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cyberosa commited on
Commit
3d497f3
·
1 Parent(s): 1c9dfec

Adding better description and same y axis for retention graphs

Browse files
Files changed (5) hide show
  1. app.py +11 -0
  2. notebooks/closed_markets.ipynb +6 -2
  3. notebooks/retention_metrics.ipynb +580 -40
  4. notebooks/unknown_traders.ipynb +556 -8
  5. scripts/retention_metrics.py +20 -8
app.py CHANGED
@@ -427,6 +427,7 @@ with demo:
427
  with gr.Row():
428
  gr.Markdown(
429
  """
 
430
  - Olas trader: agent using Mech, with a service ID and the corresponding safe in the registry
431
  - Non-Olas trader: agent using Mech, with no service ID
432
  - Unclassified trader: agent (safe/EOAs) not using Mechs
@@ -457,6 +458,16 @@ with demo:
457
  gr.Markdown(
458
  "The Cohort groups are organized by cohort weeks. A trader is part of a cohort group/week where it was detected the FIRST activity ever of that trader."
459
  )
 
 
 
 
 
 
 
 
 
 
460
  with gr.Row():
461
  with gr.Column(scale=1):
462
  gr.Markdown("## Cohort retention of 🌊 Olas traders")
 
427
  with gr.Row():
428
  gr.Markdown(
429
  """
430
+ Activity based on mech interactions for Olas and non_Olas traders and based on trading acitivity for the unclassified ones.
431
  - Olas trader: agent using Mech, with a service ID and the corresponding safe in the registry
432
  - Non-Olas trader: agent using Mech, with no service ID
433
  - Unclassified trader: agent (safe/EOAs) not using Mechs
 
458
  gr.Markdown(
459
  "The Cohort groups are organized by cohort weeks. A trader is part of a cohort group/week where it was detected the FIRST activity ever of that trader."
460
  )
461
+ with gr.Row():
462
+ gr.Markdown(
463
+ """
464
+ Week 0 for a cohort group is the same cohort week of the FIRST detected activity ever of that trader.
465
+ Only two values are possible for this Week 0:
466
+
467
+ 1. 100% if the cohort size is > 0, meaning all traders active that first cohort week
468
+ 2. 0% if the cohort size = 0, meaning no totally new traders started activity that cohort week.
469
+ """
470
+ )
471
  with gr.Row():
472
  with gr.Column(scale=1):
473
  gr.Markdown("## Cohort retention of 🌊 Olas traders")
notebooks/closed_markets.ipynb CHANGED
@@ -11,7 +11,7 @@
11
  },
12
  {
13
  "cell_type": "code",
14
- "execution_count": 4,
15
  "metadata": {},
16
  "outputs": [
17
  {
@@ -354,7 +354,11 @@
354
  "execution_count": null,
355
  "metadata": {},
356
  "outputs": [],
357
- "source": []
 
 
 
 
358
  },
359
  {
360
  "cell_type": "code",
 
11
  },
12
  {
13
  "cell_type": "code",
14
+ "execution_count": 1,
15
  "metadata": {},
16
  "outputs": [
17
  {
 
354
  "execution_count": null,
355
  "metadata": {},
356
  "outputs": [],
357
+ "source": [
358
+ "filtered_traders[\"month_year_week\"] =(\n",
359
+ " pd.to_datetime(filtered_traders[\"creation_timestamp\"]).dt.to_period(\"W\").dt.strftime(\"%b-%d-%Y\")\n",
360
+ ")"
361
+ ]
362
  },
363
  {
364
  "cell_type": "code",
notebooks/retention_metrics.ipynb CHANGED
@@ -21,7 +21,7 @@
21
  },
22
  {
23
  "cell_type": "code",
24
- "execution_count": 113,
25
  "metadata": {},
26
  "outputs": [],
27
  "source": [
@@ -50,7 +50,7 @@
50
  },
51
  {
52
  "cell_type": "code",
53
- "execution_count": 91,
54
  "metadata": {},
55
  "outputs": [
56
  {
@@ -61,7 +61,7 @@
61
  " dtype='object')"
62
  ]
63
  },
64
- "execution_count": 91,
65
  "metadata": {},
66
  "output_type": "execute_result"
67
  }
@@ -72,27 +72,27 @@
72
  },
73
  {
74
  "cell_type": "code",
75
- "execution_count": 115,
76
  "metadata": {},
77
  "outputs": [
78
  {
79
  "data": {
80
  "text/plain": [
81
  "month_year_week\n",
82
- "Jan-12-2025 148192\n",
83
- "Dec-15-2024 136640\n",
84
- "Dec-22-2024 136529\n",
85
- "Jan-05-2025 136190\n",
86
- "Dec-29-2024 128665\n",
87
- "Dec-08-2024 109545\n",
88
- "Nov-24-2024 72777\n",
89
- "Dec-01-2024 67851\n",
90
- "Jan-19-2025 53165\n",
91
- "Nov-17-2024 31089\n",
92
  "Name: count, dtype: int64"
93
  ]
94
  },
95
- "execution_count": 115,
96
  "metadata": {},
97
  "output_type": "execute_result"
98
  }
@@ -103,55 +103,303 @@
103
  },
104
  {
105
  "cell_type": "code",
106
- "execution_count": 111,
107
  "metadata": {},
108
  "outputs": [
109
  {
110
  "data": {
111
  "text/plain": [
112
- "month_year_week\n",
113
- "Jan-14-2025 30424\n",
114
- "Jan-01-2025 28494\n",
115
- "Jan-07-2025 28200\n",
116
- "Jan-11-2025 26174\n",
117
- "Jan-09-2025 22810\n",
118
- " ... \n",
119
- "Nov-14-2024 8757\n",
120
- "Nov-25-2024 8112\n",
121
- "Nov-15-2024 7658\n",
122
- "Nov-17-2024 4967\n",
123
- "Jan-15-2025 3728\n",
124
- "Name: count, Length: 63, dtype: int64"
125
  ]
126
  },
127
- "execution_count": 111,
128
  "metadata": {},
129
  "output_type": "execute_result"
130
  }
131
  ],
132
  "source": [
133
- "retention_df.month_year_week.value_counts()"
 
