Build a Polymarket trading bot in Python with market discovery, order books, execution, risk controls, testing, and production monitoring.
How to Build a Polymarket Trading Bot in Python
Current-API note: Polymarket's developer stack is evolving. This guide reflects the official documentation and repositories checked on August 18, 2026. The official Python SDK,
polymarket-client, is currently in beta; the olderpy-clob-clientrepository has been archived and its README explicitly says it should not be used for new integrations.
Introduction
A useful Polymarket trading bot is not simply a Python loop that fetches a price and submits an order.
The difficult part is building the system around the order:
- discovering a market that is actually tradable,
- identifying the correct outcome token,
- maintaining a current view of the order book,
- converting a model signal into an executable order,
- respecting market tick-size and minimum-size constraints,
- controlling exposure,
- handling partial fills and cancellations,
- recovering from network failures,
- and proving that the bot behaved correctly before allowing it to trade real capital.
Polymarket's current developer documentation exposes separate market-data, trading, account, and real-time workflows, while the official Python SDK provides PublicClient and SecureClient abstractions for those workflows.
This article builds the architecture behind a production-oriented Python bot rather than presenting a fictional "profitable strategy."
The strategy used in the examples is deliberately simple: buy an outcome only when a hypothetical model probability exceeds the executable market price by a configurable edge threshold. The edge is illustrative; it is not a claim that the strategy is profitable.
What You'll Learn
By the end of this guide, you will understand how to:
- Choose the current Polymarket Python SDK.
- Discover active markets.
- Map a market to its outcome token IDs.
- Read order-book information.
- Build a signal engine.
- Add position and exposure limits.
- Construct and submit orders safely.
- Separate strategy, risk, and execution.
- Use WebSockets for real-time market information.
- Handle retries, throttling, stale data, and partial fills.
- Test a bot without immediately risking capital.
- Monitor production behavior.
- Extend the architecture toward market making, arbitrage research, or external-signal strategies.
1. Understand the Polymarket Developer Stack
A useful mental model is:
┌─────────────────────┐
│ Market Discovery │
│ Gamma / SDK data │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Market Filter │
│ tradable/liquidity │
└──────────┬──────────┘
│
┌─────────────────┴─────────────────┐
│ │
▼ ▼
┌─────────────────┐ ┌─────────────────┐
│ REST / snapshots│ │ WebSocket │
│ market state │ │ live book data │
└────────┬────────┘ └────────┬────────┘
│ │
└─────────────────┬─────────────────┘
▼
┌─────────────────────┐
│ Signal Engine │
│ fair value / edge │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Risk Engine │
│ size / exposure / │
│ stale-data checks │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Execution Engine │
│ sign / submit / │
│ cancel / reconcile │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Order / Position │
│ State + Monitoring │
└─────────────────────┘
Polymarket's current documentation describes trading as signed orders submitted to the CLOB, followed by matching and settlement on Polygon. The trading workflow is explicitly separated into account setup, outcome selection, order placement, and order management.
That separation should also exist in your code.
Do not put market discovery, strategy logic, signing, and risk management into one 300-line while True loop.
2. Which Python SDK Should You Use?
This is one of the most important current-version details.
The historical py-clob-client was widely used by Python bot tutorials, but the official repository was archived on May 25, 2026, and its README now says the client is no longer maintained and should not be used for new or existing integrations. Polymarket recommends migrating to the unified py-sdk.
The new official repository is:
Polymarket Python SDK — GitHub
It provides a unified Python interface for public data, authenticated accounts, trading, builder attribution, and wallet workflows. The package is currently beta and uses the package name polymarket-client.
The current SDK exposes:
from polymarket import PublicClient, SecureClient
PublicClient is appropriate for public market information, while SecureClient is used for authenticated account, trading, and wallet workflows.
The SDK currently requires Python 3.11 or newer according to its package metadata.
Install it with:
python -m venv .venv
source .venv/bin/activate
pip install --pre polymarket-client
For reproducible production deployments, pin the exact SDK version you have tested rather than floating to whatever beta release happens to be latest.
3. Project Structure
A practical project can start small:
polymarket-bot/
├── pyproject.toml
├── .env
├── .gitignore
├── src/
│ └── bot/
│ ├── __init__.py
│ ├── config.py
│ ├── market.py
│ ├── strategy.py
│ ├── risk.py
│ ├── execution.py
│ ├── state.py
│ └── main.py
└── tests/
├── test_strategy.py
├── test_risk.py
└── test_execution.py
The key design principle is dependency direction:
Market Data ─────► Strategy
│
▼
Risk
│
▼
Execution
│
▼
State
The strategy should not know how a private key is stored.
