DEV Community: Dexoryn

How to Calculate Expected Value for a Polymarket Trading Bot in Python

Dexoryn — Wed, 14 Oct 2026 10:33:42 +0000

A Polymarket trading bot can identify a promising outcome and still make a poor entry.

The problem is that predicting what will happen and determining whether a trade is worth taking are two different tasks.

A bot might estimate that an outcome has a 65% chance of winning. But if the price is already $0.63, the estimated advantage is small before accounting for trading costs and uncertainty.

This is where expected value becomes useful.

In this tutorial, we will build a small Python component that calculates the estimated expected value of a Polymarket outcome token, applies a minimum threshold, and rejects trades that do not meet the strategy's requirements.

The example uses hypothetical inputs. It does not connect to a live market, submit orders, or guarantee profitable results.

Understand the expected value calculation

For a binary outcome token held until resolution, a winning share pays $1 and a losing share pays $0.

Let:

(q) be your estimated probability that the outcome wins.
(p) be the purchase price per share.

The simplified expected profit per share before costs is:

[
EV=q(1-p)+(1-q)(-p)
]

This simplifies to:

[
EV=q-p
]

Suppose your model estimates a 65% probability of winning.

If the token costs $0.45:

[
EV=0.65-0.45=$0.20
]

If the token costs $0.62:

[
EV=0.65-0.62=$0.03
]

The estimated expected profit falls from $0.20 to $0.03 per share before fees and other costs.

This calculation assumes the probability estimate is accurate enough to be useful and that the position is held until resolution. Selling before resolution requires a different model that accounts for the exit price.

Expected value is an estimate, not a guaranteed return.

Implement the calculation in Python

Use "Decimal" for financial values when you want explicit decimal arithmetic rather than binary floating-point calculations.

from decimal import Decimal

def expected_value_per_share(
probability: Decimal,
price: Decimal,
cost_per_share: Decimal = Decimal("0"),
) -> Decimal:
if not Decimal("0") <= probability <= Decimal("1"):
raise ValueError("Probability must be between 0 and 1")

if not Decimal("0") < price < Decimal("1"):
    raise ValueError("Price must be between 0 and 1")

if cost_per_share < Decimal("0"):
    raise ValueError("Cost cannot be negative")

return probability - price - cost_per_share

probability = Decimal("0.65")
price = Decimal("0.45")
cost = Decimal("0.01")

ev = expected_value_per_share(
probability=probability,
price=price,
cost_per_share=cost,
)

print(f"Estimated EV per share: ${ev}")

Expected output:

Estimated EV per share: $0.19

The example subtracts an assumed cost of $0.01 per share. This is only a placeholder. Actual costs depend on the applicable market fee schedule and execution conditions.

The function deliberately keeps probability estimation separate from expected-value calculation. That makes it easier to replace the hypothetical probability with an estimate from a model, research process, or calibrated forecasting system.

Reject trades with insufficient expected value

Calculating expected value is only useful if the bot uses the result to make a decision.

A strategy can define a minimum expected-value threshold. If a proposed trade falls below it, the bot rejects the trade rather than submitting an order.

from decimal import Decimal

def evaluate_trade(
probability: Decimal,
price: Decimal,
cost_per_share: Decimal,
minimum_ev: Decimal,
) -> dict:
ev = expected_value_per_share(
probability=probability,
price=price,
cost_per_share=cost_per_share,
)

if ev < minimum_ev:
    return {
        "approved": False,
        "reason": "EXPECTED_VALUE_TOO_LOW",
        "expected_value": str(ev),
    }

return {
    "approved": True,
    "reason": "APPROVED",
    "expected_value": str(ev),
}

result = evaluate_trade(
probability=Decimal("0.65"),
price=Decimal("0.62"),
cost_per_share=Decimal("0.01"),
minimum_ev=Decimal("0.05"),
)

print(result)

The result is:

{
"approved": False,
"reason": "EXPECTED_VALUE_TOO_LOW",
"expected_value": "0.02"
}

The trade is rejected because its estimated expected value is $0.02 per share, below the configured minimum of $0.05.

The threshold is an example, not a universally correct value. It should be selected and evaluated based on the strategy's assumptions, uncertainty, execution costs, and risk requirements.

A positive expected value alone does not mean a trade is worth taking.

Use the executable price, not an assumed price

One of the most important implementation details is choosing the correct price input.

Polymarket provides market data through its APIs. The CLOB API exposes order-book information and price-related endpoints, while the Gamma API provides market metadata and discovery information. The "Polymarket Institute's data guide" (https://institute.polymarket.com/data) includes examples of retrieving CLOB prices and historical prices.

For a potential buy order, the bot should evaluate the price available to buyers, not blindly use a last-traded price or a midpoint.

For larger orders, even the best ask may not represent the average price of the entire order. The bot may need to inspect available ask levels and estimate the cost of filling the intended quantity.

Consider this simplified order book:

Ask price| Available shares
$0.50| 20
$0.52| 30
$0.55| 50

A purchase of 20 shares might be available at $0.50. A purchase of 50 shares would require buying across at least two price levels if those quantities remain available.

The estimated average entry price therefore depends on the size of the order.

For a live implementation, the process should be:

Retrieve the relevant outcome token's current order book.
Validate that the response is complete and sufficiently fresh.
Estimate the average execution price for the intended quantity.
Calculate expected value using that estimate and applicable costs.
Reject the trade if the resulting expected value falls below the strategy's threshold.

