1278 lines
50 KiB
Python
1278 lines
50 KiB
Python
"""
|
||
信号分析器 - 多周期技术分析和 LLM 深度分析
|
||
"""
|
||
import pandas as pd
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||
from typing import Dict, Any, Optional, List
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||
from app.utils.logger import logger
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||
from app.services.llm_service import llm_service
|
||
|
||
|
||
class SignalAnalyzer:
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"""交易信号分析器 - 波段交易优化版"""
|
||
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# LLM 系统提示词 - 波段交易版
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||
CRYPTO_ANALYST_PROMPT = """你是一位经验丰富的加密货币波段交易员，专注于捕捉 1-7 天的中等波段行情。
|
||
|
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## 交易风格
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- **波段交易**：持仓 1-7 天，不做超短线
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- **顺势回调**：在趋势中寻找回调入场机会
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- **风险控制**：单笔亏损不超过本金 2%
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## 多周期分析框架
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1. **4H 周期**：判断主趋势方向和强度
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- 趋势明确：价格在 MA20 同侧运行 3 根以上 K 线
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- 趋势强度：看 MACD 柱状图是否放大
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2. **1H 周期**：确认趋势 + 寻找回调位置
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- 上涨趋势中：等待回调到 MA20 或前低支撑
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- 下跌趋势中：等待反弹到 MA20 或前高阻力
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3. **15M 周期**：入场信号确认
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- 做多：RSI 从超卖回升 + MACD 金叉 + K 线企稳
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- 做空：RSI 从超买回落 + MACD 死叉 + K 线见顶
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## 入场条件（波段做多）
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1. 4H 趋势向上（价格 > MA20，MACD > 0 或底背离）
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2. 1H 回调到支撑位（MA20 附近或前低）
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3. 15M 出现止跌信号（RSI < 40 回升，或 MACD 金叉）
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4. 止损明确（前低下方），风险收益比 >= 1:2
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## 入场条件（波段做空）
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1. 4H 趋势向下（价格 < MA20，MACD < 0 或顶背离）
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2. 1H 反弹到阻力位（MA20 附近或前高）
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3. 15M 出现见顶信号（RSI > 60 回落，或 MACD 死叉）
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4. 止损明确（前高上方），风险收益比 >= 1:2
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## 特殊情况处理
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- **极度超卖（RSI < 20）**：不追空，等待反弹做多机会
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- **极度超买（RSI > 80）**：不追多，等待回调做空机会
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- **震荡市**：观望，等待突破方向
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## 输出格式（JSON）
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```json
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{
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"market_structure": {
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"trend": "uptrend/downtrend/sideways",
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"strength": "strong/moderate/weak",
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"phase": "impulse/correction/reversal"
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},
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"key_levels": {
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"resistance": [阻力位1, 阻力位2],
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"support": [支撑位1, 支撑位2]
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},
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"signal": {
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"quality": "A/B/C/D",
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"action": "buy/sell/wait",
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"confidence": 0-100,
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"entry_zone": [入场区间下限, 入场区间上限],
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"stop_loss": 止损价,
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"targets": [目标1, 目标2],
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"reason": "入场理由"
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},
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"risk_warning": "风险提示"
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}
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```
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信号质量说明：
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- A级：趋势明确 + 回调到位 + 多重信号共振（置信度 80+）
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- B级：趋势明确 + 信号较好（置信度 60-80）
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- C级：有机会但需要更多确认（置信度 40-60）
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- D级：不建议交易（置信度 < 40）
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重要：波段交易要有耐心，宁可错过也不要在不理想的位置入场。"""
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def __init__(self):
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"""初始化信号分析器"""
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logger.info("信号分析器初始化完成")
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# ==================== K线形态识别 ====================
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def _detect_candlestick_patterns(self, df: pd.DataFrame) -> Dict[str, Any]:
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"""
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识别 K 线形态
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Args:
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df: K线数据（至少需要3根K线）
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Returns:
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{
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'bullish_patterns': [...], # 看涨形态
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'bearish_patterns': [...], # 看跌形态
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'pattern_weight': float # 形态权重
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}
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"""
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if len(df) < 3:
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return {'bullish_patterns': [], 'bearish_patterns': [], 'pattern_weight': 0}
