Dinger
2e9c7cd69e
Core changes: - Refactor FastAnalysisService: single LLM multi-factor analysis replaces 7-agent pipeline; add multi-timeframe consensus, threshold calibration, confidence calibration, multi-model ensemble voting - Add RAG memory injection and reflection validation (analysis_memory + reflection worker) - Simplify billing config: remove unused strategy_run/backtest/portfolio_monitor, add ai_code_gen separate billing (different token consumption scale) - Settings hot-reload after save, no backend restart needed Frontend: - Global dark theme overhaul: pure black palette replacing blue-tinted colors across sidebar/header/dashboard/analysis/K-line/user-manage/profile/settings/billing - Fix USDT payment modal dark theme (portal rendering broke CSS selectors) - Refactor position modal: direction + quantity + entry price, remove add/reduce logic, show raw DB values on re-open, save exactly what user inputs - Fix Polymarket prediction market dark text - i18n for position modal title Backend: - Position management: one record per symbol (DELETE+INSERT replacing ON CONFLICT with side), fixes PnL showing 0 when switching long/short - MarketDataCollector data fetching optimization - portfolio_monitor scheduled monitoring improvements - env.example reorganized: common config first, advanced config last Documentation: - README architecture diagram updated to FastAnalysisService flow - Add virtual position, AI tuning config, billing items documentation - Add INDICATOR_DEFINITIONS_CN.md, FRONTEND_FAST_ANALYSIS.md Made-with: Cursor
1322 lines
60 KiB
Python
1322 lines
60 KiB
Python
"""
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Portfolio Monitor Service.
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Runs scheduled AI analysis on manual positions and sends notifications.
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"""
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from __future__ import annotations
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import hashlib
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import json
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import threading
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import time
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import traceback
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from typing import Any, Dict, List, Optional
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from app.utils.db import get_db_connection
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from app.utils.logger import get_logger
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from app.services.fast_analysis import get_fast_analysis_service
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from app.services.signal_notifier import SignalNotifier
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from app.services.kline import KlineService
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from app.services.billing_service import get_billing_service
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logger = get_logger(__name__)
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DEFAULT_USER_ID = 1
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_monitor_thread: Optional[threading.Thread] = None
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_stop_event = threading.Event()
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# 多语言消息模板
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ALERT_MESSAGES = {
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'zh-CN': {
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'price_above': '🔔 价格突破预警: {symbol} 当前价格 ${current_price:.4f} 已突破 ${threshold:.4f}',
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'price_below': '🔔 价格跌破预警: {symbol} 当前价格 ${current_price:.4f} 已跌破 ${threshold:.4f}',
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'pnl_above': '🎉 盈利预警: {symbol} 当前盈亏 {pnl_percent:.1f}% 已达到 {threshold:.1f}% 目标',
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'pnl_below': '⚠️ 亏损预警: {symbol} 当前盈亏 {pnl_percent:.1f}% 已触及 {threshold:.1f}% 止损线',
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'alert_title': '价格/盈亏预警'
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},
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'en-US': {
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'price_above': '🔔 Price Alert: {symbol} current price ${current_price:.4f} has exceeded ${threshold:.4f}',
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'price_below': '🔔 Price Alert: {symbol} current price ${current_price:.4f} has dropped below ${threshold:.4f}',
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'pnl_above': '🎉 Profit Alert: {symbol} P&L {pnl_percent:.1f}% has reached {threshold:.1f}% target',
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'pnl_below': '⚠️ Loss Alert: {symbol} P&L {pnl_percent:.1f}% has hit {threshold:.1f}% stop-loss',
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'alert_title': 'Price/P&L Alert'
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}
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}
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def _get_alert_message(alert_type: str, language: str = 'en-US', **kwargs) -> str:
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"""Get localized alert message."""
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lang = 'zh-CN' if language and language.startswith('zh') else 'en-US'
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templates = ALERT_MESSAGES.get(lang, ALERT_MESSAGES['en-US'])
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template = templates.get(alert_type, '')
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if template:
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return template.format(**kwargs)
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return ''
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def _get_alert_title(language: str = 'en-US') -> str:
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"""Get localized alert title."""
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lang = 'zh-CN' if language and language.startswith('zh') else 'en-US'
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return ALERT_MESSAGES.get(lang, ALERT_MESSAGES['en-US']).get('alert_title', 'Alert')
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def _now_ts() -> int:
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return int(time.time())
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def _safe_json_loads(value, default=None):
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"""Safely parse JSON string."""
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if default is None:
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default = {}
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if isinstance(value, dict):
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return value
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if isinstance(value, list):
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return value
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if isinstance(value, str) and value.strip():
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try:
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return json.loads(value)
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except Exception:
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return default
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return default
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def _get_positions_for_monitor(position_ids: List[int] = None, user_id: int = None) -> List[Dict[str, Any]]:
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"""Get positions, optionally filtered by IDs and user_id."""
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try:
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kline_service = KlineService()
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effective_user_id = user_id if user_id is not None else DEFAULT_USER_ID
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with get_db_connection() as db:
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cur = db.cursor()
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if position_ids:
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placeholders = ','.join(['?' for _ in position_ids])
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cur.execute(
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f"""
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SELECT id, market, symbol, name, side, quantity, entry_price, group_name
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FROM qd_manual_positions
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WHERE user_id = ? AND id IN ({placeholders})
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""",
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[effective_user_id] + list(position_ids)
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)
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else:
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cur.execute(
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"""
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SELECT id, market, symbol, name, side, quantity, entry_price, group_name
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FROM qd_manual_positions
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WHERE user_id = ?
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""",
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(effective_user_id,)
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)
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rows = cur.fetchall() or []
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cur.close()
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positions = []
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for row in rows:
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market = row.get('market')
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symbol = row.get('symbol')
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entry_price = float(row.get('entry_price') or 0)
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quantity = float(row.get('quantity') or 0)
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side = row.get('side') or 'long'
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group_name = row.get('group_name')
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# Get current price (use realtime price API)
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current_price = 0
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try:
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price_data = kline_service.get_realtime_price(market, symbol)
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current_price = float(price_data.get('price') or 0)
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except Exception:
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pass
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# Calculate PnL
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if side == 'long':
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pnl = (current_price - entry_price) * quantity
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else:
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pnl = (entry_price - current_price) * quantity
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pnl_percent = round(pnl / (entry_price * quantity) * 100, 2) if entry_price * quantity > 0 else 0
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positions.append({
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'id': row.get('id'),
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'market': market,
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'symbol': symbol,
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'name': row.get('name') or symbol,
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'side': side,
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'quantity': quantity,
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'entry_price': entry_price,
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'current_price': current_price,
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'pnl': round(pnl, 2),
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'pnl_percent': pnl_percent,
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'group_name': group_name
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})
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return positions
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except Exception as e:
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logger.error(f"_get_positions_for_monitor failed: {e}")
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return []
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MAX_PARALLEL_ANALYSIS = 5
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def _analyze_single_position(pos: Dict[str, Any], language: str) -> Dict[str, Any]:
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"""Analyze a single position (designed to run inside a thread pool)."""