134
  ]
135
  },
136
  {
137
  "cell_type": "code",
138
- "execution_count": 129,
139
  "metadata": {},
140
  "outputs": [
141
  {
142
  "data": {
143
  "text/plain": [
144
- "136190"
 
 
 
 
 
145
  ]
146
  },
147
- "execution_count": 129,
148
  "metadata": {},
149
  "output_type": "execute_result"
150
  }
151
  ],
152
  "source": [
153
- "jan5 = retention_df.loc[retention_df[\"month_year_week\"]==\"Jan-05-2025\"]\n",
154
- "len(jan5)"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
155
  ]
156
  },
157
  {
@@ -5686,17 +5934,309 @@
5686
  },
5687
  {
5688
  "cell_type": "code",
5689
- "execution_count": null,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5690
  "metadata": {},
5691
  "outputs": [],
5692
- "source": []
 
 
 
5693
  },
5694
  {
5695
  "cell_type": "code",
5696
- "execution_count": null,
5697
  "metadata": {},
5698
  "outputs": [],
5699
- "source": []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5700
  },
5701
  {
5702
  "cell_type": "code",
 
21
  },
22
  {
23
  "cell_type": "code",
24
+ "execution_count": 230,
25
  "metadata": {},
26
  "outputs": [],
27
  "source": [
 
50
  },
51
  {
52
  "cell_type": "code",
53
+ "execution_count": 231,
54
  "metadata": {},
55
  "outputs": [
56
  {
 
61
  " dtype='object')"
62
  ]
63
  },
64
+ "execution_count": 231,
65
  "metadata": {},
66
  "output_type": "execute_result"
67
  }
 
72
  },
73
  {
74
  "cell_type": "code",
75
+ "execution_count": 232,
76
  "metadata": {},
77
  "outputs": [
78
  {
79
  "data": {
80
  "text/plain": [
81
  "month_year_week\n",
82
+ "Jan-06-2025 148192\n",
83
+ "Dec-09-2024 136640\n",
84
+ "Dec-16-2024 136529\n",
85
+ "Dec-30-2024 136190\n",
86
+ "Dec-23-2024 128665\n",
87
+ "Dec-02-2024 109545\n",
88
+ "Nov-18-2024 72777\n",
89
+ "Nov-25-2024 67851\n",
90
+ "Jan-13-2025 53165\n",
91
+ "Nov-11-2024 31089\n",
92
  "Name: count, dtype: int64"
93
  ]
94
  },
95
+ "execution_count": 232,
96
  "metadata": {},
97
  "output_type": "execute_result"
98
  }
 
103
  },
104
  {
105
  "cell_type": "code",
106
+ "execution_count": 233,
107
  "metadata": {},
108
  "outputs": [
109
  {
110
  "data": {
111
  "text/plain": [
112
+ "148192"
 
 
 
 
 
 
 
 
 
 
 