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bullish = []
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bearish = []
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weight = 0
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# 获取最近3根K线
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curr = df.iloc[-1]
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prev = df.iloc[-2]
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prev2 = df.iloc[-3]
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curr_body = curr['close'] - curr['open']
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curr_body_abs = abs(curr_body)
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curr_range = curr['high'] - curr['low']
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prev_body = prev['close'] - prev['open']
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prev_body_abs = abs(prev_body)
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# 避免除零
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if curr_range == 0:
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curr_range = 0.0001
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# === 锤子线 / 倒锤子线 ===
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upper_shadow = curr['high'] - max(curr['open'], curr['close'])
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lower_shadow = min(curr['open'], curr['close']) - curr['low']
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# 锤子线：下影线长，实体小，出现在下跌后
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if lower_shadow > curr_body_abs * 2 and upper_shadow < curr_body_abs * 0.5:
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if prev_body < 0: # 前一根是阴线
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bullish.append("锤子线")
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weight += 1.5
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# 倒锤子线：上影线长，实体小，出现在下跌后
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if upper_shadow > curr_body_abs * 2 and lower_shadow < curr_body_abs * 0.5:
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if prev_body < 0:
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bullish.append("倒锤子线")
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weight += 1
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# 上吊线：锤子线形态但出现在上涨后
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if lower_shadow > curr_body_abs * 2 and upper_shadow < curr_body_abs * 0.5:
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if prev_body > 0:
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bearish.append("上吊线")
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weight -= 1.5
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# === 吞没形态 ===
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# 看涨吞没：阳线实体完全包住前一根阴线
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if curr_body > 0 and prev_body < 0:
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if curr['open'] <= prev['close'] and curr['close'] >= prev['open']:
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if curr_body_abs > prev_body_abs * 1.2:
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bullish.append("看涨吞没")
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weight += 2
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# 看跌吞没：阴线实体完全包住前一根阳线
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if curr_body < 0 and prev_body > 0:
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if curr['open'] >= prev['close'] and curr['close'] <= prev['open']:
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if curr_body_abs > prev_body_abs * 1.2:
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bearish.append("看跌吞没")
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weight -= 2
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# === 十字星 ===
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if curr_body_abs < curr_range * 0.1: # 实体很小
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if upper_shadow > curr_range * 0.3 and lower_shadow > curr_range * 0.3:
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# 十字星本身是中性的，需要结合前一根K线判断
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if prev_body > 0:
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bearish.append("十字星(上涨后)")
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weight -= 1
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elif prev_body < 0:
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bullish.append("十字星(下跌后)")
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weight += 1
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# === 早晨之星 / 黄昏之星 (3根K线形态) ===
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prev2_body = prev2['close'] - prev2['open']
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prev_range = prev['high'] - prev['low'] if prev['high'] != prev['low'] else 0.0001
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# 早晨之星：大阴线 + 小实体(星) + 大阳线
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if prev2_body < 0 and abs(prev2_body) > prev_range * 0.5: # 第一根大阴线
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if abs(prev_body) < prev_range * 0.3: # 第二根小实体
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if curr_body > 0 and curr_body_abs > curr_range * 0.5: # 第三根大阳线
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if curr['close'] > (prev2['open'] + prev2['close']) / 2:
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bullish.append("早晨之星")
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weight += 2.5
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# 黄昏之星：大阳线 + 小实体(星) + 大阴线
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if prev2_body > 0 and prev2_body > prev_range * 0.5:
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if abs(prev_body) < prev_range * 0.3:
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if curr_body < 0 and curr_body_abs > curr_range * 0.5:
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if curr['close'] < (prev2['open'] + prev2['close']) / 2:
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bearish.append("黄昏之星")
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weight -= 2.5
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return {
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'bullish_patterns': bullish,
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'bearish_patterns': bearish,
