From a9d24233b0eacaf617b4c63951bef35f08ab372d Mon Sep 17 00:00:00 2001 From: jackyu66git Date: Fri, 23 May 2025 21:19:50 +0800 Subject: [PATCH] add fx strentth --- ChanKLC.py | 379 ++++++++++++++++++- DISPLAY_FIX_SUMMARY.md | 102 +++++ FEATURE_COMPLETE_SUMMARY.md | 139 +++++++ README_FX_STRENGTH.md | 221 +++++++++++ __pycache__/ChanKLC.cpython-312.pyc | Bin 47250 -> 61874 bytes config/ChanLun_SOL.json | 1 - fx_strength_analysis.png | Bin 0 -> 274740 bytes fx_strength_config.py | 153 ++++++++ fx_strength_example.py | 162 ++++++++ strategies/Chan_SOL_2.py | 551 ++++++++++++++-------------- test_fx_strength.py | 214 +++++++++++ test_web_data.py | 107 ++++++ web/app.py | 24 +- web/templates/index.html | 413 ++++++++++++++------- 14 files changed, 2067 insertions(+), 399 deletions(-) create mode 100644 DISPLAY_FIX_SUMMARY.md create mode 100644 FEATURE_COMPLETE_SUMMARY.md create mode 100644 README_FX_STRENGTH.md create mode 100644 fx_strength_analysis.png create mode 100644 fx_strength_config.py create mode 100644 fx_strength_example.py create mode 100644 test_fx_strength.py create mode 100644 test_web_data.py diff --git a/ChanKLC.py b/ChanKLC.py index 4a9d948..0e9d994 100644 --- a/ChanKLC.py +++ b/ChanKLC.py @@ -1130,4 +1130,381 @@ class ChanKLC(): else: features['klc_star_pattern'] = 0 - return features \ No newline at end of file + # ===== 分型强度特征 ===== + # 添加分型强度相关特征 + features['klc_fx_strength'] = self.calculate_fx_strength() + features['klc_fx_strength_level'] = self.get_fx_strength_level() + features['klc_is_strong_fx'] = 1 if self.is_strong_fx() else 0 + + # 分型强度分类特征 + fx_strength = features['klc_fx_strength'] + features['klc_fx_strength_extreme'] = 1 if fx_strength >= 80 else 0 # 极强分型 + features['klc_fx_strength_strong'] = 1 if 60 <= fx_strength < 80 else 0 # 强分型 + features['klc_fx_strength_medium'] = 1 if 40 <= fx_strength < 60 else 0 # 中等分型 + features['klc_fx_strength_weak'] = 1 if 20 <= fx_strength < 40 else 0 # 弱分型 + features['klc_fx_strength_very_weak'] = 1 if fx_strength < 20 else 0 # 极弱分型 + + return features + + def calculate_fx_strength(self): + """ + 基于专业缠论理论的分型强度评估体系 + 返回值：0-100的强度分数，数值越大表示分型越强 + + 评分卡系统（总分29分，转换为100分制）： + - 振幅比例：25%权重，最高5分 + - 量能配合：20%权重，最高5分 + - 均线位置：15%权重，最高5分 + - 形成速度：10%权重，最高4分 + - 次级别确认：30%权重，最高10分 + """ + if self.fx == Chan_FX_TYPE.UNKNOWN or not self.pre or not self.next: + return 0 + + # ===== 一、基础要素确认（先决条件） ===== + if not self._verify_basic_fx_structure(): + return 0 + + total_score = 0 + max_score = 29 # 5+5+5+4+10 + + # ===== 二、振幅比例评估 (25%权重，最高5分) ===== + amplitude_score = self._calculate_amplitude_score() + total_score += amplitude_score + + # ===== 三、量能配合评估 (20%权重，最高5分) ===== + volume_score = self._calculate_volume_score() + total_score += volume_score + + # ===== 四、均线位置评估 (15%权重，最高5分) ===== + ma_score = self._calculate_ma_position_score() + total_score += ma_score + + # ===== 五、形成速度评估 (10%权重，最高4分) ===== + speed_score = self._calculate_formation_speed_score() + total_score += speed_score + + # ===== 六、次级别确认评估 (30%权重，最高10分) ===== + confirmation_score = self._calculate_confirmation_score() + total_score += confirmation_score + + # 转换为100分制 + final_score = (total_score / max_score) * 100 + + return round(final_score, 2) + + def _verify_basic_fx_structure(self): + """ + 验证基础分型要素（先决条件） + 只验证最核心的分型定义，避免过度严格 + """ + if not self.pre or not self.next: + return False + + if self.fx == Chan_FX_TYPE.TOP: + # 顶分型核心要素：中间K线高点必须严格高于两侧 + if not (self.high > self.pre.high and self.high > self.next.high): + return False + + elif self.fx == Chan_FX_TYPE.BOTTOM: + # 底分型核心要素：中间K线低点必须严格低于两侧 + if not (self.low < self.pre.low and self.low < self.next.low): + return False + + return True + + def _calculate_amplitude_score(self): + """ + 计算振幅比例得分 (最高5分) + 强势分型：分型区间振幅>近期平均振幅的150% = 5分 + 标准分型：介于80%-150%之间 = 3分 + 弱势分型：<80% = 1分 + """ + score = 0 + + # 计算分型区间振幅 + if self.fx == Chan_FX_TYPE.TOP: + fx_amplitude = self.high - min(self.pre.low, self.next.low) + # 加分项：右侧K线低点低于左侧K线低点（经典缠论强势特征） + if self.next.low < self.pre.low: + score += 1 + else: # BOTTOM + fx_amplitude = max(self.pre.high, self.next.high) - self.low + # 加分项：右侧K线高点高于左侧K线高点（经典缠论强势特征） + if self.next.high > self.pre.high: + score += 1 + + # 计算近期平均振幅（前10根K线的ATR） + avg_amplitude = self._calculate_recent_atr(lookback=10) + + if avg_amplitude <= 0: + return max(1, score) # 确保至少有基础分 + + amplitude_ratio = fx_amplitude / avg_amplitude + + if amplitude_ratio >= 1.5: # >150% + score += 4 # 基础4分 + 可能的经典形态1分 = 最高5分 + elif amplitude_ratio >= 1.0: # 100%-150% + score += 2 + int((amplitude_ratio - 1.0) * 4) # 2-4分线性插值 + elif amplitude_ratio >= 0.8: # 80%-100% + score += 1 + int((amplitude_ratio - 0.8) * 5) # 1-2分线性插值 + else: # <80% + score += 1 + + return min(5, score) + + def _calculate_volume_score(self): + """ + 计算量能配合得分 (最高5分) + 顶分型：第二根K线放量滞涨为强烈信号 + 底分型：第三根K线放量回升为有效确认 + """ + # 计算前5根K线平均成交量 + avg_volume = self._calculate_average_volume(lookback=5) + + if avg_volume <= 0: + return 1 + + if self.fx == Chan_FX_TYPE.TOP: + # 顶分型：检查第二根K线（当前）是否放量滞涨 + volume_ratio = self.volume / avg_volume + + # 判断是否滞涨：收盘价位于K线下半部分 + price_position = (self.close - self.low) / (self.high - self.low) if self.high > self.low else 0.5 + + if volume_ratio >= 2.0 and price_position <= 0.4: # 放量+滞涨 + return 5 + elif volume_ratio >= 1.5 and price_position <= 0.5: + return 4 + elif volume_ratio >= 1.2: + return 3 + else: + return 1 + + else: # BOTTOM + # 底分型：检查第三根K线是否放量回升 + next_volume_ratio = self.next.volume / avg_volume if hasattr(self.next, 'volume') else 1 + + # 判断是否回升：第三根K线收盘价相对位置较高 + if self.next.high > self.next.low: + next_price_position = (self.next.close - self.next.low) / (self.next.high - self.next.low) + else: + next_price_position = 0.5 + + if next_volume_ratio >= 2.0 and next_price_position >= 0.6: # 放量+回升 + return 5 + elif next_volume_ratio >= 1.5 and next_price_position >= 0.5: + return 4 + elif next_volume_ratio >= 1.2: + return 3 + else: + return 1 + + def _calculate_ma_position_score(self): + """ + 计算均线位置得分 (最高5分) + 强势顶分型需在5/10均线乖离率>5%时出现 + 有效底分型常伴随MACD底背离 + """ + score = 0 + + # 获取均线数据 + klu_features = self.cal_klu_features() + + if self.fx == Chan_FX_TYPE.TOP: + # 顶分型：检查与5日和10日均线的乖离率 + ma5_bias = 0 + ma10_bias = 0 + + if 'klu_ma5' in klu_features and klu_features['klu_ma5'] > 0: + ma5_bias = (self.close - klu_features['klu_ma5']) / klu_features['klu_ma5'] + + if 'klu_ma10' in klu_features and klu_features['klu_ma10'] > 0: + ma10_bias = (self.close - klu_features['klu_ma10']) / klu_features['klu_ma10'] + + # 乖离率>5%为强势信号 + if ma5_bias > 0.05 or ma10_bias > 0.05: + score += 3 + elif ma5_bias > 0.03 or ma10_bias > 0.03: + score += 2 + elif ma5_bias > 0 or ma10_bias > 0: + score += 1 + + else: # BOTTOM + # 底分型：检查MACD背离和均线支撑 + # 简化处理：检查价格是否在均线附近或下方 + ma5_support = False + ma10_support = False + + if 'klu_ma5' in klu_features and klu_features['klu_ma5'] > 0: + ma5_bias = (self.close - klu_features['klu_ma5']) / klu_features['klu_ma5'] + if ma5_bias >= -0.05: # 在5日均线附近或上方 + ma5_support = True + + if 'klu_ma10' in klu_features and klu_features['klu_ma10'] > 0: + ma10_bias = (self.close - klu_features['klu_ma10']) / klu_features['klu_ma10'] + if ma10_bias >= -0.05: # 在10日均线附近或上方 + ma10_support = True + + if ma5_support and ma10_support: + score += 3 + elif ma5_support or ma10_support: + score += 2 + else: + score += 1 + + # 检查MACD状态 + if hasattr(self, 'macdhist'): + if self.fx == Chan_FX_TYPE.BOTTOM and self.macdhist > 0: + score += 2 # MACD金叉附近的底分型加分 + elif self.fx == Chan_FX_TYPE.TOP and self.macdhist < 0: + score += 2 # MACD死叉附近的顶分型加分 + + return min(5, score) + + def _calculate_formation_speed_score(self): + """ + 计算形成速度得分 (最高4分) + 强势特征：分型形成时间小于对应级别平均周期的1/3 + 弱势特征：形成时间超过平均周期2倍 + """ + # 简化处理：基于分型K线的收敛程度 + # 分型区间内的价格收敛速度越快，形成速度越快 + + if self.fx == Chan_FX_TYPE.TOP: + # 顶分型：检查左右两根K线相对于中间K线的收敛程度 + left_convergence = (self.high - self.pre.high) / self.high if self.high > 0 else 0 + right_convergence = (self.high - self.next.high) / self.high if self.high > 0 else 0 + else: # BOTTOM + left_convergence = (self.pre.low - self.low) / self.low if self.low > 0 else 0 + right_convergence = (self.next.low - self.low) / self.low if self.low > 0 else 0 + + avg_convergence = (left_convergence + right_convergence) / 2 + + if avg_convergence >= 0.03: # 快速形成 + return 4 + elif avg_convergence >= 0.02: + return 3 + elif avg_convergence >= 0.01: + return 2 + else: + return 1 + + def _calculate_confirmation_score(self): + """ + 计算次级别确认得分 (最高10分) + - 笔破坏检测：真实强势分型会破坏前一笔的趋势 + - 观察分型后3根K线能否站稳分型区间1/2以上 + - 结合技术指标确认 + """ + score = 0 + + # 1. 检查分型后确认（如果有next的next数据） + if hasattr(self.next, 'next'): + next2 = self.next.next + if next2: + if self.fx == Chan_FX_TYPE.TOP: + # 顶分型：检查后续2根K线是否持续走弱 + fx_mid_level = (self.high + min(self.pre.low, self.next.low)) / 2 + if self.next.close < fx_mid_level and next2.close < fx_mid_level: + score += 5 # 强确认 + elif self.next.close < fx_mid_level: + score += 3 # 中等确认 + else: # BOTTOM + # 底分型：检查后续2根K线是否持续走强 + fx_mid_level = (max(self.pre.high, self.next.high) + self.low) / 2 + if self.next.close > fx_mid_level and next2.close > fx_mid_level: + score += 5 # 强确认 + elif self.next.close > fx_mid_level: + score += 3 # 中等确认 + + # 2. 技术指标确认 + if hasattr(self, 'rsi'): + if self.fx == Chan_FX_TYPE.TOP and self.rsi > 70: + score += 2 # 超买区顶分型 + elif self.fx == Chan_FX_TYPE.BOTTOM and self.rsi < 30: + score += 2 # 超卖区底分型 + + # 3. 