TA-Lib
Is it possible to add KDJ?
It is a momentum indicator frequently used in Asia. http://vb.letsfx.com/showthread.php/797-I-want-to-know-abou-KDJ-indicator
%K = 100[(C – L5close)/(H5 – L5)]
C = the most recent closing price
L5 = the low of the five previous trading sessions
H5 = the highest price traded during the same 5 day period.
The formula for the more important %D line looks like this:
%D = 100 X (H3/L3)
Now calculate %J = 3 x D – 2 x K
That looks easy to implement, I generally haven't been interested in adding indicators that aren't in the underlying ta-lib project (http://ta-lib.org)...
Seems like you could implement it yourself by doing something like this:
import pandas as pd
def KDJ(H, L, C):
L5 = pd.rolling_min(L, 5)
H5 = pd.rolling_max(H, 5)
K = 100 * (C - L5 / (H5 - L5) )
D = 100 * H3 / L3
J = (3 * D) - (2 * K)
return K, D, J
Note: I'm not sure if H3 and L3 are typos for H5 and L5, or if they mean the 3-day versions.
Also, not sure what L5close is in the pseudocode.
It is a typo. Usually people use 9 days instead of 5 days or 3 days. I think I can implement it by myself. Thanks.
@zhenyiy you can use talib.STOCH to get K and D and then J = (3 * D) - (2 * K)
But the k d value is not correct as what you can see in other website with the same parameter 9,3,3
This is one example of the result of what I have when using talib.STOCH:
2019-04-21 20:00:00 84.757888 87.737427 93.696504
This is what I got from tradingview:
I set parameter to 9, 3, 3 for generating the STOCH value for the sake of comparing the vlaue with the kdj value in tradingview. The value at 20:00:00 should be consistent with the value at 18:00:00 here
Also, even if I set parameter to 14,3,3 to compare stoch value with the same indicator of what I saw in Trdaingview, it is also different.
def KDJ(H, L, C):
L5 = pd.rolling_min(L, 9)
H5 = pd.rolling_max(H, 9)
RSV = 100 * ((C - L5) / (H5 - L5)).values
k0 = 50
k_out = []
for j in range(len(RSV)):
if RSV[j] == RSV[j]: # check for nan
k0 = 1/3 * RSV[j] + 2/3 * k0
k_out.append(k0)
else:
k_out.append(np.nan)
d0 = 50
d_out = []
for j in range(len(RSV)):
if k_out[j] == k_out[j]:
d0 =1/3 * k_out[j] + 2/3 * d0
d_out.append(d0)
else:
d_out.append(np.nan)
J = (3 * np.array(k_out)) - (2 * np.array(d_out))
return pd.Series(J, H.index)
The above might give KDJ values similar to the ones you see on trading view.
def KDJ(H, L, C): L5 = pd.rolling_min(L, 9) H5 = pd.rolling_max(H, 9) RSV = 100 * ((C - L5) / (H5 - L5)).values k0 = 50 k_out = [] for j in range(len(RSV)): if RSV[j] == RSV[j]: # check for nan k0 = 1/3 * RSV[j] + 2/3 * k0 k_out.append(k0) else: k_out.append(np.nan) d0 = 50 d_out = [] for j in range(len(RSV)): if k_out[j] == k_out[j]: d0 =1/3 * k_out[j] + 2/3 * d0 d_out.append(d0) else: d_out.append(np.nan) J = (3 * np.array(k_out)) - (2 * np.array(d_out)) return pd.Series(J, H.index)The above might give KDJ values similar to the ones you see on trading view.
Hi Prashant,
Does your code work for kdj calculation?