Market data can change between retrieval and order submission. The execution layer should enforce suitable price limits and handle cases where the market moves before the order is filled.

Add unit tests

Expected-value logic is small enough that it should be tested independently before being integrated into a trading bot.

Using "pytest", create a file called "test_expected_value.py":

from decimal import Decimal
import pytest

from trading_logic import expected_value_per_share

def test_positive_expected_value():
result = expected_value_per_share(
Decimal("0.65"),
Decimal("0.45"),
Decimal("0.01"),
)

assert result == Decimal("0.19")

def test_zero_expected_value():
result = expected_value_per_share(
Decimal("0.50"),
Decimal("0.50"),
)

assert result == Decimal("0.00")

def test_invalid_probability():
with pytest.raises(ValueError):
expected_value_per_share(
Decimal("1.10"),
Decimal("0.50"),
)

def test_invalid_price():
with pytest.raises(ValueError):
expected_value_per_share(
Decimal("0.60"),
Decimal("1.00"),
)

Place the calculation function in "trading_logic.py", then run:

pip install pytest
pytest -q

These tests verify the arithmetic and basic input validation. They do not establish that a probability model is accurate or that a live trading system will execute orders as expected.

What this calculation does not tell you

Expected value is useful, but it is only one part of a trading decision.

First, the probability estimate may be wrong. A model that consistently overestimates an outcome's chances can report positive expected value while making poor decisions.

Second, costs may be more complicated than a fixed amount per share. The actual implementation must account for the applicable fee schedule and the economics of the order.

Third, expected value does not measure the uncertainty of the estimate. Two opportunities can have the same calculated expected value but very different levels of model uncertainty.

Fourth, it does not account for portfolio concentration. Several individually attractive positions may depend on the same underlying event and create excessive combined exposure.

A production trading bot should combine expected-value evaluation with data-quality checks, executable-price validation, position limits, order-state management, and monitoring.

It should also record rejected trades and the reasons for rejection. This makes it possible to assess whether the rules are too permissive, too restrictive, or based on assumptions that do not hold in live conditions.

Conclusion

Expected value gives a Polymarket trading bot a clear mathematical basis for evaluating a proposed entry. Implementing it as an independent Python component makes the logic easier to test, audit, and improve.

The calculation is straightforward. The difficult work lies in producing reliable probability estimates, using realistic execution prices, accounting for costs, and enforcing the result consistently throughout the trading system.

At Dexoryn Labs, we work on Polymarket trading automation, including existing bot projects and custom development. You can explore our "open-source projects on GitHub" (https://github.com/dexorynlabs) or visit "dexoryn.xyz" (https://dexoryn.xyz/) to learn more.

Build the decision logic first, test its assumptions, and only then consider connecting it to live execution.

How to Build a Trade-Rejection Layer for a Polymarket Bot in Python

Dexoryn — Wed, 14 Oct 2026 00:17:10 +0000

A Polymarket trading bot needs more than a strategy that identifies potential opportunities. It needs a reliable way to reject trades that fail its execution or risk requirements.

Without this layer, a bot may submit orders using stale market data, accept an unattractive price, exceed its exposure limits, or act on a signal that no longer makes sense.

In this tutorial, we'll build a small Python trade-validation layer that evaluates a proposed trade before it reaches the execution component.

The implementation is deliberately independent of any specific Polymarket SDK. That makes it easier to test the decision logic without connecting a wallet, signing orders, or risking real funds.

Separate signal generation from trade validation

A common architectural mistake is allowing strategy logic to submit orders directly.

A cleaner design separates the process into distinct stages:

Signal generation: Identifies a possible trade.
Market validation: Checks data quality and market eligibility.
Trade validation: Evaluates expected edge, execution price, and risk limits.
Execution: Submits an approved order through the appropriate trading client.
Monitoring: Tracks order status, fills, errors, and resulting exposure.

The strategy should not be able to bypass the validation layer simply because it produces a strong signal.

This separation also makes the system easier to test. You can evaluate whether a trade should be accepted without making an API request or interacting with a wallet.

Polymarket provides APIs for market data and trading. Before integrating the execution stage, consult the "official API overview" (https://help.polymarket.com/en/articles/13364254-does-polymarket-have-an-api) and the current developer documentation for the specific endpoints and SDK version you use.

Define a trade proposal

Start by representing a proposed trade as structured data.

from dataclasses import dataclass
from decimal import Decimal

@dataclass(frozen=True)
class TradeProposal:
token_id: str
side: str
estimated_probability: Decimal
execution_price: Decimal
expected_cost_per_share: Decimal
data_age_seconds: Decimal
proposed_exposure: Decimal
current_exposure: Decimal

Each field serves a specific purpose.

"token_id" identifies the outcome token.
"side" identifies whether the proposal is to buy or sell.
"estimated_probability" represents the strategy's estimated probability for the relevant outcome.
"execution_price" is the estimated executable price, not necessarily the last traded price.
"expected_cost_per_share" represents estimated costs expressed per share.
"data_age_seconds" measures the age of the market data used for the decision.
"proposed_exposure" and "current_exposure" represent the relevant risk allocation.

This is a simplified data model. A production system should define exposure units precisely, distinguish order notional from maximum loss where appropriate, and validate the token and market identifiers against trusted market metadata.

It should also distinguish between buy and sell calculations. The example below focuses on buying an outcome token.

