Your Polymarket Bot Has Live Prices. That Doesn't Mean It Has the Latest Order Book.
A trading bot can receive price updates every second and still make decisions using a market state that no longer exists.
That sounds contradictory until you look at how an order book changes.
A bot sees an attractive ask, calculates an edge, and prepares an order. Before that order reaches the exchange, someone cancels the liquidity, another trader takes the offer, or the best available price changes.
The bot's calculation may have been correct when it started. The market it calculated against is gone.
This is one of the problems that separates a bot that merely consumes market data from one that handles live trading conditions properly.
A price is not the same thing as a market state
Imagine your Polymarket bot estimates that a YES share is worth $0.64.
Its market-data handler reports an ask of $0.59, so the strategy identifies a potential five-cent edge.
But three details matter:
- How recently was that ask observed?
- How much liquidity was available at that price?
- Does the price still exist when the order is submitted?
Suppose the $0.59 ask disappears and the next available ask is $0.63.
Your original edge has almost vanished before accounting for fees and other costs.
A system that stores only the latest price can miss the sequence of events that produced this situation. A system that tracks the state of the order book has a better chance of detecting it.
The distinction matters because live market data is a stream of changing observations, not a guarantee that a displayed opportunity will remain executable.
Why WebSocket updates need careful handling
Polymarket's CLOB WebSocket market channel provides events such as order-book snapshots and price changes. Developers can use these events to maintain a local view of the market. See the "Polymarket WebSocket documentation" (github.com/Polymarket/agent-skills/blob/main/webs…).
The challenge is keeping that local view trustworthy.
A basic implementation might update a variable whenever a new price arrives:
best_ask = incoming_update["best_ask"]
That can be useful for a simple example, but a production system needs more context. Not every event necessarily represents a complete replacement for the order book. Some updates describe changes to particular prices or assets.
If the bot treats every message as a full snapshot, it can lose information. If it applies updates incorrectly, its local book can diverge from the exchange's current state.
The result is a strategy making decisions from data that looks valid but is incomplete.
Build a local book, not just a price variable
A more robust architecture separates market-data ingestion from strategy execution.
The data layer receives events and updates the local order book. The strategy reads a consistent view of that book, evaluates the opportunity, and passes a proposed order to an execution layer.
That execution layer performs its own checks before submission.
A simplified flow looks like this:
WebSocket events
|
v
Event validation
|
v
Local order-book state
|
v
Strategy evaluation
|
v
Pre-trade validation
|
v
Order submission
|
v
Fill and order-status tracking
Each stage has a separate responsibility.
The data layer should understand the event types it receives. The strategy should calculate its edge using the appropriate side of the book. The execution layer should reject orders that no longer satisfy the strategy's price, size, or risk constraints.
This separation also makes debugging easier. If a trade performs badly, you can investigate whether the problem came from the signal, the book state, or execution.
What should happen after a disconnect?
Consider a more dangerous situation.
Your bot loses its WebSocket connection for several seconds. During that period, prices move and orders are cancelled. The connection reconnects, and new messages begin arriving.
Should the bot immediately resume trading?
Not necessarily.
The system first needs to establish whether its local market state is trustworthy. Depending on the documented protocol and the events received, that may require obtaining a fresh book snapshot and rebuilding local state before allowing new decisions.
A conservative recovery procedure is:
1. Mark the affected market data as unavailable or untrusted.
2. Stop generating new orders that depend on that state.
3. Reconnect and restore the local book using the supported snapshot-and-update process.
4. Validate the reconstructed state before enabling the strategy again.
5. Reconcile outstanding orders and fills separately from market-data recovery.
The last point is easy to overlook. Restoring the order book does not tell you whether an order submitted before the disconnect was accepted, partially filled, or still resting. Account and order state need their own reconciliation process.
A reconnect is not proof that everything is synchronized.
Add a pre-trade freshness check
Even a correctly maintained local book can become outdated between the strategy's calculation and order submission.
Your bot should therefore define what it considers an acceptable data state before placing an order.
For example, a pre-trade validator might check:
- Whether the market-data connection is healthy.
- Whether the local book has been initialized and remains valid.
- Whether the latest relevant update falls within the strategy's configured freshness limit.
- Whether the intended size is supported by available liquidity.
- Whether the executable price still leaves enough expected edge after estimated costs.
The freshness limit should depend on the strategy. A bot reacting to rapid price changes may require stricter limits than one trading infrequently.
Also, do not assume that a recent message automatically proves that the entire book is synchronized. Freshness and consistency are related, but they are not interchangeable.
Test the failures, not just the happy path
A bot that works while the network is stable has passed only one part of its engineering test.
Build tests that simulate:
- A price changing immediately before order submission.
- Liquidity disappearing at the intended entry price.
- A disconnect followed by reconnection.
- Duplicate or unexpected events.
- A partially filled order during a market-data interruption.
- A book that cannot be confidently reconstructed.
For each scenario, define the expected behavior before running the test. Should the bot reject the trade, pause the market, rebuild state, or reconcile an outstanding order?
That turns resilience from a vague objective into something measurable.
You can start with recorded market-data events and simulated order responses before introducing real capital. A deterministic replay also makes it easier to reproduce a bug that occurred during a live session.
The real objective is trustworthy decisions
Real-time data is valuable only when the system understands what that data represents and when it is safe to act on it.
For Polymarket bot developers, this means treating the order book as state that must be maintained and validated, not as a number that can simply be overwritten whenever a message arrives.
The strongest implementation is not necessarily the one that reacts to every update fastest. It is the one that knows when its view of the market is reliable, when an opportunity has disappeared, and when it should refuse to trade.
That discipline will not guarantee profitable trades. It will, however, help prevent avoidable execution mistakes from being mistaken for strategy failures.
Building a Polymarket trading bot, copy-trading system, or custom market-data workflow? Explore what Dexoryn Labs builds at dexorynlabs.com.
Polymarket Trading Bot: Build a Market Maker Bot in Python- Project Setup and API Integration
Day 3 of 7: Building a Polymarket Market Maker Bot From Zero to Complete
In the first two parts of this series, we explored what a Polymarket market maker bot is and how the Polymarket order book works.
We covered YES and NO outcome tokens, bid-ask spreads, market depth, limit orders, and the risks of providing liquidity.
Now, let's start building.
In this tutorial, we'll set up a Python project, connect to Polymarket's public market data API, discover available markets, retrieve outcome asset identifiers, and inspect an order book.
By the end, you'll have the foundation of a Python market maker bot that can read market data and calculate basic pricing metrics.
Important: This tutorial is read-only. It does not submit orders, spend funds, or require a private key.
