PorterandCursor
2c1232555e
将核心结构、指标与分析拆到 chan/{core,indicators,analysis,pipeline}；
根目录保留兼容 shim；strategies 改为从 chan 包导入；买卖点经 bsp_macd 与 MACD 接合。
Co-authored-by: Cursor <cursoragent@cursor.com>
220 lines
7.4 KiB
Python
220 lines
7.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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实时K线分型强弱判断示例
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解决KLC滞后问题，提供即时的分型信号
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"""
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from ..core.ChanKLU import ChanKLU
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from ..core.ChanEnum import Chan_FX_TYPE
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import pandas as pd
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from datetime import datetime, timedelta
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class RealtimeFxAnalyzer:
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"""实时分型分析器"""
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def __init__(self):
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self.klu_list = []
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self.latest_signals = []
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def add_kline(self, time, open_price, high, low, close, volume, indicators=None):
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"""
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添加新的K线数据并进行实时分析
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Args:
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time: 时间
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open_price, high, low, close, volume: K线数据
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indicators: 技术指标字典 {'macd': xx, 'rsi': xx, 'ma5': xx, ...}
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"""
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# 创建新的KLU对象
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new_klu = ChanKLU(time, open_price, high, low, close, volume)
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# 设置技术指标
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if indicators:
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new_klu.set_indicators(indicators)
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# 设置索引
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new_klu.set_idx(len(self.klu_list))
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# 建立前后关系链
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if len(self.klu_list) >= 1:
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prev_klu = self.klu_list[-1]
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new_klu.set_pre(prev_klu)
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prev_klu.set_next(new_klu)
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# 如果有足够的数据，设置前一根K线的next关系
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if len(self.klu_list) >= 2:
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prev_prev_klu = self.klu_list[-2]
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prev_prev_klu.set_next(self.klu_list[-1])
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self.klu_list.append(new_klu)
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# 实时分析最近的K线分型
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self._analyze_recent_fractals()
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return new_klu
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def _analyze_recent_fractals(self):
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"""分析最近的分型情况"""
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if len(self.klu_list) < 3:
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return
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# 检查倒数第二根K线的分型（因为需要左右两根K线确认）
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target_idx = len(self.klu_list) - 2
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if target_idx >= 1:
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target_klu = self.klu_list[target_idx]
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# 进行实时分型分析
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target_klu.update_realtime_analysis()
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# 如果发现分型，记录信号
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if target_klu.fx_confirmed:
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signal = target_klu.get_fx_signal()
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signal_info = {
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'time': target_klu.time,
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'price': target_klu.close,
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'signal_type': signal[0],
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'strength': signal[1],
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'suggestion': signal[2],
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'fx_type': target_klu.fx_type
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}
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self.latest_signals.append(signal_info)
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# 保持最近20个信号
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if len(self.latest_signals) > 20:
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self.latest_signals.pop(0)
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print(f"🔔 分型信号: {signal_info['time']} - {signal_info['signal_type']} "
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f"(强度: {signal_info['strength']}) - {signal_info['suggestion']}")
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def get_latest_signal(self):
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"""获取最新的分型信号"""
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return self.latest_signals[-1] if self.latest_signals else None
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def get_current_fx_status(self):
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"""获取当前分型状态统计"""
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if len(self.klu_list) < 10:
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return {"status": "数据不足"}
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recent_10 = self.klu_list[-10:]
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top_fx_count = sum(1 for klu in recent_10 if klu.fx_type == Chan_FX_TYPE.TOP)
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bottom_fx_count = sum(1 for klu in recent_10 if klu.fx_type == Chan_FX_TYPE.BOTTOM)
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strong_fx_count = sum(1 for klu in recent_10 if klu.fx_strength >= 65)
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return {
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"最近10根K线": len(recent_10),
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"顶分型数量": top_fx_count,
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"底分型数量": bottom_fx_count,
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"强分型数量": strong_fx_count,
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"最新K线时间": recent_10[-1].time,
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"最新信号": self.get_latest_signal()
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}
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def simulate_realtime_trading():
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"""模拟实时交易场景"""
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print("=== 实时K线分型分析示例 ===\n")
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# 创建分析器
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analyzer = RealtimeFxAnalyzer()
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# 模拟实时K线数据流
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base_time = datetime.now()
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base_price = 100.0
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print("开始接收K线数据...\n")
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for i in range(20):
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# 模拟价格波动
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if i < 5: # 上涨阶段
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price_change = 0.5
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elif i < 10: # 下跌阶段
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price_change = -0.8
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elif i < 15: # 震荡阶段
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price_change = 0.3 * ((-1) ** i)
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else: # 再次上涨
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price_change = 0.6
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current_price = base_price + price_change
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# 构造K线数据
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open_price = base_price
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high = max(open_price, current_price) + abs(price_change) * 0.2
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low = min(open_price, current_price) - abs(price_change) * 0.2
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close = current_price
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volume = 1000 + i * 50
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# 模拟技术指标
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indicators = {
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'ma5': base_price + (i - 10) * 0.1,
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'ma10': base_price + (i - 10) * 0.05,
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'rsi': 50 + (i % 7 - 3) * 10,
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'macd': (i % 6 - 3) * 0.01,
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'macdhist': (i % 4 - 2) * 0.005,
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'volume_ratio': 1.0 + (i % 3 - 1) * 0.2
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}
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# 添加K线数据
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kline_time = base_time + timedelta(minutes=i)
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analyzer.add_kline(
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time=kline_time.strftime("%Y-%m-%d %H:%M:%S"),
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open_price=open_price,
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high=high,
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low=low,
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close=close,
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volume=volume,
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indicators=indicators
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)
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base_price = current_price
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# 每5根K线显示一次状态
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if (i + 1) % 5 == 0:
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status = analyzer.get_current_fx_status()
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print(f"\n--- 第{i+1}根K线后的状态 ---")
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for key, value in status.items():
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if key != "最新信号":
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print(f"{key}: {value}")
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if "最新信号" in status and status["最新信号"]:
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signal = status["最新信号"]
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print(f"最新信号: {signal['signal_type']} (强度: {signal['strength']})")
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print()
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print("\n=== 所有分型信号汇总 ===")
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for signal in analyzer.latest_signals:
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print(f"{signal['time']} | {signal['signal_type']} | 强度: {signal['strength']} | {signal['suggestion']}")
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def compare_latency():
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"""对比KLC和KLU方法的延迟差异"""
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print("\n=== 延迟对比分析 ===")
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print("假设场景：连续包含关系的K线序列")
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print("原始K线: K1, K2(包含K1), K3(包含K2), K4(突破), K5, K6")
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print()
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print("KLC方法:")
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print("- 需要等待K4确认包含关系结束")
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print("- KLC1 = [K1+K2+K3], 在K4完成时才确定")
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print("- 分型检测: 需要等待KLC1, KLC2, KLC3")
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print("- 实际延迟: 可能6-8根原始K线")
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print()
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print("KLU实时方法:")
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print("- 每根K线完成时立即检测")
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print("- K3完成时就能检测K2的分型状态")
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print("- 实际延迟: 最多1根K线")
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print()
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print("延迟改善: 从6-8根K线缩短到1根K线")
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print("时间价值: 在5分钟K线下，可节省25-40分钟的反应时间")
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if __name__ == "__main__":
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# 运行模拟
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simulate_realtime_trading()
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# 显示延迟对比
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compare_latency() |