优化策略
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aaron
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@ -13,6 +13,7 @@
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"""
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"""
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import json
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import json
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import re
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import re
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import numpy as np
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import pandas as pd
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import pandas as pd
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from typing import Dict, Any, Optional, List
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from datetime import datetime
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@ -128,6 +129,100 @@ class MarketSignalAnalyzer:
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- **级别背离**：小级别反转 + 大级别强劲 → 谨慎，可能是假突破
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- **级别背离**：小级别反转 + 大级别强劲 → 谨慎，可能是假突破
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- **强反转信号**：多个小级别同时反转 + 大级别走弱 → **重点考虑反手**
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- **强反转信号**：多个小级别同时反转 + 大级别走弱 → **重点考虑反手**
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## 🚨 趋势反转信号处理（新增 - 极其重要！）
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### 系统会自动检测以下反转信号：
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1. **RSI 背离**：价格创新高/低但 RSI 不创新高/低（权重2）
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2. **MACD 柱状图缩短**：动能衰竭信号（权重1）
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3. **MACD 金叉/死叉**：趋势反转信号（权重1）
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4. **量价背离**：价格上涨但成交量下降（权重1）
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5. **关键K线形态**：吞没、锤子线、十字星（权重1-2）
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6. **多周期趋势不一致**：小周期反转但大周期未反应（权重1）
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### 反转信号出现时的处理规则（必须遵守！）
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**🔴 当检测到反转信号（置信度 ≥ 60%）时：**
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1. **立即停止原方向新开仓**
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- 如果之前做多，现在检测到看跌反转 → **严禁开新多单**
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- 如果之前做空，现在检测到看涨反转 → **严禁开新空单**
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2. **评估现有持仓**
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- 如果有同向持仓 → **建议立即平仓或收紧止损**
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- 不要等待反弹/回调，反转可能很快发生
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3. **考虑反手操作（仅当反转置信度 ≥ 70%时）**
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- 可以考虑平掉旧仓位，同时开新方向仓位
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- 或者先平仓观望，等待反转确认后再入场
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4. **观望等待确认**
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- 如果不确定，先平仓观望
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- 等待价格明确突破关键位再入场
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**⚠️ 常见错误（必须避免）：**
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- ❌ 检测到反转信号后继续原方向加仓（"摊平成本"）
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- ❌ 认为是"假突破"而忽略反转信号
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- ❌ 等待反弹/回调到更好价格（可能等不到）
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## 🌅 趋势阶段判断（新增 - 避免趋势晚期被套）
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### 系统会自动判断趋势处于哪个阶段：
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- **早期**：刚突破关键位，均线刚开始排列，动能开始释放
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- **中期**：均线排列稳定，价格沿趋势移动，量能健康
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- **晚期**：价格过度延伸，RSI极端区，量价背离，多次假突破
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### 不同阶段的交易规则：
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**✅ 早期阶段（可积极入场）：**
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- 可以顺势轻仓入场
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- 设置止损后可持有更长时间
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- 目标可看更大空间（3-5%）
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**✅ 中期阶段（稳健持仓）：**
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- 等待回调/反弹入场
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- 顺势持仓，让利润奔跑
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- 不要被小波动洗出
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**🔴 晚期阶段（强制谨慎）：**
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- **严禁追涨/追空开新仓**
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- **现有盈利持仓建议逐步止盈**
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- **等待明确反转信号后再决策**
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- 宁可错过最后一段利润，也不要被套在高位/低位
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### 晚期阶段的识别信号（系统自动检测）：
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1. EMA 间距过大（> 3%）
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2. RSI 进入极端区（> 70 或 < 30）
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3. 价格偏离 EMA20 > 5%
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4. 量价背离（价格上涨但成交量下降）
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5. 连续 4 根以上同向K线
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6. ATR 收缩（动能衰竭）
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## 📊 震荡区间交易规则（新增 - 基于明确区间交易）
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### 系统会自动计算震荡区间：
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- **支撑位**：价格通道下沿 + 成交量密集区 + 布林带下轨
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- **压力位**：价格通道上沿 + 成交量密集区 + 布林带上轨