Create explicit rejection reasons

Returning only "True" or "False" tells you whether a trade was rejected, but not why.

Use a structured result instead.

from dataclasses import dataclass

@dataclass(frozen=True)
class ValidationResult:
approved: bool
reason: str

The "reason" field makes decisions observable. It can later be recorded in logs, test reports, and performance analysis.

For example, a bot might reject a proposal because its data is stale, its estimated edge is too small, or its exposure limit would be exceeded.

Implement the validation function

The following implementation uses explicit thresholds and returns the first rejection reason encountered.

from decimal import Decimal

def validate_trade(
trade: TradeProposal,
*,
minimum_edge: Decimal,
maximum_data_age: Decimal,
maximum_exposure: Decimal,
maximum_price: Decimal,
) -> ValidationResult:

if not trade.token_id:
    return ValidationResult(False, "MISSING_TOKEN_ID")

if trade.side != "BUY":
    return ValidationResult(False, "UNSUPPORTED_SIDE")

if not (
    Decimal("0") < trade.estimated_probability
    < Decimal("1")
):
    return ValidationResult(False, "INVALID_PROBABILITY")

if not (
    Decimal("0") < trade.execution_price
    < Decimal("1")
):
    return ValidationResult(False, "INVALID_PRICE")

if trade.data_age_seconds < Decimal("0"):
    return ValidationResult(False, "INVALID_DATA_AGE")

if trade.data_age_seconds > maximum_data_age:
    return ValidationResult(False, "STALE_DATA")

if trade.execution_price > maximum_price:
    return ValidationResult(False, "PRICE_LIMIT_EXCEEDED")

edge = (
    trade.estimated_probability
    - trade.execution_price
    - trade.expected_cost_per_share
)

if edge < minimum_edge:
    return ValidationResult(False, "EDGE_TOO_LOW")

resulting_exposure = (
    trade.current_exposure
    + trade.proposed_exposure
)

if resulting_exposure > maximum_exposure:
    return ValidationResult(False, "EXPOSURE_LIMIT")

if trade.proposed_exposure <= Decimal("0"):
    return ValidationResult(False, "INVALID_EXPOSURE")

return ValidationResult(True, "APPROVED")

The edge calculation is intentionally simplified.

For a binary outcome token purchased at price (p), a probability estimate (q) gives a basic expected value per share of approximately (q-p), assuming a $1 payout if the outcome wins and $0 otherwise.

The code subtracts an estimated per-share cost as well. Real calculations must account for the applicable fee schedule, price constraints, execution uncertainty, and the precise strategy being evaluated. Selling positions requires different logic.

Also, the maximum price and minimum edge are separate controls. A trade can satisfy one and fail the other.

The thresholds should come from a documented strategy configuration and testing process, not arbitrary values chosen to make the bot trade more often.

Test both accepted and rejected trades

The validation layer should be tested before it is connected to a live execution client.

Here is a basic pytest example.

from decimal import Decimal

def make_trade(**overrides):
values = {
"token_id": "example-token",
"side": "BUY",
"estimated_probability": Decimal("0.60"),
"execution_price": Decimal("0.50"),
"expected_cost_per_share": Decimal("0.01"),
"data_age_seconds": Decimal("1"),
"proposed_exposure": Decimal("20"),
"current_exposure": Decimal("30"),
}

values.update(overrides)
return TradeProposal(**values)

def test_approves_valid_trade():
result = validate_trade(
make_trade(),
minimum_edge=Decimal("0.05"),
maximum_data_age=Decimal("5"),
maximum_exposure=Decimal("100"),
maximum_price=Decimal("0.55"),
)

assert result.approved is True
assert result.reason == "APPROVED"

def test_rejects_stale_data():
result = validate_trade(
make_trade(data_age_seconds=Decimal("10")),
minimum_edge=Decimal("0.05"),
maximum_data_age=Decimal("5"),
maximum_exposure=Decimal("100"),
maximum_price=Decimal("0.55"),
)

assert result.approved is False
assert result.reason == "STALE_DATA"

def test_rejects_excessive_exposure():
result = validate_trade(
make_trade(
proposed_exposure=Decimal("80"),
current_exposure=Decimal("30"),
),
minimum_edge=Decimal("0.05"),
maximum_data_age=Decimal("5"),
maximum_exposure=Decimal("100"),
maximum_price=Decimal("0.55"),
)

assert result.approved is False
assert result.reason == "EXPOSURE_LIMIT"

Save the implementation and tests in separate Python files, import the relevant classes and functions, and run:

pytest -q

The expected behavior is straightforward: the valid proposal is approved, the stale proposal is rejected, and the proposal that exceeds the exposure limit is rejected.

These are unit tests, not evidence that a live trading strategy is profitable or that an execution integration works correctly.

Add failure scenarios before integrating execution

The first set of tests covers only a few conditions. A more complete suite should test:

Missing or invalid token identifiers.
Probabilities outside the permitted range.
Invalid prices and negative data ages.
Prices above the strategy's maximum.
Expected edge exactly at the minimum threshold.
Exposure exactly at the limit.
Non-positive proposed exposure.
Unexpected API responses and missing order-book fields.
Market data becoming stale between validation and submission.
Duplicate signals and uncertain order-submission outcomes.

One particularly important case is a price change between validation and execution.

Suppose a proposal passes validation at an estimated price of $0.50, but the available price moves to $0.57 before the order is submitted. The original approval should not automatically authorize the more expensive trade.