 
113
  ]
114
  },
115
+ "execution_count": 233,
116
  "metadata": {},
117
  "output_type": "execute_result"
118
  }
119
  ],
120
  "source": [
121
+ "jan6 = retention_df.loc[retention_df[\"month_year_week\"]==\"Jan-06-2025\"]\n",
122
+ "len(jan6)"
123
  ]
124
  },
125
  {
126
  "cell_type": "code",
127
+ "execution_count": 234,
128
  "metadata": {},
129
  "outputs": [
130
  {
131
  "data": {
132
  "text/plain": [
133
+ "staking\n",
134
+ "non_Olas 112597\n",
135
+ "non_staking 23586\n",
136
+ "pearl 7657\n",
137
+ "quickstart 4352\n",
138
+ "Name: count, dtype: int64"
139
  ]
140
  },
141
+ "execution_count": 234,
142
  "metadata": {},
143
  "output_type": "execute_result"
144
  }
145
  ],
146
  "source": [
147
+ "jan6.staking.value_counts()"
148
+ ]
149
+ },
150
+ {
151
+ "cell_type": "code",
152
+ "execution_count": 235,
153
+ "metadata": {},
154
+ "outputs": [
155
+ {
156
+ "data": {
157
+ "text/plain": [
158
+ "185"
159
+ ]
160
+ },
161
+ "execution_count": 235,
162
+ "metadata": {},
163
+ "output_type": "execute_result"
164
+ }
165
+ ],
166
+ "source": [
167
+ "olas_jan6 = jan6.loc[jan6[\"staking\"]!= \"non_Olas\"]\n",
168
+ "olas_jan6.trader_address.nunique()"
169
+ ]
170
+ },
171
+ {
172
+ "cell_type": "code",
173
+ "execution_count": 237,
174
+ "metadata": {},
175
+ "outputs": [],
176
+ "source": [
177
+ "retention_df[\"trader_type\"] = retention_df[\"staking\"].apply(\n",
178
+ " lambda x: \"non_Olas\" if x == \"non_Olas\" else \"Olas\"\n",
179
+ " )"
180
+ ]
181
+ },
182
+ {
183
+ "cell_type": "code",
184
+ "execution_count": 238,
185
+ "metadata": {},
186
+ "outputs": [],
187
+ "source": [
188
+ " \n",
189
+ "active_traders = (\n",
190
+ " retention_df.groupby(by=[\"month_year_week\", \"trader_type\"])[\n",
191
+ " \"trader_address\"\n",
192
+ " ]\n",
193
+ " .nunique()\n",
194
+ " .reset_index(name=\"nr_traders\")\n",
195
+ ")"
196
+ ]
197
+ },
198
+ {
199
+ "cell_type": "code",
200
+ "execution_count": 239,
201
+ "metadata": {},
202
+ "outputs": [
203
+ {
204
+ "data": {
205
+ "text/html": [
206
+ "<div>\n",
207
+ "<style scoped>\n",
208
+ " .dataframe tbody tr th:only-of-type {\n",
209
+ " vertical-align: middle;\n",
210
+ " }\n",
211
+ "\n",
212
+ " .dataframe tbody tr th {\n",
213
+ " vertical-align: top;\n",
214
+ " }\n",
215
+ "\n",
216
+ " .dataframe thead th {\n",
217
+ " text-align: right;\n",
218
+ " }\n",
219
+ "</style>\n",
220
+ "<table border=\"1\" class=\"dataframe\">\n",
221
+ " <thead>\n",
222
+ " <tr style=\"text-align: right;\">\n",
223
+ " <th></th>\n",
224
+ " <th>month_year_week</th>\n",
225
+ " <th>trader_type</th>\n",
226
+ " <th>nr_traders</th>\n",
227
+ " </tr>\n",
228
+ " </thead>\n",
229
+ " <tbody>\n",
230
+ " <tr>\n",
231
+ " <th>0</th>\n",
232
+ " <td>Dec-02-2024</td>\n",
233
+ " <td>Olas</td>\n",
234
+ " <td>233</td>\n",
235
+ " </tr>\n",
236
+ " <tr>\n",
237
+ " <th>1</th>\n",
238
+ " <td>Dec-02-2024</td>\n",
239
+ " <td>non_Olas</td>\n",
240
+ " <td>324</td>\n",
241
+ " </tr>\n",
242
+ " <tr>\n",
243
+ " <th>2</th>\n",
244
+ " <td>Dec-09-2024</td>\n",
245
+ " <td>Olas</td>\n",
246
+ " <td>227</td>\n",
247
+ " </tr>\n",
248
+ " <tr>\n",
249
+ " <th>3</th>\n",
250
+ " <td>Dec-09-2024</td>\n",
251
+ " <td>non_Olas</td>\n",
252
+ " <td>325</td>\n",
253
+ " </tr>\n",
254
+ " <tr>\n",
255
+ " <th>4</th>\n",
256
+ " <td>Dec-16-2024</td>\n",
257
+ " <td>Olas</td>\n",
258
+ " <td>214</td>\n",
259
+ " </tr>\n",
260
+ " <tr>\n",
261
+ " <th>5</th>\n",
262
+ " <td>Dec-16-2024</td>\n",
263
+ " <td>non_Olas</td>\n",
264
+ " <td>344</td>\n",
265