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market = pos.get('market')
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symbol = pos.get('symbol')
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name = pos.get('name') or symbol
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group_name = pos.get('group_name')
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if not market or not symbol:
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return {'market': market, 'symbol': symbol, 'name': name, 'error': 'missing market/symbol'}
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try:
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logger.info(f"Running fast AI analysis for {market}:{symbol}")
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service = get_fast_analysis_service()
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analysis_result = service.analyze(
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market=market, symbol=symbol, language=language, timeframe='1D'
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)
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detailed = analysis_result.get('detailed_analysis', {})
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trading_plan = analysis_result.get('trading_plan', {})
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scores = analysis_result.get('scores', {})
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risks = analysis_result.get('risks', [])
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risk_report = '\n'.join([f"• {r}" for r in risks]) if risks else ''
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result = {
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'market': market, 'symbol': symbol, 'name': name, 'group_name': group_name,
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'entry_price': pos.get('entry_price'),
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'current_price': pos.get('current_price') or analysis_result.get('market_data', {}).get('current_price'),
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'pnl': pos.get('pnl'), 'pnl_percent': pos.get('pnl_percent'),
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'quantity': pos.get('quantity'), 'side': pos.get('side'),
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'final_decision': analysis_result.get('decision', 'HOLD'),
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'confidence': analysis_result.get('confidence', 50),
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'reasoning': analysis_result.get('summary', ''),
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'trader_decision': analysis_result.get('decision', 'HOLD'),
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'trader_reasoning': analysis_result.get('summary', ''),
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'overview_report': detailed.get('technical', ''),
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'fundamental_report': detailed.get('fundamental', ''),
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'sentiment_report': detailed.get('sentiment', ''),
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'risk_report': risk_report,
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'suggested_entry': trading_plan.get('entry_price'),
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'suggested_stop_loss': trading_plan.get('stop_loss'),
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'suggested_take_profit': trading_plan.get('take_profit'),
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'technical_score': scores.get('technical', 50),
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'fundamental_score': scores.get('fundamental', 50),
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'sentiment_score': scores.get('sentiment', 50),
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'key_reasons': analysis_result.get('reasons', []),
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'error': analysis_result.get('error')
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}
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logger.info(f"Fast analysis completed for {market}:{symbol}: {analysis_result.get('decision', 'N/A')}")
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return result
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except Exception as e:
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logger.error(f"Failed to analyze {market}:{symbol}: {e}")
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return {'market': market, 'symbol': symbol, 'name': name, 'error': str(e)}
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def _run_ai_analysis(positions: List[Dict[str, Any]], config: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Run fast AI analysis on positions **in parallel** using a thread pool.
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"""
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try:
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language = config.get('language', 'en-US')
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custom_prompt = config.get('prompt', '')
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workers = min(len(positions), MAX_PARALLEL_ANALYSIS)
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position_analyses: List[Dict[str, Any]] = [None] * len(positions)
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with ThreadPoolExecutor(max_workers=workers) as executor:
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future_to_idx = {
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executor.submit(_analyze_single_position, pos, language): idx
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for idx, pos in enumerate(positions)
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}
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for future in as_completed(future_to_idx):
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idx = future_to_idx[future]
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try:
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position_analyses[idx] = future.result()
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except Exception as e:
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pos = positions[idx]
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position_analyses[idx] = {
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'market': pos.get('market'), 'symbol': pos.get('symbol'),
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'name': pos.get('name') or pos.get('symbol'), 'error': str(e)
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}
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analysis_report = _build_comprehensive_report(positions, position_analyses, language, custom_prompt)
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return {
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'success': True,
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'analysis': analysis_report,
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'position_analyses': position_analyses,
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'position_count': len(positions),
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'analyzed_count': len([p for p in position_analyses if not p.get('error')]),
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'timestamp': _now_ts()
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}
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except Exception as e:
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logger.error(f"_run_ai_analysis failed: {e}")
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logger.error(traceback.format_exc())
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return {'success': False, 'error': str(e), 'timestamp': _now_ts()}
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def _build_comprehensive_report(
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positions: List[Dict[str, Any]],
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position_analyses: List[Dict[str, Any]],
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language: str,
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custom_prompt: str = ''
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) -> str:
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"""Build a comprehensive text report (backward compatible)."""
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# Use HTML report as the main format
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return _build_html_report(positions, position_analyses, language, custom_prompt)
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def _build_html_report(
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positions: List[Dict[str, Any]],
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position_analyses: List[Dict[str, Any]],
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language: str,
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custom_prompt: str = ''
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) -> str:
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"""Build a beautiful HTML report with collapsible sections."""
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# Calculate portfolio summary
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total_cost = sum(float(p.get('entry_price', 0)) * float(p.get('quantity', 0)) for p in positions)
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total_pnl = sum(float(p.get('pnl', 0)) for p in positions)
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total_pnl_percent = round(total_pnl / total_cost * 100, 2) if total_cost > 0 else 0