The execution component should enforce an appropriate price limit, and the system should revalidate when necessary. The exact mechanism depends on the supported order types and current API behavior.

Likewise, if an order request times out, do not immediately assume the order failed and submit another one. The first request may have reached the service even though the response was lost. Reconcile the order state before retrying.

Record rejected trades for later analysis

A validation layer becomes more useful when its decisions can be examined.

A rejection record might contain:

rejection_record = {
"token_id": "example-token",
"reason": "EDGE_TOO_LOW",
"estimated_probability": "0.60",
"execution_price": "0.56",
"data_age_seconds": "1",
}

In a real application, add a timestamp, strategy version, market identifier, relevant threshold values, and a unique proposal identifier.

Avoid storing private keys, API secrets, or other sensitive credentials in logs.

Over time, analyze rejection counts by reason. If most signals fail because of execution price, the strategy may be generating signals too slowly or relying on unrealistic price assumptions. If stale data causes repeated rejections, the market-data pipeline may need attention.

Do not judge rejected trades solely by whether the market later moved in the predicted direction. A hypothetical trade is not the same as a confirmed fill at the expected price.

What this example does not cover

This implementation is a starting point, not a complete production risk engine.

It does not retrieve live Polymarket order books, calculate market-specific fees, estimate fills across multiple price levels, manage open orders, track partial fills, or maintain portfolio exposure from confirmed account state.

Those responsibilities should be implemented and tested in separate components.

For a production system, the validation layer should consume trusted, current market data and a consistent view of account exposure. The execution layer should enforce order constraints, reconcile uncertain outcomes, and record order-state transitions.

The architecture matters as much as the individual checks. A perfect validation function cannot protect a bot if another code path can submit orders without using it.

Conclusion

A trade-rejection layer turns risk rules into explicit, testable software behavior.

By validating data freshness, executable price, estimated edge, and exposure before order submission, developers can make a trading bot easier to inspect and maintain. Structured rejection reasons also make it easier to identify weaknesses in the strategy and infrastructure.

At Dexoryn Labs, we work on Polymarket automation through existing bot projects and custom development. Explore our "open-source Polymarket projects on GitHub" (https://github.com/dexorynlabs) or visit "dexoryn.xyz" (https://dexoryn.xyz/) if you are evaluating a bot or planning a custom trading workflow.

The goal is not to approve every signal. It is to make sure every approved trade has passed the rules the system was designed to enforce.

How to Test a Polymarket Trading Bot in Python Before Going Live

Dexoryn — Tue, 13 Oct 2026 14:19:49 +0000

A Polymarket trading bot can run without crashing and still make serious mistakes. It might submit duplicate orders, calculate the wrong position size, trade using stale prices, or assume an order was filled when it was not.

Testing only whether the script starts successfully is not enough.

A reliable trading bot needs tests for its decision logic, risk controls, execution behaviour, and recovery from failures. You can test most of these components without placing real orders or risking funds.

This tutorial shows how to structure a basic Python testing workflow using a simple trading decision, a risk check, a simulated execution layer, and automated tests.

Separate trading logic from order execution

One of the most useful design decisions is to separate the component that decides what to do from the component that communicates with the trading API.

A simple architecture looks like this:

Market data: Supplies prices, order-book information, and market identifiers.
Strategy: Evaluates whether a trading signal meets the strategy's conditions.
Risk manager: Checks position limits, available capital, and permitted prices.
Execution layer: Submits, tracks, and reconciles orders.
Test suite: Verifies the behaviour of each component independently.

This separation makes testing easier because you can evaluate the strategy without connecting a wallet or sending an order.

It also helps identify which component failed when a test does not behave as expected.

Write a small, testable risk-control function

Start with a function that decides whether a proposed order is allowed.

For this example, the bot accepts an order only when its price, size, and total cost meet predefined limits. These are illustrative rules, not a complete trading strategy.

risk.py

from decimal import Decimal

def validate_order(
price: Decimal,
shares: Decimal,
available_cash: Decimal,
max_order_cost: Decimal,
) -> bool:
if price <= Decimal("0") or price >= Decimal("1"):
return False

if shares <= Decimal("0"):
    return False

order_cost = price * shares

if order_cost > available_cash:
    return False

if order_cost > max_order_cost:
    return False

return True

The price restriction reflects the conventional probability-token range used in this simplified example. A production implementation must also validate the actual market's supported price increments, order-size constraints, applicable fees, and current API requirements.

Notice that the function does not place an order. It only evaluates whether the proposed order satisfies these particular checks.

That makes it deterministic and straightforward to test.

Add automated unit tests with pytest

Install the testing library:

pip install pytest

Create a file named "test_risk.py":

from decimal import Decimal

from risk import validate_order

def test_accepts_order_within_limits():
assert validate_order(
price=Decimal("0.40"),
shares=Decimal("10"),
available_cash=Decimal("20"),
max_order_cost=Decimal("10"),
)

def test_rejects_order_exceeding_available_cash():
assert not validate_order(
price=Decimal("0.50"),
shares=Decimal("50"),
available_cash=Decimal("20"),
max_order_cost=Decimal("100"),
)

def test_rejects_order_exceeding_order_limit():
assert not validate_order(
price=Decimal("0.50"),
shares=Decimal("30"),
available_cash=Decimal("100"),
max_order_cost=Decimal("10"),
)

def test_rejects_zero_shares():
assert not validate_order(
price=Decimal("0.40"),
shares=Decimal("0"),
available_cash=Decimal("100"),
max_order_cost=Decimal("10"),
)

def test_rejects_invalid_price():
assert not validate_order(
price=Decimal("1.00"),
shares=Decimal("10"),
available_cash=Decimal("100"),
max_order_cost=Decimal("10"),
)

Run the tests:

python -m pytest -v

These tests check normal behaviour and several invalid inputs. They do not prove that the entire bot is safe, but they establish that the specific risk rules behave as intended.

