update
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aaron
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569
backend/app/stock_agent/analysis_tools.py
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569
backend/app/stock_agent/analysis_tools.py
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"""
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股票分析工具模块
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提供各种辅助计算功能：
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- ATR计算
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- 多周期共振分析
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- 基本面评分
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- 支撑阻力位识别
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, Any, List, Tuple, Optional
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from app.utils.logger import logger
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class StockAnalysisTools:
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"""股票分析工具类"""
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@staticmethod
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def calculate_atr(df: pd.DataFrame, period: int = 14) -> float:
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"""
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计算ATR (真实波动幅度)
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Args:
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df: 包含 high, low, close 的数据
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period: ATR周期
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Returns:
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ATR值
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"""
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if df is None or len(df) < period + 1:
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return 0.0
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try:
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high = df['high'].values
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low = df['low'].values
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close = df['close'].values
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tr_list = []
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for i in range(1, len(df)):
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tr1 = high[i] - low[i]
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tr2 = abs(high[i] - close[i-1])
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tr3 = abs(low[i] - close[i-1])
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tr = max(tr1, tr2, tr3)
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tr_list.append(tr)
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if len(tr_list) < period:
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return 0.0
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atr = pd.Series(tr_list).rolling(window=period).mean().iloc[-1]
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return float(atr) if not np.isnan(atr) else 0.0
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except Exception as e:
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logger.warning(f"ATR计算失败: {e}")
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return 0.0
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@staticmethod
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def calculate_volume_ratio(df: pd.DataFrame, period: int = 20) -> float:
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"""
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计算量比（当前成交量 / 过去N周期平均成交量）
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Args:
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df: 包含 volume 的数据
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period: 均量周期
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Returns:
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量比值
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"""
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if df is None or len(df) < period + 1:
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return 1.0
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try:
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current_vol = df['volume'].iloc[-1]
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avg_vol = df['volume'].iloc[-period-1:-1].mean()
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if avg_vol > 0:
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return float(current_vol / avg_vol)
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return 1.0
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except Exception as e:
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logger.warning(f"量比计算失败: {e}")
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return 1.0
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@staticmethod
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def detect_trend(df: pd.DataFrame) -> Dict[str, Any]:
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"""
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检测趋势方向和强度
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使用EMA系统判断趋势
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Returns:
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{
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'direction': 'uptrend'/'downtrend'/'neutral',
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'strength': 'strong'/'medium'/'weak',
