel:
|
||||
|
||||
```bash
|
||||
# On your PC
|
||||
ssh -L 5433:localhost:5433 admin@SYNOLOGY_IP
|
||||
|
||||
# Now connect to localhost:5433 (tunnels to Synology)
|
||||
export DB_HOST=localhost
|
||||
export DB_PORT=5433
|
||||
python sync_indicators.py
|
||||
```
|
||||
|
||||
This encrypts all traffic and doesn't require opening PostgreSQL port.
|
||||
|
||||
---
|
||||
|
||||
**Questions or issues?** Check the logs and verify each step works before proceeding to the next.
|
||||
107
scripts/check_db_stats.py
Normal file
107
scripts/check_db_stats.py
Normal file
@ -0,0 +1,107 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Quick database statistics checker
|
||||
Shows oldest date, newest date, and count for each interval
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import asyncpg
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
async def check_database_stats():
|
||||
# Database connection (uses same env vars as your app)
|
||||
conn = await asyncpg.connect(
|
||||
host=os.getenv('DB_HOST', 'localhost'),
|
||||
port=int(os.getenv('DB_PORT', 5432)),
|
||||
database=os.getenv('DB_NAME', 'btc_data'),
|
||||
user=os.getenv('DB_USER', 'btc_bot'),
|
||||
password=os.getenv('DB_PASSWORD', '')
|
||||
)
|
||||
|
||||
try:
|
||||
print("=" * 70)
|
||||
print("DATABASE STATISTICS")
|
||||
print("=" * 70)
|
||||
print()
|
||||
|
||||
# Check for each interval
|
||||
intervals = ['1m', '3m', '5m', '15m', '30m', '37m', '1h', '2h', '4h', '8h', '12h', '1d']
|
||||
|
||||
for interval in intervals:
|
||||
stats = await conn.fetchrow("""
|
||||
SELECT
|
||||
COUNT(*) as count,
|
||||
MIN(time) as oldest,
|
||||
MAX(time) as newest
|
||||
FROM candles
|
||||
WHERE symbol = 'BTC' AND interval = $1
|
||||
""", interval)
|
||||
|
||||
if stats['count'] > 0:
|
||||
oldest = stats['oldest'].strftime('%Y-%m-%d %H:%M') if stats['oldest'] else 'N/A'
|
||||
newest = stats['newest'].strftime('%Y-%m-%d %H:%M') if stats['newest'] else 'N/A'
|
||||
count = stats['count']
|
||||
|
||||
# Calculate days of data
|
||||
if stats['oldest'] and stats['newest']:
|
||||
days = (stats['newest'] - stats['oldest']).days
|
||||
print(f"{interval:6} | {count:>8,} candles | {days:>4} days | {oldest} to {newest}")
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
|
||||
# Check indicators
|
||||
print("\nINDICATORS AVAILABLE:")
|
||||
indicators = await conn.fetch("""
|
||||
SELECT DISTINCT indicator_name, interval, COUNT(*) as count
|
||||
FROM indicators
|
||||
WHERE symbol = 'BTC'
|
||||
GROUP BY indicator_name, interval
|
||||
ORDER BY interval, indicator_name
|
||||
""")
|
||||
|
||||
if indicators:
|
||||
for ind in indicators:
|
||||
print(f" {ind['indicator_name']:10} on {ind['interval']:6} | {ind['count']:>8,} values")
|
||||
else:
|
||||
print(" No indicators found in database")
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
|
||||
# Check 1m specifically with more detail
|
||||
print("\n1-MINUTE DATA DETAIL:")
|
||||
one_min_stats = await conn.fetchrow("""
|
||||
SELECT
|
||||
COUNT(*) as count,
|
||||
MIN(time) as oldest,
|
||||
MAX(time) as newest,
|
||||
COUNT(*) FILTER (WHERE time > NOW() - INTERVAL '24 hours') as last_24h
|
||||
FROM candles
|
||||
WHERE symbol = 'BTC' AND interval = '1m'
|
||||
""")
|
||||
|
||||
if one_min_stats['count'] > 0:
|
||||
total_days = (one_min_stats['newest'] - one_min_stats['oldest']).days
|
||||
expected_candles = total_days * 24 * 60 # 1 candle per minute
|
||||
actual_candles = one_min_stats['count']
|
||||
coverage = (actual_candles / expected_candles) * 100 if expected_candles > 0 else 0
|
||||
|
||||
print(f" Total candles: {actual_candles:,}")
|
||||
print(f" Date range: {one_min_stats['oldest'].strftime('%Y-%m-%d')} to {one_min_stats['newest'].strftime('%Y-%m-%d')}")
|
||||
print(f" Total days: {total_days}")
|
||||
print(f" Expected candles: {expected_candles:,} (if complete)")
|
||||
print(f" Coverage: {coverage:.1f}%")
|
||||
print(f" Last 24 hours: {one_min_stats['last_24h']:,} candles")
|
||||
else:
|
||||
print(" No 1m data found")
|
||||
|
||||
print()
|
||||
print("=" * 70)
|
||||
|
||||
finally:
|
||||
await conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(check_database_stats())
|
||||
18
scripts/check_status.sh
Normal file
18
scripts/check_status.sh
Normal file
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
# Check the status of the indicators table (constraints and compression)
|
||||
|
||||
docker exec -i btc_timescale psql -U btc_bot -d btc_data <<EOF
|
||||
\x
|
||||
SELECT 'Checking constraints...' as step;
|
||||
SELECT conname, pg_get_constraintdef(oid)
|
||||
FROM pg_constraint
|
||||
WHERE conrelid = 'indicators'::regclass;
|
||||
|
||||
SELECT 'Checking compression settings...' as step;
|
||||
SELECT * FROM timescaledb_information.hypertables
|
||||
WHERE hypertable_name = 'indicators';
|
||||
|
||||
SELECT 'Checking compression jobs...' as step;
|
||||
SELECT * FROM timescaledb_information.jobs
|
||||
WHERE hypertable_name = 'indicators';
|
||||
EOF
|
||||
54
scripts/fix_indicators_v2.sh
Normal file
54
scripts/fix_indicators_v2.sh
Normal file
@ -0,0 +1,54 @@
|
||||
#!/bin/bash
|
||||
# Fix indicators table schema - Version 2 (Final)
|
||||
# Handles TimescaleDB compression constraints properly
|
||||
|
||||
echo "Fixing indicators table schema (v2)..."
|
||||
|
||||
# 1. Decompress chunks individually (safest method)
|
||||
# We fetch the list of compressed chunks and process them one by one
|
||||
echo "Checking for compressed chunks..."
|
||||
CHUNKS=$(docker exec -i btc_timescale psql -U btc_bot -d btc_data -t -c "SELECT chunk_schema || '.' || chunk_name FROM timescaledb_information.chunks WHERE hypertable_name = 'indicators' AND is_compressed = true;")
|
||||
|
||||
for chunk in $CHUNKS; do
|
||||
# Trim whitespace
|
||||
chunk=$(echo "$chunk" | xargs)
|
||||
if [[ ! -z "$chunk" ]]; then
|
||||
echo "Decompressing chunk: $chunk"
|
||||
docker exec -i btc_timescale psql -U btc_bot -d btc_data -c "SELECT decompress_chunk('$chunk');"
|
||||
fi
|
||||
done
|
||||
|
||||
# 2. Execute the schema changes
|
||||
docker exec -i btc_timescale psql -U btc_bot -d btc_data <<EOF
|
||||
BEGIN;
|
||||
|
||||
-- Remove policy first
|
||||
SELECT remove_compression_policy('indicators', if_exists => true);
|
||||
|
||||
-- Disable compression setting (REQUIRED to add unique constraint)
|
||||
ALTER TABLE indicators SET (timescaledb.compress = false);
|
||||
|
||||
-- Deduplicate data (just in case duplicates exist)
|
||||
DELETE FROM indicators a USING indicators b
|
||||
WHERE a.ctid < b.ctid
|
||||
AND a.time = b.time
|
||||
AND a.symbol = b.symbol
|
||||
AND a.interval = b.interval
|
||||
AND a.indicator_name = b.indicator_name;
|
||||
|
||||
-- Add the unique constraint
|
||||
ALTER TABLE indicators ADD CONSTRAINT indicators_unique UNIQUE (time, symbol, interval, indicator_name);
|
||||
|
||||
-- Re-enable compression configuration
|
||||
ALTER TABLE indicators SET (
|
||||
timescaledb.compress,
|
||||
timescaledb.compress_segmentby = 'symbol,interval,indicator_name'
|
||||
);
|
||||
|
||||
-- Re-add compression policy (7 days)
|
||||
SELECT add_compression_policy('indicators', INTERVAL '7 days', if_not_exists => true);
|
||||
|
||||
COMMIT;
|
||||
|
||||
SELECT 'Indicators schema fix v2 completed successfully' as status;
|
||||
EOF
|
||||
11
scripts/run_test.sh
Normal file
11
scripts/run_test.sh
Normal file
@ -0,0 +1,11 @@
|
||||
#!/bin/bash
|
||||
# Run performance test inside Docker container
|
||||
# Usage: ./run_test.sh [days] [interval]
|
||||
|
||||
DAYS=${1:-7}
|
||||
INTERVAL=${2:-1m}
|
||||
|
||||
echo "Running MA44 performance test: ${DAYS} days of ${INTERVAL} data"
|
||||
echo "=================================================="
|
||||
|
||||
docker exec btc_collector python scripts/test_ma44_performance.py --days $DAYS --interval $INTERVAL
|
||||
187
scripts/test_ma44_performance.py
Normal file
187
scripts/test_ma44_performance.py
Normal file
@ -0,0 +1,187 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Performance Test Script for MA44 Strategy
|
||||
Tests backtesting performance on Synology DS218+ with 6GB RAM
|
||||
|
||||
Usage:
|
||||
python test_ma44_performance.py [--days DAYS] [--interval INTERVAL]
|
||||
|
||||
Example:
|
||||
python test_ma44_performance.py --days 7 --interval 1m
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import argparse
|
||||
import time
|
||||
import sys
|
||||
import os
|
||||
from datetime import datetime, timedelta, timezone
|
||||
|
||||
# Add src to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
|
||||
|
||||
from data_collector.database import DatabaseManager
|
||||
from data_collector.indicator_engine import IndicatorEngine, IndicatorConfig
|
||||
from data_collector.brain import Brain
|
||||
from data_collector.backtester import Backtester
|
||||
|
||||
|
||||
async def run_performance_test(days: int = 7, interval: str = "1m"):
|
||||
"""Run MA44 backtest and measure performance"""
|
||||
|
||||
print("=" * 70)
|
||||
print(f"PERFORMANCE TEST: MA44 Strategy")
|
||||
print(f"Timeframe: {interval}")
|
||||
print(f"Period: Last {days} days")
|
||||
print(f"Hardware: Synology DS218+ (6GB RAM)")
|
||||
print("=" * 70)
|
||||
print()
|
||||
|
||||
# Database connection (adjust these if needed)
|
||||
db = DatabaseManager(
|
||||
host=os.getenv('DB_HOST', 'localhost'),
|
||||
port=int(os.getenv('DB_PORT', 5432)),
|
||||
database=os.getenv('DB_NAME', 'btc_data'),
|
||||
user=os.getenv('DB_USER', 'btc_bot'),
|
||||
password=os.getenv('DB_PASSWORD', '')
|
||||
)
|
||||
|
||||
try:
|
||||
await db.connect()
|
||||
print("✓ Database connected")
|
||||
|
||||
# Calculate date range
|
||||
end_date = datetime.now(timezone.utc)
|
||||
start_date = end_date - timedelta(days=days)
|
||||
|
||||
print(f"✓ Date range: {start_date.date()} to {end_date.date()}")
|
||||
print(f"✓ Symbol: BTC")
|
||||
print(f"✓ Strategy: MA44 (44-period SMA)")
|
||||
print()
|
||||
|
||||
# Check data availability
|
||||
async with db.acquire() as conn:
|
||||
count = await conn.fetchval("""
|
||||
SELECT COUNT(*) FROM candles
|
||||
WHERE symbol = 'BTC'
|
||||
AND interval = $1
|
||||
AND time >= $2
|
||||
AND time <= $3
|
||||
""", interval, start_date, end_date)
|
||||
|
||||
print(f"📊 Data points: {count:,} {interval} candles")
|
||||
|
||||
if count == 0:
|
||||
print("❌ ERROR: No data found for this period!")