+ " </tr>\n",
266
+ " <tr>\n",
267
+ " <th>6</th>\n",
268
+ " <td>Dec-23-2024</td>\n",
269
+ " <td>Olas</td>\n",
270
+ " <td>194</td>\n",
271
+ " </tr>\n",
272
+ " <tr>\n",
273
+ " <th>7</th>\n",
274
+ " <td>Dec-23-2024</td>\n",
275
+ " <td>non_Olas</td>\n",
276
+ " <td>353</td>\n",
277
+ " </tr>\n",
278
+ " <tr>\n",
279
+ " <th>8</th>\n",
280
+ " <td>Dec-30-2024</td>\n",
281
+ " <td>Olas</td>\n",
282
+ " <td>193</td>\n",
283
+ " </tr>\n",
284
+ " <tr>\n",
285
+ " <th>9</th>\n",
286
+ " <td>Dec-30-2024</td>\n",
287
+ " <td>non_Olas</td>\n",
288
+ " <td>360</td>\n",
289
+ " </tr>\n",
290
+ " <tr>\n",
291
+ " <th>10</th>\n",
292
+ " <td>Jan-06-2025</td>\n",
293
+ " <td>Olas</td>\n",
294
+ " <td>185</td>\n",
295
+ " </tr>\n",
296
+ " <tr>\n",
297
+ " <th>11</th>\n",
298
+ " <td>Jan-06-2025</td>\n",
299
+ " <td>non_Olas</td>\n",
300
+ " <td>358</td>\n",
301
+ " </tr>\n",
302
+ " <tr>\n",
303
+ " <th>12</th>\n",
304
+ " <td>Jan-13-2025</td>\n",
305
+ " <td>Olas</td>\n",
306
+ " <td>153</td>\n",
307
+ " </tr>\n",
308
+ " <tr>\n",
309
+ " <th>13</th>\n",
310
+ " <td>Jan-13-2025</td>\n",
311
+ " <td>non_Olas</td>\n",
312
+ " <td>344</td>\n",
313
+ " </tr>\n",
314
+ " <tr>\n",
315
+ " <th>14</th>\n",
316
+ " <td>Nov-11-2024</td>\n",
317
+ " <td>Olas</td>\n",
318
+ " <td>202</td>\n",
319
+ " </tr>\n",
320
+ " <tr>\n",
321
+ " <th>15</th>\n",
322
+ " <td>Nov-11-2024</td>\n",
323
+ " <td>non_Olas</td>\n",
324
+ " <td>180</td>\n",
325
+ " </tr>\n",
326
+ " <tr>\n",
327
+ " <th>16</th>\n",
328
+ " <td>Nov-18-2024</td>\n",
329
+ " <td>Olas</td>\n",
330
+ " <td>221</td>\n",
331
+ " </tr>\n",
332
+ " <tr>\n",
333
+ " <th>17</th>\n",
334
+ " <td>Nov-18-2024</td>\n",
335
+ " <td>non_Olas</td>\n",
336
+ " <td>186</td>\n",
337
+ " </tr>\n",
338
+ " <tr>\n",
339
+ " <th>18</th>\n",
340
+ " <td>Nov-25-2024</td>\n",
341
+ " <td>Olas</td>\n",
342
+ " <td>202</td>\n",
343
+ " </tr>\n",
344
+ " <tr>\n",
345
+ " <th>19</th>\n",
346
+ " <td>Nov-25-2024</td>\n",
347
+ " <td>non_Olas</td>\n",
348
+ " <td>196</td>\n",
349
+ " </tr>\n",
350
+ " </tbody>\n",
351
+ "</table>\n",
352
+ "</div>"
353
+ ],
354
+ "text/plain": [
355
+ " month_year_week trader_type nr_traders\n",
356
+ "0 Dec-02-2024 Olas 233\n",
357
+ "1 Dec-02-2024 non_Olas 324\n",
358
+ "2 Dec-09-2024 Olas 227\n",
359
+ "3 Dec-09-2024 non_Olas 325\n",
360
+ "4 Dec-16-2024 Olas 214\n",
361
+ "5 Dec-16-2024 non_Olas 344\n",
362
+ "6 Dec-23-2024 Olas 194\n",
363
+ "7 Dec-23-2024 non_Olas 353\n",
364
+ "8 Dec-30-2024 Olas 193\n",
365
+ "9 Dec-30-2024 non_Olas 360\n",
366
+ "10 Jan-06-2025 Olas 185\n",
367
+ "11 Jan-06-2025 non_Olas 358\n",
368
+ "12 Jan-13-2025 Olas 153\n",
369
+ "13 Jan-13-2025 non_Olas 344\n",
370
+ "14 Nov-11-2024 Olas 202\n",
371
+ "15 Nov-11-2024 non_Olas 180\n",
372
+ "16 Nov-18-2024 Olas 221\n",
373
+ "17 Nov-18-2024 non_Olas 186\n",
374
+ "18 Nov-25-2024 Olas 202\n",
375
+ "19 Nov-25-2024 non_Olas 196"
376
+ ]
377
+ },
378
+ "execution_count": 239,
379
+ "metadata": {},
380
+ "output_type": "execute_result"
381
+ }
382
+ ],
383
+ "source": [
384
+ "active_traders"
385
+ ]
386
+ },
387
+ {
388
+ "cell_type": "code",
389
+ "execution_count": 236,
390
+ "metadata": {},
391
+ "outputs": [],
392
+ "source": [
393
+ "olas_data = retention_df.loc[retention_df[\"staking\"]!= \"non_Olas\"]"
394
+ ]
395
+ },
396
+ {
397
+ "cell_type": "code",
398
+ "execution_count": null,
399
+ "metadata": {},
400
+ "outputs": [],
401
+ "source": [
402
+ "olas_data.groupby([\"month_year_week\"])[\"trader_address\"].nunique"
403
  ]
404
  },
405
  {
 