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'pattern_weight': weight
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}
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# ==================== 支撑阻力位计算 ====================
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def _calculate_support_resistance(self, df: pd.DataFrame, current_price: float) -> Dict[str, Any]:
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"""
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计算支撑位和阻力位
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Args:
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df: K线数据（建议使用1H或4H数据）
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current_price: 当前价格
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Returns:
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{
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'supports': [支撑位1, 支撑位2],
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'resistances': [阻力位1, 阻力位2],
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'nearest_support': float,
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'nearest_resistance': float,
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'at_support': bool,
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'at_resistance': bool
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}
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"""
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if len(df) < 20:
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return {
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'supports': [], 'resistances': [],
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'nearest_support': 0, 'nearest_resistance': 0,
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'at_support': False, 'at_resistance': False
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}
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# 方法1：使用近期高低点
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highs = df['high'].tail(50).values
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lows = df['low'].tail(50).values
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# 找局部高点和低点
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local_highs = []
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local_lows = []
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for i in range(2, len(highs) - 2):
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# 局部高点
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if highs[i] > highs[i-1] and highs[i] > highs[i-2] and \
|
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highs[i] > highs[i+1] and highs[i] > highs[i+2]:
|
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local_highs.append(highs[i])
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# 局部低点
|
||
if lows[i] < lows[i-1] and lows[i] < lows[i-2] and \
|
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lows[i] < lows[i+1] and lows[i] < lows[i+2]:
|
||
local_lows.append(lows[i])
|
||
|
||
# 方法2：使用均线作为动态支撑阻力
|
||
ma20 = df['ma20'].iloc[-1] if 'ma20' in df.columns and pd.notna(df['ma20'].iloc[-1]) else 0
|
||
ma50 = df['ma50'].iloc[-1] if 'ma50' in df.columns and pd.notna(df['ma50'].iloc[-1]) else 0
|
||
|
||
# 方法3：布林带
|
||
bb_upper = df['bb_upper'].iloc[-1] if 'bb_upper' in df.columns and pd.notna(df['bb_upper'].iloc[-1]) else 0
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||
bb_lower = df['bb_lower'].iloc[-1] if 'bb_lower' in df.columns and pd.notna(df['bb_lower'].iloc[-1]) else 0
|
||
|
||
# 合并所有支撑位（低于当前价格）
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all_supports = []
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if local_lows:
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||
all_supports.extend([l for l in local_lows if l < current_price])
|
||
if ma20 and ma20 < current_price:
|
||
all_supports.append(ma20)
|
||
if ma50 and ma50 < current_price:
|
||
all_supports.append(ma50)
|
||
if bb_lower and bb_lower < current_price:
|
||
all_supports.append(bb_lower)
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||
|
||
# 合并所有阻力位（高于当前价格）
|
||
all_resistances = []
|
||
if local_highs:
|
||
all_resistances.extend([h for h in local_highs if h > current_price])
|
||
if ma20 and ma20 > current_price:
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||
all_resistances.append(ma20)
|
||
if ma50 and ma50 > current_price:
|
||
all_resistances.append(ma50)
|
||
if bb_upper and bb_upper > current_price:
|
||
all_resistances.append(bb_upper)
|
||
|
||
# 排序并去重（合并相近的价位）
|
||
supports = sorted(set(all_supports), reverse=True)[:3] # 最近的3个支撑
|
||
resistances = sorted(set(all_resistances))[:3] # 最近的3个阻力
|
||
|
||
# 找最近的支撑和阻力
|
||
nearest_support = supports[0] if supports else 0
|
||
nearest_resistance = resistances[0] if resistances else 0
|
||
|
||
# 判断是否在支撑/阻力位附近（1%范围内）
|
||
at_support = nearest_support > 0 and abs(current_price - nearest_support) / current_price < 0.01
|
||
at_resistance = nearest_resistance > 0 and abs(current_price - nearest_resistance) / current_price < 0.01
|
||
|
||
return {
|
||
'supports': supports,
|
||
'resistances': resistances,
|
||
'nearest_support': nearest_support,
|
||
'nearest_resistance': nearest_resistance,
|
||
'at_support': at_support,
|
||
'at_resistance': at_resistance
|
||
}
|
||
|
||
# ==================== 成交量分析 ====================
|
||
|
||
def _analyze_volume(self, df: pd.DataFrame) -> Dict[str, Any]:
|
||
"""
|
||
分析成交量
|
||
|
||
Args:
|
||
df: K线数据（需要包含 volume, volume_ma20, volume_ratio）
|
||
|
||
Returns:
|
||
{
|
||
'volume_signal': 'high' | 'normal' | 'low',
|
||
'volume_trend': 'increasing' | 'decreasing' | 'stable',
|
||
'volume_confirms': bool, # 成交量是否确认价格走势
|
||
'volume_weight': float
|
||
}
|
||
"""
|
||
if len(df) < 5 or 'volume' not in df.columns:
|
||
return {
|
||
'volume_signal': 'normal',
|
||
'volume_trend': 'stable',
|
||
'volume_confirms': False,
|
||
'volume_weight': 0
|
||
}
|
||
|
||
latest = df.iloc[-1]
|
||
prev = df.iloc[-2]
|
||
|
||
# 量比判断
|
||
volume_ratio = latest.get('volume_ratio', 1)
|
||
if pd.isna(volume_ratio):
|
||
volume_ratio = 1
|
||
|
||
if volume_ratio > 2:
|
||
volume_signal = 'high'
|
||
elif volume_ratio < 0.5:
|
||
volume_signal = 'low'
|
||
else:
|
||
volume_signal = 'normal'
|
||
|
||
# 成交量趋势（最近5根K线）
|
||
recent_volumes = df['volume'].tail(5)
|
||
if len(recent_volumes) >= 5:
|
||
first_half = recent_volumes.iloc[:2].mean()
|
||
