stevekeyharvey/Polymarket
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

First commit

Browse Source
This commit is contained in:
stevekeyharvey
2026-03-27 12:40:49 -04:00
commit adf3e592eb
7 changed files with 939 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored Normal file
View File

@@ -0,0 +1 @@
.env

4
.vscode/settings.json vendored Normal file
View File

@@ -0,0 +1,4 @@
{
"python-envs.defaultEnvManager": "ms-python.python:conda",
"python-envs.defaultPackageManager": "ms-python.python:conda"
}

BIN
modules/__pycache__/api.cpython-313.pyc Normal file
View File

Binary file not shown.

42
modules/api.py Normal file
View File

@@ -0,0 +1,42 @@
import requests
from py_clob_client.client import ClobClient
import os
from dotenv import load_dotenv
### Functions ###
def check_geoblock() -> None:
response = requests.get("https://polymarket.com/api/geoblock")
geo = response.json()
if geo["blocked"]:
print(f"Trading not available in {geo['country']}")
raise ValueError('GEOBLOCKED - KILLING SCRIPT')
else:
print("Trading available")
def create_client():
print('creating client...')
load_dotenv()
private_key = os.getenv("PRIVATE_KEY")
host = "https://clob.polymarket.com"
chain_id = 137 # Polygon mainnet
temp_client = ClobClient(host, key=private_key, chain_id=chain_id)
api_creds = temp_client.create_or_derive_api_creds()
client = ClobClient(
host,
key=private_key,
chain_id=chain_id,
creds=api_creds,
signature_type=2, # Rabby
funder=os.getenv("FUNDER")
# funder="0xb2967A7e578E700E27611238B7F762BdADC72CcB"
# 0x2bb5be619b8f348954dd2290abcd267735a9f4a0
)
# View your trade history
trades = client.get_trades()
print(f"You've made {len(trades)} trades")
print('client created successfully!')
return client