What We'll Build
Our first version will perform five tasks:
1. Connect to Polymarket's public API.
2. Discover available markets.
3. Identify a market's YES and NO assets.
4. Retrieve the order book for a selected asset.
5. Calculate the best bid, best ask, spread, and midpoint.
The data flow looks like this:
Polymarket Market Data
|
v
Market Discovery
|
v
Select a Market
|
v
Identify YES / NO Assets
|
v
Fetch Order Book
|
v
Calculate Spread & Midpoint
|
v
Print Results
This is a small program, but it establishes an important component of every trading bot: the market data layer.
!polymarket trading bot
A production market maker would extend this foundation with real-time data subscriptions, strategy logic, order management, inventory tracking, risk controls, and monitoring.
1. Choose the Python SDK
Polymarket provides developer interfaces for market discovery, order books, pricing, and trading.
For this tutorial, we'll use the unified Python SDK identified in the project references:
Official repository: Polymarket Python SDK
The package used in the examples is polymarket-client.
The SDK is intended to provide access to market information and other Polymarket workflows. Public market-data access does not require you to configure trading credentials for this read-only exercise.
Before installing it, check the repository's current installation instructions and examples. SDK interfaces can change, and code written for an older client may not work with the current version.
2. Set Up Your Development Environment
You can follow this tutorial on Windows, macOS, or Linux.
Check Python
python --version
On some systems, use:
python3 --version
Create a project directory
mkdir polymarket-market-maker
cd polymarket-market-maker
Create a virtual environment
python -m venv .venv
Activate it on Windows PowerShell:
.venv\Scripts\Activate.ps1
On macOS or Linux:
source .venv/bin/activate
Install the SDK
python -m pip install --upgrade pip
pip install polymarket-client
Verify the import:
python -c "from polymarket import PublicClient; print('SDK import successful')"
If the import fails, check the SDK's current Python requirements and installation instructions before proceeding.
3. Create a Clean Project Structure
A common beginner mistake is putting market discovery, pricing calculations, and trading logic into one large Python file.
Let's separate responsibilities from the beginning.
polymarket-market-maker/
├── src/
│ └── polymarket_mm/
│ ├── __init__.py
│ ├── market_data.py
│ ├── orderbook.py
│ ├── pricing.py
│ └── main.py
├── tests/
├── .gitignore
└── README.md
Each module has a specific responsibility:
- market_data.py — discovers markets and retrieves market information.
- orderbook.py — retrieves and inspects order book levels.
- pricing.py — calculates spread and midpoint.
- main.py — connects the modules and runs the example.
- tests/ — contains tests for pricing calculations and other logic.
This structure is intentionally simple. We don't need a complex architecture before our first API request works.
4. Discover Markets Through the SDK
Before inspecting an order book, the bot needs to discover which markets are available.
Create src/polymarket_mm/market_data.py:
from polymarket import PublicClient
def list_markets(limit: int = 10):
"""Return a page of markets from Polymarket."""
with PublicClient() as client:
page = client.list_markets(
closed=False,
page_size=limit,
).first_page()
return page.items
if __name__ == "__main__":
markets = list_markets()
for market in markets:
print(
{
"question": market.question,
"slug": market.slug,
"market_id": market.id,
}
)
Run the module from your project root.
On macOS or Linux:
PYTHONPATH=src python -m polymarket_mm.market_data
On Windows PowerShell:
$env:PYTHONPATH = "src"
python -m polymarket_mm.market_data
The program requests a page of markets and prints basic information about each result.
One important detail: closed=False does not necessarily mean a market is currently accepting orders. A production bot should separately validate market status, trading permissions, and the availability of a usable order book.
For the current implementation, consult the official market discovery example.
5. Understand Market IDs and Asset IDs
A market and a tradable asset are not the same thing.
A market represents an event or question.
An asset identifier identifies a tradable outcome or position associated with that market.
For a conventional binary market, there are typically YES and NO outcomes. However, market infrastructure can evolve, so avoid assuming every market exposes its tradable identifiers in exactly the same format.
The SDK's market model may expose a token ID, position ID, or another identifier depending on the market representation.
The key principle is simple:
Use the current SDK's market model and official examples instead of extracting identifiers from URLs or relying on undocumented raw JSON structures.
You can review the SDK's market helper examples to understand how outcome identifiers are accessed.
6. Retrieve an Order Book
Now let's inspect an actual market.
The following example searches a limited page of open markets until it finds one with a usable YES asset identifier. It then requests the corresponding order book.
Create src/polymarket_mm/orderbook.py:
from polymarket import PublicClient
def inspect_first_available_market():
with PublicClient() as client:
markets = client.list_markets(
closed=False,
page_size=100,
).first_page().items
for market in markets:
# Conventional binary-market example.
# Check the SDK model for your market version.
yes = market.outcomes.yes
asset_id = yes.token_id or yes.position_id
if asset_id is None:
continue
try:
book = client.get_order_book(
asset_id=asset_id
)
except Exception as exc:
print(
f"Skipping market {market.id}: "
f"could not retrieve its book: {exc}"
)
continue
if not book.bids and not book.asks:
continue
return market, asset_id, book
raise RuntimeError(
"No market with a usable YES order book was found. "
"Try again or refine market discovery."
)
def main():
market, asset_id, book = inspect_first_available_market()
print("Market:", market.question)
print("Market ID:", market.id)
print("Asset ID:", asset_id)
print("Number of bids:", len(book.bids))
print("Number of asks:", len(book.asks))
print("\nTop bids:")
for level in sorted(
book.bids,
key=lambda item: item.price,
reverse=True,
)[:5]:
print("Price:", level.price, "Size:", level.size)
print("\nTop asks:")
for level in sorted(
book.asks,
key=lambda item: item.price,
)[:5]:
print("Price:", level.price, "Size:", level.size)
if __name__ == "__main__":
main()
Run it:
PYTHONPATH=src python -m polymarket_mm.orderbook
On Windows PowerShell, use the PYTHONPATH command from the previous section.
The program prints the market question, asset identifier, number of bid and ask levels, and the five best levels on each side.
A few things to remember:
- The best bid is the highest available buy price.
- The best ask is the lowest available sell price.
- The size indicates the quantity available at a price level.
- An empty order book does not provide enough information to calculate both sides of the spread.
The code is an educational starting point, not a guarantee that every market returned by the API will expose these exact fields. Verify the current SDK model before running it.
For another reference, see the official market prices example.
7. Calculate the Spread and Midpoint
Once we have the order book, we can calculate two basic market metrics.
Bid-ask spread
The spread measures the difference between the best ask and the best bid.
