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1
+ {
2
+ "nbformat": 4,
3
+ "nbformat_minor": 0,
4
+ "metadata": {
5
+ "colab": {
6
+ "provenance": []
7
+ },
8
+ "kernelspec": {
9
+ "name": "python3",
10
+ "display_name": "Python 3"
11
+ },
12
+ "language_info": {
13
+ "name": "python"
14
+ }
15
+ },
16
+ "cells": [
17
+ {
18
+ "cell_type": "code",
19
+ "source": [
20
+ "import yfinance\n",
21
+ "\n",
22
+ "stocks = ['ARM', 'META', 'SPY', 'TSLA']\n",
23
+ "\n",
24
+ "data = yfinance.download(stocks,\n",
25
+ " '2024-04-01',\n",
26
+ " '2024-05-09')['Close']"
27
+ ],
28
+ "metadata": {
29
+ "colab": {
30
+ "base_uri": "https://localhost:8080/"
31
+ },
32
+ "id": "mfJAaMYDQPfy",
33
+ "outputId": "ae6d7974-6068-40fa-ed90-c2c6dbc9cec0"
34
+ },
35
+ "execution_count": null,
36
+ "outputs": [
37
+ {
38
+ "output_type": "stream",
39
+ "name": "stderr",
40
+ "text": [
41
+ "[*********************100%%**********************] 4 of 4 completed\n"
42
+ ]
43
+ }
44
+ ]
45
+ },
46
+ {
47
+ "cell_type": "code",
48
+ "source": [
49
+ "data.head()"
50
+ ],
51
+ "metadata": {
52
+ "colab": {
53
+ "base_uri": "https://localhost:8080/",
54
+ "height": 238
55
+ },
56
+ "id": "nNuUrUiIQqRA",
57
+ "outputId": "92863eca-deef-4765-ca5b-74ddf230f3ef"
58
+ },
59
+ "execution_count": null,
60
+ "outputs": [
61
+ {
62
+ "output_type": "execute_result",
63
+ "data": {
64
+ "text/plain": [
65
+ "Ticker ARM META SPY TSLA\n",
66
+ "Date \n",
67
+ "2024-04-01 126.910004 491.350006 522.159973 175.220001\n",
68
+ "2024-04-02 124.279999 497.369995 518.840027 166.630005\n",
69
+ "2024-04-03 125.309998 506.739990 519.409973 168.380005\n",
70
+ "2024-04-04 122.010002 510.920013 513.070007 171.110001\n",
71
+ "2024-04-05 124.820000 527.340027 518.429993 164.899994"
72
+ ],
73
+ "text/html": [
74
+ "\n",
75
+ " <div id=\"df-0e5eb836-574f-4203-bb84-939eec9bd41e\" class=\"colab-df-container\">\n",
76
+ " <div>\n",
77
+ "<style scoped>\n",
78
+ " .dataframe tbody tr th:only-of-type {\n",
79
+ " vertical-align: middle;\n",
80
+ " }\n",
81
+ "\n",
82
+ " .dataframe tbody tr th {\n",
83
+ " vertical-align: top;\n",
84
+ " }\n",
85
+ "\n",
86
+ " .dataframe thead th {\n",
87
+ " text-align: right;\n",
88
+ " }\n",
89
+ "</style>\n",
90
+ "<table border=\"1\" class=\"dataframe\">\n",
91
+ " <thead>\n",
92
+ " <tr style=\"text-align: right;\">\n",
93
+ " <th>Ticker</th>\n",
94
+ " <th>ARM</th>\n",
95
+ " <th>META</th>\n",
96
+ " <th>SPY</th>\n",
97
+ " <th>TSLA</th>\n",
98
+ " </tr>\n",
99
+ " <tr>\n",
100
+ " <th>Date</th>\n",
101
+ " <th></th>\n",
102
+ " <th></th>\n",
103
+ " <th></th>\n",
104
+ " <th></th>\n",
105
+ " </tr>\n",
106
+ " </thead>\n",
107
+ " <tbody>\n",
108
+ " <tr>\n",
109
+ " <th>2024-04-01</th>\n",
110
+ " <td>126.910004</td>\n",
111
+ " <td>491.350006</td>\n",
112
+ " <td>522.159973</td>\n",
113
+ " <td>175.220001</td>\n",
114
+ " </tr>\n",
115
+ " <tr>\n",
116
+ " <th>2024-04-02</th>\n",
117
+ " <td>124.279999</td>\n",
118
+ " <td>497.369995</td>\n",
119
+ " <td>518.840027</td>\n",
120
+ " <td>166.630005</td>\n",
121
+ " </tr>\n",
122
+ " <tr>\n",
123
+ " <th>2024-04-03</th>\n",
124
+ " <td>125.309998</td>\n",
125
+ " <td>506.739990</td>\n",
126
+ " <td>519.409973</td>\n",
127
+ " <td>168.380005</td>\n",
128
+ " </tr>\n",
129
+ " <tr>\n",
130
+ " <th>2024-04-04</th>\n",
131
+ " <td>122.010002</td>\n",
132
+ " <td>510.920013</td>\n",
133
+ " <td>513.070007</td>\n",
134
+ " <td>171.110001</td>\n",
135
+ " </tr>\n",
136
+ " <tr>\n",
137
+ " <th>2024-04-05</th>\n",
138
+ " <td>124.820000</td>\n",
139
+ " <td>527.340027</td>\n",
140
+ " <td>518.429993</td>\n",
141
+ " <td>164.899994</td>\n",
142
+ " </tr>\n",
143
+ " </tbody>\n",
144
+ "</table>\n",
145
+ "</div>\n",
146
+ " <div class=\"colab-df-buttons\">\n",
147
+ "\n",
148
+ " <div class=\"colab-df-container\">\n",
149
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e5eb836-574f-4203-bb84-939eec9bd41e')\"\n",
150
+ " title=\"Convert this dataframe to an interactive table.\"\n",
151
+ " style=\"display:none;\">\n",
152
+ "\n",
153
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
154
+ " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
155
+ " </svg>\n",
156
+ " </button>\n",
157
+ "\n",
158
+ " <style>\n",
159
+ " .colab-df-container {\n",
160
+ " display:flex;\n",
161
+ " gap: 12px;\n",
162
+ " }\n",
163
+ "\n",
164
+ " .colab-df-convert {\n",
165
+ " background-color: #E8F0FE;\n",
166
+ " border: none;\n",
167
+ " border-radius: 50%;\n",
168
+ " cursor: pointer;\n",
169
+ " display: none;\n",
170
+ " fill: #1967D2;\n",
171
+ " height: 32px;\n",
172
+ " padding: 0 0 0 0;\n",
173
+ " width: 32px;\n",
174
+ " }\n",
175
+ "\n",
176
+ " .colab-df-convert:hover {\n",
177
+ " background-color: #E2EBFA;\n",
178
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
179
+ " fill: #174EA6;\n",
