| | |
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| | | import datetime |
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| | | import json |
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| | | import time |
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| | | |
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| | | import constant |
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| | | from log_module import async_log_util |
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| | | from log_module.log import logger_common, logger_Overall_market_strength_score, logger_debug |
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| | | # import time |
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| | | # 引入掘金API |
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| | | # from gm.api import * |
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| | | from strategy import basic_methods, kpl_api |
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| | | from strategy import data_cache |
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| | | |
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| | |
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| | | |
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| | | # 获取logger实例 |
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| | | logger = logger_common |
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| | | |
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| | | # ====================== |
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| | | # 模拟数据生成（完整版） |
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| | | # ====================== |
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| | | # data = { |
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| | | # 'total_stocks': 5000, # 全市场股票总数 |
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| | | # 'limit_up': 120, # 涨停股数量 |
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| | | # 'limit_down': 5, # 跌停股数量 |
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| | | # 'advance_count': 2800, # 上涨家数 |
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| | | # 'decline_count': 1500, # 下跌家数 |
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| | | # 'high_retreat_count': 30, # 当日回撤超10%的个股数量 |
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| | | # 'turnover': 1.2e12, # 当日成交额（元） |
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| | | # 'turnover_prev_day': 1.0e12, # 前一日成交额 |
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| | | # 'northbound_inflow': 8e9, # 北向资金净流入（元） |
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| | | # 'max_northbound_30d': 15e9, # 近30日最大北向净流入 |
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| | | # 'margin_buy': 8e10, # 融资买入额 |
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| | | # 'sector_gains': [3.5, 2.8, 1.9, -0.5], # 前4大板块涨幅（%） |
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| | | # 'max_continuous_boards': 7, # 最高连板数（如7连板） |
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| | | # 'continuous_boards': [7, 5, 3, 2], # 连板梯队（各层级连板数量） |
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| | | # } |
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| | | # |
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| | | # |
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| | | # # ====================== |
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| | | # # 因子计算与标准化处理 |
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| | | # # ====================== |
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| | | # def calculate_factors(data): |
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| | | # factors = {} |
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| | | # |
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| | | # # === 新增核心因子 === |
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| | | # # 1. 涨跌家数比（0-100分） |
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| | | # if data['decline_count'] == 0: |
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| | | # factors['advance_ratio'] = 100.0 |
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| | | # else: |
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| | | # factors['advance_ratio'] = min( |
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| | | # (data['advance_count'] / data['decline_count']) * 50, # 比值1:2对应100分 |
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| | | # 100.0 |
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| | | # ) |
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| | | # |
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| | | # # 2. 涨停强度（标准化到0-100） |
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| | | # factors['limit_strength'] = ( |
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| | | # (data['limit_up'] - data['limit_down']) / data['total_stocks'] * 1000 # 放大差异 |
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| | | # ) |
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| | | # |
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| | | # # 3. 大幅回撤比例（0-100分） |
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| | | # factors['high_retreat_ratio'] = ( |
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| | | # data['high_retreat_count'] / data['total_stocks'] * 1000 # 千分比更敏感 |
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| | | # ) |
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| | | # |
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| | | # # 4. 连板高度与梯队（根据历史极值归一化） |
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| | | # factors['max_continuous'] = ( |
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| | | # data['max_continuous_boards'] / 10 * 100 # 假设历史最高10连板 |
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| | | # ) |
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| | | # factors['board_ladder'] = ( |
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| | | # len(data['continuous_boards']) / 5 * 100 # 假设最多5个连板层级 |
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| | | # ) |
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| | | # |
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| | | # # === 原有因子优化 === |
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| | | # # 5. 量能变化（成交额增长率） |