[
\text{Spread} = \text{Best Ask} - \text{Best Bid}
]
For example, suppose the best bid is $0.48 and the best ask is $0.52.
The spread is:
0.52 - 0.48 = 0.04
The spread is four cents per share.
Midpoint
The midpoint is the average of the best bid and best ask.
[
\text{Midpoint} =
\frac{\text{Best Bid}+\text{Best Ask}}{2}
]
Using the same example:
(0.48 + 0.52) / 2 = 0.50
The midpoint is $0.50.
Important: The midpoint is a reference price, not a guaranteed execution price. A market order or limit order may execute at a different price depending on liquidity and market conditions.
Implement the calculations
Create src/polymarket_mm/pricing.py:
def calculate_book_metrics(bids, asks):
"""Calculate basic metrics from order book levels."""
best_bid = max(
(float(level.price) for level in bids),
default=None,
)
best_ask = min(
(float(level.price) for level in asks),
default=None,
)
if best_bid is None or best_ask is None:
return {
"best_bid": best_bid,
"best_ask": best_ask,
"spread": None,
"midpoint": None,
}
if best_bid > best_ask:
raise ValueError(
"The book appears crossed; inspect the data "
"and API response before using it."
)
return {
"best_bid": best_bid,
"best_ask": best_ask,
"spread": best_ask - best_bid,
"midpoint": (best_bid + best_ask) / 2,
}
You can import this function into the order book example:
from polymarket_mm.pricing import calculate_book_metrics
metrics = calculate_book_metrics(
book.bids,
book.asks,
)
print("\nBook metrics:")
for name, value in metrics.items():
print(f"{name}: {value}")
For financial calculations and order construction, prefer Decimal or the SDK's decimal-compatible types over binary floating-point arithmetic.
The example uses float for simplicity and educational display.
8. Add Error Handling and Logging
Real-world API requests can fail.
Common causes include:
- Network timeouts.
- Rate limits.
- Invalid asset identifiers.
- Temporary service errors.
- Disconnected WebSocket connections.
- Missing or stale market data.
Different errors require different responses.
A timeout may justify a controlled retry. A rate-limit response should trigger appropriate backoff. An invalid identifier may indicate a market-selection problem.
Stale data is particularly important for a market maker. If the order book is outdated, the bot should not continue generating quotes as if the information were current.
Start with Python's standard logging module:
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s %(levelname)s %(message)s",
)
logger = logging.getLogger(__name__)
logger.info("Market data module started")
As the project grows, structured logs will help investigate market data updates, order submissions, fills, cancellations, and risk-limit events.
Avoid infinite retry loops. A robust bot should stop making trading decisions when its data cannot be trusted.
9. Why We Are Not Placing Orders Yet
At this stage, our program can retrieve market information, but it is not yet a market maker.
It does not know:
- What prices it should quote.
- How large its orders should be.
- When it should cancel or replace orders.
- How to manage inventory.
- How to control exposure and capital usage.
- How to reconcile partial fills and failed requests.
Adding live order placement before implementing these components would be premature.
A safer development sequence is:
1. Retrieve market data reliably.
2. Calculate candidate quotes.
3. Validate quotes against price and risk limits.
4. Simulate decisions using recorded or live market data.
5. Implement order lifecycle management.
6. Test failures, cancellations, and partial fills.
7. Consider controlled live testing only after sufficient validation.
Separating market data from order execution makes the bot easier to test and reduces the risk of an early coding mistake spending real funds.
10. What We Built Today
By completing Day 3, we have established the initial market data layer.
We now have a foundation for:
- Installing and importing the Python SDK.
- Discovering open markets.
- Identifying a usable YES asset identifier.
- Retrieving an order book.
- Inspecting bid and ask levels.
- Calculating spread and midpoint metrics.
- Recognizing the importance of API errors and stale data.
This is the first step toward a more complete market maker bot.
Day 4: Design the Market-Making Strategy
In Day 4, we'll move from observing the market to generating candidate quotes.
We'll explore:
- How to select a reference price.
- How to calculate bid and ask quotes.
- How to set a target spread.
- How to choose order sizes.
- How to adjust quotes based on inventory.
- Why fixed-distance-from-midpoint strategies can perform poorly during rapid price movements.
The objective is to build a strategy layer that can be tested before connecting it to live order execution.
Follow the Project
GitHub repository: Polymarket Trading Bot in Python
YouTube tutorial: Watch the video
Contact and discussion: Telegram — BenjaminCup
Follow the series as we build a Polymarket market maker bot from the fundamentals toward a complete Python implementation.
#Python #Polymarket #TradingBot #AlgoTrading #Crypto
5 reactions
Polymarket's official agents repo is worth reading if you're building prediction market bots. It wires up market data, LLM research, and CLOB order placement. Check key handling and order limits before letting it trade live. Polymarket/agents
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How to Build a Reliable Polymarket WebSocket Monitor in Python
A Polymarket trading bot needs timely market data to make informed execution decisions. But connecting to a WebSocket and receiving messages is only the beginning. Connections can drop, messages can be delayed, and a bot can accidentally keep using an old order-book snapshot after reconnecting.
A reliable monitor must do three things well: consume market updates, detect when its data is no longer trustworthy, and rebuild its state after a connection failure.
This tutorial builds a small Python monitor around Polymarket's public market WebSocket. It focuses on top-of-book data, connection recovery, and stale-data protection rather than order execution.
1. Connect to the Polymarket market WebSocket
Polymarket provides a public market channel for real-time order-book and trade updates. The documented endpoint is:
"wss://ws-subscriptions-clob.polymarket.com/ws/market"
The market channel subscribes using asset IDs, also called token IDs. Each outcome has its own token ID, so you need the appropriate ID for the outcome you want to monitor.
The official "Polymarket WebSocket documentation" (github.com/Polymarket/agent-skills/blob/main/webs…) describes the subscription format and event types.
Install the Python WebSocket library:
pip install websockets
The example below uses the raw WebSocket protocol so the connection lifecycle is visible rather than hidden inside a larger framework.
2. Understand the messages you receive
Three message types are particularly useful for a basic monitor.
"book" provides an order-book snapshot with bid and ask levels. Use it to initialize or rebuild local market state.
"price_change" reports changes to price levels. Its "price_changes" array can contain updates for different assets and levels.
"last_trade_price" reports a trade that has occurred. A last-traded price is not necessarily the price at which your next order can execute.
For monitoring the best bid and ask, the "price_change" payload includes "best_bid" and "best_ask" fields. The code should still distinguish these updates from a complete order-book snapshot.
That distinction matters after reconnecting: receiving one price update does not necessarily mean your local view of the entire book has been rebuilt.
