CTA择时：缠论30分钟笔非多即空
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CTA择时：缠论30分钟笔非多即空
CTA择时：缠论30分钟笔非多即空
飞书用户73952023年9月9日创建
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JSON策略回放V230911
CTA策略回测V230911
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一、策略描述
30分钟笔向上持有多头；30分钟笔向下持有空头。策略优化的难点在于提高30分钟笔方向判断的准确性。
与本策略相关的信号函数：
二、策略编写
代码块
import czsc
from pathlib import Path
from loguru import logger
from czsc.connectors import research
from czsc import Event, Position
def create_long_short_V230908(symbol, **kwargs):
"""30分钟笔非多即空策略
使用的信号函数：
https://czsc.readthedocs.io/en/latest/api/czsc.signals.cxt_bi_status_V230101.html
"""
opens = [
{
"operate": "开多",
"signals_all": [],
"signals_any": [],
"signals_not": [],
"factors": [
{
"signals_all": [
"30分钟_D1_表里关系V230101_向上_任意_任意_0"
],
"signals_any": [],
"signals_not": [
"30分钟_D1_涨跌停V230331_涨停_任意_任意_0"
]
}
]
},
{
"operate": "开空",
"signals_all": [],
"signals_any": [],
"signals_not": [],
"factors": [
{
"signals_all": [
"30分钟_D1_表里关系V230101_向下_任意_任意_0"
],
"signals_any": [],
"signals_not": [
"30分钟_D1_涨跌停V230331_跌停_任意_任意_0"
]
}
]
}
]
exits = []
pos = Position(name="30分钟笔非多即空", symbol=symbol,
opens=[Event.load(x) for x in opens],
exits=[Event.load(x) for x in exits],
interval=3600 * 4, timeout=16 * 30, stop_loss=500)
return pos
def create_long_short_V230909(symbol, **kwargs):
"""笔非多即空策略
使用的信号函数：
https://czsc.readthedocs.io/en/latest/api/czsc.signals.cxt_bi_status_V230101.html
"""
base_freq = kwargs.get('base_freq', '30分钟')
opens = [
{
"operate": "开多",
"signals_all": [],
"signals_any": [],
"signals_not": [],
"factors": [
{
"signals_all": [
f"{base_freq}_D1_表里关系V230101_向上_任意_任意_0"
],
"signals_any": [],
"signals_not": [
f"{base_freq}_D1_涨跌停V230331_涨停_任意_任意_0"
]
}
]
},
{
"operate": "开空",
"signals_all": [],
"signals_any": [],
"signals_not": [],
"factors": [
{
"signals_all": [
f"{base_freq}_D1_表里关系V230101_向下_任意_任意_0"
],
"signals_any": [],
"signals_not": [
f"{base_freq}_D1_涨跌停V230331_跌停_任意_任意_0"
]
}
]
}
]
exits = []
pos = Position(name=f"{base_freq}笔非多即空", symbol=symbol,
opens=[Event.load(x) for x in opens],
exits=[Event.load(x) for x in exits],
interval=3600 * 4, timeout=16 * 30, stop_loss=500)
return pos
class Strategy(czsc.CzscStrategyBase):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.is_stocks = kwargs.get('is_stocks', True)
@property
def positions(self):
pos_list = [
# create_long_short_V230908(self.symbol),
create_long_short_V230909(self.symbol, base_freq='30分钟'),
create_long_short_V230909(self.symbol, base_freq='60分钟'),
create_long_short_V230909(self.symbol, base_freq='日线'),
]
return pos_list
if __name__ == '__main__':
results_path = Path(r'D:\策略研究\笔非多即空')
logger.add(results_path / "czsc.log", rotation="1 week", encoding="utf-8")
results_path.mkdir(exist_ok=True, parents=True)
symbols = research.get_symbols('中证500成分股')[:30]
symbol = symbols[0]
tactic = Strategy(symbol=symbol, is_stocks=True)
# 使用 logger 记录策略的基本信息
logger.info(f"K线周期列表：{tactic.freqs}")
logger.info(f"信号函数配置列表：{tactic.signals_config}")
# replay 查看策略的编写是否正确，执行过程是否符合预期
bars = research.get_raw_bars(symbol, freq=tactic.base_freq, sdt='20150101', edt='20220101')
trader = tactic.replay(bars, sdt='20210101', res_path=results_path / "replay", refresh=True)
# 当策略执行过程符合预期后，将持仓策略保存到本地 json 文件中
tactic.save_positions(results_path / "positions")