180
+ " }\n",
181
+ "\n",
182
+ " .colab-df-buttons div {\n",
183
+ " margin-bottom: 4px;\n",
184
+ " }\n",
185
+ "\n",
186
+ " [theme=dark] .colab-df-convert {\n",
187
+ " background-color: #3B4455;\n",
188
+ " fill: #D2E3FC;\n",
189
+ " }\n",
190
+ "\n",
191
+ " [theme=dark] .colab-df-convert:hover {\n",
192
+ " background-color: #434B5C;\n",
193
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
194
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
195
+ " fill: #FFFFFF;\n",
196
+ " }\n",
197
+ " </style>\n",
198
+ "\n",
199
+ " <script>\n",
200
+ " const buttonEl =\n",
201
+ " document.querySelector('#df-0e5eb836-574f-4203-bb84-939eec9bd41e button.colab-df-convert');\n",
202
+ " buttonEl.style.display =\n",
203
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
204
+ "\n",
205
+ " async function convertToInteractive(key) {\n",
206
+ " const element = document.querySelector('#df-0e5eb836-574f-4203-bb84-939eec9bd41e');\n",
207
+ " const dataTable =\n",
208
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
209
+ " [key], {});\n",
210
+ " if (!dataTable) return;\n",
211
+ "\n",
212
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
213
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
214
+ " + ' to learn more about interactive tables.';\n",
215
+ " element.innerHTML = '';\n",
216
+ " dataTable['output_type'] = 'display_data';\n",
217
+ " await google.colab.output.renderOutput(dataTable, element);\n",
218
+ " const docLink = document.createElement('div');\n",
219
+ " docLink.innerHTML = docLinkHtml;\n",
220
+ " element.appendChild(docLink);\n",
221
+ " }\n",
222
+ " </script>\n",
223
+ " </div>\n",
224
+ "\n",
225
+ "\n",
226
+ "<div id=\"df-2a98e692-2e15-4bf2-b320-373fa67edeb6\">\n",
227
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a98e692-2e15-4bf2-b320-373fa67edeb6')\"\n",
228
+ " title=\"Suggest charts\"\n",
229
+ " style=\"display:none;\">\n",
230
+ "\n",
231
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
232
+ " width=\"24px\">\n",
233
+ " <g>\n",
234
+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
235
+ " </g>\n",
236
+ "</svg>\n",
237
+ " </button>\n",
238
+ "\n",
239
+ "<style>\n",
240
+ " .colab-df-quickchart {\n",
241
+ " --bg-color: #E8F0FE;\n",
242
+ " --fill-color: #1967D2;\n",
243
+ " --hover-bg-color: #E2EBFA;\n",
244
+ " --hover-fill-color: #174EA6;\n",
245
+ " --disabled-fill-color: #AAA;\n",
246
+ " --disabled-bg-color: #DDD;\n",
247
+ " }\n",
248
+ "\n",
249
+ " [theme=dark] .colab-df-quickchart {\n",
250
+ " --bg-color: #3B4455;\n",
251
+ " --fill-color: #D2E3FC;\n",
252
+ " --hover-bg-color: #434B5C;\n",
253
+ " --hover-fill-color: #FFFFFF;\n",
254
+ " --disabled-bg-color: #3B4455;\n",
255
+ " --disabled-fill-color: #666;\n",
256
+ " }\n",
257
+ "\n",
258
+ " .colab-df-quickchart {\n",
259
+ " background-color: var(--bg-color);\n",
260
+ " border: none;\n",
261
+ " border-radius: 50%;\n",
262
+ " cursor: pointer;\n",
263
+ " display: none;\n",
264
+ " fill: var(--fill-color);\n",
265
+ " height: 32px;\n",
266
+ " padding: 0;\n",
267
+ " width: 32px;\n",
268
+ " }\n",
269
+ "\n",
270
+ " .colab-df-quickchart:hover {\n",
271
+ " background-color: var(--hover-bg-color);\n",
272
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
273
+ " fill: var(--button-hover-fill-color);\n",
274
+ " }\n",
275
+ "\n",
276
+ " .colab-df-quickchart-complete:disabled,\n",
277
+ " .colab-df-quickchart-complete:disabled:hover {\n",
278
+ " background-color: var(--disabled-bg-color);\n",
279
+ " fill: var(--disabled-fill-color);\n",
280
+ " box-shadow: none;\n",
281
+ " }\n",
282
+ "\n",
283
+ " .colab-df-spinner {\n",
284
+ " border: 2px solid var(--fill-color);\n",
285
+ " border-color: transparent;\n",
286
+ " border-bottom-color: var(--fill-color);\n",
287
+ " animation:\n",
288
+ " spin 1s steps(1) infinite;\n",
289
+ " }\n",
290
+ "\n",
291
+ " @keyframes spin {\n",
292
+ " 0% {\n",
293
+ " border-color: transparent;\n",
294
+ " border-bottom-color: var(--fill-color);\n",
295
+ " border-left-color: var(--fill-color);\n",
296
+ " }\n",
297
+ " 20% {\n",
298
+ " border-color: transparent;\n",
299
+ " border-left-color: var(--fill-color);\n",
300
+ " border-top-color: var(--fill-color);\n",
301
+ " }\n",
302
+ " 30% {\n",
303
+ " border-color: transparent;\n",
304
+ " border-left-color: var(--fill-color);\n",
305
+ " border-top-color: var(--fill-color);\n",
306
+ " border-right-color: var(--fill-color);\n",
307
+ " }\n",
308
+ " 40% {\n",
309
+ " border-color: transparent;\n",
310
+ " border-right-color: var(--fill-color);\n",
311
+ " border-top-color: var(--fill-color);\n",
312
+ " }\n",
313
+ " 60% {\n",
314
+ " border-color: transparent;\n",
315
+ " border-right-color: var(--fill-color);\n",
316
+ " }\n",
317
+ " 80% {\n",
318
+ " border-color: transparent;\n",