3. Build a monitor with stale-data protection
The monitor below tracks one outcome token. It starts in an unready state, subscribes to the market, sends the required application-level heartbeat, and marks the data ready only after receiving a fresh book snapshot.
If the connection drops, the monitor retries with exponential backoff. Every new connection begins unready, preventing the application from treating an old snapshot as current.
Replace "YOUR_OUTCOME_TOKEN_ID" with a valid token ID.
import asyncio
import json
import time
import websockets
WS_URL = "wss://ws-subscriptions-clob.polymarket.com/ws/market"
ASSET_ID = "YOUR_OUTCOME_TOKEN_ID"
class MarketMonitor:
def init(self):
self.best_bid = None
self.best_ask = None
self.book_ready = False
self.last_message_at = None
def reset(self):
self.best_bid = None
self.best_ask = None
self.book_ready = False
self.last_message_at = None
def handle(self, message):
event_type = message.get("event_type")
self.last_message_at = time.monotonic()
if event_type == "book":
if message.get("asset_id") != ASSET_ID:
return
bids = [
float(level["price"])
for level in message.get("bids", [])
if float(level["size"]) > 0
]
asks = [
float(level["price"])
for level in message.get("asks", [])
if float(level["size"]) > 0
]
self.best_bid = max(bids) if bids else None
self.best_ask = min(asks) if asks else None
self.book_ready = True
print("Fresh book snapshot received.")
elif event_type == "price_change":
for change in message.get("price_changes", []):
if change.get("asset_id") != ASSET_ID:
continue
# Do not trust incremental updates until a fresh
# snapshot has initialized this connection's state.
if not self.book_ready:
continue
if "best_bid" in change:
self.best_bid = float(change["best_bid"])
if "best_ask" in change:
self.best_ask = float(change["best_ask"])
if self.book_ready:
print(
f"Bid: {self.best_bid} | "
f"Ask: {self.best_ask} | "
f"Spread: {self.spread()}"
)
def spread(self):
if self.best_bid is None or self.best_ask is None:
return None
return round(self.best_ask - self.best_bid, 6)
async def heartbeat(ws):
while True:
await asyncio.sleep(10)
await ws.send("PING")
async def run_monitor():
monitor = MarketMonitor()
backoff = 1
while True:
monitor.reset()
try:
async with websockets.connect(
WS_URL,
open_timeout=10,
close_timeout=5,
ping_interval=None,
) as ws:
await ws.send(json.dumps({
"type": "market",
"assets_ids": [ASSET_ID],
"custom_feature_enabled": True,
}))
print("Connected and subscribed.")
backoff = 1
heartbeat_task = asyncio.create_task(heartbeat(ws))
try:
async for raw in ws:
if raw == "PONG":
continue
message = json.loads(raw)
if isinstance(message, dict):
monitor.handle(message)
finally:
heartbeat_task.cancel()
await asyncio.gather(
heartbeat_task,
return_exceptions=True,
)
except (
OSError,
asyncio.TimeoutError,
websockets.exceptions.WebSocketException,
json.JSONDecodeError,
) as error:
print(f"Connection or message error: {error}")
monitor.reset()
print(f"Reconnecting in {backoff} seconds...")
await asyncio.sleep(backoff)
backoff = min(backoff * 2, 30)
if name == "main":
asyncio.run(run_monitor())
This is a teaching example, not a complete trading data service. It uses the best-bid and best-ask fields for top-of-book monitoring; it does not maintain a complete local order book after every incremental change. A production implementation should apply price-level updates correctly, validate messages, track data timestamps, and test the current protocol against the official documentation.
4. Why the monitor resets its state
Imagine the connection drops while the best ask is $0.48. During the outage, the market moves to $0.53.
If the bot reconnects but keeps using its old $0.48 value, a strategy might believe an attractive entry is still available when it is not.
Resetting the state avoids presenting that old value as current. The monitor waits for a new book snapshot before declaring its top-of-book view ready.
The same principle should apply to a trading bot's decision-making layer:
- Do not submit new trades when market data is stale.
- Do not assume a successful reconnect means the local book is synchronized.
- Rebuild state from a fresh snapshot before trusting incremental updates.
- Reconcile open orders and positions separately from public market data.
A market-data connection does not tell you whether your own order was accepted or filled. Account and order state require their own reliable tracking process.
5. Detect stale data explicitly
A connected socket does not guarantee that the data your strategy needs is fresh.
A production monitor should track the time of the last valid market update and define a maximum acceptable age. If no qualifying update arrives within that period, it should mark the feed stale and prevent dependent trading decisions.
The threshold should reflect the application. A dashboard may tolerate a longer delay than a strategy attempting to react to small price changes.
It is also important to distinguish a quiet market from a broken connection. A lack of price changes does not necessarily mean the feed is unhealthy. Heartbeat responses, connection state, server timestamps, and application-level freshness checks should be considered together.
Polymarket's documented market and user channels require sending "PING" every ten seconds, with the server responding "PONG". The example disables the WebSocket library's own ping frames because it sends the protocol's application-level heartbeat instead.
6. What to improve before using this in a trading bot
A reliable production implementation needs additional safeguards.
Full order-book maintenance. A "book" event supplies a snapshot. Subsequent "price_change" events can add, update, or remove price levels. A local book must apply these changes correctly, including updates where size becomes zero.
Snapshot and update consistency. If the application loses messages or reconnects, it should rebuild the book rather than assuming its previous state remains valid.
Structured logging. Record connection attempts, reconnects, stale-feed incidents, malformed messages, and snapshot recovery. Avoid logging private credentials.
Backpressure. If updates arrive faster than the application can process them, an unbounded queue can increase memory usage and delay decisions. Measure processing lag and choose a queue policy appropriate to the strategy.
Execution safeguards. Before placing an order, validate the age of the market data, price limits, available balance, and portfolio exposure. A fresh feed alone does not make a trade safe.
Testing. Simulate disconnects, malformed messages, empty books, missing fields, and delayed updates. Verify that the bot refuses to act while its market state is unready or stale.
Conclusion
A Polymarket WebSocket monitor is more than a connection that prints prices. It is a stateful component that must know when its data is valid, when it has become stale, and when it needs to rebuild its view of the market.
Starting with snapshot recovery, heartbeat handling, freshness checks, and bounded reconnect delays gives developers a stronger foundation for market-data systems.
Dexoryn Labs maintains a public "Polymarket copy-trading bot repository" (dexorynlabs/polymarket-copy-trading-bot) for developers evaluating existing automation. Teams with more specific data-processing, monitoring, or execution requirements may need a custom implementation.
