如何解决在python中获取RSI
我一直在尝试计算股票的 14 RSI 并且我设法让它起作用,在某种程度上,它给了我不准确的数字
import pandas as pd
import datetime as dt
import pandas_datareader as web
ticker = 'TSLA'
start = dt.datetime(2018,1,1)
end = dt.datetime.now()
data = web.DataReader(ticker,'yahoo',start,end)
delta = data['Adj Close'].diff(1)
delta.dropna(inplace=True)
positive = delta.copy()
negative = delta.copy()
positive[positive < 0] = 0
negative[negative > 0] = 0
days = 14
average_gain = positive.rolling(window=days).mean()
average_loss = abs(negative.rolling(window=days).mean())
relative_strenght = average_gain / average_loss
rsi = 100.0 - (100.0 / (1.0 + relative_strenght))
print(ticker + str(rsi))
当我应该得到 70.13(14 天 RSI)时,它最终给了我 77.991564(14 天 RSI),有人知道我做错了什么吗?
也是的,我读过Calculating RSI in Python,但它对我需要的东西没有帮助
解决方法
这是一种自行计算 RSI 的方法。代码可以优化,但我更喜欢让它容易理解,让你优化。
例如,我们假设您有一个名为 df 的 DataFrame,其中有一列名为“Close”,用于表示收盘价。顺便说一句,请注意,例如,如果您将 RSI 的结果与一个站点进行比较,您应该确保比较相同的值。例如,如果在站内,您已经接近出价,并且您自己计算中位或叫价,则结果不会相同。
让我们看看代码:
def rsi(df,_window=14,_plot=0,_start=None,_end=None):
"""[RSI function]
Args:
df ([DataFrame]): [DataFrame with a column 'Close' for the close price]
_window ([int]): [The lookback window.](default : {14})
_plot ([int]): [1 if you want to see the plot](default : {0})
_start ([Date]):[if _plot=1,start of plot](default : {None})
_end ([Date]):[if _plot=1,end of plot](default : {None})
"""
##### Diff for the différences between last close and now
df['Diff'] = df['Close'].transform(lambda x: x.diff())
##### In 'Up',just keep the positive values
df['Up'] = df['Diff']
df.loc[(df['Up']<0),'Up'] = 0
##### Diff for the différences between last close and now
df['Down'] = df['Diff']
##### In 'Down',just keep the negative values
df.loc[(df['Down']>0),'Down'] = 0
df['Down'] = abs(df['Down'])
##### Moving average on Up & Down
df['avg_up'+str(_window)] = df['Up'].transform(lambda x: x.rolling(window=_window).mean())
df['avg_down'+str(_window)] = df['Down'].transform(lambda x: x.rolling(window=_window).mean())
##### RS is the ratio of the means of Up & Down
df['RS_'+str(_window)] = df['avg_up'+str(_window)] / df['avg_down'+str(_window)]
##### RSI Calculation
##### 100 - (100/(1 + RS))
df['RSI_'+str(_window)] = 100 - (100/(1+df['RS_'+str(_fast)]))
##### Drop useless columns
df = df.drop(['Diff','Up','Down','avg_up'+str(_window),'avg_down'+str(_window),'RS_'+str(_window)],axis=1)
##### If asked,plot it!
if _plot == 1:
sns.set()
fig = plt.figure(facecolor = 'white',figsize = (30,5))
ax0 = plt.subplot2grid((6,4),(1,0),rowspan=4,colspan=4)
ax0.plot(df[(df.index<=end)&(df.index>=start)&(df.Symbol==_ticker.replace('/',''))]['Close'])
ax0.set_facecolor('ghostwhite')
ax0.legend(['Close'],ncol=3,loc = 'upper left',fontsize = 15)
plt.title(_ticker+" Close from "+str(start)+' to '+str(end),fontsize = 20)
ax1 = plt.subplot2grid((6,(5,rowspan=1,colspan=4,sharex = ax0)
ax1.plot(df[(df.index<=end)&(df.index>=start)&(df.Symbol==_ticker.replace('/',''))]['RSI_'+str(_window)],color = 'blue')
ax1.legend(['RSI_'+str(_window)],fontsize = 12)
ax1.set_facecolor('silver')
plt.subplots_adjust(left=.09,bottom=.09,right=1,top=.95,wspace=.20,hspace=0)
plt.show()
return(df)
要调用函数,你只需要输入
df = rsi(df)
如果您保留默认值,或者更改参数的 _window 和/或 _plot。 请注意,如果您输入 _plot=1,则需要使用字符串或日期时间提供绘图的开始和结束。
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