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| | | # turnover_growth = ( |
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| | | # (data['turnover'] - data['turnover_prev_day']) / |
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| | | # data['turnover_prev_day'] * 100 |
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| | | # ) |
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| | | # factors['liquidity'] = turnover_growth |
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| | | # |
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| | | # # 6. 板块强度（前3板块平均涨幅） |
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| | | # top_sectors = sorted(data['sector_gains'], reverse=True)[:3] |
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| | | # sector_avg = sum(top_sectors) / len(top_sectors) |
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| | | # |
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| | | # factors['sector_strength'] = ((sector_avg - (-5.0)) / (10.0 - (-5.0)) * 100) # 历史范围-5%~10% |
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| | | # # 7. 北向资金强度 |
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| | | # factors['northbound_ratio'] = (data['northbound_inflow'] / data['max_northbound_30d'] * 100) |
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| | | # |
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| | | # # 8. 融资买入占比 |
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| | | # factors['margin_ratio'] = (data['margin_buy'] / data['turnover'] * 100) |
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| | | # |
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| | | # # # 强制所有因子在0-100范围内 |
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| | | # for key in factors: |
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| | | # factors[key] = max(0.0, min(factors[key], 100.0)) |
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| | | # |
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| | | # return factors |
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| | | # |
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| | | # |
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| | | # # ====================== |
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| | | # # 权重分配（总权重1.0） |
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| | | # # ====================== |
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| | | # def get_weights(): |
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| | | # return { |
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| | | # # 新增因子权重 |
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| | | # 'advance_ratio': 0.15, # 涨跌家数比 |
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| | | # 'limit_strength': 0.2, # 涨停强度 |
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| | | # 'high_retreat_ratio': 0.1, # 大幅回撤 |
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| | | # 'max_continuous': 0.1, # 连板高度 |
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| | | # 'board_ladder': 0.05, # 连板梯队 |
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| | | # # 原有因子权重 |
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| | | # 'liquidity': 0.15, # 量能变化 |
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| | | # 'sector_strength': 0.1, # 板块强度 |
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| | | # 'northbound_ratio': 0.1, # 北向资金 |
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| | | # 'margin_ratio': 0.05 # 融资买入 |
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| | | # } |
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| | | # |
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| | | # |
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| | | # # ====================== |
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| | | # # 综合强度分数计算（含动态修正） |
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| | | # # ====================== |
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| | | # def composite_strength_score(data): |
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| | | # factors = calculate_factors(data) |
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| | | # weights = get_weights() |
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| | | # |
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| | | # # 基础加权得分 |
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| | | # score = sum(factors[key] * weights[key] for key in factors) |
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| | | # |
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| | | # # === 动态修正规则 === |
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| | | # # 规则1：涨停数量超过100家时额外加分 |
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| | | # if data['limit_up'] > 100: |
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| | | # score += min((data['limit_up'] - 100) * 0.2, 10) # 最多加10分 |
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| | | # |
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| | | # # 规则2：连板梯队断裂时扣分（如最高板与次高板差距≥3） |
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| | | # continuous_boards = sorted(data['continuous_boards'], reverse=True) |
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| | | # if len(continuous_boards) >= 2 and (continuous_boards[0] - continuous_boards[1] >= 3): |
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| | | # score -= 15 # 梯队断裂惩罚 |
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| | | # |
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| | | # # 规则3：北向资金与涨跌家数背离时调整 |
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| | | # if (factors['northbound_ratio'] > 50) and (factors['advance_ratio'] < 40): |
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| | | # score *= 0.9 # 权重股拉升导致的虚假繁荣 |
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| | | # |
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| | | # return max(0.0, min(score, 100.0)) |
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| | | |
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| | | |
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| | | # ====================== |
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| | | # 执行计算与结果输出 |
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| | | # ====================== |
