so, the instantaneous amplitude function M and instantaneous phase function Z of the signal can be obtained as follows:的另一种英语说法
时间: 2024-05-19 14:13:51 浏览: 9
An alternative way to express the statement "so, the instantaneous amplitude function M and instantaneous phase function Z of the signal can be obtained as follows" is:
Thus, it is possible to derive the instantaneous amplitude function M and instantaneous phase function Z of the signal through the following means:
相关问题
First, we compared the STSA estimator (20) with the MMSE STSA estimator derived in [ 1, formula (7)]. The enhanced speech obtained by using (20) suffers much less residual noise, while no difference in the speech itself was noticed. The residual noise obtained with (20) sounds a little less uniform than when the MMSE STSA estimator is used. However, because of the lower residual noise level, this effect appears insignificant. The reduction in the residual noise level obtained when (20) is used is probably a result of the lower gain [see (22)] , particularly in regions of low instantaneous SNR values (see Fig. 1). 翻译成中文
首先,我们将STSA估计器(20)与[1]中导出的MMSE STSA估计器[公式(7)]进行了比较。使用(20)得到的增强语音残余噪声明显较少,而语音本身没有注意到任何差异。使用(20)得到的残余噪声听起来比使用MMSE STSA估计器时略微不均匀。然而,由于残余噪声水平更低,这种效应似乎不太明显。当使用(20)时,残余噪声水平的降低可能是低增益[见(22)],特别是在低瞬时信噪比值的区域(见图1)。
你是量化工程师,请使用tushare的数据用Python 写一个 使用Instantaneous phase synchrony作为算法的关于中证1000指数和上证50指数比值的配对交易策略,然后做出回溯检验,最后以日期为横坐标画出收益图形和最大回撤图形以及交易信号图形.
好的,我会尽力为你提供这个策略的Python代码。首先,我们需要安装tushare库,如果你还没有安装的话,可以使用以下命令进行安装:
```
!pip install tushare
```
接下来,我们需要获取中证1000指数和上证50指数的历史数据。可以使用以下代码:
```python
import tushare as ts
# 获取中证1000指数历史数据
zz1000 = ts.get_k_data('000852', index=True, start='2015-01-01', end='2021-12-31')
# 获取上证50指数历史数据
sz50 = ts.get_k_data('000016', index=True, start='2015-01-01', end='2021-12-31')
```
接下来,我们需要使用Instantaneous phase synchrony算法计算两个时间序列的相位同步性,这里我们可以使用PyPI上的MFC-Python库,可以使用以下命令进行安装:
```
!pip install mfc-py
```
接下来,我们需要编写一个函数来计算两个时间序列的相位同步性。
```python
from mfc.mfc import MFC
def get_sync_signal(x, y):
# 计算x和y的相位
phase_x = MFC(x).phase
phase_y = MFC(y).phase
# 计算x和y的相位同步性
sync_signal = np.cos(phase_x - phase_y)
return sync_signal
```
接下来,我们可以使用该函数来计算中证1000指数和上证50指数的相位同步性,并绘制其交易信号图形。
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 计算中证1000指数和上证50指数的相位同步性
sync_signal = get_sync_signal(zz1000['close'], sz50['close'])
# 设置交易信号阈值
threshold = 0.5
# 计算交易信号
buy_signal = np.where(sync_signal > threshold, 1, 0)
sell_signal = np.where(sync_signal < -threshold, -1, 0)
signal = buy_signal + sell_signal
# 绘制交易信号图形
plt.figure(figsize=(16, 6))
plt.plot(zz1000['date'], signal, label='Signal')
plt.axhline(y=0, color='black', linestyle='--')
plt.axhline(y=1, color='green', linestyle='--')
plt.axhline(y=-1, color='red', linestyle='--')
plt.legend()
plt.title('Trading Signal')
plt.show()
```
接下来,我们可以使用pandas库计算每个交易日的收益率,并绘制收益图形和最大回撤图形。
```python
# 计算每个交易日的收益率
zz1000['return'] = zz1000['close'].pct_change()
sz50['return'] = sz50['close'].pct_change()
# 计算配对交易策略的收益率
portfolio = pd.DataFrame({'zz1000': zz1000['return'], 'sz50': sz50['return'], 'signal': signal})
portfolio['return'] = portfolio['zz1000'] - portfolio['sz50'] * portfolio['signal']
# 计算累积收益率和最大回撤
portfolio['cumulative_return'] = (1 + portfolio['return']).cumprod()
portfolio['max_drawdown'] = portfolio['cumulative_return'].cummax() - portfolio['cumulative_return']
portfolio['max_drawdown_pct'] = portfolio['max_drawdown'] / portfolio['cumulative_return'].cummax()
# 绘制收益图形和最大回撤图形
plt.figure(figsize=(16, 6))
plt.plot(zz1000['date'], portfolio['cumulative_return'], label='Cumulative Return')
plt.plot(zz1000['date'], portfolio['max_drawdown'], label='Max Drawdown')
plt.legend()
plt.title('Performance')
plt.show()
```
上述代码将生成一个包含收益图形和最大回撤图形的图表。