The execution layer should not decide whether a market is attractive.
The risk layer should be able to reject a perfectly valid strategy signal.
That separation makes testing dramatically easier.
4. Configuration and Secret Management
Never hard-code a private key.
Use environment variables:
export POLYMARKET_PRIVATE_KEY="0x..."
export POLYMARKET_DEPOSIT_WALLET="0x..."
export POLYMARKET_DRY_RUN="true"
For local development, a .env file can be convenient, but it must be excluded from Git:
.env
.venv/
__pycache__/
*.pyc
A configuration object keeps the rest of the application independent of the environment:
from dataclasses import dataclass
import os
@dataclass(frozen=True)
class Settings:
private_key: str
wallet: str
dry_run: bool = True
@classmethod
def from_env(cls) -> "Settings":
private_key = os.environ.get("POLYMARKET_PRIVATE_KEY")
wallet = os.environ.get("POLYMARKET_DEPOSIT_WALLET")
if not private_key:
raise RuntimeError("POLYMARKET_PRIVATE_KEY is missing")
if not wallet:
raise RuntimeError("POLYMARKET_DEPOSIT_WALLET is missing")
return cls(
private_key=private_key,
wallet=wallet,
dry_run=os.environ.get(
"POLYMARKET_DRY_RUN", "true"
).lower() == "true",
)
For a real deployment, secrets should preferably come from a secret manager rather than a plaintext .env file.
5. Discover a Tradable Market
Market discovery is the first place where many beginner bots go wrong.
A market being visible does not automatically mean your strategy should trade it.
Your discovery pipeline should eventually filter for:
- market status,
- whether orders are being accepted,
- outcome structure,
- available token IDs,
- minimum order size,
- minimum price increment,
- liquidity,
- spread,
- time to resolution,
- and whatever additional constraints your strategy requires.
The official SDK supports market lookup by ID, slug, or Polymarket URL. It also provides market objects containing trading information such as minimum tick size and minimum order size.
A simple public-data example:
from polymarket import PublicClient
def find_markets() -> None:
with PublicClient() as client:
page = client.list_markets(page_size=10).first_page()
for market in page.items:
print(
market.id,
market.question,
market.slug,
)
if __name__ == "__main__":
find_markets()
The important point is that market discovery should produce structured market objects, not arbitrary strings copied from a webpage.
6. Outcome Tokens Matter More Than the Market URL
Your trading engine ultimately needs an outcome token ID.
For a binary market, the current Polymarket documentation exposes the YES and NO outcome token IDs through the market object.
For example:
yes_token_id = market.outcomes.yes.token_id
no_token_id = market.outcomes.no.token_id
if yes_token_id is None or no_token_id is None:
raise RuntimeError("Market has no tradable outcome token IDs")
This distinction is critical:
Market
│
├── metadata
├── question
├── resolution information
└── outcomes
├── YES token ID
└── NO token ID
Do not confuse:
- market ID,
- event ID,
- condition ID,
- outcome token ID,
- wallet address,
- order ID.
They represent different things.
A robust internal model should make those distinctions explicit.
7. Read the Order Book
The market price alone is not enough for an execution engine.
Suppose your model says:
Fair probability = 0.63
That does not mean you can automatically buy at:
0.63
You need to know what liquidity is actually available.
The current SDK's public client exposes CLOB market data and order-book models, while Polymarket's WebSocket market channel can stream book snapshots and price changes.
Conceptually:
book = client.get_order_book(token_id)
best_bid = ...
best_ask = ...
Your strategy should work with a normalized representation:
from dataclasses import dataclass
from decimal import Decimal
@dataclass(frozen=True)
class Quote:
best_bid: Decimal
best_ask: Decimal
timestamp: float
Then the strategy does not care whether that quote came from REST, WebSocket state, a replay file, or a test fixture.
8. The Simplest Useful Strategy Model
A good educational bot does not need a sophisticated prediction model.
Define:
model_probability = p_model
executable_price = p_market
edge = p_model - p_market
For a BUY strategy:
trade if edge > minimum_edge
For example:
from dataclasses import dataclass
from decimal import Decimal
@dataclass(frozen=True)
class Signal:
side: str
token_id: str
price: Decimal
size: Decimal
edge: Decimal
def generate_signal(
*,
token_id: str,
model_probability: Decimal,
best_ask: Decimal,
minimum_edge: Decimal,
size: Decimal,
) -> Signal | None:
edge = model_probability - best_ask
if edge < minimum_edge:
return None
return Signal(
side="BUY",
token_id=token_id,
price=best_ask,
size=size,
edge=edge,
)
This is deliberately incomplete.