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| | | # final_score = composite_strength_score(data) |
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| | | # print("=== 综合强度分数 ===") |
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| | | # print(f"当前得分: {final_score:.1f}/100") |
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| | | # |
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| | | # # 输出因子贡献度分析 |
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| | | # factors = calculate_factors(data) |
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| | | # weights = get_weights() |
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| | | # print("\n=== 因子贡献度明细 ===") |
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| | | # for key in factors: |
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| | | # print(f"{key:20s}: {factors[key]:5.1f} × {weights[key]:.0%} = {factors[key] * weights[key]:5.1f}") |
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| | | |
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| | | ''' 代码输出示例： |
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| | | === 综合强度分数 === |
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| | | 当前得分: 78.4/100 |
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| | | |
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| | | === 因子贡献度明细 === |
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| | | advance_ratio : 93.3 × 15% = 14.0 |
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| | | limit_strength : 23.0 × 20% = 4.6 |
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| | | high_retreat_ratio : 6.0 × 10% = 0.6 |
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| | | max_continuous : 70.0 × 10% = 7.0 |
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| | | board_ladder : 80.0 × 5% = 4.0 |
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| | | liquidity : 20.0 × 15% = 3.0 |
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| | | sector_strength : 60.0 × 10% = 6.0 |
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| | | northbound_ratio : 53.3 × 10% = 5.3 |
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| | | margin_ratio : 10.0 × 5% = 0.5 |
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| | | ''' |
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| | | |
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| | | |
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| | | # 指数行情策略函数 |
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| | |
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| | | 'TSXV': {} |
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| | | } |
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| | | if current_info is not None and len_current_info > 0: |
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| | | now_time_str = tool.get_now_time_str() |
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| | | if data_cache.L1_DATA_START_TIME < now_time_str < data_cache.CLOSING_TIME: |
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| | | # print(f"current_info------------{current_info}") |
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| | | # 上证指数数据 |
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| | | Shanghai_index_data = current_info.get('000001') |
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| | |
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| | | |
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| | | |
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| | | # 计算市场分布形态因子 函数 |
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| | | def calculate_market_distribution_pattern_factor(data): |
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| | | # ====================== 输入数据 ====================== |
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| | | data = {'-1': '210', '-10': '4', '-2': '134', '-3': '71', '-4': '45', '-5': '18', '-6': '15', '-7': '7', '-8': '6', '-9': '2', '0': '83', '1': '650', '10': '41', '2': '1242', '3': '1182', '4': '784', '5': '334', '6': '139', '7': '77', '8': '46', '9': '16', 'DT': 5, 'SJDT': '1', 'SJZT': '17', 'STDT': '4', 'STZT': '2', 'SZJS': 4530, 'XDJS': 517, 'ZSZDFB': '1929,247,56,435,61,3,259,29,4,44,5,1,38,9,3,239,51,10,', 'ZT': 19, 'sign': '市场人气较好', 'szln': 1723817, 'qscln': 5291241, 's_zrcs': 2413498, 'q_zrcs': 6183792, 's_zrtj': 75543024, 'q_zrtj': 169960513} |
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| | | |
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| | | # ====================== 数据预处理 ====================== |
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| | | def preprocess_data(data): |
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| | | """ |
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| | | 原生Python数据预处理 |
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| | | 返回： |
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| | | sorted_bins : 排序后的涨跌幅区间（整数列表） |
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| | | sorted_counts : 对应区间的股票数量 |
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| | | total : 总股票数 |
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| | | pos_counts : 正涨跌区间的数量列表 |
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| | | neg_counts : 负涨跌区间的数量列表 |
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| | | 预处理原始数据： |
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| | | 1. 转换所有数值字段为整数 |
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| | | 2. 解析涨跌分布(ZSZDFB)为整数列表 |
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| | | 3. 分离涨跌幅分布数据 |
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| | | """ |
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| | | # 将字符串键转为整数并排序 |
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| | | sorted_items = sorted(data.items(), key=lambda x: int(x[0])) |
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| | | sorted_bins = [int(k) for k, v in sorted_items] |
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| | | sorted_counts = [v for k, v in sorted_items] |
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| | | processed = {} |
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| | | |
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| | | # 计算总股票数 |
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| | | total = sum(sorted_counts) |