|
||||
print(f" Run: python -m data_collector.backfill --days {days} --intervals {interval}")
|
||||
return
|
||||
|
||||
print(f" (Expected: ~{count * int(interval.replace('m','').replace('h','').replace('d',''))} minutes of data)")
|
||||
print()
|
||||
|
||||
# Setup indicator configuration
|
||||
indicator_configs = [
|
||||
IndicatorConfig("ma44", "sma", 44, [interval])
|
||||
]
|
||||
|
||||
engine = IndicatorEngine(db, indicator_configs)
|
||||
brain = Brain(db, engine)
|
||||
backtester = Backtester(db, engine, brain)
|
||||
|
||||
print("⚙️ Running backtest...")
|
||||
print("-" * 70)
|
||||
|
||||
# Measure execution time
|
||||
start_time = time.time()
|
||||
|
||||
await backtester.run("BTC", [interval], start_date, end_date)
|
||||
|
||||
end_time = time.time()
|
||||
execution_time = end_time - start_time
|
||||
|
||||
print("-" * 70)
|
||||
print()
|
||||
|
||||
# Fetch results from database
|
||||
async with db.acquire() as conn:
|
||||
latest_backtest = await conn.fetchrow("""
|
||||
SELECT id, strategy, start_time, end_time, intervals, results, created_at
|
||||
FROM backtest_runs
|
||||
WHERE strategy LIKE '%ma44%'
|
||||
ORDER BY created_at DESC
|
||||
LIMIT 1
|
||||
""")
|
||||
|
||||
if latest_backtest and latest_backtest['results']:
|
||||
import json
|
||||
results = json.loads(latest_backtest['results'])
|
||||
|
||||
print("📈 RESULTS:")
|
||||
print("=" * 70)
|
||||
print(f" Total Trades: {results.get('total_trades', 'N/A')}")
|
||||
print(f" Win Rate: {results.get('win_rate', 0):.1f}%")
|
||||
print(f" Win Count: {results.get('win_count', 0)}")
|
||||
print(f" Loss Count: {results.get('loss_count', 0)}")
|
||||
print(f" Total P&L: ${results.get('total_pnl', 0):.2f}")
|
||||
print(f" P&L Percent: {results.get('total_pnl_pct', 0):.2f}%")
|
||||
print(f" Initial Balance: ${results.get('initial_balance', 1000):.2f}")
|
||||
print(f" Final Balance: ${results.get('final_balance', 1000):.2f}")
|
||||
print(f" Max Drawdown: {results.get('max_drawdown', 0):.2f}%")
|
||||
print()
|
||||
print("⏱️ PERFORMANCE:")
|
||||
print(f" Execution Time: {execution_time:.2f} seconds")
|
||||
print(f" Candles/Second: {count / execution_time:.0f}")
|
||||
print(f" Backtest ID: {latest_backtest['id']}")
|
||||
print()
|
||||
|
||||
# Performance assessment
|
||||
if execution_time < 30:
|
||||
print("✅ PERFORMANCE: Excellent (< 30s)")
|
||||
elif execution_time < 60:
|
||||
print("✅ PERFORMANCE: Good (< 60s)")
|
||||
elif execution_time < 300:
|
||||
print("⚠️ PERFORMANCE: Acceptable (1-5 min)")
|
||||
else:
|
||||
print("❌ PERFORMANCE: Slow (> 5 min) - Consider shorter periods or higher TFs")
|
||||
|
||||
print()
|
||||
print("💡 RECOMMENDATIONS:")
|
||||
if execution_time > 60:
|
||||
print(" • For faster results, use higher timeframes (15m, 1h, 4h)")
|
||||
print(" • Or reduce date range (< 7 days)")
|
||||
else:
|
||||
print(" • Hardware is sufficient for this workload")
|
||||
print(" • Can handle larger date ranges or multiple timeframes")
|
||||
|
||||
else:
|
||||
print("❌ ERROR: No results found in database!")
|
||||
print(" The backtest may have failed. Check server logs.")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ ERROR: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
finally:
|
||||
await db.disconnect()
|
||||
print()
|
||||
print("=" * 70)
|
||||
print("Test completed")
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='Test MA44 backtest performance')
|
||||
parser.add_argument('--days', type=int, default=7,
|
||||
help='Number of days to backtest (default: 7)')
|
||||
parser.add_argument('--interval', type=str, default='1m',
|
||||
help='Candle interval (default: 1m)')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Run the async test
|
||||
asyncio.run(run_performance_test(args.days, args.interval))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
87
scripts/update_schema.sh
Normal file
87
scripts/update_schema.sh
Normal file
@ -0,0 +1,87 @@
|
||||
#!/bin/bash
|
||||
# Apply schema updates to a running TimescaleDB container without wiping data
|
||||
|
||||
echo "Applying schema updates to btc_timescale container..."
|
||||
|
||||
# Execute the schema SQL inside the container
|
||||
# We use psql with the environment variables set in docker-compose
|
||||
docker exec -i btc_timescale psql -U btc_bot -d btc_data <<EOF
|
||||
-- 1. Unique constraint for indicators (if not exists)
|
||||
DO \$\$
|
||||
BEGIN
|
||||
IF NOT EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'indicators_unique') THEN
|
||||
ALTER TABLE indicators ADD CONSTRAINT indicators_unique UNIQUE (time, symbol, interval, indicator_name);
|
||||
END IF;
|
||||
END \$\$;
|
||||
|
||||
-- 2. Index for indicators
|
||||
CREATE INDEX IF NOT EXISTS idx_indicators_lookup ON indicators (symbol, interval, indicator_name, time DESC);
|
||||
|
||||
-- 3. Data health view update
|
||||
CREATE OR REPLACE VIEW data_health AS
|
||||
SELECT
|
||||
symbol,
|
||||
COUNT(*) as total_candles,
|
||||
COUNT(*) FILTER (WHERE validated) as validated_candles,
|
||||
MAX(time) as latest_candle,
|
||||
MIN(time) as earliest_candle,
|
||||
NOW() - MAX(time) as time_since_last
|
||||
FROM candles
|
||||
GROUP BY symbol;
|
||||
|
||||
-- 4. Decisions table
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
time TIMESTAMPTZ NOT NULL,
|
||||
symbol TEXT NOT NULL,
|
||||
interval TEXT NOT NULL,
|
||||
decision_type TEXT NOT NULL,
|
||||
strategy TEXT NOT NULL,
|
||||
confidence DECIMAL(5,4),
|
||||
price_at_decision DECIMAL(18,8),
|
||||
indicator_snapshot JSONB NOT NULL,
|
||||
candle_snapshot JSONB NOT NULL,
|
||||
reasoning TEXT,
|
||||
backtest_id TEXT,
|
||||
executed BOOLEAN DEFAULT FALSE,
|
||||
execution_price DECIMAL(18,8),
|
||||
execution_time TIMESTAMPTZ,
|
||||
created_at TIMESTAMPTZ DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- 5. Decisions hypertable (ignore error if already exists)
|
||||
DO \$\$
|
||||
BEGIN
|
||||
PERFORM create_hypertable('decisions', 'time', chunk_time_interval => INTERVAL '7 days', if_not_exists => TRUE);
|
||||
EXCEPTION WHEN OTHERS THEN
|
||||
NULL; -- Ignore if already hypertable
|
||||
END \$\$;
|
||||
|
||||
-- 6. Decisions indexes
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_live ON decisions (symbol, interval, time DESC) WHERE backtest_id IS NULL;
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_backtest ON decisions (backtest_id, symbol, time DESC) WHERE backtest_id IS NOT NULL;
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type ON decisions (symbol, decision_type, time DESC);
|
||||
|
||||
-- 7. Backtest runs table
|
||||
CREATE TABLE IF NOT EXISTS backtest_runs (
|
||||
id TEXT PRIMARY KEY,
|
||||
strategy TEXT NOT NULL,
|
||||
symbol TEXT NOT NULL DEFAULT 'BTC',
|
||||
start_time TIMESTAMPTZ NOT NULL,
|
||||
end_time TIMESTAMPTZ NOT NULL,
|
||||
intervals TEXT[] NOT NULL,
|
||||
config JSONB,
|
||||
results JSONB,
|
||||
created_at TIMESTAMPTZ DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- 8. Compression policies
|
||||
DO \$\$
|
||||
BEGIN
|
||||
ALTER TABLE decisions SET (timescaledb.compress, timescaledb.compress_segmentby = 'symbol,interval,strategy');
|
||||
PERFORM add_compression_policy('decisions', INTERVAL '7 days', if_not_exists => TRUE);
|
||||
EXCEPTION WHEN OTHERS THEN
|
||||
NULL; -- Ignore compression errors if already set
|
||||
END \$\$;
|
||||
|
||||
SELECT 'Schema update completed successfully' as status;
|
||||
EOF
|
||||
@ -396,13 +396,173 @@
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
@media (max-width: 1200px) {
|
||||
/* Simulation Panel Styles */
|
||||
.sim-strategies {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 4px;
|
||||
margin: 8px 0;
|
||||
}
|
||||
|
||||
.sim-strategy-option {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
padding: 8px;
|
||||
border-radius: 4px;
|
||||
cursor: pointer;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.sim-strategy-option:hover {
|
||||
background: var(--tv-hover);
|
||||
}
|
||||
|
||||
.sim-strategy-option input[type="radio"] {
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.sim-strategy-option label {
|
||||
cursor: pointer;
|
||||
flex: 1;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.sim-strategy-info {
|
||||
color: var(--tv-text-secondary);
|
||||
cursor: help;
|
||||
position: relative;
|
||||
font-size: 14px;
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
border-radius: 50%;
|
||||
transition: background 0.2s;
|
||||
}
|
||||
|
||||
.sim-strategy-info:hover {
|
||||
background: var(--tv-hover);
|
||||
color: var(--tv-text);
|
||||
}
|
||||
|
||||
/* Tooltip */
|
||||
.sim-strategy-info:hover::after {
|
||||
content: attr(data-tooltip);
|
||||
position: absolute;
|
||||
bottom: 100%;
|
||||
right: 0;
|
||||
background: var(--tv-panel-bg);
|
||||
border: 1px solid var(--tv-border);
|
||||
padding: 8px 12px;
|
||||
border-radius: 4px;
|
||||
font-size: 12px;
|
||||
width: 250px;
|
||||
z-index: 100;
|
||||
box-shadow: 0 4px 12px rgba(0,0,0,0.3);
|
||||
color: var(--tv-text);
|
||||
margin-bottom: 4px;
|
||||
line-height: 1.4;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
.sim-input-group {
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
|
||||
.sim-input-group label {
|
||||
display: block;
|
||||
font-size: 11px;
|
||||
color: var(--tv-text-secondary);
|
||||
margin-bottom: 4px;
|
||||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
.sim-input {
|
||||
width: 100%;
|
||||
padding: 8px 12px;
|
||||
background: var(--tv-bg);
|
||||
border: 1px solid var(--tv-border);
|
||||
border-radius: 4px;
|
||||
color: var(--tv-text);
|
||||
font-size: 13px;
|
||||
font-family: inherit;
|
||||
}
|
||||
|
||||
.sim-input:focus {
|
||||
outline: none;
|
||||
border-color: var(--tv-blue);
|
||||
}
|
||||
|
||||
.sim-run-btn {
|
||||
width: 100%;
|
||||
padding: 10px;
|
||||
background: var(--tv-blue);
|
||||
color: white;
|
||||
border: none;
|
||||
border-radius: 4px;
|
||||
cursor: pointer;
|
||||
font-size: 13px;
|
||||
font-weight: 600;
|
||||
transition: opacity 0.2s;
|
||||
margin-top: 8px;
|
||||
}
|
||||
|
||||
.sim-run-btn:hover:not(:disabled) {
|
||||
opacity: 0.9;
|
||||
}
|
||||
|
||||
.sim-run-btn:disabled {
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.sim-results {
|
||||
margin-top: 16px;
|
||||
padding-top: 16px;
|
||||
border-top: 1px solid var(--tv-border);
|
||||
}
|
||||
|
||||
.sim-stat-row {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
padding: 6px 0;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.sim-stat-row span:first-child {
|
||||
color: var(--tv-text-secondary);
|
||||
}
|
||||
|
||||
.sim-value {
|
||||
font-weight: 600;
|
||||
font-family: 'Courier New', monospace;
|
||||
}
|
||||
|
||||
.sim-value.positive { color: var(--tv-green); }
|
||||
.sim-value.negative { color: var(--tv-red); }
|
||||
|
||||
.loading-strategies {
|
||||
padding: 12px;
|
||||
text-align: center;
|
||||
color: var(--tv-text-secondary);
|
||||
font-size: 12px;
|
||||
}
|
||||
|
||||
@media (max-width: 1400px) {
|
||||
.ta-content {
|
||||
grid-template-columns: repeat(3, 1fr);
|
||||
}
|
||||
}
|
||||
|
||||
@media (max-width: 1000px) {
|
||||
.ta-content {
|
||||
grid-template-columns: repeat(2, 1fr);
|
||||
}
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
|
||||
@media (max-width: 600px) {
|
||||
.ta-content {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
@ -834,18 +994,47 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="ta-section">
|
||||
<div class="ta-section-title">Price Info</div>