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total_market_value = sum(float(p.get('current_price', 0)) * float(p.get('quantity', 0)) for p in positions)
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# Count recommendations
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buy_count = len([p for p in position_analyses if p.get('final_decision') == 'BUY'])
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sell_count = len([p for p in position_analyses if p.get('final_decision') == 'SELL'])
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hold_count = len([p for p in position_analyses if p.get('final_decision') == 'HOLD'])
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is_zh = language.startswith('zh')
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# Text translations
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texts = {
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'title': '投资组合AI分析报告' if is_zh else 'Portfolio AI Analysis Report',
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'subtitle': '由 QuantDinger AI 快速分析引擎生成' if is_zh else 'Generated by QuantDinger Fast AI Analysis Engine',
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'overview': '组合概览' if is_zh else 'Portfolio Overview',
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'positions': '持仓数量' if is_zh else 'Positions',
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'total_value': '总市值' if is_zh else 'Total Value',
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'total_cost': '总成本' if is_zh else 'Total Cost',
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'total_pnl': '总盈亏' if is_zh else 'Total P&L',
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'ai_recommendations': '🤖 AI智能分析建议' if is_zh else '🤖 AI Recommendations',
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'buy': '买入' if is_zh else 'Buy',
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'sell': '卖出' if is_zh else 'Sell',
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'hold': '持有' if is_zh else 'Hold',
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'position_analysis': '📈 各持仓详细分析' if is_zh else '📈 Position Analysis',
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'current_price': '当前价格' if is_zh else 'Current',
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'entry_price': '买入价' if is_zh else 'Entry',
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'pnl': '盈亏' if is_zh else 'P&L',
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'quantity': '数量' if is_zh else 'Qty',
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'side': '方向' if is_zh else 'Side',
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'long': '做多' if is_zh else 'Long',
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'short': '做空' if is_zh else 'Short',
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'ai_decision': 'AI决策' if is_zh else 'AI Decision',
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'confidence': '置信度' if is_zh else 'Confidence',
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'reasoning': '分析摘要' if is_zh else 'Summary',
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'trader_report': '📋 交易员详细评估' if is_zh else '📋 Trader Analysis',
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'risk_report': '⚠️ 风险评估' if is_zh else '⚠️ Risk Assessment',
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'overview_report': '📊 市场概览' if is_zh else '📊 Market Overview',
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'click_expand': '点击展开详情' if is_zh else 'Click to expand',
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'user_focus': '👤 用户关注点' if is_zh else '👤 User Focus',
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'generated_at': '报告生成时间' if is_zh else 'Generated at',
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'disclaimer': '本报告仅供参考，不构成投资建议。' if is_zh else 'For reference only. Not investment advice.',
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'analysis_failed': '分析失败' if is_zh else 'Analysis failed'
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}
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# CSS Styles
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css = '''
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<style>
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.qd-report { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 16px; }
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.qd-report * { box-sizing: border-box; }
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.qd-header { text-align: center; color: #fff; padding: 20px 0 30px; }
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.qd-header h1 { margin: 0 0 8px; font-size: 24px; font-weight: 700; text-shadow: 0 2px 4px rgba(0,0,0,0.2); }
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.qd-header .subtitle { font-size: 13px; opacity: 0.9; }
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.qd-content { background: #fff; border-radius: 12px; padding: 24px; box-shadow: 0 10px 40px rgba(0,0,0,0.15); }
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.qd-section { margin-bottom: 24px; }
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.qd-section:last-child { margin-bottom: 0; }
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.qd-section-title { font-size: 16px; font-weight: 600; color: #1a1a2e; margin: 0 0 16px; padding-bottom: 8px; border-bottom: 2px solid #667eea; }
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.qd-overview-grid { display: grid; grid-template-columns: repeat(4, 1fr); gap: 12px; }
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.qd-stat-card { background: linear-gradient(135deg, #f5f7fa 0%, #e8ecf3 100%); border-radius: 10px; padding: 16px; text-align: center; }
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.qd-stat-card .label { font-size: 12px; color: #666; margin-bottom: 6px; }
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.qd-stat-card .value { font-size: 20px; font-weight: 700; color: #1a1a2e; }
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.qd-stat-card .value.positive { color: #10b981; }
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.qd-stat-card .value.negative { color: #ef4444; }
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.qd-stat-card .percent { font-size: 12px; font-weight: 500; margin-left: 4px; }
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.qd-rec-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 12px; }
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.qd-rec-card { border-radius: 10px; padding: 16px; text-align: center; }
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.qd-rec-card.buy { background: linear-gradient(135deg, #d1fae5 0%, #a7f3d0 100%); }
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.qd-rec-card.sell { background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%); }
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.qd-rec-card.hold { background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%); }
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.qd-rec-card .emoji { font-size: 28px; margin-bottom: 8px; }
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.qd-rec-card .count { font-size: 24px; font-weight: 700; }
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.qd-rec-card.buy .count { color: #059669; }
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.qd-rec-card.sell .count { color: #dc2626; }
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.qd-rec-card.hold .count { color: #d97706; }
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.qd-rec-card .label { font-size: 13px; color: #666; margin-top: 4px; }
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.qd-position { background: #f8fafc; border-radius: 12px; margin-bottom: 16px; overflow: hidden; border: 1px solid #e2e8f0; }
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.qd-position:last-child { margin-bottom: 0; }
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.qd-pos-header { display: flex; justify-content: space-between; align-items: center; padding: 16px; background: #fff; cursor: default; }
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.qd-pos-symbol { display: flex; align-items: center; gap: 12px; }
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.qd-pos-symbol .icon { width: 40px; height: 40px; border-radius: 10px; display: flex; align-items: center; justify-content: center; font-weight: 700; font-size: 14px; color: #fff; }
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.qd-pos-symbol .icon.buy { background: linear-gradient(135deg, #10b981 0%, #059669 100%); }
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.qd-pos-symbol .icon.sell { background: linear-gradient(135deg, #ef4444 0%, #dc2626 100%); }
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.qd-pos-symbol .icon.hold { background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%); }
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.qd-pos-symbol .name { font-weight: 600; font-size: 15px; color: #1a1a2e; }
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.qd-pos-symbol .market { font-size: 12px; color: #666; }
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.qd-pos-decision { text-align: right; }
|
|
.qd-pos-decision .decision-tag { display: inline-block; padding: 6px 14px; border-radius: 20px; font-weight: 600; font-size: 13px; }
|
|
.qd-pos-decision .decision-tag.buy { background: #d1fae5; color: #059669; }
|
|
.qd-pos-decision .decision-tag.sell { background: #fee2e2; color: #dc2626; }
|
|
.qd-pos-decision .decision-tag.hold { background: #fef3c7; color: #d97706; }
|
|