second_half = recent_volumes.iloc[-2:].mean()
|
||
if second_half > first_half * 1.3:
|
||
volume_trend = 'increasing'
|
||
elif second_half < first_half * 0.7:
|
||
volume_trend = 'decreasing'
|
||
else:
|
||
volume_trend = 'stable'
|
||
else:
|
||
volume_trend = 'stable'
|
||
|
||
# 判断成交量是否确认价格走势
|
||
price_up = latest['close'] > prev['close']
|
||
volume_up = latest['volume'] > prev['volume']
|
||
|
||
# 价涨量增 或 价跌量缩 = 确认
|
||
volume_confirms = (price_up and volume_up) or (not price_up and not volume_up)
|
||
|
||
# 计算权重
|
||
weight = 0
|
||
if volume_signal == 'high' and volume_confirms:
|
||
weight = 1.5
|
||
elif volume_signal == 'high' and not volume_confirms:
|
||
weight = -0.5 # 放量但不确认，可能是假突破
|
||
elif volume_signal == 'low':
|
||
weight = -0.5 # 缩量，信号可靠性降低
|
||
|
||
return {
|
||
'volume_signal': volume_signal,
|
||
'volume_trend': volume_trend,
|
||
'volume_confirms': volume_confirms,
|
||
'volume_weight': weight
|
||
}
|
||
|
||
# ==================== 短线信号分析 ====================
|
||
|
||
def _analyze_short_term_signal(self, m5_data: pd.DataFrame, m15_data: pd.DataFrame,
|
||
trend_direction: str) -> Dict[str, Any]:
|
||
"""
|
||
分析短线交易信号（超跌反弹/超涨回落）
|
||
|
||
短线信号特点：
|
||
- 不依赖大趋势方向，主要看短周期超买超卖
|
||
- 快进快出，持仓时间短
|
||
- 置信度上限较低，建议轻仓
|
||
|
||
Args:
|
||
m5_data: 5分钟K线数据
|
||
m15_data: 15分钟K线数据
|
||
trend_direction: 大趋势方向（用于顺势加分）
|
||
|
||
Returns:
|
||
{
|
||
'action': 'buy' | 'sell' | 'hold',
|
||
'confidence': 0-100,
|
||
'reasons': [...],
|
||
'signal_type': 'short_term'
|
||
}
|
||
"""
|
||
if len(m5_data) < 5 or len(m15_data) < 5:
|
||
return {'action': 'hold', 'confidence': 0, 'reasons': [], 'signal_type': 'short_term'}
|
||
|
||
m5_latest = m5_data.iloc[-1]
|
||
m5_prev = m5_data.iloc[-2]
|
||
m15_latest = m15_data.iloc[-1]
|
||
m15_prev = m15_data.iloc[-2]
|
||
|
||
buy_score = 0
|
||
sell_score = 0
|
||
buy_reasons = []
|
||
sell_reasons = []
|
||
|
||
# === 15M RSI 超卖/超买 ===
|
||
m15_rsi = m15_latest.get('rsi', 50)
|
||
if pd.notna(m15_rsi):
|
||
if m15_rsi < 25:
|
||
buy_reasons.append(f"15M RSI极度超卖({m15_rsi:.1f})")
|
||
buy_score += 3
|
||
elif m15_rsi < 35:
|
||
buy_reasons.append(f"15M RSI超卖({m15_rsi:.1f})")
|
||
buy_score += 2
|
||
elif m15_rsi > 75:
|
||
sell_reasons.append(f"15M RSI极度超买({m15_rsi:.1f})")
|
||
sell_score += 3
|
||
elif m15_rsi > 65:
|
||
sell_reasons.append(f"15M RSI超买({m15_rsi:.1f})")
|
||
sell_score += 2
|
||
|
||
# === 5M RSI 反转确认 ===
|
||
m5_rsi = m5_latest.get('rsi', 50)
|
||
m5_prev_rsi = m5_prev.get('rsi', 50)
|
||
if pd.notna(m5_rsi) and pd.notna(m5_prev_rsi):
|
||
# 超卖后回升
|
||
if m5_rsi < 40 and m5_rsi > m5_prev_rsi and m5_prev_rsi < 35:
|
||
buy_reasons.append(f"5M RSI反转回升({m5_prev_rsi:.1f}→{m5_rsi:.1f})")
|
||
buy_score += 2
|
||
# 超买后回落
|
||
if m5_rsi > 60 and m5_rsi < m5_prev_rsi and m5_prev_rsi > 65:
|
||
sell_reasons.append(f"5M RSI反转回落({m5_prev_rsi:.1f}→{m5_rsi:.1f})")
|
||
sell_score += 2
|
||
|
||
# === 布林带触轨 ===
|
||
if 'bb_lower' in m15_latest and pd.notna(m15_latest['bb_lower']):
|
||
if m15_latest['close'] <= m15_latest['bb_lower']:
|
||
buy_reasons.append("15M触及布林下轨")
|
||
buy_score += 2
|
||
elif m15_latest['close'] >= m15_latest['bb_upper']:
|
||
sell_reasons.append("15M触及布林上轨")
|
||
sell_score += 2
|
||
|
||
# === KDJ 超卖/超买 ===
|
||
m15_k = m15_latest.get('k', 50)
|
||
m15_d = m15_latest.get('d', 50)
|
||
if pd.notna(m15_k) and pd.notna(m15_d):
|
||
if m15_k < 20 and m15_d < 20:
|
||
buy_reasons.append(f"15M KDJ超卖区(K={m15_k:.1f})")
|
||
buy_score += 1.5
|
||
elif m15_k > 80 and m15_d > 80:
|
||
sell_reasons.append(f"15M KDJ超买区(K={m15_k:.1f})")
|
||
sell_score += 1.5
|
||
|
||
# === 5M K线反转形态 ===
|
||
if m5_latest['close'] > m5_latest['open'] and m5_prev['close'] < m5_prev['open']:
|
||
# 阴转阳
|
||
if m5_rsi < 40:
|
||
buy_reasons.append("5M阴转阳反转")
|
||
buy_score += 1.5
|
||
elif m5_latest['close'] < m5_latest['open'] and m5_prev['close'] > m5_prev['open']:
|
||
# 阳转阴
|
||
if m5_rsi > 60:
|
||
sell_reasons.append("5M阳转阴反转")
|
||
sell_score += 1.5
|
||
|
||
# === 5M MACD 金叉/死叉 ===
|
||
m5_macd = m5_latest.get('macd', 0)
|
||
m5_macd_signal = m5_latest.get('macd_signal', 0)
|
||
m5_prev_macd = m5_prev.get('macd', 0)
|
||
m5_prev_macd_signal = m5_prev.get('macd_signal', 0)
|
||
if pd.notna(m5_macd) and pd.notna(m5_prev_macd):
|
||
if m5_prev_macd <= m5_prev_macd_signal and m5_macd > m5_macd_signal:
|
||
buy_reasons.append("5M MACD金叉")
|
||
buy_score += 1.5
|
||
elif m5_prev_macd >= m5_prev_macd_signal and m5_macd < m5_macd_signal:
|
||
sell_reasons.append("5M MACD死叉")
|
||
sell_score += 1.5
|
||
|
||
# === 顺势加分 ===
|
||
if trend_direction == 'bullish' and buy_score > 0:
|
||
buy_score += 1
|
||
buy_reasons.append("顺大势做多")
|
||
elif trend_direction == 'bearish' and sell_score > 0:
|
||
sell_score += 1
|
||
sell_reasons.append("顺大势做空")
|
||
|
||
# === 决策 ===
|
||
action = 'hold'
|
||
confidence = 0
|
||
reasons = []
|
||
|
||
# 短线信号阈值较低，但置信度上限也低
|
||
if buy_score >= 4 and buy_score > sell_score:
|
||
action = 'buy'
|
||
confidence = min(35 + buy_score * 6, 65) # 短线最高65%
|
||
reasons = buy_reasons + ["📈 短线超跌反弹"]
|
||
elif sell_score >= 4 and sell_score > buy_score:
|
||
action = 'sell'
|
||
confidence = min(35 + sell_score * 6, 65)
|
||
reasons = sell_reasons + ["📉 短线超涨回落"]
|
||
|
||
return {
|
||
'action': action,
|
||
'confidence': confidence,
|
||
'reasons': reasons,
|
||
'signal_type': 'short_term',
|
||
'scores': {'buy': buy_score, 'sell': sell_score}
|
||
}
|
||
|
||
# ==================== 5M 精确入场 ====================
|
||
|
||
def _analyze_5m_entry(self, m5_data: pd.DataFrame, action: str) -> Dict[str, Any]:
|
||
"""
|
||
使用 5M 数据寻找精确入场点
|
||
|
||
Args:
|
||
m5_data: 5分钟K线数据
|
||
action: 'buy' 或 'sell'
|
||
|
||
Returns:
|
||
{
|
||
'entry_confirmed': bool,
|
||
'entry_reasons': [...],
|
||
'entry_weight': float
|
||
}
|
||
"""
|
||
if len(m5_data) < 5:
|
||
return {'entry_confirmed': False, 'entry_reasons': [], 'entry_weight': 0}
|
||
|
||
latest = m5_data.iloc[-1]
|
||
prev = m5_data.iloc[-2]
|
||
reasons = []
|
||
weight = 0
|
||
|
||
# 获取指标
|
||
rsi = latest.get('rsi', 50)
|
||
prev_rsi = prev.get('rsi', 50)
|
||
macd = latest.get('macd', 0)
|
||
macd_signal = latest.get('macd_signal', 0)
|
||
prev_macd = prev.get('macd', 0)
|
||
prev_macd_signal = prev.get('macd_signal', 0)
|
||
|
||
if action == 'buy':
|
||
# 5M RSI 从超卖回升
|
||
if pd.notna(rsi) and pd.notna(prev_rsi):
|
||
if rsi < 40 and rsi > prev_rsi:
|
||
reasons.append("5M RSI回升")
|
||
weight += 1
|
||
if rsi < 30:
|
||
