├── Chapter02
├── A.csv
├── B.csv
├── C.csv
└── eda.ipynb
├── Chapter03
├── arr.csv
├── arr.npy
└── numpy.ipynb
├── Chapter04
├── df.csv
├── df.json
└── pandas.ipynb
├── Chapter05
├── fig.png
└── matplotlib.ipynb
├── Chapter06
├── sklearn.ipynb
└── statsmodels_pmdarima_fbprophet.ipynb
├── Chapter07
├── cache.sqlite
└── marketdata.ipynb
├── Chapter08
├── A.csv
├── quandl_eod.py
├── risk_management.ipynb
└── zipline_bundles-master.zip
├── Chapter09
└── signals_and_strategies.ipynb
├── LICENSE
├── README.md
└── environment.yml
/Chapter02/C.csv:
--------------------------------------------------------------------------------
1 | Date,Open,High,Low,Close,Adj Close,Volume
2 | 2015-05-15,12.46,13.09,12.35,12.38,12.38,0
3 | 2015-05-18,13.08,13.22,,12.73,12.73,0
4 | 2015-05-19,12.95,13.13,12.55,12.85,12.85,0
5 | 2015-05-20,12.9,13.27,12.62,12.88,12.88,0
6 | 2015-05-21,13.03,13.09,12.09,12.11,12.11,0
7 | 2015-05-22,12.37,12.37,11.82,12.13,12.13,0
8 | 2015-05-26,13.45,14.63,13.34,14.06,14.06,0
9 | 2015-05-27,14.16,14.41,13.05,13.27,13.27,0
10 | 2015-05-28,13.49,13.99,13.31,13.31,13.31,0
11 | 2015-05-29,13.59,14.43,13.4,13.84,13.84,0
12 | 2015-06-01,13.92,14.86,13.47,13.97,13.97,0
13 | 2015-06-02,14.72,15.05,13.59,14.24,14.24,0
14 | 2015-06-03,13.73,14.2,13.4,13.66,13.66,0
15 | 2015-06-04,14.57,15.49,13.99,14.71,14.71,0
16 | 2015-06-05,15.01,15.65,14.21,14.21,14.21,0
17 | 2015-06-08,14.84,15.5,14.67,15.29,15.29,0
18 | 2015-06-09,15.18,15.74,14.47,14.47,14.47,0
19 | 2015-06-10,14.24,14.37,12.96,13.22,13.22,0
20 | 2015-06-11,13.04,13.22,12.56,12.85,12.85,0
21 | 2015-06-12,13.31,14.02,,13.78,13.78,0
22 | 2015-06-15,15.48,15.57,14.91,15.39,15.39,0
23 | 2015-06-16,15.62,15.62,14.81,14.81,14.81,0
24 | 2015-06-17,14.66,15.49,14.07,14.5,14.5,0
25 | 2015-06-18,14.03,14.03,12.54,13.19,13.19,0
26 | 2015-06-19,13.35,14.0,12.96,13.96,13.96,0
27 | 2015-06-22,13.42,13.46,12.43,12.74,12.74,0
28 | 2015-06-23,12.5,12.68,11.93,12.11,12.11,0
29 | 2015-06-24,12.57,13.33,,13.26,13.26,0
30 | 2015-06-25,12.96,14.16,12.92,14.01,14.01,0
31 | 2015-06-26,14.13,14.91,13.64,14.02,14.02,0
32 | 2015-06-29,16.700001,19.5,15.82,18.85,18.85,0
33 | 2015-06-30,17.6,19.799999,17.49,18.23,18.23,0
34 | 2015-07-01,16.629998999999998,17.26,15.65,16.09,16.09,0
35 | 2015-07-02,15.43,17.48,15.39,16.790001,16.790001,0
36 | 2015-07-07,17.219998999999998,19.200001,,16.09,16.09,0
37 | 2015-07-08,17.379998999999998,19.76,16.940001000000002,19.66,19.66,0
38 | 2015-07-09,17.459999,20.049999,17.200001,19.969998999999998,19.969998999999998,0
39 | 2015-07-10,17.450001,18.17,330.0,16.83,16.83,0
40 | 2015-07-13,15.29,15.36,13.82,13.9,13.9,0
41 | 2015-07-14,13.91,13.95,12.9,13.37,13.37,0
42 | 2015-07-15,13.35,13.97,12.81,13.23,13.23,0
43 | 2015-07-16,12.59,12.61,11.87,12.11,12.11,0
44 | 2015-07-17,11.77,12.22,11.77,11.95,11.95,0
45 | 2015-07-20,12.25,12.37,11.71,12.25,12.25,0
46 | 2015-07-21,12.42,12.79,12.21,12.22,12.22,0
47 | 2015-07-22,12.77,12.83,12.05,12.12,12.12,0
48 | 2015-07-23,12.06,13.08,11.73,12.64,12.64,0
49 | 2015-07-24,12.87,14.73,12.86,13.74,13.74,0
50 | 2015-07-27,15.6,16.27,15.03,15.6,15.6,0
51 | 2015-07-28,14.87,15.62,13.32,13.44,13.44,0
52 | 2015-07-29,13.57,13.59,11.85,12.5,12.5,0
53 | 2015-07-30,12.72,13.42,,12.13,12.13,0
54 | 2015-07-31,12.03,12.63,11.82,12.12,12.12,0
55 | 2015-08-03,12.85,13.55,12.32,12.56,12.56,0
56 | 2015-08-04,12.66,13.22,12.29,13.0,13.0,0
57 | 2015-08-05,12.02,12.72,10.88,12.51,12.51,0
58 | 2015-08-06,12.2,14.25,12.16,13.77,13.77,0
59 | 2015-08-07,13.57,14.58,13.29,13.39,13.39,0
60 | 2015-08-10,12.73,12.78,12.18,12.23,12.23,0
61 | 2015-08-11,13.24,14.33,13.02,13.71,13.71,0
62 | 2015-08-12,15.19,16.280001000000002,13.45,13.61,13.61,0
63 | 2015-08-13,13.87,14.33,13.06,13.49,13.49,0
64 | 2015-08-14,13.69,13.87,12.8,12.83,12.83,0
65 | 2015-08-17,14.32,14.52,13.01,13.02,13.02,0
66 | 2015-08-18,13.41,13.94,13.17,13.79,13.79,0
67 | 2015-08-19,14.84,15.96,13.73,15.25,15.25,0
68 | 2015-08-20,16.549999,19.24,16.129998999999998,19.139999,19.139999,0
69 | 2015-08-21,22.549999,28.379998999999998,20.799999,28.030001000000002,28.030001000000002,0
70 | 2015-08-24,28.030001000000002,53.290001000000004,28.030001000000002,40.740002000000004,40.740002000000004,0
71 | 2015-08-25,31.129998999999998,38.060001,28.08,36.02,36.02,0
72 | 2015-08-26,31.129998999999998,35.619999,28.67,30.32,30.32,0
73 | 2015-08-27,27.110001,29.9,24.49,26.1,26.1,0
74 | 2015-08-28,26.690001000000002,29.200001,25.77,26.049999,26.049999,0
75 | 2015-08-31,27.030001000000002,29.370001000000002,26.629998999999998,28.43,28.43,0
76 | 2015-09-01,31.91,33.82,29.91,31.4,31.4,0
77 | 2015-09-02,29.139999,30.450001,,26.09,26.09,0
78 | 2015-09-03,25.209999,26.309998999999998,23.450001,25.610001,25.610001,0
79 | 2015-09-04,27.43,29.469998999999998,25.68,27.799999,27.799999,0
80 | 2015-09-08,25.049999,26.25,24.129998999999998,24.9,24.9,0
81 | 2015-09-09,22.389999,26.82,21.51,26.23,26.23,0
82 | 2015-09-10,26.870001000000002,27.219998999999998,23.530001000000002,24.370001000000002,24.370001000000002,0
83 | 2015-09-11,25.379998999999998,25.809998999999998,23.15,23.200001,23.200001,0
84 | 2015-09-14,24.030001000000002,25.32,23.639999,24.25,24.25,0
85 | 2015-09-15,23.280001000000002,23.77,22.129998999999998,22.540001,22.540001,0
86 | 2015-09-16,22.57,22.940001000000002,21.09,21.35,21.35,0
87 | 2015-09-17,21.540001,23.33,,21.139999,21.139999,0
88 | 2015-09-18,23.07,23.99,20.98,22.280001000000002,22.280001000000002,0
89 | 2015-09-21,21.969998999999998,22.48,20.049999,20.139999,20.139999,0
90 | 2015-09-22,22.969998999999998,26.290001,22.25,22.440001000000002,22.440001000000002,0
91 | 2015-09-23,22.09,23.200001,21.139999,22.129998999999998,22.129998999999998,0
92 | 2015-09-25,21.120001000000002,24.290001,20.809998999999998,23.620001000000002,23.620001000000002,0
93 | 2015-09-28,25.02,28.33,24.940001000000002,27.629998999999998,27.629998999999998,0
94 | 2015-09-30,24.639999,25.879998999999998,23.25,24.5,24.5,0
95 | 2015-10-01,23.139999,25.23,22.549999,22.549999,22.549999,0
96 | 2015-10-02,23.99,24.469998999999998,20.35,20.940001000000002,20.940001000000002,0
97 | 2015-10-05,20.309998999999998,20.42,19.139999,19.540001,19.540001,0
98 | 2015-10-06,19.540001,20.32,18.82,19.4,19.4,0
99 | 2015-10-07,18.959999,19.73,18.33,18.4,18.4,0
100 | 2015-10-08,18.620001000000002,19.02,16.34,17.42,17.42,0
101 | 2015-10-09,17.15,18.200001,16.889999,17.08,17.08,0
102 | 2015-10-12,17.68,17.809998999999994,16.15,16.17,16.17,0
103 | 2015-10-13,17.08,17.700001,16.139999,17.67,17.67,0
104 | 2015-10-14,17.67,18.780001000000002,17.299999,18.030001000000002,18.030001000000002,0
105 | 2015-10-15,17.620001000000006,17.85,16.040001,16.049999,16.049999,0
106 | 2015-10-16,15.64,16.860001,15.05,15.05,15.05,0
107 | 2015-10-19,15.68,16.23,14.82,14.98,14.98,0
108 | 2015-10-20,15.17,16.34,,15.75,15.75,0
109 | 2015-10-21,14.98,16.700001,14.41,16.700001,16.700001,0
110 | 2015-10-22,15.02,15.92,14.45,14.45,14.45,0
111 | 2015-10-23,13.46,15.12,13.24,14.46,14.46,0
112 | 2015-10-26,14.76,15.43,14.68,15.29,15.29,0
113 | 2015-10-27,15.75,15.99,14.78,15.43,15.43,0
114 | 2015-10-28,15.14,15.73,12.8,14.33,14.33,0
115 | 2015-10-29,14.8,15.46,14.33,14.61,14.61,0
116 | 2015-10-30,14.6,15.39,14.0,15.07,15.07,0
117 | 2015-11-02,15.41,15.51,13.67,14.15,14.15,0
118 | 2015-11-03,14.33,14.73,13.81,14.54,14.54,0
119 | 2015-11-04,14.04,15.88,13.96,15.51,15.51,0
120 | 2015-11-05,15.39,16.389999,15.0,15.05,15.05,0
121 | 2015-11-06,14.91,16.0,14.32,14.33,14.33,0
122 | 2015-11-09,15.34,17.09,15.14,16.52,16.52,0
123 | 2015-11-10,16.690001000000002,16.959999,15.24,15.29,15.29,0
124 | 2015-11-11,15.07,16.15,15.02,16.059998999999998,16.059998999999998,0
125 | 2015-11-12,17.059998999999998,18.5,16.65,18.370001000000002,18.370001000000002,0
126 | 2015-11-13,18.68,20.67,,20.08,20.08,0
127 | 2015-11-16,20.51,20.549999,17.25,18.16,18.16,0
128 | 2015-11-17,17.82,19.59,16.860001,18.84,18.84,0
129 | 2015-11-18,19.01,19.450001,16.799999,16.85,16.85,0
130 | 2015-11-19,16.25,18.26,16.0,16.99,16.99,0
131 | 2015-11-23,16.15,16.74,15.38,15.62,15.62,0
132 | 2015-11-24,16.530001000000002,17.209999,15.48,15.93,15.93,0
133 | 2015-11-25,15.55,15.89,15.05,15.19,15.19,0
134 | 2015-11-27,15.31,16.09,15.12,15.12,15.12,0
135 | 2015-11-30,15.55,16.57,15.52,16.129998999999998,16.129998999999998,0
136 | 2015-12-01,15.61,16.34,14.63,14.67,14.67,0
137 | 2015-12-02,15.04,16.49,14.71,15.91,15.91,0
138 | 2015-12-03,15.87,19.35,15.86,18.110001,18.110001,0
139 | 2015-12-04,17.43,17.65,14.69,14.81,14.81,0
140 | 2015-12-07,15.65,17.18,15.58,15.84,15.84,0
141 | 2015-12-08,17.690001000000006,18.33,16.52,17.6,17.6,0
142 | 2015-12-10,19.25,19.719998999999998,18.129998999999998,19.34,19.34,0
143 | 2015-12-11,21.360001,25.27,20.879998999999998,24.389999,24.389999,0
144 | 2015-12-14,24.700001,26.809998999999998,21.469998999999998,22.73,22.73,0
145 | 2015-12-15,20.76,21.620001000000002,20.02,20.950001,20.950001,0
146 | 2015-12-16,19.25,20.24,17.120001000000002,17.860001,17.860001,0
147 | 2015-12-17,16.18,19.049999,16.129998999999998,18.940001000000002,18.940001000000002,0
148 | 2015-12-21,19.639999,20.209999,18.700001,18.700001,18.700001,0
149 | 2015-12-22,17.610001,18.219998999999998,,16.6,16.6,0
150 | 2015-12-23,15.86,16.25,15.33,15.57,15.57,0
151 | 2015-12-24,15.44,15.88,14.45,15.74,15.74,0
152 | 2015-12-28,17.65,18.129998999999998,16.879998999999998,16.91,16.91,0
153 | 2015-12-29,15.91,16.48,15.63,16.08,16.08,0
154 | 2015-12-30,16.5,17.42,16.5,17.290001,17.290001,0
155 | 2015-12-31,17.969998999999994,20.389999,17.51,18.209999,18.209999,0
156 | 2016-01-04,22.48,23.360001,20.67,20.700001,20.700001,0
157 | 2016-01-05,20.75,21.059998999999998,19.25,19.34,19.34,0
158 | 2016-01-06,21.67,21.860001,19.799999,20.59,20.59,0
159 | 2016-01-07,23.219998999999998,25.860001,22.4,24.99,24.99,0
160 | 2016-01-08,22.959999,27.08,22.48,27.01,27.01,0
161 | 2016-01-11,25.58,27.389999,23.83,24.299999,24.299999,0
162 | 2016-01-12,22.969998999999998,23.93,21.91,22.469998999999998,22.469998999999998,0
163 | 2016-01-13,21.719998999999998,26.110001,21.440001000000002,25.219998999999998,25.219998999999998,0
164 | 2016-01-14,24.75,26.280001000000002,23.07,23.950001,23.950001,0
165 | 2016-01-15,28.959999,30.950001,26.67,27.02,27.02,0
166 | 2016-01-19,25.4,27.59,,26.049999,26.049999,0
167 | 2016-01-20,27.780001000000002,32.09,26.59,27.59,27.59,0
168 | 2016-01-21,27.790001,28.43,25.01,26.690001000000002,26.690001000000002,0
169 | 2016-01-22,24.209999,24.549999,22.219998999999998,22.34,22.34,0
170 | 2016-01-25,23.299999,24.309998999999998,22.379998999999998,24.15,24.15,0
171 | 2016-01-26,23.75,24.02,22.33,22.5,22.5,0
172 | 2016-01-27,22.879998999999998,27.219998999999998,20.42,23.110001,23.110001,0
173 | 2016-01-28,22.15,23.809998999999998,21.9,22.42,22.42,0
174 | 2016-01-29,21.59,21.74,19.5,20.200001,20.200001,0
175 | 2016-02-01,21.32,23.66,19.610001,19.98,19.98,0
176 | 2016-02-02,21.34,22.42,21.059998999999998,21.98,21.98,0
177 | 2016-02-03,21.49,27.700001,21.42,21.65,21.65,0
178 | 2016-02-04,22.290001,23.139999,21.24,21.84,21.84,0
179 | 2016-02-05,22.09,24.110001,21.91,23.379998999999998,23.379998999999998,0
180 | 2016-02-08,25.889999,27.719998999999998,25.559998999999998,26.0,26.0,0
181 | 2016-02-09,28.299999,28.309998999999998,25.99,26.540001,26.540001,0
182 | 2016-02-10,25.75,26.6,24.469998999999998,26.290001,26.290001,0
183 | 2016-02-11,29.01,30.9,26.67,28.139999,28.139999,0
184 | 2016-02-12,27.16,27.57,24.92,25.4,25.4,0
185 | 2016-02-16,24.959999,25.52,23.32,24.110001,24.110001,0
186 | 2016-02-17,23.4,24.16,21.83,22.309998999999998,22.309998999999998,0
187 | 2016-02-19,22.389999,23.440001000000002,20.52,20.530001000000002,20.530001000000002,0
188 | 2016-02-22,20.139999,20.35,19.02,19.379998999999998,19.379998999999998,0
189 | 2016-02-23,19.75,21.16,19.540001,20.98,20.98,0
190 | 2016-02-24,22.280001000000002,22.870001000000002,20.26,20.719998999999998,20.719998999999998,0
191 | 2016-02-25,20.540001,21.26,19.1,19.110001,19.110001,0
192 | 2016-02-26,-400.0,20.129998999999998,18.459999,19.809998999999998,19.809998999999998,0
193 | 2016-02-29,20.49,20.809998999999998,18.379998999999998,20.549999,20.549999,0
194 | 2016-03-01,19.84,20.17,,17.700001,17.700001,0
195 | 2016-03-02,17.98,18.41,16.780001000000002,17.09,17.09,0
196 | 2016-03-03,17.25,17.559998999999998,16.32,16.700001,16.700001,0
197 | 2016-03-04,16.48,17.35,16.049999,16.860001,16.860001,0
198 | 2016-03-07,17.98,18.040001,16.870001000000002,17.35,17.35,0
199 | 2016-03-08,18.379998999999998,18.889999,17.82,18.67,18.67,0
200 | 2016-03-09,18.559998999999998,19.110001,18.309998999999998,18.34,18.34,0
201 | 2016-03-10,18.17,19.59,17.059998999999998,18.049999,18.049999,0
202 | 2016-03-11,17.09,17.27,16.280001000000002,16.5,16.5,0
203 | 2016-03-14,17.01,17.67,16.690001000000002,16.92,16.92,0
204 | 2016-03-15,17.6,17.85,16.84,16.84,16.84,0
205 | 2016-03-16,15.96,16.33,14.89,14.99,14.99,0
206 | 2016-03-17,15.34,15.38,13.82,14.44,14.44,0
207 | 2016-03-18,14.05,14.36,13.75,14.02,14.02,0
208 | 2016-03-21,14.57,14.73,13.79,13.79,13.79,0
209 | 2016-03-22,14.57,14.76,13.75,14.17,14.17,0
210 | 2016-03-24,16.299999,16.440001000000002,330.0,14.74,14.74,0
211 | 2016-03-28,15.65,16.040001,14.89,15.24,15.24,0
212 | 2016-03-29,15.74,15.89,,13.82,13.82,0
213 | 2016-03-30,13.69,13.89,13.06,13.56,13.56,0
214 | 2016-03-31,13.73,14.28,13.49,13.95,13.95,0
215 | 2016-04-01,15.23,15.28,,13.1,13.1,0
216 | 2016-04-04,13.88,14.24,13.66,14.12,14.12,0
217 | 2016-04-05,15.39,15.72,14.93,15.42,15.42,0
218 | 2016-04-06,15.61,15.98,14.0,14.09,14.09,0
219 | 2016-04-07,15.14,16.77,14.68,16.16,16.16,0
220 | 2016-04-08,15.34,15.93,14.84,15.36,15.36,0
221 | 2016-04-11,15.34,16.26,14.83,16.26,16.26,0
222 | 2016-04-12,15.98,16.57,14.84,14.85,14.85,0
223 | 2016-04-13,14.49,14.53,13.6,13.84,13.84,0
224 | 2016-04-14,13.9,14.12,13.38,13.72,13.72,0
225 | 2016-04-15,13.77,14.19,13.58,13.62,13.62,0
226 | 2016-04-18,14.87,14.94,13.23,13.35,13.35,0
227 | 2016-04-19,13.18,13.88,12.98,13.24,13.24,0
228 | 2016-04-20,13.39,13.5,12.5,13.28,13.28,0
229 | 2016-04-21,13.2,14.14,13.16,13.95,13.95,0
230 | 2016-04-22,13.7,14.19,13.15,13.22,13.22,0
231 | 2016-04-25,14.07,14.76,13.86,14.08,14.08,0
232 | 2016-04-26,14.01,14.43,13.66,13.96,13.96,0
233 | 2016-04-27,14.15,14.95,13.5,13.77,13.77,0
234 | 2016-04-28,14.53,15.61,13.3,15.22,15.22,0
235 | 2016-04-29,15.21,17.09,,15.7,15.7,0
236 | 2016-05-02,16.33,16.5,14.48,14.68,14.68,0
237 | 2016-05-03,14.92,16.42,14.91,15.6,15.6,0
238 | 2016-05-04,15.47,16.85,15.39,16.049999,16.049999,0
239 | 2016-05-05,15.54,16.450001,15.22,15.91,15.91,0
240 | 2016-05-06,16.200001,16.58,14.71,14.72,14.72,0
241 | 2016-05-09,15.2,15.39,14.17,14.57,14.57,0
242 | 2016-05-10,13.98,14.35,13.55,13.63,13.63,0
243 | 2016-05-11,13.92,14.69,13.29,14.69,14.69,0
244 | 2016-05-12,14.55,15.42,13.95,14.41,14.41,0
245 | 2016-05-13,15.15,15.47,13.97,15.04,15.04,0
246 | 2016-05-16,15.72,15.98,14.28,14.68,14.68,0
247 | 2016-05-17,14.57,16.120001000000002,14.48,15.57,15.57,0
248 | 2016-05-18,15.72,16.469998999999998,14.86,15.95,15.95,0
249 | 2016-05-19,16.370001000000002,17.65,16.280001000000002,16.33,16.33,0
250 | 2016-05-20,16.129998999999998,16.299999,15.11,15.2,15.2,0
251 | 2016-05-23,16.33,16.469998999999998,15.4,15.82,15.82,0
252 | 2016-05-24,16.030001000000002,16.059998999999998,14.36,14.42,14.42,0
253 | 2016-05-26,13.8,14.11,13.43,13.43,13.43,0
254 | 2016-05-27,13.49,13.76,13.04,13.12,13.12,0
255 | 2016-05-31,13.94,15.0,13.45,14.19,14.19,0
256 | 2016-06-01,14.45,15.25,14.18,14.2,14.2,0
257 | 2016-06-02,14.42,14.92,13.62,13.63,13.63,0
258 | 2016-06-03,13.78,14.66,12.9,13.47,13.47,0
259 | 2016-06-06,13.84,14.27,13.42,13.65,13.65,0
260 | 2016-06-07,12.77,14.05,12.72,14.05,14.05,0
261 | 2016-06-08,13.84,14.27,13.7,14.08,14.08,0
262 | 2016-06-09,14.01,14.85,13.99,14.64,14.64,0
263 | 2016-06-10,14.89,17.33,14.85,17.030001000000002,17.030001000000002,0
264 | 2016-06-13,18.24,21.01,17.889999,20.969998999999998,20.969998999999998,0
265 | 2016-06-14,21.280001000000002,22.16,20.27,20.5,20.5,0
266 | 2016-06-15,20.25,20.450001,18.629998999999998,20.139999,20.139999,0
267 | 2016-06-16,20.799999,22.889999,19.24,19.370001000000002,19.370001000000002,0
268 | 2016-06-17,19.42,20.030001000000002,18.709999,19.41,19.41,0
269 | 2016-06-20,17.42,18.549999,16.59,18.370001000000002,18.370001000000002,0
270 | 2016-06-21,17.67,18.959999,16.91,18.48,18.48,0
271 | 2016-06-22,18.26,21.219998999999998,17.83,21.17,21.17,0
272 | 2016-06-23,19.540001,19.790001,17.25,17.25,17.25,0
273 | 2016-06-24,26.059998999999998,26.24,,25.76,25.76,0