Add tests whenever a new rule is introduced. If you change how order size is calculated, for example, test both the expected allocation and the conditions that should cause the order to be rejected.

Simulate execution instead of sending real orders

Unit tests cannot establish how the bot behaves when an execution request times out, an order is partially filled, or the market price changes.

For those scenarios, use a simulated execution layer.

A minimal interface might look like this:

class SimulatedBroker:
def init(self):
self.orders = []

def submit(self, token_id, side, price, shares):
    order = {
        "token_id": token_id,
        "side": side,
        "price": price,
        "shares": shares,
        "status": "SIMULATED",
    }

    self.orders.append(order)
    return order

Your strategy can call this interface in tests instead of the real exchange client.

The simulator records the order request but does not sign, submit, or settle a live trade. This lets you inspect what the strategy intended to do.

However, a simulated broker is not a realistic market simulator by itself. It does not model order-book depth, queue position, latency, fees, partial fills, or adverse price movement.

Those behaviours must be added explicitly if you want the simulation to represent them.

Test failures, not just successful trades

Many important bugs appear when something unexpected happens.

A useful test suite should include the following scenarios.

Insufficient balance: The strategy detects an opportunity but does not submit an order that exceeds available capital.

Stale market data: The bot refuses to use a price snapshot older than its configured freshness limit.

Duplicate signal: Processing the same source event twice does not create two independent orders.

Partial fill: The system records the filled quantity separately from the remaining quantity.

Rejected order: The bot records the rejection and does not mark the position as successfully opened.

Network timeout: The bot treats the result as unknown until it can reconcile the order state, rather than blindly submitting a duplicate.

Restart recovery: The bot reloads persistent state and reconciles open orders and positions before resuming execution.

These tests should verify state transitions as well as return values. For example, after a timeout, the order should enter an unresolved state and trigger reconciliation. It should not automatically become a failed order simply because the response was missing.

Test against historical data carefully

Historical data can help evaluate whether a strategy's rules would have generated signals under past market conditions.

But a historical simulation is only as realistic as its assumptions.

A test that assumes every order fills at the price recorded in a historical dataset may ignore spread, order-book depth, execution delays, and the fact that the desired quantity might not have been available.

For a more useful evaluation, document:

Which price or order-book data the simulation uses.
How orders are assumed to fill.
How fees and execution costs are handled.
What happens when data is missing.
Whether the strategy uses only information that would have been available at the decision time.

Avoid using future information to make past trading decisions. That creates look-ahead bias and can make an ineffective strategy appear successful.

Keep historical strategy evaluation separate from software correctness testing. A strategy can pass every unit test and still have no profitable trading edge.

Use a staged testing process

Before connecting a bot to live execution, progress through distinct stages.

Stage 1: Unit testing

Test calculations, validation rules, signal processing, and state transitions using controlled inputs.

Stage 2: Integration testing

Test how the data, strategy, risk, persistence, and execution components work together. Mock external services where appropriate.

Stage 3: Historical simulation

Evaluate the strategy against historical data using explicit assumptions about fills, fees, and data availability.

Stage 4: Dry-run monitoring

Connect to the relevant market-data feeds and record what the bot would do without submitting real orders. Compare intended decisions with current market conditions.

Stage 5: Controlled live testing

Only after the preceding stages have been reviewed should live execution be considered. Use a dedicated wallet with limited funds, conservative exposure limits, protected credentials, and a tested emergency stop.

Passing one stage does not automatically guarantee success at the next. In particular, dry-run execution cannot fully reproduce real order matching or settlement.

Define what passing actually means

A test suite is more useful when its acceptance criteria are explicit.

For example, a team might require that:

Invalid orders are rejected before submission.
Repeated source events are deduplicated.
Unknown order outcomes trigger reconciliation.
Stale market data blocks new trading decisions.
Partial fills are represented accurately.
Restart recovery does not blindly duplicate previous orders.
Secrets never appear in logs.
Risk limits remain enforced during errors and reconnects.

These are testable engineering requirements. They are more meaningful than simply declaring that a bot is ready because it ran for an hour without crashing.

The appropriate criteria will depend on the bot's architecture and intended use.

Conclusion

Testing a Polymarket trading bot is not just about checking whether its strategy produces signals. It is about verifying that the entire system behaves correctly when inputs are invalid, data becomes stale, orders fail, and the application restarts.

Start with deterministic unit tests, isolate external execution behind a testable interface, simulate failure conditions, and evaluate historical performance with realistic assumptions. Then progress to dry-run monitoring before considering live execution.

Dexoryn Labs maintains a public "Polymarket copy-trading bot repository" (https://github.com/dexorynlabs/polymarket-copy-trading-bot) that developers can inspect when evaluating an existing implementation. Teams that need different testing, monitoring, execution, or risk-control workflows may require custom development.

The goal is not to prove that a bot can never fail. It is to identify predictable failures before they become expensive, and to make unexpected failures easier to detect and recover from.

