Renko
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Install / Use
/learn @gmoncarz/RenkoREADME
Renko
Renko library that transforms stock market prices to Renko bricks. This library has two current implementations. One is a user friendly class, which is easy to use, and another which is much more efficient in CPU, ideal for reinfocement learning.
In [1]: from renko_fast import RenkoFixBrickSize_Fast
In [2]: renko = RenkoFixBrickSize_Fast(10)
In [3]: renko.new_quotes([95])
In [4]: renko.new_quotes([105, 95, 86, 85])
...: renko.new_quotes([84, 86, 76, 74, 65, 77, 54, 33, 40, 45])
...: renko.new_quotes([50, 54, 55, 64, 156, 94, 93, 92, 91, 90, 89])
In [5]: renko.get_renko()
Out[5]:
array([[ 95., 95., 85., 105., nan, nan, 0., nan, 1., 0., 0.],
[ 86., 105., 85., 115., nan, nan, 1., 0., 3., 1., 0.],
[ 76., 85., 75., 105., nan, nan, -1., 0., 4., 0., 1.],
[ 74., 75., 65., 95., nan, nan, -1., 0., 1., 0., 2.],
[ 77., 65., 55., 85., nan, nan, -1., 0., 2., 0., 3.],
[ 54., 55., 45., 75., nan, nan, -1., 0., 1., 0., 4.],
[ 33., 45., 35., 65., nan, nan, -1., 0., 0., 0., 5.],
[ 54., 35., 25., 55., nan, nan, -1., 0., 5., 0., 6.],
[ 64., 55., 35., 65., nan, nan, 1., 0., 2., 1., 0.],
[156., 65., 45., 75., nan, nan, 1., 0., 0., 2., 0.],
[156., 75., 55., 85., nan, nan, 1., 0., 0., 3., 0.],
[156., 85., 65., 95., nan, nan, 1., 0., 0., 4., 0.],
[156., 95., 75., 105., nan, nan, 1., 0., 0., 5., 0.],
[156., 105., 85., 115., nan, nan, 1., 0., 0., 6., 0.],
[156., 115., 95., 125., nan, nan, 1., 0., 0., 7., 0.],
[156., 125., 105., 135., nan, nan, 1., 0., 0., 8., 0.],
[156., 135., 115., 145., nan, nan, 1., 0., 0., 9., 0.],
[156., 145., 125., 155., nan, nan, 1., 0., 0., 10., 0.],
[156., 155., 135., 165., nan, nan, 1., 0., 1., 11., 0.],
[ 94., 135., 125., 155., nan, nan, -1., 0., 0., 0., 1.],
[ 94., 125., 115., 145., nan, nan, -1., 0., 0., 0., 2.],
[ 94., 115., 105., 135., nan, nan, -1., 0., 0., 0., 3.],
[ 94., 105., 95., 125., nan, nan, -1., 0., 0., 0., 4.],
[ 89., 95., 85., 115., nan, nan, -1., 0., 6., 0., 5.]])
In [6]: renko.get_renko(renko.AS_DATAFRAME)
Out[6]:
price_last price_renko price_min price_max dt_start dt_end trend volume count cons_up cons_down
0 95.0 95.0 85.0 105.0 NaN NaN 0.0 NaN 1.0 0.0 0.0
1 86.0 105.0 85.0 115.0 NaN NaN 1.0 0.0 3.0 1.0 0.0
2 76.0 85.0 75.0 105.0 NaN NaN -1.0 0.0 4.0 0.0 1.0
3 74.0 75.0 65.0 95.0 NaN NaN -1.0 0.0 1.0 0.0 2.0
4 77.0 65.0 55.0 85.0 NaN NaN -1.0 0.0 2.0 0.0 3.0
5 54.0 55.0 45.0 75.0 NaN NaN -1.0 0.0 1.0 0.0 4.0
6 33.0 45.0 35.0 65.0 NaN NaN -1.0 0.0 0.0 0.0 5.0
7 54.0 35.0 25.0 55.0 NaN NaN -1.0 0.0 5.0 0.0 6.0
8 64.0 55.0 35.0 65.0 NaN NaN 1.0 0.0 2.0 1.0 0.0
9 156.0 65.0 45.0 75.0 NaN NaN 1.0 0.0 0.0 2.0 0.0
10 156.0 75.0 55.0 85.0 NaN NaN 1.0 0.0 0.0 3.0 0.0
11 156.0 85.0 65.0 95.0 NaN NaN 1.0 0.0 0.0 4.0 0.0
12 156.0 95.0 75.0 105.0 NaN NaN 1.0 0.0 0.0 5.0 0.0
13 156.0 105.0 85.0 115.0 NaN NaN 1.0 0.0 0.0 6.0 0.0
14 156.0 115.0 95.0 125.0 NaN NaN 1.0 0.0 0.0 7.0 0.0
15 156.0 125.0 105.0 135.0 NaN NaN 1.0 0.0 0.0 8.0 0.0
16 156.0 135.0 115.0 145.0 NaN NaN 1.0 0.0 0.0 9.0 0.0
17 156.0 145.0 125.0 155.0 NaN NaN 1.0 0.0 0.0 10.0 0.0
18 156.0 155.0 135.0 165.0 NaN NaN 1.0 0.0 1.0 11.0 0.0
19 94.0 135.0 125.0 155.0 NaN NaN -1.0 0.0 0.0 0.0 1.0
20 94.0 125.0 115.0 145.0 NaN NaN -1.0 0.0 0.0 0.0 2.0
21 94.0 115.0 105.0 135.0 NaN NaN -1.0 0.0 0.0 0.0 3.0
22 94.0 105.0 95.0 125.0 NaN NaN -1.0 0.0 0.0 0.0 4.0
23 89.0 95.0 85.0 115.0 NaN NaN -1.0 0.0 6.0 0.0 5.0
In [7]: renko.graph()