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| | | |
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| | | # 分离正负区间（排除0%） |
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| | | pos_counts = [v for k, v in sorted_items if int(k) > 0] |
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| | | neg_counts = [v for k, v in sorted_items if int(k) < 0] |
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| | | |
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| | | return sorted_bins, sorted_counts, total, pos_counts, neg_counts |
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| | | |
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| | | |
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| | | # 执行预处理 |
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| | | bins, counts, total, pos_counts, neg_counts = calculate_market_distribution_pattern_factor(data_cache.rise_and_fall_statistics_dirt) |
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| | | |
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| | | |
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| | | # ====================== 因子计算模块 ====================== |
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| | | class NativeFactorCalculator: |
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| | | def __init__(self, bins, counts, total, pos_counts, neg_counts): |
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| | | self.bins = bins |
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| | | self.counts = counts |
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| | | # todo total 会默认为0的情况 |
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| | | if total != 0: |
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| | | self.total = total |
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| | | # 通用字段类型转换 |
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| | | for key, value in data.items(): |
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| | | if isinstance(value, str) and value.isdigit(): # 处理字符串数字 |
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| | | processed[key] = int(value) |
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| | | elif key == 'ZSZDFB': # 特殊处理涨跌分布 |
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| | | # 过滤空值并转换为整数列表 |
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| | | processed[key] = [int(x) for x in value.strip(',').split(',')] |
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| | | else: |
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| | | self.total = 1 |
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| | | self.pos_counts = pos_counts |
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| | | self.neg_counts = neg_counts |
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| | | processed[key] = value # 保留原始类型 |
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| | | |
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| | | def calculate_bdr(self): |
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| | | """原生涨跌比计算""" |
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| | | sum_pos = sum(self.pos_counts) |
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| | | sum_neg = sum(self.neg_counts) |
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| | | return sum_pos / sum_neg if sum_neg != 0 else float('nan') |
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| | | |
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| | | def calculate_extreme_ratio(self): |
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| | | """极端波动比例计算""" |
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| | | extreme_total = 0 |
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| | | for b, c in zip(self.bins, self.counts): |
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| | | if b >= 5 or b <= -5: |
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| | | extreme_total += c |
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| | | return extreme_total / self.total |
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| | | |
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| | | def calculate_market_breadth(self): |
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| | | """市场宽度因子计算""" |
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| | | diff = sum(self.pos_counts) - sum(self.neg_counts) |
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| | | return diff / self.total |
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| | | |
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| | | def _expand_distribution(self): |
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| | | """将分箱数据展开为原始数据点（用于计算统计指标）""" |
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| | | expanded = [] |
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| | | for value, count in zip(self.bins, self.counts): |
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| | | expanded.extend([value] * count) |
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| | | return expanded |
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| | | |
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| | | def calculate_distribution_metrics(self): |
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| | | """原生偏度与峰度计算""" |
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| | | data = self._expand_distribution() |
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| | | n = len(data) |
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| | | if n < 2: |
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| | | return (float('nan'), float('nan')) |
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| | | |
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| | | # 计算均值 |
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| | | mean = sum(data) / n |
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| | | |
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| | | # 计算标准差 |
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| | | variance = sum((x - mean) ** 2 for x in data) / (n - 1) |
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| | | std_dev = variance ** 0.5 |
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| | | |
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| | | # 计算偏度 |