5934
  },
5935
  {
5936
  "cell_type": "code",
5937
+ "execution_count": 217,
5938
+ "metadata": {},
5939
+ "outputs": [
5940
+ {
5941
+ "name": "stdout",
5942
+ "output_type": "stream",
5943
+ "text": [
5944
+ "start of the week = 2025-01-12 00:00:00\n"
5945
+ ]
5946
+ }
5947
+ ],
5948
+ "source": [
5949
+ "from datetime import timedelta\n",
5950
+ "now = datetime.now()\n",
5951
+ "\n",
5952
+ "# Get start of the current week (Sunday)\n",
5953
+ "start_of_week = now - timedelta(days=(now.weekday()) + 1)\n",
5954
+ "start_of_week = start_of_week.replace(hour=0, minute=0, second=0, microsecond=0)\n",
5955
+ "print(f\"start of the week = {start_of_week}\")"
5956
+ ]
5957
+ },
5958
+ {
5959
+ "cell_type": "code",
5960
+ "execution_count": 218,
5961
+ "metadata": {},
5962
+ "outputs": [
5963
+ {
5964
+ "data": {
5965
+ "text/plain": [
5966
+ "Timestamp('2025-01-15 14:11:00+0000', tz='UTC')"
5967
+ ]
5968
+ },
5969
+ "execution_count": 218,
5970
+ "metadata": {},
5971
+ "output_type": "execute_result"
5972
+ }
5973
+ ],
5974
+ "source": [
5975
+ "max(retention_df.creation_timestamp)"
5976
+ ]
5977
+ },
5978
+ {
5979
+ "cell_type": "code",
5980
+ "execution_count": 219,
5981
  "metadata": {},
5982
  "outputs": [],
5983
+ "source": [
5984
+ "all_traders[\"creation_date\"] = all_traders[\"creation_timestamp\"].dt.date\n",
5985
+ "all_traders[\"creation_date\"] = pd.to_datetime(all_traders[\"creation_date\"])"
5986
+ ]
5987
  },
5988
  {
5989
  "cell_type": "code",
5990
+ "execution_count": 222,
5991
  "metadata": {},
5992
  "outputs": [],
5993
+ "source": [
5994
+ "filtered_traders = all_traders[all_traders[\"creation_date\"] < start_of_week]\n"
5995
+ ]
5996
+ },
5997
+ {
5998
+ "cell_type": "code",
5999
+ "execution_count": 223,
6000
+ "metadata": {},
6001
+ "outputs": [
6002
+ {
6003
+ "data": {
6004
+ "text/plain": [
6005
+ "Timestamp('2025-01-11 00:00:00')"
6006
+ ]
6007
+ },
6008
+ "execution_count": 223,
6009
+ "metadata": {},
6010
+ "output_type": "execute_result"
6011
+ }
6012
+ ],
6013
+ "source": [
6014
+ "max(filtered_traders.creation_date)"
6015
+ ]
6016
+ },
6017
+ {
6018
+ "cell_type": "code",
6019
+ "execution_count": 228,
6020
+ "metadata": {},
6021
+ "outputs": [
6022
+ {
6023
+ "name": "stderr",
6024
+ "output_type": "stream",
6025
+ "text": [
6026
+ "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_51242/777837414.py:2: UserWarning:\n",
6027
+ "\n",
6028
+ "Converting to PeriodArray/Index representation will drop timezone information.\n",
6029
+ "\n"
6030
+ ]
6031
+ },
6032
+ {
6033
+ "name": "stdout",
6034
+ "output_type": "stream",
6035
+ "text": [
6036
+ "['Nov-17-2024' 'Nov-24-2024' 'Dec-01-2024' 'Dec-08-2024' 'Dec-15-2024'\n",
6037
+ " 'Dec-22-2024' 'Dec-29-2024' 'Jan-05-2025' 'Jan-12-2025']\n"
6038
+ ]
6039
+ },
6040
+ {
6041
+ "name": "stderr",
6042
+ "output_type": "stream",
6043
+ "text": [
6044
+ "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_51242/777837414.py:1: SettingWithCopyWarning:\n",
6045
+ "\n",
6046
+ "\n",
6047
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
6048
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
6049
+ "\n",
6050
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
6051
+ "\n"
6052
+ ]
6053
+ }
6054
+ ],
6055
+ "source": [
6056
+ "filtered_traders[\"month_year_week\"] =(\n",
6057
+ " pd.to_datetime(filtered_traders[\"creation_timestamp\"]).dt.to_period(\"W\").dt.strftime(\"%b-%d-%Y\")\n",
6058
+ ")\n",
6059
+ "\n",
6060
+ "print(filtered_traders.month_year_week.unique())"
6061
+ ]
6062
+ },
6063
+ {
6064
+ "cell_type": "code",
6065
+ "execution_count": 229,
6066
+ "metadata": {},
6067
+ "outputs": [
6068
+ {
6069
+ "data": {
6070
+ "text/html": [
6071
+ "<div>\n",
6072
+ "<style scoped>\n",
6073
+ " .dataframe tbody tr th:only-of-type {\n",
6074
+ " vertical-align: middle;\n",
6075
+ " }\n",
6076
+ "\n",
6077
+ " .dataframe tbody tr th {\n",
6078
+ " vertical-align: top;\n",
6079
+ " }\n",
6080
+ "\n",
6081
+ " .dataframe thead th {\n",
6082
+ " text-align: right;\n",
6083
+ " }\n",
6084
+ "</style>\n",
6085
+ "<table border=\"1\" class=\"dataframe\">\n",
6086
+ " <thead>\n",
6087
+ " <tr style=\"text-align: right;\">\n",
6088
+ " <th></th>\n",
6089
+ " <th>trader_type</th>\n",
6090
+ " <th>market_creator</th>\n",
6091
+ " <th>trader_address</th>\n",
6092
+ " <th>creation_timestamp</th>\n",
6093
+ " <th>month_year_week</th>\n",
6094
+ " <th>creation_date</th>\n",
6095
+ " </tr>\n",
6096
+ " </thead>\n",
6097
+ " <tbody>\n",
6098
+ " <tr>\n",
6099
+ " <th>819286</th>\n",
6100
+ " <td>non_Olas</td>\n",
6101
+ " <td>quickstart</td>\n",
6102
+ " <td>0xd37ea497bfc5303eba99756c700b701443cb9c3d</td>\n",
6103
+ " <td>2025-01-06 00:00:10+00:00</td>\n",
6104
+ " <td>Jan-12-2025</td>\n",
6105
+ " <td>2025-01-06</td>\n",
6106
+ " </tr>\n",
6107
+ " <tr>\n",
6108
+ " <th>819287</th>\n",
6109
+ " <td>Olas</td>\n",
6110
+ " <td>quickstart</td>\n",
6111
+ " <td>0x080a6922f4d85e288f956402bebe6310b13af8e6</td>\n",
6112
+ " <td>2025-01-06 00:00:25+00:00</td>\n",
6113
+ " <td>Jan-12-2025</td>\n",
6114
+ " <td>2025-01-06</td>\n",
6115
+ " </tr>\n",
6116
+ " <tr>\n",
6117
+ " <th>819289</th>\n",
6118
+ " <td>non_Olas</td>\n",
6119
+ " <td>quickstart</td>\n",
6120
+ " <td>0x5f35a67f81f7278e0e61ae797fc1f34969055b3e</td>\n",
6121
+ " <td>2025-01-06 00:00:45+00:00</td>\n",
6122
+ " <td>Jan-12-2025</td>\n",
6123
+ " <td>2025-01-06</td>\n",
6124
+ " </tr>\n",
6125
+ " <tr>\n",
6126
+ " <th>819288</th>\n",
6127
+ " <td>Olas</td>\n",
6128
+ " <td>quickstart</td>\n",
6129
+ " <td>0x17c17ca981b7e244d0bad80b632a082dc1db36e5</td>\n",
6130
+ " <td>2025-01-06 00:00:45+00:00</td>\n",
6131
+ " <td>Jan-12-2025</td>\n",
6132
+ " <td>2025-01-06</td>\n",
6133
+ " </tr>\n",
6134
+ " <tr>\n",
6135
+ " <th>819291</th>\n",
6136
+ " <td>non_Olas</td>\n",