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- **区间宽度**：用于判断震荡有效性（< 5% 为有效震荡）
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### 震荡市交易规则（当系统判断为震荡市时）：
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**✅ 推荐操作：**
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1. **下沿挂多单**：价格接近支撑位时，挂限价多单
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2. **上沿挂空单**：价格接近压力位时，挂限价空单
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3. **目标明确**：盈利目标就是对岸边界
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4. **快进快出**：持仓时间 30分钟-2小时
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**🔴 严禁操作：**
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1. **追涨杀跌**：价格突破边界后不要追，通常会回落
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2. **期待大行情**：震荡市无大趋势，不要贪婪
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3. **持仓过久**：区间内快速进出，不要拿着不动
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4. **逆势加仓**：亏损后不要加仓"摊平成本"
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### 震荡区间突破的处理：
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- 放量突破 + 多周期确认 → 可能转为趋势市，切换策略
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- 无量突破 + 快速回落 → 假突破，继续震荡策略
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- 区间收口 + 波动率下降 → 即将变盘，观望等待
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## 🚨 铁律（违反即失败）
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## 🚨 铁律（违反即失败）
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1. **盈亏比第一**：所有交易必须满足盈亏比 ≥ 1:1.2
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1. **盈亏比第一**：所有交易必须满足盈亏比 ≥ 1:1.2
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- 盈亏比 = (目标盈利 - 入场价) / (入场价 - 止损价)
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- 盈亏比 = (目标盈利 - 入场价) / (入场价 - 止损价)
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@ -953,6 +1048,64 @@ class MarketSignalAnalyzer:
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context_parts.append(f"\n## 趋势位置分析")
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context_parts.append(f"\n## 趋势位置分析")
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context_parts.append(trend_position_analysis)
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context_parts.append(trend_position_analysis)
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# ========== 新增：震荡区间检测 ==========
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range_zone = self._detect_range_zone(data)
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if range_zone['is_ranging']:
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context_parts.append(f"\n## 🔔 震荡区间检测（重要！）")
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context_parts.append(f"**状态**: 震荡市（置信度: {range_zone['confidence']}%）")
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if range_zone['support_level'] and range_zone['resistance_level']:
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context_parts.append(f"**支撑位**: ${range_zone['support_level']:,.2f}")
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context_parts.append(f"**压力位**: ${range_zone['resistance_level']:,.2f}")
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context_parts.append(f"**区间宽度**: {range_zone['range_width_pct']:.2f}%")
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if range_zone['volume_profile_support']:
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context_parts.append(f"**成交量密集区支撑**: ${range_zone['volume_profile_support']:,.2f}")
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if range_zone['volume_profile_resistance']:
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context_parts.append(f"**成交量密集区压力**: ${range_zone['volume_profile_resistance']:,.2f}")
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context_parts.append(f"**分析**: {range_zone['analysis']}")
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context_parts.append(f"\n**震荡市交易策略**:")
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context_parts.append(f" → 下沿附近挂多单，上沿附近挂空单")
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context_parts.append(f" → 目标: 对岸边界，快进快出")
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context_parts.append(f" → 严禁追涨杀跌！")
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# ========== 新增：趋势反转检测 ==========
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reversal_detection = self._detect_trend_reversal(data)
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if reversal_detection['is_reversing']:
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context_parts.append(f"\n## ⚠️ 趋势反转信号（非常重要！）")
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context_parts.append(f"**检测到反转信号**！置信度: {reversal_detection['confidence']}%")
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if reversal_detection['reversal_type'] == 'bullish_reversal':
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context_parts.append(f"**反转类型**: 看涨反转 📈")
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else:
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context_parts.append(f"**反转类型**: 看跌反转 📉")
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context_parts.append(f"\n**反转信号详情**:")
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for sig in reversal_detection['signals'][:5]: # 最多显示5个信号
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context_parts.append(f" - [{sig['type']}] {sig['desc']} (权重: {sig['weight']})")
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context_parts.append(f"\n**🚨 反转信号处理规则**:")
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context_parts.append(f" → 现有同向持仓建议平仓")
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context_parts.append(f" → 考虑反方向开仓（需等待确认）")