reasons.append("5M RSI超卖")
|
||
weight += 0.5
|
||
|
||
# 5M MACD 金叉
|
||
if pd.notna(macd) and pd.notna(prev_macd):
|
||
if prev_macd <= prev_macd_signal and macd > macd_signal:
|
||
reasons.append("5M MACD金叉")
|
||
weight += 1.5
|
||
|
||
# 5M K线企稳（阳线）
|
||
if latest['close'] > latest['open']:
|
||
if prev['close'] < prev['open']: # 前一根是阴线
|
||
reasons.append("5M阳线反转")
|
||
weight += 1
|
||
|
||
elif action == 'sell':
|
||
# 5M RSI 从超买回落
|
||
if pd.notna(rsi) and pd.notna(prev_rsi):
|
||
if rsi > 60 and rsi < prev_rsi:
|
||
reasons.append("5M RSI回落")
|
||
weight += 1
|
||
if rsi > 70:
|
||
reasons.append("5M RSI超买")
|
||
weight += 0.5
|
||
|
||
# 5M MACD 死叉
|
||
if pd.notna(macd) and pd.notna(prev_macd):
|
||
if prev_macd >= prev_macd_signal and macd < macd_signal:
|
||
reasons.append("5M MACD死叉")
|
||
weight += 1.5
|
||
|
||
# 5M K线见顶（阴线）
|
||
if latest['close'] < latest['open']:
|
||
if prev['close'] > prev['open']:
|
||
reasons.append("5M阴线反转")
|
||
weight += 1
|
||
|
||
entry_confirmed = weight >= 2
|
||
|
||
return {
|
||
'entry_confirmed': entry_confirmed,
|
||
'entry_reasons': reasons,
|
||
'entry_weight': weight
|
||
}
|
||
|
||
def analyze_trend(self, h1_data: pd.DataFrame, h4_data: pd.DataFrame) -> Dict[str, Any]:
|
||
"""
|
||
分析趋势方向和强度（波段交易优化版）
|
||
|
||
Args:
|
||
h1_data: 1小时K线数据（含技术指标）
|
||
h4_data: 4小时K线数据（含技术指标）
|
||
|
||
Returns:
|
||
{
|
||
'direction': 'bullish' | 'bearish' | 'neutral',
|
||
'strength': 'strong' | 'moderate' | 'weak',
|
||
'phase': 'impulse' | 'correction' | 'reversal',
|
||
'h4_score': float,
|
||
'h1_score': float
|
||
}
|
||
"""
|
||
if h1_data.empty or h4_data.empty:
|
||
return {
|
||
'direction': 'neutral',
|
||
'strength': 'weak',
|
||
'phase': 'sideways',
|
||
'h4_score': 0,
|
||
'h1_score': 0
|
||
}
|
||
|
||
# 获取最新数据
|
||
h1_latest = h1_data.iloc[-1]
|
||
h4_latest = h4_data.iloc[-1]
|
||
|
||
# 计算各周期的趋势得分
|
||
h4_score, h4_details = self._calculate_trend_score(h4_latest)
|
||
h1_score, h1_details = self._calculate_trend_score(h1_latest)
|
||
|
||
# 判断趋势方向（4H 为主）
|
||
if h4_score > 0.3:
|
||
direction = 'bullish'
|
||
elif h4_score < -0.3:
|
||
direction = 'bearish'
|
||
else:
|
||
direction = 'neutral'
|
||
|
||
# 判断趋势强度
|
||
strength = self._assess_trend_strength(h4_data, h1_data)
|
||
|
||
# 判断当前阶段（是主升/主跌还是回调）
|
||
phase = self._detect_market_phase(h4_data, h1_data, direction)
|
||
|
||
# 检查极端情况
|
||
h4_rsi = h4_latest.get('rsi', 50)
|
||
extreme_warning = ""
|
||
if pd.notna(h4_rsi):
|
||
if h4_rsi < 20:
|
||
extreme_warning = f" [RSI={h4_rsi:.1f} 极度超卖]"
|
||
phase = 'oversold'
|
||
elif h4_rsi > 80:
|
||
extreme_warning = f" [RSI={h4_rsi:.1f} 极度超买]"
|
||
phase = 'overbought'
|
||
|
||
logger.info(f"趋势分析: 方向={direction}, 强度={strength}, 阶段={phase} | "
|
||
f"4H={h4_score:.2f}{h4_details}, 1H={h1_score:.2f}{h1_details}{extreme_warning}")
|
||
|
||
return {
|
||
'direction': direction,
|
||
'strength': strength,
|
||
'phase': phase,
|
||
'h4_score': h4_score,
|
||
'h1_score': h1_score
|
||
}
|
||
|
||
def _assess_trend_strength(self, h4_data: pd.DataFrame, h1_data: pd.DataFrame) -> str:
|
||
"""评估趋势强度"""
|
||
if len(h4_data) < 5:
|
||
return 'weak'
|
||
|
||
h4_latest = h4_data.iloc[-1]
|
||
|
||
# 检查 MACD 柱状图是否放大
|
||
macd_hist = h4_data['macd_hist'].tail(5)
|
||
macd_expanding = False
|
||
if len(macd_hist) >= 3:
|
||
recent_abs = abs(macd_hist.iloc[-1])
|
||
prev_abs = abs(macd_hist.iloc[-3])
|
||
if recent_abs > prev_abs * 1.2:
|
||
macd_expanding = True
|
||
|
||
# 检查价格是否持续在 MA20 同侧
|
||
close_prices = h4_data['close'].tail(5)
|
||
ma20_values = h4_data['ma20'].tail(5)
|
||
consistent_side = True
|
||
if pd.notna(ma20_values.iloc[-1]):
|
||
above_ma = close_prices > ma20_values
|
||
consistent_side = above_ma.all() or (~above_ma).all()
|
||
|
||
# 检查 RSI 是否在趋势区间
|
||
rsi = h4_latest.get('rsi', 50)
|
||
rsi_trending = 40 < rsi < 60 # 中性区间表示趋势不强
|
||
|
||
if macd_expanding and consistent_side and not rsi_trending:
|
||
return 'strong'
|
||
elif consistent_side:
|
||
return 'moderate'
|
||
else:
|
||
return 'weak'
|
||
|
||
def _detect_market_phase(self, h4_data: pd.DataFrame, h1_data: pd.DataFrame,
|
||
direction: str) -> str:
|
||
"""检测市场阶段（主升/主跌 vs 回调）"""
|
||
if len(h1_data) < 10:
|
||
return 'unknown'
|
||
|
||
h1_latest = h1_data.iloc[-1]
|
||
h4_latest = h4_data.iloc[-1]
|
||
|
||
# 获取 1H 的短期趋势
|
||
h1_ma5 = h1_latest.get('ma5', 0)
|
||
h1_ma20 = h1_latest.get('ma20', 0)
|
||
h1_close = h1_latest.get('close', 0)
|
||
|
||
if not (pd.notna(h1_ma5) and pd.notna(h1_ma20)):
|
||
return 'unknown'
|
||
|
||
# 判断 1H 是否在回调
|
||
if direction == 'bullish':
|
||
# 上涨趋势中，1H 价格回落到 MA20 附近 = 回调
|
||
if h1_close < h1_ma5 and h1_close > h1_ma20 * 0.98:
|
||
return 'correction' # 回调中，可能是入场机会
|
||
elif h1_close > h1_ma5:
|
||
return 'impulse' # 主升浪
|
||
elif direction == 'bearish':
|
||
# 下跌趋势中，1H 价格反弹到 MA20 附近 = 反弹
|
||
if h1_close > h1_ma5 and h1_close < h1_ma20 * 1.02:
|
||
return 'correction' # 反弹中，可能是做空机会
|
||
elif h1_close < h1_ma5:
|
||
return 'impulse' # 主跌浪
|
||
|
||
return 'sideways'
|
||
|
||
def _calculate_trend_score(self, data: pd.Series) -> tuple:
|
||
"""
|
||
计算单周期趋势得分
|
||
|
||
Args:
|
||
data: 包含技术指标的数据行
|
||
|
||
Returns:
|
||
(得分, 详情字符串)
|
||
"""
|
||
score = 0.0
|
||
count = 0
|
||
details = []
|
||
|
||
# 价格与均线关系
|
||
if 'close' in data and 'ma20' in data and pd.notna(data['ma20']):
|
||
if data['close'] > data['ma20']:
|
||
score += 1
|
||
details.append("价格>MA20")
|
||
else:
|
||
score -= 1
|
||
details.append("价格<MA20")
|
||
count += 1
|
||
|
||
# MA5 与 MA20 关系
|
||
if 'ma5' in data and 'ma20' in data and pd.notna(data['ma5']) and pd.notna(data['ma20']):
|
||
if data['ma5'] > data['ma20']:
|
||
score += 1
|
||
details.append("MA5>MA20")
|
||
else:
|
||
score -= 1
|
||
details.append("MA5<MA20")
|
||
count += 1
|
||
|
||
# MACD
|
||
if 'macd' in data and 'macd_signal' in data and pd.notna(data['macd']):
|
||
if data['macd'] > data['macd_signal']:
|
||
score += 1
|
||
details.append("MACD多")
|
||
else:
|
||
score -= 1
|
||
details.append("MACD空")
|
||
count += 1
|
||
|
||
# RSI
|
||
if 'rsi' in data and pd.notna(data['rsi']):
|
||
if data['rsi'] > 50:
|
||
score += 0.5
|
||
else:
|
||
score -= 0.5
|
||
count += 0.5
|
||
|
||
final_score = score / count if count > 0 else 0
|
||
detail_str = f"({','.join(details)})" if details else ""
|
||
return final_score, detail_str
|
||
|
||
def analyze_entry_signal(self, m5_data: pd.DataFrame, m15_data: pd.DataFrame,
|
||