.qd-pos-decision .confidence { font-size: 12px; color: #666; margin-top: 4px; }
|
|
.qd-pos-stats { display: grid; grid-template-columns: repeat(4, 1fr); gap: 1px; background: #e2e8f0; }
|
|
.qd-pos-stats .stat { background: #fff; padding: 12px; text-align: center; }
|
|
.qd-pos-stats .stat .label { font-size: 11px; color: #666; margin-bottom: 4px; }
|
|
.qd-pos-stats .stat .value { font-size: 14px; font-weight: 600; color: #1a1a2e; }
|
|
.qd-pos-stats .stat .value.positive { color: #10b981; }
|
|
.qd-pos-stats .stat .value.negative { color: #ef4444; }
|
|
.qd-pos-reasoning { padding: 16px; background: #fff; border-top: 1px solid #e2e8f0; }
|
|
.qd-pos-reasoning .label { font-size: 12px; font-weight: 600; color: #666; margin-bottom: 6px; }
|
|
.qd-pos-reasoning .text { font-size: 13px; color: #374151; line-height: 1.6; }
|
|
.qd-collapsible { border-top: 1px solid #e2e8f0; }
|
|
.qd-collapsible input[type="checkbox"] { display: none; }
|
|
.qd-collapsible-header { display: flex; justify-content: space-between; align-items: center; padding: 12px 16px; background: #f1f5f9; cursor: pointer; user-select: none; }
|
|
.qd-collapsible-header:hover { background: #e2e8f0; }
|
|
.qd-collapsible-header .title { font-size: 13px; font-weight: 600; color: #475569; }
|
|
.qd-collapsible-header .arrow { transition: transform 0.2s; color: #94a3b8; display: inline-block; }
|
|
.qd-collapsible-content { display: none; padding: 16px; background: #fff; font-size: 13px; color: #475569; line-height: 1.7; border-top: 1px solid #e2e8f0; }
|
|
.qd-collapsible input[type="checkbox"]:checked ~ .qd-collapsible-content { display: block; }
|
|
.qd-collapsible input[type="checkbox"]:checked + .qd-collapsible-header .arrow { transform: rotate(180deg); }
|
|
.qd-user-focus { background: linear-gradient(135deg, #ede9fe 0%, #ddd6fe 100%); border-radius: 10px; padding: 16px; font-size: 13px; color: #5b21b6; line-height: 1.6; }
|
|
.qd-footer { text-align: center; padding: 20px 0 0; font-size: 12px; color: #666; border-top: 1px solid #e2e8f0; margin-top: 24px; }
|
|
.qd-footer .time { margin-bottom: 4px; }
|
|
.qd-footer .disclaimer { opacity: 0.8; }
|
|
.qd-error { background: #fef2f2; border: 1px solid #fecaca; border-radius: 8px; padding: 12px; color: #dc2626; font-size: 13px; }
|
|
@media (max-width: 600px) {
|
|
.qd-report { padding: 12px; border-radius: 0; }
|
|
.qd-overview-grid { grid-template-columns: repeat(2, 1fr); }
|
|
.qd-rec-grid { grid-template-columns: repeat(3, 1fr); }
|
|
.qd-pos-stats { grid-template-columns: repeat(2, 1fr); }
|
|
}
|
|
</style>
|
|
'''
|
|
|
|
# Build HTML
|
|
pnl_class = 'positive' if total_pnl >= 0 else 'negative'
|
|
pnl_sign = '+' if total_pnl >= 0 else ''
|
|
|
|
html = f'''
|
|
{css}
|
|
<div class="qd-report">
|
|
<div class="qd-header">
|
|
<h1>{texts['title']}</h1>
|
|
<div class="subtitle">{texts['subtitle']}</div>
|
|
</div>
|
|
<div class="qd-content">
|
|
<!-- Overview Section -->
|
|
<div class="qd-section">
|
|
<h2 class="qd-section-title">{texts['overview']}</h2>
|
|
<div class="qd-overview-grid">
|
|
<div class="qd-stat-card">
|
|
<div class="label">{texts['positions']}</div>
|
|
<div class="value">{len(positions)}</div>
|
|
</div>
|
|
<div class="qd-stat-card">
|
|
<div class="label">{texts['total_value']}</div>
|
|
<div class="value">${total_market_value:,.2f}</div>
|
|
</div>
|
|
<div class="qd-stat-card">
|
|
<div class="label">{texts['total_cost']}</div>
|
|
<div class="value">${total_cost:,.2f}</div>
|
|
</div>
|
|
<div class="qd-stat-card">
|
|
<div class="label">{texts['total_pnl']}</div>
|
|
<div class="value {pnl_class}">{pnl_sign}${total_pnl:,.2f}<span class="percent">({pnl_sign}{total_pnl_percent:.1f}%)</span></div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- AI Recommendations Section -->
|
|
<div class="qd-section">
|
|
<h2 class="qd-section-title">{texts['ai_recommendations']}</h2>
|
|
<div class="qd-rec-grid">
|
|
<div class="qd-rec-card buy">
|
|
<div class="emoji">🟢</div>
|
|
<div class="count">{buy_count}</div>
|
|
<div class="label">{texts['buy']}</div>
|
|
</div>
|
|
<div class="qd-rec-card sell">
|
|
<div class="emoji">🔴</div>
|
|
<div class="count">{sell_count}</div>
|
|
<div class="label">{texts['sell']}</div>
|
|
</div>
|
|
<div class="qd-rec-card hold">
|
|
<div class="emoji">🟡</div>
|
|
<div class="count">{hold_count}</div>
|
|
<div class="label">{texts['hold']}</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Position Analysis Section -->
|
|
<div class="qd-section">
|
|
<h2 class="qd-section-title">{texts['position_analysis']}</h2>
|
|
'''
|
|
|
|
for pa in position_analyses:
|
|
symbol = pa.get('symbol', '')
|
|
name = pa.get('name', symbol)
|
|
market = pa.get('market', '')
|
|
group_name = pa.get('group_name', '')
|
|
|
|
if pa.get('error'):
|
|
html += f'''
|
|
<div class="qd-position">
|
|
<div class="qd-pos-header">
|
|
<div class="qd-pos-symbol">
|
|
<div class="icon hold">⚠️</div>
|
|
<div>
|
|
<div class="name">{name}</div>
|
|
<div class="market">{market}/{symbol}</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
<div class="qd-error" style="margin: 16px;">{texts['analysis_failed']}: {pa.get('error')}</div>
|
|
</div>
|
|
'''
|
|
continue
|
|
|
|
decision = pa.get('final_decision', 'HOLD')
|
|
decision_lower = decision.lower()
|
|
decision_text = texts.get(decision_lower, decision)
|
|
confidence = pa.get('confidence', 50)
|
|
|
|
current_price = pa.get('current_price', 0)
|
|
entry_price = pa.get('entry_price', 0)
|
|
pnl = pa.get('pnl', 0)
|
|
pnl_pct = pa.get('pnl_percent', 0)
|
|
quantity = pa.get('quantity', 0)
|
|
side = pa.get('side', 'long')
|
|
side_text = texts['long'] if side == 'long' else texts['short']
|
|
|
|
pnl_class = 'positive' if pnl >= 0 else 'negative'
|
|
pnl_sign = '+' if pnl >= 0 else ''
|
|
|
|
reasoning = pa.get('reasoning', '')
|
|
trader_reasoning = pa.get('trader_reasoning', '')
|
|
overview_report = pa.get('overview_report', '')
|
|
risk_report = pa.get('risk_report', '')
|
|
|
|
html += f'''
|
|
<div class="qd-position">
|
|
<div class="qd-pos-header">
|
|
<div class="qd-pos-symbol">
|
|
<div class="icon {decision_lower}">{decision[0]}</div>
|
|
<div>
|
|
<div class="name">{name}</div>
|
|
<div class="market">{market}/{symbol}</div>
|
|
</div>
|
|
</div>
|
|
<div class="qd-pos-decision">
|
|
<div class="decision-tag {decision_lower}">{decision_text}</div>
|
|
<div class="confidence">{texts['confidence']}: {confidence}%</div>
|
|
</div>
|
|
</div>
|
|
<div class="qd-pos-stats">
|
|
<div class="stat">
|
|
<div class="label">{texts['current_price']}</div>
|
|
<div class="value">${current_price:.4f}</div>
|
|
</div>
|
|
<div class="stat">
|
|
<div class="label">{texts['entry_price']}</div>
|
|
<div class="value">${entry_price:.4f}</div>
|
|
</div>
|
|
<div class="stat">
|
|
<div class="label">{texts['pnl']}</div>
|
|
<div class="value {pnl_class}">{pnl_sign}${pnl:.2f} ({pnl_sign}{pnl_pct:.1f}%)</div>
|
|
</div>
|
|
<div class="stat">
|
|
<div class="label">{texts['quantity']} / {texts['side']}</div>
|
|
<div class="value">{quantity} / {side_text}</div>
|
|
</div>
|
|
</div>
|
|
'''
|
|
|
|
# Reasoning summary
|
|
if reasoning:
|
|
html += f'''
|
|
<div class="qd-pos-reasoning">
|
|
<div class="label">{texts['reasoning']}</div>
|
|
<div class="text">{reasoning[:500]}{'...' if len(reasoning) > 500 else ''}</div>
|
|
</div>
|
|
'''
|
|
|
|
# Generate unique ID for collapsible sections (use symbol hash to avoid special chars)
|
|
section_id_base = hashlib.md5(f"{symbol}_{market}_{group_name}".encode()).hexdigest()[:8]
|
|
|
|
# Collapsible: Trader Analysis
|
|
if trader_reasoning:
|
|
trader_id = f"trader_{section_id_base}"
|
|
html += f'''
|
|
<div class="qd-collapsible">
|
|
<input type="checkbox" id="{trader_id}">
|
|
<label for="{trader_id}" class="qd-collapsible-header">
|
|
<span class="title">{texts['trader_report']}</span>
|
|
<span class="arrow">▼</span>
|
|
</label>
|
|
<div class="qd-collapsible-content">{trader_reasoning.replace(chr(10), '<br>')}</div>
|
|
</div>
|
|
'''
|
|
|
|
# Collapsible: Market Overview
|
|
if overview_report:
|
|
overview_id = f"overview_{section_id_base}"
|
|
html += f'''
|
|
<div class="qd-collapsible">
|
|
<input type="checkbox" id="{overview_id}">
|
|
<label for="{overview_id}" class="qd-collapsible-header">
|
|
<span class="title">{texts['overview_report']}</span>
|
|
<span class="arrow">▼</span>
|
|
</label>
|
|
<div class="qd-collapsible-content">{overview_report.replace(chr(10), '<br>')}</div>
|
|
</div>
|
|
'''
|
|
|
|
# Collapsible: Risk Assessment
|
|
if risk_report:
|
|
risk_id = f"risk_{section_id_base}"
|
|
html += f'''
|
|
<div class="qd-collapsible">
|
|
<input type="checkbox" id="{risk_id}">
|
|
<label for="{risk_id}" class="qd-collapsible-header">
|
|
<span class="title">{texts['risk_report']}</span>
|
|
<span class="arrow">▼</span>
|
|
</label>
|
|
<div class="qd-collapsible-content">{risk_report.replace(chr(10), '<br>')}</div>
|
|
</div>
|
|
'''
|
|
|
|
html += '''
|
|
</div>
|
|
'''
|
|
|
|
# User focus section
|
|
if custom_prompt:
|
|
html += f'''
|
|
</div>
|
|
<div class="qd-section">
|
|
<h2 class="qd-section-title">{texts['user_focus']}</h2>
|
|
<div class="qd-user-focus">{custom_prompt}</div>
|
|
'''
|
|
|
|
# Footer
|
|
html += f'''
|
|
</div>
|
|
<div class="qd-footer">
|
|
<div class="time">{texts['generated_at']}: {time.strftime('%Y-%m-%d %H:%M:%S')}</div>
|
|
<div class="disclaimer">{texts['disclaimer']}</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
'''
|
|
|
|
return html
|
|
|
|
|
|
def _build_telegram_report(
|
|
positions: List[Dict[str, Any]],
|
|
position_analyses: List[Dict[str, Any]],
|
|
language: str,
|
|
custom_prompt: str = ''
|
|
) -> str:
|
|
"""Build a concise report suitable for Telegram (HTML format)."""
