Refactor raw trade management and enhance database operations
- Removed the `RawDataManager` class and integrated its functionality directly into the `RawTradeRepository`, streamlining the management of raw trade data. - Implemented the `cleanup_old_raw_data` method to delete outdated records, preventing table bloat and improving performance. - Added the `get_raw_data_stats` method to retrieve statistics about raw data storage, enhancing data management capabilities. - Updated documentation to reflect the new methods and their usage, ensuring clarity for future developers. These changes improve the maintainability and efficiency of the database operations related to raw trade data.
This commit is contained in:
Vasily.onl
parent
b30c16bc33
commit
1466223b85
@ -382,106 +382,4 @@ def test_connection() -> bool:
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def get_pool_status() -> Dict[str, Any]:
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"""Get connection pool status (convenience function)"""
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return db_manager.get_pool_status()
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class RawDataManager:
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"""
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Utility class for managing raw data storage and retention
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"""
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def __init__(self, db_manager: DatabaseManager):
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self.db_manager = db_manager
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def store_raw_data(self, exchange: str, symbol: str, data_type: str,
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raw_data: Dict[str, Any], timestamp: Optional[datetime] = None) -> None:
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"""
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Store raw API data
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Args:
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exchange: Exchange name (e.g., 'okx')
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symbol: Trading symbol (e.g., 'BTC-USDT')
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data_type: Type of data (ticker, trade, orderbook, candle, balance)
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raw_data: Complete API response
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timestamp: Data timestamp (defaults to now)
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"""
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from .models import RawTrade
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if timestamp is None:
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timestamp = datetime.utcnow()
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try:
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with self.db_manager.get_session() as session:
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raw_trade = RawTrade(
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exchange=exchange,
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symbol=symbol,
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timestamp=timestamp,
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data_type=data_type,
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raw_data=raw_data
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)
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session.add(raw_trade)
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logger.debug(f"Stored raw data: {exchange} {symbol} {data_type}")
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except Exception as e:
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logger.error(f"Failed to store raw data: {e}")
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raise
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def cleanup_old_raw_data(self, days_to_keep: int = 7) -> int:
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"""
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Clean up old raw data to prevent table bloat
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Args:
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days_to_keep: Number of days to retain data
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Returns:
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Number of records deleted
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"""
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try:
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cutoff_date = datetime.utcnow() - timedelta(days=days_to_keep)
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with self.db_manager.get_session() as session:
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deleted_count = session.execute(
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text("DELETE FROM raw_trades WHERE created_at < :cutoff_date"),
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{"cutoff_date": cutoff_date}
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).rowcount
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logger.info(f"Cleaned up {deleted_count} old raw data records")
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return deleted_count
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except Exception as e:
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logger.error(f"Failed to cleanup raw data: {e}")
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raise
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def get_raw_data_stats(self) -> Dict[str, Any]:
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"""Get statistics about raw data storage"""
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try:
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with self.db_manager.get_session() as session:
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result = session.execute(text("""
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SELECT
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COUNT(*) as total_records,
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COUNT(DISTINCT symbol) as unique_symbols,
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COUNT(DISTINCT data_type) as data_types,
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MIN(created_at) as oldest_record,
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MAX(created_at) as newest_record,
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pg_size_pretty(pg_total_relation_size('raw_trades')) as table_size
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FROM raw_trades
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""")).fetchone()
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if result:
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return {
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"total_records": result.total_records,
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"unique_symbols": result.unique_symbols,
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"data_types": result.data_types,
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"oldest_record": result.oldest_record,
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"newest_record": result.newest_record,
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"table_size": result.table_size
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}
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else:
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return {"status": "No data available"}
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except Exception as e:
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logger.error(f"Failed to get raw data stats: {e}")
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return {"error": str(e)}
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# Add raw data manager to the global manager
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def get_raw_data_manager() -> RawDataManager:
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"""Get raw data manager instance"""
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return RawDataManager(db_manager)
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return db_manager.get_pool_status()
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@ -1,9 +1,9 @@
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"""Repository for raw_trades table operations."""