Reliable market data does not guarantee profitable trading. It helps prevent a bot from making decisions based on information it should no longer trust.
This article is for educational purposes only. Automated trading involves financial risk. Test in a non-trading environment before connecting market-data logic to live order execution.
@suoyu007 El Agents SDK oficial ya está en Polymarket/agents. El cuello de botella real para edge es el harness de datos L2 históricos, no el LLM. ¿Lo conectaste ya a un replay de order book?
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Polymarket dropped their open-source Agents SDK. It gives AI agents the toolkit to discover prediction markets, synthesize news alpha, and execute trades autonomously on-chain. Prediction markets are officially becoming agent-first.
Polymarket/agents
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Your Polymarket Bot Has Live Prices. That Doesn't Mean It Has the Latest Order Book. A trading bot can receive price updates every second and still make decisions using a market state that no longer exists. That sounds contradictory until you look at how an order book changes. A bot sees an attractive ask, calculates an edge, and prepares an order. Before that order reaches the exchange, someone cancels the liquidity, another trader takes the offer, or the best available price changes. The bot's calculation may have been correct when it started. The market it calculated against is gone. This is one of the problems that separates a bot that merely consumes market data from one that handles live trading conditions properly. A price is not the same thing as a market state Imagine your Polymarket bot estimates that a YES share is worth $0.64. Its market-data handler reports an ask of $0.59, so the strategy identifies a potential five-cent edge. But three details matter: - How recently was that ask observed? - How much liquidity was available at that price? - Does the price still exist when the order is submitted? Suppose the $0.59 ask disappears and the next available ask is $0.63. Your original edge has almost vanished before accounting for fees and other costs. A system that stores only the latest price can miss the sequence of events that produced this situation. A system that tracks the state of the order book has a better chance of detecting it. The distinction matters because live market data is a stream of changing observations, not a guarantee that a displayed opportunity will remain executable. Why WebSocket updates need careful handling Polymarket's CLOB WebSocket market channel provides events such as order-book snapshots and price changes. Developers can use these events to maintain a local view of the market. See the "Polymarket WebSocket documentation" (github.com/Polymarket/agent-skills/blob/main/webs…). The challenge is keeping that local view trustworthy. A basic implementation might update a variable whenever a new price arrives: best_ask = incoming_update["best_ask"] That can be useful for a simple example, but a production system needs more context. Not every event necessarily represents a complete replacement for the order book. Some updates describe changes to particular prices or assets. If the bot treats every message as a full snapshot, it can lose information. If it applies updates incorrectly, its local book can diverge from the exchange's current state. The result is a strategy making decisions from data that looks valid but is incomplete. Build a local book, not just a price variable A more robust architecture separates market-data ingestion from strategy execution. The data layer receives events and updates the local order book. The strategy reads a consistent view of that book, evaluates the opportunity, and passes a proposed order to an execution layer. That execution layer performs its own checks before submission. A simplified flow looks like this: WebSocket events | v Event validation | v Local order-book state | v Strategy evaluation | v Pre-trade validation | v Order submission | v Fill and order-status tracking Each stage has a separate responsibility. The data layer should understand the event types it receives. The strategy should calculate its edge using the appropriate side of the book. The execution layer should reject orders that no longer satisfy the strategy's price, size, or risk constraints. This separation also makes debugging easier. If a trade performs badly, you can investigate whether the problem came from the signal, the book state, or execution. What should happen after a disconnect? Consider a more dangerous situation. Your bot loses its WebSocket connection for several seconds. During that period, prices move and orders are cancelled. The connection reconnects, and new messages begin arriving. Should the bot immediately resume trading? Not necessarily. The system first needs to establish whether its local market state is trustworthy. Depending on the documented protocol and the events received, that may require obtaining a fresh book snapshot and rebuilding local state before allowing new decisions. A conservative recovery procedure is: 1. Mark the affected market data as unavailable or untrusted. 2. Stop generating new orders that depend on that state. 3. Reconnect and restore the local book using the supported snapshot-and-update process. 4. Validate the reconstructed state before enabling the strategy again. 5. Reconcile outstanding orders and fills separately from market-data recovery. The last point is easy to overlook. Restoring the order book does not tell you whether an order submitted before the disconnect was accepted, partially filled, or still resting. Account and order state need their own reconciliation process. A reconnect is not proof that everything is synchronized. Add a pre-trade freshness check Even a correctly maintained local book can become outdated between the strategy's calculation and order submission. Your bot should therefore define what it considers an acceptable data state before placing an order. For example, a pre-trade validator might check: - Whether the market-data connection is healthy. - Whether the local book has been initialized and remains valid. - Whether the latest relevant update falls within the strategy's configured freshness limit. - Whether the intended size is supported by available liquidity. - Whether the executable price still leaves enough expected edge after estimated costs. The freshness limit should depend on the strategy. A bot reacting to rapid price changes may require stricter limits than one trading infrequently. Also, do not assume that a recent message automatically proves that the entire book is synchronized. Freshness and consistency are related, but they are not interchangeable. Test the failures, not just the happy path A bot that works while the network is stable has passed only one part of its engineering test. Build tests that simulate: - A price changing immediately before order submission. - Liquidity disappearing at the intended entry price. - A disconnect followed by reconnection. - Duplicate or unexpected events. - A partially filled order during a market-data interruption. - A book that cannot be confidently reconstructed. For each scenario, define the expected behavior before running the test. Should the bot reject the trade, pause the market, rebuild state, or reconcile an outstanding order? That turns resilience from a vague objective into something measurable. You can start with recorded market-data events and simulated order responses before introducing real capital. A deterministic replay also makes it easier to reproduce a bug that occurred during a live session. The real objective is trustworthy decisions Real-time data is valuable only when the system understands what that data represents and when it is safe to act on it. For Polymarket bot developers, this means treating the order book as state that must be maintained and validated, not as a number that can simply be overwritten whenever a message arrives. The strongest implementation is not necessarily the one that reacts to every update fastest. It is the one that knows when its view of the market is reliable, when an opportunity has disappeared, and when it should refuse to trade. That discipline will not guarantee profitable trades. It will, however, help prevent avoidable execution mistakes from being mistaken for strategy failures. Building a Polymarket trading bot, copy-trading system, or custom market-data workflow? Explore what Dexoryn Labs builds at dexorynlabs.com.