319
+ " border-right-color: var(--fill-color);\n",
320
+ " border-bottom-color: var(--fill-color);\n",
321
+ " }\n",
322
+ " 90% {\n",
323
+ " border-color: transparent;\n",
324
+ " border-bottom-color: var(--fill-color);\n",
325
+ " }\n",
326
+ " }\n",
327
+ "</style>\n",
328
+ "\n",
329
+ " <script>\n",
330
+ " async function quickchart(key) {\n",
331
+ " const quickchartButtonEl =\n",
332
+ " document.querySelector('#' + key + ' button');\n",
333
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
334
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
335
+ " try {\n",
336
+ " const charts = await google.colab.kernel.invokeFunction(\n",
337
+ " 'suggestCharts', [key], {});\n",
338
+ " } catch (error) {\n",
339
+ " console.error('Error during call to suggestCharts:', error);\n",
340
+ " }\n",
341
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
342
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
343
+ " }\n",
344
+ " (() => {\n",
345
+ " let quickchartButtonEl =\n",
346
+ " document.querySelector('#df-2a98e692-2e15-4bf2-b320-373fa67edeb6 button');\n",
347
+ " quickchartButtonEl.style.display =\n",
348
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
349
+ " })();\n",
350
+ " </script>\n",
351
+ "</div>\n",
352
+ "\n",
353
+ " </div>\n",
354
+ " </div>\n"
355
+ ],
356
+ "application/vnd.google.colaboratory.intrinsic+json": {
357
+ "type": "dataframe",
358
+ "variable_name": "data",
359
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762
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772
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779
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+ ],
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+ "application/vnd.google.colaboratory.intrinsic+json": {
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+ "type": "dataframe",
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+ "variable_name": "returns",
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+ "summary": "{\n \"name\": \"returns\",\n \"rows\": 27,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2024-04-02 00:00:00\",\n \"max\": \"2024-05-08 00:00:00\",\n \"num_unique_values\": 27,\n \"samples\": [\n \"2024-04-12 00:00:00\",\n \"2024-04-19 00:00:00\",\n \"2024-04-15 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ARM\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05166065364845084,\n \"min\": -0.16898585624248386,\n \"max\": 0.06789767178781414,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.03660491781455699,\n -0.16898585624248386,\n -0.03174227877866398\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"META\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02753598854622982,\n \"min\": -0.1056129586974417,\n \"max\": 0.03213812924957349,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.021523013659411117,\n -0.04131124147675891,\n -0.022797388258059104\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SPY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0084925113729149,\n \"min\": -0.015841247325688257,\n \"max\": 0.012395322615995186,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.013803077020240084,\n -0.00872835011101658,\n -0.012528127278102708\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"TSLA\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04872259693001104,\n \"min\": -0.05594859487563397,\n \"max\": 0.1530691710339953,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.020332204625772565,\n -0.0192088959161848,\n -0.05594859487563397\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
1005
+ }
1006
+ },
1007
+ "metadata": {},
1008
+ "execution_count": 4
1009
+ }
1010
+ ]
1011
+ },
1012
+ {
1013
+ "cell_type": "code",
1014
+ "source": [
1015
+ "average_daily_returns = returns.mean()\n",
1016
+ "print(average_daily_returns)"
1017
+ ],
1018
+ "metadata": {
1019
+ "colab": {
1020
+ "base_uri": "https://localhost:8080/"
1021
+ },
1022
+ "id": "0LSi1EluSjRA",
1023
+ "outputId": "a851a7e7-3e9b-4d2b-d06d-8f3ea46def2d"
1024
+ },
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+ "execution_count": null,
1026
+ "outputs": [
1027
+ {
1028
+ "output_type": "stream",
1029
+ "name": "stdout",
1030
+ "text": [
1031
+ "Ticker\n",
1032
+ "ARM -0.005259\n",
1033
+ "META -0.001060\n",
1034
+ "SPY -0.000319\n",
1035
+ "TSLA 0.000984\n",
1036
+ "dtype: float64\n"
1037
+ ]
1038
+ }
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+ ]
1040
+ },
1041
+ {
1042
+ "cell_type": "code",
1043
+ "source": [
1044
+ "standard_deviation_daily_returns = returns.std()\n",
1045
+ "\n",
1046
+ "print(standard_deviation_daily_returns)"
1047
+ ],
1048
+ "metadata": {
1049
+ "colab": {
1050
+ "base_uri": "https://localhost:8080/"
1051
+ },
1052
+ "id": "BsjEGO46SwRP",
1053
+ "outputId": "d64644d9-7aee-4fd5-9b64-dfbebb695d10"