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'ema_alignment': 'bullish'/'bearish'/'mixed',
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'price_vs_ema20': float, # 百分比
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'signals': List[str]
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}
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"""
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if df is None or len(df) < 200:
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return {
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'direction': 'neutral',
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'strength': 'weak',
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'ema_alignment': 'mixed',
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'price_vs_ema20': 0.0,
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'signals': []
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}
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try:
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# 计算EMA
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close = df['close']
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ema20 = close.ewm(span=20, adjust=False).mean().iloc[-1]
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ema50 = close.ewm(span=50, adjust=False).mean().iloc[-1]
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ema200 = close.ewm(span=200, adjust=False).mean().iloc[-1]
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current_price = close.iloc[-1]
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# EMA排列判断
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if ema20 > ema50 > ema200:
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ema_alignment = 'bullish'
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direction = 'uptrend'
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elif ema20 < ema50 < ema200:
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ema_alignment = 'bearish'
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direction = 'downtrend'
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else:
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ema_alignment = 'mixed'
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direction = 'neutral'
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# 价格与EMA20的关系
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price_vs_ema20 = ((current_price - ema20) / ema20 * 100) if ema20 > 0 else 0
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# 趋势强度判断
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strength = 'weak'
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signals = []
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if ema_alignment == 'bullish':
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if price_vs_ema20 > 1:
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strength = 'strong'
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signals.append("强势上涨：价格站稳 EMA20 之上")
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elif price_vs_ema20 > 0:
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strength = 'medium'
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signals.append("上涨趋势：价格在 EMA20 附近")
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else:
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strength = 'weak'
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signals.append("上涨趋势减弱：价格跌破 EMA20")
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elif ema_alignment == 'bearish':
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if price_vs_ema20 < -1:
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strength = 'strong'
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signals.append("强势下跌：价格跌破 EMA20 之下")
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elif price_vs_ema20 < 0:
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strength = 'medium'
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signals.append("下跌趋势：价格在 EMA20 附近")
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else:
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strength = 'weak'
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signals.append("下跌趋势减弱：价格站上 EMA20")
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else:
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signals.append("震荡市：EMA 交织")
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return {
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'direction': direction,
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'strength': strength,
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'ema_alignment': ema_alignment,
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'price_vs_ema20': round(price_vs_ema20, 2),
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'signals': signals
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}
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except Exception as e:
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logger.warning(f"趋势检测失败: {e}")
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return {
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'direction': 'neutral',
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'strength': 'weak',
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'ema_alignment': 'mixed',