274 | 2016-06-27,24.379998999999998,26.719998999999998,22.93,23.85,23.85,0
275 | 2016-06-28,21.76,22.07,18.75,18.75,18.75,0
276 | 2016-06-29,18.120001000000002,18.27,16.48,16.639999,16.639999,0
277 | 2016-07-01,15.59,15.86,14.61,14.77,14.77,0
278 | 2016-07-05,16.049999,16.620001000000002,15.49,15.58,15.58,0
279 | 2016-07-06,15.87,17.040001,14.96,14.96,14.96,0
280 | 2016-07-07,14.8,15.98,14.33,14.76,14.76,0
281 | 2016-07-11,13.25,13.67,13.0,13.54,13.54,0
282 | 2016-07-12,12.93,13.93,,13.55,13.55,0
283 | 2016-07-13,13.32,13.79,12.92,13.04,13.04,0
284 | 2016-07-14,12.5,13.37,12.14,12.82,12.82,0
285 | 2016-07-15,13.12,13.22,12.27,12.67,12.67,0
286 | 2016-07-18,12.75,13.12,12.33,12.44,12.44,0
287 | 2016-07-19,12.53,12.83,11.94,11.97,11.97,0
288 | 2016-07-21,11.8,13.06,11.69,12.74,12.74,0
289 | 2016-07-22,12.8,12.88,11.97,12.02,12.02,0
290 | 2016-07-25,12.64,13.72,12.39,12.87,12.87,0
291 | 2016-07-26,12.88,13.5,12.8,13.05,13.05,0
292 | 2016-07-27,12.61,13.74,12.5,12.83,12.83,0
293 | 2016-07-28,12.51,13.52,12.36,12.72,12.72,0
294 | 2016-07-29,12.85,12.9,11.77,11.87,11.87,0
295 | 2016-08-01,11.89,12.98,11.86,12.44,12.44,0
296 | 2016-08-02,12.39,14.24,12.35,13.37,13.37,0
297 | 2016-08-03,13.53,13.91,12.73,12.86,12.86,0
298 | 2016-08-04,12.73,12.98,11.79,12.42,12.42,0
299 | 2016-08-05,12.08,12.26,11.18,11.39,11.39,0
300 | 2016-08-08,11.66,11.78,11.41,11.5,11.5,0
301 | 2016-08-09,11.4,11.92,,11.66,11.66,0
302 | 2016-08-10,11.55,12.5,11.37,12.05,12.05,0
303 | 2016-08-11,11.93,12.11,11.38,11.68,11.68,0
304 | 2016-08-16,12.04,12.78,11.87,12.64,12.64,0
305 | 2016-08-17,12.57,13.71,12.14,12.19,12.19,0
306 | 2016-08-18,12.2,12.53,11.42,11.43,11.43,0
307 | 2016-08-22,12.53,13.02,11.94,12.27,12.27,0
308 | 2016-08-23,12.15,12.44,,12.38,12.38,0
309 | 2016-08-24,12.7,14.01,12.3,13.45,13.45,0
310 | 2016-08-25,13.62,14.09,13.29,13.63,13.63,0
311 | 2016-08-26,13.54,14.93,12.13,13.65,13.65,0
312 | 2016-08-29,14.09,14.43,330.0,12.94,12.94,0
313 | 2016-08-30,12.94,13.6,12.7,13.12,13.12,0
314 | 2016-08-31,13.14,14.34,12.97,13.42,13.42,0
315 | 2016-09-01,13.07,14.61,12.99,13.48,13.48,0
316 | 2016-09-02,13.47,13.9,11.9,11.98,11.98,0
317 | 2016-09-06,12.42,12.93,11.85,12.02,12.02,0
318 | 2016-09-07,11.89,12.45,11.77,11.94,11.94,0
319 | 2016-09-08,11.74,12.6,11.65,12.51,12.51,0
320 | 2016-09-09,12.52,17.540001,12.52,17.5,17.5,0
321 | 2016-09-12,20.129998999999998,20.51,14.76,15.16,15.16,0
322 | 2016-09-13,15.98,18.969998999999998,15.83,17.85,17.85,0
323 | 2016-09-14,17.629998999999994,18.139999,16.34,18.139999,18.139999,0
324 | 2016-09-15,17.969998999999994,18.07,15.74,16.299999,16.299999,0
325 | 2016-09-16,16.41,17.1,15.28,15.37,15.37,0
326 | 2016-09-19,15.14,15.96,14.6,15.53,15.53,0
327 | 2016-09-20,14.98,16.09,14.69,15.92,15.92,0
328 | 2016-09-21,15.07,15.65,12.98,13.3,13.3,0
329 | 2016-09-22,13.39,13.39,11.76,12.02,12.02,0
330 | 2016-09-23,12.0,12.58,11.93,12.29,12.29,0
331 | 2016-09-26,13.26,14.63,13.26,14.5,14.5,0
332 | 2016-09-27,13.36,14.76,12.97,13.1,13.1,0
333 | 2016-09-28,12.9,13.57,12.24,12.39,12.39,0
334 | 2016-09-29,12.53,15.69,12.14,14.02,14.02,0
335 | 2016-09-30,14.91,15.2,12.53,13.29,13.29,0
336 | 2016-10-03,13.75,14.42,13.42,13.57,13.57,0
337 | 2016-10-05,13.56,13.68,12.7,12.99,12.99,0
338 | 2016-10-06,13.11,13.84,12.8,12.84,12.84,0
339 | 2016-10-10,14.19,14.36,13.29,13.38,13.38,0
340 | 2016-10-11,13.71,16.469998999999998,13.69,15.36,15.36,0
341 | 2016-10-12,15.53,16.34,15.26,15.91,15.91,0
342 | 2016-10-13,16.879998999999998,17.950001,16.139999,16.690001000000002,16.690001000000002,0
343 | 2016-10-14,16.49,16.5,15.25,16.120001000000002,16.120001000000002,0
344 | 2016-10-17,17.01,17.110001,16.129998999999998,16.209999,16.209999,0
345 | 2016-10-18,15.82,15.85,15.03,15.28,15.28,0
346 | 2016-10-19,15.45,15.68,13.87,14.41,14.41,0
347 | 2016-10-20,14.43,14.72,13.74,13.75,13.75,0
348 | 2016-10-21,14.04,14.53,13.27,13.34,13.34,0
349 | 2016-10-24,13.19,13.26,12.83,13.02,13.02,0
350 | 2016-10-25,12.91,13.86,12.73,13.46,13.46,0
351 | 2016-10-26,13.66,14.8,13.66,14.24,14.24,0
352 | 2016-10-27,14.37,15.43,13.6,15.36,15.36,0
353 | 2016-10-28,15.67,17.35,14.65,16.190001000000002,16.190001000000002,0
354 | 2016-10-31,16.309998999999998,17.629998999999994,16.25,17.059998999999998,17.059998999999998,0
355 | 2016-11-01,16.540001,20.43,16.51,18.559998999999998,18.559998999999998,0
356 | 2016-11-02,19.09,19.82,18.559998999999998,19.32,19.32,0
357 | 2016-11-03,19.85,22.57,18.84,22.08,22.08,0
358 | 2016-11-04,21.860001,23.01,19.200001,22.51,22.51,0
359 | 2016-11-07,19.780001000000002,19.860001,18.389999,18.709999,18.709999,0
360 | 2016-11-08,18.92,19.91,17.700001,18.74,18.74,0
361 | 2016-11-09,20.700001,21.48,14.33,14.38,14.38,0
362 | 2016-11-10,14.01,16.299999,13.26,14.74,14.74,0
363 | 2016-11-11,14.83,16.0,14.15,14.17,14.17,0
364 | 2016-11-14,14.69,15.56,14.39,14.48,14.48,0
365 | 2016-11-15,14.16,14.65,13.3,13.37,13.37,0
366 | 2016-11-16,13.51,14.49,13.51,13.72,13.72,0
367 | 2016-11-17,13.37,13.55,12.97,13.35,13.35,0
368 | 2016-11-18,13.56,13.74,12.85,12.85,12.85,0
369 | 2016-11-21,13.27,13.44,12.16,12.42,12.42,0
370 | 2016-11-22,12.26,12.83,12.2,12.41,12.41,0
371 | 2016-11-23,12.34,13.01,12.19,12.43,12.43,0
372 | 2016-11-25,12.52,12.74,12.31,12.34,12.34,0
373 | 2016-11-28,13.4,13.5,12.74,13.15,13.15,0
374 | 2016-11-29,13.07,13.55,12.62,12.9,12.9,0
375 | 2016-11-30,12.6,13.42,12.23,13.33,13.33,0
376 | 2016-12-01,13.4,14.72,13.05,14.07,14.07,0
377 | 2016-12-02,14.16,14.48,12.39,14.12,14.12,0
378 | 2016-12-05,13.75,13.77,12.14,12.14,12.14,0
379 | 2016-12-06,12.19,12.3,,11.79,11.79,0
380 | 2016-12-07,11.59,12.24,11.33,12.22,12.22,0
381 | 2016-12-08,12.1,13.4,11.3,12.64,12.64,0
382 | 2016-12-09,12.59,12.72,11.67,11.75,11.75,0
383 | 2016-12-12,12.23,12.78,12.07,12.64,12.64,0
384 | 2016-12-13,12.46,13.42,12.34,12.72,12.72,0
385 | 2016-12-14,12.88,13.39,12.48,13.19,13.19,0
386 | 2016-12-15,13.07,13.24,12.46,12.79,12.79,0
387 | 2016-12-16,12.88,12.95,12.15,12.2,12.2,0
388 | 2016-12-19,12.5,12.52,11.67,11.71,11.71,0
389 | 2016-12-20,11.65,11.75,11.38,11.45,11.45,0
390 | 2016-12-21,11.44,11.49,10.93,11.27,11.27,0
391 | 2016-12-22,11.32,11.67,11.14,11.43,11.43,0
392 | 2016-12-23,11.38,11.81,11.35,11.44,11.44,0
393 | 2016-12-27,12.26,12.33,11.84,11.99,11.99,0
394 | 2016-12-28,11.89,13.04,11.85,12.95,12.95,0
395 | 2016-12-29,13.15,13.71,12.95,13.37,13.37,0
396 | 2016-12-30,13.2,14.68,13.05,14.04,14.04,0
397 | 2017-01-03,14.07,14.07,12.85,12.85,12.85,0
398 | 2017-01-04,12.78,12.8,11.63,11.85,11.85,0
399 | 2017-01-05,11.96,12.09,11.4,11.67,11.67,0
400 | 2017-01-06,11.7,11.74,10.98,11.32,11.32,0
401 | 2017-01-09,11.71,12.08,,11.56,11.56,0
402 | 2017-01-10,11.59,11.79,11.31,11.49,11.49,0
403 | 2017-01-11,11.56,12.23,11.21,11.26,11.26,0
404 | 2017-01-12,11.48,12.6,11.32,11.54,11.54,0
405 | 2017-01-13,11.45,11.62,10.94,11.23,11.23,0
406 | 2017-01-17,12.2,12.75,11.79,11.87,11.87,0
407 | 2017-01-18,11.79,12.81,11.69,12.48,12.48,0
408 | 2017-01-19,12.58,13.28,12.17,12.78,12.78,0
409 | 2017-01-23,12.3,12.62,11.59,11.77,11.77,0
410 | 2017-01-25,10.79,11.05,10.51,10.81,10.81,0
411 | 2017-01-26,10.61,11.01,10.6,10.63,10.63,0
412 | 2017-01-27,10.57,10.82,10.3,10.58,10.58,0
413 | 2017-01-30,11.1,12.9,11.1,11.88,11.88,0
414 | 2017-01-31,12.29,12.99,11.79,11.99,11.99,0
415 | 2017-02-01,11.79,12.05,9.97,11.81,11.81,0
416 | 2017-02-02,12.37,12.47,11.62,11.93,11.93,0
417 | 2017-02-03,11.84,11.84,10.72,10.97,10.97,0
418 | 2017-02-06,11.37,11.84,11.09,11.37,11.37,0
419 | 2017-02-07,11.39,11.67,11.06,11.29,11.29,0
420 | 2017-02-08,11.19,11.82,11.15,11.45,11.45,0
421 | 2017-02-09,11.44,11.53,,10.88,10.88,0
422 | 2017-02-10,10.85,10.95,10.55,10.85,10.85,0
423 | 2017-02-13,11.36,11.4,11.07,11.07,11.07,0
424 | 2017-02-14,11.17,11.34,10.73,10.74,10.74,0
425 | 2017-02-15,10.84,12.01,10.8,11.97,11.97,0
426 | 2017-02-16,12.02,12.86,11.69,11.76,11.76,0
427 | 2017-02-17,11.84,12.26,11.37,11.49,11.49,0
428 | 2017-02-21,12.05,12.09,11.5,11.57,11.57,0
429 | 2017-02-22,11.48,12.07,11.44,11.74,11.74,0
430 | 2017-02-23,11.66,12.46,11.54,11.71,11.71,0
431 | 2017-02-24,11.81,12.59,11.34,11.47,11.47,0
432 | 2017-02-27,11.78,12.14,11.53,12.09,12.09,0
433 | 2017-02-28,12.19,12.96,12.13,12.92,12.92,0
434 | 2017-03-01,12.31,12.58,11.78,12.54,12.54,0
435 | 2017-03-02,12.43,12.71,11.32,11.81,11.81,0
436 | 2017-03-03,11.96,11.97,10.94,10.96,10.96,0
437 | 2017-03-06,11.59,11.72,11.06,11.24,11.24,0
438 | 2017-03-07,11.27,11.58,11.04,11.45,11.45,0
439 | 2017-03-08,11.49,11.86,,11.86,11.86,0
440 | 2017-03-09,11.75,12.43,11.62,12.3,12.3,0
441 | 2017-03-10,11.97,12.09,11.46,11.66,11.66,0
442 | 2017-03-14,11.39,12.54,11.28,12.3,12.3,0
443 | 2017-03-15,12.12,12.25,10.6,11.63,11.63,0
444 | 2017-03-16,11.29,11.55,11.16,11.21,11.21,0
445 | 2017-03-17,11.38,11.38,10.78,11.28,11.28,0
446 | 2017-03-20,-400.0,11.72,11.03,11.34,11.34,0
447 | 2017-03-21,11.15,12.85,10.92,12.47,12.47,0
448 | 2017-03-22,12.95,13.16,11.99,12.81,12.81,0
449 | 2017-03-23,12.65,13.17,12.18,13.12,13.12,0
450 | 2017-03-24,12.86,14.16,12.27,12.96,12.96,0
451 | 2017-03-27,14.78,15.11,12.48,12.5,12.5,0
452 | 2017-03-28,12.44,12.67,11.34,11.53,11.53,0
453 | 2017-03-29,11.54,11.7,11.03,11.42,11.42,0
454 | 2017-03-30,11.37,11.64,11.12,11.54,11.54,0
455 | 2017-03-31,11.61,12.54,11.5,12.37,12.37,0
456 | 2017-04-03,12.59,13.59,12.27,12.38,12.38,0
457 | 2017-04-04,12.71,13.07,11.7,11.79,11.79,0
458 | 2017-04-05,11.89,12.89,10.9,12.89,12.89,0
459 | 2017-04-06,13.11,13.22,,12.39,12.39,0
460 | 2017-04-07,13.17,13.43,12.23,12.87,12.87,0
461 | 2017-04-10,13.24,14.11,12.94,14.05,14.05,0
462 | 2017-04-11,14.32,15.88,14.17,15.07,15.07,0
463 | 2017-04-12,15.16,16.16,14.84,15.77,15.77,0
464 | 2017-04-13,15.89,16.219998999999998,14.97,15.96,15.96,0
465 | 2017-04-17,16.190001000000002,16.280001000000002,14.6,14.66,14.66,0
466 | 2017-04-18,14.5,15.5,14.29,14.42,14.42,0
467 | 2017-04-19,14.0,15.15,13.46,14.93,14.93,0
468 | 2017-04-20,14.59,14.81,13.83,14.15,14.15,0
469 | 2017-04-21,13.85,15.33,13.85,14.63,14.63,0
470 | 2017-04-24,11.56,12.01,10.82,10.84,10.84,0
471 | 2017-04-25,10.81,11.15,10.22,10.76,10.76,0
472 | 2017-04-26,10.61,10.89,,10.85,10.85,0
473 | 2017-04-27,10.72,11.08,10.33,10.36,10.36,0
474 | 2017-04-28,10.39,11.16,10.29,10.82,10.82,0
475 | 2017-05-01,10.92,11.04,9.9,10.11,10.11,0
476 | 2017-05-02,10.15,10.59,10.04,10.59,10.59,0
477 | 2017-05-03,10.56,11.15,10.4,10.68,10.68,0
478 | 2017-05-04,10.71,11.24,10.27,10.46,10.46,0
479 | 2017-05-05,10.51,10.98,9.99,10.57,10.57,0
480 | 2017-05-08,10.53,10.55,9.67,9.77,9.77,0
481 | 2017-05-09,9.87,10.14,9.56,9.96,9.96,0
482 | 2017-05-10,9.75,10.24,9.62,10.21,10.21,0
483 | 2017-05-11,10.34,11.23,10.32,10.6,10.6,0
484 | 2017-05-12,10.72,10.87,10.28,10.4,10.4,0
485 | 2017-05-15,10.54,10.88,10.25,10.42,10.42,0
486 | 2017-05-16,10.46,10.67,10.18,10.65,10.65,0
487 | 2017-05-17,11.89,15.59,11.53,15.59,15.59,0
488 | 2017-05-18,14.06,16.299999,14.03,14.66,14.66,0
489 | 2017-05-19,14.23,14.23,11.72,12.04,12.04,0
490 | 2017-05-22,12.29,12.52,10.89,10.93,10.93,0
491 | 2017-05-23,11.0,11.0,10.56,10.72,10.72,0
492 | 2017-05-24,10.61,10.9,9.88,10.02,10.02,0
493 | 2017-05-25,9.82,10.29,9.72,9.99,9.99,0
494 | 2017-05-26,9.93,10.48,9.65,9.81,9.81,0
495 | 2017-05-30,10.61,10.84,10.14,10.38,10.38,0
496 | 2017-05-31,10.1,11.3,9.93,10.41,10.41,0
497 | 2017-06-01,10.42,10.54,9.69,9.89,9.89,0
498 | 2017-06-02,10.08,10.3,9.58,9.75,9.75,0
499 | 2017-06-05,10.08,10.28,9.6,10.07,10.07,0
500 | 2017-06-06,10.19,10.77,9.86,10.45,10.45,0
501 | 2017-06-07,10.48,10.93,10.12,10.39,10.39,0
502 | 2017-06-08,10.27,10.53,9.73,10.16,10.16,0
503 | 2017-06-09,9.93,12.11,9.37,10.7,10.7,0
504 | 2017-06-12,11.19,12.37,11.19,11.46,11.46,0
505 | 2017-06-13,11.12,11.14,10.26,10.42,10.42,0
506 | 2017-06-14,10.33,11.26,10.01,10.64,10.64,0
507 | 2017-06-15,11.06,12.01,10.74,10.9,10.9,0
508 | 2017-06-16,10.63,11.35,10.26,10.38,10.38,0
509 | 2017-06-19,10.57,10.6,10.01,10.37,10.37,0
510 | 2017-06-20,10.28,11.15,10.24,10.86,10.86,0
511 | 2017-06-21,11.03,11.4,10.4,10.75,10.75,0
512 | 2017-06-22,10.81,11.01,10.24,10.48,10.48,0
513 | 2017-06-23,10.25,10.69,9.85,10.02,10.02,0
514 | 2017-06-26,10.13,10.44,9.68,9.9,9.9,0
515 | 2017-06-27,10.04,11.31,9.8,11.06,11.06,0
516 | 2017-06-28,11.17,11.49,9.76,10.03,10.03,0
517 | 2017-06-29,9.79,15.16,9.73,11.44,11.44,0
518 | 2017-06-30,11.74,11.85,10.4,11.18,11.18,0
519 | 2017-07-03,11.07,11.45,10.28,11.22,11.22,0
520 | 2017-07-05,11.2,12.03,10.79,11.07,11.07,0
521 | 2017-07-06,11.2,13.05,11.18,12.54,12.54,0
522 | 2017-07-07,12.48,12.57,10.98,11.19,11.19,0
523 | 2017-07-10,11.26,11.73,10.61,11.11,11.11,0
524 | 2017-07-11,10.79,12.14,10.68,10.89,10.89,0
525 | 2017-07-12,10.85,10.85,10.08,10.3,10.3,0
526 | 2017-07-13,10.07,10.4,9.9,9.9,9.9,0
527 | 2017-07-14,10.09,10.14,9.5,9.51,9.51,0
528 | 2017-07-18,9.86,10.35,9.66,9.89,9.89,0
529 | 2017-07-19,9.69,9.94,9.58,9.79,9.79,0
530 | 2017-07-20,9.66,10.28,9.5,9.58,9.58,0
531 | 2017-07-21,9.52,9.98,9.3,9.36,9.36,0
532 | 2017-07-24,9.94,9.97,9.26,9.43,9.43,0
533 | 2017-07-25,9.4,9.52,9.04,9.43,9.43,0
534 | 2017-07-26,9.25,9.66,8.84,9.6,9.6,0
535 | 2017-07-27,9.33,11.5,9.16,10.11,10.11,0
536 | 2017-07-28,10.62,11.3,10.26,10.29,10.29,0
537 | 2017-08-01,10.19,10.56,9.95,10.09,10.09,0
538 | 2017-08-02,10.08,10.81,9.8,10.28,10.28,0
539 | 2017-08-03,10.47,10.6,9.9,10.44,10.44,0
540 | 2017-08-04,10.48,10.5,9.68,10.03,10.03,0
541 | 2017-08-07,10.19,10.32,9.76,9.93,9.93,0
542 | 2017-08-08,10.04,11.52,9.52,10.96,10.96,0
543 | 2017-08-09,11.49,12.63,11.11,11.11,11.11,0
544 | 2017-08-10,11.57,16.17,,16.040001,16.040001,0
545 | 2017-08-11,16.17,17.280001000000002,14.5,15.51,15.51,0
546 | 2017-08-14,14.05,14.05,12.06,12.33,12.33,0
547 | 2017-08-15,11.78,12.37,11.45,12.04,12.04,0
548 | 2017-08-16,11.59,12.54,11.25,11.74,11.74,0
549 | 2017-08-17,11.81,15.77,11.54,15.55,15.55,0
550 | 2017-08-18,15.38,16.040001,13.32,14.26,14.26,0
551 | 2017-08-21,14.59,14.74,13.07,13.19,13.19,0
552 | 2017-08-22,12.6,12.94,11.35,11.35,11.35,0
553 | 2017-08-23,11.51,12.59,11.39,12.25,12.25,0
554 | 2017-08-24,12.06,12.83,11.55,12.23,12.23,0
555 | 2017-08-25,12.2,12.45,11.1,11.28,11.28,0
556 | 2017-08-28,12.09,12.11,11.23,11.32,11.32,0
557 | 2017-08-29,13.33,14.34,11.48,11.7,11.7,0
558 | 2017-08-30,11.4,11.98,10.96,11.22,11.22,0
559 | 2017-08-31,11.07,11.22,10.34,10.59,10.59,0
560 | 2017-09-01,10.33,10.46,10.02,10.13,10.13,0
561 | 2017-09-05,11.75,14.06,11.41,12.23,12.23,0
562 | 2017-09-06,12.27,12.59,11.35,11.63,11.63,0
563 | 2017-09-07,11.93,12.07,11.32,11.55,11.55,0
564 | 2017-09-08,11.87,12.6,11.84,12.12,12.12,0
565 | 2017-09-11,11.38,11.39,10.51,10.73,10.73,0
566 | 2017-09-12,10.66,10.95,10.29,10.58,10.58,0
567 | 2017-09-13,10.72,10.86,10.33,10.5,10.5,0
568 | 2017-09-14,10.57,11.04,10.28,10.44,10.44,0
569 | 2017-09-15,10.51,10.74,10.0,10.17,10.17,0
570 | 2017-09-19,10.16,10.3,9.85,10.18,10.18,0
571 | 2017-09-20,10.04,10.67,9.67,9.78,9.78,0
572 | 2017-09-22,9.9,10.2,9.5,9.59,9.59,0
573 | 2017-09-25,10.08,11.21,9.79,10.21,10.21,0
574 | 2017-09-26,10.42,10.68,9.94,10.17,10.17,0
575 | 2017-09-27,9.9,10.42,9.63,9.87,9.87,0
576 | 2017-09-28,9.74,10.13,9.55,9.55,9.55,0
577 | 2017-09-29,9.59,9.83,,9.51,9.51,0
578 | 2017-10-02,9.59,10.04,9.37,9.45,9.45,0
579 | 2017-10-03,9.3,9.75,9.3,9.51,9.51,0
580 | 2017-10-04,9.53,9.88,9.53,9.63,9.63,0
581 | 2017-10-05,9.48,9.62,9.13,9.19,9.19,0
582 | 2017-10-06,9.23,10.27,9.11,9.65,9.65,0
583 | 2017-10-09,9.92,10.53,9.88,10.33,10.33,0
584 | 2017-10-10,10.15,10.66,9.94,10.08,10.08,0
585 | 2017-10-11,9.95,10.38,9.72,9.85,9.85,0
586 | 2017-10-13,9.95,9.98,9.44,9.61,9.61,0
587 | 2017-10-16,9.95,10.02,9.75,9.91,9.91,0
588 | 2017-10-17,9.85,10.46,9.78,10.31,10.31,0
589 | 2017-10-18,10.34,10.41,9.87,10.07,10.07,0
590 | 2017-10-19,10.22,11.77,9.99,10.05,10.05,0
591 | 2017-10-20,9.92,10.04,9.29,9.97,9.97,0
592 | 2017-10-23,10.25,11.08,9.94,11.07,11.07,0
593 | 2017-10-24,10.89,11.16,10.39,11.16,11.16,0
594 | 2017-10-25,11.34,13.2,10.99,11.23,11.23,0
595 | 2017-10-27,11.12,11.12,9.74,9.8,9.8,0
596 | 2017-10-30,10.28,10.89,10.1,10.5,10.5,0
597 | 2017-10-31,10.34,10.37,9.9,10.18,10.18,0
598 | 2017-11-02,10.44,10.89,9.67,9.93,9.93,0
599 | 2017-11-03,9.83,9.91,8.99,9.14,9.14,0
600 | 2017-11-06,9.63,9.74,,9.4,9.4,0
601 | 2017-11-07,9.31,10.31,9.29,9.89,9.89,0
602 | 2017-11-08,9.79,10.27,9.5,9.78,9.78,0
603 | 2017-11-09,9.94,12.19,9.79,10.5,10.5,0
604 | 2017-11-10,10.78,11.58,10.5,11.29,11.29,0
605 | 2017-11-13,11.43,12.18,11.0,11.5,11.5,0
606 | 2017-11-14,11.53,12.61,11.45,11.59,11.59,0
607 | 2017-11-15,12.52,14.51,12.33,13.13,13.13,0
608 | 2017-11-16,12.47,12.52,11.38,11.76,11.76,0
