如何将Numpy/Pandas中的Stochastic Oscillator的值从(14,1,3)平滑到(14,3,3)

Question

我有一个值，它产生与TradingView网站上任何股票相同的确切结果。该结果适用于值为(14,1,3)的随机振荡器。我想知道如果我想把值平滑到(14,3,3)需要做什么?

这个博客使用了我所说的相同的想法，下面是我的代码:

df.sort_index(ascending=False,inplace=True) #My stock is Newest First order
k_period = 14
d_period = 3
LOW,HIGH,CLOSE = "LOW", "HIGH", "CLOSE" # Column names
# Adds a "n_high" column with max value of previous 14 periods
df['n_high'] = df[HIGH].rolling(k_period).max()
# Adds an "n_low" column with min value of previous 14 periods
df['n_low'] = df[LOW].rolling(k_period).min()
# Uses the min/max values to calculate the %k (as a percentage)
df['%K'] = (df[CLOSE] - df['n_low']) * 100 / (df['n_high'] - df['n_low'])
# Uses the %k to calculates a SMA over the past 3 values of %k
df['%D'] = df['%K'].rolling(d_period).mean()

Answer 1

找到解决方案。这是一个愚蠢的调整。你也需要对蓝线进行.rolling_average()。以下是调整后的代码。

def Stochastic(data, k_period:int = 14, d_period:int = 3, smooth_k = 3, names:tuple = ('OPEN','CLOSE','LOW','HIGH'),return_df:bool=False):
'''
Implementation of the Stochastic Oscillator. Returns the Fast and Slow lines values or the whole DataFrame
args:
data: Pandas Dataframe of the stock
k_period: Period for the %K /Fast / Blue line
d_period: Period for the %D / Slow /Red / Signal Line
smooth_k: Smoothening the Fast line value. With increase/ decrease in number, it becomes the Fast or Slow Stochastic
names: Names of the columns which contains the corresponding values
return_df: Whether to return the DataFrame or the Values
out:
Returns either the Array containing (fast_line,slow_line) values or the entire DataFrame
'''
OPEN, CLOSE, LOW, HIGH = names
df = data.copy()
if df.iloc[0,0] > df.iloc[1,0]: # if the first Date entry [0,0] is > previous data entry [1,0] then it is in descending order, then reverse it for calculation
df.sort_index(ascending=False, inplace = True)
# Adds a "n_high" column with max value of previous 14 periods
df['n_high'] = df[HIGH].rolling(k_period).max()
# Adds an "n_low" column with min value of previous 14 periods
df['n_low'] = df[LOW].rolling(k_period).min()
# Uses the min/max values to calculate the %k (as a percentage)
df['Blue Line'] = (df[CLOSE] - df['n_low']) * 100 / (df['n_high'] - df['n_low']) # %K or so called Fast Line
if smooth_k > 1: # Smoothen the fast, blue line
df['Blue Line'] = df['Blue Line'].rolling(smooth_k).mean()

# Uses the %k to calculates a SMA over the past 3 values of %k
df['Red Line'] = df['Blue Line'].rolling(d_period).mean() # %D of so called Slow Line
df.drop(['n_high','n_low'],inplace=True,axis=1)

df.sort_index(ascending = True, inplace = True)
if return_df:
return df
return df.iloc[0,-2:] # Fast