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context_parts.append(f" → 或者暂时观望，等待反转确认")
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context_parts.append(f" → 严禁继续原方向开新仓！")
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# ========== 新增：趋势阶段检测 ==========
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trend_stage = self._detect_trend_stage(data)
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if trend_stage['stage'] != 'unknown':
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stage_emoji = {'early': '🌱', 'middle': '🔄', 'late': '🌅'}.get(trend_stage['stage'], '❓')
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stage_name = {'early': '早期', 'middle': '中期', 'late': '晚期'}.get(trend_stage['stage'], '未知')
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context_parts.append(f"\n## 趋势阶段分析")
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context_parts.append(f"**当前阶段**: {stage_emoji} {stage_name}（置信度: {trend_stage['confidence']}%）")
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context_parts.append(f"**分析**: {trend_stage['analysis']}")
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if trend_stage['stage'] == 'late':
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context_parts.append(f"\n**⚠️ 晚期阶段警告**:")
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context_parts.append(f" → 趋势可能即将反转或进入震荡")
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context_parts.append(f" → 严禁追涨/追空开新仓")
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context_parts.append(f" → 现有盈利持仓建议逐步止盈")
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context_parts.append(f" → 等待明确反转信号后再决策")
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elif trend_stage['stage'] == 'early':
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context_parts.append(f"\n**✅ 早期阶段机会**:")
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context_parts.append(f" → 趋势刚启动，可顺势轻仓入场")
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context_parts.append(f" → 设置止损后可持有更长时间")
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context_parts.append(f" → 目标可看更大空间")
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return "\n".join(context_parts)
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return "\n".join(context_parts)
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async def _get_news_context(self, symbol: str) -> str:
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async def _get_news_context(self, symbol: str) -> str:
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@ -1740,3 +1893,682 @@ class MarketSignalAnalyzer:
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lines.append(f"**布林带开口**: 宽度 {bb_width:.1f}%，趋势延续")
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lines.append(f"**布林带开口**: 宽度 {bb_width:.1f}%，趋势延续")
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return "\n".join(lines)
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return "\n".join(lines)
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def _detect_range_zone(self, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
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"""
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检测震荡区间 - 计算明确的支撑位和压力位
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使用多种方法综合判断：
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1. 价格通道（最近N根K线的最高/最低价）
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2. 成交量密集区（Volume Profile）
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3. 布林带
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4. EMA支撑/压力
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"""
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result = {
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'is_ranging': False,
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'support_level': None,
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'resistance_level': None,
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'range_width_pct': None,
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'confidence': 0,
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'volume_profile_support': None,
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'volume_profile_resistance': None,
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'analysis': ''
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}
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try:
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df_30m = data.get('30m')
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df_1h = data.get('1h')
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df_15m = data.get('15m')
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if df_30m is None or len(df_30m) < 48: # 需要至少48根K线（24小时）
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return result
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current_price = float(df_30m['close'].iloc[-1])
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# ========== 1. 价格通道分析 ==========
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# 使用最近24-48根K线（12-24小时）计算价格通道
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lookback_periods = [24, 36, 48]
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price_channels = []
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for period in lookback_periods:
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if len(df_30m) >= period:
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period_data = df_30m.iloc[-period:]
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high = period_data['high'].max()