|
||||
<div class="ta-level">
|
||||
<span class="ta-level-label">Current</span>
|
||||
<span class="ta-level-value">$${data.current_price.toLocaleString()}</span>
|
||||
<div class="ta-section" id="simulationPanel">
|
||||
<div class="ta-section-title">Strategy Simulation</div>
|
||||
|
||||
<!-- Date picker -->
|
||||
<div class="sim-input-group" style="margin: 0 0 8px 0;">
|
||||
<label style="font-size: 10px; text-transform: uppercase; color: var(--tv-text-secondary);">Start Date:</label>
|
||||
<input type="datetime-local" id="simStartDate" class="sim-input" style="margin-top: 2px;">
|
||||
</div>
|
||||
<div style="font-size: 12px; color: var(--tv-text-secondary); margin-top: 8px;">
|
||||
Based on last 200 candles<br>
|
||||
Strategy: Trend following with MA crossovers
|
||||
|
||||
<!-- Strategies loaded dynamically here -->
|
||||
<div id="strategyList" class="sim-strategies" style="max-height: 100px; overflow-y: auto;">
|
||||
<div class="loading-strategies">Loading strategies...</div>
|
||||
</div>
|
||||
|
||||
<button class="sim-run-btn" onclick="runSimulation()" id="runSimBtn" disabled style="padding: 6px; font-size: 12px; margin-top: 6px;">
|
||||
Run Simulation
|
||||
</button>
|
||||
|
||||
<!-- Results -->
|
||||
<div id="simResults" class="sim-results" style="display: none; margin-top: 8px; padding-top: 8px;">
|
||||
<div class="sim-stat-row" style="padding: 2px 0; font-size: 11px;">
|
||||
<span>Trades:</span>
|
||||
<span id="simTrades" class="sim-value">--</span>
|
||||
</div>
|
||||
<div class="sim-stat-row" style="padding: 2px 0; font-size: 11px;">
|
||||
<span>Win Rate:</span>
|
||||
<span id="simWinRate" class="sim-value">--</span>
|
||||
</div>
|
||||
<div class="sim-stat-row" style="padding: 2px 0; font-size: 11px;">
|
||||
<span>Total P&L:</span>
|
||||
<span id="simPnL" class="sim-value">--</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Load strategies after simulation panel is rendered
|
||||
setTimeout(() => {
|
||||
loadStrategies();
|
||||
setDefaultStartDate();
|
||||
}, 0);
|
||||
}
|
||||
|
||||
updateStats(candle) {
|
||||
@ -891,6 +1080,226 @@
|
||||
window.open(geminiUrl, '_blank');
|
||||
}
|
||||
|
||||
// Load strategies on page load
|
||||
async function loadStrategies() {
|
||||
try {
|
||||
console.log('Fetching strategies from API...');
|
||||
|
||||
// Add timeout to fetch
|
||||
const controller = new AbortController();
|
||||
const timeoutId = setTimeout(() => controller.abort(), 5000); // 5 second timeout
|
||||
|
||||
const response = await fetch('/api/v1/strategies', {
|
||||
signal: controller.signal
|
||||
});
|
||||
clearTimeout(timeoutId);
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`HTTP error! status: ${response.status}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
console.log('Strategies loaded:', data);
|
||||
|
||||
if (!data.strategies) {
|
||||
throw new Error('Invalid response format: missing strategies array');
|
||||
}
|
||||
|
||||
renderStrategies(data.strategies);
|
||||
} catch (error) {
|
||||
console.error('Error loading strategies:', error);
|
||||
|
||||
let errorMessage = error.message;
|
||||
if (error.name === 'AbortError') {
|
||||
errorMessage = 'Request timeout - API server not responding';
|
||||
} else if (error.message.includes('Failed to fetch')) {
|
||||
errorMessage = 'Cannot connect to API server - is it running?';
|
||||
}
|
||||
|
||||
document.getElementById('strategyList').innerHTML =
|
||||
`<div class="loading-strategies" style="color: var(--tv-red);">
|
||||
${errorMessage}<br>
|
||||
<small>Check console (F12) for details</small>
|
||||
</div>`;
|
||||
}
|
||||
}
|
||||
|
||||
// Render strategy list
|
||||
function renderStrategies(strategies) {
|
||||
const container = document.getElementById('strategyList');
|
||||
|
||||
if (!strategies || strategies.length === 0) {
|
||||
container.innerHTML = '<div class="loading-strategies">No strategies available</div>';
|
||||
return;
|
||||
}
|
||||
|
||||
container.innerHTML = strategies.map((s, index) => `
|
||||
<div class="sim-strategy-option">
|
||||
<input type="radio" name="strategy" id="strat_${s.id}"
|
||||
value="${s.id}" ${index === 0 ? 'checked' : ''}>
|
||||
<label for="strat_${s.id}">${s.name}</label>
|
||||
<span class="sim-strategy-info" data-tooltip="${s.description}">ⓘ</span>
|
||||
</div>
|
||||
`).join('');
|
||||
|
||||
// Enable run button
|
||||
document.getElementById('runSimBtn').disabled = false;
|
||||
}
|
||||
|
||||
// Run simulation
|
||||
async function runSimulation() {
|
||||
const selectedStrategy = document.querySelector('input[name="strategy"]:checked');
|
||||
|
||||
if (!selectedStrategy) {
|
||||
alert('Please select a strategy');
|
||||
return;
|
||||
}
|
||||
|
||||
const strategyId = selectedStrategy.value;
|
||||
const startDateInput = document.getElementById('simStartDate').value;
|
||||
|
||||
if (!startDateInput) {
|
||||
alert('Please select a start date');
|
||||
return;
|
||||
}
|
||||
|
||||
// Format date for API
|
||||
const startDate = new Date(startDateInput).toISOString().split('T')[0];
|
||||
|
||||
// Disable button during simulation
|
||||
const runBtn = document.getElementById('runSimBtn');
|
||||
runBtn.disabled = true;
|
||||
runBtn.textContent = 'Running...';
|
||||
|
||||
try {
|
||||
// Trigger backtest via API
|
||||
const response = await fetch('/api/v1/backtests', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify({
|
||||
symbol: 'BTC',
|
||||
intervals: [window.dashboard?.currentInterval || '1d'],
|
||||
start_date: startDate
|
||||
})
|
||||
});
|
||||
|
||||
const result = await response.json();
|
||||
|
||||
if (result.message) {
|
||||
// Show that simulation is running
|
||||
runBtn.textContent = 'Running...';
|
||||
|
||||
// Poll for results
|
||||
setTimeout(() => {
|
||||
pollForBacktestResults(strategyId, startDate);
|
||||
}, 2000); // Wait 2 seconds then poll
|
||||
} else {
|
||||
alert('Failed to start simulation');
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error running simulation:', error);
|
||||
alert('Error running simulation: ' + error.message);
|
||||
// Reset button only on error
|
||||
runBtn.disabled = false;
|
||||
runBtn.textContent = 'Run Simulation';
|
||||
}
|
||||
// Button stays as "Running..." until polling completes or times out
|
||||
}
|
||||
|
||||
// Poll for backtest results
|
||||
async function pollForBacktestResults(strategyId, startDate, attempts = 0) {
|
||||
const runBtn = document.getElementById('runSimBtn');
|
||||
|
||||
if (attempts > 30) { // Stop after 30 attempts (60 seconds)
|
||||
console.log('Backtest polling timeout');
|
||||
runBtn.textContent = 'Run Simulation';
|
||||
runBtn.disabled = false;
|
||||
|
||||
// Show timeout message in results area
|
||||
const simResults = document.getElementById('simResults');
|
||||
if (simResults) {
|
||||
simResults.innerHTML = `
|
||||
<div class="sim-stat-row" style="color: var(--tv-text-secondary); font-size: 11px; text-align: center;">
|
||||
<span>Simulation timeout - no results found after 60s.<br>Check server logs or try again.</span>
|
||||
</div>
|
||||
`;
|
||||
simResults.style.display = 'block';
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const response = await fetch('/api/v1/backtests?limit=5');
|
||||
const backtests = await response.json();
|
||||
|
||||
// Find the most recent backtest that matches our criteria
|
||||
const recentBacktest = backtests.find(bt =>
|
||||
bt.strategy && bt.strategy.includes(strategyId) ||
|
||||
bt.created_at > new Date(Date.now() - 60000).toISOString() // Created in last minute
|
||||
);
|
||||
|
||||
if (recentBacktest && recentBacktest.results) {
|
||||
// Parse JSON string if needed (database stores results as text)
|
||||
const parsedBacktest = {
|
||||
...recentBacktest,
|
||||
results: typeof recentBacktest.results === 'string'
|
||||
? JSON.parse(recentBacktest.results)
|
||||
: recentBacktest.results
|
||||
};
|
||||
// Results found! Display them
|
||||
displayBacktestResults(parsedBacktest);
|
||||
runBtn.textContent = 'Run Simulation';
|
||||
runBtn.disabled = false;
|
||||
return;
|
||||
}
|
||||
|
||||
// No results yet, poll again in 2 seconds
|
||||
setTimeout(() => {
|
||||
pollForBacktestResults(strategyId, startDate, attempts + 1);
|
||||
}, 2000);
|
||||
|
||||
} catch (error) {
|
||||
console.error('Error polling for backtest results:', error);
|
||||
runBtn.textContent = 'Run Simulation';
|
||||
runBtn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
// Display backtest results in the UI
|
||||
function displayBacktestResults(backtest) {
|
||||
// Parse JSON string if needed (database stores results as text)
|
||||
const results = typeof backtest.results === 'string'
|
||||
? JSON.parse(backtest.results)
|
||||
: backtest.results;
|
||||
|
||||
// Update the results display
|
||||
document.getElementById('simTrades').textContent = results.total_trades || '--';
|
||||
document.getElementById('simWinRate').textContent = results.win_rate ? results.win_rate.toFixed(1) + '%' : '--';
|
||||
|
||||
const pnlElement = document.getElementById('simPnL');
|
||||
const pnl = results.total_pnl || 0;
|
||||
pnlElement.textContent = (pnl >= 0 ? '+' : '') + '$' + pnl.toFixed(2);
|
||||
pnlElement.className = 'sim-value ' + (pnl >= 0 ? 'positive' : 'negative');
|
||||
|
||||
// Show results section
|
||||
document.getElementById('simResults').style.display = 'block';
|
||||
|
||||
console.log('Backtest results:', backtest);
|
||||
}
|
||||
|
||||
// Set default start date (7 days ago)
|
||||
function setDefaultStartDate() {
|
||||
const startDateInput = document.getElementById('simStartDate');
|
||||
if (startDateInput) {
|
||||
const sevenDaysAgo = new Date();
|
||||
sevenDaysAgo.setDate(sevenDaysAgo.getDate() - 7);
|
||||
// Format as datetime-local: YYYY-MM-DDTHH:mm
|
||||
startDateInput.value = sevenDaysAgo.toISOString().slice(0, 16);
|
||||
}
|
||||
}
|
||||
|
||||
document.addEventListener('DOMContentLoaded', () => {
|
||||
window.dashboard = new TradingDashboard();
|
||||
});
|
||||
|
||||
@ -6,17 +6,28 @@ Removes the complex WebSocket manager that was causing issues
|
||||
import os
|
||||
import asyncio
|
||||
import logging
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import Optional
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Query
|
||||
from fastapi import FastAPI, HTTPException, Query, BackgroundTasks
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from fastapi.responses import StreamingResponse
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
import asyncpg
|
||||
import csv
|
||||
import io
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
# Imports for backtest runner
|
||||
from src.data_collector.database import DatabaseManager
|
||||
from src.data_collector.indicator_engine import IndicatorEngine, IndicatorConfig
|
||||
from src.data_collector.brain import Brain
|
||||
from src.data_collector.backtester import Backtester
|
||||
|
||||
# Imports for strategy discovery
|
||||
import importlib
|
||||
from src.strategies.base import BaseStrategy
|
||||
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
@ -88,6 +99,41 @@ async def root():
|
||||
}
|
||||
|
||||
|
||||
@app.get("/api/v1/strategies")
|
||||
async def list_strategies():
|
||||
"""List all available trading strategies with metadata"""
|
||||
# Strategy registry from brain.py
|
||||
strategy_registry = {
|
||||
"ma44_strategy": "src.strategies.ma44_strategy.MA44Strategy",
|
||||
