使用Python编写指数移动平均值
Ant*_*pin 3 python math finance python-3.x pandas
我想对数据帧的三列进行计算df.为了做到这一点,我想在三列表中运行资产(加密货币)列表的价格,以便在有足够的数据后计算它们的指数移动平均值.
def calculateAllEMA(self,values_array):
df = pd.DataFrame(values_array, columns=['BTC', 'ETH', 'DASH'])
column_by_search = ["BTC", "ETH", "DASH"]
print(df)
for i,column in enumerate(column_by_search):
ema=[]
# over and over for each day that follows day 23 to get the full range of EMA
for j in range(0, len(column)-24):
# Add the closing prices for the first 22 days together and divide them by 22.
EMA_yesterday = column.iloc[1+j:22+j].mean()
k = float(2)/(22+1)
# getting the first EMA day by taking the following day’s (day 23) closing price multiplied by k, then multiply the previous day’s moving average by (1-k) and add the two.
ema.append(column.iloc[23 + j]*k+EMA_yesterday*(1-k))
print("ema")
print(ema)
mean_exp[i] = ema[-1]
return mean_exp
然而,当我打印出来的时候len(column)-24我得到-21(-24 + 3？).因此,我无法通过循环.我如何应对此错误以获得资产的指数移动平均线？
我试图将这个链接从iexplain.com应用于指数移动平均线的伪代码.
如果你有任何更容易的想法,我很乐意听到它.
这是我用来计算错误的数据:
BTC ETH DASH
0 4044.59 294.40 196.97
1 4045.25 294.31 196.97
2 4044.59 294.40 196.97
3 4045.25 294.31 196.97
4 4044.59 294.40 196.97
5 4045.25 294.31 196.97
6 4044.59 294.40 196.97
7 4045.25 294.31 196.97
8 4045.25 294.31 196.97
9 4044.59 294.40 196.97
10 4045.25 294.31 196.97
11 4044.59 294.40 196.97
12 4045.25 294.31 196.97
13 4045.25 294.32 197.07
14 4045.25 294.31 196.97
15 4045.41 294.46 197.07
16 4045.25 294.41 197.07
17 4045.41 294.41 197.07
18 4045.41 294.47 197.07
19 4045.25 294.41 197.07
20 4045.25 294.32 197.07
21 4045.43 294.35 197.07
22 4045.41 294.46 197.07
23 4045.25 294.41 197.07
您可以pandas.stats.moments.ewma按照此处的说明使用.
以下是我对可能的解决方案的建议:
带有随机值的数据框应符合您的描述:
# imports
import pandas as pd
import numpy as np
np.random.seed(123)
rows = 50
df = pd.DataFrame(np.random.randint(90,110,size=(rows, 3)), columns=['BTC', 'ETH', 'DASH'])
datelist = pd.date_range(pd.datetime(2017, 1, 1).strftime('%Y-%m-%d'), periods=rows).tolist()
df['dates'] = datelist
df = df.set_index(['dates'])
df.index = pd.to_datetime(df.index)
def ewmas(df, win, keepSource):
"""Add exponentially weighted moving averages for all columns in a dataframe.
Arguments:
df -- pandas dataframe
win -- length of ewma estimation window
keepSource -- True or False for keep or drop source data in output dataframe
"""
df_temp = df.copy()
# Manage existing column names
colNames = list(df_temp.columns.values).copy()
removeNames = colNames.copy()
i = 0
for col in colNames:
# Make new names for ewmas
ewmaName = colNames[i] + '_ewma_' + str(win)
# Add ewmas
#df_temp[ewmaName] = pd.stats.moments.ewma(df[colNames[i]], span = win)
df_temp[ewmaName] = df[colNames[i]].ewm(span = win, adjust=True).mean()
i = i + 1
# Remove estimates with insufficient window length
df_temp = df_temp.iloc[win:]
# Remove or keep source data
if keepSource == False:
df_temp = df_temp.drop(removeNames,1)
return df_temp
# Test run
df_new = ewmas(df = df, win = 22, keepSource = True)
print(df_new.tail())
以下代码段生成原始数据框的副本,并在给定定义窗口长度的情况下添加具有ewma的列:
BTC ETH DASH BTC_ewma_22 ETH_ewma_22 DASH_ewma_22
dates
2017-02-15 91 96 98 98.752431 100.081052 97.926787
2017-02-16 100 102 102 98.862445 100.250270 98.285973
2017-02-17 100 107 97 98.962634 100.844749 98.172712
2017-02-18 103 102 91 99.317826 100.946384 97.541684
2017-02-19 99 104 91 99.289894 101.214755 96.966758
我们不得不做一些调整.pandas.DataFrame.ewm如果没有足够的观察值来填充您的估算窗口,则不会引发错误.因此,您应该删除与窗口长度相对应的第一个观察值.