This article is for educational purposes only and is not financial advice. Automated trading involves risk, and software tests cannot guarantee profitability or prevent every production failure.

How to Build a Reliable Polymarket WebSocket Monitor in Python

Dexoryn — Mon, 12 Oct 2026 09:50:42 +0000

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.

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" (https://github.com/Polymarket/agent-skills/blob/main/websocket.md) 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.

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.

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.

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.

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.

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" (https://github.com/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.

How to Prevent Duplicate Orders in a Polymarket Trading Bot with Python

Dexoryn — Sat, 10 Oct 2026 21:15:05 +0000

A Polymarket trading bot can receive the same event more than once, restart after a crash, or lose its connection while an order is being submitted. If the application treats every repeated signal as a new instruction, it can place duplicate orders and expose more capital than intended.

The solution is not simply to add a delay between trades. A reliable bot needs idempotent event processing, persistent state, and order reconciliation.

This tutorial explains how to design those safeguards in Python, using a copy-trading bot as the example. The same principles apply to other automated trading systems.

Why duplicate orders happen

Consider a bot that monitors a target wallet and copies its trades.

The bot detects a buy event and submits a corresponding order. Several failure scenarios can produce an unintended second order.

Repeated events: A reconnect or polling cycle causes the same source activity to be processed again.

Application restarts: The process crashes after detecting a trade but before recording that the event has been handled.

Network timeouts: The order request reaches the trading API, but the response never reaches the bot. The application cannot tell whether the order was accepted.

Concurrent workers: Two workers process the same event before either records it as complete.

Partial failures: The bot saves a trade as processed before submitting its order, then crashes. On restart, it may skip a trade that was never submitted.

These are different failure modes. A single in-memory set of processed events will not solve all of them.

Give every source event a stable identity

The first safeguard is to distinguish a genuinely new trade from a repeated notification about an existing trade.

Ideally, the source provides a stable event identifier. Depending on the data source, this might be a transaction hash combined with a log index, or another unique activity identifier.

Do not use only the wallet address, market ID, and trade direction as the deduplication key. A trader can legitimately buy the same outcome multiple times.

Here is a simple Python example:

def event_key(event: dict) -> str:
tx_hash = event.get("transaction_hash")
log_index = event.get("log_index")

if tx_hash is None or log_index is None:
    raise ValueError("Missing stable event identity")

return f"{tx_hash.lower()}:{log_index}"

This example assumes the source exposes both fields and that the combination uniquely identifies the relevant event. Verify the actual schema before using it.

If your data provider exposes a different stable identifier, use that instead. If no reliable identifier exists, a composite fingerprint may be necessary, but it must be designed around the source's semantics and can accidentally merge legitimate trades.

The principle is simple: deduplicate events by identity, not by similarity.

Store processed events persistently

An in-memory set disappears when the process restarts. A persistent database allows the bot to remember which source events it has already accepted for processing.

SQLite is a practical starting point for a single-process Python bot.

import sqlite3

db = sqlite3.connect("bot_state.db")

db.execute("""
CREATE TABLE IF NOT EXISTS source_events (
event_key TEXT PRIMARY KEY,
status TEXT NOT NULL,
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP
)
""")

db.commit()

The primary key prevents the database from storing the same event identity twice.

You can attempt to register an event with an insert that ignores an existing key:

def register_event(event_id: str) -> bool:
cursor = db.execute(
"""
INSERT OR IGNORE INTO source_events
(event_key, status)
VALUES (?, ?)
""",
(event_id, "received"),
)

db.commit()
return cursor.rowcount == 1

If the function returns "True", the event was newly registered. If it returns "False", an event with that identity already exists.

This is more reliable than checking a set and inserting later, because the database enforces uniqueness. In a multi-worker system, the claim operation and associated state transitions still need appropriate transaction and concurrency handling.

Most importantly, registering an event does not mean its order has been executed.

That distinction leads to the next safeguard.

Track order states explicitly

A robust bot should represent the lifecycle of an event and its associated order.

A simplified state model might include:

"received": the source event has been registered.
"validated": the event passed market, price, balance, and risk checks.
"submitting": the bot is attempting to send the order.
"submitted": the API acknowledged the order.
"filled": the order was confirmed filled.
"rejected": the order was rejected.
"unknown": the request outcome cannot yet be established.

These states should not be treated as interchangeable.

For example, a submitted order might remain open, fill partially, or be cancelled. A rejected order is different from an order whose response timed out.

A useful database design separates the source event from the order attempt. One source event may produce no order because it fails validation, while a valid event may require more than one order attempt under a carefully defined recovery policy.

The system should record the source event identity, intended market and outcome, requested size, order identifier when available, current state, timestamps, and any error details.

That record makes recovery possible without blindly repeating the original action.

Treat timeouts as unknown outcomes

This is one of the most important rules in automated execution.

Suppose the bot sends an order request. The server accepts it, but the network connection drops before the response arrives.

The bot sees a timeout. It does not know whether the order was accepted.

If it immediately submits the same order again, the account could end up with two orders.

A safer recovery process is:

Mark the attempt as "unknown".
Do not blindly resubmit it.
Query the trading API for the order or relevant activity using the identifiers available.
Reconcile the response with locally stored state.
Retry only when the original request is known not to have produced an order and the retry remains valid.

The exact reconciliation procedure depends on the current API and the information it exposes. Read the current "Polymarket developer documentation" (https://docs.polymarket.com/) before implementing it.