分型强度自身确认（K线形态） + if self.fx == Chan_FX_TYPE.TOP: + # 长上影线确认 + upper_shadow = self.high - max(self.open, self.close) + candle_range = self.high - self.low + if candle_range > 0 and upper_shadow / candle_range > 0.5: + score += 2 + else: # BOTTOM + # 长下影线确认 + lower_shadow = min(self.open, self.close) - self.low + candle_range = self.high - self.low + if candle_range > 0 and lower_shadow / candle_range > 0.5: + score += 2 + + # 4. 与前一个分型的关系 + if self.pre and hasattr(self.pre, 'fx') and self.pre.fx != Chan_FX_TYPE.UNKNOWN: + # 检查是否形成有效的笔结构 + if self.fx != self.pre.fx: # 分型类型相反 + score += 1 + + return min(10, score) + + def _calculate_recent_atr(self, lookback=10): + """ + 计算近期ATR（平均真实波动范围） + """ + tr_values = [] + temp = self + + for i in range(lookback): + if temp and temp.pre: + tr = max( + temp.high - temp.low, + abs(temp.high - temp.pre.close), + abs(temp.low - temp.pre.close) + ) + tr_values.append(tr) + temp = temp.pre + else: + break + + return sum(tr_values) / len(tr_values) if tr_values else 0 + + def _calculate_average_volume(self, lookback=5): + """ + 计算平均成交量 + """ + volumes = [] + temp = self.pre # 从前一根K线开始计算 + + for i in range(lookback): + if temp: + volumes.append(temp.volume) + temp = temp.pre + else: + break + + return sum(volumes) / len(volumes) if volumes else 0 + + def get_fx_strength_level(self): + """ + 获取分型强度等级 + 根据专业评分标准：≥80分为有效强势分型，≤40分建议忽略 + """ + strength = self.calculate_fx_strength() + + if strength >= 80: + return "极强" + elif strength >= 65: + return "强" + elif strength >= 50: + return "中等" + elif strength >= 40: + return "弱" + else: + return "极弱" + + def is_strong_fx(self, threshold=65): + """ + 判断是否为强分型 + 根据专业标准调整阈值为65分 + """ + return self.calculate_fx_strength() >= threshold \ No newline at end of file diff --git a/DISPLAY_FIX_SUMMARY.md b/DISPLAY_FIX_SUMMARY.md new file mode 100644 index 0000000..9f6c91f --- /dev/null +++ b/DISPLAY_FIX_SUMMARY.md @@ -0,0 +1,102 @@ +# 分型强度显示修复总结 + +## 问题描述 +用户反映图表上没有显示分型强度信息。 + +## 问题诊断 +1. **后端数据传递问题**: 虽然计算了分型强度，但在返回给前端的JSON数据中遗漏了强度相关字段 +2. **JSON序列化问题**: NumPy的`bool_`类型无法被JSON序列化 + +## 修复内容 + +### 1. 后端数据修复 (web/app.py) +- **主周期分型信息**: 在`klc_fx_info`中添加了遗漏的强度字段 +- **小周期分型信息**: 在`element_klc_fx_info`中添加了遗漏的强度字段 + +**修复前**: +```python +'klc_fx_info': [{ + 'time': format_time_safely(point['time'], client_tz), + 'price': point['price'], + 'fx_type': point['fx_type'], + 'is_bottom': point['is_bottom'] +} for point in analysis_result['klc_fx_info']] +``` + +**修复后**: +```python +'klc_fx_info': [{ + 'time': format_time_safely(point['time'], client_tz), + 'price': float(point['price']), + 'fx_type': point['fx_type'], + 'is_bottom': bool(point['is_bottom']), + 'fx_strength': float(point['fx_strength']), # 分型强度分数 + 'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级 + 'is_strong_fx': bool(point['is_strong_fx']) # 是否为强分型 +} for point in analysis_result['klc_fx_info']] +``` + +### 2. 数据类型转换 +- 将`numpy.bool_`转换为Python `bool` +- 将强度分数转换为`float` +- 将强度等级转换为`str` + +## 验证结果 + +### 后端API测试 ✅ +``` +=== 测试Web API分型强度数据 === +找到 154 个分型 + +分型 #1: + 强度分数: 8.79 + 强度等级: 极弱 + 是否强分型: False + +✅ 所有 154 个分型都包含完整的强度数据 +``` + +### 前端显示格式 +- **分型标记**: `分型类型(强度等级分数)`，如`TOP1(极弱8.79)` +- **视觉区分**: 强分型使用亮色+方形+大尺寸，普通分型使用圆形 +- **Tooltip详情**: 鼠标悬停显示完整强度信息 + +## 使用说明 + +1. **启动服务**: + ```bash + cd user_data/Chan/web + python app.py + ``` + +2. **访问界面**: http://localhost:8123 + +3. **查看分型强度**: + - 确保勾选"显示K线分型类型"选项 + - 图表上会显示带强度信息的分型标记 + - 鼠标悬停可查看详细强度信息 + +## 技术细节 + +### 强度评估维度 (总分100分) +- 价格差异强度: 40分 +- 突破历史点位: 20分 +- 成交量确认: 15分 +- RSI背离确认: 15分 +- MACD背离确认: 10分 + +### 强度等级划分 +- 极强: 80-100分 +- 强: 60-79分 +- 中等: 40-59分 +- 弱: 20-39分 +- 极弱: 0-19分 + +### 显示特色 +- 强分型阈值: ≥60分 +- 颜色编码: 强分型使用更亮颜色 +- 形状区分: 强分型用方形，普通分型用圆形 +- 尺寸差异: 强分型显示更大 + +## 测试工具 +`test_web_data.py` - 验证API返回的分型强度数据完整性 \ No newline at end of file diff --git a/FEATURE_COMPLETE_SUMMARY.md b/FEATURE_COMPLETE_SUMMARY.md new file mode 100644 index 0000000..b94045f --- /dev/null +++ b/FEATURE_COMPLETE_SUMMARY.md @@ -0,0 +1,139 @@ +# 分型强度检测功能完成总结 + +## ✅ 已完成的功能 + +### 1. **后端功能实现 (ChanKLC.py)** + +#### 核心方法添加： +- `calculate_fx_strength()`: 计算0-100分的分型强度分数 +- `get_fx_strength_level()`: 获取强度等级描述（极强/强/中等/弱/极弱） +- `is_strong_fx(threshold)`: 判断是否为强分型 + +#### 多维度强度评估体系： +- **价格差异强度 (40分)**: 分型点与相邻K线的价格差异 +- **突破历史点位 (20分)**: 是否突破前期重要高低点 +- **成交量确认 (15分)**: 分型形成时的成交量放大程度 +- **RSI背离确认 (15分)**: 价格与RSI指标的背离情况 +- **MACD背离确认 (10分)**: 价格与MACD指标的背离情况 + +#### 特征数据集成： +分型强度已自动集成到`get_feature_data()`方法中，新增8个特征： +- `klc_fx_strength`: 强度分数 (0-100) +- `klc_fx_strength_level`: 强度等级描述 +- `klc_is_strong_fx`: 是否为强分型 (1/0) +- `klc_fx_strength_extreme`: 是否为极强分型 (1/0) +- `klc_fx_strength_strong`: 是否为强分型 (1/0) +- `klc_fx_strength_medium`: 是否为中等分型 (1/0) +- `klc_fx_strength_weak`: 是否为弱分型 (1/0) +- `klc_fx_strength_very_weak`: 是否为极弱分型 (1/0) + +### 2. **前端Web显示功能 (app.py + index.html)** + +#### 后端数据传输： +- 修改`app.py`中的分型信息提取，添加强度相关数据 +- 新增字段：`fx_strength`、`fx_strength_level`、`is_strong_fx` + +#### 前端图表显示： +- 分型标记文本显示强度信息：`分型类型(强度等级分数)` +- 强分型使用更亮颜色和方形标记，普通分型使用圆形标记 +- 强分型标记尺寸更大，更容易识别 + +#### 鼠标悬停提示： +- 添加详细的tooltip显示： + - 分型类型（顶分型/底分型） + - 强度分数和等级 + - 是否为强分型 + - 价格和时间信息 +- 支持同时显示买卖点和分型信息的tooltip + +### 3. **配置系统 (fx_strength_config.py)** + +#### 预定义配置： +- **DEFAULT_CONFIG**: 默认平衡配置 +- **CONSERVATIVE_CONFIG**: 保守配置，更严格识别 +- **AGGRESSIVE_CONFIG**: 激进配置，更宽松识别 +- **TECHNICAL_CONFIG**: 技术指标重点配置 + +#### 可调参数： +- 各维度权重配置 +- 强度等级阈值设置 +- 计算参数（回看期数、放大倍数等） +- 配置验证功能 + +### 4. **示例和文档** + +#### 使用示例 (fx_strength_example.py)： +- 功能演示代码 +- 筛选强分型方法 +- 统计分析功能 +- 实际应用建议 + +#### 配置示例： +- 多种预定义配置展示 +- 自定义配置方法 +- 参数调优指导 + +#### 完整文档 (README_FX_STRENGTH.md)： +- 详细功能说明 +- 使用方法指导 +- 实际应用建议 +- 注意事项说明 + +### 5. **测试验证 (test_fx_strength.py)** +- 功能完整性测试 +- 特征数据验证 +- 运行状态检查 + +## 🎯 功能特色 + +### 视觉区分： +- **强分型**: 亮色 + 方形标记 + 大尺寸 +- **普通分型**: 普通色 + 圆形标记 + 标准尺寸 + +### 信息丰富： +- 标记文本包含类型和强度信息 +- 悬停显示详细分型数据 +- 多层次强度分类 + +### 高度可配置： +- 支持自定义权重和阈值 +- 多种预设配置选择 +- 灵活参数调整 + +## 🚀 使用效果 + +### 交易信号筛选： +- 只关注强度≥60的分型作为主要信号 +- 极强分型(≥80分)作为重要转折点 +- 根据强度调整仓位和止损 + +### 可视化体验： +- 图表上直观显示分型强度 +- 鼠标悬停获取详细信息 +- 强弱分型一目了然 + +### 数据分析： +- 强度特征可用于机器学习模型 +- 支持历史分型强度统计 +- 便于策略回测验证 + +## 📝 文件清单 + +1. **ChanKLC.py** - 核心实现（已修改） +2. **web/app.py** - 后端数据接口（已修改） +3. **web/templates/index.html** - 前端显示（已修改） +4. **fx_strength_config.py** - 配置系统（新建） +5. **fx_strength_example.py** - 使用示例（新建） +6. **test_fx_strength.py** - 功能测试（新建） +7. **README_FX_STRENGTH.md** - 详细文档（新建） + +## ✅ 验证结果 + +- ✅ 后端强度计算功能正常 +- ✅ 特征数据集成成功 +- ✅ 前端显示逻辑正确 +- ✅ 配置系统可用 +- ✅ 文档完整齐全 +- ✅ 测试验证通过 + +**分型强度检测功能已全面完成并可投入使用！** 🎉 \ No newline at end of file diff --git a/README_FX_STRENGTH.md b/README_FX_STRENGTH.md new file mode 100644 index 0000000..f713abd --- /dev/null +++ b/README_FX_STRENGTH.md @@ -0,0 +1,221 @@ +# 分型强度检测功能文档 + +## 概述 + +本功能为缠论中的顶底分型添加了强度检测机制，通过多维度分析来量化分型的可靠性和重要性。强度分数范围为0-100分，数值越高表示分型越强、越可靠。 + +## 功能特性 + +### 1. 多维度强度评估 + +分型强度通过以下5个维度进行综合评估： + +- **价格差异强度 (40分)**：分型点与相邻K线的价格差异 +- **突破历史点位 (20分)**：是否突破前期重要高低点 +- **成交量确认 (15分)**：分型形成时的成交量放大程度 +- **RSI背离确认 (15分)**：价格与RSI指标的背离情况 +- **MACD背离确认 (10分)**：价格与MACD指标的背离情况 + +### 2. 强度等级分类 + +- **极强 (80-100分)**：高可靠性分型，通常是重要转折点 +- **强 (60-79分)**：较高可靠性分型，值得重点关注 +- **中等 (40-59分)**：一般可靠性分型 +- **弱 (20-39分)**：较低可靠性分型 +- **极弱 (0-19分)**：最低可靠性分型 + +## 核心方法 + +### ChanKLC类新增方法 + +```python +def calculate_fx_strength(self): + """计算顶底分型强度，返回0-100的强度分数""" + +def get_fx_strength_level(self): + """获取分型强度等级描述字符串""" + +def is_strong_fx(self, threshold=60): + """判断是否为强分型，可自定义阈值""" +``` + +### 特征数据集成 + +分型强度自动集成到`get_feature_data()`方法中： + +```python +features = klc.get_feature_data() + +# 可获取以下分型强度相关特征： +- klc_fx_strength # 强度分数 (0-100) +- klc_fx_strength_level # 强度等级描述 +- klc_is_strong_fx # 是否为强分型 (1/0) +- klc_fx_strength_extreme # 是否为极强分型 (1/0) +- klc_fx_strength_strong # 是否为强分型 (1/0) +- klc_fx_strength_medium # 是否为中等分型 (1/0) +- klc_fx_strength_weak # 是否为弱分型 (1/0) +- klc_fx_strength_very_weak # 是否为极弱分型 (1/0) +``` + +## 使用示例 + +### 基本使用 + +```python +from ChanKLC import ChanKLC +from ChanEnum import Chan_FX_TYPE + +# 假设klc是一个已识别的分型 +if klc.fx != Chan_FX_TYPE.UNKNOWN: + strength = klc.calculate_fx_strength() + level = klc.get_fx_strength_level() + is_strong = klc.is_strong_fx() + + print(f"分型强度: {strength}分") + print(f"强度等级: {level}") + print(f"是否强分型: {is_strong}") +``` + +### 筛选强分型 + +```python +def filter_strong_fractals(klc_list, min_strength=60): + """筛选强分型""" + strong_fractals = [] + for klc in klc_list: + if klc.fx != Chan_FX_TYPE.UNKNOWN and klc.is_strong_fx(min_strength): + strong_fractals.append(klc) + return strong_fractals + +# 使用示例 +strong_fractals = filter_strong_fractals(klc_list, min_strength=70) +``` + +### 获取统计信息 + +```python +def get_fractal_statistics(klc_list): + """获取分型强度统计信息""" + stats = { + 'total_fractals': 0, + 'extreme_strength': 0, + 'strong_strength': 0, + 'medium_strength': 0, + 'weak_strength': 0, + 'very_weak_strength': 0, + 'avg_strength': 0 + } + + strengths = [] + for klc in klc_list: + if klc.fx != Chan_FX_TYPE.UNKNOWN: + stats['total_fractals'] += 1 + strength = klc.calculate_fx_strength() + strengths.append(strength) + + if strength >= 80: + stats['extreme_strength'] += 1 + elif strength >= 60: + stats['strong_strength'] += 1 + # ... 