609 | 2017-11-17,11.75,12.01,11.16,11.43,11.43,0
610 | 2017-11-20,11.96,12.08,10.44,10.65,10.65,0
611 | 2017-11-22,9.6,9.88,9.32,9.88,9.88,0
612 | 2017-11-24,9.82,9.96,8.56,9.67,9.67,0
613 | 2017-11-27,10.07,10.26,9.79,9.87,9.87,0
614 | 2017-11-28,9.72,10.31,9.53,10.03,10.03,0
615 | 2017-11-29,9.91,10.93,9.81,10.7,10.7,0
616 | 2017-11-30,10.49,12.05,10.25,11.28,11.28,0
617 | 2017-12-01,11.19,14.58,10.54,11.43,11.43,0
618 | 2017-12-04,11.05,11.86,10.26,11.68,11.68,0
619 | 2017-12-05,11.38,11.67,10.65,11.33,11.33,0
620 | 2017-12-06,11.63,11.68,10.86,11.02,11.02,0
621 | 2017-12-07,10.9,11.32,10.12,10.16,10.16,0
622 | 2017-12-08,10.0,10.06,9.43,9.58,9.58,0
623 | 2017-12-11,9.74,10.08,9.28,9.34,9.34,0
624 | 2017-12-12,9.36,9.92,9.21,9.92,9.92,0
625 | 2017-12-13,9.78,10.21,9.65,10.18,10.18,0
626 | 2017-12-14,9.98,10.54,9.78,10.49,10.49,0
627 | 2017-12-15,10.12,10.2,,9.42,9.42,0
628 | 2017-12-18,9.46,9.89,9.24,9.53,9.53,0
629 | 2017-12-19,9.4,10.15,9.18,10.03,10.03,0
630 | 2017-12-20,9.69,9.85,8.9,9.72,9.72,0
631 | 2017-12-21,9.59,9.86,9.2,9.62,9.62,0
632 | 2017-12-22,9.37,10.18,9.35,9.9,9.9,0
633 | 2017-12-26,10.19,10.46,10.13,10.25,10.25,0
634 | 2017-12-28,10.29,10.44,10.07,10.18,10.18,0
635 | 2017-12-29,10.03,11.06,9.95,11.04,11.04,0
636 | 2018-01-02,10.95,11.07,9.52,9.77,9.77,0
637 | 2018-01-03,9.56,9.65,8.94,9.15,9.15,0
638 | 2018-01-04,9.01,9.31,8.92,9.22,9.22,0
639 | 2018-01-05,9.1,9.54,9.0,9.22,9.22,0
640 | 2018-01-08,9.61,9.89,9.32,9.52,9.52,0
641 | 2018-01-10,10.11,10.85,9.82,9.82,9.82,0
642 | 2018-01-11,9.69,10.02,9.62,9.88,9.88,0
643 | 2018-01-12,9.74,10.31,9.54,10.16,10.16,0
644 | 2018-01-16,10.42,12.41,10.4,11.66,11.66,0
645 | 2018-01-17,11.35,12.81,11.18,11.91,11.91,0
646 | 2018-01-18,12.01,12.4,11.62,12.22,12.22,0
647 | 2018-01-19,12.3,12.33,11.18,11.27,11.27,0
648 | 2018-01-22,11.59,11.62,10.84,11.03,11.03,0
649 | 2018-01-23,10.77,11.57,10.76,11.1,11.1,0
650 | 2018-01-24,11.0,12.19,10.89,11.47,11.47,0
651 | 2018-01-25,11.27,12.01,11.2,11.58,11.58,0
652 | 2018-01-29,11.71,13.84,11.68,13.84,13.84,0
653 | 2018-01-30,13.93,15.42,13.88,14.79,14.79,0
654 | 2018-01-31,14.23,14.44,,13.54,13.54,0
655 | 2018-02-01,13.05,14.3,12.5,13.47,13.47,0
656 | 2018-02-02,13.64,17.860001,13.64,17.309998999999998,17.309998999999998,0
657 | 2018-02-05,18.440001000000002,38.799999,16.799999,37.32,37.32,0
658 | 2018-02-06,37.32,50.299999,22.42,29.98,29.98,0
659 | 2018-02-07,31.379998999999998,31.639999,21.17,27.73,27.73,0
660 | 2018-02-08,27.290001,36.169998,24.41,33.459998999999996,33.459998999999996,0
661 | 2018-02-09,32.18,41.060001,27.73,29.059998999999998,29.059998999999998,0
662 | 2018-02-12,27.25,29.700001,24.42,25.610001,25.610001,0
663 | 2018-02-13,26.940001000000002,27.82,24.469998999999998,24.969998999999998,24.969998999999998,0
664 | 2018-02-14,23.48,25.719998999999998,18.99,19.26,19.26,0
665 | 2018-02-15,18.389999,20.66,17.6,19.129998999999998,19.129998999999998,0
666 | 2018-02-16,18.74,20.99,17.440001000000002,19.459999,19.459999,0
667 | 2018-02-20,20.530001000000002,21.610001,19.75,20.6,20.6,0
668 | 2018-02-21,20.76,21.040001,16.969998999999998,20.02,20.02,0
669 | 2018-02-22,20.57,20.610001,18.07,18.719998999999998,18.719998999999998,0
670 | 2018-02-23,17.959999,18.799999,16.469998999999998,16.49,16.49,0
671 | 2018-02-26,16.530001000000002,16.940001000000002,15.8,15.8,15.8,0
672 | 2018-02-27,15.83,18.98,15.29,18.59,18.59,0
673 | 2018-02-28,18.08,20.440001000000002,15.65,19.85,19.85,0
674 | 2018-03-01,19.959999,25.299999,19.57,22.469998999999998,22.469998999999998,0
675 | 2018-03-02,22.469998999999998,26.219998999999998,19.360001,19.59,19.59,0
676 | 2018-03-05,21.549999,21.57,17.940001000000006,18.73,18.73,0
677 | 2018-03-06,18.25,19.639999,17.68,18.360001,18.360001,0
678 | 2018-03-07,20.110001,20.49,17.52,17.76,17.76,0
679 | 2018-03-08,17.559998999999998,17.68,14.91,16.540001,16.540001,0
680 | 2018-03-09,16.41,16.75,13.31,14.64,14.64,0
681 | 2018-03-12,15.28,16.35,15.18,15.78,15.78,0
682 | 2018-03-13,15.7,16.98,15.03,16.35,16.35,0
683 | 2018-03-14,16.59,17.59,14.94,17.23,17.23,0
684 | 2018-03-15,16.99,17.41,15.96,16.59,16.59,0
685 | 2018-03-16,16.6,16.719998999999998,15.23,15.8,15.8,0
686 | 2018-03-19,16.629998999999998,21.870001000000002,16.559998999999998,19.02,19.02,0
687 | 2018-03-20,18.379998999999998,19.309998999999998,18.09,18.200001,18.200001,0
688 | 2018-03-21,17.76,18.370001000000002,16.26,17.860001,17.860001,0
689 | 2018-03-22,18.129998999999998,23.809998999999998,18.120001000000002,23.34,23.34,0
690 | 2018-03-23,24.02,26.01,21.629998999999998,24.870001000000002,24.870001000000002,0
691 | 2018-03-26,23.41,24.540001,20.709999,21.030001000000002,21.030001000000002,0
692 | 2018-03-27,20.33,24.059998999999998,19.84,22.5,22.5,0
693 | 2018-03-28,22.52,24.940001000000002,21.709999,22.870001000000002,22.870001000000002,0
694 | 2018-03-29,22.870001000000002,23.049999,19.6,19.969998999999998,19.969998999999998,0
695 | 2018-04-02,21.07,25.719998999999998,20.440001000000002,23.620001000000002,23.620001000000002,0
696 | 2018-04-03,23.030001000000002,23.379998999999998,20.92,21.1,21.1,0
697 | 2018-04-04,21.68,24.51,19.860001,20.059998999999998,20.059998999999998,0
698 | 2018-04-05,19.76,20.209999,18.57,18.940001000000002,18.940001000000002,0
699 | 2018-04-06,20.33,23.120001000000002,18.6,21.49,21.49,0
700 | 2018-04-09,21.27,22.02,20.34,21.77,21.77,0
701 | 2018-04-10,20.51,21.68,20.24,20.469998999999998,20.469998999999998,0
702 | 2018-04-11,20.950001,21.66,19.639999,20.24,20.24,0
703 | 2018-04-12,19.83,19.92,18.16,18.49,18.49,0
704 | 2018-04-13,18.27,18.450001,17.26,17.41,17.41,0
705 | 2018-04-16,17.59,17.66,16.379998999999998,16.559998999999998,16.559998999999998,0
706 | 2018-04-17,16.16,16.27,14.57,15.25,15.25,0
707 | 2018-04-18,15.3,16.9,,15.6,15.6,0
708 | 2018-04-19,15.55,16.92,15.16,15.96,15.96,0
709 | 2018-04-20,16.16,17.5,15.19,16.879998999999998,16.879998999999998,0
710 | 2018-04-23,17.290001,17.559998999999998,15.79,16.34,16.34,0
711 | 2018-04-24,16.16,19.66,15.37,18.02,18.02,0
712 | 2018-04-25,18.139999,19.84,17.75,17.84,17.84,0
713 | 2018-04-26,18.07,18.120001000000002,16.24,16.24,16.24,0
714 | 2018-04-27,16.219998999999998,16.77,15.25,15.41,15.41,0
715 | 2018-04-30,15.31,16.35,15.13,15.93,15.93,0
716 | 2018-05-01,16.0,16.82,15.42,15.49,15.49,0
717 | 2018-05-02,15.48,15.97,14.75,15.97,15.97,0
718 | 2018-05-03,15.78,18.66,15.43,15.9,15.9,0
719 | 2018-05-07,15.05,15.27,14.51,14.75,14.75,0
720 | 2018-05-09,14.54,14.63,13.38,13.42,13.42,0
721 | 2018-05-10,13.36,13.63,12.92,13.23,13.23,0
722 | 2018-05-11,13.22,13.44,12.65,12.65,12.65,0
723 | 2018-05-14,13.09,13.28,12.81,12.93,12.93,0
724 | 2018-05-15,13.13,15.01,12.5,14.63,14.63,0
725 | 2018-05-17,13.54,13.86,12.65,13.43,13.43,0
726 | 2018-05-18,13.18,13.87,13.06,13.42,13.42,0
727 | 2018-05-21,12.99,13.59,12.78,13.08,13.08,0
728 | 2018-05-22,13.03,13.42,12.77,13.22,13.22,0
729 | 2018-05-23,13.5,14.6,12.49,12.58,12.58,0
730 | 2018-05-24,12.73,14.24,12.53,12.53,12.53,0
731 | 2018-05-29,14.39,18.780001000000002,14.39,17.02,17.02,0
732 | 2018-05-30,16.6,16.639999,14.65,14.94,14.94,0
733 | 2018-05-31,14.93,16.290001,14.2,15.43,15.43,0
734 | 2018-06-01,14.92,14.93,13.37,13.46,13.46,0
735 | 2018-06-04,13.91,13.91,12.69,12.74,12.74,0
736 | 2018-06-05,12.91,13.34,12.3,12.4,12.4,0
737 | 2018-06-06,12.1,12.56,11.62,11.64,11.64,0
738 | 2018-06-07,11.66,13.28,11.22,12.13,12.13,0
739 | 2018-06-08,12.54,13.31,12.09,12.18,12.18,0
740 | 2018-06-11,12.52,12.69,,12.35,12.35,0
741 | 2018-06-12,12.29,12.6,11.88,12.34,12.34,0
742 | 2018-06-13,12.13,12.95,11.98,12.94,12.94,0
743 | 2018-06-14,12.98,13.07,11.88,12.12,12.12,0
744 | 2018-06-15,12.19,13.16,11.93,11.98,11.98,0
745 | 2018-06-18,12.79,13.74,,12.31,12.31,0
746 | 2018-06-19,14.61,14.68,13.21,13.35,13.35,0
747 | 2018-06-20,12.9,13.02,12.25,12.79,12.79,0
748 | 2018-06-21,12.54,15.18,12.18,14.64,14.64,0
749 | 2018-06-22,14.6,14.6,13.11,13.77,13.77,0
750 | 2018-06-25,15.07,19.610001,14.56,17.33,17.33,0
751 | 2018-06-26,16.709999,17.540001,15.1,15.92,15.92,0
752 | 2018-06-27,16.52,18.190001000000002,14.76,17.91,17.91,0
753 | 2018-06-28,17.540001,18.99,16.4,16.85,16.85,0
754 | 2018-06-29,15.73,16.51,,16.09,16.09,0
755 | 2018-07-02,17.700001,18.08,15.54,15.6,15.6,0
756 | 2018-07-03,15.37,16.450001,14.68,16.139999,16.139999,0
757 | 2018-07-05,15.62,16.219998999999998,14.47,14.97,14.97,0
758 | 2018-07-06,14.99,15.45,13.34,13.37,13.37,0
759 | 2018-07-09,13.02,13.22,12.6,12.69,12.69,0
760 | 2018-07-10,12.52,13.21,11.93,12.64,12.64,0
761 | 2018-07-11,14.05,14.15,13.09,13.63,13.63,0
762 | 2018-07-12,13.07,13.33,12.42,12.58,12.58,0
763 | 2018-07-13,12.39,12.97,11.62,12.18,12.18,0
764 | 2018-07-16,12.77,12.97,12.46,12.83,12.83,0
765 | 2018-07-17,12.91,13.18,11.85,12.06,12.06,0
766 | 2018-07-19,12.2,13.09,11.79,12.87,12.87,0
767 | 2018-07-20,13.0,13.58,12.49,12.86,12.86,0
768 | 2018-07-24,12.61,13.21,11.66,12.41,12.41,0
769 | 2018-07-25,12.37,12.82,11.8,12.29,12.29,0
770 | 2018-07-26,12.45,12.53,11.78,12.14,12.14,0
771 | 2018-07-30,13.63,14.46,12.98,14.26,14.26,0
772 | 2018-07-31,13.98,14.12,12.81,12.83,12.83,0
773 | 2018-08-01,13.09,13.63,12.45,13.15,13.15,0
774 | 2018-08-02,13.57,14.53,12.17,12.19,12.19,0
775 | 2018-08-03,12.43,12.46,11.07,11.64,11.64,0
776 | 2018-08-06,11.91,12.15,11.17,11.27,11.27,0
777 | 2018-08-07,11.12,11.24,10.52,10.93,10.93,0
778 | 2018-08-08,10.93,11.18,10.52,10.85,10.85,0
779 | 2018-08-09,10.94,11.31,10.17,11.27,11.27,0
780 | 2018-08-10,12.34,13.82,12.3,13.16,13.16,0
781 | 2018-08-13,14.5,15.02,12.95,14.78,14.78,0
782 | 2018-08-14,13.93,14.22,13.11,13.31,13.31,0
783 | 2018-08-15,13.52,16.860001,13.44,14.64,14.64,0
784 | 2018-08-16,14.18,14.36,12.82,13.45,13.45,0
785 | 2018-08-17,13.18,13.96,12.4,12.64,12.64,0
786 | 2018-08-20,12.57,12.79,12.26,12.49,12.49,0
787 | 2018-08-21,12.47,12.89,12.09,12.86,12.86,0
788 | 2018-08-22,13.09,13.29,11.97,12.25,12.25,0
789 | 2018-08-23,12.03,12.68,11.65,12.41,12.41,0
790 | 2018-08-24,12.13,12.17,11.83,11.99,11.99,0
791 | 2018-08-27,12.37,12.48,12.02,12.16,12.16,0
792 | 2018-08-28,12.01,12.5,11.87,12.5,12.5,0
793 | 2018-08-29,12.34,12.55,12.09,12.25,12.25,0
794 | 2018-08-30,12.25,13.95,12.24,13.53,13.53,0
795 | 2018-08-31,13.54,14.03,12.82,12.86,12.86,0
796 | 2018-09-04,13.13,14.35,13.12,13.16,13.16,0
797 | 2018-09-05,13.69,14.3,13.23,13.91,13.91,0
798 | 2018-09-06,14.21,15.41,13.72,14.65,14.65,0
799 | 2018-09-07,14.72,15.63,14.31,14.88,14.88,0
800 | 2018-09-10,15.09,15.2,13.93,14.16,14.16,0
801 | 2018-09-11,-400.0,14.92,13.21,13.22,13.22,0
802 | 2018-09-12,13.07,13.86,12.91,13.14,13.14,0
803 | 2018-09-13,12.91,12.91,12.3,12.37,12.37,0
804 | 2018-09-14,12.13,13.15,11.93,12.07,12.07,0
805 | 2018-09-17,12.72,13.75,12.32,13.68,13.68,0
806 | 2018-09-18,13.48,13.48,12.56,12.79,12.79,0
807 | 2018-09-19,12.61,12.77,11.66,11.75,11.75,0
808 | 2018-09-20,11.82,11.96,11.31,11.8,11.8,0
809 | 2018-09-21,11.76,12.03,11.1,11.68,11.68,0
810 | 2018-09-24,12.46,12.92,12.18,12.2,12.2,0
811 | 2018-09-25,12.28,12.6,11.8,12.42,12.42,0
812 | 2018-09-26,12.21,13.13,11.55,12.89,12.89,0
813 | 2018-09-27,12.77,13.0,11.94,12.41,12.41,0
814 | 2018-09-28,12.59,13.22,12.09,12.12,12.12,0
815 | 2018-10-01,11.99,12.4,,12.0,12.0,0
816 | 2018-10-02,12.47,12.69,11.61,12.05,12.05,0
817 | 2018-10-03,11.66,12.14,11.34,11.61,11.61,0
818 | 2018-10-04,12.84,15.84,12.42,14.22,14.22,0
819 | 2018-10-05,14.29,17.360001,11.72,14.82,14.82,0
820 | 2018-10-08,16.049999,18.379998999999998,15.69,15.69,15.69,0
821 | 2018-10-09,16.120001000000002,17.49,15.27,15.95,15.95,0
822 | 2018-10-10,16.030001000000002,22.959999,15.83,22.959999,22.959999,0
823 | 2018-10-11,23.07,28.84,20.65,24.98,24.98,0
824 | 2018-10-12,21.629998999999998,26.799999,20.879998999999998,21.309998999999998,21.309998999999998,0
825 | 2018-10-15,21.969998999999998,22.889999,19.469998999999998,21.299999,21.299999,0
826 | 2018-10-17,17.059998999999998,19.549999,17.059998999999998,17.4,17.4,0
827 | 2018-10-18,18.02,21.57,17.51,20.059998999999998,20.059998999999998,0
828 | 2018-10-19,19.24,21.08,18.389999,19.889999,19.889999,0
829 | 2018-10-22,19.379998999999998,20.889999,18.82,19.639999,19.639999,0
830 | 2018-10-23,22.18,24.66,20.18,20.709999,20.709999,0
831 | 2018-10-24,21.6,26.379998999999998,20.4,25.23,25.23,0
832 | 2018-10-25,24.780001000000002,24.780001000000002,22.059998999999998,24.219998999999998,24.219998999999998,0
833 | 2018-10-26,24.51,27.52,,24.16,24.16,0
834 | 2018-10-29,24.48,27.860001,22.01,24.700001,24.700001,0
835 | 2018-10-30,23.940001000000002,25.549999,23.110001,23.35,23.35,0
836 | 2018-10-31,22.719998999999998,22.799999,20.389999,21.23,21.23,0
837 | 2018-11-01,20.959999,21.209999,19.030001000000002,19.34,19.34,0
838 | 2018-11-02,18.52,21.610001,18.049999,19.51,19.51,0
839 | 2018-11-05,20.58,20.870001000000002,19.639999,19.959999,19.959999,0
840 | 2018-11-06,-400.0,20.6,19.690001000000002,19.91,19.91,0
841 | 2018-11-08,16.18,17.200001,16.09,16.719998999999998,16.719998999999998,0
842 | 2018-11-09,16.870001000000002,18.41,16.84,17.360001,17.360001,0
843 | 2018-11-12,17.26,20.709999,17.25,20.450001,20.450001,0
844 | 2018-11-13,19.450001,21.25,19.110001,20.02,20.02,0
845 | 2018-11-14,20.52,22.360001,19.299999,21.25,21.25,0
846 | 2018-11-15,20.41,22.969998999999998,19.940001000000002,19.98,19.98,0
847 | 2018-11-16,20.040001,21.360001,18.1,18.139999,18.139999,0
848 | 2018-11-19,18.780001000000002,20.99,18.52,20.1,20.1,0
849 | 2018-11-20,20.76,23.809998999999998,20.370001000000002,22.48,22.48,0
850 | 2018-11-21,21.66,22.309998999999998,20.110001,20.799999,20.799999,0
851 | 2018-11-23,21.23,22.65,20.65,21.52,21.52,0
852 | 2018-11-26,20.780001000000002,20.799999,18.67,18.9,18.9,0
853 | 2018-11-27,19.41,19.93,18.700001,19.02,19.02,0
854 | 2018-11-28,18.870001000000002,19.379998999999998,18.18,18.49,18.49,0
855 | 2018-11-29,18.65,20.48,18.59,18.790001,18.790001,0
856 | 2018-11-30,19.18,19.91,18.030001000000002,18.07,18.07,0
857 | 2018-12-04,16.84,21.940001000000002,16.26,20.74,20.74,0
858 | 2018-12-06,23.530001000000002,25.940001000000002,20.940001000000002,21.190001000000002,21.190001000000002,0
859 | 2018-12-07,22.42,24.709999,19.9,23.23,23.23,0
860 | 2018-12-10,23.950001,25.940001000000002,22.5,22.639999,22.639999,0
861 | 2018-12-11,22.959999,23.6,21.389999,21.76,21.76,0
862 | 2018-12-12,21.91,21.91,20.5,21.459999,21.459999,0
863 | 2018-12-13,21.0,21.57,20.34,20.65,20.65,0
864 | 2018-12-14,21.57,22.469998999999998,20.950001,21.629998999999998,21.629998999999998,0
865 | 2018-12-17,22.360001,25.879998999999998,21.969998999999998,24.52,24.52,0
866 | 2018-12-18,24.68,26.139999,23.639999,25.58,25.58,0
867 | 2018-12-19,25.15,26.639999,330.0,25.58,25.58,0
868 | 2018-12-20,26.09,30.299999,24.68,28.379998999999998,28.379998999999998,0
869 | 2018-12-21,28.24,31.35,25.709999,30.110001,30.110001,0
870 | 2018-12-24,29.290001,36.099998,29.16,36.07,36.07,0
871 | 2018-12-26,35.5,36.200001,29.59,30.41,30.41,0
872 | 2018-12-27,31.200001,33.799999,29.66,29.959999,29.959999,0
873 | 2018-12-28,29.969998999999998,31.049999,27.43,28.34,28.34,0
874 | 2018-12-31,27.59,27.639999,25.33,25.42,25.42,0
875 | 2019-01-02,27.540001,28.530001000000002,23.049999,23.219998999999998,23.219998999999998,0
876 | 2019-01-03,25.68,26.6,24.049999,25.450001,25.450001,0
877 | 2019-01-04,24.360001,24.48,21.190001000000002,21.379998999999998,21.379998999999998,0
878 | 2019-01-07,22.059998999999998,22.709999,20.91,21.4,21.4,0
879 | 2019-01-08,20.959999,22.030001000000002,20.09,20.469998999999998,20.469998999999998,0
880 | 2019-01-09,20.440001000000002,22.799999,19.48,19.98,19.98,0
881 | 2019-01-10,20.98,21.32,19.290001,19.5,19.5,0
882 | 2019-01-11,19.68,20.16,18.120001000000002,18.190001000000002,18.190001000000002,0
883 | 2019-01-14,19.84,20.27,18.700001,19.07,19.07,0
884 | 2019-01-15,18.530001000000002,19.200001,17.98,18.6,18.6,0
885 | 2019-01-16,18.08,19.040001,17.76,19.040001,19.040001,0
886 | 2019-01-17,19.51,19.719998999999998,17.85,18.059998999999998,18.059998999999998,0
887 | 2019-01-18,17.799999,18.18,17.17,17.799999,17.799999,0
888 | 2019-01-22,18.530001000000002,21.15,18.200001,20.799999,20.799999,0
889 | 2019-01-23,20.85,22.02,,19.52,19.52,0
890 | 2019-01-24,19.690001000000002,20.25,18.629998999999998,18.889999,18.889999,0
891 | 2019-01-25,18.43,18.459999,17.309998999999998,17.42,17.42,0
892 | 2019-01-28,18.559998999999998,20.42,18.42,18.870001000000002,18.870001000000002,0
893 | 2019-01-29,19.450001,19.93,18.42,19.129998999999998,19.129998999999998,0
894 | 2019-01-30,19.15,19.309998999999998,17.540001,17.66,17.66,0
895 | 2019-01-31,17.389999,17.719998999999994,16.540001,16.57,16.57,0
