194 lines
8.1 KiB
Python
194 lines
8.1 KiB
Python
#!/usr/bin/env python3
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"""
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入场诊断统计分析脚本
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分析回测日志中的入场诊断数据，生成统计报告
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"""
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import re
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import sys
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from collections import defaultdict
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def parse_log_file(log_file):
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"""解析日志文件，提取入场诊断数据"""
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entries = []
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with open(log_file, 'r', encoding='utf-8') as f:
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for line in f:
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if '[入场诊断]' not in line:
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continue
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entry = {}
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# 提取币对
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pair_match = re.search(r'\[入场诊断\]\s+(\S+)\s+\|', line)
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if pair_match:
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entry['pair'] = pair_match.group(1)
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# 提取价格
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price_match = re.search(r'价格:\s+([0-9.]+)', line)
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if price_match:
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entry['price'] = float(price_match.group(1))
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# 提取 vs 5K高点
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high_match = re.search(r'vs 5K高点:\s+([+-]?[0-9.]+)%', line)
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if high_match:
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entry['vs_5k_high'] = float(high_match.group(1))
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# 提取 vs EMA5
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ema_match = re.search(r'vs EMA5:\s+([+-]?[0-9.]+)%', line)
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if ema_match:
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entry['vs_ema5'] = float(ema_match.group(1))
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# 提取布林位置
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bb_match = re.search(r'布林位置:\s+([0-9.-]+)', line)
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if bb_match:
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entry['bb_position'] = float(bb_match.group(1))
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# 提取 RSI
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rsi_match = re.search(r'RSI:\s+([0-9.]+)', line)
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if rsi_match:
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entry['rsi'] = float(rsi_match.group(1))
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# 提取 MACD
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macd_match = re.search(r'MACD:\s+(\w+)', line)
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if macd_match:
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entry['macd'] = macd_match.group(1)
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# 提取市场状态
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market_match = re.search(r'市场:\s+(\w+)', line)
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if market_match:
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entry['market_state'] = market_match.group(1)
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# 提取 ML概率
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ml_match = re.search(r'ML概率:\s+([0-9.-]+)', line)
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if ml_match:
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entry['ml_prob'] = float(ml_match.group(1))
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if entry:
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entries.append(entry)
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return entries
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def analyze_entries(entries):
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"""分析入场数据，生成统计报告"""
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if not entries:
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print("❌ 没有找到入场诊断数据")
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return
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total = len(entries)
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# 统计各项指标
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bb_over_1 = sum(1 for e in entries if e.get('bb_position', 0) > 1.0)
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bb_over_0_8 = sum(1 for e in entries if e.get('bb_position', 0) > 0.8)
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rsi_over_70 = sum(1 for e in entries if e.get('rsi', 0) > 70)
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rsi_over_60 = sum(1 for e in entries if e.get('rsi', 0) > 60)
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high_positive = sum(1 for e in entries if e.get('vs_5k_high', -1) > 0)
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high_near_zero = sum(1 for e in entries if -0.1 <= e.get('vs_5k_high', -1) <= 0)
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ml_zero = sum(1 for e in entries if e.get('ml_prob', -1) == 0.0)
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ml_very_low = sum(1 for e in entries if 0 < e.get('ml_prob', -1) < 0.2)
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ml_low = sum(1 for e in entries if 0.2 <= e.get('ml_prob', -1) < 0.5)
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ml_medium = sum(1 for e in entries if 0.5 <= e.get('ml_prob', -1) < 0.7)
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ml_high = sum(1 for e in entries if e.get('ml_prob', -1) >= 0.7)
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macd_up = sum(1 for e in entries if e.get('macd') == 'up')
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macd_down = sum(1 for e in entries if e.get('macd') == 'down')
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# 市场状态统计
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market_states = defaultdict(int)
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for e in entries:
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if 'market_state' in e:
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market_states[e['market_state']] += 1
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# 币对统计
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pair_counts = defaultdict(int)