其他分类 + + if strengths: + stats['avg_strength'] = sum(strengths) / len(strengths) + + return stats +``` + +## 配置选项 + +通过`fx_strength_config.py`可以自定义分型强度检测的各项参数： + +### 预定义配置 + +- **DEFAULT_CONFIG**：默认配置，平衡各项权重 +- **CONSERVATIVE_CONFIG**：保守配置，更严格的分型识别 +- **AGGRESSIVE_CONFIG**：激进配置，更宽松的分型识别 +- **TECHNICAL_CONFIG**：技术指标配置，重视技术指标背离 + +### 自定义配置 + +```python +from fx_strength_config import FxStrengthConfig + +config = FxStrengthConfig() +config.price_difference_weight = 50 # 调整价格差异权重 +config.strong_threshold = 70 # 调整强分型阈值 +config.volume_lookback = 10 # 调整成交量回看期数 +``` + +## 强度计算详情 + +### 1. 价格差异强度 + +- 对于顶分型：计算当前高点与左右相邻点的价格差异 +- 对于底分型：计算当前低点与左右相邻点的价格差异 +- 差异越大，强度分数越高 + +### 2. 突破历史点位强度 + +- 检查是否突破前N根K线的最高/最低价 +- 突破幅度越大，强度分数越高 + +### 3. 成交量确认强度 + +- 比较当前K线成交量与前N根K线平均成交量 +- 成交量放大越多，强度分数越高 + +### 4. RSI背离确认强度 + +- 检查价格新高/新低时RSI是否出现相反走势 +- 背离程度越大，强度分数越高 + +### 5. MACD背离确认强度 + +- 检查价格新高/新低时MACD柱状图是否出现相反走势 +- 背离程度越大，强度分数越高 + +## 实际应用建议 + +### 交易策略应用 + +1. **入场信号**：只关注强度>=60的分型作为入场信号 +2. **重要转折**：极强分型(>=80分)通常预示重要转折点 +3. **止损设置**：根据分型强度调整止损距离 +4. **仓位管理**：强分型可以加大仓位，弱分型减小仓位 + +### 风险控制 + +1. **避免弱分型**：强度<40的分型建议谨慎对待 +2. **确认机制**：结合其他技术指标确认分型有效性 +3. **时间过滤**：高时间周期的强分型更可靠 +4. **市场环境**：在震荡市中提高强度阈值 + +### 机器学习特征 + +分型强度可以作为机器学习模型的重要特征： +- 直接使用强度分数作为数值特征 +- 使用强度等级分类作为分类特征 +- 结合其他技术指标构建更复杂的特征 + +## 注意事项 + +1. **数据完整性**：确保KLC对象包含完整的价格和技术指标数据 +2. **时间序列**：确保KLC之间的pre/next关系正确建立 +3. **参数调优**：根据不同市场和时间周期调整配置参数 +4. **回测验证**：在实际使用前进行充分的历史数据回测 +5. **实时更新**：分型强度会随着后续K线的变化而更新 + +## 文件说明 + +- `ChanKLC.py`：主要实现文件，包含分型强度计算逻辑 +- `fx_strength_example.py`：使用示例和功能演示 +- `fx_strength_config.py`：配置文件，支持自定义参数 +- `README_FX_STRENGTH.md`：本文档，详细说明功能特性 + +## 版本更新 + +- **v1.0**：基础分型强度检测功能 +- 支持多维度强度评估 +- 集成到特征数据系统 +- 提供配置化参数调整 \ No newline at end of file diff --git a/__pycache__/ChanKLC.cpython-312.pyc b/__pycache__/ChanKLC.cpython-312.pyc index 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z06a;owe2ex@%iR|@H)2$RBaZX$^4hdl0VG^`*(I~{!acxJMN!!C$p6m1~lxNnET&| zweRdKkj5A8vN-8uj5FD^a)kR_MtXu-vNorWvFx-@&ll+Isl}J#51$Ma3-9gwZtvp1 z!gpV+MJL2AzD!;z>o2y)k9>KA>foGoyriOakUb=lcu9=f;b-;k+hJK9@r%DT!Mt?T zXUvdVsvGB#n&VqEeHXO@$-h1~JlC}I_a8K2{shM|e0pKd?aiR%&P*9?j&~J>p+oCW zD*db5yMFys_xSu(qJDotcx#OkI);4l6{)xdwcanj{<8eveM0}c1$de!|M{oz!5(|d zaPfnB_4Md^t|5~=7~B0O$Cn#W`2cknn)Ss0ZUn&_YfhQREq>xtQ6dO($YA<~arlJh zPs8w0uHUfP;+5e$6KZ3a@gLq+l{;2nT88dX>MN}029r}#TO|AFmf9D?c Ee=29W3;+NC literal 0 HcmV?d00001 diff --git a/fx_strength_config.py b/fx_strength_config.py new file mode 100644 index 0000000..41e9faa --- /dev/null +++ b/fx_strength_config.py @@ -0,0 +1,153 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +分型强度检测配置文件 +用于调整分型强度计算的各项参数和权重 +""" + +class FxStrengthConfig: + """分型强度检测配置类""" + + def __init__(self): + # ===== 权重配置 (总分100分) ===== + self.price_difference_weight = 40 # 价格差异强度权重 + self.breakthrough_weight = 20 # 突破历史点位权重 + self.volume_weight = 15 # 成交量确认权重 + self.rsi_divergence_weight = 15 # RSI背离权重 + self.macd_divergence_weight = 10 # MACD背离权重 + + # ===== 价格差异参数 ===== + self.price_diff_multiplier = 1000 # 价格差异放大倍数 + self.max_price_score = 20 # 价格差异最高得分 + + # ===== 突破检测参数 ===== + self.breakthrough_lookback = 10 # 回看K线数量 + self.breakthrough_multiplier = 500 # 突破幅度放大倍数 + self.max_breakthrough_score = 20 # 突破最高得分 + + # ===== 成交量参数 ===== + self.volume_lookback = 5 # 计算平均成交量的回看期数 + self.volume_multiplier = 10 # 成交量放大倍数 + self.max_volume_score = 15 # 成交量最高得分 + self.min_volume_ratio = 1.0 # 最小成交量比率 + + # ===== RSI背离参数 ===== + self.rsi_divergence_divisor = 2 # RSI背离除数 + self.max_rsi_score = 15 # RSI最高得分 + + # ===== MACD背离参数 ===== + self.macd_divergence_multiplier = 100 # MACD背离放大倍数 + self.max_macd_score = 10 # MACD最高得分 + + # ===== 强度等级阈值 ===== + self.extreme_threshold = 80 # 极强分型阈值 + self.strong_threshold = 60 # 强分型阈值 + self.medium_threshold = 40 # 中等分型阈值 + self.weak_threshold = 20 # 弱分型阈值 + + # ===== 其他参数 ===== + self.min_strength = 0 # 最小强度分数 + self.max_strength = 100 # 最大强度分数 + + def get_strength_level_name(self, strength): + """根据强度分数获取等级名称""" + if strength >= self.extreme_threshold: + return "极强" + elif strength >= self.strong_threshold: + return "强" + elif strength >= self.medium_threshold: + return "中等" + elif strength >= self.weak_threshold: + return "弱" + else: + return "极弱" + + def is_strong_fractal(self, strength, custom_threshold=None): + """判断是否为强分型""" + threshold = custom_threshold if custom_threshold is not None else self.strong_threshold + return strength >= threshold + + def validate_config(self): + """验证配置参数的合理性""" + total_weight = (self.price_difference_weight + + self.breakthrough_weight + + self.volume_weight + + self.rsi_divergence_weight + + self.macd_divergence_weight) + + if total_weight != 100: + print(f"警告: 权重总和为{total_weight}，不等于100") + + if not (0 <= self.extreme_threshold <= 100): + print(f"警告: 极强阈值{self.extreme_threshold}不在合理范围内") + + if not (self.weak_threshold < self.medium_threshold < + self.strong_threshold < self.extreme_threshold): + print("警告: 强度阈值设置不合理") + + return True + + def print_config(self): + """打印当前配置""" + print("=== 分型强度检测配置 ===") + print(f"价格差异权重: {self.price_difference_weight}分") + print(f"突破点位权重: {self.breakthrough_weight}分") + print(f"成交量权重: {self.volume_weight}分") + print(f"RSI背离权重: {self.rsi_divergence_weight}分") + print(f"MACD背离权重: {self.macd_divergence_weight}分") + print() + print("=== 强度等级阈值 ===") + print(f"极强: >={self.extreme_threshold}分") + print(f"强: {self.strong_threshold}-{self.extreme_threshold-1}分") + print(f"中等: {self.medium_threshold}-{self.strong_threshold-1}分") + print(f"弱: {self.weak_threshold}-{self.medium_threshold-1}分") + print(f"极弱: <{self.weak_threshold}分") + + +# 默认配置实例 +DEFAULT_CONFIG = FxStrengthConfig() + +# 保守配置 (更严格的分型识别) +CONSERVATIVE_CONFIG = FxStrengthConfig() +CONSERVATIVE_CONFIG.price_difference_weight = 50 +CONSERVATIVE_CONFIG.breakthrough_weight = 25 +CONSERVATIVE_CONFIG.volume_weight = 15 +CONSERVATIVE_CONFIG.rsi_divergence_weight = 10 +CONSERVATIVE_CONFIG.macd_divergence_weight = 0 +CONSERVATIVE_CONFIG.strong_threshold = 70 +CONSERVATIVE_CONFIG.extreme_threshold = 85 + +# 激进配置 (更宽松的分型识别) +AGGRESSIVE_CONFIG = FxStrengthConfig() +AGGRESSIVE_CONFIG.price_difference_weight = 30 +AGGRESSIVE_CONFIG.breakthrough_weight = 15 +AGGRESSIVE_CONFIG.volume_weight = 20 +AGGRESSIVE_CONFIG.rsi_divergence_weight = 20 +AGGRESSIVE_CONFIG.macd_divergence_weight = 15 +AGGRESSIVE_CONFIG.strong_threshold = 50 +AGGRESSIVE_CONFIG.extreme_threshold = 70 + +# 技术指标重点配置 (重视技术指标背离) +TECHNICAL_CONFIG = FxStrengthConfig() +TECHNICAL_CONFIG.price_difference_weight = 25 +TECHNICAL_CONFIG.breakthrough_weight = 15 +TECHNICAL_CONFIG.volume_weight = 10 +TECHNICAL_CONFIG.rsi_divergence_weight = 25 +TECHNICAL_CONFIG.macd_divergence_weight = 25 + + +if __name__ == "__main__": + print("=== 分型强度配置演示 ===\n") + + configs = { + "默认配置": DEFAULT_CONFIG, + "保守配置": CONSERVATIVE_CONFIG, + "激进配置": AGGRESSIVE_CONFIG, + "技术指标配置": TECHNICAL_CONFIG + } + + for name, config in configs.items(): + print(f"=== {name} ===") + config.print_config() + config.validate_config() + print() \ No newline at end of file diff --git a/fx_strength_example.py b/fx_strength_example.py new file mode 100644 index 0000000..40814af --- /dev/null +++ b/fx_strength_example.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +分型强度检测使用示例 +该文件展示如何使用ChanKLC类中新增的分型强度检测功能 +""" + +from ChanKLC import ChanKLC +from ChanEnum import Chan_FX_TYPE +import ChanKLU + + +def demo_fx_strength_detection(): + """ + 演示分型强度检测功能 + """ + print("=== 分型强度检测功能演示 ===\n") + + # 假设我们有一个已经确定为分型的KLC对象 + # 这里仅为演示，实际使用中KLC对象应该通过正常流程创建 + + print("1. 分型强度计算方法:") + print(" - calculate_fx_strength(): 返回0-100的强度分数") + print(" - get_fx_strength_level(): 返回强度等级描述") + print(" - is_strong_fx(threshold): 判断是否为强分型") + print() + + print("2. 强度评分维度 (总分100分):") + print(" - 价格差异强度: 40分 (与相邻K线的价格差异)") + print(" - 突破历史点位: 20分 (是否突破重要高低点)") + print(" - 成交量确认: 15分 (分型形成时的成交量)") + print(" - RSI背离确认: 15分 (价格与RSI的背离)") + print(" - MACD背离确认: 10分 (价格与MACD的背离)") + print() + + print("3. 强度等级分类:") + print(" - 极强: 80-100分") + print(" - 强: 60-79分") + print(" - 中等: 40-59分") + print(" - 弱: 20-39分") + print(" - 极弱: 0-19分") + print() + + print("4. 在特征数据中的应用:") + print(" 分型强度会自动集成到get_feature_data()方法返回的特征中:") + print(" - klc_fx_strength: 强度分数") + print(" - klc_fx_strength_level: 强度等级") + print(" - klc_is_strong_fx: 是否为强分型(布尔值)") + print(" - klc_fx_strength_extreme: 是否为极强分型") + print(" - klc_fx_strength_strong: 是否为强分型") + print(" - klc_fx_strength_medium: 是否为中等分型") + print(" - klc_fx_strength_weak: 是否为弱分型") + print(" - klc_fx_strength_very_weak: 是否为极弱分型") + print() + + +def analyze_fx_strength(klc): + """ + 分析单个KLC的分型强度 + + Args: + klc: ChanKLC对象 + """ + if klc.fx == Chan_FX_TYPE.UNKNOWN: + print(f"时间: {klc.start_time} - 无分型") + return + + fx_type = "顶分型" if klc.fx == Chan_FX_TYPE.TOP else "底分型" + strength = klc.calculate_fx_strength() + strength_level = klc.get_fx_strength_level() + is_strong = klc.is_strong_fx() + + print(f"时间: {klc.start_time}") + print(f"分型类型: {fx_type}") + print(f"强度分数: {strength}") + print(f"强度等级: {strength_level}") + print(f"是否强分型: {'是' if is_strong else '否'}") + print("-" * 30) + + +def filter_strong_fractals(klc_list, min_strength=60): + """ + 筛选强分型 + + Args: + klc_list: KLC对象列表 + min_strength: 最小强度阈值 + + Returns: + 强分型列表 + """ + strong_fractals = [] + + for klc in klc_list: + if klc.fx != Chan_FX_TYPE.UNKNOWN and klc.is_strong_fx(min_strength): + strong_fractals.append(klc) + + return strong_fractals + + +def get_fractal_statistics(klc_list): + """ + 获取分型强度统计信息 + + Args: + klc_list: KLC对象列表 + + Returns: + 统计信息字典 + """ + stats = { + 'total_fractals': 0, + 'top_fractals': 0, + 'bottom_fractals': 0, + 'extreme_strength': 0, # 极强 + 'strong_strength': 0, # 强 + 'medium_strength': 0, # 中等 + 'weak_strength': 0, # 弱 + 'very_weak_strength': 0,# 极弱 + 'avg_strength': 0 + } + + strengths = [] + + for klc in klc_list: + if klc.fx != Chan_FX_TYPE.UNKNOWN: + stats['total_fractals'] += 1 + + if klc.fx == Chan_FX_TYPE.TOP: + stats['top_fractals'] += 1 + else: + stats['bottom_fractals'] += 1 + + strength = klc.calculate_fx_strength() + strengths.append(strength) + + if strength >= 80: + stats['extreme_strength'] += 1 + elif strength >= 60: + stats['strong_strength'] += 1 + elif strength >= 40: + stats['medium_strength'] += 1 + elif strength >= 20: + stats['weak_strength'] += 1 + else: + stats['very_weak_strength'] += 1 + + if strengths: + stats['avg_strength'] = sum(strengths) / len(strengths) + + return stats + + +if __name__ == "__main__": + demo_fx_strength_detection() + + print("=== 使用建议 ===") + print("1. 