896 | 2019-02-01,16.629998999999998,16.83,16.08,16.139999,16.139999,0
897 | 2019-02-04,16.719998999999998,16.73,15.6,15.73,15.73,0
898 | 2019-02-05,15.79,15.94,15.04,15.57,15.57,0
899 | 2019-02-06,15.44,15.87,15.09,15.38,15.38,0
900 | 2019-02-07,15.66,17.889999,15.51,16.370001000000002,16.370001000000002,0
901 | 2019-02-08,16.809998999999998,17.629998999999994,15.62,15.72,15.72,0
902 | 2019-02-11,16.17,16.5,15.34,15.97,15.97,0
903 | 2019-02-12,15.23,15.65,14.95,15.43,15.43,0
904 | 2019-02-13,15.46,15.91,15.0,15.65,15.65,0
905 | 2019-02-14,15.61,17.27,15.3,16.219998999999998,16.219998999999998,0
906 | 2019-02-15,16.82,16.82,14.79,14.91,14.91,0
907 | 2019-02-19,15.65,16.16,14.64,14.88,14.88,0
908 | 2019-02-20,14.92,15.19,13.99,14.02,14.02,0
909 | 2019-02-21,14.0,15.17,13.85,14.46,14.46,0
910 | 2019-02-22,14.46,14.47,13.51,13.51,13.51,0
911 | 2019-02-25,13.91,14.9,13.44,14.85,14.85,0
912 | 2019-02-26,15.16,15.28,14.52,15.17,15.17,0
913 | 2019-02-27,15.53,16.17,14.7,14.7,14.7,0
914 | 2019-03-01,14.57,14.84,13.41,13.57,13.57,0
915 | 2019-03-04,13.77,16.98,13.38,14.63,14.63,0
916 | 2019-03-05,14.52,15.39,14.26,14.74,14.74,0
917 | 2019-03-06,14.91,16.110001,14.74,15.74,15.74,0
918 | 2019-03-07,16.33,17.809998999999994,15.54,16.59,16.59,0
919 | 2019-03-08,17.379998999999998,18.33,16.02,16.049999,16.049999,0
920 | 2019-03-11,16.280001000000002,16.43,14.33,14.33,14.33,0
921 | 2019-03-12,13.97,14.7,13.61,13.77,13.77,0
922 | 2019-03-13,14.0,14.05,13.25,13.41,13.41,0
923 | 2019-03-14,13.35,13.84,13.16,13.5,13.5,0
924 | 2019-03-15,13.21,13.28,12.5,12.88,12.88,0
925 | 2019-03-18,13.13,13.8,13.0,13.1,13.1,0
926 | 2019-03-19,12.89,13.77,12.37,13.56,13.56,0
927 | 2019-03-20,13.54,14.3,13.05,13.91,13.91,0
928 | 2019-03-21,14.11,14.56,13.26,13.63,13.63,0
929 | 2019-03-22,13.77,17.52,13.62,16.48,16.48,0
930 | 2019-03-25,17.76,17.85,16.26,16.33,16.33,0
931 | 2019-03-26,16.219998999999998,16.299999,14.67,14.68,14.68,0
932 | 2019-03-27,14.52,16.709999,14.51,15.15,15.15,0
933 | 2019-03-29,14.19,14.43,13.64,13.71,13.71,0
934 | 2019-04-01,13.9,14.01,13.32,13.4,13.4,0
935 | 2019-04-02,13.62,13.68,13.13,13.36,13.36,0
936 | 2019-04-04,13.84,14.03,13.4,13.58,13.58,0
937 | 2019-04-05,13.46,13.47,12.17,12.82,12.82,0
938 | 2019-04-08,13.55,13.77,13.1,13.18,13.18,0
939 | 2019-04-10,14.09,14.29,13.27,13.3,13.3,0
940 | 2019-04-11,13.37,13.58,12.91,13.02,13.02,0
941 | 2019-04-12,12.96,12.96,330.0,12.01,12.01,0
942 | 2019-04-15,12.46,13.14,12.24,12.32,12.32,0
943 | 2019-04-16,12.26,12.47,11.85,12.18,12.18,0
944 | 2019-04-17,12.12,13.02,11.03,12.6,12.6,0
945 | 2019-04-18,12.8,13.12,12.02,12.09,12.09,0
946 | 2019-04-22,13.21,13.36,12.38,12.42,12.42,0
947 | 2019-04-23,12.66,12.69,12.08,12.28,12.28,0
948 | 2019-04-24,12.53,13.23,12.26,13.14,13.14,0
949 | 2019-04-25,13.29,14.3,12.81,13.25,13.25,0
950 | 2019-04-26,13.44,13.62,,12.73,12.73,0
951 | 2019-04-29,13.04,13.27,12.65,13.11,13.11,0
952 | 2019-04-30,13.11,14.05,12.88,13.12,13.12,0
953 | 2019-05-01,12.86,14.83,12.74,14.8,14.8,0
954 | 2019-05-02,14.35,15.92,13.8,14.42,14.42,0
955 | 2019-05-03,14.11,14.15,,12.87,12.87,0
956 | 2019-05-06,12.89,18.799999,12.89,15.44,15.44,0
957 | 2019-05-07,15.9,21.84,15.8,19.32,19.32,0
958 | 2019-05-08,18.950001,21.74,18.290001,19.4,19.4,0
959 | 2019-05-09,21.4,23.379998999999998,18.870001000000002,19.1,19.1,0
960 | 2019-05-10,18.790001,20.190001000000002,15.57,16.040001,16.040001,0
961 | 2019-05-13,18.620001000000002,21.32,18.35,20.549999,20.549999,0
962 | 2019-05-14,19.4,19.65,17.450001,18.059998999999998,18.059998999999998,0
963 | 2019-05-15,17.57,19.15,16.41,16.440001000000002,16.440001000000002,0
964 | 2019-05-16,16.690001000000002,17.129998999999998,15.16,15.29,15.29,0
965 | 2019-05-17,15.89,16.809998999999998,,15.96,15.96,0
966 | 2019-05-21,15.86,16.219998999999998,14.79,14.95,14.95,0
967 | 2019-05-22,15.06,15.44,14.42,14.75,14.75,0
968 | 2019-05-23,15.93,18.049999,15.28,16.92,16.92,0
969 | 2019-05-24,16.34,16.52,15.52,15.85,15.85,0
970 | 2019-05-28,16.549999,17.700001,15.9,17.5,17.5,0
971 | 2019-05-29,18.549999,19.040001,17.620001000000006,17.9,17.9,0
972 | 2019-05-30,17.469998999999998,18.110001,16.719998999999998,17.299999,17.299999,0
973 | 2019-05-31,19.049999,19.719998999999998,18.01,18.709999,18.709999,0
974 | 2019-06-03,19.41,19.75,,18.860001,18.860001,0
975 | 2019-06-04,18.74,18.74,16.969998999999998,16.969998999999998,16.969998999999998,0
976 | 2019-06-06,16.25,16.540001,15.36,15.93,15.93,0
977 | 2019-06-07,15.65,16.440001000000002,15.33,16.299999,16.299999,0
978 | 2019-06-10,16.299999,16.469998999999998,15.84,15.94,15.94,0
979 | 2019-06-11,15.84,16.700001,15.5,15.99,15.99,0
980 | 2019-06-12,16.26,16.43,15.78,15.91,15.91,0
981 | 2019-06-13,16.16,16.209999,15.61,15.82,15.82,0
982 | 2019-06-14,16.0,16.4,15.21,15.28,15.28,0
983 | 2019-06-17,15.65,15.76,15.24,15.35,15.35,0
984 | 2019-06-18,14.89,15.54,,15.15,15.15,0
985 | 2019-06-19,15.05,15.71,14.15,14.33,14.33,0
986 | 2019-06-20,14.04,16.030001000000002,13.19,14.75,14.75,0
987 | 2019-06-21,14.74,15.48,14.42,15.4,15.4,0
988 | 2019-06-24,15.46,15.56,15.23,15.26,15.26,0
989 | 2019-06-25,15.43,16.68,15.1,16.280001000000002,16.280001000000002,0
990 | 2019-06-26,16.1,16.6,15.47,16.209999,16.209999,0
991 | 2019-06-27,15.66,16.4,15.66,15.82,15.82,0
992 | 2019-06-28,15.7,16.129998999999998,15.08,15.08,15.08,0
993 | 2019-07-01,13.85,14.64,13.8,14.06,14.06,0
994 | 2019-07-02,14.16,14.3,12.9,12.93,12.93,0
995 | 2019-07-03,13.18,13.19,12.56,12.57,12.57,0
996 | 2019-07-05,13.0,14.47,12.04,13.28,13.28,0
997 | 2019-07-08,13.73,14.44,13.64,13.96,13.96,0
998 | 2019-07-09,14.47,14.71,13.99,14.09,14.09,0
999 | 2019-07-10,14.38,14.69,12.98,13.03,13.03,0
1000 | 2019-07-11,13.01,13.33,12.39,12.93,12.93,0
1001 | 2019-07-12,12.76,12.82,12.28,12.39,12.39,0
1002 | 2019-07-15,12.58,13.02,12.49,12.68,12.68,0
1003 | 2019-07-16,12.61,13.14,12.28,12.86,12.86,0
1004 | 2019-07-17,12.62,13.97,12.24,13.97,13.97,0
1005 | 2019-07-18,-400.0,14.5,13.19,13.53,13.53,0
1006 | 2019-07-19,13.31,14.45,13.09,14.45,14.45,0
1007 | 2019-07-22,14.55,14.7,13.42,13.53,13.53,0
1008 | 2019-07-23,13.42,13.52,12.55,12.61,12.61,0
1009 | 2019-07-24,12.8,13.1,11.98,12.07,12.07,0
1010 | 2019-07-25,12.24,13.54,11.69,12.74,12.74,0
1011 | 2019-07-26,12.58,12.72,12.01,12.16,12.16,0
1012 | 2019-07-29,12.15,13.17,12.15,12.83,12.83,0
1013 | 2019-07-30,12.87,14.18,12.87,13.94,13.94,0
1014 | 2019-07-31,13.83,16.549999,,16.120001000000002,16.120001000000002,0
1015 | 2019-08-01,15.41,19.459999,13.73,17.870001000000006,17.870001000000006,0
1016 | 2019-08-02,17.690001000000006,20.110001,17.040001,17.610001,17.610001,0
1017 | 2019-08-05,19.959999,24.809998999999998,19.91,24.59,24.59,0
1018 | 2019-08-06,22.290001,22.870001000000002,19.77,20.17,20.17,0
1019 | 2019-08-07,20.700001,23.67,18.940001000000002,19.49,19.49,0
1020 | 2019-08-08,19.360001,19.889999,16.82,16.91,16.91,0
1021 | 2019-08-09,18.139999,19.440001000000002,17.309998999999998,17.969998999999994,17.969998999999994,0
1022 | 2019-08-12,17.870001000000006,21.26,17.77,21.09,21.09,0
1023 | 2019-08-13,21.280001000000002,21.639999,17.52,17.52,17.52,0
1024 | 2019-08-14,17.809998999999994,22.709999,17.75,22.1,22.1,0
1025 | 2019-08-15,21.58,24.1,20.780001000000002,21.18,21.18,0
1026 | 2019-08-16,20.48,20.5,18.41,18.469998999999998,18.469998999999998,0
1027 | 2019-08-19,17.93,18.219998999999998,16.52,16.879998999999998,16.879998999999998,0
1028 | 2019-08-20,16.780001000000002,17.700001,16.450001,17.5,17.5,0
1029 | 2019-08-21,17.01,17.040001,15.51,15.8,15.8,0
1030 | 2019-08-22,16.120001000000002,17.68,15.63,16.68,16.68,0
1031 | 2019-08-23,16.15,21.07,,19.870001000000002,19.870001000000002,0
1032 | 2019-08-26,20.34,21.33,19.059998999999998,19.32,19.32,0
1033 | 2019-08-27,20.18,21.040001,18.49,20.309998999999998,20.309998999999998,0
1034 | 2019-08-28,20.549999,21.639999,19.1,19.35,19.35,0
1035 | 2019-08-29,19.02,19.200001,17.6,17.879998999999994,17.879998999999994,0
1036 | 2019-08-30,17.940001000000006,19.18,17.09,18.98,18.98,0
1037 | 2019-09-03,20.959999,21.15,19.41,19.66,19.66,0
1038 | 2019-09-04,18.23,18.83,17.26,17.33,17.33,0
1039 | 2019-09-05,16.92,17.049999,15.45,16.27,16.27,0
1040 | 2019-09-06,15.92,16.059998999999998,14.91,15.0,15.0,0
1041 | 2019-09-09,15.26,16.129998999999998,14.95,15.27,15.27,0
1042 | 2019-09-10,15.53,16.52,15.11,15.2,15.2,0
1043 | 2019-09-11,15.33,15.52,14.55,14.61,14.61,0
1044 | 2019-09-12,14.69,14.94,13.85,14.22,14.22,0
1045 | 2019-09-13,14.16,14.31,13.51,13.74,13.74,0
1046 | 2019-09-16,14.89,15.29,14.5,14.67,14.67,0
1047 | 2019-09-17,14.92,15.03,14.4,14.44,14.44,0
1048 | 2019-09-18,14.61,15.8,13.8,13.95,13.95,0
1049 | 2019-09-19,14.66,14.66,13.31,14.05,14.05,0
1050 | 2019-09-20,13.94,15.84,13.35,15.32,15.32,0
1051 | 2019-09-23,15.35,16.0,14.71,14.91,14.91,0
1052 | 2019-09-24,14.77,17.620001000000006,14.33,17.049999,17.049999,0
1053 | 2019-09-25,17.049999,18.450001,15.69,15.96,15.96,0
1054 | 2019-09-26,16.23,17.09,15.35,16.07,16.07,0
1055 | 2019-09-27,15.77,18.690001000000002,15.15,17.219998999999998,17.219998999999998,0
1056 | 2019-09-30,17.23,17.35,16.200001,16.24,16.24,0
1057 | 2019-10-01,16.02,18.620001000000002,15.79,18.559998999999998,18.559998999999998,0
1058 | 2019-10-03,20.110001,21.440001000000002,19.030001000000002,19.120001000000002,19.120001000000002,0
1059 | 2019-10-04,19.42,19.969998999999998,16.969998999999998,17.040001,17.040001,0
1060 | 2019-10-08,17.610001,20.379998999999998,17.42,20.280001000000002,20.280001000000002,0
1061 | 2019-10-09,19.940001000000002,20.0,17.77,18.639999,18.639999,0
1062 | 2019-10-10,19.280001000000002,19.799999,17.559998999999998,17.57,17.57,0
1063 | 2019-10-11,17.35,17.440001000000002,15.11,15.58,15.58,0
1064 | 2019-10-14,15.66,16.5,14.51,14.57,14.57,0
1065 | 2019-10-15,13.94,14.43,13.39,13.54,13.54,0
1066 | 2019-10-16,13.94,14.26,13.6,13.68,13.68,0
1067 | 2019-10-17,13.79,14.18,13.31,13.79,13.79,0
1068 | 2019-10-18,14.2,15.16,13.78,14.25,14.25,0
1069 | 2019-10-21,14.24,14.63,14.0,14.0,14.0,0
1070 | 2019-10-22,13.99,14.61,13.7,14.46,14.46,0
1071 | 2019-10-23,14.83,15.12,14.01,14.01,14.01,0
1072 | 2019-10-24,13.98,14.34,13.4,13.71,13.71,0
1073 | 2019-10-25,13.53,13.79,12.62,12.65,12.65,0
1074 | 2019-10-28,13.01,13.14,12.66,13.11,13.11,0
1075 | 2019-10-30,13.08,13.37,12.27,12.33,12.33,0
1076 | 2019-10-31,13.51,13.95,13.18,13.22,13.22,0
1077 | 2019-11-01,12.5,12.6,12.26,12.3,12.3,0
1078 | 2019-11-04,12.68,13.13,12.44,12.83,12.83,0
1079 | 2019-11-05,12.85,13.28,12.25,13.1,13.1,0
1080 | 2019-11-06,13.18,13.39,12.6,12.62,12.62,0
1081 | 2019-11-07,12.59,12.89,12.26,12.73,12.73,0
1082 | 2019-11-08,12.98,13.05,12.0,12.07,12.07,0
1083 | 2019-11-11,13.15,13.49,12.66,12.69,12.69,0
1084 | 2019-11-12,12.64,13.1,12.36,12.68,12.68,0
1085 | 2019-11-13,12.91,13.9,12.88,13.0,13.0,0
1086 | 2019-11-14,13.16,13.81,12.93,13.05,13.05,0
1087 | 2019-11-15,12.95,12.97,11.92,12.05,12.05,0
1088 | 2019-11-18,12.39,13.1,12.32,12.46,12.46,0
1089 | 2019-11-19,12.33,13.01,12.16,12.86,12.86,0
1090 | 2019-11-20,13.42,14.17,12.61,12.78,12.78,0
1091 | 2019-11-21,13.35,13.86,12.49,13.13,13.13,0
1092 | 2019-11-22,12.82,13.25,12.33,12.34,12.34,0
1093 | 2019-11-25,12.51,12.59,11.73,11.87,11.87,0
1094 | 2019-11-26,11.88,12.04,11.42,11.54,11.54,0
1095 | 2019-11-27,11.55,11.79,11.44,11.75,11.75,0
1096 | 2019-11-29,12.5,12.83,12.12,12.62,12.62,0
1097 | 2019-12-02,12.69,15.27,12.55,14.91,14.91,0
1098 | 2019-12-03,14.68,17.99,14.61,15.96,15.96,0
1099 | 2019-12-04,16.379998999999998,16.4,14.12,14.8,14.8,0
1100 | 2019-12-05,14.46,15.37,14.17,14.52,14.52,0
1101 | 2019-12-06,14.37,14.47,13.19,13.62,13.62,0
1102 | 2019-12-09,14.25,16.07,12.25,15.86,15.86,0
1103 | 2019-12-10,15.8,16.9,14.93,15.68,15.68,0
1104 | 2019-12-11,15.57,15.97,14.98,14.99,14.99,0
1105 | 2019-12-12,14.94,15.55,13.6,13.94,13.94,0
1106 | 2019-12-13,13.18,14.35,12.54,12.63,12.63,0
1107 | 2019-12-16,12.47,12.53,11.71,12.14,12.14,0
1108 | 2019-12-17,12.23,12.47,11.9,12.29,12.29,0
1109 | 2019-12-18,12.24,12.7,11.93,12.58,12.58,0
1110 | 2019-12-19,12.55,12.78,12.43,12.5,12.5,0
1111 | 2019-12-20,12.55,12.61,12.04,12.51,12.51,0
1112 | 2019-12-23,12.81,12.9,,12.61,12.61,0
1113 | 2019-12-24,12.65,12.84,12.56,12.67,12.67,0
1114 | 2019-12-26,12.74,12.75,11.72,12.65,12.65,0
1115 | 2019-12-27,12.61,13.72,,13.43,13.43,0
1116 | 2019-12-30,13.74,15.14,13.44,14.82,14.82,0
1117 | 2019-12-31,14.84,15.39,13.75,13.78,13.78,0
1118 | 2020-01-02,13.46,13.72,,12.47,12.47,0
1119 | 2020-01-03,15.01,16.200001,13.13,14.02,14.02,0
1120 | 2020-01-06,15.45,16.389999,13.54,13.85,13.85,0
1121 | 2020-01-07,13.84,14.46,13.39,13.79,13.79,0
1122 | 2020-01-08,15.16,15.24,12.83,13.45,13.45,0
1123 | 2020-01-09,12.95,13.24,12.53,12.54,12.54,0
1124 | 2020-01-10,12.42,12.87,12.09,12.56,12.56,0
1125 | 2020-01-13,12.84,13.09,12.32,12.32,12.32,0
1126 | 2020-01-14,12.72,13.82,12.05,12.39,12.39,0
1127 | 2020-01-15,12.79,12.83,11.95,12.42,12.42,0
1128 | 2020-01-16,12.2,12.42,11.78,12.32,12.32,0
1129 | 2020-01-17,12.21,12.48,11.75,12.1,12.1,0
1130 | 2020-01-21,13.23,13.33,12.32,12.85,12.85,0
1131 | 2020-01-22,12.45,13.01,12.31,12.91,12.91,0
1132 | 2020-01-23,13.26,14.15,12.94,12.98,12.98,0
1133 | 2020-01-24,12.75,15.98,12.62,14.56,14.56,0
1134 | 2020-01-27,17.42,19.02,16.82,18.23,18.23,0
1135 | 2020-01-29,15.68,16.65,14.94,16.389999,16.389999,0
1136 | 2020-01-30,17.82,18.389999,15.3,15.49,15.49,0
1137 | 2020-01-31,16.25,19.99,16.18,18.84,18.84,0
1138 | 2020-02-03,18.639999,18.879998999999998,17.1,17.969998999999994,17.969998999999994,0
1139 | 2020-02-04,16.450001,16.459999,15.63,16.049999,16.049999,0
1140 | 2020-02-05,16.290001,16.32,15.02,15.15,15.15,0
1141 | 2020-02-06,15.13,15.66,14.7,14.96,14.96,0
1142 | 2020-02-07,15.07,16.16,14.81,15.47,15.47,0
1143 | 2020-02-10,15.88,16.43,15.01,15.04,15.04,0
1144 | 2020-02-11,14.91,15.27,14.38,15.18,15.18,0
1145 | 2020-02-12,14.86,14.88,,13.74,13.74,0
1146 | 2020-02-13,14.43,15.44,14.0,14.15,14.15,0
1147 | 2020-02-14,14.12,14.54,13.38,13.68,13.68,0
1148 | 2020-02-18,14.98,15.49,14.53,14.83,14.83,0
1149 | 2020-02-19,14.66,14.74,14.21,14.38,14.38,0
1150 | 2020-02-20,14.54,17.209999,14.49,15.56,15.56,0
1151 | 2020-02-21,17.33,18.209999,16.190001000000002,17.08,17.08,0
1152 | 2020-02-24,22.25,26.35,22.0,25.030001000000002,25.030001000000002,0
1153 | 2020-02-25,23.280001000000002,30.25,22.620001000000002,27.85,27.85,0
1154 | 2020-02-26,26.629998999999998,29.57,24.76,27.559998999999998,27.559998999999998,0
1155 | 2020-02-27,32.139998999999996,39.310001,29.370001000000002,39.16,39.16,0
1156 | 2020-02-28,42.02,49.48,39.369999,40.110001000000004,40.110001000000004,0
1157 | 2020-03-02,38.540001000000004,40.41,31.5,33.419998,33.419998,0
1158 | 2020-03-03,33.650002,41.060001,24.93,36.82,36.82,0
1159 | 2020-03-04,34.439999,35.580002,30.299999,31.99,31.99,0
1160 | 2020-03-05,33.610001000000004,42.84,33.540001000000004,39.619999,39.619999,0
1161 | 2020-03-06,48.93,54.389998999999996,40.84,41.939999,41.939999,0
1162 | 2020-03-09,41.939999,62.119999,41.939999,54.459998999999996,54.459998999999996,0
1163 | 2020-03-10,49.68,55.66,43.560001,47.299999,47.299999,0
1164 | 2020-03-11,52.240002000000004,55.82,49.98,53.900002,53.900002,0
1165 | 2020-03-12,61.459998999999996,76.83000200000002,59.91,75.470001,75.470001,0
1166 | 2020-03-13,65.07,77.57,55.169998,57.830002,57.830002,0
1167 | 2020-03-16,57.830002,83.559998,57.830002,82.690002,82.690002,0
1168 | 2020-03-17,82.690002,84.83000200000002,70.370003,75.910004,75.910004,0
1169 | 2020-03-18,69.370003,85.470001,69.370003,76.449997,76.449997,0
1170 | 2020-03-19,80.620003,84.260002,68.57,72.0,72.0,0
1171 | 2020-03-20,67.860001,69.510002,57.419998,66.040001,66.040001,0
1172 | 2020-03-23,74.08000200000002,76.739998,60.459998999999996,61.59,61.59,0
1173 | 2020-03-24,58.759997999999996,61.880001,36.240002000000004,61.669998,61.669998,0
1174 | 2020-03-25,61.439999,68.860001,58.029999,63.950001,63.950001,0
1175 | 2020-03-26,65.66999799999999,67.059998,57.66,61.0,61.0,0
1176 | 2020-03-30,66.300003,67.690002,56.599998,57.080002,57.080002,0
1177 | 2020-03-31,56.689999,58.75,50.880001,53.540001000000004,53.540001000000004,0
1178 | 2020-04-01,57.380001,60.59,52.759997999999996,57.060001,57.060001,0
1179 | 2020-04-02,54.459998999999996,57.240002000000004,,50.91,50.91,0
1180 | 2020-04-06,44.169998,45.73,43.450001,45.240002000000004,45.240002000000004,0
1181 | 2020-04-07,44.830002,47.509997999999996,43.509997999999996,46.700001,46.700001,0
1182 | 2020-04-08,45.900002,47.279999,42.529999,43.349998,43.349998,0
1183 | 2020-04-09,43.0,45.73,41.389998999999996,41.669998,41.669998,0
1184 | 2020-04-13,44.599998,45.040001000000004,41.169998,41.169998,41.169998,0