681
order_entry.ipynb Normal file
View File

@@ -0,0 +1,681 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 64,
"id": "c0bfb3b5",
"metadata": {},
"outputs": [],
"source": [
"import modules.api as api\n",
"import pandas as pd\n",
"from datetime import datetime, timezone, timedelta\n",
"import math\n",
"import requests\n",
"import json\n",
"from dataclasses import dataclass\n",
"\n",
"from py_clob_client.clob_types import OrderArgs, OrderType, PostOrdersArgs, PartialCreateOrderOptions\n",
"from py_clob_client.order_builder.constants import BUY, SELL\n"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "7d7dc787",
"metadata": {},
"outputs": [],
"source": [
"def time_round_down(dt, interval_mins=5) -> int: # returns timestamp in seconds\n",
" interval_secs = interval_mins * 60\n",
" seconds = dt.timestamp()\n",
" rounded_seconds = math.floor(seconds / interval_secs) * interval_secs\n",
" \n",
" return rounded_seconds\n",
"\n",
"def get_mkt_details_by_slug(slug: str) -> dict[str, str, str]: # {'Up' : 123, 'Down': 456, 'isActive': True, 'MinTickSize': 0.01, 'isNegRisk': False}\n",
" r = requests.get(f\"https://gamma-api.polymarket.com/events/slug/{slug}\")\n",
" market = r.json()['markets'][0]\n",
" token_ids = json.loads(market.get(\"clobTokenIds\", \"[]\"))\n",
" outcomes = json.loads(market.get(\"outcomes\", \"[]\"))\n",
" d = dict(zip(outcomes, token_ids))\n",
" d['isActive'] = market['negRisk']\n",
" d['MinTickSize'] = market['orderPriceMinTickSize']\n",
" d['isNegRisk'] = market['negRisk']\n",
" d['ConditionId'] = market['conditionId']\n",
"\n",
" return d, market"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "c3e07e21",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2026-03-27 03:15:00')"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"slug_prefix = 'btc-updown-5m-'\n",
"slug_time_id = time_round_down(dt=datetime.now(timezone.utc))\n",
"slug_full = slug_prefix + str(slug_time_id)\n",
"market_details, market = get_mkt_details_by_slug(slug_full)\n",
"pd.to_datetime(slug_time_id, unit='s')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "5ba43ffc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Up': '97875487643168796351669326324566509161830383659944117871160601839654217457417',\n",
" 'Down': '96344823573113580705457152659674776966355813491715728702490170635510049560213',\n",
" 'isActive': False,\n",
" 'MinTickSize': 0.01,\n",
" 'isNegRisk': False,\n",
" 'ConditionId': '0x071d8568d3d736502bd450e150ef93481992d1d26df0c094cc119246d8931a23'}"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"market_details"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "5d356d3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"creating client...\n",
"You've made 41 trades\n",
"client created successfully!\n"
]
}
],
"source": [
"client = api.create_client()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "bebb53eb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'price': '0.84', 'side': 'BUY'}\n"
]
}
],
"source": [
"last = client.get_last_trade_price(market_details['Up'])\n",
"print(last)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bae5e6a9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 63,
"id": "52c0c38a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'errorMsg': '', 'orderID': '0x4e8e8b193d91c2d4e7b455d3d654a26697ee3289399a9140b2b1888dda7b9a16', 'takingAmount': '10', 'makingAmount': '1.7', 'status': 'matched', 'transactionsHashes': ['0x4f66978cc001a819d9ba266f708148f0512414627a59ffd9a48d8e4b92e5a716'], 'success': True}, {'errorMsg': '', 'orderID': '0x6c5aad6b231c8a6d7a91c1260fe31955e3715e6557aaa773029bd9ec42f26917', 'takingAmount': '', 'makingAmount': '', 'status': 'live', 'success': True}]\n"
]
}
],
"source": [
"response = client.post_orders([\n",
" PostOrdersArgs(\n",
" order=client.create_order(\n",
" order_args=OrderArgs(\n",
" token_id=market_details['Up'],\n",
" price=0.90,\n",
" size=10,\n",
" side=BUY,\n",
" ),\n",
" options=PartialCreateOrderOptions(\n",
" tick_size=str(market_details['MinTickSize']),\n",
" neg_risk=market_details['isNegRisk']\n",
" ),\n",
" ),\n",
" orderType=OrderType.GTC,\n",
" postOnly=False,\n",
" ),\n",
" PostOrdersArgs(\n",
" order=client.create_order(\n",
" order_args=OrderArgs(\n",
" token_id=market_details['Down'],\n",
" price=0.10,\n",
" size=10,\n",
" side=BUY,\n",
" ),\n",
" options=PartialCreateOrderOptions(\n",