trend: Dict[str, Any], h1_data: pd.DataFrame = None) -> Dict[str, Any]:
|
||
"""
|
||
分析 15M 进场信号（波段交易优化版 - 增强版）
|
||
|
||
新增功能：
|
||
- 成交量确认
|
||
- K线形态识别
|
||
- 支撑阻力位判断
|
||
- 5M精确入场
|
||
|
||
Args:
|
||
m5_data: 5分钟K线数据（用于精确入场）
|
||
m15_data: 15分钟K线数据（主要入场周期）
|
||
trend: 趋势分析结果
|
||
h1_data: 1小时K线数据（用于支撑阻力位计算，可选）
|
||
|
||
Returns:
|
||
{
|
||
'action': 'buy' | 'sell' | 'hold',
|
||
'confidence': 0-100,
|
||
'signal_grade': 'A' | 'B' | 'C' | 'D',
|
||
'reasons': [...],
|
||
'indicators': {...},
|
||
'patterns': {...},
|
||
'levels': {...},
|
||
'volume_analysis': {...}
|
||
}
|
||
"""
|
||
if m5_data.empty or m15_data.empty:
|
||
return {'action': 'hold', 'confidence': 0, 'signal_grade': 'D',
|
||
'reasons': ['数据不足'], 'indicators': {}}
|
||
|
||
# 兼容旧格式（如果 trend 是字符串）
|
||
if isinstance(trend, str):
|
||
trend_direction = trend
|
||
trend_phase = 'unknown'
|
||
trend_strength = 'moderate'
|
||
else:
|
||
trend_direction = trend.get('direction', 'neutral')
|
||
trend_phase = trend.get('phase', 'unknown')
|
||
trend_strength = trend.get('strength', 'moderate')
|
||
|
||
m15_latest = m15_data.iloc[-1]
|
||
current_price = float(m15_latest['close'])
|
||
|
||
# 收集信号
|
||
buy_signals = []
|
||
sell_signals = []
|
||
signal_weights = {'buy': 0, 'sell': 0}
|
||
|
||
# ==================== 1. 传统技术指标信号 ====================
|
||
|
||
# === RSI 信号 ===
|
||
if 'rsi' in m15_latest and pd.notna(m15_latest['rsi']):
|
||
rsi = m15_latest['rsi']
|
||
if rsi < 30:
|
||
buy_signals.append(f"RSI超卖({rsi:.1f})")
|
||
signal_weights['buy'] += 2
|
||
elif rsi < 40 and len(m15_data) >= 2:
|
||
prev_rsi = m15_data.iloc[-2].get('rsi', 50)
|
||
if pd.notna(prev_rsi) and rsi > prev_rsi:
|
||
buy_signals.append(f"RSI回升({prev_rsi:.1f}→{rsi:.1f})")
|
||
signal_weights['buy'] += 1.5
|
||
elif rsi > 70:
|
||
sell_signals.append(f"RSI超买({rsi:.1f})")
|
||
signal_weights['sell'] += 2
|
||
elif rsi > 60 and len(m15_data) >= 2:
|
||
prev_rsi = m15_data.iloc[-2].get('rsi', 50)
|
||
if pd.notna(prev_rsi) and rsi < prev_rsi:
|
||
sell_signals.append(f"RSI回落({prev_rsi:.1f}→{rsi:.1f})")
|
||
signal_weights['sell'] += 1.5
|
||
|
||
# === MACD 信号 ===
|
||
if len(m15_data) >= 2:
|
||
prev = m15_data.iloc[-2]
|
||
if 'macd' in m15_latest and 'macd_signal' in m15_latest:
|
||
if pd.notna(m15_latest['macd']) and pd.notna(prev['macd']):
|
||
if prev['macd'] <= prev['macd_signal'] and m15_latest['macd'] > m15_latest['macd_signal']:
|
||
buy_signals.append("MACD金叉")
|
||
signal_weights['buy'] += 2
|
||
elif prev['macd'] >= prev['macd_signal'] and m15_latest['macd'] < m15_latest['macd_signal']:
|
||
sell_signals.append("MACD死叉")
|
||
signal_weights['sell'] += 2
|
||
elif abs(m15_latest['macd_hist']) < abs(prev['macd_hist']) * 0.7:
|
||
if m15_latest['macd_hist'] > 0:
|
||
sell_signals.append("MACD动能减弱")
|
||
signal_weights['sell'] += 0.5
|
||
else:
|
||
buy_signals.append("MACD动能减弱")
|
||
signal_weights['buy'] += 0.5
|
||
|
||
# === 布林带信号 ===
|
||
if 'close' in m15_latest and 'bb_lower' in m15_latest and 'bb_upper' in m15_latest:
|
||
if pd.notna(m15_latest['bb_lower']) and pd.notna(m15_latest['bb_upper']):
|
||
bb_middle = m15_latest.get('bb_middle', (m15_latest['bb_upper'] + m15_latest['bb_lower']) / 2)
|
||
if m15_latest['close'] < m15_latest['bb_lower']:
|
||
buy_signals.append("触及布林下轨")
|
||
signal_weights['buy'] += 1.5
|
||
elif m15_latest['close'] > m15_latest['bb_upper']:
|
||
sell_signals.append("触及布林上轨")
|
||
signal_weights['sell'] += 1.5
|
||
elif len(m15_data) >= 2:
|
||
prev_close = m15_data.iloc[-2]['close']
|
||
if prev_close < bb_middle and m15_latest['close'] > bb_middle:
|
||
buy_signals.append("突破布林中轨")
|
||
signal_weights['buy'] += 1
|
||
elif prev_close > bb_middle and m15_latest['close'] < bb_middle:
|
||
sell_signals.append("跌破布林中轨")
|
||
signal_weights['sell'] += 1
|
||
|
||
# === KDJ 信号 ===
|
||
if 'k' in m15_latest and 'd' in m15_latest and len(m15_data) >= 2:
|
||
prev = m15_data.iloc[-2]
|
||
if pd.notna(m15_latest['k']) and pd.notna(prev['k']):
|
||
if prev['k'] <= prev['d'] and m15_latest['k'] > m15_latest['d']:
|
||
if m15_latest['k'] < 30:
|
||
buy_signals.append("KDJ低位金叉")
|
||
signal_weights['buy'] += 1.5
|
||
else:
|
||
buy_signals.append("KDJ金叉")
|
||
signal_weights['buy'] += 0.5
|
||
elif prev['k'] >= prev['d'] and m15_latest['k'] < m15_latest['d']:
|
||
if m15_latest['k'] > 70:
|
||
sell_signals.append("KDJ高位死叉")
|
||
signal_weights['sell'] += 1.5
|
||
else:
|
||
sell_signals.append("KDJ死叉")
|
||
signal_weights['sell'] += 0.5
|
||
|
||
# ==================== 2. K线形态识别 ====================
|
||
patterns = self._detect_candlestick_patterns(m15_data)
|
||
if patterns['bullish_patterns']:
|
||
buy_signals.extend(patterns['bullish_patterns'])
|
||
signal_weights['buy'] += patterns['pattern_weight']
|
||
if patterns['bearish_patterns']:
|
||
sell_signals.extend(patterns['bearish_patterns'])
|
||
signal_weights['sell'] += abs(patterns['pattern_weight'])
|
||
|
||
# ==================== 3. 成交量分析 ====================
|
||
volume_analysis = self._analyze_volume(m15_data)
|
||
if volume_analysis['volume_signal'] == 'high':
|
||
if volume_analysis['volume_confirms']:
|
||
# 放量确认
|
||
if signal_weights['buy'] > signal_weights['sell']:
|
||
buy_signals.append(f"放量确认(量比{m15_latest.get('volume_ratio', 1):.1f})")
|
||
signal_weights['buy'] += volume_analysis['volume_weight']
|
||
else:
|
||
sell_signals.append(f"放量确认(量比{m15_latest.get('volume_ratio', 1):.1f})")
|
||
signal_weights['sell'] += volume_analysis['volume_weight']
|
||
else:
|
||
# 放量不确认，可能是假信号
|
||
if signal_weights['buy'] > signal_weights['sell']:
|
||
buy_signals.append("放量但不确认(警惕)")
|
||
signal_weights['buy'] += volume_analysis['volume_weight'] # 负权重
|
||
else:
|
||
sell_signals.append("放量但不确认(警惕)")
|
||
signal_weights['sell'] += volume_analysis['volume_weight']
|
||
|
||
# ==================== 4. 支撑阻力位分析 ====================
|
||
levels = {}
|
||
if h1_data is not None and not h1_data.empty:
|
||
levels = self._calculate_support_resistance(h1_data, current_price)
|
||
if levels.get('at_support') and trend_direction == 'bullish':
|
||
buy_signals.append(f"触及支撑位({levels['nearest_support']:.2f})")
|
||
signal_weights['buy'] += 1.5
|
||
if levels.get('at_resistance') and trend_direction == 'bearish':
|
||
sell_signals.append(f"触及阻力位({levels['nearest_resistance']:.2f})")
|
||
signal_weights['sell'] += 1.5
|
||
|
||
# ==================== 5. 根据趋势和阶段决定动作（中长线信号）====================
|
||
action = 'hold'
|
||
confidence = 0
|
||
reasons = []
|
||