1185 | 2020-04-14,40.240002000000004,40.57,37.310001,37.759997999999996,37.759997999999996,0
1186 | 2020-04-15,42.57,43.23,40.049999,40.84,40.84,0
1187 | 2020-04-16,41.919998,43.02,39.869999,40.110001000000004,40.110001000000004,0
1188 | 2020-04-17,39.5,40.259997999999996,37.630001,38.150002,38.150002,0
1189 | 2020-04-20,40.68,43.830002,39.880001,43.830002,43.830002,0
1190 | 2020-04-21,44.279999,47.77,43.77,45.41,45.41,0
1191 | 2020-04-22,44.91,45.07,41.41,41.98,41.98,0
1192 | 2020-04-23,41.299999,42.470001,39.060001,41.380001,41.380001,0
1193 | 2020-04-24,38.799999,40.169998,35.599998,35.93,35.93,0
1194 | 2020-04-27,36.290001000000004,36.439999,32.509997999999996,33.290001000000004,33.290001000000004,0
1195 | 2020-04-28,33.209998999999996,35.389998999999996,30.540001,33.57,33.57,0
1196 | 2020-04-29,32.48,33.189999,30.709999,31.23,31.23,0
1197 | 2020-04-30,30.99,35.939999,30.93,34.150002,34.150002,0
1198 | 2020-05-01,38.169998,39.57,36.59,37.189999,37.189999,0
1199 | 2020-05-04,39.130001,40.32,35.529999,35.970001,35.970001,0
1200 | 2020-05-05,34.82,36.220001,31.950001,33.610001000000004,33.610001000000004,0
1201 | 2020-05-06,32.689999,35.689999,31.68,34.119999,34.119999,0
1202 | 2020-05-07,32.119999,32.279999,30.370001000000002,31.440001000000002,31.440001000000002,0
1203 | 2020-05-08,30.139999,30.389999,27.889999,27.98,27.98,0
1204 | 2020-05-11,28.459999,31.459999,26.969998999999998,27.57,27.57,0
1205 | 2020-05-12,28.469998999999998,33.040001000000004,26.0,33.040001000000004,33.040001000000004,0
1206 | 2020-05-13,32.740002000000004,37.419998,30.77,35.279999,35.279999,0
1207 | 2020-05-14,35.16,39.279999,32.330002,32.610001000000004,32.610001000000004,0
1208 |
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/Chapter03/arr.csv:
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1 | 0.86,-0.74,2.27
2 | nan,nan,nan
3 | 1.53,1.47,0.15
4 |
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/Chapter03/arr.npy:
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https://raw.githubusercontent.com/PacktPublishing/Hands-On-Financial-Trading-with-Python/57d12a947b6bb1bcce8af5ddcf95c9775602a9fb/Chapter03/arr.npy
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/Chapter03/numpy.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "import numpy as np\n",
10 | "np.random.seed(0)"
11 | ]
12 | },
13 | {
14 | "cell_type": "markdown",
15 | "metadata": {},
16 | "source": [
17 | "# Working with Numpy ndarrays"
18 | ]
19 | },
20 | {
21 | "cell_type": "markdown",
22 | "metadata": {},
23 | "source": [
24 | "## Creating 1D ndarrays"
25 | ]
26 | },
27 | {
28 | "cell_type": "code",
29 | "execution_count": 2,
30 | "metadata": {},
31 | "outputs": [
32 | {
33 | "data": {
34 | "text/plain": [
35 | "array([1.1, 2.2, 3.3, 4.4, 5.5])"
36 | ]
37 | },
38 | "execution_count": 2,
39 | "metadata": {},
40 | "output_type": "execute_result"
41 | }
42 | ],
43 | "source": [
44 | "arr1D = np.array([1.1, 2.2, 3.3, 4.4, 5.5])\n",
45 | "arr1D"
46 | ]
47 | },
48 | {
49 | "cell_type": "code",
50 | "execution_count": 3,
51 | "metadata": {},
52 | "outputs": [
53 | {
54 | "data": {
55 | "text/plain": [
56 | "numpy.ndarray"
57 | ]
58 | },
59 | "execution_count": 3,
60 | "metadata": {},
61 | "output_type": "execute_result"
62 | }
63 | ],
64 | "source": [
65 | "type(arr1D)"
66 | ]
67 | },
68 | {
69 | "cell_type": "markdown",
70 | "metadata": {},
71 | "source": [
72 | "## Creating 2D ndarrays"
73 | ]
74 | },
75 | {
76 | "cell_type": "code",
77 | "execution_count": 4,
78 | "metadata": {},
79 | "outputs": [
80 | {
81 | "data": {
82 | "text/plain": [
83 | "array([[1, 2],\n",
84 | " [3, 4]])"
85 | ]
86 | },
87 | "execution_count": 4,
88 | "metadata": {},
89 | "output_type": "execute_result"
90 | }
91 | ],
92 | "source": [
93 | "arr2D = np.array([[1, 2], [3, 4]])\n",
94 | "arr2D"
95 | ]
96 | },
97 | {
98 | "cell_type": "markdown",
99 | "metadata": {},
100 | "source": [
101 | "## Creating any dimension ndarrays"
102 | ]
103 | },
104 | {
105 | "cell_type": "code",
106 | "execution_count": 5,
107 | "metadata": {},
108 | "outputs": [
109 | {
110 | "data": {
111 | "text/plain": [
112 | "array([[[[ 0, 1],\n",
113 | " [ 2, 3]],\n",
114 | "\n",
115 | " [[ 4, 5],\n",
116 | " [ 6, 7]]],\n",
117 | "\n",
118 | "\n",
119 | " [[[ 8, 9],\n",
120 | " [10, 11]],\n",
121 | "\n",
122 | " [[12, 13],\n",
123 | " [14, 15]]]])"
124 | ]
125 | },
126 | "execution_count": 5,
127 | "metadata": {},
128 | "output_type": "execute_result"
129 | }
130 | ],
131 | "source": [
132 | "arr4D = np.array(range(16)).reshape((2, 2, 2, 2)); \n",
133 | "arr4D"
134 | ]
135 | },
136 | {
137 | "cell_type": "code",
138 | "execution_count": 6,
139 | "metadata": {},
140 | "outputs": [
141 | {
142 | "data": {
143 | "text/plain": [
144 | "(5,)"
145 | ]
146 | },
147 | "execution_count": 6,
148 | "metadata": {},
149 | "output_type": "execute_result"
150 | }
151 | ],
152 | "source": [
153 | "arr1D.shape"
154 | ]
155 | },
156 | {
157 | "cell_type": "code",
158 | "execution_count": 7,
159 | "metadata": {},
160 | "outputs": [
161 | {
162 | "data": {
163 | "text/plain": [
164 | "(2, 2)"
165 | ]
166 | },
167 | "execution_count": 7,
168 | "metadata": {},
169 | "output_type": "execute_result"
170 | }
171 | ],
172 | "source": [
173 | "arr2D.shape"
174 | ]
175 | },
176 | {
177 | "cell_type": "code",
178 | "execution_count": 8,
179 | "metadata": {},
180 | "outputs": [
181 | {
182 | "data": {
183 | "text/plain": [
184 | "(2, 2, 2, 2)"
185 | ]
186 | },
187 | "execution_count": 8,
188 | "metadata": {},
189 | "output_type": "execute_result"
190 | }
191 | ],
192 | "source": [
193 | "arr4D.shape"
194 | ]
195 | },
196 | {
197 | "cell_type": "markdown",
198 | "metadata": {},
199 | "source": [
200 | "## numpy.zeros(...)"
201 | ]
202 | },
203 | {
204 | "cell_type": "code",
205 | "execution_count": 9,
206 | "metadata": {},
207 | "outputs": [
208 | {
209 | "data": {
210 | "text/plain": [
211 | "array([[0., 0., 0., 0., 0.],\n",
212 | " [0., 0., 0., 0., 0.]])"
213 | ]
214 | },
215 | "execution_count": 9,
216 | "metadata": {},
217 | "output_type": "execute_result"
218 | }
219 | ],
220 | "source": [
221 | "np.zeros(shape=(2,5))"
222 | ]
223 | },
224 | {
225 | "cell_type": "markdown",
226 | "metadata": {},
227 | "source": [
228 | "## numpy.ones(...)"
229 | ]
230 | },
231 | {
232 | "cell_type": "code",
233 | "execution_count": 10,
234 | "metadata": {},
235 | "outputs": [
236 | {
237 | "data": {
238 | "text/plain": [
239 | "array([[1., 1.],\n",
240 | " [1., 1.]])"
241 | ]
242 | },
243 | "execution_count": 10,
244 | "metadata": {},
245 | "output_type": "execute_result"
246 | }
247 | ],
248 | "source": [
249 | "np.ones(shape=(2,2))"
250 | ]
251 | },
252 | {
253 | "cell_type": "markdown",
254 | "metadata": {},
255 | "source": [
256 | "## numpy.identity(...)"
257 | ]
258 | },
259 | {
260 | "cell_type": "code",
261 | "execution_count": 11,
262 | "metadata": {},
263 | "outputs": [
264 | {
265 | "data": {
266 | "text/plain": [
267 | "array([[1., 0., 0.],\n",
268 | " [0., 1., 0.],\n",
269 | " [0., 0., 1.]])"
270 | ]
271 | },
272 | "execution_count": 11,
273 | "metadata": {},
274 | "output_type": "execute_result"
275 | }
276 | ],
277 | "source": [
278 | "np.identity(3)"
279 | ]
280 | },
281 | {
282 | "cell_type": "markdown",
283 | "metadata": {},
284 | "source": [
285 | "## numpy.arange(...)"
286 | ]
287 | },
288 | {
289 | "cell_type": "code",
290 | "execution_count": 12,
291 | "metadata": {},
292 | "outputs": [
293 | {
294 | "data": {
295 | "text/plain": [
296 | "array([0, 1, 2, 3, 4])"
297 | ]
298 | },
299 | "execution_count": 12,
300 | "metadata": {},
301 | "output_type": "execute_result"
302 | }
303 | ],
304 | "source": [
305 | "np.arange(5)"
306 | ]
307 | },
308 | {
309 | "cell_type": "markdown",
310 | "metadata": {},
311 | "source": [
312 | "## numpy.random.randn(…)"
313 | ]
314 | },
315 | {
316 | "cell_type": "code",
317 | "execution_count": 13,
318 | "metadata": {},
319 | "outputs": [
320 | {
321 | "data": {
322 | "text/plain": [
323 | "array([[1.76405235, 0.40015721],\n",
324 | " [0.97873798, 2.2408932 ]])"
325 | ]
326 | },
327 | "execution_count": 13,
328 | "metadata": {},
329 | "output_type": "execute_result"
330 | }
331 | ],
332 | "source": [
333 | "np.random.randn(2,2)"
334 | ]
335 | },
336 | {
337 | "cell_type": "markdown",
338 | "metadata": {},
339 | "source": [
340 | "# Data types used with Numpy ndarrays"
341 | ]
342 | },
343 | {
344 | "cell_type": "markdown",
345 | "metadata": {},
346 | "source": [
347 | "## Creation of numpy.float64 array"
348 | ]
349 | },
350 | {
351 | "cell_type": "code",
352 | "execution_count": 14,
353 | "metadata": {},
354 | "outputs": [
355 | {
356 | "data": {
357 | "text/plain": [
358 | "array([-1., 0., 1.])"
359 | ]
360 | },
361 | "execution_count": 14,
362 | "metadata": {},
363 | "output_type": "execute_result"
364 | }
365 | ],
366 | "source": [
367 | "np.array([-1, 0, 1], dtype=np.float64)"
368 | ]
369 | },
370 | {
371 | "cell_type": "markdown",
372 | "metadata": {},
373 | "source": [
374 | "## Creation of numpy.bool array"
375 | ]
376 | },
377 | {
378 | "cell_type": "code",
379 | "execution_count": 15,
380 | "metadata": {},
381 | "outputs": [
382 | {
383 | "data": {
384 | "text/plain": [
385 | "array([ True, False, True])"
386 | ]
387 | },
388 | "execution_count": 15,
389 | "metadata": {},
390 | "output_type": "execute_result"
391 | }
392 | ],
393 | "source": [
394 | "np.array([-1, 0, 1], dtype=np.bool)"
395 | ]
396 | },
397 | {
398 | "cell_type": "markdown",
399 | "metadata": {},
400 | "source": [
401 | "## ndarrays' dtype attribute"
402 | ]
403 | },
404 | {
405 | "cell_type": "code",
406 | "execution_count": 16,
407 | "metadata": {},
408 | "outputs": [
409 | {
410 | "data": {
411 | "text/plain": [
412 | "dtype('float64')"
413 | ]
414 | },
415 | "execution_count": 16,
416 | "metadata": {},
417 | "output_type": "execute_result"
418 | }
419 | ],
420 | "source": [
421 | "arr1D.dtype"
422 | ]
423 | },
424 | {
425 | "cell_type": "markdown",
426 | "metadata": {},
427 | "source": [
428 | "## Conversion of underlying types of ndarray with numpy.ndarrays.astype(...)"
429 | ]
430 | },
431 | {
432 | "cell_type": "code",
433 | "execution_count": 17,
434 | "metadata": {},
435 | "outputs": [
436 | {
437 | "data": {
438 | "text/plain": [
439 | "dtype('int64')"
440 | ]
441 | },
442 | "execution_count": 17,
443 | "metadata": {},
444 | "output_type": "execute_result"
445 | }
446 | ],
447 | "source": [
448 | "arr1D.astype(np.int64).dtype"
449 | ]
450 | },
451 | {
452 | "cell_type": "code",
453 | "execution_count": 18,
454 | "metadata": {},
455 | "outputs": [
456 | {
457 | "data": {
458 | "text/plain": [
459 | "array([1, 2, 3, 4, 5], dtype=int64)"
460 | ]
461 | },
462 | "execution_count": 18,
463 | "metadata": {},
464 | "output_type": "execute_result"
465 | }
466 | ],
467 | "source": [
468 | "arr1D.astype(np.int64)"
469 | ]
470 | },
471 | {
472 | "cell_type": "markdown",
473 | "metadata": {},
474 | "source": [
475 | "# Indexing and slicing in Numpy arrays"
476 | ]
477 | },
478 | {
479 | "cell_type": "markdown",
480 | "metadata": {},
481 | "source": [
482 | "## Direct Access to an ndarray's Element"
483 | ]
484 | },
485 | {
486 | "cell_type": "code",
487 | "execution_count": 19,
488 | "metadata": {},
489 | "outputs": [
490 | {
491 | "data": {
492 | "text/plain": [
493 | "array([[ 1.86755799, -0.97727788, 0.95008842],\n",
494 | " [-0.15135721, -0.10321885, 0.4105985 ],\n",
495 | " [ 0.14404357, 1.45427351, 0.76103773]])"
496 | ]
497 | },
498 | "execution_count": 19,
499 | "metadata": {},
500 | "output_type": "execute_result"
501 | }
502 | ],
503 | "source": [
504 | "arr = np.random.randn(3,3)\n",
505 | "arr"
506 | ]
507 | },
508 | {
509 | "cell_type": "code",
510 | "execution_count": 20,
511 | "metadata": {},
512 | "outputs": [
513 | {
514 | "data": {
515 | "text/plain": [
516 | "array([ 1.86755799, -0.97727788, 0.95008842])"
517 | ]
518 | },
519 | "execution_count": 20,
520 | "metadata": {},
521 | "output_type": "execute_result"
522 | }
523 | ],
524 | "source": [
525 | "arr[0]"
526 | ]
527 | },
528 | {
529 | "cell_type": "code",
530 | "execution_count": 21,
531 | "metadata": {},
532 | "outputs": [
533 | {
534 | "data": {
535 | "text/plain": [
536 | "-0.977277879876411"
537 | ]
538 | },
539 | "execution_count": 21,
540 | "metadata": {},
541 | "output_type": "execute_result"
542 | }
543 | ],
544 | "source": [
545 | "arr[0][1]"
546 | ]
547 | },
548 | {
549 | "cell_type": "code",
550 | "execution_count": 22,
551 | "metadata": {},
552 | "outputs": [
553 | {
554 | "data": {
555 | "text/plain": [
556 | "-0.977277879876411"
557 | ]
558 | },
559 | "execution_count": 22,
560 | "metadata": {},
561 | "output_type": "execute_result"
562 | }
563 | ],
564 | "source": [
565 | "arr[0, 1]"
566 | ]
567 | },
568 | {
569 | "cell_type": "code",
570 | "execution_count": 23,
571 | "metadata": {},
572 | "outputs": [
573 | {
574 | "data": {
575 | "text/plain": [
576 | "array([0.14404357, 1.45427351, 0.76103773])"
577 | ]
578 | },
579 | "execution_count": 23,
580 | "metadata": {},
581 | "output_type": "execute_result"
582 | }
583 | ],
584 | "source": [
585 | "arr[-1]"
586 | ]
587 | },
588 | {
589 | "cell_type": "markdown",
590 | "metadata": {},
591 | "source": [
592 | "## ndarray's Slicing"
593 | ]
594 | },
595 | {
596 | "cell_type": "code",
597 | "execution_count": 24,
598 | "metadata": {},
599 | "outputs": [
600 | {
601 | "data": {
602 | "text/plain": [
603 | "array([[-0.15135721, -0.10321885, 0.4105985 ],\n",
604 | " [ 0.14404357, 1.45427351, 0.76103773]])"
605 | ]
606 | },
607 | "execution_count": 24,
608 | "metadata": {},
609 | "output_type": "execute_result"
610 | }
611 | ],
612 | "source": [
613 | "arr[1:]"
614 | ]
615 | },
616 | {
617 | "cell_type": "markdown",
618 | "metadata": {},
619 | "source": [
620 | "### Accessing all ndarray elements after the first one"
621 | ]
622 | },
623 | {
624 | "cell_type": "code",
625 | "execution_count": 25,
626 | "metadata": {},
627 | "outputs": [
628 | {
629 | "data": {
630 | "text/plain": [
631 | "array([[-0.15135721, -0.10321885],\n",
632 | " [ 0.14404357, 1.45427351]])"
633 | ]
634 | },
635 | "execution_count": 25,
636 | "metadata": {},
637 | "output_type": "execute_result"
638 | }
639 | ],
640 | "source": [
641 | "arr[1:, :2]"
642 | ]
643 | },
644 | {
645 | "cell_type": "markdown",
646 | "metadata": {},
647 | "source": [
648 | "### Fetching all rows starting from teh 2nd and columns 1 and 2"
649 | ]
650 | },
651 | {
652 | "cell_type": "code",
653 | "execution_count": 26,
654 | "metadata": {},
655 | "outputs": [
656 | {
657 | "data": {
658 | "text/plain": [
659 | "array([[-0.10321885]])"
660 | ]
661 | },
662 | "execution_count": 26,
663 | "metadata": {},
664 | "output_type": "execute_result"
665 | }
666 | ],
667 | "source": [
668 | "arr[1:2, -2:-1]"
669 | ]
670 | },
671 | {
672 | "cell_type": "markdown",
673 | "metadata": {},
674 | "source": [
675 | "### Slicing with no indices"
676 | ]
677 | },
678 | {
679 | "cell_type": "code",
680 | "execution_count": 27,
681 | "metadata": {},
682 | "outputs": [
683 | {
684 | "data": {
685 | "text/plain": [
686 | "array([0.14404357, 1.45427351, 0.76103773])"
687 | ]
688 | },
689 | "execution_count": 27,
690 | "metadata": {},
691 | "output_type": "execute_result"
692 | }
693 | ],
694 | "source": [
695 | "arr[:][2]"
696 | ]
697 | },
698 | {
699 | "cell_type": "code",
700 | "execution_count": 28,
701 | "metadata": {},
702 | "outputs": [
703 | {
704 | "data": {
705 | "text/plain": [
706 | "array([[ 1.86755799, -0.97727788, 0.95008842],\n",
707 | " [-0.15135721, -0.10321885, 0.4105985 ],\n",
708 | " [ 0.14404357, 1.45427351, 0.76103773]])"
709 | ]
710 | },
711 | "execution_count": 28,
712 | "metadata": {},
713 | "output_type": "execute_result"
714 | }
715 | ],
716 | "source": [
717 | "arr[:][:]"
718 | ]
719 | },
720 | {
721 | "cell_type": "markdown",
722 | "metadata": {},
723 | "source": [
724 | "### Setting values of a slice to zero"
725 | ]
726 | },
727 | {
728 | "cell_type": "code",
729 | "execution_count": 29,
730 | "metadata": {},
731 | "outputs": [
732 | {
733 | "data": {
734 | "text/plain": [
735 | "array([[-0.15135721, -0.10321885, 0.4105985 ]])"
736 | ]
737 | },
738 | "execution_count": 29,
739 | "metadata": {},
740 | "output_type": "execute_result"
741 | }
742 | ],
743 | "source": [
744 | "arr1 = arr[1:2]\n",
745 | "arr1"
746 | ]
747 | },
748 | {
749 | "cell_type": "code",
750 | "execution_count": 30,
751 | "metadata": {},
752 | "outputs": [
753 | {
754 | "data": {
755 | "text/plain": [
756 | "array([[0., 0., 0.]])"
