682 lines
16 KiB
Plaintext
682 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 64,
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"id": "c0bfb3b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import modules.api as api\n",
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"import pandas as pd\n",
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"from datetime import datetime, timezone, timedelta\n",
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"import math\n",
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"import requests\n",
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"import json\n",
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"from dataclasses import dataclass\n",
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"\n",
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"from py_clob_client.clob_types import OrderArgs, OrderType, PostOrdersArgs, PartialCreateOrderOptions\n",
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"from py_clob_client.order_builder.constants import BUY, SELL\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": 65,
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"id": "7d7dc787",
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"metadata": {},
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"outputs": [],
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"source": [
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"def time_round_down(dt, interval_mins=5) -> int: # returns timestamp in seconds\n",
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" interval_secs = interval_mins * 60\n",
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" seconds = dt.timestamp()\n",
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" rounded_seconds = math.floor(seconds / interval_secs) * interval_secs\n",
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" \n",
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" return rounded_seconds\n",
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"\n",
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"def get_mkt_details_by_slug(slug: str) -> dict[str, str, str]: # {'Up' : 123, 'Down': 456, 'isActive': True, 'MinTickSize': 0.01, 'isNegRisk': False}\n",
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" r = requests.get(f\"https://gamma-api.polymarket.com/events/slug/{slug}\")\n",
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" market = r.json()['markets'][0]\n",
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" token_ids = json.loads(market.get(\"clobTokenIds\", \"[]\"))\n",
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" outcomes = json.loads(market.get(\"outcomes\", \"[]\"))\n",
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" d = dict(zip(outcomes, token_ids))\n",
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" d['isActive'] = market['negRisk']\n",
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" d['MinTickSize'] = market['orderPriceMinTickSize']\n",
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" d['isNegRisk'] = market['negRisk']\n",
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" d['ConditionId'] = market['conditionId']\n",
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"\n",
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" return d, market"
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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": 66,
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"id": "c3e07e21",
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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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"Timestamp('2026-03-27 03:15:00')"
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]
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},
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"execution_count": 66,
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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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"slug_prefix = 'btc-updown-5m-'\n",
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"slug_time_id = time_round_down(dt=datetime.now(timezone.utc))\n",
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"slug_full = slug_prefix + str(slug_time_id)\n",
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"market_details, market = get_mkt_details_by_slug(slug_full)\n",
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"pd.to_datetime(slug_time_id, unit='s')"
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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": 67,
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"id": "5ba43ffc",
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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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"{'Up': '97875487643168796351669326324566509161830383659944117871160601839654217457417',\n",
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" 'Down': '96344823573113580705457152659674776966355813491715728702490170635510049560213',\n",
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" 'isActive': False,\n",
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" 'MinTickSize': 0.01,\n",
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" 'isNegRisk': False,\n",
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" 'ConditionId': '0x071d8568d3d736502bd450e150ef93481992d1d26df0c094cc119246d8931a23'}"
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]
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},
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"execution_count": 67,
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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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"market_details"
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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": 61,
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"id": "5d356d3b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"creating client...\n",
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"You've made 41 trades\n",
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"client created successfully!\n"
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]
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}
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],
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"source": [
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"client = api.create_client()"
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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": 62,
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"id": "bebb53eb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'price': '0.84', 'side': 'BUY'}\n"
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]
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}
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],
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"source": [
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"last = client.get_last_trade_price(market_details['Up'])\n",
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"print(last)"
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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": "bae5e6a9",
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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": 63,
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"id": "52c0c38a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'errorMsg': '', 'orderID': '0x4e8e8b193d91c2d4e7b455d3d654a26697ee3289399a9140b2b1888dda7b9a16', 'takingAmount': '10', 'makingAmount': '1.7', 'status': 'matched', 'transactionsHashes': ['0x4f66978cc001a819d9ba266f708148f0512414627a59ffd9a48d8e4b92e5a716'], 'success': True}, {'errorMsg': '', 'orderID': '0x6c5aad6b231c8a6d7a91c1260fe31955e3715e6557aaa773029bd9ec42f26917', 'takingAmount': '', 'makingAmount': '', 'status': 'live', 'success': True}]\n"
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]
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}
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],
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"source": [
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"response = client.post_orders([\n",
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" PostOrdersArgs(\n",
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" order=client.create_order(\n",
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" order_args=OrderArgs(\n",
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" token_id=market_details['Up'],\n",
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" price=0.90,\n",
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" size=10,\n",
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" side=BUY,\n",
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" ),\n",
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" options=PartialCreateOrderOptions(\n",
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" tick_size=str(market_details['MinTickSize']),\n",