|
|
|
|
# Calculate summary
|
|
total_cost = sum(float(p.get('entry_price', 0)) * float(p.get('quantity', 0)) for p in positions)
|
|
total_pnl = sum(float(p.get('pnl', 0)) for p in positions)
|
|
total_pnl_percent = round(total_pnl / total_cost * 100, 2) if total_cost > 0 else 0
|
|
|
|
buy_count = len([p for p in position_analyses if p.get('final_decision') == 'BUY'])
|
|
sell_count = len([p for p in position_analyses if p.get('final_decision') == 'SELL'])
|
|
hold_count = len([p for p in position_analyses if p.get('final_decision') == 'HOLD'])
|
|
|
|
is_zh = language.startswith('zh')
|
|
pnl_sign = '+' if total_pnl >= 0 else ''
|
|
|
|
if is_zh:
|
|
lines = [
|
|
"<b>📊 投资组合AI分析报告</b>",
|
|
"",
|
|
"<b>📈 组合概览</b>",
|
|
f"• 持仓: {len(positions)} 个",
|
|
f"• 总成本: ${total_cost:,.2f}",
|
|
f"• 总盈亏: {pnl_sign}${total_pnl:,.2f} ({pnl_sign}{total_pnl_percent:.1f}%)",
|
|
"",
|
|
"<b>🤖 AI建议汇总</b>",
|
|
f"🟢 买入: {buy_count} | 🔴 卖出: {sell_count} | 🟡 持有: {hold_count}",
|
|
"",
|
|
"<b>📋 持仓分析</b>"
|
|
]
|
|
|
|
for pa in position_analyses:
|
|
if pa.get('error'):
|
|
lines.append(f"⚠️ <b>{pa.get('name', pa.get('symbol'))}</b>: 分析失败")
|
|
continue
|
|
|
|
decision = pa.get('final_decision', 'HOLD')
|
|
emoji = {'BUY': '🟢', 'SELL': '🔴', 'HOLD': '🟡'}.get(decision, '⚪')
|
|
text = {'BUY': '买入', 'SELL': '卖出', 'HOLD': '持有'}.get(decision, '持有')
|
|
pnl = pa.get('pnl', 0)
|
|
pnl_pct = pa.get('pnl_percent', 0)
|
|
pnl_s = '+' if pnl >= 0 else ''
|
|
|
|
lines.append(f"\n{emoji} <b>{pa.get('name', pa.get('symbol'))}</b> ({pa.get('market')}/{pa.get('symbol')})")
|
|
lines.append(f" 💰 ${pa.get('current_price', 0):.2f} | 盈亏: {pnl_s}${pnl:.2f} ({pnl_s}{pnl_pct:.1f}%)")
|
|
lines.append(f" 🎯 建议: <b>{text}</b> (置信度 {pa.get('confidence', 50)}%)")
|
|
|
|
reasoning = pa.get('reasoning', '')
|
|
if reasoning:
|
|
lines.append(f" 📝 {reasoning[:150]}{'...' if len(reasoning) > 150 else ''}")
|
|
|
|
if custom_prompt:
|
|
lines.extend(["", f"<b>👤 关注点:</b> {custom_prompt}"])
|
|
|
|
lines.extend([
|
|
"",
|
|
"─────────────────────",
|
|
f"<i>⏰ {time.strftime('%Y-%m-%d %H:%M')}</i>",
|
|
"<i>由 QuantDinger 多智能体系统生成</i>"
|
|
])
|
|
else:
|
|
lines = [
|
|
"<b>📊 Portfolio AI Analysis Report</b>",
|
|
"",
|
|
"<b>📈 Overview</b>",
|
|
f"• Positions: {len(positions)}",
|
|
f"• Total Cost: ${total_cost:,.2f}",
|
|
f"• Total P&L: {pnl_sign}${total_pnl:,.2f} ({pnl_sign}{total_pnl_percent:.1f}%)",
|
|
"",
|
|
"<b>🤖 AI Recommendations</b>",
|
|
f"🟢 Buy: {buy_count} | 🔴 Sell: {sell_count} | 🟡 Hold: {hold_count}",
|
|
"",
|
|
"<b>📋 Position Analysis</b>"
|
|
]
|
|
|
|
for pa in position_analyses:
|
|
if pa.get('error'):
|
|
lines.append(f"⚠️ <b>{pa.get('name', pa.get('symbol'))}</b>: Analysis failed")
|
|
continue
|
|
|
|
decision = pa.get('final_decision', 'HOLD')
|
|
emoji = {'BUY': '🟢', 'SELL': '🔴', 'HOLD': '🟡'}.get(decision, '⚪')
|
|
pnl = pa.get('pnl', 0)
|
|
pnl_pct = pa.get('pnl_percent', 0)
|
|
pnl_s = '+' if pnl >= 0 else ''
|
|
|
|
lines.append(f"\n{emoji} <b>{pa.get('name', pa.get('symbol'))}</b> ({pa.get('market')}/{pa.get('symbol')})")
|
|
lines.append(f" 💰 ${pa.get('current_price', 0):.2f} | P&L: {pnl_s}${pnl:.2f} ({pnl_s}{pnl_pct:.1f}%)")
|
|
lines.append(f" 🎯 Rec: <b>{decision}</b> (Conf: {pa.get('confidence', 50)}%)")
|
|
|
|
reasoning = pa.get('reasoning', '')
|
|
if reasoning:
|
|
lines.append(f" 📝 {reasoning[:150]}{'...' if len(reasoning) > 150 else ''}")
|
|
|
|
if custom_prompt:
|
|
lines.extend(["", f"<b>👤 Focus:</b> {custom_prompt}"])
|
|
|
|
lines.extend([
|
|
"",
|
|
"─────────────────────",
|
|
f"<i>⏰ {time.strftime('%Y-%m-%d %H:%M')}</i>",
|
|
"<i>Generated by QuantDinger Multi-Agent System</i>"
|
|
])
|
|
|
|
return '\n'.join(lines)
|
|
|
|
|
|
def _send_monitor_notification(
|
|
monitor_name: str,
|
|
result: Dict[str, Any],
|
|
notification_config: Dict[str, Any],
|
|
positions: List[Dict[str, Any]] = None,
|
|
position_analyses: List[Dict[str, Any]] = None,
|
|
language: str = 'en-US',
|
|
custom_prompt: str = '',
|
|
user_id: int = None
|
|
) -> None:
|
|
"""Send notification with analysis result using appropriate format for each channel."""
