用python写一个卡夫曼策略
时间: 2024-05-16 21:13:37 浏览: 18
卡夫曼策略是一种趋势跟踪策略,基于移动平均线和波动率计算,可以用Python来实现。下面是一个简单的示例代码:
```python
import pandas as pd
def kaufman_strategy(df, n=10, m=2, atr=10):
# 计算ER
df['change'] = abs(df['close'] - df['close'].shift(1))
df['volatility'] = df['change'].rolling(n).sum()
df['ER'] = df['change'] / df['volatility']
# 计算SC和FCR
df['SC'] = ((m - 1) * df['ATR'] + df['TR']) / m
df['FCR'] = ((m - 1) * df['SC'] + df['ER']) / m
# 计算KAMA
df['KAMA'] = df['close']
for i in range(n, len(df)):
df['KAMA'][i] = df['KAMA'][i-1] + df['FCR'][i] * (df['close'][i] - df['KAMA'][i-1])
# 生成交易信号
df['position'] = 0
df['position'][n:] = (df['KAMA'][n:] > df['KAMA'][n-1:-1].values) * 1
# 计算收益率
df['strategy_return'] = df['position'] * df['pct_chg']
return df['strategy_return'].cumsum()
# 示例运行
data = pd.read_csv('data.csv', index_col='date')
data.index = pd.to_datetime(data.index)
data['pct_chg'] = data['close'].pct_change()
data['TR'] = pd.concat([data['high']-data['low'], abs(data['high']-data['close'].shift()), abs(data['low']-data['close'].shift())], axis=1).max(axis=1)
data['ATR'] = data['TR'].rolling(10).mean()
kaufman_strategy(data).plot()
```
其中,`n`代表计算ER和KAMA的移动平均周期,`m`代表计算SC和FCR的权重,`atr`代表计算ATR的移动平均周期。`df`是一个包含股票数据的DataFrame,包括日期、开盘价、最高价、最低价、收盘价等信息。最后,该函数返回一个Series,包含了Kaufman策略的收益率序列,并画出了策略的累计收益曲线。