1054
+ },
1055
+ "execution_count": null,
1056
+ "outputs": [
1057
+ {
1058
+ "output_type": "stream",
1059
+ "name": "stdout",
1060
+ "text": [
1061
+ "Ticker\n",
1062
+ "ARM 0.051661\n",
1063
+ "META 0.027536\n",
1064
+ "SPY 0.008493\n",
1065
+ "TSLA 0.048723\n",
1066
+ "dtype: float64\n"
1067
+ ]
1068
+ }
1069
+ ]
1070
+ },
1071
+ {
1072
+ "cell_type": "code",
1073
+ "source": [
1074
+ "import numpy\n",
1075
+ "\n",
1076
+ "weights = numpy.array([0.25, 0.25, 0.25, 0.25])"
1077
+ ],
1078
+ "metadata": {
1079
+ "id": "aHVZp77fS_5T"
1080
+ },
1081
+ "execution_count": null,
1082
+ "outputs": []
1083
+ },
1084
+ {
1085
+ "cell_type": "code",
1086
+ "source": [
1087
+ "covariance_matrix = (returns.cov())*250\n",
1088
+ "print(covariance_matrix)"
1089
+ ],
1090
+ "metadata": {
1091
+ "colab": {
1092
+ "base_uri": "https://localhost:8080/"
1093
+ },
1094
+ "id": "wOj-twh_Tnf5",
1095
+ "outputId": "837404eb-1556-4b86-c405-565169571db8"
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+ },
1097
+ "execution_count": null,
1098
+ "outputs": [
1099
+ {
1100
+ "output_type": "stream",
1101
+ "name": "stdout",
1102
+ "text": [
1103
+ "Ticker ARM META SPY TSLA\n",
1104
+ "Ticker \n",
1105
+ "ARM 0.667206 0.131290 0.072585 0.167230\n",
1106
+ "META 0.131290 0.189558 0.024381 -0.086532\n",
1107
+ "SPY 0.072585 0.024381 0.018031 0.027256\n",
1108
+ "TSLA 0.167230 -0.086532 0.027256 0.593473\n"
1109
+ ]
1110
+ }
1111
+ ]
1112
+ },
1113
+ {
1114
+ "cell_type": "code",
1115
+ "source": [
1116
+ "expected_portfolio_performance = numpy.sum(average_daily_returns * weights)\n",
1117
+ "\n",
1118
+ "print(expected_portfolio_performance)"
1119
+ ],
1120
+ "metadata": {
1121
+ "colab": {
1122
+ "base_uri": "https://localhost:8080/"
1123
+ },
1124
+ "id": "3Y7MpadBT1na",
1125
+ "outputId": "af1e83be-0f2e-48c6-b43f-635ae9e21d80"
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+ },
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+ "execution_count": null,
1128
+ "outputs": [
1129
+ {
1130
+ "output_type": "stream",
1131
+ "name": "stdout",
1132
+ "text": [
1133
+ "-0.001413485369940147\n"
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+ ]
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+ }
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+ ]
1137
+ },
1138
+ {
1139
+ "cell_type": "code",
1140
+ "source": [
1141
+ "returns['Portfolio Returns'] = returns.dot(weights)"
1142
+ ],
1143
+ "metadata": {
1144
+ "id": "U_tB0NrYUCCt"
1145
+ },
1146
+ "execution_count": null,
1147
+ "outputs": []
1148
+ },
1149
+ {
1150
+ "cell_type": "code",
1151
+ "source": [
1152
+ "returns.head()"
1153
+ ],
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+ "metadata": {
1155
+ "colab": {
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+ "base_uri": "https://localhost:8080/",
1157
+ "height": 238
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+ },
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+ "outputId": "a95b5cd1-071d-4fc3-e07b-b13412450a53"
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+ },
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+ "execution_count": null,
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+ "outputs": [
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+ {
1165
+ "output_type": "execute_result",
1166
+ "data": {
1167
+ "text/plain": [
1168
+ "Ticker ARM META SPY TSLA Portfolio Returns\n",
1169
+ "Date \n",
1170
+ "2024-04-02 -0.020723 0.012252 -0.006358 -0.049024 -0.015963\n",
1171
+ "2024-04-03 0.008288 0.018839 0.001099 0.010502 0.009682\n",
1172
+ "2024-04-04 -0.026335 0.008249 -0.012206 0.016213 -0.003520\n",
1173
+ "2024-04-05 0.023031 0.032138 0.010447 -0.036292 0.007331\n",
1174
+ "2024-04-08 0.035491 -0.015341 0.000559 0.048999 0.017427"
1175
+ ],
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+ "\n",
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+ " <div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " <th>Ticker</th>\n",
1197
+ " <th>ARM</th>\n",
1198
+ " <th>META</th>\n",
1199
+ " <th>SPY</th>\n",
1200
+ " <th>TSLA</th>\n",
1201
+ " <th>Portfolio Returns</th>\n",
1202
+ " </tr>\n",
1203
+ " <tr>\n",
1204
+ " <th>Date</th>\n",
1205
+ " <th></th>\n",
1206
+ " <th></th>\n",
1207
+ " <th></th>\n",
1208
+ " <th></th>\n",
1209
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1210
+ " </tr>\n",
1211
+ " </thead>\n",
1212
+ " <tbody>\n",
1213
+ " <tr>\n",
1214
+ " <th>2024-04-02</th>\n",
1215
+ " <td>-0.020723</td>\n",
1216
+ " <td>0.012252</td>\n",
1217
+ " <td>-0.006358</td>\n",
1218
+ " <td>-0.049024</td>\n",
1219
+ " <td>-0.015963</td>\n",
1220
+ " </tr>\n",
1221
+ " <tr>\n",
1222
+ " <th>2024-04-03</th>\n",
1223
+ " <td>0.008288</td>\n",
1224
+ " <td>0.018839</td>\n",
1225
+ " <td>0.001099</td>\n",
1226
+ " <td>0.010502</td>\n",
1227