Polymarket Trading Bot: Build a Market Maker Bot in Python- Project Setup and API Integration Day 3 of 7: Building a Polymarket Market Maker Bot From Zero to Complete In the first two parts of this series, we explored what a Polymarket market maker bot is and how the Polymarket order book works. We covered YES and NO outcome tokens, bid-ask spreads, market depth, limit orders, and the risks of providing liquidity. Now, let's start building. In this tutorial, we'll set up a Python project, connect to Polymarket's public market data API, discover available markets, retrieve outcome asset identifiers, and inspect an order book. By the end, you'll have the foundation of a Python market maker bot that can read market data and calculate basic pricing metrics. Important: This tutorial is read-only. It does not submit orders, spend funds, or require a private key. What We'll Build Our first version will perform five tasks: 1. Connect to Polymarket's public API. 2. Discover available markets. 3. Identify a market's YES and NO assets. 4. Retrieve the order book for a selected asset. 5. Calculate the best bid, best ask, spread, and midpoint. The data flow looks like this: Polymarket Market Data | v Market Discovery | v Select a Market | v Identify YES / NO Assets | v Fetch Order Book | v Calculate Spread & Midpoint | v Print Results This is a small program, but it establishes an important component of every trading bot: the market data layer. !polymarket trading bot A production market maker would extend this foundation with real-time data subscriptions, strategy logic, order management, inventory tracking, risk controls, and monitoring. 1. Choose the Python SDK Polymarket provides developer interfaces for market discovery, order books, pricing, and trading. For this tutorial, we'll use the unified Python SDK identified in the project references: Official repository: Polymarket Python SDK The package used in the examples is polymarket-client. The SDK is intended to provide access to market information and other Polymarket workflows. Public market-data access does not require you to configure trading credentials for this read-only exercise. Before installing it, check the repository's current installation instructions and examples. SDK interfaces can change, and code written for an older client may not work with the current version. 2. Set Up Your Development Environment You can follow this tutorial on Windows, macOS, or Linux. Check Python python --version On some systems, use: python3 --version Create a project directory mkdir polymarket-market-maker cd polymarket-market-maker Create a virtual environment python -m venv .venv Activate it on Windows PowerShell: .venv\Scripts\Activate.ps1 On macOS or Linux: source .venv/bin/activate Install the SDK python -m pip install --upgrade pip pip install polymarket-client Verify the import: python -c "from polymarket import PublicClient; print('SDK import successful')" If the import fails, check the SDK's current Python requirements and installation instructions before proceeding. 3. Create a Clean Project Structure A common beginner mistake is putting market discovery, pricing calculations, and trading logic into one large Python file. Let's separate responsibilities from the beginning. polymarket-market-maker/ ├── src/ │ └── polymarket_mm/ │ ├── __init__.py │ ├── market_data.py │ ├── orderbook.py │ ├── pricing.py │ └── main.py ├── tests/ ├── .gitignore └── README.md Each module has a specific responsibility: - market_data.py — discovers markets and retrieves market information. - orderbook.py — retrieves and inspects order book levels. - pricing.py — calculates spread and midpoint. - main.py — connects the modules and runs the example. - tests/ — contains tests for pricing calculations and other logic. This structure is intentionally simple. We don't need a complex architecture before our first API request works. 4. Discover Markets Through the SDK Before inspecting an order book, the bot needs to discover which markets are available. Create src/polymarket_mm/market_data.py: from polymarket import PublicClient def list_markets(limit: int = 10): """Return a page of markets from Polymarket.""" with PublicClient() as client: page = client.list_markets( closed=False, page_size=limit, ).first_page() return page.items if __name__ == "__main__": markets = list_markets() for market in markets: print( { "question": market.question, "slug": market.slug, "market_id": market.id, } ) Run the module from your project root. On macOS or Linux: PYTHONPATH=src python -m polymarket_mm.market_data On Windows PowerShell: $env:PYTHONPATH = "src" python -m polymarket_mm.market_data The program requests a page of markets and prints basic information about each result. One important detail: closed=False does not necessarily mean a market is currently accepting orders. A production bot should separately validate market status, trading permissions, and the availability of a usable order book. For the current implementation, consult the official market discovery example. 5. Understand Market IDs and Asset IDs A market and a tradable asset are not the same thing. A market represents an event or question. An asset identifier identifies a tradable outcome or position associated with that market. For a conventional binary market, there are typically YES and NO outcomes. However, market infrastructure can evolve, so avoid assuming every market exposes its tradable identifiers in exactly the same format. The SDK's market model may expose a token ID, position ID, or another identifier depending on the market representation. The key principle is simple: Use the current SDK's market model and official examples instead of extracting identifiers from URLs or relying on undocumented raw JSON structures. You can review the SDK's market helper examples to understand how outcome identifiers are accessed. 6. Retrieve an Order Book Now let's inspect an actual market. The following example searches a limited page of open markets until it finds one with a usable YES asset identifier. It then requests the corresponding order book. Create src/polymarket_mm/orderbook.py: from polymarket import PublicClient def inspect_first_available_market(): with PublicClient() as client: markets = client.list_markets( closed=False, page_size=100, ).first_page().items for market in markets: # Conventional binary-market example. # Check the SDK model for your market version. yes = market.outcomes.yes asset_id = yes.token_id or yes.position_id if asset_id is None: continue try: book = client.get_order_book( asset_id=asset_id ) except Exception as exc: print( f"Skipping market {market.id}: " f"could not retrieve its book: {exc}" ) continue if not book.bids and not book.asks: continue return market, asset_id, book raise RuntimeError( "No market with a usable YES order book was found. " "Try again or refine market discovery." ) def main(): market, asset_id, book = inspect_first_available_market() print("Market:", market.question) print("Market ID:", market.id) print("Asset ID:", asset_id) print("Number of bids:", len(book.bids)) print("Number of asks:", len(book.asks)) print("\nTop bids:") for level in sorted( book.bids, key=lambda item: item.price, reverse=True, )[:5]: print("Price:", level.price, "Size:", level.size) print("\nTop asks:") for level in sorted( book.asks, key=lambda item: item.price, )[:5]: print("Price:", level.price, "Size:", level.size) if __name__ == "__main__": main() Run it: PYTHONPATH=src python -m polymarket_mm.orderbook On Windows PowerShell, use the PYTHONPATH command from the previous section. The program prints the market question, asset identifier, number of bid and ask levels, and the five best levels on each side. A few things to remember: - The best bid is the highest available buy price. - The best ask is the lowest available sell price. - The size indicates the quantity available at a price level. - An empty order book does not provide enough information to calculate both sides of the spread. The code is an educational starting point, not a guarantee that every market returned by the API will expose these exact fields. Verify the current SDK model before running it. For another reference, see the official market prices example. 