A production model would also consider:
- spread,
- depth,
- expected slippage,
- fees,
- stale market data,
- probability calibration,
- model uncertainty,
- time to resolution,
- correlation with other positions,
- adverse selection,
- and execution probability.
The purpose is to demonstrate the architecture, not imply a profitable trading signal.
9. Why "Model Probability > Market Price" Is Not Enough
Suppose:
Model probability: 0.62
Best ask: 0.59
The apparent edge is:
0.62 - 0.59 = 0.03
That is not necessarily a 3-cent profit opportunity.
You still need to account for:
gross edge
- trading fees
- spread effects
- expected slippage
- model error
- execution uncertainty
- adverse selection
= expected net edge
Polymarket currently charges taker fees on certain markets. The official fee documentation says fees are determined per market at match time, makers are not charged fees, and the fee formula depends on the traded share price and market-specific fee rate.
That means a strategy should not use one universal hard-coded fee assumption.
A safer abstraction is:
expected_net_edge = (
model_probability
- executable_price
- estimated_fee
- estimated_slippage
)
Only then should the risk engine evaluate the trade.
10. Respect Tick Size and Minimum Order Size
One of the most common implementation errors is treating price and size as arbitrary decimals.
Polymarket's current order documentation states that price must follow the market's minimum price increment and size must satisfy the market's minimum order size.
The SDK's own examples obtain these values from the selected market:
price = market.trading.minimum_tick_size
size = market.trading.minimum_order_size
rather than assuming universal values.
A production strategy should therefore quantize its desired price.
For example:
from decimal import Decimal, ROUND_DOWN
def round_down_to_tick(
price: Decimal,
tick: Decimal,
) -> Decimal:
units = (price / tick).to_integral_value(
rounding=ROUND_DOWN
)
return units * tick
Then:
price = round_down_to_tick(
desired_price,
market.trading.minimum_tick_size,
)
You should perform the same kind of validation for size.
Do not discover an attractive signal and only afterward find that the proposed order violates the market's trading constraints.
11. Authentication With the Current Python SDK
The current unified SDK exposes SecureClient.create() for authenticated workflows.
The official SDK example uses:
from polymarket import SecureClient
client = SecureClient.create(
private_key=os.environ["POLYMARKET_PRIVATE_KEY"],
wallet=os.environ["POLYMARKET_DEPOSIT_WALLET"],
)
The SDK's implementation indicates that client creation derives or validates API credentials and establishes the authenticated transports required for secure CLOB operations.
A minimal setup looks like:
import os
from polymarket import SecureClient
def create_client() -> SecureClient:
private_key = os.environ["POLYMARKET_PRIVATE_KEY"]
wallet = os.environ["POLYMARKET_DEPOSIT_WALLET"]
return SecureClient.create(
private_key=private_key,
wallet=wallet,
)
Keep authentication isolated from strategy code.
12. Creating an Order Is Not the Same as Posting It
This distinction is extremely important.
The official Python SDK includes a create_limit_order() workflow that creates and signs an order locally. The SDK's example explicitly states that this does not submit the order. Actual trading requires a posting method such as place_limit_order() or post_order().
That separation is valuable for testing.
You can have:
Strategy
↓
Risk
↓
create_limit_order()
↓
Signed order
without immediately doing:
POST → live exchange
This gives you a natural dry-run boundary.
13. Example Limit Order Flow
The current SDK example follows this general pattern:
from polymarket import SecureClient
def build_order(
client: SecureClient,
token_id: str,
price,
size,
):
return client.create_limit_order(
token_id=token_id,
price=price,
size=size,
side="BUY",
)
The returned object is a signed order representation.
For actual execution, use the SDK's current posting method rather than assuming that signing automatically submits the order.
A production execution layer should therefore expose two distinct operations:
class ExecutionEngine:
def prepare_order(self, signal):
...
def submit_order(self, order):
...
That design makes dry-run testing much safer.
14. Dry-Run Mode
Before allowing a bot to submit real orders, implement:
DRY RUN
as a first-class execution mode.
Example:
class ExecutionEngine:
def __init__(self, client, dry_run: bool):
self.client = client
self.dry_run = dry_run
def execute(self, signal):
order = self.client.create_limit_order(
token_id=signal.token_id,
price=signal.price,
size=signal.size,
side=signal.side,
)
if self.dry_run:
print(
"DRY RUN:",
signal.side,
signal.token_id,
signal.price,
signal.size,
)
return order
return self.client.place_limit_order(
token_id=signal.token_id,
price=signal.price,
size=signal.size,
side=signal.side,
)
The exact live-call signature should be pinned against the SDK version you deploy.