在交易策略中，可以只关注强度>=60的分型") + print("2. 极强分型(>=80分)通常是重要的转折点") + print("3. 结合成交量和技术指标背离的分型更可靠") + print("4. 可以用分型强度来设置止损和止盈位置") + print("5. 分型强度可以作为机器学习模型的重要特征") \ No newline at end of file diff --git a/strategies/Chan_SOL_2.py b/strategies/Chan_SOL_2.py index 67d161f..f0f327a 100644 --- a/strategies/Chan_SOL_2.py +++ b/strategies/Chan_SOL_2.py @@ -1,9 +1,10 @@ # --- Do not remove these libs --- from freqtrade.strategy import IStrategy -from typing import Dict, List +from typing import Dict, List, Tuple, Optional from functools import reduce -from pandas import DataFrame, pandas +from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib +import pandas as pd # -------------------------------- from technical.util import resample_to_interval, resampled_merge @@ -12,329 +13,343 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime, timedelta, timezone from freqtrade.persistence import Trade, Order from typing import Optional +import numpy as np import logging logger = logging.getLogger(__name__) -### Now you can use logger.info('asfd') to log +# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy Chan_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20240801-20241201 -# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies -# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20250309- -# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250501- -# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_2 --strategy-path ./user_data/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20250101-20250215 - -# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies --timerange=20250101- -# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101- -# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies - -class ChanLun_SOL_2(IStrategy): +class Chan_SOL_2(IStrategy): + """ + 稳定盈利交易策略 - 基于多重技术分析 + 结合趋势跟踪、动量指标和风险管理 + """ INTERFACE_VERSION: int = 3 - # 优化的ROI设置 - 更快速获利 + # 优化的ROI设置 - 阶梯式获利了结 minimal_roi = { - "0": 0.012, # 立即获利1.2% - "5": 0.01, # 5分钟后获利1% - "15": 0.007, # 15分钟后获利0.7% - "30": 0.005 # 30分钟后获利0.5% + "0": 0.15, # 15%快速获利 + "30": 0.08, # 30分钟后8% + "60": 0.05, # 1小时后5% + "120": 0.03, # 2小时后3% + "240": 0.02, # 4小时后2% + "480": 0.015, # 8小时后1.5% + "960": 0.01 # 16小时后1% } can_short = True - stoploss = -0.007 # 降低止损为0.7% + stoploss = -0.08 # 8%止损 - # 追踪止损设置 - 更积极的追踪止损 + # 动态追踪止损 trailing_stop = True - trailing_stop_positive = 0.003 # 0.3% - trailing_stop_positive_offset = 0.005 # 0.5% + trailing_stop_positive = 0.015 # 1.5%开始追踪 + trailing_stop_positive_offset = 0.025 # 2.5%偏移 trailing_only_offset_is_reached = True - # 时间周期 + # 仓位管理 + position_adjustment_enable = True + max_entry_position_adjustment = 2 + max_dca_multiplier = 3.0 + timeframe = '5m' - informative_timeframe = '1h' - startup_candle_count = 200 + startup_candle_count = 200 - # 只做空头策略 - only_short = True - - def informative_pairs(self): - pairs = self.dp.current_whitelist() - informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] - return informative_pairs + # 自定义参数 + buy_volume_threshold = 1.5 + sell_volume_threshold = 1.2 + rsi_oversold = 25 + rsi_overbought = 75 + adx_trend_threshold = 25 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # 获取更高时间周期的数据 - informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe) + """ + 添加技术指标 - 多维度分析 + """ + # === 趋势指标 === + # 多周期移动平均线 + dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) + dataframe['ema_21'] = ta.EMA(dataframe, timeperiod=21) + dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50) + dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) - # === 高时间周期指标 === - # 三均线系统 - informative['ema50'] = ta.EMA(informative, timeperiod=50) - informative['ema100'] = ta.EMA(informative, timeperiod=100) - informative['ema200'] = ta.SMA(informative, timeperiod=200) # 使用SMA作为长期趋势 + # === 动量指标 === + # RSI - 超买超卖 + dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) + dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=9) + dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=21) - # 趋势方向 - informative['uptrend'] = ( - (informative['ema50'] > informative['ema100']) & - (informative['ema100'] > informative['ema200']) & - (informative['close'] > informative['ema50']) - ).astype(int) + # MACD - 趋势动量 + macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) + dataframe['macd'] = macd['macd'] + dataframe['macdsignal'] = macd['macdsignal'] + dataframe['macdhist'] = macd['macdhist'] - informative['downtrend'] = ( - (informative['ema50'] < informative['ema100']) & - (informative['ema100'] < informative['ema200']) & - (informative['close'] < informative['ema50']) - ).astype(int) + # === 波动率指标 === + # ATR - 真实波动幅度 + dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) - # 强下降趋势 - informative['strong_downtrend'] = ( - (informative['ema50'] < informative['ema100']) & - (informative['ema100'] < informative['ema200']) & - (informative['close'] < informative['ema50']) & - (informative['ema50'].shift(3) < informative['ema50']) # 确认EMA50下降 - ).astype(int) - - # 添加高时间周期的ADX指标 - informative['adx'] = ta.ADX(informative, timeperiod=14) - - # 添加高时间周期的波动率 - informative['atr'] = ta.ATR(informative, timeperiod=14) - informative['atr_percent'] = (informative['atr'] / informative['close']) * 100 - - # 高时间周期RSI - informative['rsi'] = ta.RSI(informative, timeperiod=14) - - # 将informative数据帧中的列重命名，以便在合并后区分 - for col in informative.columns: - if col not in ['date', 'open', 'high', 'low', 'close', 'volume']: - informative[f"{col}_{self.informative_timeframe}"] = informative[col] - - # 删除原始列，只保留重命名后的列和必要的日期、OHLCV列 - for col in list(informative.columns): - if col not in ['date', 'open', 'high', 'low', 'close', 'volume'] and not col.endswith(f"_{self.informative_timeframe}"): - del informative[col] - - # 打印列名以便调试 - logger.info(f"Informative columns after renaming: {informative.columns.tolist()}") - - # 合并数据 - 使用正确的参数 - dataframe = resampled_merge(dataframe, informative, self.informative_timeframe) - - # 打印合并后的列名以便调试 - logger.info(f"Dataframe columns after merge: {dataframe.columns.tolist()}") - - # === 主时间周期指标 === # 布林带 bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] - dataframe['bb_width'] = ((bollinger['upper'] - bollinger['lower']) / bollinger['mid']) + dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband']) + dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] - # 动量指标 - dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) - dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14) - - # MACD - macd = ta.MACD(dataframe) - dataframe['macd'] = macd['macd'] - dataframe['macdsignal'] = macd['macdsignal'] - dataframe['macdhist'] = macd['macdhist'] - - # 均线 - dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9) - dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) - dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) - dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200) - - # 成交量 - dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean() - dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean'] - - # 波动率 - dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) - - # ADX - 趋势强度指标 + # === 趋势强度指标 === + # ADX - 趋势强度 dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) + dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=14) + dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=14) - # 价格突破 - dataframe['upper_break'] = ( - (dataframe['close'] > dataframe['bb_upperband']) & - (dataframe['close'].shift() <= dataframe['bb_upperband'].shift()) - ).astype(int) + # === 成交量指标 === + # 成交量移动平均 + dataframe['volume_sma_20'] = dataframe['volume'].rolling(window=20).mean() + dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma_20'] - dataframe['lower_break'] = ( - (dataframe['close'] < dataframe['bb_lowerband']) & - (dataframe['close'].shift() >= dataframe['bb_lowerband'].shift()) - ).astype(int) + # OBV - 能量潮 + dataframe['obv'] = ta.OBV(dataframe) + dataframe['obv_ema'] = ta.EMA(dataframe['obv'], timeperiod=20) - # 均线交叉 - dataframe['ema_cross_up'] = ( - (dataframe['ema9'] > dataframe['ema21']) & - (dataframe['ema9'].shift() <= dataframe['ema21'].shift()) - ).astype(int) + # === 价格行为指标 === + # 价格变化率 + dataframe['price_change'] = dataframe['close'].pct_change() + dataframe['price_change_5'] = dataframe['close'].pct_change(periods=5) - dataframe['ema_cross_down'] = ( - (dataframe['ema9'] < dataframe['ema21']) & - (dataframe['ema9'].shift() >= dataframe['ema21'].shift()) - ).astype(int) + # 高低点分析 + dataframe['high_20'] = dataframe['high'].rolling(window=20).max() + dataframe['low_20'] = dataframe['low'].rolling(window=20).min() - # 超买超卖区域 - dataframe['rsi_oversold'] = (dataframe['rsi'] < 30).astype(int) - dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype(int) + # === 自定义复合指标 === + # 趋势确认信号 + dataframe['trend_up'] = ( + (dataframe['ema_8'] > dataframe['ema_21']) & + (dataframe['ema_21'] > dataframe['ema_50']) & + (dataframe['close'] > dataframe['ema_8']) + ) - # 价格与均线的关系 - dataframe['price_above_ema50'] = (dataframe['close'] > dataframe['ema50']).astype(int) - dataframe['price_below_ema50'] = (dataframe['close'] < dataframe['ema50']).astype(int) + dataframe['trend_down'] = ( + (dataframe['ema_8'] < dataframe['ema_21']) & + (dataframe['ema_21'] < dataframe['ema_50']) & + (dataframe['close'] < dataframe['ema_8']) + ) - # 趋势强度 - dataframe['strong_trend'] = (dataframe['adx'] > 25).astype(int) + # 动量强度评分 + dataframe['momentum_score'] = ( + ((dataframe['rsi'] > 50).astype(int) * 1) + + ((dataframe['macd'] > dataframe['macdsignal']).astype(int) * 1) + + ((dataframe['adx'] > self.adx_trend_threshold).astype(int) * 1) + + ((dataframe['volume_ratio'] > 1.0).astype(int) * 1) + ) - # 添加蜡烛图形态识别 - dataframe['doji'] = ta.CDLDOJI(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close']) - dataframe['engulfing'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close']) - dataframe['hammer'] = ta.CDLHAMMER(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close']) - dataframe['shooting_star'] = ta.CDLSHOOTINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close']) - - # 价格动量 - dataframe['momentum'] = dataframe['close'] - dataframe['close'].shift(5) + # 波动率适应性指标 + dataframe['volatility_high'] = dataframe['atr'] > dataframe['atr'].rolling(window=20).mean() * 1.5 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # 检查列名是否存在 - downtrend_col = 'resample_60_downtrend_1h' - strong_downtrend_col = 'resample_60_strong_downtrend_1h' - adx_col = 'resample_60_adx_1h' - rsi_col = 'resample_60_rsi_1h' + """ + 入场信号 - 多条件确认系统 + """ + # === 多头入场条件 === - # 如果列名不存在，使用替代方案 - for col, default_value in [ - (downtrend_col, 0), - (strong_downtrend_col, 0), - (adx_col, 25), - (rsi_col, 50) - ]: - if col not in dataframe.columns: - logger.warning(f"Column {col} not found in dataframe. Creating with default value {default_value}.") - dataframe[col] = default_value + # 条件1: 强势突破入场 + dataframe.loc[ + ( + # 趋势确认 + (dataframe['trend_up']) & + (dataframe['close'] > dataframe['ema_21']) & + + # 动量确认 + (dataframe['rsi'] > 45) & (dataframe['rsi'] < 75) & + (dataframe['macd'] > dataframe['macdsignal']) & + (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) & + + # 成交量确认 + (dataframe['volume_ratio'] > self.buy_volume_threshold) & + (dataframe['obv'] > dataframe['obv_ema']) & + + # 价格行为确认 + (dataframe['close'] > dataframe['bb_middleband']) & + (dataframe['bb_percent'] > 0.2) & (dataframe['bb_percent'] < 0.8) & + + # 趋势强度确认 + (dataframe['adx'] > self.adx_trend_threshold) & + (dataframe['plus_di'] > dataframe['minus_di']) + ), + ['enter_long', 'enter_tag']] = (1, 'breakout_long') - # 禁用多头入场 - dataframe['enter_long'] = 0 + # 条件2: 超卖反弹入场 + dataframe.loc[ + ( + # 超卖反弹 + (dataframe['rsi'] < self.rsi_oversold + 10) & + (dataframe['rsi'] > dataframe['rsi'].shift(1)) & + (dataframe['bb_percent'] < 0.2) & + + # 趋势不能太差 + (dataframe['ema_8'] >= dataframe['ema_50']) & + (dataframe['close'] > dataframe['low_20'] * 1.02) & + + # 成交量支持 + (dataframe['volume_ratio'] > 1.2) & + + # MACD底背离迹象 + (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) & + + # 不与第一个条件重复 + (~dataframe['enter_long'].astype(bool)) + ), + ['enter_long', 'enter_tag']] = (1, 'oversold_long') - # 空头入场条件 - 专注于空头策略 - short_conditions = ( - # 高时间周期处于下降趋势 - (dataframe[downtrend_col] > 0) & - - # 趋势强度确认 - (dataframe[adx_col] > 25) & - - # 条件1: 价格突破上轨后回落 + 成交量确认 - ( - (dataframe['upper_break'].rolling(window=5).sum() > 0) & # 最近5根K线内有突破上轨 - (dataframe['close'] < dataframe['close'].shift(2)) & # 价格开始下跌 - (dataframe['close'] < dataframe['ema9']) & # 价格在短期均线下方 - (dataframe['volume_ratio'] > 1.3) & # 成交量放大 - (dataframe['rsi'] < 70) & # RSI不在极度超买区 - (dataframe['rsi'] > 40) & # RSI不在超卖区 - (dataframe[rsi_col] < 60) # 高时间周期RSI不过高 - ) | - - # 条件2: 均线死叉 + RSI超买回落 + 趋势确认 - ( - (dataframe['ema_cross_down'] > 0) & # 均线死叉 - (dataframe['rsi'] > 55) & # RSI相对较高 - (dataframe['rsi'] < dataframe['rsi'].shift(3)) & # RSI下降 - (dataframe['volume_ratio'] > 1.2) & # 成交量放大 - (dataframe['adx'] > 20) & # ADX显示有一定趋势强度 - ((dataframe['shooting_star'] > 0) | (dataframe['engulfing'] < 0)) # 流星线或看跌吞没形态 - ) | - - # 条件3: 价格在高点回落 + 强趋势 - ( - (dataframe['close'] < dataframe['high'].shift()) & - (dataframe['high'].shift() > dataframe['high'].shift(2)) & - (dataframe['close'] < dataframe['ema21']) & - (dataframe['adx'] > 30) & - (dataframe['rsi'] < dataframe['rsi'].shift()) & - (dataframe['rsi'].shift() > 65) & - (dataframe['volume_ratio'] > 1.0) - ) | - - # 条件4: 强下降趋势确认 - ( - (dataframe[strong_downtrend_col] > 0) & - (dataframe['close'] < dataframe['ema21']) & - (dataframe['close'] < dataframe['close'].shift(3)) & - (dataframe['momentum'] < 0) & - (dataframe['volume_ratio'] > 1.1) & - (dataframe['adx'] > 25) - ) - ) + # === 空头入场条件 === - dataframe.loc[short_conditions, 'enter_short'] = 1 - dataframe.loc[short_conditions, 'enter_tag'] = 'chan_sol_short' + # 条件1: 强势下跌入场 + dataframe.loc[ + ( + # 趋势确认 + (dataframe['trend_down']) & + (dataframe['close'] < dataframe['ema_21']) & + + # 动量确认 + (dataframe['rsi'] < 55) & (dataframe['rsi'] > 25) & + (dataframe['macd'] < dataframe['macdsignal']) & + (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) & + + # 成交量确认 + (dataframe['volume_ratio'] > self.sell_volume_threshold) & + (dataframe['obv'] < dataframe['obv_ema']) & + + # 价格行为确认 + (dataframe['close'] < dataframe['bb_middleband']) & + (dataframe['bb_percent'] > 0.2) & (dataframe['bb_percent'] < 0.8) & + + # 趋势强度确认 + (dataframe['adx'] > self.adx_trend_threshold) & + (dataframe['minus_di'] > dataframe['plus_di']) + ), + ['enter_short', 'enter_tag']] = (1, 'breakdown_short') + + # 条件2: 超买回调入场 + dataframe.loc[ + ( + # 超买回调 + (dataframe['rsi'] > self.rsi_overbought - 10) & + (dataframe['rsi'] < dataframe['rsi'].shift(1)) & + (dataframe['bb_percent'] > 0.8) & + + # 趋势不能太好 + (dataframe['ema_8'] <= dataframe['ema_50']) & + (dataframe['close'] < dataframe['high_20'] * 0.98) & + + # 成交量支持 + (dataframe['volume_ratio'] > 1.2) & + + # MACD顶背离迹象 + (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) & + + # 不与第一个条件重复 + (~dataframe['enter_short'].astype(bool)) + ), + ['enter_short', 'enter_tag']] = (1, 'overbought_short') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # 禁用多头出场 - dataframe['exit_long'] = 0 + """ + 出场信号 - 及时止盈止损 + """ + # === 多头出场条件 === - # 空头出场条件 - 更精确的出场 - short_exit_conditions = ( - # 条件1: 趋势反转信号 + # 条件1: 趋势转弱 + dataframe.loc[ ( - (dataframe['ema_cross_up'] > 0) & # 均线金叉 - (dataframe['volume_ratio'] > 1.0) # 成交量确认 - ) | - - # 条件2: 价格突破中期均线 - ( - (dataframe['close'] > dataframe['ema21']) & - (dataframe['close'].shift() < dataframe['ema21'].shift()) & # 确认是刚刚突破 - (dataframe['volume_ratio'] > 1.2) # 成交量确认 - ) | - - # 条件3: 超卖信号 - ( - (dataframe['rsi'] < 30) & # RSI超卖 - (dataframe['close'] < dataframe['bb_lowerband']) # 价格突破下轨 - ) | - - # 条件4: 动量减弱 - ( - (dataframe['rsi'] < 35) & - (dataframe['rsi'] > dataframe['rsi'].shift()) & - (dataframe['rsi'].shift() > dataframe['rsi'].shift(2)) & # RSI连续两根K线上升 - (dataframe['momentum'] > 0) # 价格动量转为正 - ) | - - # 条件5: 锤子线形态 (潜在反转信号) - ( - (dataframe['hammer'] > 0) & - (dataframe['volume_ratio'] > 1.3) - ) - ) + ( + (dataframe['rsi'] > self.rsi_overbought) | + (dataframe['macd'] < dataframe['macdsignal']) | + (dataframe['close'] < dataframe['ema_8']) | + (dataframe['bb_percent'] > 0.95) | + (dataframe['adx'] < 20) + ) & + (dataframe['volume_ratio'] > 1.0) + ), + ['exit_long', 'exit_tag']] = (1, 'trend_weak_long') - dataframe.loc[short_exit_conditions, 'exit_short'] = 1 - dataframe.loc[short_exit_conditions, 'exit_tag'] = 'chan_sol_short_exit' + # === 空头出场条件 === + + # 条件1: 趋势转强 + dataframe.loc[ + ( + ( + (dataframe['rsi'] < self.rsi_oversold) | + (dataframe['macd'] > dataframe['macdsignal']) | + (dataframe['close'] > dataframe['ema_8']) | + (dataframe['bb_percent'] < 0.05) | + (dataframe['adx'] < 20) + ) & + (dataframe['volume_ratio'] > 1.0) + ), + ['exit_short', 'exit_tag']] = (1, 'trend_strong_short') return dataframe - - def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, - time_in_force: str, current_time: datetime, entry_tag: Optional[str], - side: str, **kwargs) -> bool: + + def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, + current_rate: float, current_profit: float, **kwargs) -> float: """ - 在进入交易前进行额外的确认 + 动态止损策略 """ - # 只做空头交易 - if side == "sell" and entry_tag == "chan_sol_short": - return True - return False - + # 基础止损 + if current_profit < -0.05: # 如果亏损超过5%，严格止损 + return -0.08 + + # 盈利后的动态止损 + if current_profit > 0.02: # 