307 lines
7.1 KiB
Plaintext
307 lines
7.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "7a3f41bd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import requests\n",
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"from datetime import datetime"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"id": "3b48e1ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"mexc_all_tickers = 'https://api.mexc.com/api/v1/contract/ticker'\n",
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"edgex_all_tickers = 'https://pro.edgex.exchange/api/v1/public/quote/getTicker/?contractId=10000001'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "ab38d984",
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"metadata": {},
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"outputs": [],
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"source": [
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"r = requests.get(mexc_all_tickers)\n",
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"data = r.json()['data']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"id": "2976b377",
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"metadata": {},
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"outputs": [],
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"source": [
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"r = requests.get(edgex_all_tickers)\n",
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"data = r.json()['data']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "1139b1a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame(data)\n",
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"df['fundingRate_pct'] = df['fundingRate']*100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b00512dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_trim = df[['symbol','fundingRate_pct','volume24']].copy()\n",
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"df_trim = df_trim.loc[df_trim['volume24'] > 10_000]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "f7b44068",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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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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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>symbol</th>\n",
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" <th>fundingRate_pct</th>\n",
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" <th>volume24</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>55</th>\n",
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" <td>DRIFT_USDT</td>\n",
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" <td>-1.1360</td>\n",
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" <td>3308466</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>RED_USDT</td>\n",
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" <td>-1.1138</td>\n",
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" <td>105826673</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>157</th>\n",
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" <td>PIXEL_USDT</td>\n",
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" <td>-0.4059</td>\n",
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" <td>12415472</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>105</th>\n",
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" <td>NIL_USDT</td>\n",
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" <td>-0.3846</td>\n",
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" <td>31438005</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>33</th>\n",
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" <td>SUPER_USDT</td>\n",
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" <td>-0.3718</td>\n",
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" <td>2469502</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>PLAY_USDT</td>\n",
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" <td>0.0838</td>\n",
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" <td>22168649</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>414</th>\n",
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" <td>PUMPBTC_USDT</td>\n",
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" <td>0.0913</td>\n",
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" <td>745129</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>398</th>\n",
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" <td>QQQSTOCK_USDT</td>\n",
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" <td>0.0969</td>\n",
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" <td>59485</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>222</th>\n",
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" <td>GUA_USDT</td>\n",
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" <td>0.0996</td>\n",
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" <td>45623</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>265</th>\n",
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" <td>BROCCOLIF3B_USDT</td>\n",
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" <td>0.1647</td>\n",
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" <td>3936939</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>837 rows × 3 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" symbol fundingRate_pct volume24\n",
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"55 DRIFT_USDT -1.1360 3308466\n",
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"10 RED_USDT -1.1138 105826673\n",
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"157 PIXEL_USDT -0.4059 12415472\n",
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"105 NIL_USDT -0.3846 31438005\n",
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"33 SUPER_USDT -0.3718 2469502\n",
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".. ... ... ...\n",
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"12 PLAY_USDT 0.0838 22168649\n",
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"414 PUMPBTC_USDT 0.0913 745129\n",
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"398 QQQSTOCK_USDT 0.0969 59485\n",
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"222 GUA_USDT 0.0996 45623\n",
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"265 BROCCOLIF3B_USDT 0.1647 3936939\n",
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"\n",
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"[837 rows x 3 columns]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_trim.sort_values('fundingRate_pct', ascending=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6775a03f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1775583908"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"round(datetime.now().timestamp())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "edca3b9f",
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"metadata": {},
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"outputs": [],
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"source": [
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"### PRIVATE API ###\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51e50e0d",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "21aabf30",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f91b1c04",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "71e551f4",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "py_313",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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