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'price_vs_ema20': 0.0,
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'signals': []
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}
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@staticmethod
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def calculate_multi_timeframe_resonance(data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
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"""
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计算多周期共振强度
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Args:
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data: 包含多个周期的数据 {'1w': df, '1d': df, '1h': df}
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Returns:
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{
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'score': 0-100,
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'level': 'strong'/'medium'/'weak',
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'weekly_trend': str,
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'daily_trend': str,
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'hourly_trend': str,
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'resonance_type': str,
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'analysis': str
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}
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"""
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trends = {}
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# 获取各周期趋势
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for tf_name in ['1w', '1d', '1h']:
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df = data.get(tf_name)
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if df is not None and len(df) > 0:
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trend_info = StockAnalysisTools.detect_trend(df)
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trends[tf_name] = trend_info['direction']
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else:
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trends[tf_name] = 'neutral'
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weekly_trend = trends.get('1w', 'neutral')
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daily_trend = trends.get('1d', 'neutral')
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hourly_trend = trends.get('1h', 'neutral')
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# 计算共振得分
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score = 0
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analysis_parts = []
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# 大周期共振（周线+日线）
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if weekly_trend == daily_trend and weekly_trend != 'neutral':
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score += 40
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analysis_parts.append(f"✅ 大周期共振（{weekly_trend}）")
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elif weekly_trend != 'neutral' and daily_trend != 'neutral':
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analysis_parts.append(f"⚠️ 大周期分歧（周线{weekly_trend} vs 日线{daily_trend}）")
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else:
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analysis_parts.append(f"➖ 大周期不明确")
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# 主周期共振（日线+1h）
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if daily_trend == hourly_trend and daily_trend != 'neutral':
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score += 35
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analysis_parts.append(f"✅ 主周期共振（{daily_trend}）")
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elif daily_trend != 'neutral' and hourly_trend != 'neutral':
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analysis_parts.append(f"⚠️ 主周期分歧（日线{daily_trend} vs 1h{hourly_trend}）")
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else:
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analysis_parts.append(f"➖ 主周期不明确")
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# 全周期共振
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if weekly_trend == daily_trend == hourly_trend and weekly_trend != 'neutral':
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score += 25
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analysis_parts.append(f"🔥 全周期共振（{weekly_trend}）")
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# 确定共振等级
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if score >= 70:
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level = 'strong'
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resonance_type = 'all_timeframe_aligned' if score >= 90 else 'strong'
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elif score >= 40:
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level = 'medium'
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resonance_type = 'large_timeframe_aligned'
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elif score >= 20:
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level = 'weak'
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resonance_type = 'partial_alignment'
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else:
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level = 'weak'
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resonance_type = 'no_resonance'