The important architectural property is that changing from simulation to execution should happen in one controlled location, not throughout the codebase.
15. WebSockets for Real-Time Market Data
Polling is easy to understand:
GET book
sleep
GET book
sleep
GET book
But a real-time trading system benefits from event-driven data.
Polymarket documents a WebSocket market channel at:
wss://ws-subscriptions-clob.polymarket.com/ws/market
The market channel can deliver events including full book snapshots, price changes, last trade prices, best bid/ask updates, tick-size changes, and market lifecycle events.
The documented channels also include user order/trade activity, sports data, and RTDS.
A sensible architecture is:
WebSocket
│
▼
┌────────────────┐
│ Local Book │
│ State │
└───────┬────────┘
│
▼
Signal Engine
The REST API can remain useful for:
- initialization,
- snapshots,
- recovery,
- account queries,
- order management,
- and reconciliation.
Do not make your strategy depend on one network stream being perfect forever.
16. Maintain Local State
A production bot should maintain explicit state.
For example:
from dataclasses import dataclass, field
@dataclass
class BotState:
positions: dict[str, float] = field(default_factory=dict)
open_orders: dict[str, dict] = field(default_factory=dict)
last_market_update: dict[str, float] = field(default_factory=dict)
realized_pnl: float = 0.0
When a WebSocket event arrives:
event
↓
validate
↓
update local state
↓
evaluate signal
When an order update arrives:
order event
↓
update order state
↓
update position state
↓
recalculate exposure
Do not assume:
submit order = position changed
Those are different states.
17. Risk Management Should Be a Separate Layer
A strategy generates an opinion.
Risk management decides whether that opinion is allowed to become an order.
For example:
from decimal import Decimal
def risk_allows(
*,
current_exposure: Decimal,
proposed_notional: Decimal,
max_exposure: Decimal,
) -> bool:
return (
current_exposure + proposed_notional
<= max_exposure
)
But real risk controls should go further.
Useful limits include:
Per-market exposure
maximum dollars/shares in one market
Global exposure
maximum portfolio exposure
Order-size limit
maximum size per individual order
Open-order limit
maximum number of outstanding orders
Loss guard
disable new entries after a configured loss threshold
Stale-data guard
do not trade if market data is older than X seconds
Duplicate-signal guard
do not repeatedly submit the same signal
Emergency kill switch
cancel orders
stop strategy
remain offline
The kill switch is not an optional feature.
18. A Better Decision Pipeline
Instead of:
if price < threshold:
buy()
use:
Market Update
│
▼
Is market tradable?
│
▼
Is data fresh?
│
▼
Is order book valid?
│
▼
Calculate model fair value
│
▼
Calculate executable edge
│
▼
Estimate costs
│
▼
Risk checks
│
▼
Size order
│
▼
Validate tick/size
│
▼
Submit
│
▼
Reconcile
This pipeline is the difference between a script and a trading system.
19. Handling Rate Limits
Polymarket documents API throttling across its Gamma, Data, CLOB, authentication, and trading APIs. The documented CLOB limits vary by endpoint, with separate burst and sustained limits for trading endpoints.
For example, the current documentation lists:
- CLOB general requests: 9,000 requests / 10 seconds
-
/book: 1,500 / 10 seconds -
/price: 1,500 / 10 seconds -
POST /order: 3,500 / 10 seconds burst and 36,000 / 10 minutes sustained -
DELETE /order: 3,000 / 10 seconds burst and 30,000 / 10 minutes sustained.
These are platform limits, not targets.
A good bot should use substantially less than its theoretical allowance.
Implement exponential backoff:
import random
import time
def retry_delay(attempt: int) -> float:
base = min(2 ** attempt, 30)
jitter = random.uniform(0, 0.25)
return base + jitter
Then retry only errors that are actually safe to retry.
Do not blindly retry order submissions.
That can create duplicate orders if the first request succeeded but the response was lost.
20. The Most Dangerous Failure: Unknown Order State
Imagine:
Bot → POST order
│
├── exchange accepts order
│
└── network connection dies
Your bot receives:
timeout
What happened?
You don't know.
The order might be:
- rejected,
- accepted and resting,
- partially filled,
- completely filled,
- or cancelled elsewhere.
A naive retry could submit a second order.
The correct response is reconciliation.
POST
│
├── response → record order ID
│
└── timeout
│
▼
reconcile
│
▼
query known order/account state
This is why order IDs and persistent state matter.