757 | ]
758 | },
759 | "execution_count": 30,
760 | "metadata": {},
761 | "output_type": "execute_result"
762 | }
763 | ],
764 | "source": [
765 | "arr1[:] = 0\n",
766 | "arr1"
767 | ]
768 | },
769 | {
770 | "cell_type": "code",
771 | "execution_count": 31,
772 | "metadata": {},
773 | "outputs": [
774 | {
775 | "data": {
776 | "text/plain": [
777 | "array([[ 1.86755799, -0.97727788, 0.95008842],\n",
778 | " [ 0. , 0. , 0. ],\n",
779 | " [ 0.14404357, 1.45427351, 0.76103773]])"
780 | ]
781 | },
782 | "execution_count": 31,
783 | "metadata": {},
784 | "output_type": "execute_result"
785 | }
786 | ],
787 | "source": [
788 | "arr"
789 | ]
790 | },
791 | {
792 | "cell_type": "code",
793 | "execution_count": 32,
794 | "metadata": {},
795 | "outputs": [],
796 | "source": [
797 | "arr_copy = arr.copy()"
798 | ]
799 | },
800 | {
801 | "cell_type": "code",
802 | "execution_count": 33,
803 | "metadata": {},
804 | "outputs": [
805 | {
806 | "data": {
807 | "text/plain": [
808 | "array([[ 1.86755799, -0.97727788, 0.95008842],\n",
809 | " [ 1. , 1. , 1. ],\n",
810 | " [ 0.14404357, 1.45427351, 0.76103773]])"
811 | ]
812 | },
813 | "execution_count": 33,
814 | "metadata": {},
815 | "output_type": "execute_result"
816 | }
817 | ],
818 | "source": [
819 | "arr_copy[1:2] = 1\n",
820 | "arr_copy"
821 | ]
822 | },
823 | {
824 | "cell_type": "code",
825 | "execution_count": 34,
826 | "metadata": {},
827 | "outputs": [
828 | {
829 | "data": {
830 | "text/plain": [
831 | "array([[ 1.86755799, -0.97727788, 0.95008842],\n",
832 | " [ 0. , 0. , 0. ],\n",
833 | " [ 0.14404357, 1.45427351, 0.76103773]])"
834 | ]
835 | },
836 | "execution_count": 34,
837 | "metadata": {},
838 | "output_type": "execute_result"
839 | }
840 | ],
841 | "source": [
842 | "arr"
843 | ]
844 | },
845 | {
846 | "cell_type": "code",
847 | "execution_count": 35,
848 | "metadata": {},
849 | "outputs": [
850 | {
851 | "data": {
852 | "text/plain": [
853 | "array([[nan, nan, nan],\n",
854 | " [nan, nan, nan],\n",
855 | " [nan, nan, nan]])"
856 | ]
857 | },
858 | "execution_count": 35,
859 | "metadata": {},
860 | "output_type": "execute_result"
861 | }
862 | ],
863 | "source": [
864 | "arr[:] = np.nan\n",
865 | "arr"
866 | ]
867 | },
868 | {
869 | "cell_type": "markdown",
870 | "metadata": {},
871 | "source": [
872 | "## Boolean Indexing"
873 | ]
874 | },
875 | {
876 | "cell_type": "code",
877 | "execution_count": 36,
878 | "metadata": {},
879 | "outputs": [
880 | {
881 | "data": {
882 | "text/plain": [
883 | "array([[ 0.12167502, 0.44386323, 0.33367433],\n",
884 | " [ 1.49407907, -0.20515826, 0.3130677 ],\n",
885 | " [-0.85409574, -2.55298982, 0.6536186 ]])"
886 | ]
887 | },
888 | "execution_count": 36,
889 | "metadata": {},
890 | "output_type": "execute_result"
891 | }
892 | ],
893 | "source": [
894 | "arr = np.random.randn(3,3)\n",
895 | "arr"
896 | ]
897 | },
898 | {
899 | "cell_type": "code",
900 | "execution_count": 37,
901 | "metadata": {},
902 | "outputs": [
903 | {
904 | "data": {
905 | "text/plain": [
906 | "array([[False, False, False],\n",
907 | " [False, True, False],\n",
908 | " [ True, True, False]])"
909 | ]
910 | },
911 | "execution_count": 37,
912 | "metadata": {},
913 | "output_type": "execute_result"
914 | }
915 | ],
916 | "source": [
917 | "arr < 0"
918 | ]
919 | },
920 | {
921 | "cell_type": "code",
922 | "execution_count": 38,
923 | "metadata": {},
924 | "outputs": [
925 | {
926 | "data": {
927 | "text/plain": [
928 | "array([-0.20515826, -0.85409574, -2.55298982])"
929 | ]
930 | },
931 | "execution_count": 38,
932 | "metadata": {},
933 | "output_type": "execute_result"
934 | }
935 | ],
936 | "source": [
937 | "arr[(arr < 0)]"
938 | ]
939 | },
940 | {
941 | "cell_type": "code",
942 | "execution_count": 39,
943 | "metadata": {},
944 | "outputs": [
945 | {
946 | "data": {
947 | "text/plain": [
948 | "array([[ True, True, True],\n",
949 | " [False, True, True],\n",
950 | " [ True, False, True]])"
951 | ]
952 | },
953 | "execution_count": 39,
954 | "metadata": {},
955 | "output_type": "execute_result"
956 | }
957 | ],
958 | "source": [
959 | "(arr > -1) & (arr < 1)"
960 | ]
961 | },
962 | {
963 | "cell_type": "code",
964 | "execution_count": 40,
965 | "metadata": {},
966 | "outputs": [
967 | {
968 | "data": {
969 | "text/plain": [
970 | "array([ 0.12167502, 0.44386323, 0.33367433, -0.20515826, 0.3130677 ,\n",
971 | " -0.85409574, 0.6536186 ])"
972 | ]
973 | },
974 | "execution_count": 40,
975 | "metadata": {},
976 | "output_type": "execute_result"
977 | }
978 | ],
979 | "source": [
980 | "arr[((arr > -1) & (arr < 1))]"
981 | ]
982 | },
983 | {
984 | "cell_type": "code",
985 | "execution_count": 41,
986 | "metadata": {},
987 | "outputs": [
988 | {
989 | "data": {
990 | "text/plain": [
991 | "array([ 1.49407907, -2.55298982])"
992 | ]
993 | },
994 | "execution_count": 41,
995 | "metadata": {},
996 | "output_type": "execute_result"
997 | }
998 | ],
999 | "source": [
1000 | "arr[(arr < -1) | (arr > 1)]"
1001 | ]
1002 | },
1003 | {
1004 | "cell_type": "markdown",
1005 | "metadata": {},
1006 | "source": [
1007 | "## Indexing with arrays"
1008 | ]
1009 | },
1010 | {
1011 | "cell_type": "code",
1012 | "execution_count": 42,
1013 | "metadata": {},
1014 | "outputs": [
1015 | {
1016 | "data": {
1017 | "text/plain": [
1018 | "array([[ 0.12167502, 0.44386323, 0.33367433],\n",
1019 | " [ 1.49407907, -0.20515826, 0.3130677 ],\n",
1020 | " [-0.85409574, -2.55298982, 0.6536186 ]])"
1021 | ]
1022 | },
1023 | "execution_count": 42,
1024 | "metadata": {},
1025 | "output_type": "execute_result"
1026 | }
1027 | ],
1028 | "source": [
1029 | "arr"
1030 | ]
1031 | },
1032 | {
1033 | "cell_type": "code",
1034 | "execution_count": 43,
1035 | "metadata": {},
1036 | "outputs": [
1037 | {
1038 | "data": {
1039 | "text/plain": [
1040 | "array([[ 0.12167502, 0.44386323, 0.33367433],\n",
1041 | " [-0.85409574, -2.55298982, 0.6536186 ]])"
1042 | ]
1043 | },
1044 | "execution_count": 43,
1045 | "metadata": {},
1046 | "output_type": "execute_result"
1047 | }
1048 | ],
1049 | "source": [
1050 | "arr[[0, 2]]"
1051 | ]
1052 | },
1053 | {
1054 | "cell_type": "code",
1055 | "execution_count": 44,
1056 | "metadata": {},
1057 | "outputs": [
1058 | {
1059 | "data": {
1060 | "text/plain": [
1061 | "array([ 0.44386323, -2.55298982])"
1062 | ]
1063 | },
1064 | "execution_count": 44,
1065 | "metadata": {},
1066 | "output_type": "execute_result"
1067 | }
1068 | ],
1069 | "source": [
1070 | "arr[[0, 2], [1]]"
1071 | ]
1072 | },
1073 | {
1074 | "cell_type": "code",
1075 | "execution_count": 45,
1076 | "metadata": {
1077 | "scrolled": true
1078 | },
1079 | "outputs": [
1080 | {
1081 | "data": {
1082 | "text/plain": [
1083 | "array([[-0.85409574, -2.55298982, 0.6536186 ],\n",
1084 | " [ 0.12167502, 0.44386323, 0.33367433]])"
1085 | ]
1086 | },
1087 | "execution_count": 45,
1088 | "metadata": {},
1089 | "output_type": "execute_result"
1090 | }
1091 | ],
1092 | "source": [
1093 | "arr[[2, 0]]"
1094 | ]
1095 | },
1096 | {
1097 | "cell_type": "markdown",
1098 | "metadata": {},
1099 | "source": [
1100 | "# Basic ndarray operations with Numpy Universal Functions"
1101 | ]
1102 | },
1103 | {
1104 | "cell_type": "code",
1105 | "execution_count": 46,
1106 | "metadata": {},
1107 | "outputs": [
1108 | {
1109 | "data": {
1110 | "text/plain": [
1111 | "array([[1, 2],\n",
1112 | " [3, 4]])"
1113 | ]
1114 | },
1115 | "execution_count": 46,
1116 | "metadata": {},
1117 | "output_type": "execute_result"
1118 | }
1119 | ],
1120 | "source": [
1121 | "arr2D"
1122 | ]
1123 | },
1124 | {
1125 | "cell_type": "markdown",
1126 | "metadata": {},
1127 | "source": [
1128 | "## Scalar multiplication with an ndarray"
1129 | ]
1130 | },
1131 | {
1132 | "cell_type": "code",
1133 | "execution_count": 47,
1134 | "metadata": {},
1135 | "outputs": [
1136 | {
1137 | "data": {
1138 | "text/plain": [
1139 | "array([[ 4, 8],\n",
1140 | " [12, 16]])"
1141 | ]
1142 | },
1143 | "execution_count": 47,
1144 | "metadata": {},
1145 | "output_type": "execute_result"
1146 | }
1147 | ],
1148 | "source": [
1149 | "arr2D * 4"
1150 | ]
1151 | },
1152 | {
1153 | "cell_type": "markdown",
1154 | "metadata": {},
1155 | "source": [
1156 | "## Linear Combinations of ndarrays"
1157 | ]
1158 | },
1159 | {
1160 | "cell_type": "code",
1161 | "execution_count": 48,
1162 | "metadata": {},
1163 | "outputs": [
1164 | {
1165 | "data": {
1166 | "text/plain": [
1167 | "array([[ 5, 10],\n",
1168 | " [15, 20]])"
1169 | ]
1170 | },
1171 | "execution_count": 48,
1172 | "metadata": {},
1173 | "output_type": "execute_result"
1174 | }
1175 | ],
1176 | "source": [
1177 | "2*arr2D + 3*arr2D"
1178 | ]
1179 | },
1180 | {
1181 | "cell_type": "markdown",
1182 | "metadata": {},
1183 | "source": [
1184 | "## Exponentiation of ndarrays"
1185 | ]
1186 | },
1187 | {
1188 | "cell_type": "code",
1189 | "execution_count": 49,
1190 | "metadata": {},
1191 | "outputs": [
1192 | {
1193 | "data": {
1194 | "text/plain": [
1195 | "array([[ 1, 4],\n",
1196 | " [ 9, 16]], dtype=int32)"
1197 | ]
1198 | },
1199 | "execution_count": 49,
1200 | "metadata": {},
1201 | "output_type": "execute_result"
1202 | }
1203 | ],
1204 | "source": [
1205 | "arr2D ** 2"
1206 | ]
1207 | },
1208 | {
1209 | "cell_type": "markdown",
1210 | "metadata": {},
1211 | "source": [
1212 | "## Addition of an ndarray with a scalar"
1213 | ]
1214 | },
1215 | {
1216 | "cell_type": "code",
1217 | "execution_count": 50,
1218 | "metadata": {},
1219 | "outputs": [
1220 | {
1221 | "data": {
1222 | "text/plain": [
1223 | "array([[11, 12],\n",
1224 | " [13, 14]])"
1225 | ]
1226 | },
1227 | "execution_count": 50,
1228 | "metadata": {},
1229 | "output_type": "execute_result"
1230 | }
1231 | ],
1232 | "source": [
1233 | "arr2D + 10"
1234 | ]
1235 | },
1236 | {
1237 | "cell_type": "markdown",
1238 | "metadata": {},
1239 | "source": [
1240 | "## Transposing a matrix"
1241 | ]
1242 | },
1243 | {
1244 | "cell_type": "code",
1245 | "execution_count": 51,
1246 | "metadata": {},
1247 | "outputs": [
1248 | {
1249 | "data": {
1250 | "text/plain": [
1251 | "array([[1, 3],\n",
1252 | " [2, 4]])"
1253 | ]
1254 | },
1255 | "execution_count": 51,
1256 | "metadata": {},
1257 | "output_type": "execute_result"
1258 | }
1259 | ],
1260 | "source": [
1261 | "arr2D.transpose()"
1262 | ]
1263 | },
1264 | {
1265 | "cell_type": "markdown",
1266 | "metadata": {},
1267 | "source": [
1268 | "## Changing layout of an ndarray"
1269 | ]
1270 | },
1271 | {
1272 | "cell_type": "code",
1273 | "execution_count": 52,
1274 | "metadata": {},
1275 | "outputs": [
1276 | {
1277 | "data": {
1278 | "text/plain": [
1279 | "array([[1],\n",
1280 | " [2],\n",
1281 | " [3],\n",
1282 | " [4]])"
1283 | ]
1284 | },
1285 | "execution_count": 52,
1286 | "metadata": {},
1287 | "output_type": "execute_result"
1288 | }
1289 | ],
1290 | "source": [
1291 | "arr2D.reshape((4, 1))"
1292 | ]
1293 | },
1294 | {
1295 | "cell_type": "code",
1296 | "execution_count": 53,
1297 | "metadata": {},
1298 | "outputs": [
1299 | {
1300 | "data": {
1301 | "text/plain": [
1302 | "array([[ 0.8644362 , -0.74216502, 2.26975462],\n",
1303 | " [-1.45436567, 0.04575852, -0.18718385],\n",
1304 | " [ 1.53277921, 1.46935877, 0.15494743]])"
1305 | ]
1306 | },
1307 | "execution_count": 53,
1308 | "metadata": {},
1309 | "output_type": "execute_result"
1310 | }
1311 | ],
1312 | "source": [
1313 | "arr = np.random.randn(9).reshape((3,3))\n",
1314 | "arr"
1315 | ]
1316 | },
1317 | {
1318 | "cell_type": "markdown",
1319 | "metadata": {},
1320 | "source": [
1321 | "## Finding the minimum value in an ndarray"
1322 | ]
1323 | },
1324 | {
1325 | "cell_type": "code",
1326 | "execution_count": 54,
1327 | "metadata": {},
1328 | "outputs": [
1329 | {
1330 | "data": {
1331 | "text/plain": [
1332 | "-1.4543656745987648"
1333 | ]
1334 | },
1335 | "execution_count": 54,
1336 | "metadata": {},
1337 | "output_type": "execute_result"
1338 | }
1339 | ],
1340 | "source": [
1341 | "np.min(arr)"
1342 | ]
1343 | },
1344 | {
1345 | "cell_type": "markdown",
1346 | "metadata": {},
1347 | "source": [
1348 | "## Calculating the absolute value"
1349 | ]
1350 | },
1351 | {
1352 | "cell_type": "code",
1353 | "execution_count": 55,
1354 | "metadata": {},
1355 | "outputs": [
1356 | {
1357 | "data": {
1358 | "text/plain": [
1359 | "array([[0.8644362 , 0.74216502, 2.26975462],\n",
1360 | " [1.45436567, 0.04575852, 0.18718385],\n",
1361 | " [1.53277921, 1.46935877, 0.15494743]])"
1362 | ]
1363 | },
1364 | "execution_count": 55,
1365 | "metadata": {},
1366 | "output_type": "execute_result"
1367 | }
1368 | ],
1369 | "source": [
1370 | "np.abs(arr)"
1371 | ]
1372 | },
1373 | {
1374 | "cell_type": "markdown",
1375 | "metadata": {},
1376 | "source": [
1377 | "## Calculating the mean of an ndarray"
1378 | ]
1379 | },
1380 | {
1381 | "cell_type": "code",
1382 | "execution_count": 56,
1383 | "metadata": {},
1384 | "outputs": [
1385 | {
1386 | "data": {
1387 | "text/plain": [
1388 | "0.4392578005636864"
1389 | ]
1390 | },
1391 | "execution_count": 56,
1392 | "metadata": {},
1393 | "output_type": "execute_result"
1394 | }
1395 | ],
1396 | "source": [
1397 | "np.mean(arr)"
1398 | ]
1399 | },
1400 | {
1401 | "cell_type": "code",
1402 | "execution_count": 57,
1403 | "metadata": {
1404 | "scrolled": true
1405 | },
1406 | "outputs": [
1407 | {
1408 | "data": {
1409 | "text/plain": [
1410 | "array([0.31428325, 0.25765076, 0.7458394 ])"
1411 | ]
1412 | },
1413 | "execution_count": 57,
1414 | "metadata": {},
1415 | "output_type": "execute_result"
1416 | }
1417 | ],
1418 | "source": [
1419 | "np.mean(arr, axis=0)"
1420 | ]
1421 | },
1422 | {
1423 | "cell_type": "code",
1424 | "execution_count": 58,
1425 | "metadata": {},
1426 | "outputs": [
1427 | {
1428 | "data": {
1429 | "text/plain": [
1430 | "array([ 0.79734193, -0.53193034, 1.0523618 ])"
1431 | ]
1432 | },
1433 | "execution_count": 58,
1434 | "metadata": {},
1435 | "output_type": "execute_result"
1436 | }
1437 | ],
1438 | "source": [
1439 | "np.mean(arr, axis=1)"
1440 | ]
1441 | },
1442 | {
1443 | "cell_type": "markdown",
1444 | "metadata": {},
1445 | "source": [
1446 | "## Finding the index of the maximum value in an ndarray"
1447 | ]
1448 | },
1449 | {
1450 | "cell_type": "code",
1451 | "execution_count": 59,
1452 | "metadata": {},
1453 | "outputs": [
1454 | {
1455 | "data": {
1456 | "text/plain": [
1457 | "2"
1458 | ]
1459 | },
1460 | "execution_count": 59,
1461 | "metadata": {},
1462 | "output_type": "execute_result"
1463 | }
1464 | ],
1465 | "source": [
1466 | "np.argmax(arr)"
1467 | ]
1468 | },
1469 | {
1470 | "cell_type": "code",
1471 | "execution_count": 60,
1472 | "metadata": {},
1473 | "outputs": [
1474 | {
1475 | "data": {
1476 | "text/plain": [
1477 | "array([2, 1, 0], dtype=int64)"
1478 | ]
1479 | },
1480 | "execution_count": 60,
1481 | "metadata": {},
1482 | "output_type": "execute_result"
1483 | }
1484 | ],
1485 | "source": [
1486 | "np.argmax(arr, axis=1)"
1487 | ]
1488 | },
1489 | {
1490 | "cell_type": "markdown",
1491 | "metadata": {},
1492 | "source": [
1493 | "## Calculating the cumulative sum of elements of an ndarray"
1494 | ]
1495 | },
1496 | {
1497 | "cell_type": "code",
1498 | "execution_count": 61,
1499 | "metadata": {
1500 | "scrolled": true
1501 | },
1502 | "outputs": [
1503 | {
1504 | "data": {
1505 | "text/plain": [
1506 | "array([0.8644362 , 0.12227118, 2.3920258 , 0.93766013, 0.98341865,\n",
1507 | " 0.7962348 , 2.32901401, 3.79837278, 3.95332021])"
1508 | ]
1509 | },
1510 | "execution_count": 61,
1511 | "metadata": {},
1512 | "output_type": "execute_result"
1513 | }
1514 | ],
1515 | "source": [
1516 | "np.cumsum(arr)"
1517 | ]
1518 | },
1519 | {
1520 | "cell_type": "code",
1521 | "execution_count": 62,
1522 | "metadata": {},
1523 | "outputs": [
1524 | {
1525 | "data": {
1526 | "text/plain": [
1527 | "array([[ 0.8644362 , 0.12227118, 2.3920258 ],\n",
1528 | " [-1.45436567, -1.40860716, -1.59579101],\n",
1529 | " [ 1.53277921, 3.00213798, 3.15708541]])"
1530 | ]
1531 | },
1532 | "execution_count": 62,
1533 | "metadata": {},
1534 | "output_type": "execute_result"
1535 | }
1536 | ],
1537 | "source": [
1538 | "np.cumsum(arr, axis=1)"
1539 | ]
1540 | },
1541 | {
1542 | "cell_type": "markdown",
1543 | "metadata": {},
1544 | "source": [
1545 | "## Finding NaNs in an ndarray"
1546 | ]
1547 | },
1548 | {
1549 | "cell_type": "code",
1550 | "execution_count": 63,
1551 | "metadata": {},
1552 | "outputs": [
1553 | {
1554 | "data": {
1555 | "text/plain": [
1556 | "array([[ 0.8644362 , -0.74216502, 2.26975462],\n",
1557 | " [ nan, nan, nan],\n",
1558 | " [ 1.53277921, 1.46935877, 0.15494743]])"
1559 | ]
1560 | },
1561 | "execution_count": 63,
1562 | "metadata": {},
1563 | "output_type": "execute_result"
1564 | }
1565 | ],
1566 | "source": [
1567 | "arr[1, :] = np.nan\n",
1568 | "arr"
1569 | ]
1570 | },
1571 | {
1572 | "cell_type": "code",
1573 | "execution_count": 64,
1574 | "metadata": {},
1575 | "outputs": [
1576 | {
1577 | "data": {
1578 | "text/plain": [
1579 | "array([[False, False, False],\n",
1580 | " [ True, True, True],\n",
1581 | " [False, False, False]])"
1582 | ]
1583 | },
1584 | "execution_count": 64,
1585 | "metadata": {},
1586 | "output_type": "execute_result"
1587 | }
1588 | ],
1589 | "source": [
1590 | "np.isnan(arr)"
1591 | ]
1592 | },
1593 | {
1594 | "cell_type": "markdown",
1595 | "metadata": {},
1596 | "source": [
1597 | "## Finding the truth values of x1>x2 of two ndarrays"
1598 | ]
1599 | },
1600 | {
1601 | "cell_type": "code",
1602 | "execution_count": 65,
1603 | "metadata": {},
1604 | "outputs": [
1605 | {
1606 | "data": {
1607 | "text/plain": [
1608 | "array([[ 0.37816252, -0.88778575, -1.98079647],\n",
1609 | " [-0.34791215, 0.15634897, 1.23029068],\n",
1610 | " [ 1.20237985, -0.38732682, -0.30230275]])"
1611 | ]
1612 | },
1613 | "execution_count": 65,
1614 | "metadata": {},
1615 | "output_type": "execute_result"
1616 | }
1617 | ],
1618 | "source": [
1619 | "arr1 = np.random.randn(9).reshape((3,3))\n",
1620 | "arr1"
1621 | ]
1622 | },
1623 | {
