我有这段代码可以使用股票价格计算最大跌幅。
#max drawdown function code...
def max_drawdown(X):
mdd = 0
peak = X[0]
for x in X:
if x > peak:
peak = x
dd = (x - peak) / peak
if dd < mdd
mdd = dd
return mdd
我使用以下功能来显示跌幅
#shows max drawdown
stocks_prices.xs(key='close',axis=1,level='Stock Info').apply(max_drawdown)
如果您想查看股价的高低
Ticker NOW BBY NOW Return BBY Return
Stock Info open high low close volume open high low close volume
date
2013-09-30 52.10 52.34 51.170 51.95 1383145 31.9605 32.3721 31.8061 32.1577 3826963 NaN NaN
2013-10-01 51.60 51.89 50.610 51.49 1288635 32.2092 32.7923 32.1834 32.5436 3607267 -0.008855 0.012000
2013-10-02 51.35 52.42 51.215 52.27 1158196 32.2606 32.4750 31.9005 32.2949 2990664 0.015149 -0.007642
2013-10-03 52.39 52.90 51.560 52.15 1512797 32.4236 32.5093 31.6089 32.3206 3413673 -0.002296 0.000796
2013-10-04 53.09 55.46 52.810 54.43 1688824 32.3206 32.9724 31.9862 32.7151 3311713 0.043720 0.012206
输出为
Ticker
NOW -0.476164
BBY -0.485378
dtype: float64
由于计算在函数内部，因此如何在数据框中输入输出。我无法使用以下代码从下拉功能的输出中创建数据框
mdd = mdd.to_frame('Maximum Drawdown')
mdd.index.name = 'Ticker'
mdd = mdd.reset_index()
我有一些类似的数据帧
Ticker Sharpe
0 NOW 8.061887
1 BBY 7.174034
Ticker Var
0 NOW -0.1
1 BBY 0.2
我如何将所有这些数据帧合并为一个，以便可以从代码开始，然后从值开始导出到excel
例如，我希望输出为
Ticker Var Sharpe Ratio
0 NOW -0.1 0.5
1 BBY 0.2 0.3
请在这两个方面提供帮助，非常感谢
答案 0 :(得分：0)
所以从您的代码开始：
mdd = stocks_prices.xs(key='close',axis=1,level='Stock Info').apply(max_drawdown)
mdd系列看起来像：
Ticker
NOW -0.008855
BBY -0.007642
dtype: float64
您可能有不同的电话号码。
不确定是否要在stocks_prices中使用-如果这样做，可以这样做：
最简单的方法是创建一个新列
stocks_prices[ 'NOW mdd' ] = float('NaN')
并获取数据框的最后一天：
last_day = stocks_prices.index[-1]
对我来说是2013-10-04，但我认为您还有更多行。无论如何，请将其粘贴到列NOW mdd的最后一行：
stocks_prices.loc[ last_day, 'NOW mdd' ] = mdd[ 'NOW' ]
所以我的stocks_prices如下：
Ticker NOW BBY NOW Return BBY Return NOW mdd
Stock Info open high low close open high low close
2013-09-30 50.95 NaN NaN 51.95 31.1577 NaN NaN 32.1577 NaN NaN NaN
2013-10-01 50.49 NaN NaN 51.49 31.5436 NaN NaN 32.5436 -0.008855 0.012000 NaN
2013-10-02 51.27 NaN NaN 52.27 31.2949 NaN NaN 32.2949 0.015149 -0.007642 NaN
2013-10-03 51.15 NaN NaN 52.15 31.3206 NaN NaN 32.3206 -0.002296 0.000796 NaN
2013-10-04 53.43 NaN NaN 54.43 31.7151 NaN NaN 32.7151 0.043720 0.012206 -0.008855
将DataFrame放在一起通常是在诸如Ticker之类的通用键上完成的。
因此，一个sharpe数据框：
Sharpe Ticker
0 8.061887 NOW
1 7.174034 BBY
和一个var数据框：
Ticker Var
0 NOW -0.1
1 BBY 0.2
可以与
合并result = sharpe.merge( var, on='Ticker', suffixes=('','') )
给予
Sharpe Ticker Var
0 8.061887 NOW -0.1
1 7.174034 BBY 0.2
如果我们想将mdd合并到其中，我们可以先将mdd变成一个DataFrame：
result2 = result.merge( mdd.to_frame('mdd'), on='Ticker', suffixes=('','') )
给予
Sharpe Ticker Var mdd
0 8.061887 NOW -0.1 -0.008855
1 7.174034 BBY 0.2 -0.007642
您可以立即更改max_drawdown函数以输出列表，例如
def max_drawdown2(X):
mdd = 0
peak = X[0]
values = [] # <-- NEW LINE
for x in X:
if x > peak:
peak = x
dd = (x - peak) / peak
if dd < mdd:
mdd = dd
values.append( mdd ) # <-- NEW LINE
return values # <-- NEW LINE
stocks_prices[ 'NOW mdd' ] = max_drawdown2( stocks_prices.loc[ :, ('NOW','close') ] )
获得
Ticker NOW BBY NOW Return BBY Return NOW mdd
Stock Info open high low close open high low close
2013-09-30 50.95 NaN NaN 51.95 31.1577 NaN NaN 32.1577 NaN NaN 0.000000
2013-10-01 50.49 NaN NaN 51.49 31.5436 NaN NaN 32.5436 -0.008855 0.012000 -0.008855
2013-10-02 51.27 NaN NaN 52.27 31.2949 NaN NaN 32.2949 0.015149 -0.007642 -0.008855
2013-10-03 51.15 NaN NaN 52.15 31.3206 NaN NaN 32.3206 -0.002296 0.000796 -0.008855
2013-10-04 53.43 NaN NaN 54.43 31.7151 NaN NaN 32.7151 0.043720 0.012206 -0.008855
请注意，.loc[ :, ('NOW','close') ]正在使用:获取所有日期，然后('NOW','close')正在获取Ticker然后是Stock Info级
可以使用相同的(Ticker, close)语法来添加子列：
stocks_prices[ ('NOW', 'mdd' ) ] = max_drawdown2( stocks_prices.loc[ :, ('NOW','close') ] )