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return {
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'score': score,
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'level': level,
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'weekly_trend': weekly_trend,
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'daily_trend': daily_trend,
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'hourly_trend': hourly_trend,
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'resonance_type': resonance_type,
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'analysis': ' | '.join(analysis_parts)
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}
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@staticmethod
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def calculate_fundamental_score(data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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计算基本面评分（0-100）
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Args:
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data: 基本面数据
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Returns:
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{
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'score': 0-100,
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'grade': 'A'/'B'/'C'/'D',
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'valuation': {'score': 0-25, 'grade': 'A'/'B'/'C'/'D'},
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'profitability': {'score': 0-35, 'grade': 'A'/'B'/'C'/'D'},
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'growth': {'score': 0-25, 'grade': 'A'/'B'/'C'/'D'},
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'financial_health': {'score': 0-15, 'grade': 'A'/'B'/'C'/'D'},
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'summary': str
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}
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"""
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if not data:
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return {
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'score': 0,
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'grade': 'D',
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'summary': '无基本面数据'
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}
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score = 0
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breakdown = {}
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# 估值评分 (25分)
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val_score = 0
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pe = data.get('pe_ratio', 0)
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pb = data.get('pb_ratio', 0)
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peg = data.get('peg_ratio', 0)
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if 0 < pe < 15:
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val_score += 10
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elif 15 <= pe <= 25:
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val_score += 7
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elif pe > 40:
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val_score += 0
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else:
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val_score += 5
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if 0 < pb < 1:
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val_score += 10
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elif 1 <= pb <= 3:
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val_score += 7
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elif pb > 5:
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val_score += 0
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else:
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val_score += 5
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if peg and 0 < peg < 1:
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val_score += 5
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elif peg and 1 <= peg <= 2:
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val_score += 3
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elif peg and peg > 2:
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val_score += 0
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else:
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val_score += 2
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breakdown['valuation'] = {
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'score': val_score,
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'grade': 'A' if val_score >= 20 else 'B' if val_score >= 15 else 'C' if val_score >= 10 else 'D'
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}
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score += val_score
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# 盈利能力评分 (35分)
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prof_score = 0
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roe = data.get('roe', 0)
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net_margin = data.get('profit_margin', 0)
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if roe > 20:
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prof_score += 20
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elif roe > 15:
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prof_score += 15
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elif roe > 10:
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prof_score += 10
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elif roe > 0:
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prof_score += 5
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if net_margin > 20:
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prof_score += 15
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elif net_margin > 10:
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prof_score += 10
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elif net_margin > 5:
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prof_score += 5
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elif net_margin > 0:
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prof_score += 2
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breakdown['profitability'] = {
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'score': prof_score,
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'grade': 'A' if prof_score >= 30 else 'B' if prof_score >= 20 else 'C' if prof_score >= 10 else 'D'
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}
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score += prof_score
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# 成长性评分 (25分)
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growth_score = 0
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revenue_growth = data.get('revenue_growth', 0)
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earnings_growth = data.get('earnings_growth', 0)
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if revenue_growth > 30:
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growth_score += 13
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elif revenue_growth > 20:
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growth_score += 10
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elif revenue_growth > 10:
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growth_score += 5
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elif revenue_growth > 0:
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growth_score += 2
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if earnings_growth > 30:
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growth_score += 12
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elif earnings_growth > 20:
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growth_score += 8
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elif earnings_growth > 10:
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growth_score += 5
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elif earnings_growth > 0:
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growth_score += 2
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elif earnings_growth < 0:
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growth_score -= 5 # 负增长扣分
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breakdown['growth'] = {
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'score': max(0, growth_score),
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'grade': 'A' if growth_score >= 20 else 'B' if growth_score >= 15 else 'C' if growth_score >= 10 else 'D'
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}
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score += max(0, growth_score)
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# 财务健康评分 (15分)
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health_score = 0
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debt_ratio = data.get('debt_to_equity', 0)
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current_ratio = data.get('current_ratio', 0)
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if debt_ratio < 1:
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health_score += 8
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elif debt_ratio < 2:
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health_score += 5
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elif debt_ratio < 3:
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health_score += 2
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else:
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health_score += 0
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if current_ratio > 2:
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health_score += 7
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elif current_ratio > 1.5:
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health_score += 5
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elif current_ratio > 1:
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health_score += 2
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else:
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health_score += 0
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breakdown['financial_health'] = {
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'score': health_score,
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'grade': 'A' if health_score >= 12 else 'B' if health_score >= 8 else 'C' if health_score >= 5 else 'D'
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}
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score += health_score