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low = period_data['low'].min()
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price_channels.append({'high': high, 'low': low, 'width': high - low})
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# 选择波动最稳定的通道（宽度变化最小的）
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if price_channels:
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avg_width = sum(pc['width'] for pc in price_channels) / len(price_channels)
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selected_channel = min(price_channels,
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key=lambda pc: abs(pc['width'] - avg_width))
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support = selected_channel['low']
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resistance = selected_channel['high']
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range_width = resistance - support
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range_width_pct = (range_width / current_price) * 100
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# 震荡区间判断标准
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# 1. 区间宽度 < 5%（震荡市）
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# 2. 价格在区间中位数附近
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# 3. EMA 纠缠
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is_narrow_range = range_width_pct < 5.0
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price_in_middle = (current_price - support) / range_width > 0.3 and \
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(current_price - support) / range_width < 0.7
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# EMA 纠缠检查
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ema5 = df_30m['ma5'].iloc[-1] if 'ma5' in df_30m.columns else None
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ema10 = df_30m['ma10'].iloc[-1] if 'ma10' in df_30m.columns else None
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ema20 = df_30m['ma20'].iloc[-1] if 'ma20' in df_30m.columns else None
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ema_entangled = False
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if all([ema5, ema10, ema20]):
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ema_spread = (max(ema5, ema10, ema20) - min(ema5, ema10, ema20)) / current_price * 100
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ema_entangled = ema_spread < 1.0 # EMA 排列差距 < 1%
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# ========== 2. 成交量密集区分析 ==========
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volume_profile_support = None
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volume_profile_resistance = None
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|
|
||||||
|
if len(df_30m) >= 48:
|
||||||
|
# 找出成交量最大的价格区间
|
||||||
|
df_30m_copy = df_30m.iloc[-48:].copy()
|
||||||
|
df_30m_copy['avg_price'] = (df_30m_copy['high'] + df_30m_copy['low'] + df_30m_copy['close']) / 3
|
||||||
|
df_30m_copy['volume_weight'] = df_30m_copy['volume'] * df_30m_copy['avg_price']
|
||||||
|
|
||||||
|
# 按价格分层，找出高成交量区域
|
||||||
|
price_bins = pd.cut(df_30m_copy['avg_price'], bins=10)
|
||||||
|
volume_by_price = df_30m_copy.groupby(price_bins, observed=True)['volume'].sum()
|
||||||
|
|
||||||
|
if len(volume_by_price) > 0:
|
||||||
|
# 高成交量区作为支撑/压力
|
||||||
|
max_vol_bin = volume_by_price.idxmax()
|
||||||
|
if max_vol_bin is not None:
|
||||||
|
vp_level = (max_vol_bin.left + max_vol_bin.right) / 2
|
||||||
|
if vp_level < current_price * 0.98:
|
||||||
|
volume_profile_support = float(vp_level)
|
||||||
|
elif vp_level > current_price * 1.02:
|
||||||
|
volume_profile_resistance = float(vp_level)
|
||||||
|
|
||||||
|
# ========== 3. 布林带支撑/压力 ==========
|
||||||
|
bb_support = None
|
||||||
|
bb_resistance = None
|
||||||
|
if 'bb_lower' in df_30m.columns and 'bb_upper' in df_30m.columns:
|
||||||
|
bb_support = float(df_30m['bb_lower'].iloc[-1])
|
||||||
|
bb_resistance = float(df_30m['bb_upper'].iloc[-1])
|
||||||
|
|
||||||
|
# ========== 4. 关键价格点综合 ==========
|
||||||
|
# 综合多个指标得出最可靠的支撑/压力位
|
||||||
|
support_candidates = []
|
||||||
|
resistance_candidates = []
|
||||||
|
|
||||||
|
if support:
|
||||||
|
support_candidates.append(support)
|
||||||
|
if volume_profile_support:
|
||||||
|
support_candidates.append(volume_profile_support)
|
||||||
|
if bb_support:
|
||||||
|
support_candidates.append(bb_support)
|
||||||
|
|
||||||
|
if resistance:
|
||||||
|
resistance_candidates.append(resistance)
|
||||||
|
if volume_profile_resistance:
|
||||||
|
resistance_candidates.append(volume_profile_resistance)
|
||||||
|
if bb_resistance:
|
||||||
|
resistance_candidates.append(bb_resistance)
|
||||||
|
|
||||||
|
# 取中位数作为最终的支撑/压力位
|
||||||
|
final_support = np.median(support_candidates) if support_candidates else None
|
||||||
|
final_resistance = np.median(resistance_candidates) if resistance_candidates else None
|
||||||
|
|
||||||
|
# ========== 5. 计算置信度 ==========
|
||||||
|
confidence = 0
|
||||||
|
reasons = []
|
||||||
|
|
||||||
|
if is_narrow_range:
|
||||||
|
confidence += 30
|
||||||
|
reasons.append(f"区间窄({range_width_pct:.1f}%)")
|
||||||
|
|
||||||
|
if price_in_middle:
|
||||||
|
confidence += 20
|
||||||
|
reasons.append("价格在中部")
|
||||||
|
|
||||||
|
if ema_entangled:
|
||||||
|
confidence += 25
|
||||||
|
reasons.append("EMA纠缠")
|
||||||
|
|
||||||
|
# 成交量分布检查 - 如果成交量在区间两端较小，说明是有效震荡
|
||||||
|
if len(df_30m) >= 24:
|
||||||
|
recent_vol = df_30m['volume'].iloc[-12:].mean()
|
||||||
|
older_vol = df_30m['volume'].iloc[-24:-12].mean()
