按照上条回复修改后输出
时间: 2024-03-25 19:36:19 浏览: 12
修改后的输出如下:
```python
def trading_strategy(df, para):
""" 根据给定的参数,计算交易信号
:param df: pandas.DataFrame, 包含股票价格数据的DataFrame
:param para: list, 交易策略的参数,包括:
- n: 取平均线和标准差的参数
- m: 标准差的倍数
- ma_n: MA指标的参数
- volatility_factor: 波动率因子,可以根据实际情况调整
:return: int, 交易信号,1表示买入,-1表示卖出,0表示持有
"""
n = int(para[0])
m = para[1]
ma_n = int(para[2])
volatility_factor = para[3]
# 计算均线和标准差
close = df['close'].values
ma = np.mean(close[-n:])
std = np.std(close[-n:], ddof=1)
# 计算上下轨道
upper = ma + volatility_factor * std
lower = ma - volatility_factor * std
# 计算MA指标
ma_values = df['close'].rolling(ma_n).mean().values
ma_current = ma_values[-1]
ma_previous = ma_values[-2]
# 计算LLT指标
llt = np.zeros(ma_n)
llt[0] = close[-1]
alpha = 2 / (ma_n + 1)
for i in range(1, ma_n):
llt[i] = alpha * close[-i-1] + (1 - alpha) * llt[i-1]
# 寻找交易信号
signal = 0
close_current = close[-1]
close_previous = close[-2]
# 做多信号
if (close_current > upper) and (close_previous <= upper) and \
(close_current > ma_current) and (close_previous <= ma_previous) and (std < volatility_factor * ma_current):
# LLT指标过滤做多信号
if close_current < llt[-1]:
signal = 0
else:
signal = 1
# 做空信号
elif (close_current < lower) and (close_previous >= lower) and \
(close_current < ma_current) and (close_previous >= ma_previous) and (std < volatility_factor * ma_current):
# LLT指标过滤做空信号
if close_current > llt[-1]:
signal = 0
else:
signal = -1
# 平仓信号
elif ((close_current < ma) and (close_previous >= ma)) or ((close_current > ma) and (close_previous <= ma)):
signal = 0
return signal
trading_strategy_para_list = {
'n': [10, 20, 30],
'm': [1.5, 2, 2.5],
'ma_n': [5, 10, 20],
'volatility_factor': [1.5, 2, 2.5]
}
```
这里将函数的参数和注释都进行了修改,同时定义了名为`trading_strategy_para_list`的字典,包含了各个参数的范围。