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from datetime import datetime
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from datetime import datetime, timedelta
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from typing import Dict, Any, Optional, List
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from sqlalchemy import desc
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from sqlalchemy import desc, text
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from ..models import RawTrade
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from data.base_collector import MarketDataPoint
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@ -106,4 +106,55 @@ class RawTradeRepository(BaseRepository):
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except Exception as e:
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self.log_error(f"Error retrieving raw trades for {symbol}: {e}")
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raise DatabaseOperationError(f"Failed to retrieve raw trades: {e}")
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raise DatabaseOperationError(f"Failed to retrieve raw trades: {e}")
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def cleanup_old_raw_data(self, days_to_keep: int = 7) -> int:
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"""
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Clean up old raw data to prevent table bloat.
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Args:
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days_to_keep: Number of days to retain data.
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Returns:
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Number of records deleted.
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"""
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try:
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cutoff_date = datetime.now(datetime.timezone.utc) - timedelta(days=days_to_keep)
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with self.get_session() as session:
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result = session.execute(
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text("DELETE FROM raw_trades WHERE created_at < :cutoff_date"),
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{"cutoff_date": cutoff_date}
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)
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deleted_count = result.rowcount
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session.commit()
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self.log_info(f"Cleaned up {deleted_count} old raw data records")
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return deleted_count
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except Exception as e:
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self.log_error(f"Failed to cleanup raw data: {e}")
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raise DatabaseOperationError(f"Failed to cleanup raw data: {e}")
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def get_raw_data_stats(self) -> Dict[str, Any]:
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"""Get statistics about raw data storage."""
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try:
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with self.get_session() as session:
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result = session.execute(text("""
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SELECT
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COUNT(*) as total_records,
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COUNT(DISTINCT symbol) as unique_symbols,
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COUNT(DISTINCT data_type) as data_types,
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MIN(created_at) as oldest_record,
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MAX(created_at) as newest_record,
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pg_size_pretty(pg_total_relation_size('raw_trades')) as table_size
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FROM raw_trades
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""")).fetchone()
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if result:
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return dict(result._mapping)
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return {"status": "No data available"}
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except Exception as e:
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self.log_error(f"Failed to get raw data stats: {e}")
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raise DatabaseOperationError(f"Failed to get raw data stats: {e}")
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@ -1,329 +0,0 @@
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-- Database Schema for Crypto Trading Bot Platform
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-- Following PRD specifications with optimized schema for time-series data
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-- Version: 1.0
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-- Author: Generated following PRD requirements
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-- Create extensions
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CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
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CREATE EXTENSION IF NOT EXISTS "timescaledb" CASCADE;
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-- Set timezone to UTC for consistency
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SET timezone = 'UTC';
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-- ========================================
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-- MARKET DATA TABLES
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-- ========================================
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-- OHLCV Market Data (primary table for bot operations)
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-- This is the main table that bots will use for trading decisions