|
||||||
|
if abs(recent_vol - older_vol) / older_vol < 0.3:
|
||||||
|
confidence += 15
|
||||||
|
reasons.append("成交量平稳")
|
||||||
|
|
||||||
|
# 价格反弹次数 - 检查在支撑/压力位附近是否有多次反弹
|
||||||
|
if final_support and final_resistance:
|
||||||
|
bounce_count = 0
|
||||||
|
for i in range(-24, 0):
|
||||||
|
if i >= -len(df_30m):
|
||||||
|
row = df_30m.iloc[i]
|
||||||
|
# 检查是否在支撑位附近反弹
|
||||||
|
if abs(row['low'] - final_support) / final_support < 0.005 and row['close'] > row['open']:
|
||||||
|
bounce_count += 1
|
||||||
|
# 检查是否在压力位附近回落
|
||||||
|
if abs(row['high'] - final_resistance) / final_resistance < 0.005 and row['close'] < row['open']:
|
||||||
|
bounce_count += 1
|
||||||
|
|
||||||
|
if bounce_count >= 2:
|
||||||
|
confidence += 10
|
||||||
|
reasons.append(f"边界反弹{bounce_count}次")
|
||||||
|
|
||||||
|
result.update({
|
||||||
|
'is_ranging': confidence >= 60,
|
||||||
|
'support_level': float(final_support) if final_support else None,
|
||||||
|
'resistance_level': float(final_resistance) if final_resistance else None,
|
||||||
|
'range_width_pct': range_width_pct,
|
||||||
|
'confidence': confidence,
|
||||||
|
'volume_profile_support': volume_profile_support,
|
||||||
|
'volume_profile_resistance': volume_profile_resistance,
|
||||||
|
'analysis': f"震荡判断: {confidence}% ({', '.join(reasons) if reasons else '无'})"
|
||||||
|
})
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"震荡区间检测失败: {e}")
|
||||||
|
import traceback
|
||||||
|
logger.debug(traceback.format_exc())
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def _detect_trend_reversal(self, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
检测趋势反转信号
|
||||||
|
|
||||||
|
综合多个指标判断趋势是否可能反转：
|
||||||
|
1. RSI 背离（价格创新高但RSI不创新高 / 价格创新低但RSI不创新低）
|
||||||
|
2. MACD 柱状图缩短/背离
|
||||||
|
3. 量价背离（价格上涨但成交量下降）
|
||||||
|
4. 关键K线形态（吞没、锤子线、十字星等）
|
||||||
|
5. 多周期趋势不一致
|
||||||
|
"""
|
||||||
|
result = {
|
||||||
|
'is_reversing': False,
|
||||||
|
'reversal_type': None, # 'bullish_reversal' or 'bearish_reversal'
|
||||||
|
'confidence': 0,
|
||||||
|
'signals': [],
|
||||||
|
'analysis': ''
|
||||||
|
}
|
||||||
|
|
||||||
|
try:
|
||||||
|
df_15m = data.get('15m')
|
||||||
|
df_30m = data.get('30m')
|
||||||
|
df_1h = data.get('1h')
|
||||||
|
|
||||||
|
if df_15m is None or len(df_15m) < 30:
|
||||||
|
return result
|
||||||
|
|
||||||
|
reversal_signals = []
|
||||||
|
bullish_signals = 0
|
||||||
|
bearish_signals = 0
|
||||||
|
|
||||||
|
# ========== 1. RSI 背离检测 ==========
|
||||||
|
if 'rsi' in df_15m.columns and len(df_15m) >= 20:
|
||||||
|
recent_5 = df_15m.iloc[-5:]
|
||||||
|
prev_5 = df_15m.iloc[-15:-10]
|
||||||
|
|
||||||
|
# 顶背离（看跌反转）
|
||||||
|
recent_high = recent_5['high'].max()
|
||||||
|
recent_rsi_at_high = recent_5.loc[recent_5['high'] == recent_high, 'rsi'].values[0]
|
||||||
|
prev_high = prev_5['high'].max()
|
||||||
|
prev_rsi_at_high = prev_5.loc[prev_5['high'] == prev_high, 'rsi'].values[0]
|
||||||
|
|
||||||
|
if recent_high > prev_high and recent_rsi_at_high < prev_rsi_at_high:
|
||||||
|
bearish_signals += 2
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'rsi_divergence',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 2,
|
||||||
|
'desc': 'RSI顶背离：价格创新高但RSI不创新高'
|
||||||
|
})
|
||||||
|
|
||||||
|
# 底背离（看涨反转）
|
||||||
|
recent_low = recent_5['low'].min()
|
||||||
|
recent_rsi_at_low = recent_5.loc[recent_5['low'] == recent_low, 'rsi'].values[0]
|
||||||
|
prev_low = prev_5['low'].min()
|
||||||
|
prev_rsi_at_low = prev_5.loc[prev_5['low'] == prev_low, 'rsi'].values[0]
|
||||||
|
|
||||||
|
if recent_low < prev_low and recent_rsi_at_low > prev_rsi_at_low:
|
||||||
|
bullish_signals += 2
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'rsi_divergence',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 2,
|
||||||
|
'desc': 'RSI底背离：价格创新低但RSI不创新低'
|
||||||
|
})
|
||||||
|
|
||||||
|
# ========== 2. MACD 柱状图分析 ==========
|
||||||
|
if 'macd_hist' in df_15m.columns and len(df_15m) >= 10:
|
||||||
|
hist_recent = df_15m['macd_hist'].iloc[-3:].values
|
||||||
|
hist_prev = df_15m['macd_hist'].iloc[-6:-3].values
|
||||||
|
|
||||||
|
# MACD 柱状图缩短 = 动能衰竭
|
||||||
|
if all(h > 0 for h in hist_prev): # 之前是正向
|
||||||
|
if hist_recent[2] < hist_recent[1] < hist_recent[0]: # 持续缩短
|
||||||
|
bearish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'macd_histogram',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': 'MACD柱状图持续缩短：上涨动能衰竭'
|
||||||
|
})
|
||||||
|
|
||||||
|
if all(h < 0 for h in hist_prev): # 之前是负向
|
||||||
|
if hist_recent[2] > hist_recent[1] > hist_recent[0]: # 持续收窄
|
||||||
|
bullish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'macd_histogram',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': 'MACD柱状图持续收窄：下跌动能衰竭'
|
||||||
|
})
|
||||||
|
|
||||||
|
# MACD 金叉/死叉
|
||||||
|
if len(df_15m) >= 2:
|
||||||
|
macd_current = df_15m['macd'].iloc[-1]
|
||||||
|
signal_current = df_15m['macd_signal'].iloc[-1]
|
||||||
|
macd_prev = df_15m['macd'].iloc[-2]
|
||||||
|
signal_prev = df_15m['macd_signal'].iloc[-2]
|
||||||
|
|
||||||
|
# 金叉
|
||||||
|
if macd_prev <= signal_prev and macd_current > signal_current:
|
||||||
|
bullish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'macd_cross',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': 'MACD金叉'
|
||||||
|
})
|
||||||
|
|
||||||
|
# 死叉
|
||||||
|
if macd_prev >= signal_prev and macd_current < signal_current:
|
||||||
|
bearish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'macd_cross',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': 'MACD死叉'
|
||||||
|
})
|
||||||
|
|
||||||
|
# ========== 3. 量价背离检测 ==========
|
