Add klc and klu fx strength check
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jackyu66git
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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KLU与KLC分型强度算法一致性测试
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验证两种算法在相同数据下是否产生一致的结果
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"""
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from ChanKLU import ChanKLU
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from ChanKLC import ChanKLC
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from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR
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import pandas as pd
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from datetime import datetime, timedelta
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def create_test_data():
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"""创建测试用的K线数据"""
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test_cases = [
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# 测试用例1：标准顶分型
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{
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'name': '标准顶分型',
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'data': [
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{'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000}, # K1
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{'open': 101, 'high': 105, 'low': 100, 'close': 103, 'volume': 1500}, # K2 (顶分型中心)
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{'open': 103, 'high': 104, 'low': 98, 'close': 99, 'volume': 1200}, # K3
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]
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},
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# 测试用例2：标准底分型
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{
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'name': '标准底分型',
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'data': [
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{'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000}, # K1
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{'open': 101, 'high': 103, 'low': 95, 'close': 97, 'volume': 1500}, # K2 (底分型中心)
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{'open': 97, 'high': 104, 'low': 96, 'close': 102, 'volume': 1200}, # K3
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]
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},
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# 测试用例3：强势顶分型（放量+下影线）
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{
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'name': '强势顶分型',
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'data': [
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{'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000}, # K1
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{'open': 101, 'high': 108, 'low': 100, 'close': 102, 'volume': 2500}, # K2 (强顶分型)
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{'open': 102, 'high': 103, 'low': 95, 'close': 96, 'volume': 1800}, # K3 (大阴线确认)
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]
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}
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]
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return test_cases
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def setup_klu_chain(data_list):
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"""设置KLU链"""
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klus = []
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base_time = datetime.now()
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for i, data in enumerate(data_list):
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time_str = (base_time + timedelta(minutes=i)).strftime("%Y-%m-%d %H:%M:%S")
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klu = ChanKLU(time_str, data['open'], data['high'], data['low'], data['close'], data['volume'])
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klu.set_idx(i)
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# 设置基础技术指标
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indicators = {
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'ma5': data['close'] + (i-1) * 0.1,
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'ma10': data['close'] + (i-1) * 0.05,
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'rsi': 50 + (i % 3 - 1) * 15,
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'macd': (i % 3 - 1) * 0.01,
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'macdhist': (i % 2) * 0.005,
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'volume_ratio': 1.0 + (i % 2) * 0.3
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}
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klu.set_indicators(indicators)
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klus.append(klu)
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# 建立前后关系
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for i in range(len(klus)):
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if i > 0:
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klus[i].set_pre(klus[i-1])
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if i < len(klus) - 1:
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klus[i].set_next(klus[i+1])
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return klus
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def setup_klc_chain(data_list):
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"""设置KLC链（基于KLU）"""
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klus = setup_klu_chain(data_list)
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klcs = []
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# 为简化测试，假设每个KLU对应一个KLC（无包含关系处理）
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for i, klu in enumerate(klus):
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klc = ChanKLC(klu, i, Chan_KLINE_DIR.UP)
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klc.set_end_klu(klu)
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klcs.append(klc)
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# 建立前后关系
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for i in range(len(klcs)):
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if i > 0:
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klcs[i].set_pre(klcs[i-1])
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if i < len(klcs) - 1:
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klcs[i].set_next(klcs[i+1])
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# 设置分型类型
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if len(klcs) >= 3:
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middle_klc = klcs[1]
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if (middle_klc.high > klcs[0].high and middle_klc.high > klcs[2].high):
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middle_klc.set_fx(Chan_FX_TYPE.TOP)
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elif (middle_klc.low < klcs[0].low and middle_klc.low < klcs[2].low):
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middle_klc.set_fx(Chan_FX_TYPE.BOTTOM)
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return klcs
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def compare_algorithms(test_cases):
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"""对比KLU和KLC算法"""
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print("=" * 80)
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print("KLU与KLC分型强度算法一致性测试")