+ " <td>0.009682</td>\n",
1228
+ " </tr>\n",
1229
+ " <tr>\n",
1230
+ " <th>2024-04-04</th>\n",
1231
+ " <td>-0.026335</td>\n",
1232
+ " <td>0.008249</td>\n",
1233
+ " <td>-0.012206</td>\n",
1234
+ " <td>0.016213</td>\n",
1235
+ " <td>-0.003520</td>\n",
1236
+ " </tr>\n",
1237
+ " <tr>\n",
1238
+ " <th>2024-04-05</th>\n",
1239
+ " <td>0.023031</td>\n",
1240
+ " <td>0.032138</td>\n",
1241
+ " <td>0.010447</td>\n",
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+ " <td>-0.036292</td>\n",
1243
+ " <td>0.007331</td>\n",
1244
+ " </tr>\n",
1245
+ " <tr>\n",
1246
+ " <th>2024-04-08</th>\n",
1247
+ " <td>0.035491</td>\n",
1248
+ " <td>-0.015341</td>\n",
1249
+ " <td>0.000559</td>\n",
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+ " <td>0.048999</td>\n",
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+ " <td>0.017427</td>\n",
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1445
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1446
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+ "application/vnd.google.colaboratory.intrinsic+json": {
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+ "type": "dataframe",
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+ "variable_name": "returns",
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+ "summary": "{\n \"name\": \"returns\",\n \"rows\": 27,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2024-04-02 00:00:00\",\n \"max\": \"2024-05-08 00:00:00\",\n \"num_unique_values\": 27,\n \"samples\": [\n \"2024-04-12 00:00:00\",\n \"2024-04-19 00:00:00\",\n \"2024-04-15 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ARM\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05166065364845084,\n \"min\": -0.16898585624248386,\n \"max\": 0.06789767178781414,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.03660491781455699,\n -0.16898585624248386,\n -0.03174227877866398\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"META\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02753598854622982,\n \"min\": -0.1056129586974417,\n \"max\": 0.03213812924957349,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.021523013659411117,\n -0.04131124147675891,\n -0.022797388258059104\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SPY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0084925113729149,\n \"min\": -0.015841247325688257,\n \"max\": 0.012395322615995186,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.013803077020240084,\n -0.00872835011101658,\n -0.012528127278102708\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"TSLA\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04872259693001104,\n \"min\": -0.05594859487563397,\n \"max\": 0.1530691710339953,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.020332204625772565,\n -0.0192088959161848,\n -0.05594859487563397\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Portfolio Returns\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02313377722945321,\n \"min\": -0.059558585936611036,\n \"max\": 0.038719721860631745,\n \"num_unique_values\": 27,\n \"samples\": [\n -0.02306580327999519,\n -0.059558585936611036,\n -0.03075409729761494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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+ }
1471
+ },
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+ "metadata": {},
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+ "execution_count": 11
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+ }
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+ ]
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+ },
1477
+ {
1478
+ "cell_type": "code",
1479
+ "source": [
1480
+ "daily_cumulative_returns= (1+returns).cumprod()\n",
1481
+ "print(daily_cumulative_returns)"
1482
+ ],
1483
+ "metadata": {
1484
+ "id": "o8z0mBEUUa77",
1485
+ "colab": {
1486
+ "base_uri": "https://localhost:8080/"
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+ },
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+ "execution_count": null,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
1495
+ "text": [
1496
+ "Ticker ARM META SPY TSLA Portfolio Returns\n",
1497
+ "Date \n",
1498
+ "2024-04-02 0.979277 1.012252 0.993642 0.950976 0.984037\n",
1499
+ "2024-04-03 0.987393 1.031322 0.994733 0.960963 0.993564\n",
1500
+ "2024-04-04 0.961390 1.039829 0.982592 0.976544 0.990067\n",
1501
+ "2024-04-05 0.983532 1.073247 0.992857 0.941103 0.997325\n",
1502
+ "2024-04-08 1.018438 1.056782 0.993412 0.987216 1.014706\n",
1503
+ "2024-04-09 0.999212 1.052000 0.994561 1.009474 1.014781\n",
1504
+ "2024-04-10 0.986368 1.057963 0.984602 0.980253 1.003075\n",
1505
+ "2024-04-11 1.033252 1.064740 0.992033 0.996462 1.022639\n",
1506
+ "2024-04-12 0.995430 1.041824 0.978340 0.976201 0.999051\n",
1507
+ "2024-04-15 0.963833 1.018073 0.966083 0.921584 0.968326\n",