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| | | skewness = (sum((x - mean) ** 3 for x in data) / n) / (std_dev ** 3) if std_dev != 0 else 0 |
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| | | |
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| | | # 计算峰度（Fisher定义，即正态分布峰度为0） |
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| | | kurtosis = (sum((x - mean) ** 4 for x in data) / n) / (std_dev ** 4) - 3 if std_dev != 0 else 0 |
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| | | |
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| | | return (skewness, kurtosis) |
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| | | # 分离涨跌幅分布数据（示例划分，具体区间需确认） |
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| | | # 假设ZSZDFB对应从-10%到10%的18个区间分布（根据数据长度推测） |
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| | | processed['price_distribution'] = { |
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| | | 'bins': list(range(-10, 11)), # 生成-10到10的整数区间标识 |
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| | | 'counts': processed['ZSZDFB'] |
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| | | } |
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| | | return processed |
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| | | |
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| | | |
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| | | # 实例化并计算因子 |
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| | | calculator = NativeFactorCalculator(bins, counts, total, pos_counts, neg_counts) |
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| | | bdr = calculator.calculate_bdr() |
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| | | extreme_ratio = calculator.calculate_extreme_ratio() |
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| | | market_breadth = calculator.calculate_market_breadth() |
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| | | skewness, kurtosis = calculator.calculate_distribution_metrics() |
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| | | # data = preprocess_data(raw_data) |
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| | | |
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| | | |
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| | | # ====================== 策略信号生成模块 ====================== |
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| | | class NativeTradingStrategy: |
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| | | def __init__(self, bdr_threshold=1.5, extreme_threshold=0.15): |
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| | | self.bdr_threshold = bdr_threshold |
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| | | self.extreme_threshold = extreme_threshold |
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| | | # ====================== 核心因子计算 ====================== |
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| | | def calculate_factors(data): |
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| | | if data is not None: |
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| | | zero = int(data.get('0')) |
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| | | rise_one = int(data.get('1')) |
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| | | rise_two = int(data.get('2')) |
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| | | rise_three = int(data.get('3')) |
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| | | rise_four = int(data.get('4')) |
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| | | rise_five = int(data.get('5')) |
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| | | rise_six = int(data.get('6')) |
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| | | rise_seven = int(data.get('7')) |
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| | | rise_eight = int(data.get('8')) |
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| | | rise_nine = int(data.get('9')) |
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| | | rise_ten = int(data.get('10')) |
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| | | fall_one = int(data.get('-1')) |
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| | | fall_two = int(data.get('-2')) |
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| | | fall_three = int(data.get('-3')) |
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| | | fall_four = int(data.get('-4')) |
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| | | fall_five = int(data.get('-5')) |
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| | | fall_six = int(data.get('-6')) |
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| | | fall_seven = int(data.get('-7')) |
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| | | fall_eight = int(data.get('-8')) |
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| | | fall_nine = int(data.get('-9')) |
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| | | fall_ten = int(data.get('-10')) |
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| | | print(type(fall_ten)) |
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| | | |
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| | | def generate_signals(self, bdr, extreme_ratio, market_breadth, skewness): |
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| | | """生成交易信号（与之前相同，保持策略逻辑）""" |
|---|
| | | """计算市场关键指标因子""" |
|---|
| | | factors = {} |
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| | | # 总票数 |
|---|
| | | total_stocks = zero + rise_one + rise_two + rise_three + rise_four + rise_five + rise_six + rise_seven + rise_eight + rise_nine + rise_ten + fall_one + fall_two + fall_three + fall_four + fall_five + fall_six + fall_seven + fall_eight + fall_nine + fall_ten |
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| | | print(f"total_stocks = {total_stocks}") |
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| | | factors['total_stocks'] = total_stocks |
|---|
| | | # 涨跌统计（排除0%） |
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| | | rise = rise_one + rise_two + rise_three + rise_four + rise_five + rise_six + rise_seven + rise_eight + rise_nine + rise_ten |
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| | | fall = fall_one + fall_two + fall_three + fall_four + fall_five + fall_six + fall_seven + fall_eight + fall_nine + fall_ten |