6137
+ " <td>quickstart</td>\n",
6138
+ " <td>0xb5f3e3e2738035eead5f3e332c04d9951b95cab2</td>\n",
6139
+ " <td>2025-01-06 00:00:50+00:00</td>\n",
6140
+ " <td>Jan-12-2025</td>\n",
6141
+ " <td>2025-01-06</td>\n",
6142
+ " </tr>\n",
6143
+ " <tr>\n",
6144
+ " <th>...</th>\n",
6145
+ " <td>...</td>\n",
6146
+ " <td>...</td>\n",
6147
+ " <td>...</td>\n",
6148
+ " <td>...</td>\n",
6149
+ " <td>...</td>\n",
6150
+ " <td>...</td>\n",
6151
+ " </tr>\n",
6152
+ " <tr>\n",
6153
+ " <th>950521</th>\n",
6154
+ " <td>non_Olas</td>\n",
6155
+ " <td>quickstart</td>\n",
6156
+ " <td>0xb43b4aee8b0d09244fb5f8df72714595c9f307bb</td>\n",
6157
+ " <td>2025-01-11 23:59:20+00:00</td>\n",
6158
+ " <td>Jan-12-2025</td>\n",
6159
+ " <td>2025-01-11</td>\n",
6160
+ " </tr>\n",
6161
+ " <tr>\n",
6162
+ " <th>950522</th>\n",
6163
+ " <td>non_Olas</td>\n",
6164
+ " <td>quickstart</td>\n",
6165
+ " <td>0x8b61e3164f546880ae26f433d51db788addc4d70</td>\n",
6166
+ " <td>2025-01-11 23:59:35+00:00</td>\n",
6167
+ " <td>Jan-12-2025</td>\n",
6168
+ " <td>2025-01-11</td>\n",
6169
+ " </tr>\n",
6170
+ " <tr>\n",
6171
+ " <th>950523</th>\n",
6172
+ " <td>non_Olas</td>\n",
6173
+ " <td>quickstart</td>\n",
6174
+ " <td>0x0b3db72453ebce8af6f577e9cf85f56bd07e05db</td>\n",
6175
+ " <td>2025-01-11 23:59:45+00:00</td>\n",
6176
+ " <td>Jan-12-2025</td>\n",
6177
+ " <td>2025-01-11</td>\n",
6178
+ " </tr>\n",
6179
+ " <tr>\n",
6180
+ " <th>950524</th>\n",
6181
+ " <td>non_Olas</td>\n",
6182
+ " <td>quickstart</td>\n",
6183
+ " <td>0x4aebc783154b7763093adb94f4f162bc0bc6d77d</td>\n",
6184
+ " <td>2025-01-11 23:59:50+00:00</td>\n",
6185
+ " <td>Jan-12-2025</td>\n",
6186
+ " <td>2025-01-11</td>\n",
6187
+ " </tr>\n",
6188
+ " <tr>\n",
6189
+ " <th>950525</th>\n",
6190
+ " <td>Olas</td>\n",
6191
+ " <td>quickstart</td>\n",
6192
+ " <td>0x080a6922f4d85e288f956402bebe6310b13af8e6</td>\n",
6193
+ " <td>2025-01-11 23:59:50+00:00</td>\n",
6194
+ " <td>Jan-12-2025</td>\n",
6195
+ " <td>2025-01-11</td>\n",
6196
+ " </tr>\n",
6197
+ " </tbody>\n",
6198
+ "</table>\n",
6199
+ "<p>131701 rows × 6 columns</p>\n",
6200
+ "</div>"
6201
+ ],
6202
+ "text/plain": [
6203
+ " trader_type market_creator trader_address \\\n",
6204
+ "819286 non_Olas quickstart 0xd37ea497bfc5303eba99756c700b701443cb9c3d \n",
6205
+ "819287 Olas quickstart 0x080a6922f4d85e288f956402bebe6310b13af8e6 \n",
6206
+ "819289 non_Olas quickstart 0x5f35a67f81f7278e0e61ae797fc1f34969055b3e \n",
6207
+ "819288 Olas quickstart 0x17c17ca981b7e244d0bad80b632a082dc1db36e5 \n",
6208
+ "819291 non_Olas quickstart 0xb5f3e3e2738035eead5f3e332c04d9951b95cab2 \n",
6209
+ "... ... ... ... \n",
6210
+ "950521 non_Olas quickstart 0xb43b4aee8b0d09244fb5f8df72714595c9f307bb \n",
6211
+ "950522 non_Olas quickstart 0x8b61e3164f546880ae26f433d51db788addc4d70 \n",
6212
+ "950523 non_Olas quickstart 0x0b3db72453ebce8af6f577e9cf85f56bd07e05db \n",
6213
+ "950524 non_Olas quickstart 0x4aebc783154b7763093adb94f4f162bc0bc6d77d \n",
6214
+ "950525 Olas quickstart 0x080a6922f4d85e288f956402bebe6310b13af8e6 \n",
6215
+ "\n",
6216
+ " creation_timestamp month_year_week creation_date \n",
6217
+ "819286 2025-01-06 00:00:10+00:00 Jan-12-2025 2025-01-06 \n",
6218
+ "819287 2025-01-06 00:00:25+00:00 Jan-12-2025 2025-01-06 \n",
6219
+ "819289 2025-01-06 00:00:45+00:00 Jan-12-2025 2025-01-06 \n",
6220
+ "819288 2025-01-06 00:00:45+00:00 Jan-12-2025 2025-01-06 \n",
6221
+ "819291 2025-01-06 00:00:50+00:00 Jan-12-2025 2025-01-06 \n",
6222
+ "... ... ... ... \n",
6223
+ "950521 2025-01-11 23:59:20+00:00 Jan-12-2025 2025-01-11 \n",
6224
+ "950522 2025-01-11 23:59:35+00:00 Jan-12-2025 2025-01-11 \n",
6225
+ "950523 2025-01-11 23:59:45+00:00 Jan-12-2025 2025-01-11 \n",
6226
+ "950524 2025-01-11 23:59:50+00:00 Jan-12-2025 2025-01-11 \n",
6227
+ "950525 2025-01-11 23:59:50+00:00 Jan-12-2025 2025-01-11 \n",
6228
+ "\n",
6229
+ "[131701 rows x 6 columns]"
6230
+ ]
6231
+ },
6232
+ "execution_count": 229,
6233
+ "metadata": {},
6234
+ "output_type": "execute_result"
6235
+ }
6236
+ ],
6237
+ "source": [
6238
+ "filtered_traders.loc[filtered_traders[\"month_year_week\"]==\"Jan-12-2025\"]"
6239
+ ]
6240
  },
6241
  {
6242
  "cell_type": "code",
notebooks/unknown_traders.ipynb CHANGED
@@ -2,7 +2,7 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 1,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
@@ -14,7 +14,7 @@
14
  },
15
  {
16
  "cell_type": "code",
17
- "execution_count": 2,
18
  "metadata": {},
19
  "outputs": [],
20
  "source": [
@@ -32,7 +32,7 @@
32
  },
33
  {
34
  "cell_type": "code",
35
- "execution_count": 5,
36
  "metadata": {},
37
  "outputs": [
38
  {
@@ -43,11 +43,11 @@
43
  " 'trade_fee_amount', 'outcomes_tokens_traded', 'current_answer',\n",
44
  " 'is_invalid', 'winning_trade', 'earnings', 'redeemed',\n",
45
  " 'redeemed_amount', 'num_mech_calls', 'mech_fee_amount', 'net_earnings',\n",
46
- " 'roi', 'staking'],\n",
47
  " dtype='object')"
48
  ]
49
  },
50
- "execution_count": 5,
51
  "metadata": {},
52
  "output_type": "execute_result"
53
  }
@@ -138,7 +138,7 @@
138
  },
139
  {
140
  "cell_type": "code",
141
- "execution_count": 14,
142
  "metadata": {},
143
  "outputs": [],
144
  "source": [
@@ -149,7 +149,7 @@
149
  },
150
  {
151
  "cell_type": "code",
152
- "execution_count": 17,
153
  "metadata": {},
154
  "outputs": [],
155
  "source": [
@@ -186,7 +186,7 @@
186
  },
187
  {
188
  "cell_type": "code",
189
- "execution_count": 20,
190
  "metadata": {},
191
  "outputs": [
192
  {
@@ -201,6 +201,554 @@
201
  "unknown_traders = add_creation_date(unknown_traders)"
202
  ]
203
  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
204
  {
205
  "cell_type": "code",
206
  "execution_count": 36,
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 2,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
 