|
|
try:
|
|
notifier = SignalNotifier()
|
|
effective_user_id = user_id if user_id is not None else DEFAULT_USER_ID
|
|
|
|
channels = notification_config.get('channels', ['browser'])
|
|
targets = notification_config.get('targets', {})
|
|
|
|
title = f"📊 资产监测: {monitor_name}" if language.startswith('zh') else f"📊 Portfolio Monitor: {monitor_name}"
|
|
|
|
if not result.get('success'):
|
|
error_title = f"⚠️ 资产监测失败: {monitor_name}" if language.startswith('zh') else f"⚠️ Monitor Failed: {monitor_name}"
|
|
error_msg = f"分析失败: {result.get('error', 'Unknown error')}" if language.startswith('zh') else f"Analysis failed: {result.get('error', 'Unknown error')}"
|
|
|
|
for channel in channels:
|
|
try:
|
|
ch = str(channel).strip().lower()
|
|
if ch == 'browser':
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
INSERT INTO qd_strategy_notifications
|
|
(user_id, strategy_id, symbol, signal_type, channels, title, message, payload_json, created_at)
|
|
VALUES (?, NULL, ?, ?, ?, ?, ?, ?, NOW())
|
|
""",
|
|
(effective_user_id, 'PORTFOLIO', 'ai_monitor', 'browser', error_title, error_msg,
|
|
json.dumps(result, ensure_ascii=False))
|
|
)
|
|
db.commit()
|
|
cur.close()
|
|
elif ch == 'telegram':
|
|
chat_id = targets.get('telegram', '')
|
|
token_override = targets.get('telegram_bot_token', '')
|
|
if chat_id:
|
|
notifier._notify_telegram(chat_id=chat_id, text=f"<b>{error_title}</b>\n\n{error_msg}", token_override=token_override, parse_mode="HTML")
|
|
elif ch == 'email':
|
|
to_email = targets.get('email', '')
|
|
if to_email:
|
|
notifier._notify_email(to_email=to_email, subject=error_title, body_text=error_msg)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to send error notification to {channel}: {e}")
|
|
return
|
|
|
|
# Generate reports for different channels
|
|
html_report = result.get('analysis', '') # This is already HTML from _build_html_report
|
|
|
|
# Generate Telegram-specific report if we have the data
|
|
telegram_report = ''
|
|
if positions is not None and position_analyses is not None:
|
|
telegram_report = _build_telegram_report(positions, position_analyses, language, custom_prompt)
|
|
else:
|
|
# Fallback: strip HTML tags for Telegram
|
|
import re
|
|
telegram_report = re.sub(r'<[^>]+>', '', html_report)
|
|
if len(telegram_report) > 4000:
|
|
telegram_report = telegram_report[:4000] + '...'
|
|
|
|
# Send to each channel
|
|
for channel in channels:
|
|
try:
|
|
ch = str(channel).strip().lower()
|
|
|
|
if ch == 'browser':
|
|
# Browser notification uses HTML report
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
INSERT INTO qd_strategy_notifications
|
|
(user_id, strategy_id, symbol, signal_type, channels, title, message, payload_json, created_at)
|
|
VALUES (?, NULL, ?, ?, ?, ?, ?, ?, NOW())
|
|
""",
|
|
(effective_user_id, 'PORTFOLIO', 'ai_monitor', 'browser', title, html_report,
|
|
json.dumps(result, ensure_ascii=False))
|
|
)
|
|
db.commit()
|
|
cur.close()
|
|
|
|
elif ch == 'telegram':
|
|
chat_id = targets.get('telegram', '')
|
|
token_override = targets.get('telegram_bot_token', '')
|
|
if chat_id:
|
|
# Use Telegram-optimized format
|
|
notifier._notify_telegram(
|
|
chat_id=chat_id,
|
|
text=telegram_report,
|
|
token_override=token_override,
|
|
parse_mode="HTML"
|
|
)
|
|
|
|
elif ch == 'email':
|
|
to_email = targets.get('email', '')
|
|
if to_email:
|
|
# Email uses full HTML report
|
|
notifier._notify_email(
|
|
to_email=to_email,
|
|
subject=title,
|
|
body_text=html_report,
|
|
body_html=html_report # Send as HTML email
|
|
)
|
|
|
|
elif ch == 'webhook':
|
|
url = targets.get('webhook', '')
|
|
if url:
|
|
notifier._notify_webhook(
|
|
url=url,
|
|
payload={
|
|
'type': 'portfolio_monitor',
|
|
'monitor_name': monitor_name,
|
|
'result': result,
|
|
'html_report': html_report
|
|
}
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Failed to send notification to {channel}: {e}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"_send_monitor_notification failed: {e}")
|
|
|
|
|
|
def run_single_monitor(monitor_id: int, override_language: str = None, user_id: int = None) -> Dict[str, Any]:
|
|
"""Run a single monitor and return the result.
|
|
|
|
Args:
|
|
monitor_id: The monitor ID to run
|
|
override_language: Optional language override (e.g., 'zh-CN', 'en-US')
|
|
If provided, will override the language in monitor config
|
|
user_id: Optional user ID for user isolation
|
|
"""
|
|
try:
|
|
# Use provided user_id or default
|
|
effective_user_id = user_id if user_id is not None else DEFAULT_USER_ID
|
|
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
SELECT id, user_id, name, position_ids, monitor_type, config, notification_config
|
|
FROM qd_position_monitors
|
|
WHERE id = ? AND user_id = ?