# imports
import pandas as pd
import numpy as np
np.random.seed(123)
rows = 50
df = pd.DataFrame(np.random.randint(90,110,size=(rows, 3)), columns=['BTC', 'ETH', 'DASH'])
datelist = pd.date_range(pd.datetime(2017, 1, 1).strftime('%Y-%m-%d'), periods=rows).tolist()
df['dates'] = datelist
df = df.set_index(['dates'])
df.index = pd.to_datetime(df.index)
def ewmas(df, win, keepSource):
"""Add exponentially weighted moving averages for all columns in a dataframe.
Arguments:
df -- pandas dataframe
win -- length of ewma estimation window
keepSource -- True or False for keep or drop source data in output dataframe
"""
df_temp = df.copy()
# Manage existing column names
colNames = list(df_temp.columns.values).copy()
removeNames = colNames.copy()
i = 0
for col in colNames:
# Make new names for ewmas
ewmaName = colNames[i] + '_ewma_' + str(win)
# Add ewmas
#df_temp[ewmaName] = pd.stats.moments.ewma(df[colNames[i]], span = win)
df_temp[ewmaName] = df[colNames[i]].ewm(span = win, adjust=True).mean()
i = i + 1
# Remove estimates with insufficient window length
df_temp = df_temp.iloc[win:]
# Remove or keep source data
if keepSource == False:
df_temp = df_temp.drop(removeNames,1)
return df_temp
# Test run
df_new = ewmas(df = df, win = 22, keepSource = True)
print(df_new.tail())
根据您是否要将源数据保存在新数据框中,可以将其删除,如下所示:
BTC ETH DASH BTC_ewma_22 ETH_ewma_22 DASH_ewma_22
dates
2017-02-15 91 96 98 98.752431 100.081052 97.926787
2017-02-16 100 102 102 98.862445 100.250270 98.285973
2017-02-17 100 107 97 98.962634 100.844749 98.172712
2017-02-18 103 102 91 99.317826 100.946384 97.541684
2017-02-19 99 104 91 99.289894 101.214755 96.966758
这是包含在函数中的整个过程:
# imports
import pandas as pd
import numpy as np
np.random.seed(123)
rows = 50
df = pd.DataFrame(np.random.randint(90,110,size=(rows, 3)), columns=['BTC', 'ETH', 'DASH'])
datelist = pd.date_range(pd.datetime(2017, 1, 1).strftime('%Y-%m-%d'), periods=rows).tolist()
df['dates'] = datelist
df = df.set_index(['dates'])
df.index = pd.to_datetime(df.index)
def ewmas(df, win, keepSource):
"""Add exponentially weighted moving averages for all columns in a dataframe.
Arguments:
df -- pandas dataframe
win -- length of ewma estimation window
keepSource -- True or False for keep or drop source data in output dataframe
"""
df_temp = df.copy()
# Manage existing column names
colNames = list(df_temp.columns.values).copy()
removeNames = colNames.copy()
i = 0
for col in colNames:
# Make new names for ewmas
ewmaName = colNames[i] + '_ewma_' + str(win)
# Add ewmas
#df_temp[ewmaName] = pd.stats.moments.ewma(df[colNames[i]], span = win)
df_temp[ewmaName] = df[colNames[i]].ewm(span = win, adjust=True).mean()
i = i + 1
# Remove estimates with insufficient window length
df_temp = df_temp.iloc[win:]
# Remove or keep source data
if keepSource == False:
df_temp = df_temp.drop(removeNames,1)
return df_temp
# Test run
df_new = ewmas(df = df, win = 22, keepSource = True)
print(df_new.tail())
这是一个测试运行:
BTC ETH DASH BTC_ewma_22 ETH_ewma_22 DASH_ewma_22
dates
2017-02-15 91 96 98 98.752431 100.081052 97.926787
2017-02-16 100 102 102 98.862445 100.250270 98.285973
2017-02-17 100 107 97 98.962634 100.844749 98.172712
2017-02-18 103 102 91 99.317826 100.946384 97.541684
2017-02-19 99 104 91 99.289894 101.214755 96.966758
小智 0
在循环中，for i,column in enumerate(column_by_search):您迭代列表中的元素column_by_search，即列依次采用值“BTC”、“ETH”、“DASH”。因此，len(column)将为您提供字符串“BTC”的长度，实际上是 3。
请尝试df[column]，这将返回一个列表，其中包含所需列中的元素，您可以对其进行迭代。
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