14
  },
15
  {
16
  "cell_type": "code",
17
+ "execution_count": 3,
18
  "metadata": {},
19
  "outputs": [],
20
  "source": [
 
32
  },
33
  {
34
  "cell_type": "code",
35
+ "execution_count": 4,
36
  "metadata": {},
37
  "outputs": [
38
  {
 
43
  " 'trade_fee_amount', 'outcomes_tokens_traded', 'current_answer',\n",
44
  " 'is_invalid', 'winning_trade', 'earnings', 'redeemed',\n",
45
  " 'redeemed_amount', 'num_mech_calls', 'mech_fee_amount', 'net_earnings',\n",
46
+ " 'roi', 'staking', 'nr_mech_calls'],\n",
47
  " dtype='object')"
48
  ]
49
  },
50
+ "execution_count": 4,
51
  "metadata": {},
52
  "output_type": "execute_result"
53
  }
 
138
  },
139
  {
140
  "cell_type": "code",
141
+ "execution_count": 6,
142
  "metadata": {},
143
  "outputs": [],
144
  "source": [
 
149
  },
150
  {
151
  "cell_type": "code",
152
+ "execution_count": 5,
153
  "metadata": {},
154
  "outputs": [],
155
  "source": [
 
186
  },
187
  {
188
  "cell_type": "code",
189
+ "execution_count": 7,
190
  "metadata": {},
191
  "outputs": [
192
  {
 
201
  "unknown_traders = add_creation_date(unknown_traders)"
202
  ]
203
  },
204
+ {
205
+ "cell_type": "code",
206
+ "execution_count": 8,
207
+ "metadata": {},
208
+ "outputs": [
209
+ {
210
+ "name": "stderr",
211
+ "output_type": "stream",
212
+ "text": [
213
+ "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_6876/1622588175.py:2: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
214
+ " pd.to_datetime(unknown_traders[\"creation_timestamp\"]).dt.to_period(\"W\").dt.strftime(\"%b-%d-%Y\")\n"
215
+ ]
216
+ }
217
+ ],
218
+ "source": [
219
+ "unknown_traders[\"month_year_week\"] =(\n",
220
+ " pd.to_datetime(unknown_traders[\"creation_timestamp\"]).dt.to_period(\"W\").dt.strftime(\"%b-%d-%Y\")\n",
221
+ ")"
222
+ ]
223
+ },
224
+ {
225
+ "cell_type": "code",
226
+ "execution_count": 9,
227
+ "metadata": {},
228
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+ " trader_address market_creator \\\n",
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+ "0 2025-01-12 12:21:40+00:00 \n",
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+ "2 2025-01-09 06:21:15+00:00 \n",
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+ "3 2024-12-27 18:21:35+00:00 \n",
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+ "0 Will federal prosecutors announce any new char... CLOSED \n",
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+ "1 Will a third fight between Usyk and Fury be of... CLOSED \n",
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+ "2 Will record-low temperatures be recorded in at... CLOSED \n",
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+ "3 Will any new unfair labor practice charges be ... CLOSED \n",
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+ "0 Jan-12-2025 \n",
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+ "1 Dec-29-2024 \n",
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+ "2 Jan-12-2025 \n",
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+ "\n",
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+ "[5 rows x 24 columns]"
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+ "execution_count": 15,
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+ "/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_6876/928490420.py:2: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
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+ " pd.to_datetime(unknown_traders[\"creation_timestamp\"]).dt.to_period(\"W\").dt.start_time.dt.strftime(\"%b-%d-%Y\")\n"
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656
+ " <td>0x05e8bbdb89c84a14d05194bbbae81caf2340db72</td>\n",
657
+ " <td>quickstart</td>\n",
658
+ " <td>0x13e42a5f5422df80a05c4b7b8d01bd21be82289b0x05...</td>\n",
659
+ " <td>2025-01-08 18:22:00+00:00</td>\n",
660
+ " <td>Will the Israeli government publicly acknowled...</td>\n",
661
+ " <td>CLOSED</td>\n",
662
+ " <td>3.227216</td>\n",
663
+ " <td>1</td>\n",
664
+ " <td>0.032272</td>\n",
665
+ " <td>7.405353</td>\n",
666
+ " <td>...</td>\n",
667
+ " <td>True</td>\n",
668
+ " <td>7.405353</td>\n",
669
+ " <td>0</td>\n",
670
+ " <td>0.0</td>\n",
671
+ " <td>4.145865</td>\n",
672
+ " <td>1.271938</td>\n",
673
+ " <td>non_Olas</td>\n",
674
+ " <td>NaN</td>\n",
675
+ " <td>2025-01-08</td>\n",
676
+ " <td>Jan-06-2025</td>\n",
677
+ " </tr>\n",
678
+ " </tbody>\n",
679
+ "</table>\n",
680
+ "<p>5 rows × 24 columns</p>\n",
681
+ "</div>"
682
+ ],
683
+ "text/plain": [
684
+ " trader_address market_creator \\\n",
685
+ "0 0x05e8bbdb89c84a14d05194bbbae81caf2340db72 quickstart \n",
686
+ "1 0x05e8bbdb89c84a14d05194bbbae81caf2340db72 quickstart \n",
687
+ "2 0x05e8bbdb89c84a14d05194bbbae81caf2340db72 quickstart \n",
688
+ "3 0x05e8bbdb89c84a14d05194bbbae81caf2340db72 quickstart \n",
689
+ "4 0x05e8bbdb89c84a14d05194bbbae81caf2340db72 quickstart \n",
690
+ "\n",
691
+ " trade_id \\\n",
692
+ "0 0x083c9f03dfc4072082b9e10aa2137151346f6fd30x05... \n",
693
+ "1 0x0b52672f29f059b1d0c3a297b28670fb8d92c4bd0x05... \n",
694
+ "2 0x0de08cc50fa523de23b95ce7ad773e038fb8590d0x05... \n",
695
+ "3 0x0f90a2ea8a8b0fbca021405563e763acb9f7d9460x05... \n",
696
+ "4 0x13e42a5f5422df80a05c4b7b8d01bd21be82289b0x05... \n",
697
+ "\n",
698
+ " creation_timestamp \\\n",
699
+ "0 2025-01-12 12:21:40+00:00 \n",
700
+ "1 2024-12-27 00:21:40+00:00 \n",
701
+ "2 2025-01-09 06:21:15+00:00 \n",
702
+ "3 2024-12-27 18:21:35+00:00 \n",
703
+ "4 2025-01-08 18:22:00+00:00 \n",
704
+ "\n",
705
+ " title market_status \\\n",
706
+ "0 Will federal prosecutors announce any new char... CLOSED \n",
707
+ "1 Will a third fight between Usyk and Fury be of... CLOSED \n",
708
+ "2 Will record-low temperatures be recorded in at... CLOSED \n",
709
+ "3 Will any new unfair labor practice charges be ... CLOSED \n",
710
+ "4 Will the Israeli government publicly acknowled... CLOSED \n",
711
+ "\n",
712
+ " collateral_amount outcome_index trade_fee_amount outcomes_tokens_traded \\\n",
713
+ "0 0.204245 1 0.002042 0.279142 \n",
714
+ "1 0.943883 1 0.009439 1.302968 \n",
715
+ "2 2.185557 1 0.021856 3.456138 \n",
716
+ "3 0.430373 0 0.004304 0.586244 \n",
717
+ "4 3.227216 1 0.032272 7.405353 \n",
718
+ "\n",
719
+ " ... redeemed redeemed_amount num_mech_calls mech_fee_amount \\\n",
720
+ "0 ... True 0.279142 0 0.0 \n",
721
+ "1 ... True 1.302968 0 0.0 \n",
722
+ "2 ... True 0.000000 0 0.0 \n",
723
+ "3 ... True 0.586244 0 0.0 \n",
724
+ "4 ... True 7.405353 0 0.0 \n",
725
+ "\n",
726
+ " net_earnings roi staking nr_mech_calls creation_date \\\n",
727
+ "0 0.072854 0.353167 non_Olas NaN 2025-01-12 \n",
728
+ "1 0.349646 0.366766 non_Olas NaN 2024-12-27 \n",
729
+ "2 -2.207412 -1.000000 non_Olas NaN 2025-01-09 \n",
730
+ "3 0.151567 0.348688 non_Olas NaN 2024-12-27 \n",
731
+ "4 4.145865 1.271938 non_Olas NaN 2025-01-08 \n",
732
+ "\n",
733
+ " month_year_week \n",
734
+ "0 Jan-06-2025 \n",
735
+ "1 Dec-23-2024 \n",
736
+ "2 Jan-06-2025 \n",
737
+ "3 Dec-23-2024 \n",
738
+ "4 Jan-06-2025 \n",
739
+ "\n",
740
+ "[5 rows x 24 columns]"
741
+ ]
742
+ },
743
+ "execution_count": 16,
744
+ "metadata": {},
745
+ "output_type": "execute_result"
746
+ }
747
+ ],
748
+ "source": [
749
+ "unknown_traders.head()"
750
+ ]
751
+ },
752
  {
753
  "cell_type": "code",
754
  "execution_count": 36,
scripts/retention_metrics.py CHANGED
@@ -46,7 +46,6 @@ def calculate_wow_retention_by_type(
46
  for key in week_to_number.keys()
47
  if week_to_number[key] == previous_week_number
48
  ][0]
49
- # previous_week = type_data.iloc[i-1]['month_year_week']
50
  # print(f"previous week = {previous_week}")
51
 