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for e in entries:
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if 'pair' in e:
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pair_counts[e['pair']] += 1
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# 打印报告
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print("=" * 80)
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print(f"📊 入场诊断统计报告（共 {total} 条候选入场）")
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print("=" * 80)
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print("\n【1. 布林位置分布】")
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print(f" 布林位置 > 1.0 (布林上轨之上): {bb_over_1:3d}/{total} ({bb_over_1*100//total:2d}%)")
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print(f" 布林位置 > 0.8 (接近上轨): {bb_over_0_8:3d}/{total} ({bb_over_0_8*100//total:2d}%)")
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print(f" 布林位置 ≤ 0.8 (相对安全): {total-bb_over_0_8:3d}/{total} ({(total-bb_over_0_8)*100//total:2d}%)")
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if bb_over_1 > total * 0.1:
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print(f" ⚠️ 警告：{bb_over_1*100//total}% 的入场在布林上轨之上（追高风险）")
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print("\n【2. RSI 超买分布】")
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print(f" RSI > 70 (严重超买): {rsi_over_70:3d}/{total} ({rsi_over_70*100//total:2d}%)")
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print(f" RSI > 60 (轻度超买): {rsi_over_60:3d}/{total} ({rsi_over_60*100//total:2d}%)")
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print(f" RSI ≤ 60 (正常/超卖): {total-rsi_over_60:3d}/{total} ({(total-rsi_over_60)*100//total:2d}%)")
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if rsi_over_70 > total * 0.2:
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print(f" ⚠️ 警告：{rsi_over_70*100//total}% 的入场 RSI 超过 70（超买风险）")
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print("\n【3. 短期高点位置】")
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print(f" vs 5K高点 > 0 (买在高位): {high_positive:3d}/{total} ({high_positive*100//total:2d}%)")
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print(f" vs 5K高点 [-0.1%, 0] (接近): {high_near_zero:3d}/{total} ({high_near_zero*100//total:2d}%)")
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print(f" vs 5K高点 < -0.1% (回调后): {total-high_positive-high_near_zero:3d}/{total} ({(total-high_positive-high_near_zero)*100//total:2d}%)")
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if high_positive > total * 0.05:
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print(f" ⚠️ 警告：{high_positive*100//total}% 的入场买在短期高点之上")
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print("\n【4. ML 置信度分布】")
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print(f" ML 概率 = 0.00 (完全不看好): {ml_zero:3d}/{total} ({ml_zero*100//total:2d}%)")
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print(f" ML 概率 (0, 0.2) (极低): {ml_very_low:3d}/{total} ({ml_very_low*100//total:2d}%)")
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print(f" ML 概率 [0.2, 0.5) (低): {ml_low:3d}/{total} ({ml_low*100//total:2d}%)")
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print(f" ML 概率 [0.5, 0.7) (中): {ml_medium:3d}/{total} ({ml_medium*100//total:2d}%)")
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print(f" ML 概率 >= 0.7 (高): {ml_high:3d}/{total} ({ml_high*100//total:2d}%)")
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print(f" ML 概率 < 0.5 小计: {ml_zero+ml_very_low+ml_low:3d}/{total} ({(ml_zero+ml_very_low+ml_low)*100//total:2d}%)")
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if ml_zero + ml_very_low + ml_low > total * 0.7:
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print(f" ⚠️ 警告：{(ml_zero+ml_very_low+ml_low)*100//total}% 的候选入场 ML 置信度 < 0.5")
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print("\n【5. MACD 趋势】")
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print(f" MACD 上升 (up): {macd_up:3d}/{total} ({macd_up*100//total:2d}%)")
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print(f" MACD 下降 (down): {macd_down:3d}/{total} ({macd_down*100//total:2d}%)")
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print("\n【6. 市场状态分布】")
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for state, count in sorted(market_states.items(), key=lambda x: -x[1]):
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print(f" {state:15s}: {count:3d}/{total} ({count*100//total:2d}%)")
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print("\n【7. 币对分布（Top 10）】")
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for pair, count in sorted(pair_counts.items(), key=lambda x: -x[1])[:10]:
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print(f" {pair:15s}: {count:3d}/{total} ({count*100//total:2d}%)")
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print("\n" + "=" * 80)
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print("💡 建议：")
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if bb_over_1 > total * 0.1:
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print(f" • 建议添加布林位置过滤：bb_position <= 0.8")
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if rsi_over_70 > total * 0.2:
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print(f" • 建议添加 RSI 过滤：rsi <= 65 或 70")
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if high_positive > total * 0.05:
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print(f" • 建议添加短期高点过滤：vs_5k_high <= 0")
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if ml_zero + ml_very_low + ml_low > total * 0.7:
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print(f" • ML 审核官可能阈值过低，建议提高 ml_entry_signal_threshold")
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print("=" * 80)
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if __name__ == '__main__':
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if len(sys.argv) < 2:
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print("用法: python analyze_entry_diagnostics.py <日志文件路径>")
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print("示例: python analyze_entry_diagnostics.py outputs/filted.log")
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sys.exit(1)
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log_file = sys.argv[1]
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try:
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entries = parse_log_file(log_file)
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analyze_entries(entries)
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except FileNotFoundError:
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print(f"❌ 错误：找不到文件 {log_file}")
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sys.exit(1)
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except Exception as e:
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print(f"❌ 错误：{e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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