feat: AI analysis engine refactor, dark theme polish & virtual position management
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
This commit is contained in:
Dinger
@@ -9,6 +9,7 @@ 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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@@ -16,6 +17,7 @@ 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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@@ -154,98 +156,92 @@ def _get_positions_for_monitor(position_ids: List[int] = None, user_id: int = No
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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.
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Uses the new FastAnalysisService (single LLM call, faster and more stable).
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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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# Get the fast analysis service
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service = get_fast_analysis_service()
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# Analyze each position
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position_analyses = []
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for pos in positions:
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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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continue
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try:
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logger.info(f"Running fast AI analysis for {market}:{symbol}")
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# Use the new FastAnalysisService (single LLM call)
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analysis_result = service.analyze(
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market=market,
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symbol=symbol,
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language=language,
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timeframe='1D'
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)
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# Extract information from the new format
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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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# Build risk report from risks list
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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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position_analysis = {
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'market': market,
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'symbol': symbol,
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'name': name,
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'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'),
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'pnl_percent': pos.get('pnl_percent'),
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'quantity': pos.get('quantity'),
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'side': pos.get('side'),
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# New fast analysis results
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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'), # Same as final for fast analysis
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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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# Trading plan
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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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# Scores
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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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position_analyses.append(position_analysis)
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logger.info(f"Fast analysis completed for {market}:{symbol}: {analysis_result.get('decision', 'N/A')}")
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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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position_analyses.append({
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'market': market,
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'symbol': symbol,
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'name': name,
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'error': str(e)
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})
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# Build comprehensive report
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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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@@ -254,15 +250,11 @@ def _run_ai_analysis(positions: List[Dict[str, Any]], config: Dict[str, Any]) ->
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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 {
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'success': False,
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'error': str(e),
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'timestamp': _now_ts()
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}
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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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@@ -914,22 +906,67 @@ def run_single_monitor(monitor_id: int, override_language: str = None, user_id:
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if override_language:
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config['language'] = override_language
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# Get positions for this user
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# Resolve interval (frontend sends run_interval_minutes, legacy uses interval_minutes)
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interval_minutes = int(
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config.get('run_interval_minutes')
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or config.get('interval_minutes')
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or 60
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)
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# Get positions (or build from config.symbol if no position_ids)
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positions = _get_positions_for_monitor(position_ids if position_ids else None, user_id=monitor_user_id)
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# If monitor was created without positions but has symbol in config, build a virtual position
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if not positions and config.get('symbol'):
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positions = [{
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'market': config.get('market', ''),
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'symbol': config.get('symbol', ''),
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'name': config.get('symbol', ''),
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'side': 'long',
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'quantity': 0,
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'entry_price': 0,
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'current_price': 0,
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'pnl': 0,
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'pnl_percent': 0,
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}]
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if not positions:
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return {'success': False, 'error': 'No positions to analyze'}
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# ── Billing: charge per symbol analyzed ──
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billing = get_billing_service()
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symbol_count = len(positions)
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per_symbol_cost = billing.get_feature_cost('ai_analysis')
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total_cost = per_symbol_cost * symbol_count
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if total_cost > 0 and billing.is_billing_enabled():
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user_credits = billing.get_user_credits(monitor_user_id)
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if user_credits < total_cost:
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logger.warning(
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f"Monitor #{monitor_id} skipped: insufficient credits "
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f"({user_credits} < {total_cost} for {symbol_count} symbols)"
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)
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return {
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'success': False,
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'error': f'Insufficient credits: need {total_cost}, have {user_credits}'
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}
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for i in range(symbol_count):
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pos = positions[i]
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ok, msg = billing.check_and_consume(
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user_id=monitor_user_id,
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feature='ai_analysis',
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reference_id=f"monitor_{monitor_id}_{pos.get('symbol', '')}"
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)
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if not ok:
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logger.warning(f"Monitor #{monitor_id} billing failed at symbol #{i+1}: {msg}")
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break
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# Run analysis based on type
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if monitor_type == 'ai':
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result = _run_ai_analysis(positions, config)
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else:
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# For other types, we can add price_alert, pnl_alert logic later
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result = {'success': False, 'error': f'Unsupported monitor type: {monitor_type}'}
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# Update monitor record
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interval_minutes = int(config.get('interval_minutes') or 60)
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with get_db_connection() as db:
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cur = db.cursor()
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cur.execute(
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