7. Calculate the Spread and Midpoint Once we have the order book, we can calculate two basic market metrics. Bid-ask spread The spread measures the difference between the best ask and the best bid. [ \text{Spread} = \text{Best Ask} - \text{Best Bid} ] For example, suppose the best bid is $0.48 and the best ask is $0.52. The spread is: 0.52 - 0.48 = 0.04 The spread is four cents per share. Midpoint The midpoint is the average of the best bid and best ask. [ \text{Midpoint} = \frac{\text{Best Bid}+\text{Best Ask}}{2} ] Using the same example: (0.48 + 0.52) / 2 = 0.50 The midpoint is $0.50. Important: The midpoint is a reference price, not a guaranteed execution price. A market order or limit order may execute at a different price depending on liquidity and market conditions. Implement the calculations Create src/polymarket_mm/pricing.py: def calculate_book_metrics(bids, asks): """Calculate basic metrics from order book levels.""" best_bid = max( (float(level.price) for level in bids), default=None, ) best_ask = min( (float(level.price) for level in asks), default=None, ) if best_bid is None or best_ask is None: return { "best_bid": best_bid, "best_ask": best_ask, "spread": None, "midpoint": None, } if best_bid > best_ask: raise ValueError( "The book appears crossed; inspect the data " "and API response before using it." ) return { "best_bid": best_bid, "best_ask": best_ask, "spread": best_ask - best_bid, "midpoint": (best_bid + best_ask) / 2, } You can import this function into the order book example: from polymarket_mm.pricing import calculate_book_metrics metrics = calculate_book_metrics( book.bids, book.asks, ) print("\nBook metrics:") for name, value in metrics.items(): print(f"{name}: {value}") For financial calculations and order construction, prefer Decimal or the SDK's decimal-compatible types over binary floating-point arithmetic. The example uses float for simplicity and educational display. 8. Add Error Handling and Logging Real-world API requests can fail. Common causes include: - Network timeouts. - Rate limits. - Invalid asset identifiers. - Temporary service errors. - Disconnected WebSocket connections. - Missing or stale market data. Different errors require different responses. A timeout may justify a controlled retry. A rate-limit response should trigger appropriate backoff. An invalid identifier may indicate a market-selection problem. Stale data is particularly important for a market maker. If the order book is outdated, the bot should not continue generating quotes as if the information were current. Start with Python's standard logging module: import logging logging.basicConfig( level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s", ) logger = logging.getLogger(__name__) logger.info("Market data module started") As the project grows, structured logs will help investigate market data updates, order submissions, fills, cancellations, and risk-limit events. Avoid infinite retry loops. A robust bot should stop making trading decisions when its data cannot be trusted. 9. Why We Are Not Placing Orders Yet At this stage, our program can retrieve market information, but it is not yet a market maker. It does not know: - What prices it should quote. - How large its orders should be. - When it should cancel or replace orders. - How to manage inventory. - How to control exposure and capital usage. - How to reconcile partial fills and failed requests. Adding live order placement before implementing these components would be premature. A safer development sequence is: 1. Retrieve market data reliably. 2. Calculate candidate quotes. 3. Validate quotes against price and risk limits. 4. Simulate decisions using recorded or live market data. 5. Implement order lifecycle management. 6. Test failures, cancellations, and partial fills. 7. Consider controlled live testing only after sufficient validation. Separating market data from order execution makes the bot easier to test and reduces the risk of an early coding mistake spending real funds. 10. What We Built Today By completing Day 3, we have established the initial market data layer. We now have a foundation for: - Installing and importing the Python SDK. - Discovering open markets. - Identifying a usable YES asset identifier. - Retrieving an order book. - Inspecting bid and ask levels. - Calculating spread and midpoint metrics. - Recognizing the importance of API errors and stale data. This is the first step toward a more complete market maker bot. Day 4: Design the Market-Making Strategy In Day 4, we'll move from observing the market to generating candidate quotes. We'll explore: - How to select a reference price. - How to calculate bid and ask quotes. - How to set a target spread. - How to choose order sizes. - How to adjust quotes based on inventory. - Why fixed-distance-from-midpoint strategies can perform poorly during rapid price movements. The objective is to build a strategy layer that can be tested before connecting it to live order execution. Follow the Project GitHub repository: Polymarket Trading Bot in Python YouTube tutorial: Watch the video Contact and discussion: Telegram — BenjaminCup Follow the series as we build a Polymarket market maker bot from the fundamentals toward a complete Python implementation. #Python #Polymarket #TradingBot #AlgoTrading #Crypto
Polymarket's official agents repo is worth reading if you're building prediction market bots. It wires up market data, LLM research, and CLOB order placement. Check key handling and order limits before letting it trade live. Polymarket/agents
How to Build a Reliable Polymarket WebSocket Monitor in Python A Polymarket trading bot needs timely market data to make informed execution decisions. But connecting to a WebSocket and receiving messages is only the beginning. Connections can drop, messages can be delayed, and a bot can accidentally keep using an old order-book snapshot after reconnecting. A reliable monitor must do three things well: consume market updates, detect when its data is no longer trustworthy, and rebuild its state after a connection failure. This tutorial builds a small Python monitor around Polymarket's public market WebSocket. It focuses on top-of-book data, connection recovery, and stale-data protection rather than order execution. 1. Connect to the Polymarket market WebSocket Polymarket provides a public market channel for real-time order-book and trade updates. The documented endpoint is: "wss://ws-subscriptions-clob.polymarket.com/ws/market" The market channel subscribes using asset IDs, also called token IDs. Each outcome has its own token ID, so you need the appropriate ID for the outcome you want to monitor. The official "Polymarket WebSocket documentation" (github.com/Polymarket/agent-skills/blob/main/webs…) describes the subscription format and event types. Install the Python WebSocket library: pip install websockets The example below uses the raw WebSocket protocol so the connection lifecycle is visible rather than hidden inside a larger framework. 2. Understand the messages you receive Three message types are particularly useful for a basic monitor. "book" provides an order-book snapshot with bid and ask levels. Use it to initialize or rebuild local market state. "price_change" reports changes to price levels. Its "price_changes" array can contain updates for different assets and levels. "last_trade_price" reports a trade that has occurred. A last-traded price is not necessarily the price at which your next order can execute. For monitoring the best bid and ask, the "price_change" payload includes "best_bid" and "best_ask" fields. The code should still distinguish these updates from a complete order-book snapshot. That distinction matters after reconnecting: receiving one price update does not necessarily mean your local view of the entire book has been rebuilt. 