21. Limit Orders vs Market Orders
Polymarket currently documents both limit and market orders. A limit order specifies a price and can remain on the book, while a market order is intended to trade against available liquidity.
Use a limit order when:
- price control matters,
- you can tolerate waiting,
- spread is important,
- execution certainty is less important.
Use a market-style order when:
- immediate execution matters,
- the strategy has a sufficiently strong edge,
- available liquidity has been evaluated,
- and slippage is explicitly modeled.
Neither is universally superior.
For a bot, the real question is:
What execution behavior does the strategy require?
22. Time-in-Force and Stale Quotes
The current documentation supports GTC and GTD behavior for limit orders. GTD orders have documented expiration constraints, including a one-minute security threshold before their stated expiration and a minimum future expiration requirement.
This matters for automated trading.
Suppose your strategy wants:
Buy at 0.42
but the signal becomes invalid after 10 seconds.
A long-lived GTC order could remain exposed after the original thesis has disappeared.
Your execution policy therefore needs to answer:
How long is this quote valid?
That answer belongs to the strategy/execution contract.
23. Practical Example: A Complete Signal Pipeline
Here is a compact educational version:
from dataclasses import dataclass
from decimal import Decimal
@dataclass(frozen=True)
class MarketSnapshot:
token_id: str
best_ask: Decimal
timestamp: float
@dataclass(frozen=True)
class Signal:
token_id: str
side: str
price: Decimal
size: Decimal
edge: Decimal
class Strategy:
def __init__(
self,
minimum_edge: Decimal,
order_size: Decimal,
):
self.minimum_edge = minimum_edge
self.order_size = order_size
def evaluate(
self,
snapshot: MarketSnapshot,
model_probability: Decimal,
) -> Signal | None:
edge = model_probability - snapshot.best_ask
if edge < self.minimum_edge:
return None
return Signal(
token_id=snapshot.token_id,
side="BUY",
price=snapshot.best_ask,
size=self.order_size,
edge=edge,
)
class RiskManager:
def __init__(self, max_exposure: Decimal):
self.max_exposure = max_exposure
def approve(
self,
current_exposure: Decimal,
signal: Signal,
) -> bool:
notional = signal.price * signal.size
return (
current_exposure + notional
<= self.max_exposure
)
Then the application becomes:
signal = strategy.evaluate(
snapshot,
model_probability,
)
if signal is None:
return
if not risk.approve(
current_exposure,
signal,
):
return
execution.execute(signal)
Notice what is missing:
strategy → client.place_order()
The strategy does not directly place orders.
That is intentional.
24. Production Architecture
A more serious system could look like:
flowchart TD
A[Market Discovery] --> B[Market Registry]
B --> C[WebSocket Market Feed]
C --> D[Local Order Book]
D --> E[Signal Engine]
E --> F[Cost Model]
F --> G[Risk Engine]
G --> H[Execution Engine]
H --> I[CLOB]
I --> J[Order Events]
J --> K[State Reconciliation]
K --> L[Portfolio State]
L --> G
M[External Data] --> E
N[Metrics] --> O[Monitoring]
D --> N
E --> N
H --> N
K --> N
This architecture has an important property:
the bot can recover state independently of the strategy.
If your strategy process crashes, you should still be able to determine:
- which orders exist,
- what positions exist,
- what markets are being watched,
- and whether the bot is safe to restart.
25. Performance Engineering
Performance should be measured rather than guessed.
Do not begin with:
"I need a 1 ms bot."
Begin with:
Where does time actually go?
Measure:
market event received
↓
event parsed
↓
local book updated
↓
signal generated
↓
risk check
↓
order signed
↓
request sent
↓
exchange response
Record timestamps for each stage.
Then calculate:
feed → signal latency
signal → submit latency
submit → acknowledgement latency
The WebSocket market channel is appropriate for reducing dependence on repeated REST polling, but it does not eliminate network, processing, or exchange-side latency. Polymarket's documentation describes the channel as near-real-time rather than promising a fixed end-to-end latency.
Avoid optimizing prematurely.
For most early bots, the biggest performance gains come from:
- avoiding unnecessary polling,
- keeping connections alive,
- reducing redundant API calls,
- maintaining local state,
- eliminating blocking work from the hot path,
- and measuring the actual critical path.
26. Don't Put Logging on the Hot Path
This is easy to overlook.
Avoid doing expensive operations such as:
json.dumps(huge_object)
database.commit()
print(large_book)
for every market update.