|
|
""",
|
|
(monitor_id, effective_user_id)
|
|
)
|
|
row = cur.fetchone()
|
|
cur.close()
|
|
|
|
if not row:
|
|
return {'success': False, 'error': 'Monitor not found'}
|
|
|
|
monitor_user_id = int(row.get('user_id') or effective_user_id)
|
|
name = row.get('name') or f'Monitor #{monitor_id}'
|
|
position_ids = _safe_json_loads(row.get('position_ids'), [])
|
|
monitor_type = row.get('monitor_type') or 'ai'
|
|
config = _safe_json_loads(row.get('config'), {})
|
|
notification_config = _safe_json_loads(row.get('notification_config'), {})
|
|
|
|
# Override language if provided (from frontend)
|
|
if override_language:
|
|
config['language'] = override_language
|
|
|
|
# Resolve interval (frontend sends run_interval_minutes, legacy uses interval_minutes)
|
|
interval_minutes = int(
|
|
config.get('run_interval_minutes')
|
|
or config.get('interval_minutes')
|
|
or 60
|
|
)
|
|
|
|
# Get positions (or build from config.symbol if no position_ids)
|
|
positions = _get_positions_for_monitor(position_ids if position_ids else None, user_id=monitor_user_id)
|
|
|
|
# If monitor was created without positions but has symbol in config, build a virtual position
|
|
if not positions and config.get('symbol'):
|
|
positions = [{
|
|
'market': config.get('market', ''),
|
|
'symbol': config.get('symbol', ''),
|
|
'name': config.get('symbol', ''),
|
|
'side': 'long',
|
|
'quantity': 0,
|
|
'entry_price': 0,
|
|
'current_price': 0,
|
|
'pnl': 0,
|
|
'pnl_percent': 0,
|
|
}]
|
|
|
|
if not positions:
|
|
return {'success': False, 'error': 'No positions to analyze'}
|
|
|
|
# ── Billing: charge per symbol analyzed ──
|
|
billing = get_billing_service()
|
|
symbol_count = len(positions)
|
|
per_symbol_cost = billing.get_feature_cost('ai_analysis')
|
|
total_cost = per_symbol_cost * symbol_count
|
|
|
|
if total_cost > 0 and billing.is_billing_enabled():
|
|
user_credits = billing.get_user_credits(monitor_user_id)
|
|
if user_credits < total_cost:
|
|
logger.warning(
|
|
f"Monitor #{monitor_id} skipped: insufficient credits "
|
|
f"({user_credits} < {total_cost} for {symbol_count} symbols)"
|
|
)
|
|
return {
|
|
'success': False,
|
|
'error': f'Insufficient credits: need {total_cost}, have {user_credits}'
|
|
}
|
|
for i in range(symbol_count):
|
|
pos = positions[i]
|
|
ok, msg = billing.check_and_consume(
|
|
user_id=monitor_user_id,
|
|
feature='ai_analysis',
|
|
reference_id=f"monitor_{monitor_id}_{pos.get('symbol', '')}"
|
|
)
|
|
if not ok:
|
|
logger.warning(f"Monitor #{monitor_id} billing failed at symbol #{i+1}: {msg}")
|
|
break
|
|
|
|
# Run analysis based on type
|
|
if monitor_type == 'ai':
|
|
result = _run_ai_analysis(positions, config)
|
|
else:
|
|
result = {'success': False, 'error': f'Unsupported monitor type: {monitor_type}'}
|
|
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
UPDATE qd_position_monitors
|
|
SET last_run_at = NOW(),
|
|
next_run_at = NOW() + INTERVAL '%s minutes',
|
|
last_result = ?,
|
|
run_count = run_count + 1,
|
|
updated_at = NOW()
|
|
WHERE id = ?
|
|
""",
|
|
(interval_minutes, json.dumps(result, ensure_ascii=False), monitor_id)
|
|
)
|
|
db.commit()
|
|
cur.close()
|
|
|
|
# Send notification
|
|
if notification_config.get('channels'):
|
|
language = config.get('language', 'en-US')
|
|
custom_prompt = config.get('prompt', '')
|
|
position_analyses = result.get('position_analyses', [])
|
|
_send_monitor_notification(
|
|
monitor_name=name,
|
|
result=result,
|
|
notification_config=notification_config,
|
|
positions=positions,
|
|
position_analyses=position_analyses,
|
|
language=language,
|
|
custom_prompt=custom_prompt,
|
|
user_id=monitor_user_id
|
|
)
|
|
|
|
return result
|
|
except Exception as e:
|
|
logger.error(f"run_single_monitor failed: {e}")
|
|
logger.error(traceback.format_exc())
|
|
return {'success': False, 'error': str(e)}
|
|
|
|
|
|
def _check_position_alerts():
|
|
"""Check all active alerts and trigger notifications if conditions are met."""
|
|
from datetime import datetime, timezone
|
|
try:
|
|
kline_service = KlineService()
|
|
notifier = SignalNotifier()
|
|
now = datetime.now(timezone.utc)
|
|
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
# Get active alerts for all users that haven't been triggered (or can repeat)
|
|
cur.execute(
|
|
"""
|
|
SELECT a.id, a.user_id, a.position_id, a.market, a.symbol, a.alert_type, a.threshold,
|
|
a.notification_config, a.is_triggered, a.last_triggered_at, a.repeat_interval,
|
|
p.entry_price, p.quantity, p.side, p.name as position_name
|
|
FROM qd_position_alerts a
|
|
LEFT JOIN qd_manual_positions p ON a.position_id = p.id
|
|
WHERE a.is_active = 1
|
|
"""
|
|
)
|
|
alerts = cur.fetchall() or []
|
|
cur.close()
|
|
|
|
for alert in alerts:
|
|
try:
|
|
alert_id = alert.get('id')
|
|
alert_user_id = int(alert.get('user_id') or 1)
|
|
alert_type = alert.get('alert_type')
|
|
threshold = float(alert.get('threshold') or 0)
|
|
market = alert.get('market')
|
|
symbol = alert.get('symbol')
|
|
is_triggered = bool(alert.get('is_triggered'))
|
|
last_triggered_at = alert.get('last_triggered_at') # datetime or None
|
|
repeat_interval = int(alert.get('repeat_interval') or 0)
|
|
notification_config = _safe_json_loads(alert.get('notification_config'), {})
|
|
|
|
# Check if we can trigger (not triggered yet, or repeat interval passed)
|
|
can_trigger = not is_triggered
|
|
if is_triggered and repeat_interval > 0 and last_triggered_at:
|
|
# Convert last_triggered_at to timezone-aware if needed
|
|
if last_triggered_at.tzinfo is None:
|
|
last_triggered_at = last_triggered_at.replace(tzinfo=timezone.utc)
|
|
elapsed_seconds = (now - last_triggered_at).total_seconds()
|
|
if elapsed_seconds >= repeat_interval:
|
|
can_trigger = True
|
|
|
|
if not can_trigger:
|
|
continue
|
|
|
|
# Get current price (use realtime price API)
|
|
current_price = 0
|
|
try:
|
|
price_data = kline_service.get_realtime_price(market, symbol)
|
|
current_price = float(price_data.get('price') or 0)
|
|
except Exception:
|
|
continue
|
|
|
|
if current_price <= 0:
|
|
continue
|
|
|
|
triggered = False
|
|
alert_message = ""
|
|
|
|
# Get language from notification_config (saved when alert was created)
|
|
alert_language = notification_config.get('language', 'en-US')
|
|
|
|
if alert_type == 'price_above':
|
|
if current_price >= threshold:
|
|
triggered = True
|
|
alert_message = _get_alert_message(
|
|
'price_above', alert_language,
|
|
symbol=symbol, current_price=current_price, threshold=threshold
|
|
)
|
|
|
|
elif alert_type == 'price_below':
|
|
if current_price <= threshold:
|
|
triggered = True
|
|
alert_message = _get_alert_message(
|
|
'price_below', alert_language,
|
|
symbol=symbol, current_price=current_price, threshold=threshold
|
|
)
|
|
|
|
elif alert_type in ('pnl_above', 'pnl_below'):
|
|
entry_price = float(alert.get('entry_price') or 0)
|
|
quantity = float(alert.get('quantity') or 0)
|
|
side = alert.get('side') or 'long'
|
|
|
|
if entry_price > 0 and quantity > 0:
|
|
if side == 'long':
|
|
pnl = (current_price - entry_price) * quantity
|
|
else:
|
|
pnl = (entry_price - current_price) * quantity
|
|
pnl_percent = pnl / (entry_price * quantity) * 100
|
|
|
|
if alert_type == 'pnl_above' and pnl_percent >= threshold:
|
|
triggered = True
|
|
alert_message = _get_alert_message(
|
|
'pnl_above', alert_language,
|
|
symbol=symbol, pnl_percent=pnl_percent, threshold=threshold
|
|
)
|
|
elif alert_type == 'pnl_below' and pnl_percent <= threshold:
|
|
triggered = True
|
|
alert_message = _get_alert_message(
|
|
'pnl_below', alert_language,
|
|
symbol=symbol, pnl_percent=pnl_percent, threshold=threshold
|
|
)
|
|
|
|
if triggered:
|
|
logger.info(f"Alert #{alert_id} triggered: {alert_message}")
|
|
|
|
# Update alert status
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
UPDATE qd_position_alerts
|
|
SET is_triggered = 1, last_triggered_at = NOW(), trigger_count = trigger_count + 1, updated_at = NOW()
|
|
WHERE id = ?