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| | | |
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| | | rise_dirt = { |
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| | | 'rise_one': rise_one, |
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| | | 'rise_two': rise_two, |
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| | | 'rise_three': rise_three, |
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| | | 'rise_four': rise_four, |
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| | | 'rise_five': rise_five, |
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| | | 'rise_six': rise_six, |
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| | | 'rise_seven': rise_seven, |
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| | | 'rise_eight': rise_eight, |
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| | | 'rise_nine': rise_nine, |
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| | | 'rise_ten': rise_ten, |
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| | | } |
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| | | fall_dirt = { |
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| | | 'fall_one': fall_one, |
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| | | 'fall_two': fall_two, |
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| | | 'fall_three': fall_three, |
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| | | 'fall_four': fall_four, |
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| | | 'fall_five': fall_five, |
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| | | 'fall_six': fall_six, |
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| | | 'fall_seven': fall_seven, |
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| | | 'fall_eight': fall_eight, |
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| | | 'fall_nine': fall_nine, |
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| | | 'fall_ten': fall_ten, |
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| | | } |
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| | | # 计算所有值的总和 |
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| | | total_sum = sum(rise_dirt.values()) |
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| | | # 计算每个键的值占总和的百分比 |
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| | | percentages = {key: round((value / total_sum) * 100, 2) for key, value in rise_dirt.items()} |
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| | | # 找到最大值对应的键 |
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| | | max_key = max(rise_dirt, key=rise_dirt.get) |
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| | | # 找到最小值对应的键 |
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| | | min_key = min(rise_dirt, key=rise_dirt.get) |
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| | | |
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| | | # 涨跌比因子 --------------------------------------------------- |
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| | | factors['rise_vs_fall'] = { |
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| | | 'rise_vs_fall_ratio': rise / fall if fall > 0 else 0, # 涨跌比 |
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| | | 'rise_gather_area': max_key, # 找到最大值 |
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| | | 'rise_scattered_area': min_key, # 找到最小值 |
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| | | 'percentages': percentages, # 找到最小值 |
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| | | } |
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| | | |
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| | | # 资金流向因子 -------------------------------------------------- |
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| | | factors['capital_flow'] = { |
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| | | 'buy': data['q_zrtj'], # 买方资金净流入 |
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| | | 'sell': data['s_zrtj'], # 卖方资金净流出 |
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| | | 'net': data['q_zrtj'] - data['s_zrtj'] # 资金净流入 |
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| | | } |
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| | | |
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| | | # 市场情绪因子 -------------------------------------------------- |
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| | | factors['sentiment'] = { |
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| | | 'zt_ratio': data['ZT'] / total_stocks, # 涨停占总比 |
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| | | 'zt_vs_dt_ratio': data['ZT'] / data['DT'], # 涨停跌停比 |
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| | | 'sign': 1 if '较好' in data['sign'] else 0, # 情绪标签量化 |
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| | | 'rise_vs_all_stocks_ratio': data['SZJS'] / total_stocks, # 活跃股占比 |
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| | | 'rise_vs_fall_ratio': data['SZJS'] / data['SZJS'], # 活跃股占比 |
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| | | 'extreme_ratio': (data['ZT'] + data['DT']) / total_stocks, # 极端波动（涨停+跌停占总比） |
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| | | } |
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| | | return factors |
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| | | |
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| | | # |
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| | | # # ====================== 策略信号生成 ====================== |
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| | | def generate_signals(factors): |
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| | | """生成多维度交易信号""" |
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| | | signals = [] |
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| | | # 信号1：趋势强度判断 |
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| | | if factors['sentiment']['rise_vs_fall_ratio'] > 1.5: |
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| | | signals.append("多头强势：涨跌比高于阈值1.5") |
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| | | elif factors['sentiment']['rise_vs_fall_ratio'] < 0.67: |
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| | | signals.append("空头强势：涨跌比低于阈值0.67") |
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| | | |
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| | | # 信号1：基于涨跌比 |