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CREATE TABLE market_data (
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id SERIAL PRIMARY KEY,
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exchange VARCHAR(50) NOT NULL DEFAULT 'okx',
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(5) NOT NULL, -- 1m, 5m, 15m, 1h, 4h, 1d
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timestamp TIMESTAMPTZ NOT NULL,
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open DECIMAL(18,8) NOT NULL,
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high DECIMAL(18,8) NOT NULL,
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low DECIMAL(18,8) NOT NULL,
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close DECIMAL(18,8) NOT NULL,
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volume DECIMAL(18,8) NOT NULL,
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trades_count INTEGER, -- number of trades in this candle
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created_at TIMESTAMPTZ DEFAULT NOW(),
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CONSTRAINT unique_market_data UNIQUE(exchange, symbol, timeframe, timestamp)
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);
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-- Convert to hypertable for TimescaleDB optimization
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SELECT create_hypertable('market_data', 'timestamp', if_not_exists => TRUE);
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-- Create optimized indexes for market data
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CREATE INDEX idx_market_data_lookup ON market_data(symbol, timeframe, timestamp);
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CREATE INDEX idx_market_data_recent ON market_data(timestamp DESC) WHERE timestamp > NOW() - INTERVAL '7 days';
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CREATE INDEX idx_market_data_symbol ON market_data(symbol);
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CREATE INDEX idx_market_data_timeframe ON market_data(timeframe);
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-- Raw Trade Data (optional, for detailed backtesting only)
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-- This table is partitioned by timestamp for better performance
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CREATE TABLE raw_trades (
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id SERIAL PRIMARY KEY,
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exchange VARCHAR(50) NOT NULL DEFAULT 'okx',
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symbol VARCHAR(20) NOT NULL,
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timestamp TIMESTAMPTZ NOT NULL,
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type VARCHAR(10) NOT NULL, -- trade, order, balance, tick, books
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data JSONB NOT NULL, -- response from the exchange
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created_at TIMESTAMPTZ DEFAULT NOW()
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) PARTITION BY RANGE (timestamp);
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-- Create initial partition for current month
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CREATE TABLE raw_trades_current PARTITION OF raw_trades
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FOR VALUES FROM (date_trunc('month', NOW())) TO (date_trunc('month', NOW()) + INTERVAL '1 month');
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-- Index for raw trades
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CREATE INDEX idx_raw_trades_symbol_time ON raw_trades(symbol, timestamp);
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CREATE INDEX idx_raw_trades_type ON raw_trades(type);
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-- ========================================
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-- BOT MANAGEMENT TABLES
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-- ========================================
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-- Bot Management (simplified)
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CREATE TABLE bots (
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id SERIAL PRIMARY KEY,
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name VARCHAR(100) NOT NULL,
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strategy_name VARCHAR(50) NOT NULL,
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(5) NOT NULL,
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status VARCHAR(20) NOT NULL DEFAULT 'inactive', -- active, inactive, error, paused
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config_file VARCHAR(200), -- path to JSON config
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virtual_balance DECIMAL(18,8) DEFAULT 10000,
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current_balance DECIMAL(18,8) DEFAULT 10000,
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last_heartbeat TIMESTAMPTZ,
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created_at TIMESTAMPTZ DEFAULT NOW(),
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updated_at TIMESTAMPTZ DEFAULT NOW(),
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CONSTRAINT chk_bot_status CHECK (status IN ('active', 'inactive', 'error', 'paused'))
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);
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-- Indexes for bot management
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CREATE INDEX idx_bots_status ON bots(status);
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CREATE INDEX idx_bots_symbol ON bots(symbol);
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CREATE INDEX idx_bots_strategy ON bots(strategy_name);
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CREATE INDEX idx_bots_last_heartbeat ON bots(last_heartbeat);
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-- ========================================
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-- TRADING SIGNAL TABLES