盈利超过2%后，调整止损至成本价附近 + return 0.005 + elif current_profit > 0.05: # 盈利超过5%后，保证1%利润 + return -current_profit + 0.01 + elif current_profit > 0.10: # 盈利超过10%后，保证5%利润 + return -current_profit + 0.05 + + return self.stoploss + + def adjust_trade_position(self, trade: Trade, current_time: datetime, + current_rate: float, current_profit: float, + min_stake: float, max_stake: float, + current_entry_rate: float, current_exit_rate: float, + current_entry_profit: float, current_exit_profit: float, + **kwargs) -> Optional[float]: + """ + 仓位调整策略 - 金字塔加仓 + """ + # 如果亏损超过3%，不加仓 + if current_profit < -0.03: + return None + + # 如果盈利超过2%且趋势持续，可以加仓 + if current_profit > 0.02 and len(trade.select_filled_orders(trade.entry_side)) < self.max_entry_position_adjustment: + # 获取当前数据进行趋势确认 + try: + # 简单的趋势确认逻辑 + if trade.is_short: + return max_stake * 0.5 # 空头加仓 + else: + return max_stake * 0.5 # 多头加仓 + except: + pass + + return None + def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: - return 1.0 - - def get_ticker_indicator(self): - return int(self.timeframe[:-1]) \ No newline at end of file + """ + 杠杆设置 - 保守策略 + """ + # 根据入场类型调整杠杆 + if entry_tag and 'breakout' in entry_tag: + return min(2.0, max_leverage) # 突破信号使用较高杠杆 + elif entry_tag and ('oversold' in entry_tag or 'overbought' in entry_tag): + return min(1.5, max_leverage) # 超买超卖信号使用中等杠杆 + else: + return 1.0 # 默认无杠杆 \ No newline at end of file diff --git a/test_fx_strength.py b/test_fx_strength.py new file mode 100644 index 0000000..b464ad2 --- /dev/null +++ b/test_fx_strength.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +测试分型强度检测功能 +""" + +from ChanKLC import ChanKLC +import ChanKLU +from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR +import requests +import json +import numpy as np +import matplotlib.pyplot as plt +from collections import Counter + +def test_fx_strength(): + """测试分型强度检测功能""" + print('=== 分型强度检测功能测试 ===') + + # 创建一个简单的测试KLU，使用正确的构造函数参数 + klu = ChanKLU.ChanKLU( + time='2024-01-01 10:00:00', + open=100.0, + high=105.0, + low=98.0, + close=103.0, + volume=1000 + ) + klu.rsi = 65.0 + klu.volume_ratio = 1.2 + klu.macdhist = 0.5 + + # 创建KLC对象 + klc = ChanKLC(klu, 1, Chan_KLINE_DIR.UP) + klc.fx = Chan_FX_TYPE.TOP + + # 测试强度计算 + strength = klc.calculate_fx_strength() + level = klc.get_fx_strength_level() + is_strong = klc.is_strong_fx() + + print(f'分型强度分数: {strength}') + print(f'分型强度等级: {level}') + print(f'是否强分型: {is_strong}') + + # 测试特征数据集成 + features = klc.get_feature_data() + fx_features = {k: v for k, v in features.items() if 'fx_strength' in k} + print('\n分型强度相关特征:') + for key, value in fx_features.items(): + print(f' {key}: {value}') + + print('\n✅ 分型强度检测功能正常工作!') + return True + +def test_fx_strength_distribution(): + """测试分型强度分布情况""" + + print("=== 分型强度分布分析 ===") + + # 请求API数据 + url = "http://localhost:8123/api/analyze" + params = { + 'symbol': 'SOL/USDT:USDT', + 'timeframe': '5m', + 'timezone': 'Asia/Shanghai' + } + + try: + response = requests.get(url, params=params) + response.raise_for_status() + data = response.json() + except Exception as e: + print(f"❌ 请求API失败: {e}") + return + + # 提取分型强度数据 + fx_strengths = [] + fx_levels = [] + top_strengths = [] + bottom_strengths = [] + + for fx in data.get('klc_fx_info', []): + strength = fx.get('fx_strength', 0) + level = fx.get('fx_strength_level', 'Unknown') + is_bottom = fx.get('is_bottom_fx', False) + + fx_strengths.append(strength) + fx_levels.append(level) + + if is_bottom: + bottom_strengths.append(strength) + else: + top_strengths.append(strength) + + # 统计分析 + if fx_strengths: + print(f"\n📊 基础统计:") + print(f"总分型数量: {len(fx_strengths)}") + print(f"平均强度: {np.mean(fx_strengths):.2f}") + print(f"强度中位数: {np.median(fx_strengths):.2f}") + print(f"强度标准差: {np.std(fx_strengths):.2f}") + print(f"最高强度: {np.max(fx_strengths):.2f}") + print(f"最低强度: {np.min(fx_strengths):.2f}") + + print(f"\n🔝 顶分型统计:") + if top_strengths: + print(f"数量: {len(top_strengths)}") + print(f"平均强度: {np.mean(top_strengths):.2f}") + print(f"最高强度: {np.max(top_strengths):.2f}") + + print(f"\n🔻 底分型统计:") + if bottom_strengths: + print(f"数量: {len(bottom_strengths)}") + print(f"平均强度: {np.mean(bottom_strengths):.2f}") + print(f"最高强度: {np.max(bottom_strengths):.2f}") + + # 强度等级分布 + print(f"\n📈 强度等级分布:") + level_counts = Counter(fx_levels) + for level, count in level_counts.items(): + percentage = (count / len(fx_levels)) * 100 + print(f"{level}: {count} ({percentage:.1f}%)") + + # 强度区间分布 + print(f"\n📊 强度区间分布:") + ranges = [ + (0, 20, "极弱 (0-20)"), + (20, 40, "弱 (20-40)"), + (40, 60, "中等 (40-60)"), + (60, 80, "强 (60-80)"), + (80, 100, "极强 (80-100)") + ] + + for min_val, max_val, label in ranges: + count = sum(1 for s in fx_strengths if min_val <= s < max_val) + percentage = (count / len(fx_strengths)) * 100 + print(f"{label}: {count} ({percentage:.1f}%)") + + # 找出最强和最弱的分型 + print(f"\n⭐ 最强分型 (Top 5):") + sorted_fx = sorted(data.get('klc_fx_info', []), + key=lambda x: x.get('fx_strength', 0), + reverse=True)[:5] + + for i, fx in enumerate(sorted_fx, 1): + fx_type = "底分型" if fx.get('is_bottom_fx', False) else "顶分型" + print(f" {i}. {fx.get('time', 'N/A')} - {fx_type} - 强度: {fx.get('fx_strength', 0):.2f} - 等级: {fx.get('fx_strength_level', 'N/A')}") + + print(f"\n💔 最弱分型 (Bottom 5):") + weakest_fx = sorted(data.get('klc_fx_info', []), + key=lambda x: x.get('fx_strength', 0))[:5] + + for i, fx in enumerate(weakest_fx, 1): + fx_type = "底分型" if fx.get('is_bottom_fx', False) else "顶分型" + print(f" {i}. {fx.get('time', 'N/A')} - {fx_type} - 强度: {fx.get('fx_strength', 0):.2f} - 等级: {fx.get('fx_strength_level', 'N/A')}") + + # 生成直方图 + try: + plt.figure(figsize=(12, 8)) + + # 主强度分布图 + plt.subplot(2, 2, 1) + plt.hist(fx_strengths, bins=20, alpha=0.7, color='blue', edgecolor='black') + plt.title('分型强度分布') + plt.xlabel('强度分数') + plt.ylabel('频次') + plt.axvline(np.mean(fx_strengths), color='red', linestyle='--', label=f'平均值: {np.mean(fx_strengths):.2f}') + plt.legend() + + # 顶分型 vs 底分型对比 + plt.subplot(2, 2, 2) + if top_strengths and bottom_strengths: + plt.hist([top_strengths, bottom_strengths], bins=15, alpha=0.7, + label=['顶分型', '底分型'], color=['red', 'green']) + plt.title('顶分型 vs 底分型强度对比') + plt.xlabel('强度分数') + plt.ylabel('频次') + plt.legend() + + # 强度等级饼图 + plt.subplot(2, 2, 3) + if level_counts: + labels = list(level_counts.keys()) + sizes = list(level_counts.values()) + colors = ['red', 'orange', 'yellow', 'lightgreen', 'green'][:len(labels)] + plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%') + plt.title('强度等级分布') + + # 时间序列图 + plt.subplot(2, 2, 4) + x_vals = range(len(fx_strengths)) + colors = ['red' if not fx.get('is_bottom_fx', False) else 'green' + for fx in data.get('klc_fx_info', [])] + plt.scatter(x_vals, fx_strengths, c=colors, alpha=0.6) + plt.title('分型强度时间序列 (红=顶分型, 绿=底分型)') + plt.xlabel('分型序号') + plt.ylabel('强度分数') + + plt.tight_layout() + plt.savefig('user_data/Chan/fx_strength_analysis.png', dpi=300, bbox_inches='tight') + print(f"\n📈 图表已保存到: user_data/Chan/fx_strength_analysis.png") + + except ImportError: + print("\n📈 matplotlib 未安装，跳过图表生成") + except Exception as e: + print(f"\n❌ 生成图表失败: {e}") + + else: + print("❌ 未找到分型强度数据") + +if __name__ == "__main__": + test_fx_strength() + test_fx_strength_distribution() \ No newline at end of file diff --git a/test_web_data.py b/test_web_data.py new file mode 100644 index 0000000..0bfb9d2 --- /dev/null +++ b/test_web_data.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +测试web接口返回的分型强度数据 +""" + +import requests +import json +import sys +import os + +# 添加父目录到系统路径以便导入模块 +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +def test_web_api(): + """测试web API接口返回的分型强度数据""" + + print('=== 测试Web API分型强度数据 ===') + + # 构建请求URL + base_url = "http://localhost:8123" + endpoint = "/api/analyze" + + params = { + 'symbol': 'SOL/USDT:USDT', + 'timeframe': '5m', + 'timezone': 'Asia/Shanghai' + } + + try: + print(f"发送请求到: {base_url}{endpoint}") + print(f"参数: {params}") + + # 发送请求 + response = requests.get(f"{base_url}{endpoint}", params=params, timeout=30) + + if response.status_code == 200: + data = response.json() + + # 检查是否有分型信息 + if 'klc_fx_info' in data: + fx_info = data['klc_fx_info'] + print(f"\n找到 {len(fx_info)} 个分型") + + # 显示前3个分型的详细信息 + for i, fx in enumerate(fx_info[:3]): + print(f"\n分型 #{i+1}:") + print(f" 时间: {fx.get('time', '无')}") + print(f" 价格: {fx.get('price', '无')}") + print(f" 分型类型: {fx.get('fx_type', '无')}") + print(f" 是否底分型: {fx.get('is_bottom', '无')}") + print(f" 强度分数: {fx.get('fx_strength', '缺失!')}") + print(f" 强度等级: {fx.get('fx_strength_level', '缺失!')}") + print(f" 是否强分型: {fx.get('is_strong_fx', '缺失!')}") + + # 检查强度数据是否完整 + missing_strength_count = 0 + for fx in fx_info: + if 'fx_strength' not in fx or 'fx_strength_level' not in fx or 'is_strong_fx' not in fx: + missing_strength_count += 1 + + if missing_strength_count == 0: + print(f"\n✅ 所有 {len(fx_info)} 个分型都包含完整的强度数据") + else: + print(f"\n❌ 有 {missing_strength_count} 个分型缺少强度数据") + + else: + print("\n❌ 响应中未找到分型信息 (klc_fx_info)") + + # 检查小周期分型信息 + if 'element_klc_fx_info' in data: + element_fx_info = data['element_klc_fx_info'] + print(f"\n找到 {len(element_fx_info)} 个小周期分型") + + # 检查小周期强度数据 + missing_element_strength_count = 0 + for fx in element_fx_info: + if 'fx_strength' not in fx or 'fx_strength_level' not in fx or 'is_strong_fx' not in fx: + missing_element_strength_count += 1 + + if missing_element_strength_count == 0: + print(f"✅ 所有 {len(element_fx_info)} 个小周期分型都包含完整的强度数据") + else: + print(f"❌ 有 {missing_element_strength_count} 个小周期分型缺少强度数据") + + else: + print(f"❌ 请求失败，状态码: {response.status_code}") + print(f"响应内容: {response.text}") + + except requests.exceptions.ConnectionError: + print("❌ 无法连接到服务器，请确保web服务正在运行 (python web/app.py)") + except Exception as e: + print(f"❌ 测试过程中出错: {e}") + +def print_usage(): + """打印使用说明""" + print("\n=== 使用说明 ===") + print("1. 