"ma125_strategy": "src.strategies.ma125_strategy.MA125Strategy",
|
||||
}
|
||||
|
||||
strategies = []
|
||||
|
||||
for strategy_id, class_path in strategy_registry.items():
|
||||
try:
|
||||
module_path, class_name = class_path.rsplit('.', 1)
|
||||
module = importlib.import_module(module_path)
|
||||
strategy_class = getattr(module, class_name)
|
||||
|
||||
# Instantiate to get metadata
|
||||
strategy_instance = strategy_class()
|
||||
|
||||
strategies.append({
|
||||
"id": strategy_id,
|
||||
"name": strategy_instance.display_name,
|
||||
"description": strategy_instance.description,
|
||||
"required_indicators": strategy_instance.required_indicators
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load strategy {strategy_id}: {e}")
|
||||
|
||||
return {
|
||||
"strategies": strategies,
|
||||
"count": len(strategies)
|
||||
}
|
||||
|
||||
|
||||
@app.get("/api/v1/candles")
|
||||
async def get_candles(
|
||||
symbol: str = Query("BTC", description="Trading pair symbol"),
|
||||
@ -215,6 +261,155 @@ async def health_check():
|
||||
raise HTTPException(status_code=503, detail=f"Health check failed: {str(e)}")
|
||||
|
||||
|
||||
@app.get("/api/v1/indicators")
|
||||
async def get_indicators(
|
||||
symbol: str = Query("BTC", description="Trading pair symbol"),
|
||||
interval: str = Query("1d", description="Candle interval"),
|
||||
name: str = Query(None, description="Filter by indicator name (e.g., ma44)"),
|
||||
start: Optional[datetime] = Query(None, description="Start time"),
|
||||
end: Optional[datetime] = Query(None, description="End time"),
|
||||
limit: int = Query(1000, le=5000)
|
||||
):
|
||||
"""Get indicator values"""
|
||||
async with pool.acquire() as conn:
|
||||
query = """
|
||||
SELECT time, indicator_name, value
|
||||
FROM indicators
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
"""
|
||||
params = [symbol, interval]
|
||||
|
||||
if name:
|
||||
query += f" AND indicator_name = ${len(params) + 1}"
|
||||
params.append(name)
|
||||
|
||||
if start:
|
||||
query += f" AND time >= ${len(params) + 1}"
|
||||
params.append(start)
|
||||
|
||||
if end:
|
||||
query += f" AND time <= ${len(params) + 1}"
|
||||
params.append(end)
|
||||
|
||||
query += f" ORDER BY time DESC LIMIT ${len(params) + 1}"
|
||||
params.append(limit)
|
||||
|
||||
rows = await conn.fetch(query, *params)
|
||||
|
||||
# Group by time for easier charting
|
||||
grouped = {}
|
||||
for row in rows:
|
||||
ts = row['time'].isoformat()
|
||||
if ts not in grouped:
|
||||
grouped[ts] = {'time': ts}
|
||||
grouped[ts][row['indicator_name']] = float(row['value'])
|
||||
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"interval": interval,
|
||||
"data": list(grouped.values())
|
||||
}
|
||||
|
||||
|
||||
@app.get("/api/v1/decisions")
|
||||
async def get_decisions(
|
||||
symbol: str = Query("BTC"),
|
||||
interval: Optional[str] = Query(None),
|
||||
backtest_id: Optional[str] = Query(None),
|
||||
limit: int = Query(100, le=1000)
|
||||
):
|
||||
"""Get brain decisions"""
|
||||
async with pool.acquire() as conn:
|
||||
query = """
|
||||
SELECT time, interval, decision_type, strategy, confidence,
|
||||
price_at_decision, indicator_snapshot, reasoning, backtest_id
|
||||
FROM decisions
|
||||
WHERE symbol = $1
|
||||
"""
|
||||
params = [symbol]
|
||||
|
||||
if interval:
|
||||
query += f" AND interval = ${len(params) + 1}"
|
||||
params.append(interval)
|
||||
|
||||
if backtest_id:
|
||||
query += f" AND backtest_id = ${len(params) + 1}"
|
||||
params.append(backtest_id)
|
||||
else:
|
||||
query += " AND backtest_id IS NULL"
|
||||
|
||||
query += f" ORDER BY time DESC LIMIT ${len(params) + 1}"
|
||||
params.append(limit)
|
||||
|
||||
rows = await conn.fetch(query, *params)
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
|
||||
@app.get("/api/v1/backtests")
|
||||
async def list_backtests(symbol: Optional[str] = None, limit: int = 20):
|
||||
"""List historical backtests"""
|
||||
async with pool.acquire() as conn:
|
||||
query = """
|
||||
SELECT id, strategy, symbol, start_time, end_time,
|
||||
intervals, results, created_at
|
||||
FROM backtest_runs
|
||||
"""
|
||||
params = []
|
||||
if symbol:
|
||||
query += " WHERE symbol = $1"
|
||||
params.append(symbol)
|
||||
|
||||
query += f" ORDER BY created_at DESC LIMIT ${len(params) + 1}"
|
||||
params.append(limit)
|
||||
|
||||
rows = await conn.fetch(query, *params)
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
|
||||
class BacktestRequest(BaseModel):
|
||||
symbol: str = "BTC"
|
||||
intervals: list[str] = ["37m"]
|
||||
start_date: str = "2025-01-01" # ISO date
|
||||
end_date: Optional[str] = None
|
||||
|
||||
|
||||
async def run_backtest_task(req: BacktestRequest):
|
||||
"""Background task to run backtest"""
|
||||
db = DatabaseManager(
|
||||
host=DB_HOST, port=DB_PORT, database=DB_NAME,
|
||||
user=DB_USER, password=DB_PASSWORD
|
||||
)
|
||||
await db.connect()
|
||||
|
||||
try:
|
||||
# Load configs (hardcoded for now to match main.py)
|
||||
configs = [
|
||||
IndicatorConfig("ma44", "sma", 44, req.intervals),
|
||||
IndicatorConfig("ma125", "sma", 125, req.intervals)
|
||||
]
|
||||
|
||||
engine = IndicatorEngine(db, configs)
|
||||
brain = Brain(db, engine)
|
||||
backtester = Backtester(db, engine, brain)
|
||||
|
||||
start = datetime.fromisoformat(req.start_date).replace(tzinfo=timezone.utc)
|
||||
end = datetime.fromisoformat(req.end_date).replace(tzinfo=timezone.utc) if req.end_date else datetime.now(timezone.utc)
|
||||
|
||||
await backtester.run(req.symbol, req.intervals, start, end)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Backtest failed: {e}")
|
||||
finally:
|
||||
await db.disconnect()
|
||||
|
||||
|
||||
@app.post("/api/v1/backtests")
|
||||
async def trigger_backtest(req: BacktestRequest, background_tasks: BackgroundTasks):
|
||||
"""Start a backtest in the background"""
|
||||
background_tasks.add_task(run_backtest_task, req)
|
||||
return {"message": "Backtest started", "params": req.dict()}
|
||||
|
||||
|
||||
@app.get("/api/v1/ta")
|
||||
async def get_technical_analysis(
|
||||
symbol: str = Query("BTC", description="Trading pair symbol"),
|
||||
@ -222,42 +417,44 @@ async def get_technical_analysis(
|
||||
):
|
||||
"""
|
||||
Get technical analysis for a symbol
|
||||
Calculates MA 44, MA 125, trend, support/resistance
|
||||
Uses stored indicators from DB if available, falls back to on-the-fly calc
|
||||
"""
|
||||
try:
|
||||
async with pool.acquire() as conn:
|
||||
# Get enough candles for MA 125 calculation
|
||||
rows = await conn.fetch("""
|
||||
SELECT time, open, high, low, close, volume
|
||||
# 1. Get latest price
|
||||
latest = await conn.fetchrow("""
|
||||
SELECT close, time
|
||||
FROM candles
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
ORDER BY time DESC
|
||||
LIMIT 200
|
||||
LIMIT 1
|
||||
""", symbol, interval)
|
||||
|
||||
if len(rows) < 50:
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"interval": interval,
|
||||
"error": "Not enough data for technical analysis",
|
||||
"min_required": 50,
|
||||
"available": len(rows)
|
||||
}
|
||||
if not latest:
|
||||
return {"error": "No candle data found"}
|
||||
|
||||
current_price = float(latest['close'])
|
||||
timestamp = latest['time']
|
||||
|
||||
# Reverse to chronological order
|
||||
candles = list(reversed(rows))
|
||||
closes = [float(c['close']) for c in candles]
|
||||
# 2. Get latest indicators from DB
|
||||
indicators = await conn.fetch("""
|
||||
SELECT indicator_name, value
|
||||
FROM indicators
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
AND time <= $3
|
||||
ORDER BY time DESC
|
||||
""", symbol, interval, timestamp)
|
||||
|
||||
# Calculate Moving Averages
|
||||
def calculate_ma(data, period):
|
||||
if len(data) < period:
|
||||
return None
|
||||
return sum(data[-period:]) / period
|
||||
# Convert list to dict, e.g. {'ma44': 65000, 'ma125': 64000}
|
||||
# We take the most recent value for each indicator
|
||||
ind_map = {}
|
||||
for row in indicators:
|
||||
name = row['indicator_name']
|
||||
if name not in ind_map:
|
||||
ind_map[name] = float(row['value'])
|
||||
|
||||
ma_44 = calculate_ma(closes, 44)
|
||||
ma_125 = calculate_ma(closes, 125)
|
||||
|
||||
current_price = closes[-1]
|
||||
ma_44 = ind_map.get('ma44')
|
||||
ma_125 = ind_map.get('ma125')
|
||||
|
||||
# Determine trend
|
||||
if ma_44 and ma_125:
|
||||
@ -274,24 +471,35 @@ async def get_technical_analysis(
|
||||
trend = "Unknown"
|
||||
trend_strength = "Insufficient data"
|
||||
|
||||
# Find support and resistance (recent swing points)
|
||||
highs = [float(c['high']) for c in candles[-20:]]
|
||||
lows = [float(c['low']) for c in candles[-20:]]
|
||||
# 3. Find support/resistance (simple recent high/low)
|
||||
rows = await conn.fetch("""
|
||||
SELECT high, low
|
||||
FROM candles
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
ORDER BY time DESC
|
||||
LIMIT 20
|
||||
""", symbol, interval)
|
||||
|
||||
resistance = max(highs)
|
||||
support = min(lows)
|
||||
|
||||
# Calculate price position
|
||||
price_range = resistance - support
|
||||
if price_range > 0:
|
||||
position = (current_price - support) / price_range * 100
|
||||
if rows:
|
||||
highs = [float(r['high']) for r in rows]
|
||||
lows = [float(r['low']) for r in rows]
|
||||
resistance = max(highs)
|
||||
support = min(lows)
|
||||
|
||||
price_range = resistance - support
|
||||
if price_range > 0:
|
||||
position = (current_price - support) / price_range * 100
|
||||
else:
|
||||
position = 50
|
||||
else:
|
||||
resistance = current_price
|
||||
support = current_price
|
||||
position = 50
|
||||
|
||||
return {
|
||||
"symbol": symbol,
|
||||
"interval": interval,
|
||||
"timestamp": datetime.utcnow().isoformat(),
|
||||
"timestamp": timestamp.isoformat(),
|
||||
"current_price": round(current_price, 2),
|
||||
"moving_averages": {
|
||||
"ma_44": round(ma_44, 2) if ma_44 else None,
|
||||
|
||||
@ -4,6 +4,9 @@ from .candle_buffer import CandleBuffer
|
||||
from .database import DatabaseManager
|
||||
from .backfill import HyperliquidBackfill
|
||||
from .custom_timeframe_generator import CustomTimeframeGenerator
|
||||
from .indicator_engine import IndicatorEngine, IndicatorConfig
|
||||
from .brain import Brain, Decision
|
||||
from .backtester import Backtester
|
||||
|
||||
__all__ = [
|
||||
'HyperliquidWebSocket',
|
||||
@ -11,5 +14,10 @@ __all__ = [
|
||||
'CandleBuffer',
|
||||
'DatabaseManager',
|
||||
'HyperliquidBackfill',
|
||||
'CustomTimeframeGenerator'
|
||||
]
|
||||
'CustomTimeframeGenerator',
|
||||
'IndicatorEngine',
|
||||
'IndicatorConfig',
|
||||
'Brain',
|
||||
'Decision',
|
||||
'Backtester'
|
||||
]
|
||||
|
||||
391
src/data_collector/backtester.py
Normal file
391
src/data_collector/backtester.py
Normal file
@ -0,0 +1,391 @@
|
||||
"""
|
||||
Backtester - Historical replay driver for IndicatorEngine + Brain
|
||||
Iterates over stored candle data to simulate live trading decisions
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
from typing import Dict, List, Optional, Any
|
||||
from uuid import uuid4
|
||||
|
||||
from .database import DatabaseManager
|
||||
from .indicator_engine import IndicatorEngine, IndicatorConfig
|
||||
from .brain import Brain, Decision
|
||||
from .simulator import Account
|
||||
from src.strategies.base import SignalType
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Backtester:
|
||||
"""
|
||||
Replays historical candle data through IndicatorEngine and Brain.