|
|
""",
|
|
(alert_id,)
|
|
)
|
|
db.commit()
|
|
cur.close()
|
|
|
|
# Send notification
|
|
channels = notification_config.get('channels', ['browser'])
|
|
targets = notification_config.get('targets', {})
|
|
alert_title = _get_alert_title(alert_language)
|
|
|
|
for channel in channels:
|
|
try:
|
|
ch = str(channel).strip().lower()
|
|
if ch == 'browser':
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
INSERT INTO qd_strategy_notifications
|
|
(user_id, strategy_id, symbol, signal_type, channels, title, message, payload_json, created_at)
|
|
VALUES (?, NULL, ?, ?, ?, ?, ?, ?, NOW())
|
|
""",
|
|
(alert_user_id, symbol, 'price_alert', 'browser', alert_title, alert_message,
|
|
json.dumps({'alert_id': alert_id, 'alert_type': alert_type}, ensure_ascii=False))
|
|
)
|
|
db.commit()
|
|
cur.close()
|
|
elif ch == 'telegram':
|
|
chat_id = targets.get('telegram', '')
|
|
token_override = targets.get('telegram_bot_token', '')
|
|
if chat_id:
|
|
notifier._notify_telegram(chat_id=chat_id, text=alert_message, token_override=token_override, parse_mode="HTML")
|
|
elif ch == 'email':
|
|
to_email = targets.get('email', '')
|
|
if to_email:
|
|
notifier._notify_email(to_email=to_email, subject=alert_title, body_text=alert_message)
|
|
except Exception as e:
|
|
logger.warning(f"Failed to send alert notification: {e}")
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Error processing alert: {e}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"_check_position_alerts failed: {e}")
|
|
|
|
|
|
def notify_strategy_signal_for_positions(market: str, symbol: str, signal_type: str, signal_detail: str, user_id: int = None):
|
|
"""
|
|
Called when a strategy signal is triggered.
|
|
Check if user has manual positions in this symbol and send notification.
|
|
"""
|
|
try:
|
|
symbol = (symbol or '').strip().upper()
|
|
if not symbol:
|
|
return
|
|
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
# Query positions for all users or specific user
|
|
if user_id is not None:
|
|
cur.execute(
|
|
"""
|
|
SELECT id, user_id, market, symbol, name, side, quantity, entry_price, group_name
|
|
FROM qd_manual_positions
|
|
WHERE user_id = ? AND symbol = ?
|
|
""",
|
|
(user_id, symbol)
|
|
)
|
|
else:
|
|
cur.execute(
|
|
"""
|
|
SELECT id, user_id, market, symbol, name, side, quantity, entry_price, group_name
|
|
FROM qd_manual_positions
|
|
WHERE symbol = ?
|
|
""",
|
|
(symbol,)
|
|
)
|
|
positions = cur.fetchall() or []
|
|
cur.close()
|
|
|
|
if not positions:
|
|
return
|
|
|
|
# User has positions in this symbol - send notification
|
|
notifier = SignalNotifier()
|
|
now = _now_ts()
|
|
|
|
for pos in positions:
|
|
pos_user_id = int(pos.get('user_id') or 1)
|
|
pos_name = pos.get('name') or symbol
|
|
pos_side = pos.get('side') or 'long'
|
|
quantity = float(pos.get('quantity') or 0)
|
|
entry_price = float(pos.get('entry_price') or 0)
|
|
|
|
title = f"🔗 策略信号联动: {pos_name}"
|
|
message = f"""策略发出 {signal_type} 信号!
|
|
|
|
标的: {market}/{symbol}
|
|
您的持仓: {pos_side.upper()} {quantity} @ {entry_price:.4f}
|
|
|
|
信号详情:
|
|
{signal_detail}
|
|
|
|
请注意检查您的持仓是否需要调整。"""
|
|
|
|
# Save browser notification
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
INSERT INTO qd_strategy_notifications
|
|
(user_id, strategy_id, symbol, signal_type, channels, title, message, payload_json, created_at)
|
|
VALUES (?, NULL, ?, ?, ?, ?, ?, ?, NOW())
|
|
""",
|
|
(pos_user_id, symbol, 'strategy_linkage', 'browser', title, message,
|
|
json.dumps({'signal_type': signal_type}, ensure_ascii=False))
|
|
)
|
|
db.commit()
|
|
cur.close()
|
|
|
|
logger.info(f"Strategy signal linkage: notified {len(positions)} position(s) for {symbol}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"notify_strategy_signal_for_positions failed: {e}")
|
|
|
|
|
|
def _monitor_loop():
|
|
"""Background loop that checks and runs due monitors."""
|
|
logger.info("Portfolio monitor background loop started")
|
|
|
|
while not _stop_event.is_set():
|
|
try:
|
|
# 1. Check position alerts (price/pnl alerts) for all users
|
|
_check_position_alerts()
|
|
|
|
# 2. Find AI monitors that are due for all users
|
|
with get_db_connection() as db:
|
|
cur = db.cursor()
|
|
cur.execute(
|
|
"""
|
|
SELECT id, user_id FROM qd_position_monitors
|
|
WHERE is_active = 1 AND next_run_at <= NOW()
|
|
ORDER BY next_run_at ASC
|
|
LIMIT 10
|
|
"""
|
|
)
|
|
rows = cur.fetchall() or []
|
|
cur.close()
|
|
|
|
for row in rows:
|
|
if _stop_event.is_set():
|
|
break
|
|
monitor_id = row.get('id')
|
|
monitor_user_id = int(row.get('user_id') or 1)
|
|
if monitor_id:
|
|
logger.info(f"Running due monitor #{monitor_id} for user #{monitor_user_id}")
|
|
try:
|
|
run_single_monitor(monitor_id, user_id=monitor_user_id)
|
|
except Exception as e:
|
|
logger.error(f"Monitor #{monitor_id} execution failed: {e}")
|
|
except Exception as e:
|
|
logger.error(f"Monitor loop error: {e}")
|
|
|
|
# Sleep for 30 seconds before next check
|
|
_stop_event.wait(30)
|
|
|
|
logger.info("Portfolio monitor background loop stopped")
|
|
|
|
|
|
def start_monitor_service():
|
|
"""Start the background monitor service."""
|
|
global _monitor_thread
|
|
|
|
if _monitor_thread and _monitor_thread.is_alive():
|
|
logger.info("Portfolio monitor service already running")
|
|
return
|
|
|
|
_stop_event.clear()
|
|
_monitor_thread = threading.Thread(target=_monitor_loop, daemon=True, name="PortfolioMonitor")
|
|
_monitor_thread.start()
|
|
logger.info("Portfolio monitor service started")
|
|
|
|
|
|
def stop_monitor_service():
|
|
"""Stop the background monitor service."""
|
|
global _monitor_thread
|
|
|
|
_stop_event.set()
|
|
if _monitor_thread:
|
|
_monitor_thread.join(timeout=5)
|
|
_monitor_thread = None
|
|
logger.info("Portfolio monitor service stopped")
|