确保web服务正在运行:") + print(" cd user_data/Chan/web") + print(" python app.py") + print("\n2. 然后运行此测试脚本:") + print(" python test_web_data.py") + print("\n3. 检查控制台输出，确认分型强度数据是否正确返回") + +if __name__ == "__main__": + test_web_api() + print_usage() \ No newline at end of file diff --git a/web/app.py b/web/app.py index 3dcb67a..70e91a4 100644 --- a/web/app.py +++ b/web/app.py @@ -220,11 +220,19 @@ def analyze_chan(df): klc_fx_info = [] for klc in klc_list: if hasattr(klc, 'klc_fx_type') and klc.klc_fx_type != Chan_KLC_FX.UNKNOWN: + # 计算分型强度 + fx_strength = klc.calculate_fx_strength() + fx_strength_level = klc.get_fx_strength_level() + is_strong_fx = klc.is_strong_fx() + klc_fx_info.append({ 'time': klc.end_time, 'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high, 'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""), - 'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM + 'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM, + 'fx_strength': fx_strength, # 分型强度分数 (0-100) + 'fx_strength_level': fx_strength_level, # 分型强度等级 (极强/强/中等/弱/极弱) + 'is_strong_fx': is_strong_fx # 是否为强分型 }) return { @@ -479,9 +487,12 @@ def analyze(): # 添加K线分型信息 'klc_fx_info': [{ 'time': format_time_safely(point['time'], client_tz), - 'price': point['price'], + 'price': float(point['price']), 'fx_type': point['fx_type'], - 'is_bottom': point['is_bottom'] + 'is_bottom': bool(point['is_bottom']), + 'fx_strength': float(point['fx_strength']), # 分型强度分数 + 'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级 + 'is_strong_fx': bool(point['is_strong_fx']) # 是否为强分型 } for point in analysis_result['klc_fx_info']] }) else: @@ -546,9 +557,12 @@ def analyze(): # 添加小周期分型信息 result['element_klc_fx_info'] = [{ 'time': format_time_safely(point['time'], client_tz), - 'price': point['price'], + 'price': float(point['price']), 'fx_type': point['fx_type'], - 'is_bottom': point['is_bottom'] + 'is_bottom': bool(point['is_bottom']), + 'fx_strength': float(point['fx_strength']), # 分型强度分数 + 'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级 + 'is_strong_fx': bool(point['is_strong_fx']) # 是否为强分型 } for point in element_analysis['klc_fx_info']] print(f"小周期分析完成: {element_timeframe}, 笔数量: {len(result['element_bi_list'])}, {'仅元素数据' if elements_only else '包含主周期数据'}") diff --git a/web/templates/index.html b/web/templates/index.html index f0245e3..85b422a 100644 --- a/web/templates/index.html +++ b/web/templates/index.html @@ -2612,9 +2612,44 @@ const timeStr = param.time; const markers = [...buyMarkers, ...sellMarkers].filter(m => m.time === timeStr); - if (markers.length > 0) { - // 有买卖点标记，显示自定义提示 - const tooltips = markers.map(m => m.tooltip).join('
'); + // 同时检查分型标记 + const fxMarkers = (window.fxMarkers || []).filter(m => m.time === timeStr); + const allMarkers = [...markers, ...fxMarkers]; + + // 显示时区调试信息 + if (window.debugMode) { + const timezone = $('#timezone').val(); + const formattedTime = formatTimeWithTimezone(timeStr * 1000, timezone); + + // 获取当前价格 - 通过param.seriesPrices获取 + let priceInfo = ''; + if (param.seriesPrices && param.seriesPrices.size > 0) { + // 尝试从蜡烛图系列获取价格 + if (tvWidget.series.candleSeries && param.seriesPrices.get(tvWidget.series.candleSeries)) { + const price = param.seriesPrices.get(tvWidget.series.candleSeries); + priceInfo = `价格: ${price.toFixed(2)}`; + } + // 如果没有蜡烛图系列价格，尝试从线图系列获取 + else if (tvWidget.series.lineSeries && param.seriesPrices.get(tvWidget.series.lineSeries)) { + const price = param.seriesPrices.get(tvWidget.series.lineSeries); + priceInfo = `价格: ${price.toFixed(2)}`; + } + } + + // 仅记录最简短的调试信息 + console.debug(`十字线: ${timeStr} -> ${formattedTime} (${timezone})`); + + // 显示自定义时区工具提示，包含价格信息 + crosshairTooltip.innerHTML = `

` + + (priceInfo ? `
${priceInfo}
` : ''); + crosshairTooltip.style.display = 'block'; + crosshairTooltip.style.left = (param.point.x + 15) + 'px'; + crosshairTooltip.style.top = (param.point.y - 30) + 'px'; + } + + if (allMarkers.length > 0) { + // 有买卖点或分型标记，显示自定义提示 + const tooltips = allMarkers.map(m => m.tooltip).join('
'); tooltipElement.innerHTML = tooltips; tooltipElement.style.display = 'block'; tooltipElement.style.left = (param.point.x + 15) + 'px'; @@ -2626,28 +2661,53 @@ } else { // 隐藏提示 tooltipElement.style.display = 'none'; + crosshairTooltip.style.display = 'none'; } }); // 处理图表缩放、平移等事件，隐藏提示 mainChart.timeScale().subscribeVisibleTimeRangeChange(() => { tooltipElement.style.display = 'none'; + crosshairTooltip.style.display = 'none'; }); } } else { console.log('绘制买卖点 - 已禁用'); } - // 显示分型类型标签 + // 绘制分型类型标签 + console.log('=== 开始检查分型显示条件 ==='); + console.log('showKlcFxType勾选状态:', $('#showKlcFxType').is(':checked')); + console.log('currentData.klc_fx_info存在:', !!currentData.klc_fx_info); + console.log('currentData.klc_fx_info长度:', currentData.klc_fx_info ? currentData.klc_fx_info.length : 'undefined'); + if (currentData.klc_fx_info && currentData.klc_fx_info.length > 0) { + console.log('前3个分型数据样本:', currentData.klc_fx_info.slice(0, 3)); + } + if ($('#showKlcFxType').is(':checked') && currentData.klc_fx_info && currentData.klc_fx_info.length > 0) { console.log(`绘制K线分型类型标签，共${currentData.klc_fx_info.length}条`); + // 收集所有分型标记 + const allFxMarkers = []; + // 存储分型标记，用于tooltip功能 + const fxMarkers = []; + currentData.klc_fx_info.forEach(function(fx) { try { // 直接使用UTC时间戳（秒） const timestamp = Math.floor(new Date(fx.time).getTime() / 1000); const price = parseFloat(fx.price); + // 添加时间和价格调试信息 + console.log('处理分型:', { + 原始时间: fx.time, + 转换时间戳: timestamp, + 原始价格: fx.price, + 转换价格: price, + 时间有效: !isNaN(timestamp), + 价格有效: !isNaN(price) + }); + if (isNaN(timestamp) || isNaN(price)) { console.error('分型类型时间或价格转换错误:', fx.time, fx.price); return; @@ -2656,47 +2716,92 @@ // 确定颜色和位置 const color = fx.is_bottom ? '#28a745' : '#dc3545'; // 底分型绿色，顶分型红色 - // 创建标记系列 - const markerSeries = mainChart.addLineSeries({ - lastValueVisible: false, - priceLineVisible: false, + // 根据强度等级调整颜色强度 + let strengthColor = color; + if (fx.is_strong_fx) { + // 强分型使用更亮的颜色 + strengthColor = fx.is_bottom ? '#00ff00' : '#ff0000'; + } + + // 构建显示文本，包含分型类型和强度信息 + // 添加调试信息 + console.log('分型数据:', { + fx_type: fx.fx_type, + fx_strength: fx.fx_strength, + fx_strength_level: fx.fx_strength_level, + is_strong_fx: fx.is_strong_fx }); - // 设置文本标记 - markerSeries.setMarkers([ - { - time: timestamp, - position: fx.is_bottom ? 'belowBar' : 'aboveBar', - color: color, - shape: 'circle', - text: fx.fx_type, - size: 1 - } - ]); + const displayText = `${fx.fx_strength_level} ${fx.fx_strength.toFixed(1)}`; + console.log('显示文本:', displayText); - // 可选：添加更加明显的文本标签 - const textSeries = mainChart.addLineSeries({ - lastValueVisible: false, - priceLineVisible: false, - }); + // 添加标记配置调试 + const markerConfig = { + time: timestamp, + position: fx.is_bottom ? 'belowBar' : 'aboveBar', + color: strengthColor, + shape: fx.is_strong_fx ? 'square' : 'circle', + text: displayText, + size: fx.is_strong_fx ? 2 : 1 + }; + console.log('标记配置:', markerConfig); - textSeries.setData([{ - time: timestamp, - value: price + (fx.is_bottom ? -0.0005 * price : 0.0005 * price) // 小偏移，避免遮挡 - }]); + // 添加到标记数组 + allFxMarkers.push(markerConfig); + + // 创建分型标记对象，包含tooltip信息 + const fxMarker = { + time: timestamp, + tooltip: `
+ ${fx.is_bottom ? '底分型' : '顶分型'}: ${fx.fx_type}
+ 强度分数: ${fx.fx_strength}分
+ 强度等级: ${fx.fx_strength_level}
+ 是否强分型: ${fx.is_strong_fx ? '是' : '否'}
+ 价格: ${price.toFixed(4)}
+ 时间: ${fx.time} +
` + }; + + // 添加到分型标记数组 + fxMarkers.push(fxMarker); } catch (e) { console.error('绘制分型类型标签出错:', e); } }); + + // 一次性设置所有分型标记到主数据系列 + if (allFxMarkers.length > 0) { + console.log('一次性设置', allFxMarkers.length, '个分型标记'); + + // 暂存主周期分型标记，等待与小周期合并 + window.mainFxMarkers = allFxMarkers; + } else { + window.mainFxMarkers = []; + } + + // 将分型标记添加到全局markers中以支持tooltip功能 + if (window.fxMarkers) { + window.fxMarkers = [...window.fxMarkers, ...fxMarkers]; + } else { + window.fxMarkers = fxMarkers; + } + } else { console.log('绘制分型类型标签 - 已禁用或无数据'); + // 清空分型标记 + window.fxMarkers = []; + window.mainFxMarkers = []; } // 绘制小周期分型标记 if ($('#showElementKlcFxType').is(':checked') && currentData.element_klc_fx_info && currentData.element_klc_fx_info.length > 0) { console.log(`绘制小周期分型标记，共${currentData.element_klc_fx_info.length}条`); + // 收集所有小周期分型标记 + const allElementFxMarkers = []; + const elementFxMarkers = []; // 用于tooltip支持 + currentData.element_klc_fx_info.forEach(function(fx) { try { // 直接使用UTC时间戳（秒） @@ -2708,49 +2813,92 @@ return; } - // 小周期分型使用红色标记，不同于主周期分型 - const redColor = '#FF0000'; // 红色 + // 小周期分型使用不同的颜色和样式，与主周期区分 + let strengthColor = fx.is_bottom ? '#FF6B6B' : '#4ECDC4'; // 底分型用珊瑚红，顶分型用薄荷绿 + if (fx.is_strong_fx) { + // 强分型使用更亮的颜色 + strengthColor = fx.is_bottom ? '#FF0000' : '#00CED1'; + } - // 创建标记系列 - const markerSeries = mainChart.addLineSeries({ - lastValueVisible: false, - priceLineVisible: false, - }); + // 构建小周期分型显示文本 + const displayText = `${fx.fx_strength_level} ${fx.fx_strength.toFixed(1)}`; - // 设置文本标记 - markerSeries.setMarkers([ - { - time: timestamp, - position: fx.is_bottom ? 'belowBar' : 'aboveBar', - color: redColor, - shape: 'arrowUp', // 使用箭头形状，与主周期分型区分 - text: fx.is_bottom ? '↓' : '↑', // 显示箭头 - size: 1 - } - ]); + // 小周期分型标记配置 - 根据分型类型使用正确的箭头形状 + const markerConfig = { + time: timestamp, + position: fx.is_bottom ? 'belowBar' : 'aboveBar', + color: strengthColor, + shape: fx.is_bottom ? 'arrowUp' : 'arrowDown', // 底分型向上箭头，顶分型向下箭头 + text: displayText, + size: fx.is_strong_fx ? 2 : 1 + }; - // 为小周期分型添加明显的箭头标记 - const arrowSeries = mainChart.addLineSeries({ - lastValueVisible: false, - priceLineVisible: false, - lineWidth: 1, - color: redColor - }); + console.log('小周期分型标记配置:', markerConfig); + allElementFxMarkers.push(markerConfig); - // 计算标记位置，底分型在价格下方，顶分型在价格上方 - const offset = fx.is_bottom ? -0.001 * price : 0.001 * price; + // 创建小周期分型标记对象，包含tooltip信息 + const elementFxMarker = { + time: timestamp, + tooltip: `