|
||||
Uses Simulator (Account) to track PnL, leverage, and fees.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
db: DatabaseManager,
|
||||
indicator_engine: IndicatorEngine,
|
||||
brain: Brain,
|
||||
):
|
||||
self.db = db
|
||||
self.indicator_engine = indicator_engine
|
||||
self.brain = brain
|
||||
self.account = Account(initial_balance=1000.0)
|
||||
|
||||
async def run(
|
||||
self,
|
||||
symbol: str,
|
||||
intervals: List[str],
|
||||
start: datetime,
|
||||
end: datetime,
|
||||
config: Optional[Dict[str, Any]] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Run a full backtest over the given time range.
|
||||
"""
|
||||
backtest_id = str(uuid4())
|
||||
|
||||
logger.info(
|
||||
f"Starting backtest {backtest_id}: {symbol} "
|
||||
f"{intervals} from {start} to {end}"
|
||||
)
|
||||
|
||||
# Reset brain state
|
||||
self.brain.reset_state()
|
||||
|
||||
# Reset account for this run
|
||||
self.account = Account(initial_balance=1000.0)
|
||||
|
||||
# Store the run metadata
|
||||
await self._save_run_start(
|
||||
backtest_id, symbol, intervals, start, end, config
|
||||
)
|
||||
|
||||
total_decisions = 0
|
||||
|
||||
for interval in intervals:
|
||||
# Only process intervals that have indicators configured
|
||||
configured = self.indicator_engine.get_configured_intervals()
|
||||
if interval not in configured:
|
||||
logger.warning(
|
||||
f"Skipping interval {interval}: no indicators configured"
|
||||
)
|
||||
continue
|
||||
|
||||
# Get all candle timestamps in range
|
||||
timestamps = await self._get_candle_timestamps(
|
||||
symbol, interval, start, end
|
||||
)
|
||||
|
||||
if not timestamps:
|
||||
logger.warning(
|
||||
f"No candles found for {symbol}/{interval} in range"
|
||||
)
|
||||
continue
|
||||
|
||||
logger.info(
|
||||
f"Backtest {backtest_id}: processing {len(timestamps)} "
|
||||
f"{interval} candles..."
|
||||
)
|
||||
|
||||
for i, ts in enumerate(timestamps):
|
||||
# 1. Compute indicators
|
||||
raw_indicators = await self.indicator_engine.compute_at(
|
||||
symbol, interval, ts
|
||||
)
|
||||
indicators = {k: v for k, v in raw_indicators.items() if v is not None}
|
||||
|
||||
# 2. Get Current Position info for Strategy
|
||||
current_pos = self.account.get_position_dict()
|
||||
|
||||
# 3. Brain Evaluate
|
||||
decision: Decision = await self.brain.evaluate(
|
||||
symbol=symbol,
|
||||
interval=interval,
|
||||
timestamp=ts,
|
||||
indicators=indicators,
|
||||
backtest_id=backtest_id,
|
||||
current_position=current_pos
|
||||
)
|
||||
|
||||
# 4. Execute Decision in Simulator
|
||||
self._execute_decision(decision)
|
||||
|
||||
total_decisions += 1
|
||||
|
||||
if (i + 1) % 200 == 0:
|
||||
logger.info(
|
||||
f"Backtest {backtest_id}: {i + 1}/{len(timestamps)} "
|
||||
f"{interval} candles processed. Eq: {self.account.equity:.2f}"
|
||||
)
|
||||
await asyncio.sleep(0.01)
|
||||
|
||||
# Compute and store summary results from Simulator
|
||||
results = self.account.get_stats()
|
||||
results['total_evaluations'] = total_decisions
|
||||
|
||||
await self._save_run_results(backtest_id, results)
|
||||
|
||||
logger.info(
|
||||
f"Backtest {backtest_id} complete. Final Balance: {results['final_balance']:.2f}"
|
||||
)
|
||||
|
||||
return backtest_id
|
||||
|
||||
def _execute_decision(self, decision: Decision):
|
||||
"""Translate Brain decision into Account action"""
|
||||
price = decision.price_at_decision
|
||||
time = decision.time
|
||||
|
||||
# Open Long
|
||||
if decision.decision_type == SignalType.OPEN_LONG.value:
|
||||
self.account.open_position(time, 'long', price, leverage=1.0) # Todo: Configurable leverage
|
||||
|
||||
# Open Short
|
||||
elif decision.decision_type == SignalType.OPEN_SHORT.value:
|
||||
self.account.open_position(time, 'short', price, leverage=1.0)
|
||||
|
||||
# Close Long (only if we are long)
|
||||
elif decision.decision_type == SignalType.CLOSE_LONG.value:
|
||||
if self.account.current_position and self.account.current_position.side == 'long':
|
||||
self.account.close_position(time, price)
|
||||
|
||||
# Close Short (only if we are short)
|
||||
elif decision.decision_type == SignalType.CLOSE_SHORT.value:
|
||||
if self.account.current_position and self.account.current_position.side == 'short':
|
||||
self.account.close_position(time, price)
|
||||
|
||||
# Update equity mark-to-market
|
||||
self.account.update_equity(price)
|
||||
|
||||
async def _get_candle_timestamps(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
start: datetime,
|
||||
end: datetime,
|
||||
) -> List[datetime]:
|
||||
"""Get all candle timestamps in a range, ordered chronologically"""
|
||||
async with self.db.acquire() as conn:
|
||||
rows = await conn.fetch("""
|
||||
SELECT time FROM candles
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
AND time >= $3 AND time <= $4
|
||||
ORDER BY time ASC
|
||||
""", symbol, interval, start, end)
|
||||
|
||||
return [row["time"] for row in rows]
|
||||
|
||||
async def _save_run_start(
|
||||
self,
|
||||
backtest_id: str,
|
||||
symbol: str,
|
||||
intervals: List[str],
|
||||
start: datetime,
|
||||
end: datetime,
|
||||
config: Optional[Dict[str, Any]],
|
||||
) -> None:
|
||||
"""Store backtest run metadata at start"""
|
||||
async with self.db.acquire() as conn:
|
||||
await conn.execute("""
|
||||
INSERT INTO backtest_runs (
|
||||
id, strategy, symbol, start_time, end_time,
|
||||
intervals, config
|
||||
)
|
||||
VALUES ($1, $2, $3, $4, $5, $6, $7)
|
||||
""",
|
||||
backtest_id,
|
||||
self.brain.strategy_name,
|
||||
symbol,
|
||||
start,
|
||||
end,
|
||||
intervals,
|
||||
json.dumps(config) if config else None,
|
||||
)
|
||||
|
||||
async def _compute_results(self, backtest_id, symbol):
|
||||
"""Deprecated: Logic moved to Account class"""
|
||||
return {}
|
||||
|
||||
async def _save_run_results(
|
||||
self,
|
||||
backtest_id: str,
|
||||
results: Dict[str, Any],
|
||||
) -> None:
|
||||
"""Update backtest run with final results"""
|
||||
# Remove trades list from stored results (can be large)
|
||||
stored_results = {k: v for k, v in results.items() if k != "trades"}
|
||||
|
||||
async with self.db.acquire() as conn:
|
||||
await conn.execute("""
|
||||
UPDATE backtest_runs
|
||||
SET results = $1
|
||||
WHERE id = $2
|
||||
""", json.dumps(stored_results), backtest_id)
|
||||
|
||||
async def get_run(self, backtest_id: str) -> Optional[Dict[str, Any]]:
|
||||
"""Get a specific backtest run with results"""
|
||||
async with self.db.acquire() as conn:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT id, strategy, symbol, start_time, end_time,
|
||||
intervals, config, results, created_at
|
||||
FROM backtest_runs
|
||||
WHERE id = $1
|
||||
""", backtest_id)
|
||||
|
||||
return dict(row) if row else None
|
||||
|
||||
async def list_runs(
|
||||
self,
|
||||
symbol: Optional[str] = None,
|
||||
limit: int = 20,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""List recent backtest runs"""
|
||||
async with self.db.acquire() as conn:
|
||||
if symbol:
|
||||
rows = await conn.fetch("""
|
||||
SELECT id, strategy, symbol, start_time, end_time,
|
||||
intervals, results, created_at
|
||||
FROM backtest_runs
|
||||
WHERE symbol = $1
|
||||
ORDER BY created_at DESC
|
||||
LIMIT $2
|
||||
""", symbol, limit)
|
||||
else:
|
||||
rows = await conn.fetch("""
|
||||
SELECT id, strategy, symbol, start_time, end_time,
|
||||
intervals, results, created_at
|
||||
FROM backtest_runs
|
||||
ORDER BY created_at DESC
|
||||
LIMIT $1
|
||||
""", limit)
|
||||
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
async def cleanup_run(self, backtest_id: str) -> int:
|
||||
"""Delete all decisions and metadata for a backtest run"""
|
||||
async with self.db.acquire() as conn:
|
||||
result = await conn.execute("""
|
||||
DELETE FROM decisions WHERE backtest_id = $1
|
||||
""", backtest_id)
|
||||
|
||||
await conn.execute("""
|
||||
DELETE FROM backtest_runs WHERE id = $1
|
||||
""", backtest_id)
|
||||
|
||||
deleted = int(result.split()[-1]) if result else 0
|
||||
logger.info(
|
||||
f"Cleaned up backtest {backtest_id}: "
|
||||
f"{deleted} decisions deleted"
|
||||
)
|
||||
return deleted
|
||||
|
||||
|
||||
async def main():
|
||||
"""CLI entry point for running backtests"""
|
||||
import argparse
|
||||
import os
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Run backtest on historical data"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--symbol", default="BTC", help="Symbol (default: BTC)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--intervals", nargs="+", default=["37m"],
|
||||
help="Intervals to backtest (default: 37m)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--start", required=True,
|
||||
help="Start date (ISO format, e.g., 2025-01-01)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--end", default=None,
|
||||
help="End date (ISO format, default: now)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--db-host", default=os.getenv("DB_HOST", "localhost"),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--db-port", type=int, default=int(os.getenv("DB_PORT", 5432)),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--db-name", default=os.getenv("DB_NAME", "btc_data"),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--db-user", default=os.getenv("DB_USER", "btc_bot"),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--db-password", default=os.getenv("DB_PASSWORD", ""),
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
)
|
||||
|
||||
# Parse dates
|
||||
start = datetime.fromisoformat(args.start).replace(tzinfo=timezone.utc)
|
||||
end = (
|
||||
datetime.fromisoformat(args.end).replace(tzinfo=timezone.utc)
|
||||
if args.end
|
||||
else datetime.now(timezone.utc)
|
||||
)
|
||||
|
||||
# Initialize components
|
||||
db = DatabaseManager(
|
||||
host=args.db_host,
|
||||
port=args.db_port,
|
||||
database=args.db_name,
|
||||
user=args.db_user,
|
||||
password=args.db_password,
|
||||
)
|
||||
await db.connect()
|
||||
|
||||
try:
|
||||
# Default indicator configs (MA44 + MA125 on selected intervals)
|
||||
configs = [
|
||||
IndicatorConfig("ma44", "sma", 44, args.intervals),
|
||||
IndicatorConfig("ma125", "sma", 125, args.intervals),
|
||||
]
|
||||
|
||||
indicator_engine = IndicatorEngine(db, configs)
|
||||
brain = Brain(db, indicator_engine)
|
||||
backtester = Backtester(db, indicator_engine, brain)
|
||||
|
||||
# Run the backtest
|
||||
backtest_id = await backtester.run(
|
||||
symbol=args.symbol,
|
||||
intervals=args.intervals,
|
||||
start=start,
|
||||
end=end,
|
||||
)
|
||||
|
||||
# Print results
|
||||
run = await backtester.get_run(backtest_id)
|
||||
if run and run.get("results"):
|
||||
results = json.loads(run["results"]) if isinstance(run["results"], str) else run["results"]
|
||||
print("\n=== Backtest Results ===")
|
||||