signal_grade = 'D'
|
||
signal_type = 'swing' # 默认波段信号
|
||
|
||
# 波段交易核心逻辑：在回调中寻找入场机会
|
||
if trend_direction == 'bullish':
|
||
if trend_phase == 'correction' and signal_weights['buy'] >= 3:
|
||
action = 'buy'
|
||
confidence = min(40 + signal_weights['buy'] * 10, 95)
|
||
reasons = buy_signals + [f"📊 波段信号: 上涨趋势回调({trend_strength})"]
|
||
signal_grade = 'A' if confidence >= 80 else ('B' if confidence >= 60 else 'C')
|
||
elif trend_phase == 'impulse' and signal_weights['buy'] >= 4:
|
||
action = 'buy'
|
||
confidence = min(30 + signal_weights['buy'] * 8, 80)
|
||
reasons = buy_signals + ["📊 波段信号: 主升浪追多"]
|
||
signal_grade = 'B' if confidence >= 60 else 'C'
|
||
elif trend_phase == 'oversold' and signal_weights['buy'] >= 3:
|
||
# 极度超卖时允许抄底，但降低置信度并提示风险
|
||
action = 'buy'
|
||
confidence = min(30 + signal_weights['buy'] * 8, 70) # 最高70%
|
||
reasons = buy_signals + ["📊 波段信号: 极度超卖抄底(高风险)", "建议轻仓试探"]
|
||
signal_grade = 'C' # 最高C级
|
||
elif trend_phase == 'overbought':
|
||
reasons = ['极度超买，不宜追多']
|
||
|
||
elif trend_direction == 'bearish':
|
||
if trend_phase == 'correction' and signal_weights['sell'] >= 3:
|
||
action = 'sell'
|
||
confidence = min(40 + signal_weights['sell'] * 10, 95)
|
||
reasons = sell_signals + [f"📊 波段信号: 下跌趋势反弹({trend_strength})"]
|
||
signal_grade = 'A' if confidence >= 80 else ('B' if confidence >= 60 else 'C')
|
||
elif trend_phase == 'impulse' and signal_weights['sell'] >= 4:
|
||
action = 'sell'
|
||
confidence = min(30 + signal_weights['sell'] * 8, 80)
|
||
reasons = sell_signals + ["📊 波段信号: 主跌浪追空"]
|
||
signal_grade = 'B' if confidence >= 60 else 'C'
|
||
elif trend_phase == 'overbought' and signal_weights['sell'] >= 3:
|
||
# 极度超买时允许做空，但降低置信度并提示风险
|
||
action = 'sell'
|
||
confidence = min(30 + signal_weights['sell'] * 8, 70) # 最高70%
|
||
reasons = sell_signals + ["📊 波段信号: 极度超买摸顶(高风险)", "建议轻仓试探"]
|
||
signal_grade = 'C' # 最高C级
|
||
elif trend_phase == 'oversold':
|
||
reasons = ['极度超卖，不宜追空']
|
||
|
||
# ==================== 5.5 短线信号检测（中长线无信号时）====================
|
||
# 如果中长线没有触发信号，检查短线超跌反弹/超涨回落机会
|
||
if action == 'hold':
|
||
short_term = self._analyze_short_term_signal(m5_data, m15_data, trend_direction)
|
||
if short_term['action'] != 'hold' and short_term['confidence'] >= 50:
|
||
action = short_term['action']
|
||
confidence = short_term['confidence']
|
||
reasons = short_term['reasons']
|
||
signal_type = 'short_term'
|
||
signal_grade = 'C' # 短线信号最高C级
|
||
|
||
# 如果还是没有信号
|
||
if action == 'hold' and trend_direction == 'neutral':
|
||
reasons = ['趋势不明确，观望']
|
||
|
||
# ==================== 6. 5M 精确入场确认 ====================
|
||
if action != 'hold' and not m5_data.empty:
|
||
entry_5m = self._analyze_5m_entry(m5_data, action)
|
||
if entry_5m['entry_confirmed']:
|
||
confidence = min(confidence + 10, 95)
|
||
reasons.extend(entry_5m['entry_reasons'])
|
||
if signal_grade == 'B':
|
||
signal_grade = 'A'
|
||
elif signal_grade == 'C':
|
||
signal_grade = 'B'
|
||
elif entry_5m['entry_weight'] < 1:
|
||
# 5M 没有确认，降低置信度
|
||
confidence = max(confidence - 10, 0)
|
||
reasons.append("5M未确认入场")
|
||
|
||
if not reasons:
|
||
reasons = ['信号不足，继续观望']
|
||
|
||
# 收集指标数据
|
||
indicators = {}
|
||
for col in ['rsi', 'macd', 'macd_signal', 'macd_hist', 'k', 'd', 'j', 'close', 'ma20', 'volume_ratio']:
|
||
if col in m15_latest and pd.notna(m15_latest[col]):
|
||
indicators[col] = float(m15_latest[col])
|
||
|
||
# 记录详细日志（简化版，详细日志在 crypto_agent 中输出）
|
||
if action != 'hold':
|
||
type_text = "短线" if signal_type == 'short_term' else "波段"
|
||
logger.debug(f"信号详情: [{type_text}] {action} {confidence}% {signal_grade} | 买权重={signal_weights['buy']:.1f} 卖权重={signal_weights['sell']:.1f}")
|
||
|
||
return {
|
||
'action': action,
|
||
'confidence': confidence,
|
||
'signal_grade': signal_grade,
|
||
'signal_type': signal_type, # 'swing' 波段 | 'short_term' 短线
|
||
'reasons': reasons,
|
||
'indicators': indicators,
|
||
'trend_info': {
|
||
'direction': trend_direction,
|
||
'phase': trend_phase,
|
||
'strength': trend_strength
|
||
},
|
||
'patterns': patterns,
|
||
'volume_analysis': volume_analysis,
|
||
'levels': levels,
|
||
'signal_weights': signal_weights
|
||
}
|
||
|
||
async def llm_analyze(self, data: Dict[str, pd.DataFrame], signal: Dict[str, Any],
|
||
symbol: str) -> Dict[str, Any]:
|
||
"""
|
||
使用 LLM 进行深度分析
|
||
|
||
Args:
|
||
data: 多周期K线数据
|
||
signal: 初步信号分析结果
|
||
symbol: 交易对
|
||
|
||
Returns:
|
||
LLM 分析结果（结构化数据）
|
||
"""
|
||
try:
|
||
# 构建分析提示
|
||
prompt = self._build_analysis_prompt(data, signal, symbol)
|
||
|
||
# 调用 LLM
|
||
response = llm_service.chat([
|
||
{"role": "system", "content": self.CRYPTO_ANALYST_PROMPT},
|
||
{"role": "user", "content": prompt}
|
||
])
|
||
|
||
if response:
|
||
logger.info(f"LLM 分析完成: {symbol}")
|
||
# 解析 JSON 响应
|
||
return self._parse_llm_response(response)
|
||
else:
|
||
return {"error": "LLM 分析暂时不可用", "raw": ""}
|
||
|
||
except Exception as e:
|
||
logger.error(f"LLM 分析失败: {e}")
|
||
return {"error": str(e), "raw": ""}
|
||
|
||
def _parse_llm_response(self, response: str) -> Dict[str, Any]:
|
||
"""
|
||
解析 LLM 的 JSON 响应
|
||
|
||
Args:
|
||
response: LLM 原始响应
|
||
|
||
Returns:
|
||
解析后的结构化数据
|
||
"""
|
||
import json
|
||
import re
|
||
|
||
result = {
|
||
"raw": response,
|
||
"parsed": None,
|
||
"summary": ""
|
||
}
|
||
|
||
try:
|
||
# 尝试提取 JSON 块
|
||
json_match = re.search(r'```json\s*([\s\S]*?)\s*```', response)
|
||
if json_match:
|
||
json_str = json_match.group(1)
|
||
else:
|
||
# 尝试直接解析整个响应
|
||
json_str = response
|
||
|
||
# 解析 JSON
|
||
parsed = json.loads(json_str)
|
||
result["parsed"] = parsed
|
||
|
||
# 生成摘要
|
||
if parsed:
|
||
recommendation = parsed.get("recommendation", {})
|
||
action = recommendation.get("action", "wait")
|
||
confidence = recommendation.get("confidence", 0)
|
||
reason = recommendation.get("reason", "")
|
||
|
||
if action == "wait":
|
||
result["summary"] = f"建议观望。{parsed.get('risk_warning', '')}"
|
||
else:
|
||
action_text = "做多" if action == "buy" else "做空"
|
||
result["summary"] = f"建议{action_text}，置信度{confidence}%。{reason}"
|
||
|
||
except json.JSONDecodeError:
|
||
# JSON 解析失败，提取关键信息
|
||
logger.warning("LLM 响应不是有效 JSON，尝试提取关键信息")
|
||
result["summary"] = self._extract_summary_from_text(response)
|
||
|
||
return result
|
||
|
||
def _extract_summary_from_text(self, text: str) -> str:
|
||
"""从非 JSON 文本中提取摘要"""
|
||
# 简单提取前 200 字符作为摘要
|
||
text = text.strip()
|
||
if len(text) > 200:
|
||
return text[:200] + "..."