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# 确定总等级
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grade = 'A' if score >= 80 else 'B' if score >= 60 else 'C' if score >= 40 else 'D'
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# 生成摘要
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summary_parts = []
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if breakdown['valuation']['grade'] == 'A':
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summary_parts.append("估值低估")
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elif breakdown['valuation']['grade'] == 'D':
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summary_parts.append("估值高估")
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if breakdown['profitability']['grade'] == 'A':
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summary_parts.append("盈利优秀")
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elif breakdown['profitability']['grade'] == 'D':
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summary_parts.append("盈利较差")
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if breakdown['growth']['grade'] == 'A':
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summary_parts.append("高成长")
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elif breakdown['growth']['grade'] == 'D':
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summary_parts.append("低成长")
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if breakdown['financial_health']['grade'] == 'A':
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summary_parts.append("财务健康")
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elif breakdown['financial_health']['grade'] == 'D':
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summary_parts.append("财务风险")
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return {
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'score': score,
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'grade': grade,
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'breakdown': breakdown,
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'summary': ' | '.join(summary_parts) if summary_parts else '一般'
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}
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@staticmethod
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def identify_key_levels(df: pd.DataFrame, lookback: int = 60) -> Dict[str, List[float]]:
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"""
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识别关键支撑位和阻力位
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Args:
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df: K线数据
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lookback: 回看周期
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Returns:
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{
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'support': [支撑位1, 支撑位2, ...],
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'resistance': [阻力位1, 阻力位2, ...]
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}
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"""
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if df is None or len(df) < lookback:
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return {'support': [], 'resistance': []}
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try:
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recent = df.tail(lookback)
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highs = recent['high'].values
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lows = recent['low'].values
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# 找局部高点和低点
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from scipy.signal import argrelextrema
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from numpy import array
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high_indices = argrelextrema(array(highs), np.greater, order=5)
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low_indices = argrelextrema(array(lows), np.less, order=5)
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resistance_levels = sorted([highs[i] for i in high_indices], reverse=True)[:3]
|
||||
support_levels = sorted([lows[i] for i in low_indices])[:3]
|
||||
|
||||
return {
|
||||
'resistance': resistance_levels,
|
||||
'support': support_levels
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning(f"关键位识别失败: {e}")
|
||||
return {'support': [], 'resistance': []}
|
||||
|
||||
@staticmethod
|
||||
def calculate_stop_loss_take_profit(
|
||||
entry_price: float,
|
||||
atr: float,
|
||||
direction: str,
|
||||
key_levels: Dict[str, List[float]] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
计算止损止盈价格
|
||||
|
||||
Args:
|
||||
entry_price: 入场价格
|
||||
atr: ATR值
|
||||
direction: 'long'/'short'
|
||||
key_levels: 支撑阻力位
|
||||
|
||||
Returns:
|
||||
{
|
||||
'stop_loss': float,
|
||||
'take_profit': float,
|
||||
'method': str,
|
||||
'risk_reward_ratio': float
|
||||
}
|
||||
"""
|
||||
if direction == 'long':
|
||||
# 做多
|
||||
# 止损：入场价 - 1.5×ATR，或设在前支撑位下方
|
||||
sl_atr = entry_price - 1.5 * atr
|
||||
sl_support = None
|
||||
|
||||
if key_levels and key_levels.get('support'):
|
||||
closest_support = [s for s in key_levels['support'] if s < entry_price]
|
||||
if closest_support:
|
||||
sl_support = min(closest_support) * 0.995 # 支撑位下方0.5%
|
||||
|
||||
# 选择更保守的止损（更远的）
|
||||
if sl_support and sl_support < sl_atr:
|
||||
stop_loss = sl_atr
|
||||
else:
|
||||
stop_loss = sl_atr
|
||||
|
||||
# 止盈：入场价 + 3×ATR（风险收益比1:2）
|
||||
take_profit = entry_price + 3 * atr
|
||||
|
||||
else:
|
||||
# 做空
|
||||
# 止损：入场价 + 1.5×ATR，或设在前阻力位上方
|
||||
sl_atr = entry_price + 1.5 * atr
|
||||
sl_resistance = None
|
||||
|
||||
if key_levels and key_levels.get('resistance'):
|
||||
closest_resistance = [r for r in key_levels['resistance'] if r > entry_price]
|
||||
if closest_resistance:
|
||||
sl_resistance = min(closest_resistance) * 1.005 # 阻力位上方0.5%
|
||||
|
||||
# 选择更保守的止损
|
||||
if sl_resistance and sl_resistance > sl_atr:
|
||||
stop_loss = sl_atr
|
||||
else:
|
||||
stop_loss = sl_atr
|
||||
|
||||
# 止盈：入场价 - 3×ATR
|
||||
take_profit = entry_price - 3 * atr
|
||||
|
||||
# 计算风险收益比
|
||||
if direction == 'long':
|
||||
risk = entry_price - stop_loss
|
||||
reward = take_profit - entry_price
|
||||
else:
|
||||
risk = stop_loss - entry_price
|
||||
reward = entry_price - take_profit
|
||||
|
||||
risk_reward_ratio = reward / risk if risk > 0 else 0
|
||||
|
||||
return {
|
||||
'stop_loss': round(stop_loss, 2),
|
||||
'take_profit': round(take_profit, 2),
|
||||
'method': 'ATR-based (1:2 risk-reward)',
|
||||
'risk_reward_ratio': round(risk_reward_ratio, 2)
|
||||
}
|
||||
@ -308,6 +308,45 @@ class StockMarketSignalAnalyzer:
|
||||
- 必须同时输出 `entry_price`（建议入场价）和 `entry_type`（入场方式）
|
||||
- 入场方式由你的市场分析判断，不是简单的价格距离计算
|
||||
|
||||
## 止损止盈计算规则
|
||||
使用ATR（真实波动幅度）动态计算止损止盈：
|
||||
|
||||
**做多**：
|
||||
- 止损 = 入场价 - 1.5 × ATR(14日)
|
||||
- 止盈 = 入场价 + 3 × ATR(14日) （风险收益比 1:2）
|
||||
- 如果附近有明显支撑位，可将止损设在支撑位下方
|
||||
|
||||
**做空**：
|
||||
- 止损 = 入场价 + 1.5 × ATR(14日)
|
||||
- 止盈 = 入场价 - 3 × ATR(14日) （风险收益比 1:2）
|
||||
- 如果附近有明显阻力位，可将止损设在阻力位上方
|
||||
|
||||
## 信号输出条件（严格遵守）
|
||||
**满足以下条件才输出信号（否则 signals 返回空数组）**：
|
||||
|
||||
### 做多信号条件：
|
||||
1. ✅ 趋势：EMA20 > EMA50 > EMA200（或处于回调中的上升趋势）
|
||||
2. ✅ 价格：站稳 EMA20 之上或回调到 EMA20 附近
|
||||
3. ✅ 量价：放量上涨或缩量回调后重新放量
|
||||
4. ✅ 共振：多周期共振得分 >= 40（至少大周期一致）
|
||||
5. ✅ 基本面：评分 >= C级（40分以上）
|
||||
6. ✅ 新闻：无重大负面消息（财报暴雷、监管处罚等）
|
||||
|
||||
### 做空信号条件：
|
||||
1. ✅ 趋势：EMA20 < EMA50 < EMA200（或处于反弹中的下降趋势）
|
||||
2. ✅ 价格：跌破 EMA20 或反弹到 EMA20 附近
|
||||
3. ✅ 量价：放量下跌或缩量反弹后继续下跌
|
||||
4. ✅ 共振：多周期共振得分 >= 40（至少大周期一致）
|
||||
5. ✅ 基本面：评分 >= C级（40分以上）
|
||||
6. ✅ 新闻：无重大正面消息
|
||||
|
||||
### 禁止输出信号的情况：
|
||||
- ❌ 趋势不明确（EMA 交织，震荡市）
|
||||
- ❌ 多周期方向完全相反
|
||||
- ❌ 基本面差（D级，<40分）
|
||||
- ❌ 有重大负面新闻（做多时）或重大正面新闻（做空时）
|
||||
- ❌ 技术指标矛盾（如RSI超买但MACD死叉）
|
||||
|
||||
## 输出格式
|
||||
请严格按照以下 JSON 格式输出：
|
||||
|
||||
@ -416,128 +455,89 @@ class StockMarketSignalAnalyzer:
|
||||
symbols: List[str] = None,
|
||||
fundamental_data: Dict[str, Any] = None,
|
||||
news_data: List[Dict[str, Any]] = None) -> str:
|
||||
"""准备市场上下文信息（技术面 + 基本面 + 新闻）"""
|
||||
"""准备市场上下文信息（结构化版本，减少token消耗）"""
|
||||
from app.stock_agent.analysis_tools import StockAnalysisTools
|
||||
|
||||
context_parts = []
|
||||
|
||||
# 当前价格和24h变化（使用日线数据）
|
||||
# 当前价格和24h变化
|
||||
df_1d = data.get('1d')
|
||||
if df_1d is None or len(df_1d) == 0:
|
||||
df_1h = data.get('1h') # 备用：使用1h数据
|
||||
if df_1d is None or len(df_1d) == 0:
|
||||
return "" # 没有数据就返回空
|
||||
return ""
|
||||
|
||||
current_price = float(df_1d.iloc[-1]['close'])
|
||||
price_change_24h = self._calculate_price_change_24h(df_1d)
|
||||
context_parts.append(f"当前价格: ${current_price:,.2f} ({price_change_24h})")
|
||||
context_parts.append(f"**当前价格**: ${current_price:,.2f} ({price_change_24h})")
|
||||
|
||||
# 多周期数据
|
||||
for tf_name, df in data.items():
|
||||
if df is None or len(df) == 0:
|
||||
continue
|
||||
# ===== 趋势分析（预计算） =====
|
||||
context_parts.append(f"\n**## 趋势分析**")
|
||||
trend_info = StockAnalysisTools.detect_trend(df_1d)
|
||||
context_parts.append(f"- 方向: {trend_info['direction']} ({trend_info['strength']})")
|
||||
context_parts.append(f"- EMA排列: {trend_info['ema_alignment']}")
|
||||
context_parts.append(f"- 价格相对EMA20: {trend_info['price_vs_ema20']:+.2f}%")
|
||||
|
||||
latest = df.iloc[-1]
|
||||
context_parts.append(f"\n## {tf_name} 数据")
|
||||
context_parts.append(f"开: {latest['open']}, 高: {latest['high']}, 低: {latest['low']}, 收: {latest['close']}")
|
||||
context_parts.append(f"成交量: {latest.get('volume', 'N/A')}")
|
||||
# 多周期共振（预计算）
|
||||
context_parts.append(f"\n**## 多周期共振**")
|
||||
resonance = StockAnalysisTools.calculate_multi_timeframe_resonance(data)
|
||||
context_parts.append(f"- 共振等级: {resonance['level'].upper()} (得分: {resonance['score']}/100)")
|
||||
context_parts.append(f"- 共振分析: {resonance['analysis']}")
|
||||
context_parts.append(f"- 周线: {resonance['weekly_trend']} | 日线: {resonance['daily_trend']} | 1h: {resonance['hourly_trend']}")
|
||||
|
||||
# 技术指标
|
||||
if 'rsi' in df.columns:
|
||||
rsi = df['rsi'].iloc[-1]
|
||||
context_parts.append(f"RSI: {rsi:.2f}")
|
||||
if 'macd' in df.columns:
|
||||
macd = df['macd'].iloc[-1]
|
||||
signal = df['macd_signal'].iloc[-1]
|
||||
context_parts.append(f"MACD: {macd:.4f}, 信号线: {signal:.4f}")
|
||||
if 'bb_upper' in df.columns:
|
||||
bb_upper = df['bb_upper'].iloc[-1]
|
||||
bb_lower = df['bb_lower'].iloc[-1]
|
||||
context_parts.append(f"布林带: 上轨 {bb_upper:.2f}, 下轨 {bb_lower:.2f}")
|
||||
# ===== 技术指标（只显示日线关键指标） =====
|
||||
context_parts.append(f"\n**## 技术指标（日线）**")
|
||||
latest = df_1d.iloc[-1]
|
||||
|
||||
# 均线系统（使用日线数据）
|
||||
context_parts.append(f"\n## 均线系统")
|
||||
df_1d = data.get('1d')
|
||||
if df_1d is not None and len(df_1d) > 0:
|
||||
