├── BTCUSDT3600.csv
├── LICENSE
├── README.md
└── perm_entropy.py


/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 neurotrader888
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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1 | # PermutationEntropy
2 | A simple python implementation of permutation entropy.
3 | 
4 | Includes hourly bitcoin data for testing. 
5 | 
6 | 
7 | 


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/perm_entropy.py:
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 1 | import pandas as pd
 2 | import numpy as np
 3 | import math
 4 | import matplotlib.pyplot as plt
 5 | 
 6 | def ordinal_patterns(arr: np.array, d: int) -> np.array:
 7 |     assert(d >= 2)
 8 |     fac = math.factorial(d);
 9 |     d1 = d - 1
10 |     mults = []
11 |     for i in range(1, d):
12 |         mult = fac / math.factorial(i + 1)
13 |         mults.append(mult)
14 |    
15 |     # Create array to put ordinal pattern in
16 |     ordinals = np.empty(len(arr))
17 |     ordinals[:] = np.nan
18 | 
19 |     for i in range(d1, len(arr)):
20 |         dat = arr[i - d1:  i+1] 
21 |         pattern_ordinal = 0
22 |         for l in range(1, d): 
23 |             count = 0
24 |             for r in range(l):
25 |                 if dat[d1 - l] >= dat[d1 - r]:
26 |                    count += 1
27 |              
28 |             pattern_ordinal += count * mults[l - 1]
29 |         ordinals[i] = int(pattern_ordinal)
30 |     
31 |     return ordinals
32 | 
33 | def permutation_entropy(arr: np.array, d:int, mult: int) -> np.array:
34 |     fac = math.factorial(d)
35 |     lookback = fac * mult
36 |     
37 |     ent = np.empty(len(arr))
38 |     ent[:] = np.nan
39 |     ordinals = ordinal_patterns(arr, d)
40 |     
41 |     for i in range(lookback + d - 1, len(arr)):
42 |         window = ordinals[i - lookback + 1 :i+1]
43 |         
44 |         # Create distribution
45 |         freqs = pd.Series(window).value_counts().to_dict()
46 |         for j in range(fac):
47 |             if j in freqs:
48 |                 freqs[j] = freqs[j] / lookback
49 |        
50 |         # Calculate entropy
51 |         perm_entropy = 0.0
52 |         for k, v in freqs.items():
53 |             perm_entropy += v * math.log2(v)
54 | 
55 |         # Normalize to 0-1
56 |         perm_entropy = -1. * (1. / math.log2(fac)) * perm_entropy
57 |         ent[i] = perm_entropy
58 |         
59 |     return ent 
60 | 
61 | if __name__ == '__main__':
62 |     data = pd.read_csv('BTCUSDT3600.csv')
63 |     data['date'] = data['date'].astype('datetime64[s]')
64 |     data = data.set_index('date')
65 | 
66 | 
67 |     plt.style.use('dark_background')
68 |     data['perm_entropy'] = permutation_entropy(data['close'].to_numpy(), 3, 28)
69 |     data['vol_perm_entropy'] = permutation_entropy(data['volume'].to_numpy(), 3, 28)
70 | 
71 |     fig, ax1 = plt.subplots()
72 |     np.log(data['close']).plot(ax=ax1, color='green', label='Close')
73 |     ax2 = ax1.twinx()     
74 |     data['perm_entropy'].plot(ax=ax2, color='white', alpha=0.75, label='Perm Entropy (Close)')
75 |     data['vol_perm_entropy'].plot(ax=ax2, color='yellow', alpha=0.75, label='Perm Entropy (Volume)')
76 |     ax1.legend()
77 |     plt.legend()
78 |     plt.show()
79 | 
80 | 
81 | 


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