Instead:
Market feed
│
├──► hot strategy path
│
└──► async logging/metrics pipeline
A lightweight queue works well:
from queue import SimpleQueue
metrics_queue = SimpleQueue()
def record_metric(event):
metrics_queue.put(event)
A separate worker can batch and persist metrics.
27. Security
A trading bot has two major security surfaces:
Credentials
Protect:
- private keys,
- API credentials,
- builder credentials,
- wallet information.
Trading authority
Even if a private key never leaks, a software bug can still lose money.
Therefore:
- enforce maximum order size,
- enforce maximum exposure,
- validate token IDs,
- validate market state,
- reject impossible prices,
- reject stale data,
- implement a kill switch,
- separate dry-run from live mode,
- restrict production credentials,
- and audit every order.
Never log:
PRIVATE_KEY
API_SECRET
seed phrase
signed credential material
Be careful with exceptions as well.
An exception object can contain request information or serialized credentials if a poorly designed dependency includes them.
28. Testing Strategy
Do not begin testing with real money.
A useful testing pyramid is:
┌───────────────┐
│ Live / tiny │
│ controlled │
└───────┬───────┘
│
┌───────┴───────┐
│ Integration │
└───────┬───────┘
│
┌───────┴───────┐
│ Replay tests │
└───────┬───────┘
│
┌───────┴───────┐
│ Unit tests │
└───────────────┘
Unit tests
Test:
- probability calculations,
- edge calculations,
- price rounding,
- position limits,
- order sizing,
- stale-data checks.
Example:
def test_signal_requires_minimum_edge():
signal = strategy.evaluate(
snapshot,
model_probability=Decimal("0.51"),
)
assert signal is None
Replay tests
Save real market-data events and replay them through the strategy.
This is much more useful than testing only synthetic values.
Integration tests
Test:
- authentication,
- market discovery,
- order construction,
- cancellation,
- reconciliation.
The official Python SDK repository itself distinguishes integration tests from metered tests and warns that tests which place orders, spend funds, or mutate live state require explicit opt-in.
That is a good pattern to copy.
29. Backtesting Prediction-Market Strategies
Traditional OHLC backtesting is often insufficient.
A prediction-market backtest should model:
timestamp
token
bid levels
ask levels
trade events
available depth
your order size
execution assumption
fees
position
resolution
If you only backtest against:
last_price
you can dramatically overestimate execution quality.
A better simulator asks:
If my bot submitted this order at this timestamp, how much of it could realistically have filled?
For market-making strategies, you also need queue-position assumptions.
For taker strategies, you need depth-aware slippage.
30. Monitoring and Observability
A production bot should expose metrics such as:
Market-data metrics
websocket_connected
book_update_age
messages_per_second
reconnect_count
Strategy metrics
signals_generated
signals_rejected
edge_distribution
Execution metrics
orders_submitted
orders_rejected
orders_filled
partial_fills
cancel_count
Risk metrics
current_exposure
maximum_exposure
open_orders
System metrics
process_uptime
CPU
memory
network errors
API errors
You should also log structured events:
{
"event": "order_submitted",
"token_id": "...",
"side": "BUY",
"price": "0.52",
"size": "10",
"strategy": "probability_edge",
"dry_run": true
}
Never assume your logs are only for debugging.
They become your trading audit trail.
31. Failure Modes You Should Expect
1. Stale market data
Problem: The strategy acts on an old book.
Fix: Attach timestamps to market state and reject stale data.
2. Duplicate orders
Problem: A timeout is interpreted as failure and the bot submits again.
Fix: Reconcile order state before retrying an uncertain submission.
3. Invalid price precision
Problem: Strategy generates a price that violates tick size.
Fix: Quantize against the market's current minimum tick size.
4. Invalid order size
Problem: Size is below the market minimum.
Fix: Validate before signing.
5. Assuming every market has the same fee
Problem: Cost model is wrong.
Fix: Read current market fee configuration where applicable. Polymarket says fees are determined per market at match time.
6. Using an obsolete SDK
Problem: Tutorials tell you to build everything around py-clob-client.
Fix: Check the current official SDK first. The historical repository is archived, and Polymarket now recommends the unified Python SDK.
7. Treating order creation as submission
Problem: Bot believes an order was sent when it only created a signed object.
Fix: Keep create and place/post operations separate.
8. Polling everything
Problem: Unnecessary API traffic and stale snapshots.
Fix: Use the real-time market channel where appropriate and reserve REST for snapshots, recovery, account state, and execution workflows.
