78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
import copy
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from typing import List
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from Chan import CChan
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from ChanConfig import CChanConfig
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from Common.CEnum import AUTYPE, DATA_FIELD, DATA_SRC, KL_TYPE
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from DataAPI.BaoStockAPI import CBaoStock
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from KLine.KLine_Unit import CKLine_Unit
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def combine_60m_klu_form_15m(klu_15m_lst: List[CKLine_Unit]) -> CKLine_Unit:
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return CKLine_Unit(
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{
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DATA_FIELD.FIELD_TIME: klu_15m_lst[-1].time,
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DATA_FIELD.FIELD_OPEN: klu_15m_lst[0].open,
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DATA_FIELD.FIELD_CLOSE: klu_15m_lst[-1].close,
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DATA_FIELD.FIELD_HIGH: max(klu.high for klu in klu_15m_lst),
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DATA_FIELD.FIELD_LOW: min(klu.low for klu in klu_15m_lst),
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}
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)
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if __name__ == "__main__":
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"""
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代码不能直接跑，仅用于展示如何实现小级别K线更新直接刷新CChan结果
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"""
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code = "sz.000001"
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begin_time = "2023-09-10"
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end_time = None
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data_src_type = DATA_SRC.BAO_STOCK
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lv_list = [KL_TYPE.K_60M, KL_TYPE.K_15M]
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config = CChanConfig({
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"trigger_step": True,
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})
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# 快照
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chan_snapshot = CChan(
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code=code,
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data_src=data_src_type,
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lv_list=lv_list,
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config=config,
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)
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CBaoStock.do_init()
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data_src = CBaoStock(code, k_type=KL_TYPE.K_15M, begin_date=begin_time, end_date=end_time, autype=AUTYPE.QFQ) # 获取最小级别
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klu_15m_lst_tmp: List[CKLine_Unit] = [] # 存储用于合成当前60M K线的15M k线
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for klu_15m in data_src.get_kl_data(): # 获取单根15分钟K线
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klu_15m_lst_tmp.append(klu_15m)
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klu_60m = combine_60m_klu_form_15m(klu_15m_lst_tmp) # 合成60分钟K线
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"""
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拷贝一份chan_snapshot
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如果是用序列化方式，这里可以采用pickle.load()
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"""
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chan: CChan = copy.deepcopy(chan_snapshot)
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chan.trigger_load({KL_TYPE.K_60M: [klu_60m], KL_TYPE.K_15M: klu_15m_lst_tmp})
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"""
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策略开始：
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这里基于chan实现你的策略
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"""
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for kl_type, ele_manager in chan.kl_datas.items():
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# 打印当前每一级别分别有多少K线
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print(klu_15m.time, kl_type, sum(len(klc) for klc in ele_manager))
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# 策略结束：
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if len(klu_15m_lst_tmp) == 4: # 已经完成4根15分钟K线了，说明这个最新的60分钟K线和里面的4根15分钟K线在将来不会再变化
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"""
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把当前完整chan重新保存成chan_snapshot
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如果是序列化方式，这里可以采用pickle.dump()
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"""
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chan_snapshot = chan
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klu_15m_lst_tmp = [] # 清空1分钟K线，用于下一个五分钟周期的合成
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CBaoStock.do_close()
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