print(f"ID: {backtest_id}")
|
||||
print(f"Strategy: {run['strategy']}")
|
||||
print(f"Period: {run['start_time']} to {run['end_time']}")
|
||||
print(f"Intervals: {run['intervals']}")
|
||||
print(f"Total evaluations: {results.get('total_evaluations', 0)}")
|
||||
print(f"Total trades: {results.get('total_trades', 0)}")
|
||||
print(f"Win rate: {results.get('win_rate', 0)}%")
|
||||
print(f"Total P&L: {results.get('total_pnl_pct', 0)}%")
|
||||
print(f"Final Balance: {results.get('final_balance', 0)}")
|
||||
|
||||
finally:
|
||||
await db.disconnect()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
223
src/data_collector/brain.py
Normal file
223
src/data_collector/brain.py
Normal file
@ -0,0 +1,223 @@
|
||||
"""
|
||||
Brain - Strategy evaluation and decision logging
|
||||
Pure strategy logic separated from DB I/O for testability
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from dataclasses import dataclass, asdict
|
||||
from datetime import datetime, timezone
|
||||
from typing import Dict, Optional, Any, List
|
||||
import importlib
|
||||
|
||||
from .database import DatabaseManager
|
||||
from .indicator_engine import IndicatorEngine
|
||||
from src.strategies.base import BaseStrategy, StrategySignal, SignalType
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Registry of available strategies
|
||||
STRATEGY_REGISTRY = {
|
||||
"ma44_strategy": "src.strategies.ma44_strategy.MA44Strategy",
|
||||
"ma125_strategy": "src.strategies.ma125_strategy.MA125Strategy",
|
||||
}
|
||||
|
||||
def load_strategy(strategy_name: str) -> BaseStrategy:
|
||||
"""Dynamically load a strategy class"""
|
||||
if strategy_name not in STRATEGY_REGISTRY:
|
||||
# Default fallback or error
|
||||
logger.warning(f"Strategy {strategy_name} not found, defaulting to MA44")
|
||||
strategy_name = "ma44_strategy"
|
||||
|
||||
module_path, class_name = STRATEGY_REGISTRY[strategy_name].rsplit('.', 1)
|
||||
module = importlib.import_module(module_path)
|
||||
cls = getattr(module, class_name)
|
||||
return cls()
|
||||
|
||||
@dataclass
|
||||
class Decision:
|
||||
"""A single brain evaluation result"""
|
||||
time: datetime
|
||||
symbol: str
|
||||
interval: str
|
||||
decision_type: str # "buy", "sell", "hold" -> Now maps to SignalType
|
||||
strategy: str
|
||||
confidence: float
|
||||
price_at_decision: float
|
||||
indicator_snapshot: Dict[str, Any]
|
||||
candle_snapshot: Dict[str, Any]
|
||||
reasoning: str
|
||||
backtest_id: Optional[str] = None
|
||||
|
||||
def to_db_tuple(self) -> tuple:
|
||||
"""Convert to positional tuple for DB insert"""
|
||||
return (
|
||||
self.time,
|
||||
self.symbol,
|
||||
self.interval,
|
||||
self.decision_type,
|
||||
self.strategy,
|
||||
self.confidence,
|
||||
self.price_at_decision,
|
||||
json.dumps(self.indicator_snapshot),
|
||||
json.dumps(self.candle_snapshot),
|
||||
self.reasoning,
|
||||
self.backtest_id,
|
||||
)
|
||||
|
||||
|
||||
class Brain:
|
||||
"""
|
||||
Evaluates market conditions using a loaded Strategy.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
db: DatabaseManager,
|
||||
indicator_engine: IndicatorEngine,
|
||||
strategy: str = "ma44_strategy",
|
||||
):
|
||||
self.db = db
|
||||
self.indicator_engine = indicator_engine
|
||||
self.strategy_name = strategy
|
||||
self.active_strategy: BaseStrategy = load_strategy(strategy)
|
||||
|
||||
logger.info(f"Brain initialized with strategy: {self.active_strategy.name}")
|
||||
|
||||
async def evaluate(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
timestamp: datetime,
|
||||
indicators: Optional[Dict[str, float]] = None,
|
||||
backtest_id: Optional[str] = None,
|
||||
current_position: Optional[Dict[str, Any]] = None,
|
||||
) -> Decision:
|
||||
"""
|
||||
Evaluate market conditions and produce a decision.
|
||||
"""
|
||||
# Get indicator values
|
||||
if indicators is None:
|
||||
indicators = await self.indicator_engine.get_values_at(
|
||||
symbol, interval, timestamp
|
||||
)
|
||||
|
||||
# Get the triggering candle
|
||||
candle = await self._get_candle(symbol, interval, timestamp)
|
||||
if not candle:
|
||||
return self._create_empty_decision(timestamp, symbol, interval, indicators, backtest_id)
|
||||
|
||||
price = float(candle["close"])
|
||||
candle_dict = {
|
||||
"time": candle["time"].isoformat(),
|
||||
"open": float(candle["open"]),
|
||||
"high": float(candle["high"]),
|
||||
"low": float(candle["low"]),
|
||||
"close": price,
|
||||
"volume": float(candle["volume"]),
|
||||
}
|
||||
|
||||
# Delegate to Strategy
|
||||
signal: StrategySignal = self.active_strategy.analyze(
|
||||
candle_dict, indicators, current_position
|
||||
)
|
||||
|
||||
# Build decision
|
||||
decision = Decision(
|
||||
time=timestamp,
|
||||
symbol=symbol,
|
||||
interval=interval,
|
||||
decision_type=signal.type.value,
|
||||
strategy=self.strategy_name,
|
||||
confidence=signal.confidence,
|
||||
price_at_decision=price,
|
||||
indicator_snapshot=indicators,
|
||||
candle_snapshot=candle_dict,
|
||||
reasoning=signal.reasoning,
|
||||
backtest_id=backtest_id,
|
||||
)
|
||||
|
||||
# Store to DB
|
||||
await self._store_decision(decision)
|
||||
|
||||
return decision
|
||||
|
||||
def _create_empty_decision(self, timestamp, symbol, interval, indicators, backtest_id):
|
||||
return Decision(
|
||||
time=timestamp,
|
||||
symbol=symbol,
|
||||
interval=interval,
|
||||
decision_type="hold",
|
||||
strategy=self.strategy_name,
|
||||
confidence=0.0,
|
||||
price_at_decision=0.0,
|
||||
indicator_snapshot=indicators or {},
|
||||
candle_snapshot={},
|
||||
reasoning="No candle data available",
|
||||
backtest_id=backtest_id,
|
||||
)
|
||||
|
||||
async def _get_candle(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
timestamp: datetime,
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""Fetch a specific candle from the database"""
|
||||
async with self.db.acquire() as conn:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT time, open, high, low, close, volume
|
||||
FROM candles
|
||||
WHERE symbol = $1 AND interval = $2 AND time = $3
|
||||
""", symbol, interval, timestamp)
|
||||
|
||||
return dict(row) if row else None
|
||||
|
||||
async def _store_decision(self, decision: Decision) -> None:
|
||||
"""Write decision to the decisions table"""
|
||||
# Note: We might want to skip writing every single HOLD to DB to save space if simulating millions of candles
|
||||
# But keeping it for now for full traceability
|
||||
async with self.db.acquire() as conn:
|
||||
await conn.execute("""
|
||||
INSERT INTO decisions (
|
||||
time, symbol, interval, decision_type, strategy,
|
||||
confidence, price_at_decision, indicator_snapshot,
|
||||
candle_snapshot, reasoning, backtest_id
|
||||
)
|
||||
VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)
|
||||
""", *decision.to_db_tuple())
|
||||
|
||||
async def get_recent_decisions(
|
||||
self,
|
||||
symbol: str,
|
||||
limit: int = 20,
|
||||
backtest_id: Optional[str] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Get recent decisions, optionally filtered by backtest_id"""
|
||||
async with self.db.acquire() as conn:
|
||||
if backtest_id is not None:
|
||||
rows = await conn.fetch("""
|
||||
SELECT time, symbol, interval, decision_type, strategy,
|
||||
confidence, price_at_decision, indicator_snapshot,
|
||||
candle_snapshot, reasoning, backtest_id
|
||||
FROM decisions
|
||||
WHERE symbol = $1 AND backtest_id = $2
|
||||
ORDER BY time DESC
|
||||
LIMIT $3
|
||||
""", symbol, backtest_id, limit)
|
||||
else:
|
||||
rows = await conn.fetch("""
|
||||
SELECT time, symbol, interval, decision_type, strategy,
|
||||
confidence, price_at_decision, indicator_snapshot,
|
||||
candle_snapshot, reasoning, backtest_id
|
||||
FROM decisions
|
||||
WHERE symbol = $1 AND backtest_id IS NULL
|
||||
ORDER BY time DESC
|
||||
LIMIT $2
|
||||
""", symbol, limit)
|
||||
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
def reset_state(self) -> None:
|
||||
"""Reset internal state tracking"""
|
||||
pass
|
||||
285
src/data_collector/indicator_engine.py
Normal file
285
src/data_collector/indicator_engine.py
Normal file
@ -0,0 +1,285 @@
|
||||
"""
|
||||
Indicator Engine - Computes and stores technical indicators
|
||||
Stateless DB-backed design: same code for live updates and backtesting
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from typing import Dict, List, Optional, Any
|
||||
|
||||
from .database import DatabaseManager
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class IndicatorConfig:
|
||||
"""Configuration for a single indicator"""
|
||||
name: str # e.g., "ma44"
|
||||
type: str # e.g., "sma"
|
||||
period: int # e.g., 44
|
||||
intervals: List[str] # e.g., ["37m", "148m", "1d"]
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, name: str, data: Dict[str, Any]) -> "IndicatorConfig":
|
||||
"""Create config from YAML dict entry"""
|
||||
return cls(
|
||||
name=name,
|
||||
type=data["type"],
|
||||
period=data["period"],
|
||||
intervals=data["intervals"],
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class IndicatorResult:
|
||||
"""Result of a single indicator computation"""
|
||||
name: str
|
||||
value: Optional[float]
|
||||
period: int
|
||||
timestamp: datetime
|
||||
|
||||
|
||||
class IndicatorEngine:
|
||||
"""
|
||||
Computes technical indicators from candle data in the database.
|
||||
|
||||
Two modes, same math:
|
||||
- on_interval_update(): called by live system after higher-TF candle update
|
||||
- compute_at(): called by backtester for a specific point in time
|
||||
Both query the DB for the required candle history and store results.
|
||||
"""
|
||||
|
||||
def __init__(self, db: DatabaseManager, configs: List[IndicatorConfig]):
|
||||
self.db = db
|
||||
self.configs = configs
|
||||
# Build lookup: interval -> list of configs that need computation
|
||||
self._interval_configs: Dict[str, List[IndicatorConfig]] = {}
|
||||
for cfg in configs:
|
||||
for interval in cfg.intervals:
|
||||
if interval not in self._interval_configs:
|
||||
self._interval_configs[interval] = []
|
||||
self._interval_configs[interval].append(cfg)
|
||||
|
||||
logger.info(
|
||||
f"IndicatorEngine initialized with {len(configs)} indicators "
|
||||
f"across intervals: {list(self._interval_configs.keys())}"
|
||||
)
|
||||
|
||||
def get_configured_intervals(self) -> List[str]:
|
||||
"""Return all intervals that have indicators configured"""
|
||||
return list(self._interval_configs.keys())
|
||||
|
||||
async def on_interval_update(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
timestamp: datetime,
|
||||
) -> Dict[str, Optional[float]]:
|
||||
"""
|
||||
Compute all indicators configured for this interval.
|
||||
Called by main.py after CustomTimeframeGenerator updates a higher TF.
|
||||
|
||||
Returns dict of indicator_name -> value (for use by Brain).