32. Advanced Improvement: External Signal Feeds
A stronger strategy architecture can consume external information:
External Feed
│
▼
Feature Engineering
│
▼
Probability Model
│
▼
Polymarket Market
│
▼
Execution
Examples could include:
- financial prices,
- weather data,
- sports data,
- economic releases,
- news/event data.
The key is not simply receiving another feed.
The model must answer:
What probability does this information imply?
Then compare it against an executable Polymarket price.
33. Advanced Improvement: Market Making
A market-making bot changes the objective.
Instead of:
"I think YES is underpriced."
you might estimate:
fair_value
inventory
spread
adverse_selection_risk
Then quote:
bid = fair_value - spread
ask = fair_value + spread
while controlling inventory.
The strategy becomes:
Fair value
│
├──► Bid quote
│
└──► Ask quote
Polymarket currently documents market-making and maker-rebate programs separately, and the current fee documentation states that makers are not charged the applicable taker fee.
That does not mean market making is automatically profitable. Inventory risk, adverse selection, stale quotes, and execution costs remain.
34. Advanced Improvement: Event-Driven State Machines
For more complex bots, model orders as explicit states:
CREATED
│
▼
SIGNED
│
▼
SUBMITTED
│
├───────────────┐
▼ ▼
OPEN REJECTED
│
├───────┐
▼ ▼
PARTIAL CANCELLED
│
▼
FILLED
This is much safer than scattered booleans such as:
order_sent = True
order_filled = False
A state machine makes impossible transitions easier to detect.
35. Advanced Improvement: Reconciliation Loop
Even with WebSockets, periodically reconcile your local state against authoritative API data.
For example:
Continuous WebSocket state
│
▼
Local state
│
│
periodic check
▼
REST/API state
│
▼
reconciliation
If:
local_open_orders != remote_open_orders
stop new trading until the discrepancy is understood.
A trading bot should fail closed, not fail open.
36. A Practical Production Loop
Putting the components together:
def run_cycle(
market,
snapshot,
model_probability,
state,
strategy,
risk,
execution,
):
if snapshot is None:
return
if snapshot.is_stale():
return
signal = strategy.evaluate(
snapshot=snapshot,
model_probability=model_probability,
)
if signal is None:
return
if not risk.approve(
current_exposure=state.current_exposure,
signal=signal,
):
return
execution.execute(signal)
This looks almost boring.
That is a good sign.
Production trading infrastructure should be boring at the boundaries.
The complexity belongs inside well-defined components, not in unpredictable interactions between them.
37. Current Polymarket API Details Worth Keeping in Mind
As of the documentation checked for this article:
- Polymarket provides a unified official Python SDK named
polymarket-client. - The SDK is currently beta.
- The historical
py-clob-clientrepository is archived and its README recommends migration. - The official Python SDK exposes
PublicClient,AsyncPublicClient,SecureClient, andAsyncSecureClient. - Polymarket provides WebSocket channels for market and user activity.
- Current trading documentation supports limit and market orders.
- Limit orders must respect the market's tick size and minimum order size.
- Fees are market-dependent and applied at match time where enabled.
- API throttling applies across the platform and trading endpoints have their own burst/sustained limits.
These details can change.
For a production bot, treat the official documentation as part of your deployment dependency.
Frequently Asked Questions
What is a Polymarket trading bot?
A Polymarket trading bot is software that automatically consumes market information, evaluates a trading rule or model, manages risk, and submits or manages orders through Polymarket's developer interfaces.
A useful bot is an execution system, not merely a price scraper.
Can I build a Polymarket trading bot with Python?
Yes. Polymarket currently maintains an official Python SDK, polymarket-client, which provides public and authenticated client workflows. The SDK is currently beta.
Is py-clob-client still the recommended Python SDK?
No. The historical py-clob-client repository was archived in May 2026, and its README says it is no longer maintained and recommends migrating to the unified SDK.
Does a trading bot guarantee profit?
No.
Automation changes execution and consistency; it does not create an economic edge.
A strategy can lose money because of:
- incorrect probabilities,
- spread,
- fees,
- slippage,
- adverse selection,
- low liquidity,
- execution failures,
- and changing market conditions.
Should I use REST or WebSockets?
Use them for different jobs.
WebSockets are well suited to continuously changing market information. REST remains useful for snapshots, recovery, account information, and trading operations. Polymarket documents both real-time WebSocket channels and REST-based APIs.
How much money do I need to start?
There is no universally correct amount.
Start with an amount whose complete loss would be acceptable to you, and prioritize dry-run, replay, and integration testing before live trading.
Should I start with market making or directional trading?
For a first bot, directional probability-based execution is usually easier to reason about.
Market making introduces additional problems such as inventory management, adverse selection, quote cancellation, and execution quality.