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-- ========================================
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-- Trading Signals (for analysis and debugging)
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CREATE TABLE signals (
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id SERIAL PRIMARY KEY,
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bot_id INTEGER REFERENCES bots(id) ON DELETE CASCADE,
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timestamp TIMESTAMPTZ NOT NULL,
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signal_type VARCHAR(10) NOT NULL, -- buy, sell, hold
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price DECIMAL(18,8),
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confidence DECIMAL(5,4), -- signal confidence score (0.0000 to 1.0000)
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indicators JSONB, -- technical indicator values
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created_at TIMESTAMPTZ DEFAULT NOW(),
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CONSTRAINT chk_signal_type CHECK (signal_type IN ('buy', 'sell', 'hold')),
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CONSTRAINT chk_confidence CHECK (confidence >= 0 AND confidence <= 1)
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);
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-- Convert signals to hypertable for TimescaleDB optimization
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SELECT create_hypertable('signals', 'timestamp', if_not_exists => TRUE);
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-- Indexes for signals
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CREATE INDEX idx_signals_bot_time ON signals(bot_id, timestamp);
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CREATE INDEX idx_signals_type ON signals(signal_type);
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CREATE INDEX idx_signals_timestamp ON signals(timestamp);
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-- ========================================
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-- TRADE EXECUTION TABLES
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-- ========================================
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-- Trade Execution Records
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CREATE TABLE trades (
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id SERIAL PRIMARY KEY,
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bot_id INTEGER REFERENCES bots(id) ON DELETE CASCADE,
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signal_id INTEGER REFERENCES signals(id) ON DELETE SET NULL,
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timestamp TIMESTAMPTZ NOT NULL,
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side VARCHAR(5) NOT NULL, -- buy, sell
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price DECIMAL(18,8) NOT NULL,
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quantity DECIMAL(18,8) NOT NULL,
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fees DECIMAL(18,8) DEFAULT 0,
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pnl DECIMAL(18,8), -- profit/loss for this trade
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balance_after DECIMAL(18,8), -- portfolio balance after trade
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created_at TIMESTAMPTZ DEFAULT NOW(),
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CONSTRAINT chk_trade_side CHECK (side IN ('buy', 'sell')),
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CONSTRAINT chk_positive_price CHECK (price > 0),
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CONSTRAINT chk_positive_quantity CHECK (quantity > 0),
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CONSTRAINT chk_non_negative_fees CHECK (fees >= 0)
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);
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-- Convert trades to hypertable for TimescaleDB optimization
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SELECT create_hypertable('trades', 'timestamp', if_not_exists => TRUE);
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-- Indexes for trades
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CREATE INDEX idx_trades_bot_time ON trades(bot_id, timestamp);
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CREATE INDEX idx_trades_side ON trades(side);
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CREATE INDEX idx_trades_timestamp ON trades(timestamp);
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-- ========================================
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-- PERFORMANCE TRACKING TABLES
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-- ========================================
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-- Performance Snapshots (for plotting portfolio over time)
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CREATE TABLE bot_performance (
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id SERIAL PRIMARY KEY,
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bot_id INTEGER REFERENCES bots(id) ON DELETE CASCADE,
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timestamp TIMESTAMPTZ NOT NULL,
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total_value DECIMAL(18,8) NOT NULL, -- current portfolio value
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cash_balance DECIMAL(18,8) NOT NULL,
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crypto_balance DECIMAL(18,8) NOT NULL,
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total_trades INTEGER DEFAULT 0,
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winning_trades INTEGER DEFAULT 0,
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total_fees DECIMAL(18,8) DEFAULT 0,
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created_at TIMESTAMPTZ DEFAULT NOW(),
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CONSTRAINT chk_non_negative_values CHECK (
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total_value >= 0 AND
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cash_balance >= 0 AND