|
||||
"""
|
||||
configs = self._interval_configs.get(interval, [])
|
||||
if not configs:
|
||||
return {}
|
||||
|
||||
return await self._compute_and_store(symbol, interval, timestamp, configs)
|
||||
|
||||
async def compute_at(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
timestamp: datetime,
|
||||
) -> Dict[str, Optional[float]]:
|
||||
"""
|
||||
Compute indicators at a specific point in time.
|
||||
Alias for on_interval_update -- used by backtester for clarity.
|
||||
"""
|
||||
return await self.on_interval_update(symbol, interval, timestamp)
|
||||
|
||||
async def compute_historical(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
start: datetime,
|
||||
end: datetime,
|
||||
) -> int:
|
||||
"""
|
||||
Batch-compute indicators for a time range.
|
||||
Iterates over every candle timestamp in [start, end] and computes.
|
||||
|
||||
Returns total number of indicator values stored.
|
||||
"""
|
||||
configs = self._interval_configs.get(interval, [])
|
||||
if not configs:
|
||||
logger.warning(f"No indicators configured for interval {interval}")
|
||||
return 0
|
||||
|
||||
# Get all candle timestamps in range
|
||||
async with self.db.acquire() as conn:
|
||||
rows = await conn.fetch("""
|
||||
SELECT time FROM candles
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
AND time >= $3 AND time <= $4
|
||||
ORDER BY time ASC
|
||||
""", symbol, interval, start, end)
|
||||
|
||||
if not rows:
|
||||
logger.warning(f"No candles found for {symbol}/{interval} in range")
|
||||
return 0
|
||||
|
||||
timestamps = [row["time"] for row in rows]
|
||||
total_stored = 0
|
||||
|
||||
logger.info(
|
||||
f"Computing {len(configs)} indicators across "
|
||||
f"{len(timestamps)} {interval} candles..."
|
||||
)
|
||||
|
||||
for i, ts in enumerate(timestamps):
|
||||
results = await self._compute_and_store(symbol, interval, ts, configs)
|
||||
total_stored += sum(1 for v in results.values() if v is not None)
|
||||
|
||||
if (i + 1) % 100 == 0:
|
||||
logger.info(f"Progress: {i + 1}/{len(timestamps)} candles processed")
|
||||
await asyncio.sleep(0.01) # Yield to event loop
|
||||
|
||||
logger.info(
|
||||
f"Historical compute complete: {total_stored} indicator values "
|
||||
f"stored for {interval}"
|
||||
)
|
||||
return total_stored
|
||||
|
||||
async def _compute_and_store(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
timestamp: datetime,
|
||||
configs: List[IndicatorConfig],
|
||||
) -> Dict[str, Optional[float]]:
|
||||
"""Core computation: fetch candles, compute indicators, store results"""
|
||||
# Determine max lookback needed
|
||||
max_period = max(cfg.period for cfg in configs)
|
||||
|
||||
# Fetch enough candles for the longest indicator
|
||||
async with self.db.acquire() as conn:
|
||||
rows = await conn.fetch("""
|
||||
SELECT time, open, high, low, close, volume
|
||||
FROM candles
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
AND time <= $3
|
||||
ORDER BY time DESC
|
||||
LIMIT $4
|
||||
""", symbol, interval, timestamp, max_period)
|
||||
|
||||
if not rows:
|
||||
return {cfg.name: None for cfg in configs}
|
||||
|
||||
# Reverse to chronological order
|
||||
candles = list(reversed(rows))
|
||||
closes = [float(c["close"]) for c in candles]
|
||||
|
||||
# Compute each indicator
|
||||
results: Dict[str, Optional[float]] = {}
|
||||
values_to_store: List[tuple] = []
|
||||
|
||||
for cfg in configs:
|
||||
value = self._compute_indicator(cfg, closes)
|
||||
results[cfg.name] = value
|
||||
|
||||
if value is not None:
|
||||
values_to_store.append((
|
||||
timestamp,
|
||||
symbol,
|
||||
interval,
|
||||
cfg.name,
|
||||
value,
|
||||
json.dumps({"type": cfg.type, "period": cfg.period}),
|
||||
))
|
||||
|
||||
# Batch upsert all computed values
|
||||
if values_to_store:
|
||||
async with self.db.acquire() as conn:
|
||||
await conn.executemany("""
|
||||
INSERT INTO indicators (time, symbol, interval, indicator_name, value, parameters)
|
||||
VALUES ($1, $2, $3, $4, $5, $6)
|
||||
ON CONFLICT (time, symbol, interval, indicator_name)
|
||||
DO UPDATE SET
|
||||
value = EXCLUDED.value,
|
||||
parameters = EXCLUDED.parameters,
|
||||
computed_at = NOW()
|
||||
""", values_to_store)
|
||||
|
||||
logger.debug(
|
||||
f"Stored {len(values_to_store)} indicator values for "
|
||||
f"{symbol}/{interval} at {timestamp}"
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
def _compute_indicator(
|
||||
self,
|
||||
config: IndicatorConfig,
|
||||
closes: List[float],
|
||||
) -> Optional[float]:
|
||||
"""Dispatch to the correct computation function"""
|
||||
if config.type == "sma":
|
||||
return self.compute_sma(closes, config.period)
|
||||
else:
|
||||
logger.warning(f"Unknown indicator type: {config.type}")
|
||||
return None
|
||||
|
||||
# ── Pure math functions (no DB, no async, easily testable) ──────────
|
||||
|
||||
@staticmethod
|
||||
def compute_sma(closes: List[float], period: int) -> Optional[float]:
|
||||
"""Simple Moving Average over the last `period` closes"""
|
||||
if len(closes) < period:
|
||||
return None
|
||||
return sum(closes[-period:]) / period
|
||||
|
||||
async def get_latest_values(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
) -> Dict[str, float]:
|
||||
"""
|
||||
Get the most recent indicator values for a symbol/interval.
|
||||
Used by Brain to read current state.
|
||||
"""
|
||||
async with self.db.acquire() as conn:
|
||||
rows = await conn.fetch("""
|
||||
SELECT DISTINCT ON (indicator_name)
|
||||
indicator_name, value, time
|
||||
FROM indicators
|
||||
WHERE symbol = $1 AND interval = $2
|
||||
ORDER BY indicator_name, time DESC
|
||||
""", symbol, interval)
|
||||
|
||||
return {row["indicator_name"]: float(row["value"]) for row in rows}
|
||||
|
||||
async def get_values_at(
|
||||
self,
|
||||
symbol: str,
|
||||
interval: str,
|
||||
timestamp: datetime,
|
||||
) -> Dict[str, float]:
|
||||
"""
|
||||
Get indicator values at a specific timestamp.
|
||||
Used by Brain during backtesting.
|
||||
"""
|
||||
async with self.db.acquire() as conn:
|
||||
rows = await conn.fetch("""
|
||||
SELECT indicator_name, value
|
||||
FROM indicators
|
||||
WHERE symbol = $1 AND interval = $2 AND time = $3
|
||||
""", symbol, interval, timestamp)
|
||||
|
||||
return {row["indicator_name"]: float(row["value"]) for row in rows}
|
||||
@ -1,6 +1,6 @@
|
||||
"""
|
||||
Main entry point for data collector service
|
||||
Integrates WebSocket client, buffer, and database
|
||||
Integrates WebSocket client, buffer, database, indicators, and brain
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
@ -8,13 +8,17 @@ import logging
|
||||
import signal
|
||||
import sys
|
||||
from datetime import datetime, timezone
|
||||
from typing import Optional
|
||||
from typing import Optional, List
|
||||
import os
|
||||
|
||||
import yaml
|
||||
|
||||
from .websocket_client import HyperliquidWebSocket, Candle
|
||||
from .candle_buffer import CandleBuffer
|
||||
from .database import DatabaseManager
|
||||
from .custom_timeframe_generator import CustomTimeframeGenerator
|
||||
from .indicator_engine import IndicatorEngine, IndicatorConfig
|
||||
from .brain import Brain
|
||||
|
||||
|
||||
# Configure logging
|
||||
@ -68,6 +72,17 @@ class DataCollector:
|
||||
self.custom_tf_generator = CustomTimeframeGenerator(self.db)
|
||||
await self.custom_tf_generator.initialize()
|
||||
|
||||
# Initialize indicator engine
|
||||
# Hardcoded config for now, eventually load from yaml
|
||||
indicator_configs = [
|
||||
IndicatorConfig("ma44", "sma", 44, ["37m", "148m", "1d"]),
|
||||
IndicatorConfig("ma125", "sma", 125, ["37m", "148m", "1d"])
|
||||
]
|
||||
self.indicator_engine = IndicatorEngine(self.db, indicator_configs)
|
||||
|
||||
# Initialize brain
|
||||
self.brain = Brain(self.db, self.indicator_engine)
|
||||
|
||||
# Initialize buffer
|
||||
self.buffer = CandleBuffer(
|
||||
max_size=1000,
|
||||
@ -166,12 +181,47 @@ class DataCollector:
|
||||
raise # Re-raise to trigger buffer retry
|
||||
|
||||
async def _update_custom_timeframes(self, candles: list) -> None:
|
||||
"""Update custom timeframes in background (non-blocking)"""
|
||||
"""
|
||||
Update custom timeframes in background, then trigger indicators/brain.
|
||||
|
||||
This chain ensures that indicators are computed on fresh candle data,
|
||||
and the brain evaluates on fresh indicator data.
|
||||
"""
|
||||
try:
|
||||
# 1. Update custom candles (37m, 148m, etc.)
|
||||
await self.custom_tf_generator.update_realtime(candles)
|
||||
logger.debug("Custom timeframes updated")
|
||||
|
||||
# 2. Trigger indicator updates for configured intervals
|
||||
# We use the timestamp of the last 1m candle as the trigger point
|
||||
trigger_time = candles[-1].time
|
||||
|
||||
if self.indicator_engine:
|
||||
intervals = self.indicator_engine.get_configured_intervals()
|
||||
for interval in intervals:
|
||||
# Get the correct bucket start time for this interval
|
||||
# e.g., if trigger_time is 09:48:00, 37m bucket might start at 09:25:00
|
||||
if self.custom_tf_generator:
|
||||
bucket_start = self.custom_tf_generator.get_bucket_start(trigger_time, interval)
|
||||
else:
|
||||
bucket_start = trigger_time
|
||||
|
||||
# Compute indicators for this bucket
|
||||
raw_indicators = await self.indicator_engine.on_interval_update(
|
||||
self.symbol, interval, bucket_start
|
||||
)
|
||||
|
||||
# Filter out None values to satisfy type checker
|
||||
indicators = {k: v for k, v in raw_indicators.items() if v is not None}
|
||||
|
||||
# 3. Evaluate brain if we have fresh indicators
|
||||
if self.brain and indicators:
|
||||
await self.brain.evaluate(
|
||||
self.symbol, interval, bucket_start, indicators
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update custom timeframes: {e}")
|
||||
logger.error(f"Failed to update custom timeframes/indicators: {e}")
|
||||
# Don't raise - this is non-critical
|
||||
|
||||
async def _on_error(self, error: Exception) -> None:
|
||||
|
||||
160
src/data_collector/simulator.py
Normal file
160
src/data_collector/simulator.py
Normal file
@ -0,0 +1,160 @@
|
||||
"""
|
||||
Simulator
|
||||
Handles account accounting, leverage, fees, and position management for backtesting.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional, List, Dict, Any
|
||||
from datetime import datetime
|
||||
from .brain import Decision # We might need to decouple this later, but reusing for now
|
||||
|
||||
@dataclass
|
||||
class Trade:
|
||||
entry_time: datetime
|
||||
exit_time: Optional[datetime]
|
||||
side: str # 'long' or 'short'
|
||||
entry_price: float
|
||||
exit_price: Optional[float]
|
||||
size: float # Quantity of asset
|
||||
leverage: float
|
||||
pnl: float = 0.0
|
||||
pnl_percent: float = 0.0
|
||||
fees: float = 0.0
|
||||
status: str = 'open' # 'open', 'closed'
|
||||
|
||||
class Account:
|
||||
def __init__(self, initial_balance: float = 1000.0, maker_fee: float = 0.0002, taker_fee: float = 0.0005):
|
||||
self.initial_balance = initial_balance
|
||||
self.balance = initial_balance
|
||||
self.equity = initial_balance
|
||||
self.maker_fee = maker_fee
|
||||
self.taker_fee = taker_fee
|
||||
self.trades: List[Trade] = []
|
||||
self.current_position: Optional[Trade] = None
|
||||
self.margin_used = 0.0
|
||||
|
||||
def update_equity(self, current_price: float):
|
||||
"""Update equity based on unrealized PnL of current position"""
|
||||
if not self.current_position:
|
||||
self.equity = self.balance
|
||||
return
|
||||
|
||||
trade = self.current_position
|
||||
if trade.side == 'long':
|
||||
unrealized_pnl = (current_price - trade.entry_price) * trade.size
|
||||
else:
|
||||
unrealized_pnl = (trade.entry_price - current_price) * trade.size
|
||||
|
||||
self.equity = self.balance + unrealized_pnl
|
||||
|
||||
def open_position(self, time: datetime, side: str, price: float, leverage: float = 1.0, portion: float = 1.0):
|
||||
"""
|
||||
Open a position.