1624 | "cell_type": "code",
1625 | "execution_count": 66,
1626 | "metadata": {},
1627 | "outputs": [
1628 | {
1629 | "data": {
1630 | "text/plain": [
1631 | "array([[-1.04855297, -1.42001794, -1.70627019],\n",
1632 | " [ 1.9507754 , -0.50965218, -0.4380743 ],\n",
1633 | " [-1.25279536, 0.77749036, -1.61389785]])"
1634 | ]
1635 | },
1636 | "execution_count": 66,
1637 | "metadata": {},
1638 | "output_type": "execute_result"
1639 | }
1640 | ],
1641 | "source": [
1642 | "arr2 = np.random.randn(9).reshape((3,3))\n",
1643 | "arr2"
1644 | ]
1645 | },
1646 | {
1647 | "cell_type": "code",
1648 | "execution_count": 67,
1649 | "metadata": {},
1650 | "outputs": [
1651 | {
1652 | "data": {
1653 | "text/plain": [
1654 | "array([[ True, True, False],\n",
1655 | " [False, True, True],\n",
1656 | " [ True, False, True]])"
1657 | ]
1658 | },
1659 | "execution_count": 67,
1660 | "metadata": {},
1661 | "output_type": "execute_result"
1662 | }
1663 | ],
1664 | "source": [
1665 | "np.greater(arr1, arr2)"
1666 | ]
1667 | },
1668 | {
1669 | "cell_type": "code",
1670 | "execution_count": 68,
1671 | "metadata": {},
1672 | "outputs": [
1673 | {
1674 | "data": {
1675 | "text/plain": [
1676 | "array([[ True, True, False],\n",
1677 | " [False, True, True],\n",
1678 | " [ True, False, True]])"
1679 | ]
1680 | },
1681 | "execution_count": 68,
1682 | "metadata": {},
1683 | "output_type": "execute_result"
1684 | }
1685 | ],
1686 | "source": [
1687 | "arr1 > arr2"
1688 | ]
1689 | },
1690 | {
1691 | "cell_type": "markdown",
1692 | "metadata": {},
1693 | "source": [
1694 | "## Boolean operations Any and All on ndarrays"
1695 | ]
1696 | },
1697 | {
1698 | "cell_type": "code",
1699 | "execution_count": 69,
1700 | "metadata": {},
1701 | "outputs": [
1702 | {
1703 | "data": {
1704 | "text/plain": [
1705 | "array([[False, False, False],\n",
1706 | " [False, False, False],\n",
1707 | " [False, False, True]])"
1708 | ]
1709 | },
1710 | "execution_count": 69,
1711 | "metadata": {},
1712 | "output_type": "execute_result"
1713 | }
1714 | ],
1715 | "source": [
1716 | "arr_bool = (arr > -0.5) & (arr < 0.5)\n",
1717 | "arr_bool"
1718 | ]
1719 | },
1720 | {
1721 | "cell_type": "code",
1722 | "execution_count": 70,
1723 | "metadata": {},
1724 | "outputs": [
1725 | {
1726 | "data": {
1727 | "text/plain": [
1728 | "True"
1729 | ]
1730 | },
1731 | "execution_count": 70,
1732 | "metadata": {},
1733 | "output_type": "execute_result"
1734 | }
1735 | ],
1736 | "source": [
1737 | "arr_bool.any()"
1738 | ]
1739 | },
1740 | {
1741 | "cell_type": "code",
1742 | "execution_count": 71,
1743 | "metadata": {},
1744 | "outputs": [
1745 | {
1746 | "data": {
1747 | "text/plain": [
1748 | "array([False, False, True])"
1749 | ]
1750 | },
1751 | "execution_count": 71,
1752 | "metadata": {},
1753 | "output_type": "execute_result"
1754 | }
1755 | ],
1756 | "source": [
1757 | "arr_bool.any(axis=1)"
1758 | ]
1759 | },
1760 | {
1761 | "cell_type": "code",
1762 | "execution_count": 72,
1763 | "metadata": {},
1764 | "outputs": [
1765 | {
1766 | "data": {
1767 | "text/plain": [
1768 | "False"
1769 | ]
1770 | },
1771 | "execution_count": 72,
1772 | "metadata": {},
1773 | "output_type": "execute_result"
1774 | }
1775 | ],
1776 | "source": [
1777 | "arr_bool.all()"
1778 | ]
1779 | },
1780 | {
1781 | "cell_type": "code",
1782 | "execution_count": 73,
1783 | "metadata": {
1784 | "scrolled": true
1785 | },
1786 | "outputs": [
1787 | {
1788 | "data": {
1789 | "text/plain": [
1790 | "array([False, False, False])"
1791 | ]
1792 | },
1793 | "execution_count": 73,
1794 | "metadata": {},
1795 | "output_type": "execute_result"
1796 | }
1797 | ],
1798 | "source": [
1799 | "arr_bool.all(axis=1)"
1800 | ]
1801 | },
1802 | {
1803 | "cell_type": "markdown",
1804 | "metadata": {},
1805 | "source": [
1806 | "## Sorting ndarrays"
1807 | ]
1808 | },
1809 | {
1810 | "cell_type": "code",
1811 | "execution_count": 74,
1812 | "metadata": {},
1813 | "outputs": [
1814 | {
1815 | "data": {
1816 | "text/plain": [
1817 | "array([-0.21274028, -0.89546656, 0.3869025 , -0.51080514, -1.18063218,\n",
1818 | " -0.02818223, 0.42833187, 0.06651722, 0.3024719 , -0.63432209])"
1819 | ]
1820 | },
1821 | "execution_count": 74,
1822 | "metadata": {},
1823 | "output_type": "execute_result"
1824 | }
1825 | ],
1826 | "source": [
1827 | "arr1D = np.random.randn(10)\n",
1828 | "arr1D"
1829 | ]
1830 | },
1831 | {
1832 | "cell_type": "code",
1833 | "execution_count": 75,
1834 | "metadata": {},
1835 | "outputs": [
1836 | {
1837 | "data": {
1838 | "text/plain": [
1839 | "array([-1.18063218, -0.89546656, -0.63432209, -0.51080514, -0.21274028,\n",
1840 | " -0.02818223, 0.06651722, 0.3024719 , 0.3869025 , 0.42833187])"
1841 | ]
1842 | },
1843 | "execution_count": 75,
1844 | "metadata": {},
1845 | "output_type": "execute_result"
1846 | }
1847 | ],
1848 | "source": [
1849 | "np.sort(arr1D)"
1850 | ]
1851 | },
1852 | {
1853 | "cell_type": "code",
1854 | "execution_count": 76,
1855 | "metadata": {},
1856 | "outputs": [
1857 | {
1858 | "data": {
1859 | "text/plain": [
1860 | "array([-0.21274028, -0.89546656, 0.3869025 , -0.51080514, -1.18063218,\n",
1861 | " -0.02818223, 0.42833187, 0.06651722, 0.3024719 , -0.63432209])"
1862 | ]
1863 | },
1864 | "execution_count": 76,
1865 | "metadata": {},
1866 | "output_type": "execute_result"
1867 | }
1868 | ],
1869 | "source": [
1870 | "arr1D"
1871 | ]
1872 | },
1873 | {
1874 | "cell_type": "code",
1875 | "execution_count": 77,
1876 | "metadata": {},
1877 | "outputs": [
1878 | {
1879 | "data": {
1880 | "text/plain": [
1881 | "array([4, 1, 9, 3, 0, 5, 7, 8, 2, 6], dtype=int64)"
1882 | ]
1883 | },
1884 | "execution_count": 77,
1885 | "metadata": {},
1886 | "output_type": "execute_result"
1887 | }
1888 | ],
1889 | "source": [
1890 | "np.argsort(arr1D)"
1891 | ]
1892 | },
1893 | {
1894 | "cell_type": "code",
1895 | "execution_count": 78,
1896 | "metadata": {},
1897 | "outputs": [
1898 | {
1899 | "data": {
1900 | "text/plain": [
1901 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
1902 | ]
1903 | },
1904 | "execution_count": 78,
1905 | "metadata": {},
1906 | "output_type": "execute_result"
1907 | }
1908 | ],
1909 | "source": [
1910 | "arr1D.sort()\n",
1911 | "np.argsort(arr1D)"
1912 | ]
1913 | },
1914 | {
1915 | "cell_type": "markdown",
1916 | "metadata": {},
1917 | "source": [
1918 | "## Searching within ndarrays"
1919 | ]
1920 | },
1921 | {
1922 | "cell_type": "code",
1923 | "execution_count": 79,
1924 | "metadata": {},
1925 | "outputs": [
1926 | {
1927 | "data": {
1928 | "text/plain": [
1929 | "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
1930 | ]
1931 | },
1932 | "execution_count": 79,
1933 | "metadata": {},
1934 | "output_type": "execute_result"
1935 | }
1936 | ],
1937 | "source": [
1938 | "arr1 = np.array(range(1, 11))\n",
1939 | "arr1"
1940 | ]
1941 | },
1942 | {
1943 | "cell_type": "code",
1944 | "execution_count": 80,
1945 | "metadata": {},
1946 | "outputs": [
1947 | {
1948 | "data": {
1949 | "text/plain": [
1950 | "array([ 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,\n",
1951 | " 10000])"
1952 | ]
1953 | },
1954 | "execution_count": 80,
1955 | "metadata": {},
1956 | "output_type": "execute_result"
1957 | }
1958 | ],
1959 | "source": [
1960 | "arr2 = arr1 * 1000\n",
1961 | "arr2"
1962 | ]
1963 | },
1964 | {
1965 | "cell_type": "code",
1966 | "execution_count": 81,
1967 | "metadata": {},
1968 | "outputs": [
1969 | {
1970 | "data": {
1971 | "text/plain": [
1972 | "array([False, False, False, False, False, True, False, False, True,\n",
1973 | " False])"
1974 | ]
1975 | },
1976 | "execution_count": 81,
1977 | "metadata": {},
1978 | "output_type": "execute_result"
1979 | }
1980 | ],
1981 | "source": [
1982 | "cond = np.random.randn(10) > 0\n",
1983 | "cond"
1984 | ]
1985 | },
1986 | {
1987 | "cell_type": "code",
1988 | "execution_count": 82,
1989 | "metadata": {},
1990 | "outputs": [
1991 | {
1992 | "data": {
1993 | "text/plain": [
1994 | "array([ 1000, 2000, 3000, 4000, 5000, 6, 7000, 8000, 9,\n",
1995 | " 10000])"
1996 | ]
1997 | },
1998 | "execution_count": 82,
1999 | "metadata": {},
2000 | "output_type": "execute_result"
2001 | }
2002 | ],
2003 | "source": [
2004 | "np.where(cond, arr1, arr2)"
2005 | ]
2006 | },
2007 | {
2008 | "cell_type": "markdown",
2009 | "metadata": {},
2010 | "source": [
2011 | "# File operations on ndarrays"
2012 | ]
2013 | },
2014 | {
2015 | "cell_type": "markdown",
2016 | "metadata": {},
2017 | "source": [
2018 | "## File operations with Text files"
2019 | ]
2020 | },
2021 | {
2022 | "cell_type": "code",
2023 | "execution_count": 83,
2024 | "metadata": {},
2025 | "outputs": [
2026 | {
2027 | "data": {
2028 | "text/plain": [
2029 | "array([[ 0.8644362 , -0.74216502, 2.26975462],\n",
2030 | " [ nan, nan, nan],\n",
2031 | " [ 1.53277921, 1.46935877, 0.15494743]])"
2032 | ]
2033 | },
2034 | "execution_count": 83,
2035 | "metadata": {},
2036 | "output_type": "execute_result"
2037 | }
2038 | ],
2039 | "source": [
2040 | "arr"
2041 | ]
2042 | },
2043 | {
2044 | "cell_type": "code",
2045 | "execution_count": 84,
2046 | "metadata": {},
2047 | "outputs": [],
2048 | "source": [
2049 | "np.savetxt('arr.csv', arr, fmt='%0.2lf', delimiter=',')"
2050 | ]
2051 | },
2052 | {
2053 | "cell_type": "code",
2054 | "execution_count": 85,
2055 | "metadata": {},
2056 | "outputs": [
2057 | {
2058 | "data": {
2059 | "text/plain": [
2060 | "\u001b[1;36m0.86\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m0.74\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2.27\u001b[0m\u001b[1;33m\n",
2061 | "\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m\n",
2062 | "\u001b[0m\u001b[1;36m1.53\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1.47\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.15\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n"
2063 | ]
2064 | },
2065 | "metadata": {},
2066 | "output_type": "display_data"
2067 | }
2068 | ],
2069 | "source": [
2070 | "%pycat arr.csv"
2071 | ]
2072 | },
2073 | {
2074 | "cell_type": "code",
2075 | "execution_count": 86,
2076 | "metadata": {},
2077 | "outputs": [
2078 | {
2079 | "data": {
2080 | "text/plain": [
2081 | "array([[ 0.86, -0.74, 2.27],\n",
2082 | " [ nan, nan, nan],\n",
2083 | " [ 1.53, 1.47, 0.15]])"
2084 | ]
2085 | },
2086 | "execution_count": 86,
2087 | "metadata": {},
2088 | "output_type": "execute_result"
2089 | }
2090 | ],
2091 | "source": [
2092 | "arr_new = np.loadtxt('arr.csv', delimiter=',')\n",
2093 | "arr_new"
2094 | ]
2095 | },
2096 | {
2097 | "cell_type": "markdown",
2098 | "metadata": {},
2099 | "source": [
2100 | "## File operations with Binary files"
2101 | ]
2102 | },
2103 | {
2104 | "cell_type": "code",
2105 | "execution_count": 87,
2106 | "metadata": {},
2107 | "outputs": [],
2108 | "source": [
2109 | "np.save('arr', arr)"
2110 | ]
2111 | },
2112 | {
2113 | "cell_type": "code",
2114 | "execution_count": 88,
2115 | "metadata": {},
2116 | "outputs": [
2117 | {
2118 | "data": {
2119 | "text/plain": [
2120 | "array([[ 0.8644362 , -0.74216502, 2.26975462],\n",
2121 | " [ nan, nan, nan],\n",
2122 | " [ 1.53277921, 1.46935877, 0.15494743]])"
2123 | ]
2124 | },
2125 | "execution_count": 88,
2126 | "metadata": {},
2127 | "output_type": "execute_result"
2128 | }
2129 | ],
2130 | "source": [
2131 | "arr_new = np.load('arr.npy')\n",
2132 | "arr_new"
2133 | ]
2134 | },
2135 | {
2136 | "cell_type": "code",
2137 | "execution_count": 89,
2138 | "metadata": {},
2139 | "outputs": [
2140 | {
2141 | "data": {
2142 | "text/plain": [
2143 | "array([[ True, True, True],\n",
2144 | " [False, False, False],\n",
2145 | " [ True, True, True]])"
2146 | ]
2147 | },
2148 | "execution_count": 89,
2149 | "metadata": {},
2150 | "output_type": "execute_result"
2151 | }
2152 | ],
2153 | "source": [
2154 | "arr == arr_new"
2155 | ]
2156 | },
2157 | {
2158 | "cell_type": "code",
2159 | "execution_count": null,
2160 | "metadata": {},
2161 | "outputs": [],
2162 | "source": []
2163 | }
2164 | ],
2165 | "metadata": {
2166 | "kernelspec": {
2167 | "display_name": "Python 3",
2168 | "language": "python",
2169 | "name": "python3"
2170 | },
2171 | "language_info": {
2172 | "codemirror_mode": {
2173 | "name": "ipython",
2174 | "version": 3
2175 | },
2176 | "file_extension": ".py",
2177 | "mimetype": "text/x-python",
2178 | "name": "python",
2179 | "nbconvert_exporter": "python",
2180 | "pygments_lexer": "ipython3",
2181 | "version": "3.6.13"
2182 | }
2183 | },
2184 | "nbformat": 4,
2185 | "nbformat_minor": 4
2186 | }
2187 |
--------------------------------------------------------------------------------
/Chapter04/df.csv:
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1 | ,A,B,C,D,E
2 | 9,0.5936127527039693,-0.542363581313287,-1.719672378632265,-0.5789087862396088,1.4269485506363453
3 | 10,0.2769969050973699,0.7896671305447348,0.32207411358372423,0.7003923770036988,0.3887166323316587
4 | 27,-0.04126386067735677,0.29588432121730995,-0.42527998805117745,1.7276391201667975,-0.8683525728302848
5 | 39,-0.8209775777189621,-1.0997420126347677,0.08672982893439583,0.4562870103794832,0.43103330308489607
6 | 44,2.072573534652599,-0.5377851185632266,-1.3784300958152218,-0.4924042501649364,2.3277381091718357
7 | 45,1.8044039661642386,-0.24942132634091757,-0.8208638292651728,-1.4933399582170666,0.524175945925593
8 | 61,0.34511317001597924,0.7243746800286961,-2.0403808422422274,-1.0797780988310783,-0.6934244111617518
9 | 71,-2.338046150957248,1.6622623438878599,-0.27535274222162914,-0.7465704488243654,1.2251034612955707
10 | 85,-0.1104324589602783,-1.2734893853043774,0.031786151729383684,0.4623583343156185,0.35354835026082904
11 | 97,-1.0619793207360517,-0.2613546728103239,-1.1713985180433313,-1.857087438077901,-0.14481625312704066
12 |
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/Chapter04/df.json:
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1 | {"A":{"9":0.5936127527,"10":0.2769969051,"27":-0.0412638607,"39":-0.8209775777},"B":{"9":-0.5423635813,"10":0.7896671305,"27":0.2958843212,"39":-1.0997420126},"C":{"9":-1.7196723786,"10":0.3220741136,"27":-0.4252799881,"39":0.0867298289},"D":{"9":-0.5789087862,"10":0.700392377,"27":1.7276391202,"39":0.4562870104},"E":{"9":1.4269485506,"10":0.3887166323,"27":-0.8683525728,"39":0.4310333031}}
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/Chapter05/fig.png:
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https://raw.githubusercontent.com/PacktPublishing/Hands-On-Financial-Trading-with-Python/57d12a947b6bb1bcce8af5ddcf95c9775602a9fb/Chapter05/fig.png
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/Chapter07/cache.sqlite:
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https://raw.githubusercontent.com/PacktPublishing/Hands-On-Financial-Trading-with-Python/57d12a947b6bb1bcce8af5ddcf95c9775602a9fb/Chapter07/cache.sqlite
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/Chapter08/quandl_eod.py:
--------------------------------------------------------------------------------
1 | """
2 | Module for building a complete daily dataset from Quandl's EOD dataset.
3 | Source Code Inspiration: https://github.com/quantopian/zipline/issues/2603
4 | """
5 | from io import BytesIO
6 | import tarfile
7 | from zipfile import ZipFile
8 |
9 | from click import progressbar
10 | from logbook import Logger
11 | import pandas as pd
12 | import requests
13 | from six.moves.urllib.parse import urlencode
14 | from six import iteritems
15 | from trading_calendars import register_calendar_alias
16 |
17 | from . import core as bundles
18 | import numpy as np
19 |
20 | log = Logger(__name__)
21 |
22 | ONE_MEGABYTE = 1024 * 1024
23 | QUANDL_DATA_URL = "https://www.quandl.com/api/v3/databases/EOD/data?"
24 |
25 |
26 | def format_metadata_url(api_key):
27 | """ Build the query URL for Quandl WIKI Prices metadata.
28 | """
29 | query_params = [("api_key", api_key), ("download_type", "full")]
30 |
31 | return QUANDL_DATA_URL + urlencode(query_params)
32 |
33 |
34 | def load_data_table(file, index_col, show_progress=False):
35 | """ Load data table from zip file provided by Quandl.
36 | """
37 | with ZipFile(file) as zip_file:
38 | file_names = zip_file.namelist()
39 | assert len(file_names) == 1, "Expected a single file from Quandl."
40 | eod_prices = file_names.pop()
41 | with zip_file.open(eod_prices) as table_file:
42 | if show_progress:
43 | log.info("Parsing raw data.")
44 | data_table = pd.read_csv(
45 | table_file,
46 | header=None,
47 | names=[
48 | "symbol",
49 | "date",
50 | "open",
51 | "high",
52 | "low",
53 | "close",
54 | "volume",
55 | "ex_dividend",
56 | "split_ratio",
57 | "adjusted_open",
58 | "adjusted_high",
59 | "adjusted_low",
60 | "adjusted_close",
61 | "adjusted_volume",
62 | ],
63 | parse_dates=["date"],
64 | index_col=index_col,
65 | usecols=[
66 | "symbol",
67 | "date",
68 | "open",
69 | "high",
70 | "low",
71 | "close",
72 | "volume",
73 | "ex_dividend",
74 | "split_ratio",
75 | ],
76 | )
77 |
78 | return data_table
79 |
80 |
81 | def fetch_data_table(api_key, show_progress, retries):
82 | """ Fetch WIKI Prices data table from Quandl
83 | """
84 | for _ in range(retries):
85 | try:
86 | if show_progress:
87 | log.info("Downloading WIKI metadata.")
88 |
89 | # Extract link from metadata and download zip file.
90 | table_url = format_metadata_url(api_key)
91 | if show_progress:
92 | raw_file = download_with_progress(
93 | table_url,
94 | chunk_size=ONE_MEGABYTE,
95 | label="Downloading WIKI Prices table from Quandl",
96 | )
97 | else:
98 | raw_file = download_without_progress(table_url)
99 |
100 | return load_data_table(
101 | file=raw_file, index_col=None, show_progress=show_progress
102 | )
103 |
104 | except Exception:
105 | log.exception("Exception raised reading Quandl data. Retrying.")
106 |
107 | else:
108 | raise ValueError(
109 | "Failed to download Quandl data after %d attempts." % (retries)
110 | )
111 |
112 |
113 | def gen_asset_metadata(data, show_progress):
114 | if show_progress:
115 | log.info("Generating asset metadata.")