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" neg_risk=market_details['isNegRisk']\n",
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" ),\n",
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" ),\n",
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" orderType=OrderType.GTC,\n",
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" postOnly=False,\n",
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" ),\n",
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" PostOrdersArgs(\n",
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" order=client.create_order(\n",
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" order_args=OrderArgs(\n",
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" token_id=market_details['Down'],\n",
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" price=0.10,\n",
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" size=10,\n",
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" side=BUY,\n",
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" ),\n",
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" options=PartialCreateOrderOptions(\n",
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" tick_size=str(market_details['MinTickSize']),\n",
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" neg_risk=market_details['isNegRisk']\n",
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" ),\n",
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" ),\n",
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" orderType=OrderType.GTC,\n",
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" postOnly=True,\n",
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" ),\n",
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"])\n",
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"\n",
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"print(response)"
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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": "52a7229b",
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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": "fb5f066a",
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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": "4b3ccf83",
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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": 68,
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"id": "75d45cd8",
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"metadata": {},
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"outputs": [],
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"source": [
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"d = [{'asset_id': '97987758532314346863331680319607711694838937984814950023901315671390566048932', 'price': '0.37', 'size': '429.41', 'side': 'BUY', 'hash': '999d5b73d83a840c0df7dc2d817ae55a242845d5', 'best_bid': '0.37', 'best_ask': '0.38'}, {'asset_id': '92959981857766705879127008770062050214089835506649207585188324269480756219695', 'price': '0.63', 'size': '429.41', 'side': 'SELL', 'hash': 'ead5af5a55f8b125b1e50f1c80a783c3c2d33187', 'best_bid': '0.62', 'best_ask': '0.63'}]"
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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": 69,
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"id": "f608378f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.microsoft.datawrangler.viewer.v0+json": {
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"columns": [
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{
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"name": "index",
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"rawType": "int64",
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"type": "integer"
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},
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{
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"name": "asset_id",
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"rawType": "object",
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"type": "string"
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},
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{
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"name": "price",
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"rawType": "object",
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"type": "string"
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},
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{
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"name": "size",
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"rawType": "object",
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"type": "string"
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},
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{
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"name": "side",
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"rawType": "object",
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"type": "string"
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},
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{
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"name": "hash",
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"rawType": "object",
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"type": "string"
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},
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{
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"name": "best_bid",
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"rawType": "object",
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"type": "string"
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},
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{
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"name": "best_ask",
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"rawType": "object",
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"type": "string"
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}
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],
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"ref": "310c4763-20db-4051-931a-ef52e1b6513b",
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"rows": [
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[
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"0",
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"97987758532314346863331680319607711694838937984814950023901315671390566048932",
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"0.37",
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"429.41",
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"BUY",
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"999d5b73d83a840c0df7dc2d817ae55a242845d5",
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"0.37",
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"0.38"
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],
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[
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"1",
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"92959981857766705879127008770062050214089835506649207585188324269480756219695",
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"0.63",
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"429.41",
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"SELL",
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"ead5af5a55f8b125b1e50f1c80a783c3c2d33187",
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"0.62",
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"0.63"
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]
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],
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"shape": {
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"columns": 7,
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"rows": 2
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}
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},
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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>asset_id</th>\n",
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" <th>price</th>\n",
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" <th>size</th>\n",
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" <th>side</th>\n",
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" <th>hash</th>\n",
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" <th>best_bid</th>\n",
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" <th>best_ask</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>0</th>\n",
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" <td>9798775853231434686333168031960771169483893798...</td>\n",
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" <td>0.37</td>\n",
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" <td>429.41</td>\n",
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" <td>BUY</td>\n",
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" <td>999d5b73d83a840c0df7dc2d817ae55a242845d5</td>\n",