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crypto_balance >= 0 AND
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total_trades >= 0 AND
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winning_trades >= 0 AND
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total_fees >= 0
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),
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CONSTRAINT chk_winning_trades_logic CHECK (winning_trades <= total_trades)
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);
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-- Convert bot_performance to hypertable for TimescaleDB optimization
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SELECT create_hypertable('bot_performance', 'timestamp', if_not_exists => TRUE);
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-- Indexes for bot performance
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CREATE INDEX idx_bot_performance_bot_time ON bot_performance(bot_id, timestamp);
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CREATE INDEX idx_bot_performance_timestamp ON bot_performance(timestamp);
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-- ========================================
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-- FUNCTIONS AND TRIGGERS
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-- ========================================
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-- Function to update bot updated_at timestamp
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CREATE OR REPLACE FUNCTION update_bot_timestamp()
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RETURNS TRIGGER AS $$
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BEGIN
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NEW.updated_at = NOW();
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RETURN NEW;
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END;
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$$ LANGUAGE plpgsql;
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-- Trigger to automatically update bot updated_at
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CREATE TRIGGER trigger_update_bot_timestamp
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BEFORE UPDATE ON bots
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FOR EACH ROW
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EXECUTE FUNCTION update_bot_timestamp();
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-- Function to create monthly partition for raw_trades
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CREATE OR REPLACE FUNCTION create_monthly_partition_for_raw_trades(partition_date DATE)
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RETURNS VOID AS $$
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DECLARE
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partition_name TEXT;
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start_date DATE;
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end_date DATE;
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BEGIN
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start_date := date_trunc('month', partition_date);
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end_date := start_date + INTERVAL '1 month';
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partition_name := 'raw_trades_' || to_char(start_date, 'YYYY_MM');
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EXECUTE format('CREATE TABLE IF NOT EXISTS %I PARTITION OF raw_trades
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FOR VALUES FROM (%L) TO (%L)',
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partition_name, start_date, end_date);
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END;
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$$ LANGUAGE plpgsql;
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-- ========================================
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-- VIEWS FOR COMMON QUERIES
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-- ========================================
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-- View for bot status overview
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CREATE VIEW bot_status_overview AS
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SELECT
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b.id,
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b.name,
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b.strategy_name,
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b.symbol,
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b.status,
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b.current_balance,
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b.virtual_balance,
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(b.current_balance - b.virtual_balance) as pnl,
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b.last_heartbeat,
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COUNT(t.id) as total_trades,
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COALESCE(SUM(CASE WHEN t.pnl > 0 THEN 1 ELSE 0 END), 0) as winning_trades
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FROM bots b
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LEFT JOIN trades t ON b.id = t.bot_id
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GROUP BY b.id, b.name, b.strategy_name, b.symbol, b.status,
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b.current_balance, b.virtual_balance, b.last_heartbeat;
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-- View for recent market data
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CREATE VIEW recent_market_data AS
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SELECT
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symbol,
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timeframe,
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timestamp,
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open,
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high,
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low,