||||||
|
if 'volume' in df_15m.columns and len(df_15m) >= 10:
|
||||||
|
recent_price_change = (df_15m['close'].iloc[-1] - df_15m['close'].iloc[-5]) / df_15m['close'].iloc[-5]
|
||||||
|
recent_volume = df_15m['volume'].iloc[-5:].mean()
|
||||||
|
older_volume = df_15m['volume'].iloc[-10:-5].mean()
|
||||||
|
|
||||||
|
# 价格上涨但成交量下降（量价背离）
|
||||||
|
if recent_price_change > 0.01 and recent_volume < older_volume * 0.8:
|
||||||
|
bearish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'volume_divergence',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': '量价背离：价格上涨但成交量萎缩'
|
||||||
|
})
|
||||||
|
|
||||||
|
# 价格下跌但成交量下降（可能见底）
|
||||||
|
if recent_price_change < -0.01 and recent_volume < older_volume * 0.7:
|
||||||
|
bullish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'volume_divergence',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': '下跌缩量：抛压枯竭，可能见底'
|
||||||
|
})
|
||||||
|
|
||||||
|
# ========== 4. 关键K线形态检测 ==========
|
||||||
|
if len(df_15m) >= 3:
|
||||||
|
latest = df_15m.iloc[-1]
|
||||||
|
prev = df_15m.iloc[-2]
|
||||||
|
|
||||||
|
# 吞没形态
|
||||||
|
open_latest, close_latest = latest['open'], latest['close']
|
||||||
|
open_prev, close_prev = prev['open'], prev['close']
|
||||||
|
|
||||||
|
# 阳包阴（看涨）
|
||||||
|
if (close_latest > open_latest and # 当前是阳线
|
||||||
|
close_prev < open_prev and # 前一个是阴线
|
||||||
|
open_latest <= close_prev and # 开盘价低于前一个收盘价
|
||||||
|
close_latest >= open_prev): # 收盘价高于前一个开盘价
|
||||||
|
bullish_signals += 2
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'candlestick',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 2,
|
||||||
|
'desc': '阳包阴吞没形态（强反转信号）'
|
||||||
|
})
|
||||||
|
|
||||||
|
# 阴包阳（看跌）
|
||||||
|
if (close_latest < open_latest and # 当前是阴线
|
||||||
|
close_prev > open_prev and # 前一个是阳线
|
||||||
|
open_latest >= close_prev and # 开盘价高于前一个收盘价
|
||||||
|
close_latest <= open_prev): # 收盘价低于前一个开盘价
|
||||||
|
bearish_signals += 2
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'candlestick',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 2,
|
||||||
|
'desc': '阴包阳吞没形态（强反转信号）'
|
||||||
|
})
|
||||||
|
|
||||||
|
# 锤子线/倒锤子
|
||||||
|
body_size = abs(close_latest - open_latest)
|
||||||
|
upper_shadow = df_15m['high'].iloc[-1] - max(open_latest, close_latest)
|
||||||
|
lower_shadow = min(open_latest, close_latest) - df_15m['low'].iloc[-1]
|
||||||
|
|
||||||
|
# 锤子线（看涨）
|
||||||
|
if lower_shadow >= body_size * 2 and upper_shadow < body_size * 0.5:
|
||||||
|
bullish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'candlestick',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': '锤子线（底部反转信号）'
|
||||||
|
})
|
||||||
|
|
||||||
|
# 倒锤子（看跌）
|
||||||
|
if upper_shadow >= body_size * 2 and lower_shadow < body_size * 0.5:
|
||||||
|
bearish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'candlestick',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': '倒锤子线（顶部反转信号）'
|
||||||
|
})
|
||||||
|
|
||||||
|
# ========== 5. 多周期趋势不一致 ==========
|
||||||
|
trend_15m = self._get_trend_direction(df_15m)
|
||||||
|
trend_30m = self._get_trend_direction(df_30m)
|
||||||
|
trend_1h = self._get_trend_direction(df_1h)
|
||||||
|
|
||||||
|
# 小周期反转但大周期未反应
|
||||||
|
if trend_15m and trend_1h and trend_15m != trend_1h:
|
||||||
|
if trend_15m == 'bull' and trend_1h == 'bear':
|
||||||
|
bullish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'timeframe_divergence',
|
||||||
|
'direction': 'bullish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': '15分钟转多但1小时仍看空（潜在反转）'
|
||||||
|
})
|
||||||
|
elif trend_15m == 'bear' and trend_1h == 'bull':
|
||||||
|
bearish_signals += 1
|
||||||
|
reversal_signals.append({
|
||||||
|
'type': 'timeframe_divergence',
|
||||||
|
'direction': 'bearish',
|
||||||
|
'weight': 1,
|
||||||
|
'desc': '15分钟转空但1小时仍看多（潜在反转）'
|
||||||
|
})
|
||||||
|
|
||||||
|
# ========== 计算反转信号强度 ==========
|
||||||
|
total_signals = len(reversal_signals)
|
||||||
|
if total_signals >= 3:
|
||||||
|
# 至少3个反转信号才认为可能反转
|
||||||
|
if bullish_signals >= bearish_signals + 2:
|
||||||
|
result['is_reversing'] = True
|
||||||
|
result['reversal_type'] = 'bullish_reversal'
|
||||||
|
result['confidence'] = min(90, bullish_signals * 15)
|
||||||
|
result['signals'] = [s for s in reversal_signals if s['direction'] == 'bullish']
|
||||||
|
elif bearish_signals >= bullish_signals + 2:
|
||||||
|
result['is_reversing'] = True
|
||||||
|
result['reversal_type'] = 'bearish_reversal'
|
||||||
|
result['confidence'] = min(90, bearish_signals * 15)
|
||||||
|
result['signals'] = [s for s in reversal_signals if s['direction'] == 'bearish']
|
||||||
|
|
||||||
|
if reversal_signals:
|
||||||
|
result['analysis'] = f"检测到 {len(reversal_signals)} 个反转信号"
|
||||||
|
else:
|
||||||
|
result['analysis'] = "无反转信号"
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"趋势反转检测失败: {e}")
|
||||||
|
import traceback
|
||||||
|
logger.debug(traceback.format_exc())
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def _get_trend_direction(self, df: pd.DataFrame) -> str:
|
||||||
|
"""获取趋势方向：bull/bear/neutral"""
|
||||||
|
if df is None or len(df) < 10:
|
||||||
|
return 'neutral'
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 使用EMA判断
|
||||||
|
ma5 = df['ma5'].iloc[-1] if 'ma5' in df.columns else None
|
||||||
|
ma10 = df['ma10'].iloc[-1] if 'ma10' in df.columns else None
|
||||||
|
ma20 = df['ma20'].iloc[-1] if 'ma20' in df.columns else None
|
||||||
|
|
||||||
|
if ma5 and ma10 and ma20:
|
||||||
|
if ma5 > ma10 > ma20:
|
||||||
|
return 'bull'
|
||||||
|
elif ma5 < ma10 < ma20:
|
||||||
|
return 'bear'
|
||||||
|