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" <td>0.37</td>\n",
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" <td>0.38</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>9295998185776670587912700877006205021408983550...</td>\n",
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" <td>0.63</td>\n",
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" <td>429.41</td>\n",
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" <td>SELL</td>\n",
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" <td>ead5af5a55f8b125b1e50f1c80a783c3c2d33187</td>\n",
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" <td>0.62</td>\n",
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" <td>0.63</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" asset_id price size side \\\n",
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"0 9798775853231434686333168031960771169483893798... 0.37 429.41 BUY \n",
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"1 9295998185776670587912700877006205021408983550... 0.63 429.41 SELL \n",
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"\n",
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" hash best_bid best_ask \n",
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"0 999d5b73d83a840c0df7dc2d817ae55a242845d5 0.37 0.38 \n",
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"1 ead5af5a55f8b125b1e50f1c80a783c3c2d33187 0.62 0.63 "
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]
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},
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"execution_count": 69,
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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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"pd.DataFrame(d)"
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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": "2fe32a82",
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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": "7603be6c",
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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": 75,
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"id": "c099da9a",
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"metadata": {},
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"outputs": [],
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"source": [
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"start = 1774585800\n",
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"end = 1774585800 + 60*5"
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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": "b631306d",
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"metadata": {},
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"outputs": [
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{
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"data": {
|
|
"text/plain": [
|
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"Timestamp('2026-03-27 04:35:00')"
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]
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},
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"execution_count": 76,
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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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"pd.to_datetime(start, unit='s')"
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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": 77,
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"id": "685d7dd9",
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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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"300"
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]
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},
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"execution_count": 77,
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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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"end - start"
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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": 78,
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"id": "bdd681cc",
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"metadata": {},
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"outputs": [],
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"source": [
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"def format_timestamp(total_seconds):\n",
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" minutes, seconds = divmod(total_seconds, 60)\n",
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" print(f\"{minutes} minutes and {seconds} seconds\")"
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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": 79,
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"id": "7ba83cea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"5 minutes and 0 seconds\n"
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]
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}
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],
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"source": [
|
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"format_timestamp(end - start)"
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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": 88,
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"id": "4e211e0d",
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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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"1774586619"
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]
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},
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"execution_count": 88,
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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": "5dabe016",
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"metadata": {},
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"outputs": [],
|
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"source": [
|
|
"1774585800"
|
|
]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 111,
|
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"id": "8a65ba6f",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
|
"data": {
|
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"text/plain": [
|
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"Timestamp('2026-03-27 03:20:00')"
|
|
]
|
|
},
|
|
"execution_count": 111,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.to_datetime(1774581600, unit='s')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 91,
|
|
"id": "83f63c10",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sdt = '2026-03-27T03:20:00Z'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 110,
|
|
"id": "1f907a71",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1774581600"
|
|
]
|
|
},
|
|
"execution_count": 110,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"round(datetime.strptime(sdt, '%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=timezone.utc).timestamp())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 113,
|
|
"id": "fe7c0c12",
|
|
"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
|
"d = {\"connection_id\":\"a4-i0ecArPECFLQ=\",\"payload\":{\"full_accuracy_value\":\"65779051825748830000000\",\"symbol\":\"btc/usd\",\"timestamp\":1774627572000,\"value\":65779.05182574883},\"timestamp\":1774627573524,\"topic\":\"crypto_prices_chainlink\",\"type\":\"update\"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 114,
|
|
"id": "a98ace05",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'connection_id': 'a4-i0ecArPECFLQ=',\n",
|
|
" 'payload': {'full_accuracy_value': '65779051825748830000000',\n",
|
|
" 'symbol': 'btc/usd',\n",
|
|
" 'timestamp': 1774627572000,\n",
|
|
" 'value': 65779.05182574883},\n",
|
|
" 'timestamp': 1774627573524,\n",
|
|
" 'topic': 'crypto_prices_chainlink',\n",
|
|
" 'type': 'update'}"
|
|
]
|
|
},
|
|
"execution_count": 114,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"d"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 120,
|
|
"id": "d2278853",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Timestamp('2026-03-27 16:06:13.524000')"
|
|
]
|
|
},
|
|
"execution_count": 120,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.to_datetime(d['timestamp'], unit='ms')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "008cb5c9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f6c647b7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e1b8d116",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3f1e3a83",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "py313",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.2"
|
|
}
|
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},
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"nbformat": 4,
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