1508
+ "2024-04-16 0.963045 1.017116 0.964321 0.896644 0.960908\n",
1509
+ "2024-04-17 0.847530 1.005739 0.958614 0.887170 0.925447\n",
1510
+ "2024-04-18 0.826728 1.021268 0.956642 0.855667 0.914649\n",
1511
+ "2024-04-19 0.687022 0.979078 0.948292 0.839231 0.860173\n",
1512
+ "2024-04-22 0.733670 0.980421 0.957025 0.810695 0.869738\n",
1513
+ "2024-04-23 0.756757 1.009667 0.968381 0.825705 0.889672\n",
1514
+ "2024-04-24 0.787014 1.004376 0.967922 0.925294 0.924120\n",
1515
+ "2024-04-25 0.771649 0.898301 0.964245 0.971236 0.905803\n",
1516
+ "2024-04-26 0.803325 0.902188 0.973380 0.960450 0.915709\n",
1517
+ "2024-04-29 0.813490 0.880472 0.976827 1.107465 0.948948\n",
1518
+ "2024-04-30 0.797494 0.875486 0.961353 1.045999 0.926015\n",
1519
+ "2024-05-01 0.750847 0.893843 0.958231 1.027223 0.912420\n",
1520
+ "2024-05-02 0.772201 0.898911 0.967194 1.027337 0.922360\n",
1521
+ "2024-05-03 0.801355 0.919833 0.979183 1.034071 0.940802\n",
1522
+ "2024-05-06 0.842960 0.947756 0.989295 1.054446 0.967216\n",
1523
+ "2024-05-07 0.849421 0.952966 0.990386 1.014781 0.961570\n",
1524
+ "2024-05-08 0.835789 0.961840 0.990482 0.997146 0.955796\n"
1525
+ ]
1526
+ }
1527
+ ]
1528
+ },
1529
+ {
1530
+ "cell_type": "code",
1531
+ "source": [
1532
+ "daily_cumulative_returns.tail()"
1533
+ ],
1534
+ "metadata": {
1535
+ "colab": {
1536
+ "base_uri": "https://localhost:8080/",
1537
+ "height": 238
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+ },
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+ "id": "Roh4BE6fUmGt",
1540
+ "outputId": "66b911b4-5268-42ad-9f45-887f377dfe77"
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+ },
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+ "execution_count": null,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
1546
+ "data": {
1547
+ "text/plain": [
1548
+ "Ticker ARM META SPY TSLA Portfolio Returns\n",
1549
+ "Date \n",
1550
+ "2024-05-02 0.772201 0.898911 0.967194 1.027337 0.922360\n",
1551
+ "2024-05-03 0.801355 0.919833 0.979183 1.034071 0.940802\n",
1552
+ "2024-05-06 0.842960 0.947756 0.989295 1.054446 0.967216\n",
1553
+ "2024-05-07 0.849421 0.952966 0.990386 1.014781 0.961570\n",
1554
+ "2024-05-08 0.835789 0.961840 0.990482 0.997146 0.955796"
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+ ],
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+ "text/html": [
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1561
+ " .dataframe tbody tr th:only-of-type {\n",
1562
+ " vertical-align: middle;\n",
1563
+ " }\n",
1564
+ "\n",
1565
+ " .dataframe tbody tr th {\n",
1566
+ " vertical-align: top;\n",
1567
+ " }\n",
1568
+ "\n",
1569
+ " .dataframe thead th {\n",
1570
+ " text-align: right;\n",
1571
+ " }\n",
1572
+ "</style>\n",
1573
+ "<table border=\"1\" class=\"dataframe\">\n",
1574
+ " <thead>\n",
1575
+ " <tr style=\"text-align: right;\">\n",
1576
+ " <th>Ticker</th>\n",
1577
+ " <th>ARM</th>\n",
1578
+ " <th>META</th>\n",
1579
+ " <th>SPY</th>\n",
1580
+ " <th>TSLA</th>\n",
1581
+ " <th>Portfolio Returns</th>\n",
1582
+ " </tr>\n",
1583
+ " <tr>\n",
1584
+ " <th>Date</th>\n",
1585
+ " <th></th>\n",
1586
+ " <th></th>\n",
1587
+ " <th></th>\n",
1588
+ " <th></th>\n",
1589
+ " <th></th>\n",
1590
+ " </tr>\n",
1591
+ " </thead>\n",
1592
+ " <tbody>\n",
1593
+ " <tr>\n",
1594
+ " <th>2024-05-02</th>\n",
1595
+ " <td>0.772201</td>\n",
1596
+ " <td>0.898911</td>\n",
1597
+ " <td>0.967194</td>\n",
1598
+ " <td>1.027337</td>\n",
1599
+ " <td>0.922360</td>\n",
1600
+ " </tr>\n",
1601
+ " <tr>\n",
1602
+ " <th>2024-05-03</th>\n",
1603
+ " <td>0.801355</td>\n",
1604
+ " <td>0.919833</td>\n",
1605
+ " <td>0.979183</td>\n",
1606
+ " <td>1.034071</td>\n",
1607
+ " <td>0.940802</td>\n",
1608
+ " </tr>\n",
1609
+ " <tr>\n",
1610
+ " <th>2024-05-06</th>\n",
1611
+ " <td>0.842960</td>\n",
1612
+ " <td>0.947756</td>\n",
1613
+ " <td>0.989295</td>\n",
1614
+ " <td>1.054446</td>\n",
1615
+ " <td>0.967216</td>\n",
1616
+ " </tr>\n",
1617
+ " <tr>\n",
1618
+ " <th>2024-05-07</th>\n",
1619
+ " <td>0.849421</td>\n",
1620
+ " <td>0.952966</td>\n",
1621
+ " <td>0.990386</td>\n",
1622
+ " <td>1.014781</td>\n",
1623
+ " <td>0.961570</td>\n",
1624
+ " </tr>\n",
1625
+ " <tr>\n",
1626
+ " <th>2024-05-08</th>\n",
1627
+ " <td>0.835789</td>\n",
1628
+ " <td>0.961840</td>\n",
1629
+ " <td>0.990482</td>\n",
1630
+ " <td>0.997146</td>\n",
1631
+ " <td>0.955796</td>\n",
1632
+ " </tr>\n",
1633
+ " </tbody>\n",
1634
+ "</table>\n",
1635
+ "</div>\n",
1636
+ " <div class=\"colab-df-buttons\">\n",
1637
+ "\n",
1638
+ " <div class=\"colab-df-container\">\n",
1639
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e16cfa33-3b61-42a3-a82b-ae1ecb73362c')\"\n",
1640
+ " title=\"Convert this dataframe to an interactive table.\"\n",
1641
+ " style=\"display:none;\">\n",
1642