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close,
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volume,
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trades_count
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FROM market_data
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WHERE timestamp > NOW() - INTERVAL '24 hours'
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ORDER BY symbol, timeframe, timestamp DESC;
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-- View for trading performance summary
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CREATE VIEW trading_performance_summary AS
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SELECT
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t.bot_id,
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b.name as bot_name,
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COUNT(t.id) as total_trades,
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SUM(CASE WHEN t.pnl > 0 THEN 1 ELSE 0 END) as winning_trades,
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ROUND((SUM(CASE WHEN t.pnl > 0 THEN 1 ELSE 0 END)::DECIMAL / COUNT(t.id)) * 100, 2) as win_rate_percent,
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ROUND(SUM(t.pnl), 4) as total_pnl,
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ROUND(SUM(t.fees), 4) as total_fees,
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MIN(t.timestamp) as first_trade,
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MAX(t.timestamp) as last_trade
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FROM trades t
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JOIN bots b ON t.bot_id = b.id
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GROUP BY t.bot_id, b.name
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ORDER BY total_pnl DESC;
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-- ========================================
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-- INITIAL DATA SEEDING
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-- ========================================
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-- Insert sample timeframes that the system supports
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CREATE TABLE IF NOT EXISTS supported_timeframes (
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timeframe VARCHAR(5) PRIMARY KEY,
|
||||
description VARCHAR(50),
|
||||
minutes INTEGER
|
||||
);
|
||||
|
||||
INSERT INTO supported_timeframes (timeframe, description, minutes) VALUES
|
||||
('1m', '1 Minute', 1),
|
||||
('5m', '5 Minutes', 5),
|
||||
('15m', '15 Minutes', 15),
|
||||
('1h', '1 Hour', 60),
|
||||
('4h', '4 Hours', 240),
|
||||
('1d', '1 Day', 1440)
|
||||
ON CONFLICT (timeframe) DO NOTHING;
|
||||
|
||||
-- Insert sample exchanges
|
||||
CREATE TABLE IF NOT EXISTS supported_exchanges (
|
||||
exchange VARCHAR(50) PRIMARY KEY,
|
||||
name VARCHAR(100),
|
||||
api_url VARCHAR(200),
|
||||
enabled BOOLEAN DEFAULT true
|
||||
);
|
||||
|
||||
INSERT INTO supported_exchanges (exchange, name, api_url, enabled) VALUES
|
||||
('okx', 'OKX Exchange', 'https://www.okx.com/api/v5', true),
|
||||
('binance', 'Binance Exchange', 'https://api.binance.com/api/v3', false),
|
||||
('coinbase', 'Coinbase Pro', 'https://api.exchange.coinbase.com', false)
|
||||
ON CONFLICT (exchange) DO NOTHING;
|
||||
|
||||
-- ========================================
|
||||
-- COMMENTS FOR DOCUMENTATION
|
||||
-- ========================================
|
||||
|
||||
COMMENT ON TABLE market_data IS 'Primary OHLCV market data table optimized for bot operations and backtesting';
|
||||
COMMENT ON TABLE raw_trades IS 'Optional raw trade data for detailed backtesting (partitioned by month)';
|
||||
COMMENT ON TABLE bots IS 'Bot instance management with JSON configuration references';
|
||||
COMMENT ON TABLE signals IS 'Trading signals generated by strategies with confidence scores';
|
||||
COMMENT ON TABLE trades IS 'Virtual trade execution records with P&L tracking';
|
||||
COMMENT ON TABLE bot_performance IS 'Portfolio performance snapshots for visualization';
|
||||
|
||||
COMMENT ON COLUMN market_data.timestamp IS 'Right-aligned timestamp (candle close time) following exchange standards';
|
||||
COMMENT ON COLUMN bots.config_file IS 'Path to JSON configuration file for strategy parameters';
|
||||
COMMENT ON COLUMN signals.confidence IS 'Signal confidence score from 0.0000 to 1.0000';
|
||||
COMMENT ON COLUMN trades.pnl IS 'Profit/Loss for this specific trade in base currency';
|
||||
COMMENT ON COLUMN bot_performance.total_value IS 'Current total portfolio value (cash + crypto)';
|
||||
|
||||
-- Grant permissions to dashboard user
|
||||
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO dashboard;
|
||||
GRANT ALL PRIVILEGES ON ALL SEQUENCES IN SCHEMA public TO dashboard;
|
||||
GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMA public TO dashboard;
|
||||
@ -342,6 +342,33 @@ trades = db.raw_trades.get_raw_trades(
|
||||
)
|
||||
```
|
||||
|
||||
##### `cleanup_old_raw_data(days_to_keep: int = 7) -> int`
|
||||
|
||||
Clean up old raw data to prevent table bloat.
|
||||
|
||||
**Parameters:**
|
||||
- `days_to_keep`: Number of days to retain raw data records.
|
||||
|
||||
**Returns:** The number of records deleted.
|
||||
|
||||
```python
|
||||
# Clean up raw data older than 14 days
|
||||
deleted_count = db.raw_trades.cleanup_old_raw_data(days_to_keep=14)
|
||||
print(f"Deleted {deleted_count} old raw data records.")
|
||||
```
|
||||
|
||||
##### `get_raw_data_stats() -> Dict[str, Any]`
|
||||
|
||||
Get statistics about raw data storage.
|
||||
|
||||
**Returns:** A dictionary with statistics like total records, table size, etc.
|
||||
|
||||
```python
|
||||
raw_stats = db.raw_trades.get_raw_data_stats()
|
||||
print(f"Raw Trades Table Size: {raw_stats.get('table_size')}")
|
||||
print(f"Total Raw Records: {raw_stats.get('total_records')}")
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The database operations module includes comprehensive error handling with custom exceptions.
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user