latest = df_1d.iloc[-1]
|
||||
context_parts.append(f"MA5: {latest.get('ma5', 'N/A')}")
|
||||
context_parts.append(f"MA10: {latest.get('ma10', 'N/A')}")
|
||||
context_parts.append(f"MA20: {latest.get('ma20', 'N/A')}")
|
||||
context_parts.append(f"MA50: {latest.get('ma50', 'N/A')}")
|
||||
# RSI
|
||||
if 'rsi' in df_1d.columns:
|
||||
rsi = df_1d['rsi'].iloc[-1]
|
||||
context_parts.append(f"- RSI(14): {rsi:.1f} {'(超买⚠️)' if rsi > 70 else '(超卖💡)' if rsi < 30 else '(正常)'}")
|
||||
|
||||
# EMA 均线（用于趋势判断）
|
||||
ema20 = latest.get('ema20', None)
|
||||
ema50 = latest.get('ema50', None)
|
||||
ema200 = latest.get('ema200', None)
|
||||
if ema20 is not None:
|
||||
context_parts.append(f"EMA20: {ema20:.2f}")
|
||||
if ema50 is not None:
|
||||
context_parts.append(f"EMA50: {ema50:.2f}")
|
||||
if ema200 is not None:
|
||||
context_parts.append(f"EMA200: {ema200:.2f}")
|
||||
# MACD
|
||||
if 'macd' in df_1d.columns:
|
||||
macd = df_1d['macd'].iloc[-1]
|
||||
signal = df_1d['macd_signal'].iloc[-1]
|
||||
context_parts.append(f"- MACD: {macd:.4f} (信号: {signal:.4f})")
|
||||
|
||||
# 判断 MA 排列
|
||||
ma5 = latest.get('ma5', 0)
|
||||
ma10 = latest.get('ma10', 0)
|
||||
ma20 = latest.get('ma20', 0)
|
||||
ma50 = latest.get('ma50', 0)
|
||||
# 布林带
|
||||
if 'bb_upper' in df_1d.columns:
|
||||
bb_upper = df_1d['bb_upper'].iloc[-1]
|
||||
bb_lower = df_1d['bb_lower'].iloc[-1]
|
||||
bb_position = (current_price - bb_lower) / (bb_upper - bb_lower) * 100 if bb_upper != bb_lower else 50
|
||||
context_parts.append(f"- 布林带: [{bb_lower:.2f}, {bb_upper:.2f}] (位置: {bb_position:.0f}%)")
|
||||
|
||||
if all([ma5, ma10, ma20, ma50]):
|
||||
if ma5 > ma10 > ma20 > ma50:
|
||||
context_parts.append("MA排列: 多头排列 📈")
|
||||
elif ma5 < ma10 < ma20 < ma50:
|
||||
context_parts.append("MA排列: 空头排列 📉")
|
||||
else:
|
||||
context_parts.append("MA排列: 交织，方向不明")
|
||||
# 量价分析
|
||||
volume_ratio = StockAnalysisTools.calculate_volume_ratio(df_1d, 20)
|
||||
context_parts.append(f"- 量比: {volume_ratio:.2f}x {'放量📊' if volume_ratio > 1.5 else '缩量📉' if volume_ratio < 0.7 else '平量'}")
|
||||
|
||||
# EMA 排列（更重要的趋势判断）
|
||||
if all([ema20, ema50, ema200]):
|
||||
if ema20 > ema50 > ema200:
|
||||
context_parts.append("EMA排列: 多头排列 (长期趋势向上) 📈")
|
||||
elif ema20 < ema50 < ema200:
|
||||
context_parts.append("EMA排列: 空头排列 (长期趋势向下) 📉")
|
||||
else:
|
||||
context_parts.append("EMA排列: 交织 (震荡市) ➡️")
|
||||
# ATR（用于止损止盈）
|
||||
atr = StockAnalysisTools.calculate_atr(df_1d, 14)
|
||||
atr_pct = (atr / current_price * 100) if current_price > 0 else 0
|
||||
context_parts.append(f"- ATR(14): ${atr:.2f} ({atr_pct:.2f}%)")
|
||||
|
||||
# 价格与 EMA20 的关系（趋势判断关键）
|
||||
if ema20 is not None:
|
||||
if latest['close'] > ema20:
|
||||
context_parts.append("价格位置: 站稳 EMA20 之上 (多头强势)")
|
||||
elif latest['close'] < ema20:
|
||||
context_parts.append("价格位置: 跌破 EMA20 (空头强势)")
|
||||
else:
|
||||
context_parts.append("价格位置: 接近 EMA20")
|
||||
# ===== 关键支撑阻力位 =====
|
||||
context_parts.append(f"\n**## 关键位**")
|
||||
key_levels = StockAnalysisTools.identify_key_levels(df_1d, lookback=60)
|
||||
if key_levels['resistance']:
|
||||
context_parts.append(f"- 阻力位: ${', '.join(f'{r:.2f}' for r in key_levels['resistance'][:3])}")
|
||||
else:
|
||||
context_parts.append(f"- 阻力位: 未明确")
|
||||
if key_levels['support']:
|
||||
context_parts.append(f"- 支撑位: ${', '.join(f'{s:.2f}' for s in key_levels['support'][:3])}")
|
||||
else:
|
||||
context_parts.append(f"- 支撑位: 未明确")
|
||||
|
||||
# 量比分析（使用日线数据）
|
||||
df_1d = data.get('1d')
|
||||
if df_1d is not None and len(df_1d) >= 20:
|
||||
vol_latest = df_1d['volume'].iloc[-1]
|
||||
vol_ma20 = df_1d['volume'].iloc[-20:-1].mean()
|
||||
volume_ratio = vol_latest / vol_ma20 if vol_ma20 > 0 else 1
|
||||
context_parts.append(f"\n## 量价分析")
|
||||
context_parts.append(f"最新成交量: {vol_latest:.0f}")
|
||||
context_parts.append(f"20周期均量: {vol_ma20:.0f}")
|
||||
context_parts.append(f"量比: {volume_ratio:.2f}")
|
||||
|
||||
if volume_ratio > 1.5:
|
||||
context_parts.append("量价状态: 放量 📊")
|
||||
elif volume_ratio < 0.7:
|
||||
context_parts.append("量价状态: 缩量 📉")
|
||||
else:
|
||||
context_parts.append("量价状态: 平量 ➖")
|
||||
|
||||
# 波动率分析
|
||||
volatility_analysis = self._analyze_volatility(data)
|
||||
if volatility_analysis:
|
||||
context_parts.append(f"\n## 波动率分析")
|
||||
context_parts.append(volatility_analysis)
|
||||
|
||||
# 基本面分析
|
||||
# ===== 基本面分析（评分） =====
|
||||
if fundamental_data:
|
||||
context_parts.append(f"\n## 基本面分析")
|
||||
context_parts.append(self._format_fundamental_data(fundamental_data))
|
||||
context_parts.append(f"\n**## 基本面分析**")
|
||||
fund_score = StockAnalysisTools.calculate_fundamental_score(fundamental_data)
|
||||
context_parts.append(f"- 综合评分: {fund_score['grade']}级 ({fund_score['score']}/100)")
|
||||
context_parts.append(f"- 估值: {fund_score['breakdown']['valuation']['grade']}级 | 盈利: {fund_score['breakdown']['profitability']['grade']}级")
|
||||
context_parts.append(f"- 成长: {fund_score['breakdown']['growth']['grade']}级 | 财务: {fund_score['breakdown']['financial_health']['grade']}级")
|
||||
context_parts.append(f"- 摘要: {fund_score['summary']}")
|
||||
|
||||
# 新闻舆情
|
||||
# ===== 新闻舆情 =====
|
||||
if news_data:
|
||||
context_parts.append(f"\n## 最新新闻")
|
||||
context_parts.append(f"\n**## 最新新闻**")
|
||||
context_parts.append(self._format_news_data(news_data))
|
||||
|
||||
return "\n".join(context_parts)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user