" tick_size=str(market_details['MinTickSize']),\n",
" neg_risk=market_details['isNegRisk']\n",
" ),\n",
" ),\n",
" orderType=OrderType.GTC,\n",
" postOnly=True,\n",
" ),\n",
"])\n",
"\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52a7229b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb5f066a",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b3ccf83",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 68,
"id": "75d45cd8",
"metadata": {},
"outputs": [],
"source": [
"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'}]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "f608378f",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
"columns": [
{
"name": "index",
"rawType": "int64",
"type": "integer"
},
{
"name": "asset_id",
"rawType": "object",
"type": "string"
},
{
"name": "price",
"rawType": "object",
"type": "string"
},
{
"name": "size",
"rawType": "object",
"type": "string"
},
{
"name": "side",
"rawType": "object",
"type": "string"
},
{
"name": "hash",
"rawType": "object",
"type": "string"
},
{
"name": "best_bid",
"rawType": "object",
"type": "string"
},
{
"name": "best_ask",
"rawType": "object",
"type": "string"
}
],
"ref": "310c4763-20db-4051-931a-ef52e1b6513b",
"rows": [
[
"0",
"97987758532314346863331680319607711694838937984814950023901315671390566048932",
"0.37",
"429.41",
"BUY",
"999d5b73d83a840c0df7dc2d817ae55a242845d5",
"0.37",
"0.38"
],
[
"1",
"92959981857766705879127008770062050214089835506649207585188324269480756219695",
"0.63",
"429.41",
"SELL",
"ead5af5a55f8b125b1e50f1c80a783c3c2d33187",
"0.62",
"0.63"
]
],
"shape": {
"columns": 7,
"rows": 2
}
},
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_id</th>\n",
" <th>price</th>\n",
" <th>size</th>\n",
" <th>side</th>\n",
" <th>hash</th>\n",
" <th>best_bid</th>\n",
" <th>best_ask</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9798775853231434686333168031960771169483893798...</td>\n",
" <td>0.37</td>\n",
" <td>429.41</td>\n",
" <td>BUY</td>\n",
" <td>999d5b73d83a840c0df7dc2d817ae55a242845d5</td>\n",
" <td>0.37</td>\n",
" <td>0.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9295998185776670587912700877006205021408983550...</td>\n",
" <td>0.63</td>\n",
" <td>429.41</td>\n",
" <td>SELL</td>\n",
" <td>ead5af5a55f8b125b1e50f1c80a783c3c2d33187</td>\n",
" <td>0.62</td>\n",
" <td>0.63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_id price size side \\\n",
"0 9798775853231434686333168031960771169483893798... 0.37 429.41 BUY \n",
"1 9295998185776670587912700877006205021408983550... 0.63 429.41 SELL \n",
"\n",
" hash best_bid best_ask \n",
"0 999d5b73d83a840c0df7dc2d817ae55a242845d5 0.37 0.38 \n",
"1 ead5af5a55f8b125b1e50f1c80a783c3c2d33187 0.62 0.63 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2fe32a82",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "7603be6c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 75,
"id": "c099da9a",
"metadata": {},
"outputs": [],
"source": [
"start = 1774585800\n",
"end = 1774585800 + 60*5"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b631306d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2026-03-27 04:35:00')"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.to_datetime(start, unit='s')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "685d7dd9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"300"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"end - start"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "bdd681cc",
"metadata": {},
"outputs": [],
"source": [
"def format_timestamp(total_seconds):\n",
" minutes, seconds = divmod(total_seconds, 60)\n",
" print(f\"{minutes} minutes and {seconds} seconds\")"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "7ba83cea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 minutes and 0 seconds\n"
]
}
],
"source": [
"format_timestamp(end - start)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "4e211e0d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1774586619"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(datetime.now().timestamp())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5dabe016",
"metadata": {},
"outputs": [],
"source": [
"1774585800"
]
},
{
"cell_type": "code",
"execution_count": 111,
"id": "8a65ba6f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"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": [],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