+ "\n",
1643
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
1644
+ " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1645
+ " </svg>\n",
1646
+ " </button>\n",
1647
+ "\n",
1648
+ " <style>\n",
1649
+ " .colab-df-container {\n",
1650
+ " display:flex;\n",
1651
+ " gap: 12px;\n",
1652
+ " }\n",
1653
+ "\n",
1654
+ " .colab-df-convert {\n",
1655
+ " background-color: #E8F0FE;\n",
1656
+ " border: none;\n",
1657
+ " border-radius: 50%;\n",
1658
+ " cursor: pointer;\n",
1659
+ " display: none;\n",
1660
+ " fill: #1967D2;\n",
1661
+ " height: 32px;\n",
1662
+ " padding: 0 0 0 0;\n",
1663
+ " width: 32px;\n",
1664
+ " }\n",
1665
+ "\n",
1666
+ " .colab-df-convert:hover {\n",
1667
+ " background-color: #E2EBFA;\n",
1668
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1669
+ " fill: #174EA6;\n",
1670
+ " }\n",
1671
+ "\n",
1672
+ " .colab-df-buttons div {\n",
1673
+ " margin-bottom: 4px;\n",
1674
+ " }\n",
1675
+ "\n",
1676
+ " [theme=dark] .colab-df-convert {\n",
1677
+ " background-color: #3B4455;\n",
1678
+ " fill: #D2E3FC;\n",
1679
+ " }\n",
1680
+ "\n",
1681
+ " [theme=dark] .colab-df-convert:hover {\n",
1682
+ " background-color: #434B5C;\n",
1683
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1684
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1685
+ " fill: #FFFFFF;\n",
1686
+ " }\n",
1687
+ " </style>\n",
1688
+ "\n",
1689
+ " <script>\n",
1690
+ " const buttonEl =\n",
1691
+ " document.querySelector('#df-e16cfa33-3b61-42a3-a82b-ae1ecb73362c button.colab-df-convert');\n",
1692
+ " buttonEl.style.display =\n",
1693
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1694
+ "\n",
1695
+ " async function convertToInteractive(key) {\n",
1696
+ " const element = document.querySelector('#df-e16cfa33-3b61-42a3-a82b-ae1ecb73362c');\n",
1697
+ " const dataTable =\n",
1698
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1699
+ " [key], {});\n",
1700
+ " if (!dataTable) return;\n",
1701
+ "\n",
1702
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1703
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1704
+ " + ' to learn more about interactive tables.';\n",
1705
+ " element.innerHTML = '';\n",
1706
+ " dataTable['output_type'] = 'display_data';\n",
1707
+ " await google.colab.output.renderOutput(dataTable, element);\n",
1708
+ " const docLink = document.createElement('div');\n",
1709
+ " docLink.innerHTML = docLinkHtml;\n",
1710
+ " element.appendChild(docLink);\n",
1711
+ " }\n",
1712
+ " </script>\n",
1713
+ " </div>\n",
1714
+ "\n",
1715
+ "\n",
1716
+ "<div id=\"df-b087f441-dd59-4af0-81da-6c3b9fe3d4a6\">\n",
1717
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b087f441-dd59-4af0-81da-6c3b9fe3d4a6')\"\n",
1718
+ " title=\"Suggest charts\"\n",
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+ " style=\"display:none;\">\n",
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+ "\n",
1721
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " <g>\n",
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+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
1725
+ " </g>\n",
1726
+ "</svg>\n",
1727
+ " </button>\n",
1728
+ "\n",
1729
+ "<style>\n",
1730
+ " .colab-df-quickchart {\n",
1731
+ " --bg-color: #E8F0FE;\n",
1732
+ " --fill-color: #1967D2;\n",
1733
+ " --hover-bg-color: #E2EBFA;\n",
1734
+ " --hover-fill-color: #174EA6;\n",
1735
+ " --disabled-fill-color: #AAA;\n",
1736
+ " --disabled-bg-color: #DDD;\n",
1737
+ " }\n",
1738
+ "\n",
1739
+ " [theme=dark] .colab-df-quickchart {\n",
1740
+ " --bg-color: #3B4455;\n",
1741
+ " --fill-color: #D2E3FC;\n",
1742
+ " --hover-bg-color: #434B5C;\n",
1743
+ " --hover-fill-color: #FFFFFF;\n",
1744
+ " --disabled-bg-color: #3B4455;\n",
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+ " --disabled-fill-color: #666;\n",
1746
+ " }\n",
1747
+ "\n",
1748
+ " .colab-df-quickchart {\n",
1749
+ " background-color: var(--bg-color);\n",
1750
+ " border: none;\n",
1751
+ " border-radius: 50%;\n",
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+ " cursor: pointer;\n",
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+ " display: none;\n",
1754
+ " fill: var(--fill-color);\n",
1755
+ " height: 32px;\n",
1756
+ " padding: 0;\n",
1757
+ " width: 32px;\n",
1758
+ " }\n",
1759
+ "\n",
1760
+ " .colab-df-quickchart:hover {\n",
1761
+ " background-color: var(--hover-bg-color);\n",
1762
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1763
+ " fill: var(--button-hover-fill-color);\n",
1764
+ " }\n",
1765
+ "\n",
1766
+ " .colab-df-quickchart-complete:disabled,\n",
1767
+ " .colab-df-quickchart-complete:disabled:hover {\n",
1768
+ " background-color: var(--disabled-bg-color);\n",
1769
+ " fill: var(--disabled-fill-color);\n",
1770
+ " box-shadow: none;\n",
1771
+ " }\n",
1772
+ "\n",
1773
+ " .colab-df-spinner {\n",
1774