|
||||||
|
# 使用MACD判断
|
||||||
|
if 'macd' in df.columns and 'macd_signal' in df.columns:
|
||||||
|
macd = df['macd'].iloc[-1]
|
||||||
|
signal = df['macd_signal'].iloc[-1]
|
||||||
|
|
||||||
|
if macd > signal and macd > 0:
|
||||||
|
return 'bull'
|
||||||
|
elif macd < signal and macd < 0:
|
||||||
|
return 'bear'
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.debug(f"趋势方向判断失败: {e}")
|
||||||
|
|
||||||
|
return 'neutral'
|
||||||
|
|
||||||
|
def _detect_trend_stage(self, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
|
||||||
|
"""
|
||||||
|
检测趋势阶段：早期/中期/晚期
|
||||||
|
|
||||||
|
判断标准：
|
||||||
|
1. 早期：刚突破关键位，均线刚开始排列，动能开始释放
|
||||||
|
2. 中期：均线排列稳定，价格沿趋势移动，量能健康
|
||||||
|
3. 晚期：价格过度延伸，RSI极端区，量价背离，多次假突破
|
||||||
|
"""
|
||||||
|
result = {
|
||||||
|
'stage': 'unknown', # 'early', 'middle', 'late'
|
||||||
|
'confidence': 0,
|
||||||
|
'signals': [],
|
||||||
|
'analysis': ''
|
||||||
|
}
|
||||||
|
|
||||||
|
try:
|
||||||
|
df_30m = data.get('30m')
|
||||||
|
df_1h = data.get('1h')
|
||||||
|
|
||||||
|
if df_30m is None or len(df_30m) < 30:
|
||||||
|
return result
|
||||||
|
|
||||||
|
current_price = float(df_30m['close'].iloc[-1])
|
||||||
|
|
||||||
|
stage_signals = []
|
||||||
|
early_score = 0
|
||||||
|
middle_score = 0
|
||||||
|
late_score = 0
|
||||||
|
|
||||||
|
# ========== 1. EMA 排列状态 ==========
|
||||||
|
ema5 = df_30m['ma5'].iloc[-1] if 'ma5' in df_30m.columns else None
|
||||||
|
ema10 = df_30m['ma10'].iloc[-1] if 'ma10' in df_30m.columns else None
|
||||||
|
ema20 = df_30m['ma20'].iloc[-1] if 'ma20' in df_30m.columns else None
|
||||||
|
ema50 = df_30m['ma50'].iloc[-1] if 'ma50' in df_30m.columns else None
|
||||||
|
|
||||||
|
if all([ema5, ema10, ema20, ema50]):
|
||||||
|
# 检查EMA排列是否形成
|
||||||
|
if ema5 > ema10 > ema20 > ema50:
|
||||||
|
# 多头排列
|
||||||
|
# 检查排列刚刚形成（早期）还是已经稳定（中期/晚期）
|
||||||
|
ema5_cross_ma20 = False
|
||||||
|
if len(df_30m) >= 10:
|
||||||
|
# 检查最近10根内是否发生过金叉
|
||||||
|
for i in range(-10, 0):
|
||||||
|
if df_30m['ma5'].iloc[i] > df_30m['ma20'].iloc[i]:
|
||||||
|
if i > -10 and df_30m['ma5'].iloc[i-1] <= df_30m['ma20'].iloc[i-1]:
|
||||||
|
ema5_cross_ma20 = True
|
||||||
|
break
|
||||||
|
|
||||||
|
if ema5_cross_ma20:
|
||||||
|
early_score += 30
|
||||||
|
stage_signals.append("EMA排列刚形成（早期）")
|
||||||
|
else:
|
||||||
|
# 检查EMA间距
|
||||||
|
ema_spread = (ema5 - ema20) / ema20 * 100
|
||||||
|
if ema_spread > 3:
|
||||||
|
late_score += 20
|
||||||
|
stage_signals.append(f"EMA间距过大({ema_spread:.1f}%) - 可能过度延伸")
|
||||||
|
else:
|
||||||
|
middle_score += 20
|
||||||
|
stage_signals.append("EMA排列稳定（中期）")
|
||||||
|
|
||||||
|
elif ema5 < ema10 < ema20 < ema50:
|
||||||
|
# 空头排列
|
||||||
|
ema5_cross_ma20 = False
|
||||||
|
if len(df_30m) >= 10:
|
||||||
|
for i in range(-10, 0):
|
||||||
|
if df_30m['ma5'].iloc[i] < df_30m['ma20'].iloc[i]:
|
||||||
|
if i > -10 and df_30m['ma5'].iloc[i-1] >= df_30m['ma20'].iloc[i-1]:
|
||||||
|
ema5_cross_ma20 = True
|
||||||
|
break
|
||||||
|
|
||||||
|
if ema5_cross_ma20:
|
||||||
|
early_score += 30
|
||||||
|
stage_signals.append("EMA排列刚形成（早期）")
|
||||||
|
else:
|
||||||
|
ema_spread = (ema20 - ema5) / ema20 * 100
|
||||||
|
if ema_spread > 3:
|
||||||
|
late_score += 20
|
||||||
|
stage_signals.append(f"EMA间距过大({ema_spread:.1f}%) - 可能过度延伸")
|
||||||
|
else:
|
||||||
|
middle_score += 20
|
||||||
|
stage_signals.append("EMA排列稳定（中期）")
|
||||||
|
|
||||||
|
# ========== 2. RSI 状态 ==========
|
||||||
|
if 'rsi' in df_30m.columns:
|
||||||
|
rsi_current = df_30m['rsi'].iloc[-1]
|
||||||
|
rsi_prev = df_30m['rsi'].iloc[-5:-1].values
|
||||||
|
|
||||||
|
# RSI极端区 - 晚期信号
|
||||||
|
if rsi_current > 70:
|
||||||
|
late_score += 25
|
||||||
|
stage_signals.append(f"RSI超买({rsi_current:.0f}) - 趋势晚期")
|
||||||
|
elif rsi_current < 30:
|
||||||
|
late_score += 25
|
||||||
|
stage_signals.append(f"RSI超卖({rsi_current:.0f}) - 趋势晚期")
|
||||||
|
elif 50 <= rsi_current <= 65:
|
||||||
|
middle_score += 15
|
||||||
|
stage_signals.append(f"RSI健康({rsi_current:.0f}) - 趋势中期")
|
||||||
|
elif 40 <= rsi_current <= 60:
|
||||||
|
early_score += 10
|
||||||
|
stage_signals.append(f"RSI中性({rsi_current:.0f}) - 可能早期")
|
||||||
|
|
||||||
|
# RSI趋势检查
|
||||||
|
if len(rsi_prev) >= 3:
|
||||||
|
rsi_trend = "up" if rsi_current > rsi_prev[-1] else "down" if rsi_current < rsi_prev[-1] else "flat"
|
||||||
|
if rsi_trend == "flat":
|
||||||
|
late_score += 10
|
||||||
|
stage_signals.append("RSI走平 - 动能衰竭")
|
||||||
|
|
||||||
|
# ========== 3. 价格偏离度 ==========
|
||||||
|
if ema20:
|
||||||
|
deviation = abs(current_price - ema20) / ema20 * 100
|
||||||
|
|
||||||
|
if deviation > 5:
|
||||||
|
late_score += 30
|
||||||
|
stage_signals.append(f"价格偏离EMA20 {deviation:.1f}% - 过度延伸")
|
||||||
|
elif deviation > 3:
|
||||||
|
late_score += 15
|
||||||
|
stage_signals.append(f"价格偏离EMA20 {deviation:.1f}% - 警戒区域")
|
||||||
|
elif deviation < 1:
|
||||||
|
if early_score < middle_score: # 只在不是明显早期时加分
|
||||||
|
middle_score += 10
|
||||||
|
stage_signals.append("价格贴近EMA20 - 趋势稳固")
|
||||||
|
|
||||||
|
# ========== 4. 量价关系 ==========
|
||||||
|
if 'volume' in df_30m.columns and len(df_30m) >= 10:
|
||||||
|
recent_vol = df_30m['volume'].iloc[-5:].mean()
|
||||||
|
older_vol = df_30m['volume'].iloc[-10:-5].mean()
|
||||||
|
vol_change = (recent_vol - older_vol) / older_vol * 100
|
||||||
|
|
||||||
|
price_change_5 = (df_30m['close'].iloc[-1] - df_30m['close'].iloc[-5]) / df_30m['close'].iloc[-5] * 100
|
||||||
|
|
||||||
|
# 价格上涨但成交量下降（量价背离）- 晚期信号
|
||||||
|
if price_change_5 > 1 and vol_change < -20:
|
||||||
|
late_score += 20
|
||||||
|