|
||||
portion: 0.0 to 1.0 (percentage of available balance to use)
|
||||
"""
|
||||
if self.current_position:
|
||||
# Already have a position, ignore for now (or could add to it)
|
||||
return
|
||||
|
||||
# Calculate position size
|
||||
# Margin = (Balance * portion)
|
||||
# Position Value = Margin * Leverage
|
||||
# Size = Position Value / Price
|
||||
|
||||
margin_to_use = self.balance * portion
|
||||
position_value = margin_to_use * leverage
|
||||
size = position_value / price
|
||||
|
||||
# Fee (Taker)
|
||||
fee = position_value * self.taker_fee
|
||||
self.balance -= fee # Deduct fee immediately
|
||||
|
||||
self.current_position = Trade(
|
||||
entry_time=time,
|
||||
exit_time=None,
|
||||
side=side,
|
||||
entry_price=price,
|
||||
exit_price=None,
|
||||
size=size,
|
||||
leverage=leverage,
|
||||
fees=fee
|
||||
)
|
||||
self.margin_used = margin_to_use
|
||||
|
||||
def close_position(self, time: datetime, price: float):
|
||||
"""Close the current position"""
|
||||
if not self.current_position:
|
||||
return
|
||||
|
||||
trade = self.current_position
|
||||
position_value = trade.size * price
|
||||
|
||||
# Calculate PnL
|
||||
if trade.side == 'long':
|
||||
pnl = (price - trade.entry_price) * trade.size
|
||||
pnl_pct = (price - trade.entry_price) / trade.entry_price * trade.leverage * 100
|
||||
else:
|
||||
pnl = (trade.entry_price - price) * trade.size
|
||||
pnl_pct = (trade.entry_price - price) / trade.entry_price * trade.leverage * 100
|
||||
|
||||
# Fee (Taker)
|
||||
fee = position_value * self.taker_fee
|
||||
self.balance -= fee
|
||||
trade.fees += fee
|
||||
|
||||
# Update Balance
|
||||
self.balance += pnl
|
||||
self.margin_used = 0.0
|
||||
|
||||
# Update Trade Record
|
||||
trade.exit_time = time
|
||||
trade.exit_price = price
|
||||
trade.pnl = pnl
|
||||
trade.pnl_percent = pnl_pct
|
||||
trade.status = 'closed'
|
||||
|
||||
self.trades.append(trade)
|
||||
self.current_position = None
|
||||
self.equity = self.balance
|
||||
|
||||
def get_position_dict(self) -> Optional[Dict[str, Any]]:
|
||||
if not self.current_position:
|
||||
return None
|
||||
return {
|
||||
'type': self.current_position.side,
|
||||
'entry_price': self.current_position.entry_price,
|
||||
'size': self.current_position.size,
|
||||
'leverage': self.current_position.leverage
|
||||
}
|
||||
|
||||
def get_stats(self) -> Dict[str, Any]:
|
||||
wins = [t for t in self.trades if t.pnl > 0]
|
||||
losses = [t for t in self.trades if t.pnl <= 0]
|
||||
|
||||
total_pnl = self.balance - self.initial_balance
|
||||
total_pnl_pct = (total_pnl / self.initial_balance) * 100
|
||||
|
||||
return {
|
||||
"initial_balance": self.initial_balance,
|
||||
"final_balance": self.balance,
|
||||
"total_pnl": total_pnl,
|
||||
"total_pnl_pct": total_pnl_pct,
|
||||
"total_trades": len(self.trades),
|
||||
"win_count": len(wins),
|
||||
"loss_count": len(losses),
|
||||
"win_rate": (len(wins) / len(self.trades) * 100) if self.trades else 0.0,
|
||||
"max_drawdown": 0.0, # Todo: implement DD tracking
|
||||
"trades": [
|
||||
{
|
||||
"entry_time": t.entry_time.isoformat(),
|
||||
"exit_time": t.exit_time.isoformat() if t.exit_time else None,
|
||||
"side": t.side,
|
||||
"entry_price": t.entry_price,
|
||||
"exit_price": t.exit_price,
|
||||
"pnl": t.pnl,
|
||||
"pnl_pct": t.pnl_percent,
|
||||
"fees": t.fees
|
||||
}
|
||||
for t in self.trades
|
||||
]
|
||||
}
|
||||
68
src/strategies/base.py
Normal file
68
src/strategies/base.py
Normal file
@ -0,0 +1,68 @@
|
||||
"""
|
||||
Base Strategy Interface
|
||||
All strategies must inherit from this class.
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, Any, List, Optional
|
||||
from enum import Enum
|
||||
|
||||
class SignalType(Enum):
|
||||
OPEN_LONG = "open_long"
|
||||
OPEN_SHORT = "open_short"
|
||||
CLOSE_LONG = "close_long"
|
||||
CLOSE_SHORT = "close_short"
|
||||
HOLD = "hold"
|
||||
|
||||
@dataclass
|
||||
class StrategySignal:
|
||||
type: SignalType
|
||||
confidence: float
|
||||
reasoning: str
|
||||
|
||||
class BaseStrategy(ABC):
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
self.config = config or {}
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def name(self) -> str:
|
||||
"""Unique identifier for the strategy"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def required_indicators(self) -> List[str]:
|
||||
"""List of indicator names required by this strategy (e.g. ['ma44'])"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def display_name(self) -> str:
|
||||
"""User-friendly name for display in UI (e.g. 'MA44 Crossover')"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def description(self) -> str:
|
||||
"""Detailed description of how the strategy works"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def analyze(
|
||||
self,
|
||||
candle: Dict[str, Any],
|
||||
indicators: Dict[str, float],
|
||||
current_position: Optional[Dict[str, Any]] = None
|
||||
) -> StrategySignal:
|
||||
"""
|
||||
Analyze market data and return a trading signal.
|
||||
|
||||
Args:
|
||||
candle: Dictionary containing 'close', 'open', 'high', 'low', 'volume', 'time'
|
||||
indicators: Dictionary of pre-computed indicator values
|
||||
current_position: Details about current open position (if any)
|
||||
{'type': 'long'/'short', 'entry_price': float, 'size': float}
|
||||
"""
|
||||
pass
|
||||
63
src/strategies/ma125_strategy.py
Normal file
63
src/strategies/ma125_strategy.py
Normal file
@ -0,0 +1,63 @@
|
||||
"""
|
||||
MA125 Strategy
|
||||
Simple trend following strategy.
|
||||
- Long when Price > MA125
|
||||
- Short when Price < MA125
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
from .base import BaseStrategy, StrategySignal, SignalType
|
||||
|
||||
class MA125Strategy(BaseStrategy):
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return "ma125_strategy"
|
||||
|
||||
@property
|
||||
def required_indicators(self) -> List[str]:
|
||||
return ["ma125"]
|
||||
|
||||
@property
|
||||
def display_name(self) -> str:
|
||||
return "MA125 Strategy"
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
return "Long-term trend following using 125-period moving average. Better for identifying major trends."
|
||||
|
||||
def analyze(
|
||||
self,
|
||||
candle: Dict[str, Any],
|
||||
indicators: Dict[str, float],
|
||||
current_position: Optional[Dict[str, Any]] = None
|
||||
) -> StrategySignal:
|
||||
|
||||
price = candle['close']
|
||||
ma125 = indicators.get('ma125')
|
||||
|
||||
if ma125 is None:
|
||||
return StrategySignal(SignalType.HOLD, 0.0, "MA125 not available")
|
||||
|
||||
# Current position state
|
||||
is_long = current_position and current_position.get('type') == 'long'
|
||||
is_short = current_position and current_position.get('type') == 'short'
|
||||
|
||||
# Logic: Price > MA125 -> Bullish
|
||||
if price > ma125:
|
||||
if is_long:
|
||||
return StrategySignal(SignalType.HOLD, 1.0, f"Price {price:.2f} > MA125 {ma125:.2f}. Stay Long.")
|
||||
elif is_short:
|
||||
return StrategySignal(SignalType.CLOSE_SHORT, 1.0, f"Price {price:.2f} crossed above MA125 {ma125:.2f}. Close Short.")
|
||||
else:
|
||||
return StrategySignal(SignalType.OPEN_LONG, 1.0, f"Price {price:.2f} > MA125 {ma125:.2f}. Open Long.")
|
||||
|
||||
# Logic: Price < MA125 -> Bearish
|
||||
elif price < ma125:
|
||||
if is_short:
|
||||
return StrategySignal(SignalType.HOLD, 1.0, f"Price {price:.2f} < MA125 {ma125:.2f}. Stay Short.")
|
||||
elif is_long:
|
||||
return StrategySignal(SignalType.CLOSE_LONG, 1.0, f"Price {price:.2f} crossed below MA125 {ma125:.2f}. Close Long.")
|
||||
else:
|
||||
return StrategySignal(SignalType.OPEN_SHORT, 1.0, f"Price {price:.2f} < MA125 {ma125:.2f}. Open Short.")
|
||||
|
||||
return StrategySignal(SignalType.HOLD, 0.0, "Price == MA125")
|
||||
63
src/strategies/ma44_strategy.py
Normal file
63
src/strategies/ma44_strategy.py
Normal file
@ -0,0 +1,63 @@
|
||||
"""
|
||||
MA44 Strategy
|
||||
Simple trend following strategy.
|
||||
- Long when Price > MA44
|
||||
- Short when Price < MA44
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
from .base import BaseStrategy, StrategySignal, SignalType
|
||||
|
||||
class MA44Strategy(BaseStrategy):
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return "ma44_strategy"
|
||||
|
||||
@property
|
||||
def required_indicators(self) -> List[str]:
|
||||
return ["ma44"]
|
||||
|
||||
@property
|
||||
def display_name(self) -> str:
|
||||
return "MA44 Strategy"
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
return "Buy when price crosses above MA44, sell when below. Good for trending markets."
|
||||
|
||||
def analyze(
|
||||
self,
|
||||
candle: Dict[str, Any],
|
||||
indicators: Dict[str, float],
|
||||
current_position: Optional[Dict[str, Any]] = None
|
||||
) -> StrategySignal:
|
||||
|
||||
price = candle['close']
|
||||
ma44 = indicators.get('ma44')
|
||||
|
||||
if ma44 is None:
|
||||
return StrategySignal(SignalType.HOLD, 0.0, "MA44 not available")
|
||||
|
||||
# Current position state
|
||||
is_long = current_position and current_position.get('type') == 'long'
|
||||
is_short = current_position and current_position.get('type') == 'short'
|
||||
|
||||
# Logic: Price > MA44 -> Bullish
|
||||
if price > ma44:
|
||||
if is_long:
|
||||
return StrategySignal(SignalType.HOLD, 1.0, f"Price {price:.2f} > MA44 {ma44:.2f}. Stay Long.")
|
||||
elif is_short:
|
||||
return StrategySignal(SignalType.CLOSE_SHORT, 1.0, f"Price {price:.2f} crossed above MA44 {ma44:.2f}. Close Short.")
|
||||
else:
|
||||
return StrategySignal(SignalType.OPEN_LONG, 1.0, f"Price {price:.2f} > MA44 {ma44:.2f}. Open Long.")
|
||||
|
||||
# Logic: Price < MA44 -> Bearish
|
||||
elif price < ma44:
|
||||
if is_short:
|
||||
return StrategySignal(SignalType.HOLD, 1.0, f"Price {price:.2f} < MA44 {ma44:.2f}. Stay Short.")
|
||||
elif is_long:
|
||||
return StrategySignal(SignalType.CLOSE_LONG, 1.0, f"Price {price:.2f} crossed below MA44 {ma44:.2f}. Close Long.")
|
||||
else:
|
||||
return StrategySignal(SignalType.OPEN_SHORT, 1.0, f"Price {price:.2f} < MA44 {ma44:.2f}. Open Short.")
|
||||
|
||||
return StrategySignal(SignalType.HOLD, 0.0, "Price == MA44")
|
||||
Reference in New Issue
Block a user