116 |
117 | data = data.groupby(by="symbol").agg({"date": [np.min, np.max]})
118 | data.reset_index(inplace=True)
119 | data["start_date"] = data.date.amin
120 | data["end_date"] = data.date.amax
121 | del data["date"]
122 | data.columns = data.columns.get_level_values(0)
123 |
124 | data["exchange"] = "QUANDL-EOD"
125 | data["auto_close_date"] = data["end_date"].values + pd.Timedelta(days=1)
126 | return data
127 |
128 |
129 | def parse_splits(data, show_progress):
130 | if show_progress:
131 | log.info("Parsing split data.")
132 |
133 | data["split_ratio"] = 1.0 / data.split_ratio
134 | data.rename(
135 | columns={"split_ratio": "ratio", "date": "effective_date"},
136 | inplace=True,
137 | copy=False,
138 | )
139 | return data
140 |
141 |
142 | def parse_dividends(data, show_progress):
143 | if show_progress:
144 | log.info("Parsing dividend data.")
145 |
146 | data["record_date"] = data["declared_date"] = data["pay_date"] = pd.NaT
147 | data.rename(
148 | columns={"ex_dividend": "amount", "date": "ex_date"}, inplace=True, copy=False
149 | )
150 | return data
151 |
152 |
153 | def parse_pricing_and_vol(data, sessions, symbol_map):
154 | for asset_id, symbol in iteritems(symbol_map):
155 | asset_data = (
156 | data.xs(symbol, level=1).reindex(sessions.tz_localize(None)).fillna(0.0)
157 | )
158 | yield asset_id, asset_data
159 |
160 |
161 | @bundles.register("quandl_eod")
162 | def quandl_eod_bundle(environ,
163 | asset_db_writer,
164 | minute_bar_writer,
165 | daily_bar_writer,
166 | adjustment_writer,
167 | calendar,
168 | start_session,
169 | end_session,
170 | cache,
171 | show_progress,
172 | output_dir):
173 | """
174 | quandl_bundle builds a daily dataset using Quandl's WIKI Prices dataset.
175 |
176 | For more information on Quandl's API and how to obtain an API key,
177 | please visit https://docs.quandl.com/docs#section-authentication
178 | """
179 | api_key = environ.get("QUANDL_API_KEY")
180 | if api_key is None:
181 | raise ValueError(
182 | "Please set your QUANDL_API_KEY environment variable and retry."
183 | )
184 |
185 | raw_data = fetch_data_table(
186 | api_key, show_progress, environ.get("QUANDL_DOWNLOAD_ATTEMPTS", 5)
187 | )
188 | asset_metadata = gen_asset_metadata(raw_data[["symbol", "date"]], show_progress)
189 | asset_db_writer.write(asset_metadata)
190 |
191 | symbol_map = asset_metadata.symbol
192 | sessions = calendar.sessions_in_range(start_session, end_session)
193 |
194 | raw_data.set_index(["date", "symbol"], inplace=True)
195 | daily_bar_writer.write(
196 | parse_pricing_and_vol(raw_data, sessions, symbol_map),
197 | show_progress=show_progress,
198 | )
199 |
200 | raw_data.reset_index(inplace=True)
201 | raw_data["symbol"] = raw_data["symbol"].astype("category")
202 | raw_data["sid"] = raw_data.symbol.cat.codes
203 | adjustment_writer.write(
204 | splits=parse_splits(
205 | raw_data[["sid", "date", "split_ratio"]].loc[raw_data.split_ratio != 1],
206 | show_progress=show_progress,
207 | ),
208 | dividends=parse_dividends(
209 | raw_data[["sid", "date", "ex_dividend"]].loc[raw_data.ex_dividend != 0],
210 | show_progress=show_progress,
211 | ),
212 | )
213 |
214 |
215 | def download_with_progress(url, chunk_size, **progress_kwargs):
216 | """
217 | Download streaming data from a URL, printing progress information to the
218 | terminal.
219 |
220 | Parameters
221 | ----------
222 | url : str
223 | A URL that can be understood by ``requests.get``.
224 | chunk_size : int
225 | Number of bytes to read at a time from requests.
226 | **progress_kwargs
227 | Forwarded to click.progressbar.
228 |
229 | Returns
230 | -------
231 | data : BytesIO
232 | A BytesIO containing the downloaded data.
233 | """
234 | resp = requests.get(url, stream=True)
235 | resp.raise_for_status()
236 |
237 | total_size = int(resp.headers["content-length"])
238 | data = BytesIO()
239 | with progressbar(length=total_size, **progress_kwargs) as pbar:
240 | for chunk in resp.iter_content(chunk_size=chunk_size):
241 | data.write(chunk)
242 | pbar.update(len(chunk))
243 |
244 | data.seek(0)
245 | return data
246 |
247 |
248 | def download_without_progress(url):
249 | """
250 | Download data from a URL, returning a BytesIO containing the loaded data.
251 |
252 | Parameters
253 | ----------
254 | url : str
255 | A URL that can be understood by ``requests.get``.
256 |
257 | Returns
258 | -------
259 | data : BytesIO
260 | A BytesIO containing the downloaded data.
261 | """
262 | resp = requests.get(url)
263 | resp.raise_for_status()
264 | return BytesIO(resp.content)
265 |
266 |
267 | register_calendar_alias("QUANDL-EOD", "NYSE")
--------------------------------------------------------------------------------
/Chapter08/zipline_bundles-master.zip:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/PacktPublishing/Hands-On-Financial-Trading-with-Python/57d12a947b6bb1bcce8af5ddcf95c9775602a9fb/Chapter08/zipline_bundles-master.zip
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2021 Packt
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 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | 
2 |
3 | ## Machine Learning Summit 2025
4 | **Bridging Theory and Practice: ML Solutions for Today’s Challenges**
5 |
6 | 3 days, 20+ experts, and 25+ tech sessions and talks covering critical aspects of:
7 | - **Agentic and Generative AI**
8 | - **Applied Machine Learning in the Real World**
9 | - **ML Engineering and Optimization**
10 |
11 | 👉 [Book your ticket now >>](https://packt.link/mlsumgh)
12 |
13 | ---
14 |
15 | ## Join Our Newsletters 📬
16 |
17 | ### DataPro
18 | *The future of AI is unfolding. Don’t fall behind.*
19 |
20 | 
21 |
22 | Stay ahead with [**DataPro**](https://landing.packtpub.com/subscribe-datapronewsletter/?link_from_packtlink=yes), the free weekly newsletter for data scientists, AI/ML researchers, and data engineers.
23 | From trending tools like **PyTorch**, **scikit-learn**, **XGBoost**, and **BentoML** to hands-on insights on **database optimization** and real-world **ML workflows**, you’ll get what matters, fast.
24 |
25 | > Stay sharp with [DataPro](https://landing.packtpub.com/subscribe-datapronewsletter/?link_from_packtlink=yes). Join **115K+ data professionals** who never miss a beat.
26 |
27 | ---
28 |
29 | ### BIPro
30 | *Business runs on data. Make sure yours tells the right story.*
31 |
32 | 
33 |
34 | [**BIPro**](https://landing.packtpub.com/subscribe-bipro-newsletter/?link_from_packtlink=yes) is your free weekly newsletter for BI professionals, analysts, and data leaders.
35 | Get practical tips on **dashboarding**, **data visualization**, and **analytics strategy** with tools like **Power BI**, **Tableau**, **Looker**, **SQL**, and **dbt**.
36 |
37 | > Get smarter with [BIPro](https://landing.packtpub.com/subscribe-bipro-newsletter/?link_from_packtlink=yes). Trusted by **35K+ BI professionals**, see what you’re missing.
38 |
39 |
40 |
41 |
42 | # Hands-On Financial Trading with Python
43 |
44 | 
45 |
46 | This is the code repository for [Hands-On Financial Trading with Python](https://www.packtpub.com/product/hands-on-financial-trading-with-python/9781838982881?utm_source=github&utm_medium=repository&utm_campaign=9781838982881), published by Packt.
47 |
48 | **A practical guide to using Zipline and other Python libraries for backtesting trading strategies**
49 |
50 | ## What is this book about?
51 | Algorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses.
52 |
53 | The book starts by introducing you to algorithmic trading and explaining why Python is the best platform for developing trading strategies. You’ll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. You’ll also focus on time series forecasting, covering pmdarima and Facebook Prophet.
54 |
55 | By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization.
56 |
57 | This book covers the following exciting features:
58 | * Discover how quantitative analysis works by covering financial statistics and ARIMA
59 | * Use core Python libraries to perform quantitative research and strategy development using real datasets
60 | * Understand how to access financial and economic data in Python
61 | * Implement effective data visualization with Matplotlib
62 | * Apply scientific computing and data visualization with popular Python libraries
63 | * Build and deploy backtesting algorithmic trading strategies
64 |
65 | If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1838982884) today!
66 |
67 | 
68 |
69 | ## Instructions and Navigations
70 | All of the code is organized into folders.
71 |
72 | The code will look like the following:
73 | ```
74 | fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, figsize=(12, 12), sharex=True)
75 |
76 | x = np.linspace(0, 10, num=20)
77 | y1 = np.exp(x)
78 | y2 = x ** 3
79 | y3 = np.sin(y2)
80 | y4 = np.random.randn(20)
81 |
82 | ax1.plot(x, y1, color='black', linestyle='--', linewidth=5, marker='x', markersize=15)
83 | ax2.plot(x, y2, color='green', linestyle='-.', linewidth=2, marker='^', markersize=10, alpha=0.9)
84 | ax3.plot(x, y3, color='red', linestyle=':', marker='*', markersize=15, drawstyle='steps')
85 | ax4.plot(x, y4, color='green', linestyle='-', marker='s', markersize=15)
86 |
87 | ```
88 |
89 | **Following is what you need for this book:**
90 | This book is for data analysts and financial traders who want to explore how to design algorithmic trading strategies using Python’s core libraries. If you are looking for a practical guide to backtesting algorithmic trading strategies and building your own strategies, then this book is for you. Beginner-level working knowledge of Python programming and statistics will be helpful.
91 |
92 | With the following software and hardware list you can run all code files present in the book (Chapter 1-9).
93 |
94 | ### Software and Hardware List
95 |
96 | | Chapter | Software required | OS required |
97 | | -------- | -------------------------------------------------------------------------------------| -----------------------------------|
98 | | 1 - 9 | Anaconda, Python 3.6, JupyterLab | Windows, Mac OS X, and Linux (Any) |
99 | | | List of packages and dependencies can be found in the environment.yml file | |
100 |
101 | We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://static.packt-cdn.com/downloads/9781838982881_ColorImages.pdf).
102 |
103 |
104 | ### Related products
105 | * Python Algorithmic Trading Cookbook [[Packt]](https://www.packtpub.com/product/python-algorithmic-trading-cookbook/9781838989354) [[Amazon]](https://www.amazon.com/dp/1838989358)
106 |
107 | * Python for Finance Cookbook [[Packt]](https://www.packtpub.com/product/python-for-finance-cookbook/9781789618518) [[Amazon]](https://www.amazon.com/dp/1789618517)
108 |
109 | ## Get to Know the Author
110 | **Jiri Pik** is an artificial intelligence architect and strategist who works with major investment banks, hedge funds, and other players. He has architected and delivered breakthrough trading, portfolio, and risk management systems, as well as decision support systems, across numerous industries. His consulting firm, Jiri Pik—RocketEdge, provides its clients with certified expertise, judgment, and execution at lightspeed.
111 |
112 | **Sourav Ghosh** has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low-latency, highthroughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making and pairs trading strategies for the most liquid global futures contracts. He works as a senior quantitative developer at a trading firm in Chicago. He holds a master's in computer science from the University of Southern California. His areas of interest include computer architecture, FinTech, probability theory and stochastic processes, statistical learning and inference methods, and natural language processing
113 |
114 | ### Download a free PDF
115 |
116 | If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
117 | https://packt.link/free-ebook/9781838982881
--------------------------------------------------------------------------------
/environment.yml:
--------------------------------------------------------------------------------
1 | channels:
2 | - conda-forge
3 | - intel
4 | - defaults
5 | dependencies:
6 | - python=3.6
7 | - pip
8 | - arviz=0.11.2=pyhd8ed1ab_0
9 | - bcolz=1.2.1=py36he350917_1001
10 | - blosc=1.21.0=h0e60522_0
11 | - bokeh=0.13.0=py36_1000
12 | - bottleneck=1.3.2=py36h6434af4_3
13 | - brotlipy=0.7.0=py36h68aa20f_1001
14 | - bzip2=1.0.8=vc14h5832a3a_5
15 | - ca-certificates=2020.12.5=h5b45459_0
16 | - cached-property=1.5.2=hd8ed1ab_1
17 | - cached_property=1.5.2=pyha770c72_1
18 | - certifi=2020.12.5=py36ha15d459_1
19 | - cffi=1.14.5=py36he58ceb7_0
20 | - cftime=1.4.1=py36h6434af4_0
21 | - chardet=4.0.0=py36ha15d459_1
22 | - click=7.1.2=pyh9f0ad1d_0
23 | - cloudpickle=1.6.0=py_0
24 | - contextvars=2.4=py_0
25 | - convertdate=2.3.2=pyhd8ed1ab_0
26 | - curl=7.71.1=h2a8f88b_1
27 | - cycler=0.10.0=py_2
28 | - cytoolz=0.11.0=py36h68aa20f_3
29 | - dask==1.2.2
30 | - decorator=4.4.2=py_0
31 | - empyrical=0.5.5=pyh9f0ad1d_0
32 | - ephem=3.7.7.1=py36h779f372_1
33 | - fbprophet=0.6=py36h6538335_0
34 | - freetype=2.10.2=vc14he233217_1
35 | - flask==1.1.2
36 | - flask-compress==1.8.0
37 | - greenlet=1.0.0=py36he2d232f_0
38 | - hdf4=4.2.13=h0e5069d_1004
39 | - hdf5=1.10.6=nompi_h5268f04_1114
40 | - heapdict=1.0.1=py_0
41 | - hijri-converter=2.1.1=pyhd8ed1ab_0
42 | - holidays=0.10.5.2=pyhd8ed1ab_0
43 | - idna=2.10=pyh9f0ad1d_0
44 | - intel-openmp=2020.3=h57928b3_311
45 | - intelpython=2021.1.1=1
46 | - intervaltree=3.0.2=py_0
47 | - iso3166=1.0.1=pyh9f0ad1d_0
48 | - iso4217=1.6.20180829=py_0
49 | - jpeg=9d=h8ffe710_0
50 | - kiwisolver=1.3.1=py36he95197e_1
51 | - korean_lunar_calendar=0.2.1=pyh9f0ad1d_0
52 | - krb5=1.18.2=hc04afaa_0
53 | - libblas=3.9.0=8_mkl
54 | - libcblas=3.9.0=8_mkl
55 | - libcurl=7.71.1=h2a8f88b_1
56 | - libiconv=1.16=he774522_0
57 | - liblapack=3.9.0=8_mkl
58 | - libnetcdf=4.7.4=nompi_h3a9aa94_107
59 | - libpng=1.6.37=vc14he233217_7
60 | - libpython=2.0=py36ha15d459_1
61 | - libssh2=1.9.0=h680486a_6
62 | - libxml2=2.9.10=hf5bbc77_3
63 | - libxslt=1.1.33=h65864e5_2
64 | - locket=0.2.0=py_2
65 | - logbook=1.5.3=py36h68aa20f_4
66 | - lru-dict=1.1.6=py36h68aa20f_3
67 | - lunarcalendar=0.0.9=py_0
68 | - lxml=4.6.2=py36hf8cb5f9_1
69 | - m2w64-binutils=2.25.1=5
70 | - m2w64-bzip2=1.0.6=6
71 | - m2w64-crt-git=5.0.0.4636.2595836=2
72 | - m2w64-gcc=5.3.0=6
73 | - m2w64-gcc-ada=5.3.0=6
74 | - m2w64-gcc-fortran=5.3.0=6
75 | - m2w64-gcc-libgfortran=5.3.0=6
76 | - m2w64-gcc-libs=5.3.0=7
77 | - m2w64-gcc-libs-core=5.3.0=7
78 | - m2w64-gcc-objc=5.3.0=6
79 | - m2w64-gmp=6.1.0=2
80 | - m2w64-headers-git=5.0.0.4636.c0ad18a=2
81 | - m2w64-isl=0.16.1=2
82 | - m2w64-libiconv=1.14=6
83 | - m2w64-libmangle-git=5.0.0.4509.2e5a9a2=2
84 | - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
85 | - m2w64-make=4.1.2351.a80a8b8=2
86 | - m2w64-mpc=1.0.3=3
87 | - m2w64-mpfr=3.1.4=4
88 | - m2w64-pkg-config=0.29.1=2
89 | - m2w64-toolchain=5.3.0=7
90 | - m2w64-toolchain_win-64=2.4.0=0
91 | - m2w64-tools-git=5.0.0.4592.90b8472=2
92 | - m2w64-windows-default-manifest=6.4=3
93 | - m2w64-winpthreads-git=5.0.0.4634.697f757=2
94 | - m2w64-zlib=1.2.8=10
95 | - markupsafe=1.1.1=py36h68aa20f_3
96 | - mkl=2020.4=hb70f87d_311
97 | - mock=4.0.3=py36ha15d459_1
98 | - msys2-conda-epoch=20160418=1
99 | - multipledispatch=0.6.0=py_0
100 | - netcdf4=1.5.6=nompi_py36h694ea1f_100
101 | - networkx=1.11=py36_0
102 | - openssl=1.1.1j=h8ffe710_0
103 | - pandas=0.22.0=py36_1
104 | - partd=1.1.0=py_0
105 | - patsy=0.5.1=py_0
106 | - psutil=5.8.0=py36h68aa20f_1
107 | - pycparser=2.20=pyh9f0ad1d_2
108 | - pymeeus=0.5.10=pyhd8ed1ab_0
109 | - pyopenssl=20.0.1=pyhd8ed1ab_0
110 | - pyparsing=2.4.7=pyh9f0ad1d_0
111 | - pyreadline=2.1=py36ha15d459_1003
112 | - pysocks=1.7.1=py36ha15d459_3
113 | - pystan=2.19.1.1=py36h12199cf_1
114 | - python=3.6.13=h39d44d4_0_cpython
115 | - python-dateutil=2.8.1=py_0
116 | - python-editor=1.0.4=py_0
117 | - python-interface=1.6.0=py_0
118 | - python_abi=3.6=1_cp36m
119 | - requests=2.25.1=pyhd3deb0d_0
120 | - scipy=1.5.3=py36h7ff6e69_0
121 | - setuptools=41.0.1=py36_0
122 | - six=1.15.0=pyh9f0ad1d_0
123 | - sortedcontainers=2.3.0=pyhd8ed1ab_0
124 | - statsmodels=0.11.1=py36h779f372_2
125 | - tblib=1.6.0=py_0
126 | - toolz=0.11.1=py_0
127 | - tornado=6.1=py36h68aa20f_1
128 | - tqdm=4.59.0=pyhd8ed1ab_0
129 | - typing_extensions=3.7.4.3=py_0
130 | - vc=14.1=h869be7e_15
131 | - vs2015_runtime=14.16.27012=hf0eaf9b_15
132 | - wheel=0.31.0=py36_3
133 | - win_inet_pton=1.1.0=py36ha15d459_2
134 | - wincertstore=0.2=py36_3
135 | - xarray=0.13.0=py_0
136 | - yaml=0.2.5=he774522_0
137 | - zict=2.0.0=py_0
138 | - zipline=1.4.1=py36he774522_0
139 | - zipp=3.4.1=pyhd8ed1ab_0
140 | - zlib=1.2.11.1=vc14hfa6e2cd_1
141 | - pip:
142 | - anyio==2.0.2
143 | - appdirs==1.4.4
144 | - argon2-cffi==20.1.0
145 | - async-generator==1.10
146 | - attrs==20.3.0
147 | - babel==2.9.0
148 | - backcall==0.2.0
149 | - beautifulsoup4==4.9.3
150 | - bleach==3.2.1
151 | - brotli==1.0.9
152 | - bs4==0.0.1
153 | - colorama==0.4.4
154 | - dacite==1.6.0
155 | - dash==1.18.1
156 | - dash-bootstrap-components==0.11.1
157 | - dash-colorscales==0.0.4
158 | - dash-core-components==1.14.1
159 | - dash-daq==0.5.0
160 | - dash-html-components==1.1.1
161 | - dash-renderer==1.8.3
162 | - dash-table==4.11.1
163 | - dataclasses==0.8
164 | - defusedxml==0.6.0
165 | - distributed==1.28.1
166 | - dtale==1.30.0
167 | - entrypoints==0.3
168 | - future==0.18.2
169 | - html5lib==1.1
170 | - iexfinance==0.5.0
171 | - inflection==0.5.1
172 | - int-date==0.1.8
173 | - ipykernel==5.4.2
174 | - ipympl==0.6.2
175 | - ipython==7.16.1
176 | - ipython-genutils==0.2.0
177 | - ipywidgets==7.6.3
178 | - itsdangerous==1.1.0
179 | - jedi==0.18.0
180 | - joblib==1.0.0
181 | - json5==0.9.5
182 | - jsonschema==3.2.0
183 | - jupyter==1.0.0
184 | - jupyter-client==6.1.11
185 | - jupyter-console==6.2.0
186 | - jupyter-contrib-core==0.3.3
187 | - jupyter-contrib-nbextensions==0.5.1
188 | - jupyter-core==4.7.0
189 | - jupyter-highlight-selected-word==0.2.0
190 | - jupyter-latex-envs==1.4.6
191 | - jupyter-nbextensions-configurator==0.4.1
192 | - jupyter-server==1.2.1
193 | - jupyterlab==3.0.3
194 | - jupyterlab-pygments==0.1.2
195 | - jupyterlab-server==2.1.1
196 | - jupyterlab-widgets==1.0.0
197 | - jupyterthemes==0.20.0
198 | - kaleido==0.1.0.post1
199 | - lesscpy==0.14.0
200 | - lz4==3.1.1
201 | - mistune==0.8.4
202 | - more-itertools==8.6.0
203 | - mpld3==0.5.2
204 | - msgpack==0.6.2
205 | - multitasking==0.0.9
206 | - nbclassic==0.2.6
207 | - nbclient==0.5.1
208 | - nbconvert==6.0.7
209 | - nbformat==5.0.8
210 | - nest-asyncio==1.4.3
211 | - notebook==6.1.6
212 | - pandocfilters==1.4.3
213 | - parso==0.8.1
214 | - pickleshare==0.7.5
215 | - pillow==8.0.1
216 | - plotly==4.14.1
217 | - ply==3.11
218 | - pmdarima==1.8.0
219 | - ppscore==1.1.0
220 | - prometheus-client==0.9.0
221 | - prompt-toolkit==3.0.10
222 | - pyfolio==0.9.2
223 | - pygments==2.7.3
224 | - pynput==1.7.2
225 | - pyportfolioopt==1.4.1
226 | - pyqt5==5.15.2
227 | - pyqt5-sip==12.8.1
228 | - pyqtwebengine==5.15.2
229 | - pyrsistent==0.17.3
230 | - pywin32==300
231 | - pywinpty==0.5.7
232 | - pyzmq==20.0.0
233 | - qgrid==1.3.1
234 | - qtconsole==5.0.1
235 | - qtpy==1.9.0
236 | - quandl==3.5.3
237 | - requests-cache==0.5.2
238 | - retrying==1.3.3
239 | - scikit-learn==0.24.0
240 | - seaborn==0.10.1
241 | - send2trash==1.5.0
242 | - sniffio==1.2.0
243 | - soupsieve==2.1
244 | - squarify==0.4.3
245 | - stockstats==0.3.2
246 | - strsimpy==0.2.0
247 | - terminado==0.9.2
248 | - testpath==0.4.4
249 | - threadpoolctl==2.1.0
250 | - traitlets==4.3.3
251 | - wcwidth==0.2.5
252 | - webencodings==0.5.1
253 | - werkzeug==1.0.1
254 | - widgetsnbextension==3.5.1
255 | - wrapt==1.12.1
256 | - yahoofinancials==1.6
257 |
--------------------------------------------------------------------------------