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print("=" * 80)
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for case in test_cases:
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print(f"\n🔍 测试用例: {case['name']}")
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print("-" * 50)
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# 准备数据
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klus = setup_klu_chain(case['data'])
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klcs = setup_klc_chain(case['data'])
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if len(klus) >= 3 and len(klcs) >= 3:
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middle_klu = klus[1]
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middle_klc = klcs[1]
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# KLU分析
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middle_klu.update_realtime_analysis()
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klu_fx_type = middle_klu.fx_type
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klu_strength = middle_klu.fx_strength
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klu_confirmed = middle_klu.fx_confirmed
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# KLC分析
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klc_fx_type = middle_klc.fx
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klc_strength_raw = middle_klc.cal_fx_strength() # -3到3
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klc_strength_converted = int((klc_strength_raw + 3) * 100 / 6) # 转换为0-100
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# 输出对比结果
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print(f"K线数据: {case['data'][1]}")
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print(f"\nKLU算法结果:")
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print(f" 分型类型: {klu_fx_type}")
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print(f" 分型强度: {klu_strength}")
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print(f" 是否确认: {klu_confirmed}")
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print(f"\nKLC算法结果:")
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print(f" 分型类型: {klc_fx_type}")
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print(f" 分型强度(原始): {klc_strength_raw}")
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print(f" 分型强度(转换): {klc_strength_converted}")
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# 一致性检查
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type_consistent = (klu_fx_type == klc_fx_type)
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strength_diff = abs(klu_strength - klc_strength_converted)
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strength_consistent = strength_diff <= 10 # 允许10分以内的差异
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print(f"\n一致性检查:")
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print(f" 分型类型一致: {'✅' if type_consistent else '❌'}")
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print(f" 强度差异: {strength_diff}分 {'✅' if strength_consistent else '❌'}")
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if not type_consistent or not strength_consistent:
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print(f" ⚠️ 算法结果不一致!")
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else:
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print(f" ✅ 算法结果一致")
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else:
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print("❌ 数据不足，无法进行对比")
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def detailed_strength_analysis():
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"""详细的强度分析对比"""
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print("\n" + "=" * 80)
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print("详细强度分析对比")
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print("=" * 80)
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# 创建一个明确的强分型案例
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strong_top_data = [
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{'open': 100, 'high': 101, 'low': 99, 'close': 100, 'volume': 1000},
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{'open': 100, 'high': 110, 'low': 99, 'close': 102, 'volume': 3000}, # 强顶分型
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{'open': 102, 'high': 103, 'low': 92, 'close': 93, 'volume': 2000}, # 强确认
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{'open': 93, 'high': 94, 'low': 90, 'close': 91, 'volume': 1500}, # 继续下跌
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{'open': 91, 'high': 92, 'low': 88, 'close': 89, 'volume': 1200}, # 进一步确认
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]
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klus = setup_klu_chain(strong_top_data)
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if len(klus) >= 5:
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target_klu = klus[1] # 目标分型K线
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print(f"分析目标: 第2根K线 (索引1)")
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print(f"K线数据: {strong_top_data[1]}")
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# 更新分析
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target_klu.update_realtime_analysis()
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print(f"\n分型检测结果:")
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print(f" 分型类型: {target_klu.fx_type}")
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print(f" 分型确认: {target_klu.fx_confirmed}")
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print(f" 最终强度: {target_klu.fx_strength}")
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# 显示中间计算过程（需要重新调用以获取详细信息）
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if target_klu.fx_confirmed:
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print(f"\n强度计算过程:")
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is_bi_end = target_klu._check_if_bi_ending_fx()
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post_confirmation = target_klu._check_post_fx_confirmation()
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fx_quality = target_klu._check_fx_quality()
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print(f" 笔终结判断: {is_bi_end}")
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print(f" 后续确认: {post_confirmation}")
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print(f" 分型质量: {fx_quality}")
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raw_score = is_bi_end + post_confirmation + fx_quality
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final_raw = max(-3, min(3, raw_score))
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converted_score = int((final_raw + 3) * 100 / 6)
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print(f" 原始总分: {raw_score} -> {final_raw}")
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print(f" 转换分数: {converted_score}")
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if __name__ == "__main__":
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# 运行测试
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test_cases = create_test_data()
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compare_algorithms(test_cases)
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# 详细分析
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detailed_strength_analysis()
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print("\n" + "=" * 80)
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print("测试完成！")
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print("=" * 80)
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