52
  # Get traders in both weeks for this type
@@ -134,10 +133,12 @@ def calculate_cohort_retention(
134
 
135
  # Get all unique weeks and cohorts
136
  all_cohorts = cohort_data["cohort_week"].unique()
 
137
  # print(f"all cohorts = {all_cohorts}")
138
  retention_data = []
139
  max_weeks = 8
140
- for cohort in all_cohorts:
 
141
  # print(f"analyzing cohort {cohort}")
142
  # Get all traders in this cohort
143
  cohort_traders = set(
@@ -146,15 +147,26 @@ def calculate_cohort_retention(
146
  cohort_size = len(cohort_traders)
147
  # print(f"cohort size = {cohort_size}")
148
 
149
- if cohort_size == 0:
150
- print(f"NO new traders for cohort week={cohort}")
151
- continue
152
-
153
  # Calculate retention for each week after the cohort week
154
  for week_idx, week in enumerate(all_weeks):
155
  # print(f"Analyzing week = {week}")
156
- weeks_since_cohort = week_idx - week_to_number[cohort]
157
- if weeks_since_cohort < 0 or weeks_since_cohort > 8:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
158
  continue
159
  # Get active traders from the cohort in current week
160
  current_traders = set(
 
46
  for key in week_to_number.keys()
47
  if week_to_number[key] == previous_week_number
48
  ][0]
 
49
  # print(f"previous week = {previous_week}")
50
 
51
  # Get traders in both weeks for this type
 
133
 
134
  # Get all unique weeks and cohorts
135
  all_cohorts = cohort_data["cohort_week"].unique()
136
+ # extend the cohort
137
  # print(f"all cohorts = {all_cohorts}")
138
  retention_data = []
139
  max_weeks = 8
140
+ # for cohort in all_cohorts:
141
+ for cohort_week_idx, cohort in enumerate(all_weeks):
142
  # print(f"analyzing cohort {cohort}")
143
  # Get all traders in this cohort
144
  cohort_traders = set(
 
147
  cohort_size = len(cohort_traders)
148
  # print(f"cohort size = {cohort_size}")
149
 
 
 
 
 
150
  # Calculate retention for each week after the cohort week
151
  for week_idx, week in enumerate(all_weeks):
152
  # print(f"Analyzing week = {week}")
153
+ weeks_since_cohort = week_idx - cohort_week_idx
154
+ if weeks_since_cohort < 0 or weeks_since_cohort > max_weeks:
155
+ continue
156
+ if cohort_size == 0:
157
+ print(f"NO new traders for cohort week={cohort}")
158
+ retention_data.append(
159
+ {
160
+ "cohort_week": cohort,
161
+ "week": week,
162
+ "weeks_since_cohort": weeks_since_cohort,
163
+ "cohort_size": cohort_size,
164
+ "active_traders": 0,
165
+ "retained_traders": 0,
166
+ "previous_traders": 0,
167
+ "retention_rate": round(0, 2),
168
+ }
169
+ )
170
  continue
171
  # Get active traders from the cohort in current week
172
  current_traders = set(