3. Build a monitor with stale-data protection The monitor below tracks one outcome token. It starts in an unready state, subscribes to the market, sends the required application-level heartbeat, and marks the data ready only after receiving a fresh book snapshot. If the connection drops, the monitor retries with exponential backoff. Every new connection begins unready, preventing the application from treating an old snapshot as current. Replace "YOUR_OUTCOME_TOKEN_ID" with a valid token ID. import asyncio import json import time import websockets WS_URL = "wss://ws-subscriptions-clob.polymarket.com/ws/market" ASSET_ID = "YOUR_OUTCOME_TOKEN_ID" class MarketMonitor: def init(self): self.best_bid = None self.best_ask = None self.book_ready = False self.last_message_at = None def reset(self): self.best_bid = None self.best_ask = None self.book_ready = False self.last_message_at = None def handle(self, message): event_type = message.get("event_type") self.last_message_at = time.monotonic() if event_type == "book": if message.get("asset_id") != ASSET_ID: return bids = [ float(level["price"]) for level in message.get("bids", []) if float(level["size"]) > 0 ] asks = [ float(level["price"]) for level in message.get("asks", []) if float(level["size"]) > 0 ] self.best_bid = max(bids) if bids else None self.best_ask = min(asks) if asks else None self.book_ready = True print("Fresh book snapshot received.") elif event_type == "price_change": for change in message.get("price_changes", []): if change.get("asset_id") != ASSET_ID: continue # Do not trust incremental updates until a fresh # snapshot has initialized this connection's state. if not self.book_ready: continue if "best_bid" in change: self.best_bid = float(change["best_bid"]) if "best_ask" in change: self.best_ask = float(change["best_ask"]) if self.book_ready: print( f"Bid: {self.best_bid} | " f"Ask: {self.best_ask} | " f"Spread: {self.spread()}" ) def spread(self): if self.best_bid is None or self.best_ask is None: return None return round(self.best_ask - self.best_bid, 6) async def heartbeat(ws): while True: await asyncio.sleep(10) await ws.send("PING") async def run_monitor(): monitor = MarketMonitor() backoff = 1 while True: monitor.reset() try: async with websockets.connect( WS_URL, open_timeout=10, close_timeout=5, ping_interval=None, ) as ws: await ws.send(json.dumps({ "type": "market", "assets_ids": [ASSET_ID], "custom_feature_enabled": True, })) print("Connected and subscribed.") backoff = 1 heartbeat_task = asyncio.create_task(heartbeat(ws)) try: async for raw in ws: if raw == "PONG": continue message = json.loads(raw) if isinstance(message, dict): monitor.handle(message) finally: heartbeat_task.cancel() await asyncio.gather( heartbeat_task, return_exceptions=True, ) except ( OSError, asyncio.TimeoutError, websockets.exceptions.WebSocketException, json.JSONDecodeError, ) as error: print(f"Connection or message error: {error}") monitor.reset() print(f"Reconnecting in {backoff} seconds...") await asyncio.sleep(backoff) backoff = min(backoff * 2, 30) if name == "main": asyncio.run(run_monitor()) This is a teaching example, not a complete trading data service. It uses the best-bid and best-ask fields for top-of-book monitoring; it does not maintain a complete local order book after every incremental change. A production implementation should apply price-level updates correctly, validate messages, track data timestamps, and test the current protocol against the official documentation. 4. Why the monitor resets its state Imagine the connection drops while the best ask is $0.48. During the outage, the market moves to $0.53. If the bot reconnects but keeps using its old $0.48 value, a strategy might believe an attractive entry is still available when it is not. Resetting the state avoids presenting that old value as current. The monitor waits for a new book snapshot before declaring its top-of-book view ready. The same principle should apply to a trading bot's decision-making layer: - Do not submit new trades when market data is stale. - Do not assume a successful reconnect means the local book is synchronized. - Rebuild state from a fresh snapshot before trusting incremental updates. - Reconcile open orders and positions separately from public market data. A market-data connection does not tell you whether your own order was accepted or filled. Account and order state require their own reliable tracking process. 5. Detect stale data explicitly A connected socket does not guarantee that the data your strategy needs is fresh. A production monitor should track the time of the last valid market update and define a maximum acceptable age. If no qualifying update arrives within that period, it should mark the feed stale and prevent dependent trading decisions. The threshold should reflect the application. A dashboard may tolerate a longer delay than a strategy attempting to react to small price changes. It is also important to distinguish a quiet market from a broken connection. A lack of price changes does not necessarily mean the feed is unhealthy. Heartbeat responses, connection state, server timestamps, and application-level freshness checks should be considered together. Polymarket's documented market and user channels require sending "PING" every ten seconds, with the server responding "PONG". The example disables the WebSocket library's own ping frames because it sends the protocol's application-level heartbeat instead. 6. What to improve before using this in a trading bot A reliable production implementation needs additional safeguards. Full order-book maintenance. A "book" event supplies a snapshot. Subsequent "price_change" events can add, update, or remove price levels. A local book must apply these changes correctly, including updates where size becomes zero. Snapshot and update consistency. If the application loses messages or reconnects, it should rebuild the book rather than assuming its previous state remains valid. Structured logging. Record connection attempts, reconnects, stale-feed incidents, malformed messages, and snapshot recovery. Avoid logging private credentials. Backpressure. If updates arrive faster than the application can process them, an unbounded queue can increase memory usage and delay decisions. Measure processing lag and choose a queue policy appropriate to the strategy. Execution safeguards. Before placing an order, validate the age of the market data, price limits, available balance, and portfolio exposure. A fresh feed alone does not make a trade safe. Testing. Simulate disconnects, malformed messages, empty books, missing fields, and delayed updates. Verify that the bot refuses to act while its market state is unready or stale. Conclusion A Polymarket WebSocket monitor is more than a connection that prints prices. It is a stateful component that must know when its data is valid, when it has become stale, and when it needs to rebuild its view of the market. Starting with snapshot recovery, heartbeat handling, freshness checks, and bounded reconnect delays gives developers a stronger foundation for market-data systems. Dexoryn Labs maintains a public "Polymarket copy-trading bot repository" (dexorynlabs/polymarket-copy-trading-bot) for developers evaluating existing automation. Teams with more specific data-processing, monitoring, or execution requirements may need a custom implementation. Reliable market data does not guarantee profitable trading. It helps prevent a bot from making decisions based on information it should no longer trust. This article is for educational purposes only. Automated trading involves financial risk. Test in a non-trading environment before connecting market-data logic to live order execution.
@suoyu007 El Agents SDK oficial ya está en Polymarket/agents. El cuello de botella real para edge es el harness de datos L2 históricos, no el LLM. ¿Lo conectaste ya a un replay de order book?
Polymarket dropped their open-source Agents SDK. It gives AI agents the toolkit to discover prediction markets, synthesize news alpha, and execute trades autonomously on-chain. Prediction markets are officially becoming agent-first. Polymarket/agents