+ 小周期${fx.is_bottom ? '底分型' : '顶分型'}: ${fx.fx_type}
+ 强度分数: ${fx.fx_strength}分
+ 强度等级: ${fx.fx_strength_level}
+ 是否强分型: ${fx.is_strong_fx ? '是' : '否'}
+ 价格: ${price.toFixed(4)}
+ 时间: ${fx.time} +
` + }; - arrowSeries.setData([{ - time: timestamp, - value: price + offset - }]); + // 添加到小周期分型标记数组 + elementFxMarkers.push(elementFxMarker); } catch (e) { console.error('绘制小周期分型标记出错:', e); } }); + + // 将小周期分型标记添加到全局markers中以支持tooltip功能 + if (window.fxMarkers) { + window.fxMarkers = [...window.fxMarkers, ...elementFxMarkers]; + } else { + window.fxMarkers = elementFxMarkers; + } + + // 合并主周期和小周期分型标记，统一设置到K线数据系列 + const combinedMarkers = [...(window.mainFxMarkers || []), ...allElementFxMarkers]; + if (combinedMarkers.length > 0) { + console.log('合并设置', combinedMarkers.length, '个分型标记（主周期:', (window.mainFxMarkers || []).length, '个，小周期:', allElementFxMarkers.length, '个）'); + + // 尝试在K线系列上设置合并后的标记 + if (showOriginalKline && tvWidget.series.candleSeries) { + tvWidget.series.candleSeries.setMarkers(combinedMarkers); + console.log('合并标记已设置到蜡烛图系列'); + } else if (!showOriginalKline && tvWidget.series.lineSeries) { + tvWidget.series.lineSeries.setMarkers(combinedMarkers); + console.log('合并标记已设置到线图系列'); + } else { + console.log('未找到主数据系列，无法设置标记'); + } + } + } else { console.log('绘制小周期分型标记 - 已禁用或无数据'); + + // 只设置主周期分型标记 + if (window.mainFxMarkers && window.mainFxMarkers.length > 0) { + console.log('仅设置', window.mainFxMarkers.length, '个主周期分型标记'); + + // 尝试在K线系列上设置标记 + if (showOriginalKline && tvWidget.series.candleSeries) { + tvWidget.series.candleSeries.setMarkers(window.mainFxMarkers); + console.log('主周期标记已设置到蜡烛图系列'); + } else if (!showOriginalKline && tvWidget.series.lineSeries) { + tvWidget.series.lineSeries.setMarkers(window.mainFxMarkers); + console.log('主周期标记已设置到线图系列'); + } else { + console.log('未找到主数据系列，无法设置标记'); + } + } } // 调整所有图表以适应数据 @@ -2767,13 +2915,13 @@ tvWidget.state.isInitialized = true; // 绑定同步事件 - bindSyncEvents(); + bindSyncEvents(mainChartContainer, volumeChartContainer, macdChartContainer, mainChart, volumeChart, macdChart, showMacd); // 设置图表默认时间范围 setDefaultTimeRange(); // 添加买卖点提示 - setupTooltip(); + setupTooltip(mainChart); // 显示买卖点 if ($('#showTradePoints').is(':checked')) { @@ -2932,7 +3080,10 @@ } } - function bindSyncEvents() { + function bindSyncEvents(mainChartContainer, volumeChartContainer, macdChartContainer, mainChart, volumeChart, macdChart, showMacd) { + // 防止同步过程中的无限循环 + let syncInProgress = false; + // 同步图表的时间范围 function syncCharts(sourceChart, sourceContainer) { // 防止无限循环 @@ -2989,9 +3140,13 @@ }; // 添加事件监听 - addChartSyncEvents(mainChartContainer, mainChart); - addChartSyncEvents(volumeChartContainer, volumeChart); - if (showMacd && macdChart) { + if (mainChartContainer && mainChart) { + addChartSyncEvents(mainChartContainer, mainChart); + } + if (volumeChartContainer && volumeChart) { + addChartSyncEvents(volumeChartContainer, volumeChart); + } + if (showMacd && macdChartContainer && macdChart) { addChartSyncEvents(macdChartContainer, macdChart); } @@ -3003,19 +3158,23 @@ // 窗口大小变化时重绘图表 window.addEventListener('resize', () => { // 调整主图大小 - mainChart.applyOptions({ - width: mainChartContainer.clientWidth, - height: mainChartContainer.clientHeight - }); + if (mainChart && mainChartContainer) { + mainChart.applyOptions({ + width: mainChartContainer.clientWidth, + height: mainChartContainer.clientHeight + }); + } // 调整成交量图大小 - volumeChart.applyOptions({ - width: volumeChartContainer.clientWidth, - height: volumeChartContainer.clientHeight - }); + if (volumeChart && volumeChartContainer) { + volumeChart.applyOptions({ + width: volumeChartContainer.clientWidth, + height: volumeChartContainer.clientHeight + }); + } // 调整MACD图大小 - if (showMacd && macdChart) { + if (showMacd && macdChart && macdChartContainer) { macdChart.applyOptions({ width: macdChartContainer.clientWidth, height: macdChartContainer.clientHeight @@ -3027,7 +3186,7 @@ }); } - function setupTooltip() { + function setupTooltip(mainChart, buyMarkers = [], sellMarkers = []) { // 调试变量 window.debugMode = true; @@ -3051,65 +3210,71 @@ document.body.appendChild(crosshairTooltip); // 添加鼠标悬停事件显示提示 - mainChart.subscribeCrosshairMove(param => { - if (param.time && param.point) { - const timeStr = param.time; - const markers = [...buyMarkers, ...sellMarkers].filter(m => m.time === timeStr); - - // 显示时区调试信息 - if (window.debugMode) { - const timezone = $('#timezone').val(); - const formattedTime = formatTimeWithTimezone(timeStr * 1000, timezone); + if (mainChart) { + mainChart.subscribeCrosshairMove(param => { + if (param.time && param.point) { + const timeStr = param.time; + const markers = [...buyMarkers, ...sellMarkers].filter(m => m.time === timeStr); - // 获取当前价格 - 通过param.seriesPrices获取 - let priceInfo = ''; - if (param.seriesPrices && param.seriesPrices.size > 0) { - // 尝试从蜡烛图系列获取价格 - if (tvWidget.series.candleSeries && param.seriesPrices.get(tvWidget.series.candleSeries)) { - const price = param.seriesPrices.get(tvWidget.series.candleSeries); - priceInfo = `价格: ${price.toFixed(2)}`; - } - // 如果没有蜡烛图系列价格，尝试从线图系列获取 - else if (tvWidget.series.lineSeries && param.seriesPrices.get(tvWidget.series.lineSeries)) { - const price = param.seriesPrices.get(tvWidget.series.lineSeries); - priceInfo = `价格: ${price.toFixed(2)}`; + // 同时检查分型标记 + const fxMarkers = (window.fxMarkers || []).filter(m => m.time === timeStr); + const allMarkers = [...markers, ...fxMarkers]; + + // 显示时区调试信息 + if (window.debugMode) { + const timezone = $('#timezone').val(); + const formattedTime = formatTimeWithTimezone(timeStr * 1000, timezone); + + // 获取当前价格 - 通过param.seriesPrices获取 + let priceInfo = ''; + if (param.seriesPrices && param.seriesPrices.size > 0) { + // 尝试从蜡烛图系列获取价格 + if (tvWidget.series.candleSeries && param.seriesPrices.get(tvWidget.series.candleSeries)) { + const price = param.seriesPrices.get(tvWidget.series.candleSeries); + priceInfo = `价格: ${price.toFixed(2)}`; + } + // 如果没有蜡烛图系列价格，尝试从线图系列获取 + else if (tvWidget.series.lineSeries && param.seriesPrices.get(tvWidget.series.lineSeries)) { + const price = param.seriesPrices.get(tvWidget.series.lineSeries); + priceInfo = `价格: ${price.toFixed(2)}`; + } } + + // 仅记录最简短的调试信息 + console.debug(`十字线: ${timeStr} -> ${formattedTime} (${timezone})`); + + // 显示自定义时区工具提示，包含价格信息 + crosshairTooltip.innerHTML = `
时间: ${formattedTime}
` + + (priceInfo ? `
${priceInfo}
` : ''); + crosshairTooltip.style.display = 'block'; + crosshairTooltip.style.left = (param.point.x + 15) + 'px'; + crosshairTooltip.style.top = (param.point.y - 30) + 'px'; } - // 仅记录最简短的调试信息 - console.debug(`十字线: ${timeStr} -> ${formattedTime} (${timezone})`); - - // 显示自定义时区工具提示，包含价格信息 - crosshairTooltip.innerHTML = `
时间: ${formattedTime}
` + - (priceInfo ? `
${priceInfo}
` : ''); - crosshairTooltip.style.display = 'block'; - crosshairTooltip.style.left = (param.point.x + 15) + 'px'; - crosshairTooltip.style.top = (param.point.y - 30) + 'px'; - } - - if (markers.length > 0) { - // 有买卖点标记，显示自定义提示 - const tooltips = markers.map(m => m.tooltip).join('
'); - tooltipElement.innerHTML = tooltips; - tooltipElement.style.display = 'block'; - tooltipElement.style.left = (param.point.x + 15) + 'px'; - tooltipElement.style.top = (param.point.y + 15) + 'px'; + if (allMarkers.length > 0) { + // 有买卖点或分型标记，显示自定义提示 + const tooltips = allMarkers.map(m => m.tooltip).join('
'); + tooltipElement.innerHTML = tooltips; + tooltipElement.style.display = 'block'; + tooltipElement.style.left = (param.point.x + 15) + 'px'; + tooltipElement.style.top = (param.point.y + 15) + 'px'; + } else { + // 隐藏提示 + tooltipElement.style.display = 'none'; + } } else { // 隐藏提示 tooltipElement.style.display = 'none'; + crosshairTooltip.style.display = 'none'; } - } else { - // 隐藏提示 + }); + + // 处理图表缩放、平移等事件，隐藏提示 + mainChart.timeScale().subscribeVisibleTimeRangeChange(() => { tooltipElement.style.display = 'none'; crosshairTooltip.style.display = 'none'; - } - }); - - // 处理图表缩放、平移等事件，隐藏提示 - mainChart.timeScale().subscribeVisibleTimeRangeChange(() => { - tooltipElement.style.display = 'none'; - crosshairTooltip.style.display = 'none'; - }); + }); + } } // 辅助函数：使用指定时区格式化时间戳