|
||
return text
|
||
|
||
def _build_analysis_prompt(self, data: Dict[str, pd.DataFrame], signal: Dict[str, Any],
|
||
symbol: str) -> str:
|
||
"""构建 LLM 分析提示 - 优化版"""
|
||
parts = [f"# {symbol} 技术分析数据\n"]
|
||
|
||
# 当前价格
|
||
current_price = float(data['5m'].iloc[-1]['close'])
|
||
parts.append(f"**当前价格**: ${current_price:,.2f}\n")
|
||
|
||
# 添加各周期指标摘要
|
||
for interval in ['4h', '1h', '15m']:
|
||
df = data.get(interval)
|
||
if df is None or df.empty:
|
||
continue
|
||
|
||
latest = df.iloc[-1]
|
||
parts.append(f"\n## {interval.upper()} 周期指标")
|
||
|
||
# 价格与均线关系
|
||
close = latest.get('close', 0)
|
||
ma20 = latest.get('ma20', 0)
|
||
ma50 = latest.get('ma50', 0)
|
||
if pd.notna(ma20):
|
||
position = "上方" if close > ma20 else "下方"
|
||
parts.append(f"- 价格在 MA20 {position} (MA20={ma20:.2f})")
|
||
if pd.notna(ma50):
|
||
parts.append(f"- MA50: {ma50:.2f}")
|
||
|
||
# RSI
|
||
rsi = latest.get('rsi', 0)
|
||
if pd.notna(rsi):
|
||
rsi_status = "超卖" if rsi < 30 else ("超买" if rsi > 70 else "中性")
|
||
parts.append(f"- RSI: {rsi:.1f} ({rsi_status})")
|
||
|
||
# MACD
|
||
macd = latest.get('macd', 0)
|
||
macd_signal = latest.get('macd_signal', 0)
|
||
if pd.notna(macd) and pd.notna(macd_signal):
|
||
macd_status = "多头" if macd > macd_signal else "空头"
|
||
parts.append(f"- MACD: {macd:.2f}, Signal: {macd_signal:.2f} ({macd_status})")
|
||
|
||
# 布林带
|
||
bb_upper = latest.get('bb_upper', 0)
|
||
bb_lower = latest.get('bb_lower', 0)
|
||
if pd.notna(bb_upper) and pd.notna(bb_lower):
|
||
parts.append(f"- 布林带: 上轨={bb_upper:.2f}, 下轨={bb_lower:.2f}")
|
||
|
||
# 添加最近 5 根 15M K 线（让 LLM 看形态）
|
||
parts.append("\n## 最近 5 根 15M K线")
|
||
parts.append("| 时间 | 开盘 | 最高 | 最低 | 收盘 | 涨跌 |")
|
||
parts.append("|------|------|------|------|------|------|")
|
||
|
||
df_15m = data.get('15m')
|
||
if df_15m is not None and len(df_15m) >= 5:
|
||
for i in range(-5, 0):
|
||
row = df_15m.iloc[i]
|
||
change = ((row['close'] - row['open']) / row['open']) * 100
|
||
change_str = f"+{change:.2f}%" if change >= 0 else f"{change:.2f}%"
|
||
time_str = row['open_time'].strftime('%H:%M') if pd.notna(row['open_time']) else 'N/A'
|
||
parts.append(f"| {time_str} | {row['open']:.2f} | {row['high']:.2f} | {row['low']:.2f} | {row['close']:.2f} | {change_str} |")
|
||
|
||
# 计算关键价位
|
||
parts.append("\n## 关键价位参考")
|
||
df_1h = data.get('1h')
|
||
if df_1h is not None and len(df_1h) >= 20:
|
||
recent_high = df_1h['high'].tail(20).max()
|
||
recent_low = df_1h['low'].tail(20).min()
|
||
parts.append(f"- 近期高点: ${recent_high:,.2f}")
|
||
parts.append(f"- 近期低点: ${recent_low:,.2f}")
|
||
|
||
# 初步信号分析结果
|
||
parts.append(f"\n## 规则引擎初步判断")
|
||
parts.append(f"- 趋势: {signal.get('trend', 'unknown')}")
|
||
parts.append(f"- 信号: {signal.get('action', 'hold')}")
|
||
parts.append(f"- 置信度: {signal.get('confidence', 0)}%")
|
||
parts.append(f"- 触发原因: {', '.join(signal.get('reasons', []))}")
|
||
|
||
parts.append("\n---")
|
||
parts.append("请基于以上数据进行分析，严格按照 JSON 格式输出你的判断。")
|
||
|
||
return "\n".join(parts)
|
||
|
||
def calculate_stop_loss_take_profit(self, price: float, action: str,
|
||
atr: float) -> Dict[str, float]:
|
||
"""
|
||
计算止损止盈位置
|
||
|
||
Args:
|
||
price: 当前价格
|
||
action: 'buy' 或 'sell'
|
||
atr: ATR 值
|
||
|
||
Returns:
|
||
{'stop_loss': float, 'take_profit': float}
|
||
"""
|
||
if action == 'buy':
|
||
stop_loss = price - atr * 2
|
||
take_profit = price + atr * 3
|
||
elif action == 'sell':
|
||
stop_loss = price + atr * 2
|
||
take_profit = price - atr * 3
|
||
else:
|
||
stop_loss = 0
|
||
take_profit = 0
|
||
|
||
return {
|
||
'stop_loss': round(stop_loss, 2),
|
||
'take_profit': round(take_profit, 2)
|
||
}
|