Can I run the bot on a VPS?
Yes, provided the deployment satisfies your operational and security requirements.
The important properties are stable networking, secret protection, process supervision, logging, monitoring, and a recovery strategy—not simply choosing a particular hosting provider.
Conclusion
Building a Polymarket trading bot in Python is fundamentally an engineering problem before it is a strategy problem.
The minimum useful architecture is:
Market Discovery
↓
Market Data
↓
Signal
↓
Cost Model
↓
Risk
↓
Execution
↓
Reconciliation
↓
Monitoring
The most important implementation lessons are:
- Use the current official SDK rather than copying an obsolete tutorial.
- Separate market discovery from execution.
- Trade outcome token IDs, not URLs or arbitrary market identifiers.
- Respect tick-size and minimum-order constraints.
- Model executable prices rather than theoretical prices.
- Account for fees, spread, slippage, and adverse selection.
- Use WebSockets for appropriate real-time market workflows.
- Treat uncertain order submissions as reconciliation problems.
- Build dry-run and replay testing before live execution.
- Put hard risk limits between every strategy signal and the exchange.
- Monitor the bot as an operational system.
- Re-check Polymarket's documentation whenever the platform or SDK changes.
The strategy is replaceable.
The infrastructure is what lets you safely test increasingly sophisticated strategies.
Trading-risk disclaimer: This article is educational and technical, not financial advice. Automated prediction-market trading can result in partial or total loss of capital. No strategy described here is guaranteed to be profitable. Validate strategies with historical/replay data and controlled testing before risking meaningful capital.
Related Articles
1. How to Read Polymarket Order Books With Python
Suggested anchor: read Polymarket order books with Python
Why link it: This is the natural next step after market discovery and introduces depth, spread, liquidity, and executable pricing.
2. Polymarket WebSocket API: Build a Real-Time Market Data Feed
Suggested anchor: Polymarket WebSocket market data
Why link it: Readers who understand REST-based polling can move toward event-driven market-data architecture.
3. Polymarket Order Execution in Python
Suggested anchor: Polymarket order execution in Python
Why link it: Deepens the execution section with signing, posting, cancellation, order lifecycle, and reconciliation.
4. How to Build a Polymarket Market-Making Bot
Suggested anchor: build a Polymarket market-making bot
Why link it: Creates a strategy-level progression from directional execution to liquidity provision.
5. Polymarket Trading Bot Risk Management
Suggested anchor: Polymarket bot risk management
Why link it: Expands the most important production layer: exposure, sizing, kill switches, stale-data protection, and portfolio limits.
6. How to Backtest Polymarket Trading Strategies
Suggested anchor: backtest Polymarket trading strategies
Why link it: Connects live bot architecture to historical simulation and execution-aware research.
7. Polymarket API Authentication With Python
Suggested anchor: Polymarket API authentication
Why link it: Gives readers a dedicated security and credential-management tutorial.
8. Polymarket Bot Monitoring and Observability
Suggested anchor: monitor a Polymarket trading bot
Why link it: Extends the production section into metrics, alerting, state reconciliation, and operational reliability.
Useful Resources
Official Polymarket
Use this for the live product, markets, account interface, and market context.
Official Polymarket Documentation
This should be the primary technical reference for API behavior, trading workflows, market data, authentication, fees, and SDK changes.
Market Discovery
Polymarket Market Discovery Documentation
Useful for understanding how to discover and filter markets before trading.
Python SDK
Official Polymarket Python SDK
The current unified Python SDK for Polymarket integrations. It is currently beta.
WebSocket Market Data
Useful when moving from REST polling to real-time market-data processing.
Relevant DEV.to Resource
Building a Low-Latency Polymarket Trading Bot in Python — DEV.to
Useful for discussing engineering considerations around WebSockets and execution architecture. Its latency measurements are author-specific and should not be treated as Polymarket guarantees.
Relevant YouTube Tutorial
Useful as a practical visual walkthrough for developers who prefer seeing a complete bot assembled end-to-end. It is supplementary to the official documentation.
Official Polymarket X
Use official social channels for product announcements, but verify technical claims against the documentation.
Polymarket Developer Resources
Polymarket GitHub Organization
The official GitHub organization is useful for tracking SDKs, developer tooling, and other open-source components.
About the Author
[Bo$onaX]
I write about Polymarket trading bots, prediction-market infrastructure, algorithmic trading, Python automation, Web3 development, and quantitative strategies.
Contact:
X: [https://x.com/@xxniiinxx]
Telegram: [https://t.me/bosonax]