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| | | if bdr > self.bdr_threshold: |
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| | | signals.append("多头趋势增强 - 加仓指数ETF") |
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| | | elif bdr < 1 / self.bdr_threshold: |
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| | | signals.append("空头趋势增强 - 减仓或反向操作") |
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| | | else: |
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| | | signals.append("趋势中性 - 保持当前仓位") |
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| | | # 信号2：资金动能判断 |
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| | | if factors['capital_flow']['net'] > 1e8: # 假设1亿为阈值 |
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| | | signals.append("资金大幅净流入：关注主力进场") |
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| | | elif factors['capital_flow']['net'] < -5e7: |
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| | | signals.append("资金大幅净流出：警惕风险") |
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| | | |
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| | | # 信号2：极端波动 |
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| | | if extreme_ratio > self.extreme_threshold: |
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| | | signals.append("波动风险升高 - 启动对冲或降低杠杆") |
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| | | else: |
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| | | signals.append("波动正常 - 维持现有风险敞口") |
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| | | # 信号3：极端波动预警 |
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| | | if factors['sentiment']['extreme_ratio'] > 0.1: |
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| | | signals.append("极端波动：涨停跌停股超10%") |
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| | | |
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| | | # 信号3：市场宽度与偏度 |
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| | | if market_breadth > 0.2 and skewness > 0.5: |
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| | | signals.append("市场普涨且偏度正向 - 超配行业龙头股") |
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| | | elif market_breadth < -0.2 and skewness < -0.5: |
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| | | signals.append("市场普跌且偏度负向 - 低估值防御型配置") |
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| | | return signals |
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| | | # 信号4：市场情绪综合 |
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| | | if factors['sentiment']['zt_ratio'] > 0.05 and factors['sentiment']['sign'] == 1: |
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| | | signals.append("高情绪热度：涨停股多且人气向好") |
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| | | |
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| | | |
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| | | # 实例化策略并生成信号 |
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| | | strategy = NativeTradingStrategy() |
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| | | signals = strategy.generate_signals(bdr, extreme_ratio, market_breadth, skewness) |
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| | | return signals if signals else ["无显著信号：维持当前策略"] |
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| | | |
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| | | |
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| | | # 实时设置计划持仓数量 函数 |
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| | |
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| | | # print(f"指数K线{data_cache.all_index_k_line_property_dict}") |
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| | | # all_index_k_line_dict_write() |
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| | | # ====================== 结果输出 ====================== |
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| | | print("========== 因子数值（原生计算） ==========") |
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| | | print(f"涨跌比(BDR): {bdr:.2f}") |
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| | | print(f"极端波动比例: {extreme_ratio:.2%}") |
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| | | print(f"市场宽度因子: {market_breadth:.2f}") |
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| | | print(f"分布偏度: {skewness:.2f}, 峰度: {kurtosis:.2f}") |
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| | | print(f"data=={data}") |
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| | | |
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| | | # 因子计算 |
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| | | factors = calculate_factors(data) |
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| | | |
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| | | # 生成信号 |
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| | | signals = generate_signals(factors) |
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| | | |
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| | | # 结果输出 |
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| | | print("========== 市场关键指标 ==========") |
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| | | print(f"总股票数: {factors['total_stocks']}") |
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| | | print(f"涨跌比(BDR): {factors['rise_vs_fall']['rise_vs_fall_ratio']:.2f}") |
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| | | print(f"极端波动比例: {factors['sentiment']['extreme_ratio']:.2%}") |
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| | | print(f"资金净流入(元): {factors['capital_flow']['net']:,}") |
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| | | print(f"涨停股占比: {factors['sentiment']['zt_ratio']:.2%}") |
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| | | print(f"市场情绪量化: {'积极' if factors['sentiment']['sign'] else '谨慎'}") |
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| | | print(f"上涨聚集区域：{factors['rise_vs_fall']['rise_gather_area']}") |
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| | | print(f"上涨零散区域：{factors['rise_vs_fall']['percentages']}") |
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| | | |
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| | | print("\n========== 策略信号 ==========") |
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| | | for i, signal in enumerate(signals, 1): |
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