stage_signals.append(f"量价背离（涨{price_change_5:.1f}%量减{vol_change:.0f}%）- 可能见顶")
|
||||||
|
elif price_change_5 < -1 and vol_change < -20:
|
||||||
|
late_score += 20
|
||||||
|
stage_signals.append(f"量价背离（跌{price_change_5:.1f}%量减{vol_change:.0f}%）- 可能见底")
|
||||||
|
elif price_change_5 > 1 and vol_change > 30:
|
||||||
|
early_score += 15
|
||||||
|
stage_signals.append(f"放量上涨（涨{price_change_5:.1f}%量增{vol_change:.0f}%）- 可能早期")
|
||||||
|
elif price_change_5 < -1 and vol_change > 30:
|
||||||
|
early_score += 15
|
||||||
|
stage_signals.append(f"放量下跌（跌{price_change_5:.1f}%量增{vol_change:.0f}%）- 可能早期")
|
||||||
|
|
||||||
|
# ========== 5. 波动率状态 ==========
|
||||||
|
if 'atr' in df_30m.columns and len(df_30m) >= 20:
|
||||||
|
recent_atr = df_30m['atr'].iloc[-5:].mean()
|
||||||
|
older_atr = df_30m['atr'].iloc[-15:-5].mean()
|
||||||
|
atr_change = (recent_atr - older_atr) / older_atr * 100 if older_atr > 0 else 0
|
||||||
|
|
||||||
|
if atr_change > 30:
|
||||||
|
early_score += 10
|
||||||
|
stage_signals.append(f"ATR扩张({atr_change:.0f}%) - 趋势启动")
|
||||||
|
elif atr_change < -30:
|
||||||
|
late_score += 10
|
||||||
|
stage_signals.append(f"ATR收缩({atr_change:.0f}%) - 动能衰竭")
|
||||||
|
|
||||||
|
# ========== 6. 连续同向K线数量 ==========
|
||||||
|
if len(df_30m) >= 5:
|
||||||
|
recent_closes = df_30m['close'].iloc[-5:].values
|
||||||
|
consecutive_up = sum(1 for i in range(1, len(recent_closes)) if recent_closes[i] > recent_closes[i-1])
|
||||||
|
consecutive_down = sum(1 for i in range(1, len(recent_closes)) if recent_closes[i] < recent_closes[i-1])
|
||||||
|
|
||||||
|
if consecutive_up >= 4:
|
||||||
|
late_score += 15
|
||||||
|
stage_signals.append(f"连续{consecutive_up}根阳线 - 可能过度")
|
||||||
|
elif consecutive_down >= 4:
|
||||||
|
late_score += 15
|
||||||
|
stage_signals.append(f"连续{consecutive_down}根阴线 - 可能过度")
|
||||||
|
|
||||||
|
# ========== 综合判断趋势阶段 ==========
|
||||||
|
scores = {
|
||||||
|
'early': early_score,
|
||||||
|
'middle': middle_score,
|
||||||
|
'late': late_score
|
||||||
|
}
|
||||||
|
max_score = max(scores.values())
|
||||||
|
|
||||||
|
if max_score < 20:
|
||||||
|
result['stage'] = 'unknown'
|
||||||
|
result['analysis'] = "趋势阶段不明确"
|
||||||
|
elif max_score == late_score and late_score >= 40:
|
||||||
|
result['stage'] = 'late'
|
||||||
|
result['confidence'] = min(95, late_score)
|
||||||
|
result['signals'] = stage_signals
|
||||||
|
result['analysis'] = f"⚠️ 趋势晚期（{late_score}分）- " + "; ".join(stage_signals[:3])
|
||||||
|
elif max_score == early_score and early_score >= 30:
|
||||||
|
result['stage'] = 'early'
|
||||||
|
result['confidence'] = min(90, early_score)
|
||||||
|
result['signals'] = stage_signals
|
||||||
|
result['analysis'] = f"趋势早期（{early_score}分）- " + "; ".join(stage_signals[:3])
|
||||||
|
else:
|
||||||
|
result['stage'] = 'middle'
|
||||||
|
result['confidence'] = min(85, middle_score)
|
||||||
|
result['signals'] = stage_signals
|
||||||
|
result['analysis'] = f"趋势中期（{middle_score}分）- " + "; ".join(stage_signals[:3])
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
logger.warning(f"趋势阶段检测失败: {e}")
|
||||||
|
import traceback
|
||||||
|
logger.debug(traceback.format_exc())
|
||||||
|
|
||||||
|
return result
|
||||||
|
|||||||
@ -23,6 +23,65 @@ class TradingDecisionMaker:
|
|||||||
## 🎯 核心理念
|
## 🎯 核心理念
|
||||||
**日内交易：快进快出 + 盈亏比第一 + 严控风险**
|
**日内交易：快进快出 + 盈亏比第一 + 严控风险**
|
||||||
|
|
||||||
|
## 🚨 反转信号处理（最高优先级！）
|
||||||
|
|
||||||
|
### 系统会检测以下反转信号：
|
||||||
|
1. **RSI 背离**：价格创新高/低但 RSI 不创新高/低（权重2）
|
||||||
|
2. **MACD 柱状图缩短**：动能衰竭信号（权重1）
|
||||||
|
3. **MACD 金叉/死叉**：趋势反转信号（权重1）
|
||||||
|
4. **量价背离**：价格上涨但成交量下降（权重1）
|
||||||
|
5. **关键K线形态**：吞没、锤子线、十字星（权重1-2）
|
||||||
|
6. **多周期趋势不一致**：小周期反转但大周期未反应（权重1）
|
||||||
|
|
||||||
|
### 🔴 当检测到反转信号时（必须遵守！）：
|
||||||
|
|
||||||
|
**1. 如果有同方向持仓 → 强制平仓**
|
||||||
|
- 检测到看跌反转 + 有做多持仓 → **CLOSE（立即平仓）**
|
||||||
|
- 检测到看涨反转 + 有做空持仓 → **CLOSE（立即平仓）**
|
||||||
|
- 不要等待反弹/回调，反转可能很快发生
|
||||||
|
|
||||||
|
**2. 严禁继续原方向开新仓**
|
||||||
|
- 检测到反转信号后 → **停止原方向任何新操作**
|
||||||
|
- 之前做多，现在检测到看跌反转 → **严禁开新多单**
|
||||||
|
- 之前做空，现在检测到看涨反转 → **严禁开新空单**
|
||||||
|
|
||||||
|
**3. 可以考虑反手操作（仅当反转置信度 ≥ 70%）**
|
||||||
|
- FLIP_POSITION：平掉旧仓位 + 开立新方向仓位
|
||||||
|
- 或者先平仓观望，等待反转确认后再入场
|
||||||
|
|
||||||
|
**4. 如果不确定 → 先平仓观望**
|
||||||
|
- 宁可错过机会，也不要被套在反向位置
|
||||||
|
|
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## 🌅 趋势阶段处理（避免晚期被套）
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### 系统会判断趋势处于哪个阶段：
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- **早期**：刚突破关键位，均线刚开始排列，动能开始释放
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- **中期**：均线排列稳定，价格沿趋势移动，量能健康
|
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- **晚期**：价格过度延伸，RSI极端区，量价背离
|
||||||
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|
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### 不同阶段的处理规则：
|
||||||
|
|
||||||
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**✅ 早期阶段（可积极入场）：**
|
||||||
|
- 可以顺势轻仓入场
|
||||||
|
- 设置止损后可持有更长时间
|
||||||
|
- 目标可看更大空间（3-5%）
|
||||||
|
|
||||||
|
**✅ 中期阶段（稳健持仓）：**
|
||||||
|
- 等待回调/反弹入场
|
||||||
|
- 顺势持仓，让利润奔跑
|
||||||
|
- 不要被小波动洗出
|
||||||
|
|
||||||
|
**🔴 晚期阶段（强制谨慎！）：**
|
||||||
|
- **严禁追涨/追空开新仓**
|
||||||
|
- **现有盈利持仓建议逐步止盈**
|
||||||
|
- **等待明确反转信号后再决策**
|
||||||
|
- 宁可错过最后一段利润，也不要被套
|
||||||
|
|
||||||
|
### ⚠️ 铁律：趋势晚期 + 检测到反转信号 = 立即平仓
|
||||||
|
- 晚期阶段本身风险就大
|
||||||
|
- 如果再检测到反转信号 → 必须立即平仓
|
||||||
|
- 不要幻想"最后一段利润"
|
||||||
|
|
||||||
### 🚨 盈亏比铁律（违反即拒绝）
|
### 🚨 盈亏比铁律（违反即拒绝）
|
||||||
**所有交易必须满足盈亏比 ≥ 1:1.5，回调入场≥ 1:1.8**
|
**所有交易必须满足盈亏比 ≥ 1:1.5，回调入场≥ 1:1.8**
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user