150
ws.py Normal file
View File

@@ -0,0 +1,150 @@
import asyncio
import json
import math
import pandas as pd
import os
from datetime import datetime, timezone
import websockets
import numpy as np
import talib
import requests
WSS_URL = "wss://ws-subscriptions-clob.polymarket.com/ws/market"
SLUG_END_TIME = 0
HIST_TRADES = np.empty((0, 2))
def format_timestamp(total_seconds) -> str:
minutes, seconds = divmod(total_seconds, 60)
return f"{minutes} minutes and {seconds} seconds"
def time_round_down(dt, interval_mins=5) -> int: # returns timestamp in seconds
interval_secs = interval_mins * 60
seconds = dt.timestamp()
rounded_seconds = math.floor(seconds / interval_secs) * interval_secs
return rounded_seconds
def get_mkt_details_by_slug(slug: str) -> dict[str, str, str]: # {'Up' : 123, 'Down': 456, 'isActive': True, 'MinTickSize': 0.01, 'isNegRisk': False}
r = requests.get(f"https://gamma-api.polymarket.com/events/slug/{slug}")
market = r.json()['markets'][0]
token_ids = json.loads(market.get("clobTokenIds", "[]"))
outcomes = json.loads(market.get("outcomes", "[]"))
d = dict(zip(outcomes, token_ids))
d['isActive'] = market['negRisk']
d['MinTickSize'] = market['orderPriceMinTickSize']
d['isNegRisk'] = market['negRisk']
d['ConditionId'] = market['conditionId']
d['EndDateTime'] = market['endDate']
return d, market
def gen_slug():
slug_prefix = 'btc-updown-5m-'
slug_time_id = time_round_down(dt=datetime.now(timezone.utc))
return slug_prefix + str(slug_time_id)
async def polymarket_stream():
global SLUG_END_TIME
global HIST_TRADES
slug_full = gen_slug()
market_details, market = get_mkt_details_by_slug(slug_full)
TARGET_ASSET_ID = market_details['Up']
SLUG_END_TIME = round(datetime.strptime(market_details['EndDateTime'], '%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=timezone.utc).timestamp())
print(f'********* NEW MKT - END DATETIME: {pd.to_datetime(SLUG_END_TIME, unit='s')} *********')
async with websockets.connect(WSS_URL) as websocket:
print(f"Connected to {WSS_URL}")
subscribe_msg = {
"assets_ids": [TARGET_ASSET_ID],
"type": "market",
"custom_feature_enabled": True
}
await websocket.send(json.dumps(subscribe_msg))
print(f"Subscribed to Asset: {TARGET_ASSET_ID}")
try:
async for message in websocket:
current_ts = round(datetime.now().timestamp())
sec_remaining = SLUG_END_TIME - current_ts
if sec_remaining <= 0:
HIST_TRADES = np.empty((0, 2))
print('*** Attempting to unsub from past 5min')
update_unsub_msg = {
"operation": 'unsubscribe',
"assets_ids": [TARGET_ASSET_ID],
"custom_feature_enabled": True
}
await websocket.send(json.dumps(update_unsub_msg))
print('*** Attempting to SUB to new 5min')
slug_full = gen_slug()
market_details, market = get_mkt_details_by_slug(slug_full)
TARGET_ASSET_ID = market_details['Up']
SLUG_END_TIME = round(datetime.strptime(market_details['EndDateTime'], '%Y-%m-%dT%H:%M:%SZ').replace(tzinfo=timezone.utc).timestamp())
update_sub_msg = {
"operation": 'subscribe',
"assets_ids": [TARGET_ASSET_ID],
"custom_feature_enabled": True
}
await websocket.send(json.dumps(update_sub_msg))
if isinstance(message, str):
data = json.loads(message)
if isinstance(data, dict):
# print(data.get("event_type", None))
pass
elif isinstance(data, list):
print('initial book: ')
print(data)
continue
else:
raise ValueError(f'Type: {type(data)} not expected: {message}')
event_type = data.get("event_type", None)
if event_type == "price_change":
# print("📈 Price Change")
# print(pd.DataFrame(data['price_changes']))
pass
elif event_type == "best_bid_ask":
# print(pd.DataFrame([data]))
pass
elif event_type == "last_trade_price":
px = float(data['price'])
qty = float(data['size'])
HIST_TRADES = np.append(HIST_TRADES, np.array([[px, qty]]), axis=0)
SMA = talib.ROC(HIST_TRADES[:,0], timeperiod=10)[-1]
print(f"✨ Last Px: {px:.2f}; ROC: {SMA:.4f}; Qty: {qty:6.2f}; Sec Left: {sec_remaining}")
elif event_type == "book":
pass
elif event_type == "new_market":
print('Received new_market')
elif event_type == "market_resolved":
print(f"Received: {event_type}")
print(data)
elif event_type == "tick_size_change": # may want for CLOB order routing
print(f"Received: {event_type}")
print(data)
else:
print(f"Received: {event_type}")
print(data)
except websockets.ConnectionClosed:
print("Connection closed by server.")
if __name__ == '__main__':
try:
asyncio.run(polymarket_stream())
except KeyboardInterrupt:
print("Stream stopped.")

61
ws_rtds.py Normal file
View File

@@ -0,0 +1,61 @@
import asyncio
import json
import math
import pandas as pd
import os
from datetime import datetime, timezone
import websockets
import numpy as np
import talib
import requests
WSS_URL = "wss://ws-live-data.polymarket.com"
HIST_TRADES = np.empty((0, 2))
async def rtds_stream():
global HIST_TRADES
async with websockets.connect(WSS_URL) as websocket:
print(f"Connected to {WSS_URL}")
subscribe_msg = {
"action": "subscribe",
"subscriptions": [
{
"topic": "crypto_prices_chainlink",
"type": "*",
"filters": "{\"symbol\":\"btc/usd\"}"
}
]
}
await websocket.send(json.dumps(subscribe_msg))
try:
async for message in websocket:
if isinstance(message, str):
try:
data = json.loads(message)
if data['payload'].get('value', None) is not None:
print(f'🤑 BTC Chainlink Last Px: {data['payload']['value']:_.4f}; TS: {pd.to_datetime(data['timestamp'], unit='ms')}')
else:
print(f'Initial or unexpected data struct, skipping: {data}')
continue
except (json.JSONDecodeError, ValueError):
print(f'Message not in JSON format, skipping: {message}')
continue
else:
raise ValueError(f'Type: {type(data)} not expected: {message}')
except websockets.ConnectionClosed:
print("Connection closed by server.")
if __name__ == '__main__':
try:
asyncio.run(rtds_stream())
except KeyboardInterrupt:
print("Stream stopped.")