There is an important limitation here: a local database transaction cannot atomically commit both a database update and a remote trading order. Without a server-supported idempotency mechanism, the application cannot guarantee exactly-once execution across every network failure.

The practical goal is to make duplicate execution unlikely, detect ambiguous outcomes, and recover conservatively.

Separate event detection from order execution

Do not place the entire trading workflow inside a WebSocket message handler.

If event detection, database writes, risk checks, network requests, and order submission all happen in one callback, a slow API response can interfere with processing incoming events.

A cleaner architecture separates the responsibilities:

The event registry prevents the same source activity from being independently accepted multiple times. The execution component handles order submission. Reconciliation checks whether the external system agrees with the bot's local records.

A queue can connect the components, but the queue alone is not a deduplication mechanism. Events may be delivered more than once, workers may crash, and messages may be retried.

The persistent event identity and state machine remain essential.

Test the failure cases, not only successful trades

A bot that works during a normal test run may still fail when requests time out or the process restarts.

At minimum, test these scenarios:

Duplicate event: Deliver the same source event twice. Confirm that it creates no more than one active processing record.

Crash before submission: Restart the application after registering an event but before sending an order. Confirm that recovery resumes safely.

Timeout after submission: Simulate a request whose result is unknown. Confirm that the bot reconciles before considering another submission.

Concurrent processing: Allow two workers to receive the same event simultaneously. Confirm that database uniqueness and transaction handling prevent duplicate processing.

Partial fill: Confirm that the bot tracks filled and remaining quantities rather than treating any fill as a fully completed order.

Stale state: Restart the bot with existing open orders and positions. Confirm that it reconciles external state before resuming new execution.

These tests should use mocked API responses or a dry-run environment. They should not require risking live funds.

Add observability to make failures diagnosable

When an order is rejected or an event is skipped, the logs should explain why.

Record the source event identifier, state transition, validation outcome, order identifier, requested size, response status, and reconciliation result. Never log private keys, API secrets, or other credentials.

Useful operational metrics include:

Duplicate source events detected.
Orders submitted and rejected.
Requests with unknown outcomes.
Orders awaiting reconciliation.
Partial fills and unresolved order states.
Time spent between event detection and order confirmation.

These measurements help distinguish a data-feed problem from an execution problem or a state-management bug.

Conclusion

Preventing duplicate orders in a Polymarket trading bot requires more than checking whether a trade was seen before.

Use stable event identities, persistent uniqueness constraints, explicit order states, conservative timeout recovery, and reconciliation with the external trading system. Then test the system under duplicate delivery, restarts, concurrency, and ambiguous network outcomes.

For developers building more advanced automation, these safeguards should be part of the trading architecture from the beginning.

Dexoryn Labs maintains a public "Polymarket copy-trading bot repository" (https://github.com/dexorynlabs/polymarket-copy-trading-bot) that developers can inspect when evaluating an existing implementation. Teams with different execution, monitoring, or risk requirements may also need custom development.

Reliable execution does not guarantee profitable trading. It helps ensure the bot behaves more predictably when software and network failures occur.

This article is for educational purposes only. Automated trading involves financial risk. Test in dry-run mode before considering live execution, protect credentials, and use only funds you can afford to lose.

Building a Polymarket Backtesting Engine in Python

Dexoryn — Fri, 09 Oct 2026 17:20:23 +0000

A Polymarket trading bot backtest is only useful if it approximates what the bot could have known and executed at the time. Historical prices help reconstruct market conditions, but they do not automatically tell you whether an order would have filled, how much slippage it would have incurred, or whether the strategy relied on information from the future.

For developers, the challenge is not simply calculating historical profit. It is building a simulation that separates strategy decisions from execution assumptions and makes those assumptions testable.

This guide covers a practical Python architecture for evaluating Polymarket trading strategies without treating historical performance as proof of future returns.

Start with the right historical data

Polymarket exposes public market-data interfaces, including the CLOB API for prices and order-book information. Its historical-price endpoint is documented at "/prices-history".

The endpoint accepts a market asset identifier and can return historical price observations. The "market" parameter should contain the relevant outcome token ID, not an arbitrary market title or condition identifier.

You can review the current endpoint details in the "official Polymarket documentation" (https://docs.polymarket.com/).

A basic request using Python looks like this:

import requests

BASE_URL = "https://clob.polymarket.com"

def fetch_price_history(token_id, start_ts, end_ts):
response = requests.get(
f"{BASE_URL}/prices-history",
params={
"market": token_id,
"startTs": start_ts,
"endTs": end_ts,
"fidelity": 1,
},
timeout=15,
)

response.raise_for_status()
payload = response.json()

return payload.get("history", [])

This function retrieves historical observations. It does not produce a complete execution history, and it does not guarantee that every requested interval has data.

Before using the output, inspect the timestamps, check for missing periods, and confirm that the returned prices fall within the expected range.

Do not silently replace missing observations with zero. A missing price is not the same as a zero price.

Normalize the data before testing

A backtest becomes difficult to trust when timestamps, price formats, and missing observations are handled inconsistently.