+ " border: 2px solid var(--fill-color);\n",
1775
+ " border-color: transparent;\n",
1776
+ " border-bottom-color: var(--fill-color);\n",
1777
+ " animation:\n",
1778
+ " spin 1s steps(1) infinite;\n",
1779
+ " }\n",
1780
+ "\n",
1781
+ " @keyframes spin {\n",
1782
+ " 0% {\n",
1783
+ " border-color: transparent;\n",
1784
+ " border-bottom-color: var(--fill-color);\n",
1785
+ " border-left-color: var(--fill-color);\n",
1786
+ " }\n",
1787
+ " 20% {\n",
1788
+ " border-color: transparent;\n",
1789
+ " border-left-color: var(--fill-color);\n",
1790
+ " border-top-color: var(--fill-color);\n",
1791
+ " }\n",
1792
+ " 30% {\n",
1793
+ " border-color: transparent;\n",
1794
+ " border-left-color: var(--fill-color);\n",
1795
+ " border-top-color: var(--fill-color);\n",
1796
+ " border-right-color: var(--fill-color);\n",
1797
+ " }\n",
1798
+ " 40% {\n",
1799
+ " border-color: transparent;\n",
1800
+ " border-right-color: var(--fill-color);\n",
1801
+ " border-top-color: var(--fill-color);\n",
1802
+ " }\n",
1803
+ " 60% {\n",
1804
+ " border-color: transparent;\n",
1805
+ " border-right-color: var(--fill-color);\n",
1806
+ " }\n",
1807
+ " 80% {\n",
1808
+ " border-color: transparent;\n",
1809
+ " border-right-color: var(--fill-color);\n",
1810
+ " border-bottom-color: var(--fill-color);\n",
1811
+ " }\n",
1812
+ " 90% {\n",
1813
+ " border-color: transparent;\n",
1814
+ " border-bottom-color: var(--fill-color);\n",
1815
+ " }\n",
1816
+ " }\n",
1817
+ "</style>\n",
1818
+ "\n",
1819
+ " <script>\n",
1820
+ " async function quickchart(key) {\n",
1821
+ " const quickchartButtonEl =\n",
1822
+ " document.querySelector('#' + key + ' button');\n",
1823
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
1824
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
1825
+ " try {\n",
1826
+ " const charts = await google.colab.kernel.invokeFunction(\n",
1827
+ " 'suggestCharts', [key], {});\n",
1828
+ " } catch (error) {\n",
1829
+ " console.error('Error during call to suggestCharts:', error);\n",
1830
+ " }\n",
1831
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1832
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1833
+ " }\n",
1834
+ " (() => {\n",
1835
+ " let quickchartButtonEl =\n",
1836
+ " document.querySelector('#df-b087f441-dd59-4af0-81da-6c3b9fe3d4a6 button');\n",
1837
+ " quickchartButtonEl.style.display =\n",
1838
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1839
+ " })();\n",
1840
+ " </script>\n",
1841
+ "</div>\n",
1842
+ "\n",
1843
+ " </div>\n",
1844
+ " </div>\n"
1845
+ ],
1846
+ "application/vnd.google.colaboratory.intrinsic+json": {
1847
+ "type": "dataframe",
1848
+ "summary": "{\n \"name\": \"daily_cumulative_returns\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2024-05-02 00:00:00\",\n \"max\": \"2024-05-08 00:00:00\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"2024-05-03 00:00:00\",\n \"2024-05-08 00:00:00\",\n \"2024-05-06 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ARM\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03268724736387041,\n \"min\": 0.7722007499181814,\n \"max\": 0.8494208489566288,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.8013552439807079,\n 0.835789115389434,\n 0.8429595797803437\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"META\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.026126472997597343,\n \"min\": 0.8989111370494823,\n \"max\": 0.9618398295164574,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.9198330840355401,\n 0.9618398295164574,\n 0.9477561552684173\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SPY\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.010174510968413989,\n \"min\": 0.9671940109425183,\n \"max\": 0.9904819002628732,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.9791826927404093,\n 0.9904819002628732,\n 0.9892945340359032\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"TSLA\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.02140969763814976,\n \"min\": 0.9971464444896896,\n \"max\": 1.0544458008199447,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.0340714597597993,\n 0.9971464444896896,\n 1.0544458008199447\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Portfolio Returns\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.01810684145461361,\n \"min\": 0.9223596625037148,\n \"max\": 0.9672162989216139,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.940802335236565,\n 0.9557962381861789,\n 0.9672162989216139\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
1849
+ }
1850
+ },
1851
+ "metadata": {},
1852
+ "execution_count": 13
1853
+ }
1854
+ ]
1855
+ },
1856
+ {
1857
+ "cell_type": "code",
1858
+ "source": [],
1859
+ "metadata": {
1860
+ "id": "HwVTimnSY8Wd"
1861
+ },
1862
+ "execution_count": null,
1863
+ "outputs": []
1864
+ }
1865
+ ]
1866
+ }