Create a consistent internal representation before applying strategy logic.

import pandas as pd

def normalize_history(history):
df = pd.DataFrame(history)

if df.empty:
    return pd.DataFrame(columns=["timestamp", "price"])

df = df.rename(columns={
    "t": "timestamp",
    "p": "price",
})

df["timestamp"] = pd.to_datetime(
    df["timestamp"],
    unit="s",
    utc=True,
)

df["price"] = pd.to_numeric(
    df["price"],
    errors="coerce",
)

df = df.dropna(subset=["timestamp", "price"])
df = df.sort_values("timestamp")
df = df.drop_duplicates(subset=["timestamp"], keep="last")

return df.reset_index(drop=True)

This example standardizes the data for analysis. A production pipeline should also validate the expected schema, flag suspicious price changes, and record gaps rather than hiding them.

One detail matters particularly for prediction markets: a price such as "0.62" represents a price of 62 cents per share. It should not be confused with a return of 62%.

Keep prices, share quantities, fees, and dollar values as separate fields throughout the simulation.

Separate signals from execution

A clean backtesting engine should have distinct components for:

Data ingestion and validation
Strategy signals
Order simulation
Position accounting
Risk checks
Performance reporting

This separation makes it possible to test a strategy without changing the execution model every time the signal logic changes.

For example, a strategy might generate a buy signal when its estimated probability differs sufficiently from the market price.

That signal does not mean the bot has bought anything.

The execution simulator must decide whether the order could have been filled, at what price, and in what quantity. The portfolio engine should update holdings only according to the simulated fills.

This distinction prevents a common accounting mistake: recording every intended trade as a completed trade.

Avoid using future information

Look-ahead bias occurs when a strategy uses information that would not have been available when its decision was made.

Suppose a strategy evaluates a price observation at 12:00. It should not use a signal calculated from data that arrived at 12:05 to decide whether the 12:00 trade was attractive.

For a simple historical series, a lagged signal is one way to illustrate the principle:

df["previous_price"] = df["price"].shift(1)

df["price_change"] = (
df["price"] - df["previous_price"]
)

df["signal"] = (
df["price_change"] > 0
).astype(int)

This is only a demonstration of lagging a signal. It is not a complete trading strategy, and it does not account for execution costs or portfolio constraints.

For more complex strategies, make the timing explicit. Record when an observation was published, when the system received it, when the strategy evaluated it, and when an order could reasonably have reached the market.

A backtest should reproduce the information available at decision time, not merely sort a finished dataset by timestamp.

Model the execution price realistically

A historical midpoint is not necessarily an executable price.

A buy order generally interacts with available asks, while a sell order interacts with available bids. Larger orders may consume multiple price levels, producing a different average fill price.

For a buy order, a simplified simulation might estimate:

def estimate_buy_cost(ask_levels, requested_shares):
remaining = requested_shares
total_cost = 0.0
filled_shares = 0.0

for price, available_shares in ask_levels:
    fill = min(remaining, available_shares)

    total_cost += fill * price
    filled_shares += fill
    remaining -= fill

    if remaining <= 0:
        break

if remaining > 0:
    return {
        "filled_shares": filled_shares,
        "average_price": None,
        "complete": False,
    }

return {
    "filled_shares": filled_shares,
    "average_price": total_cost / filled_shares,
    "complete": True,
}

Here, "ask_levels" is assumed to contain price and available-share pairs ordered from the lowest ask upward.

The function estimates the cost of consuming the supplied depth. It does not reconstruct historical order-book depth, predict future fills, or model queue priority.

If your dataset contains only sampled historical prices, you cannot honestly claim to have reproduced the historical order book from those prices alone.

Use the strongest historical data available, document what is missing, and test how sensitive the results are to less favorable execution assumptions.

Include fees and partial fills

Once the simulator estimates an execution price, it still needs to account for applicable fees.

Do not hard-code one fee rate across every market and period without checking the relevant fee rules. Polymarket's current documentation should be treated as the source of truth for the markets and trading conditions being modeled.

A useful execution record might contain:

trade = {
"token_id": token_id,
"side": "BUY",
"requested_shares": 50,
"filled_shares": 35,
"average_fill_price": 0.61,
"fees": None,
"timestamp": timestamp,
}

The "fees" field is deliberately left unset here. It should be populated using the applicable fee calculation rather than an invented default.

The example also records a partial fill. A real execution simulator should distinguish the requested quantity from the quantity actually filled.

That difference affects cash, exposure, average entry price, and subsequent exit calculations.

Track the portfolio, not just individual trades

A strategy can show profitable individual trades and still behave poorly at portfolio level.

For example, several positions may depend on the same underlying event. Evaluating each trade independently can hide the concentration of risk.

The portfolio engine should track cash, open positions, average entry prices, realized profit and loss, and any unresolved holdings. It should also enforce the strategy's position-size and exposure limits.

Avoid calculating realized profit by simply subtracting the historical entry price from the latest observed price. That may represent an unrealized mark, not a completed exit.

For positions held until resolution, settlement must be modeled according to the market's rules and the applicable payout. Do not assume that every position can be closed at the final observed historical price.

Test on data the strategy has not seen

After developing a strategy, reserve a separate period for evaluation.

Use one dataset to develop the strategy and another to evaluate it. If you repeatedly adjust the strategy based on the evaluation period, that period is no longer an independent test.

Measure more than cumulative profit. Examine maximum drawdown, trade count, average net result, exposure, and how concentrated the returns are across trades and markets.

Also compare performance under more conservative execution assumptions.

If a small increase in estimated slippage destroys the entire edge, that is important evidence about the strategy's fragility.

A strong backtest should make it easier to identify weaknesses, not merely produce an attractive performance chart.

Move from backtesting to paper trading

Before risking real capital, run the strategy against live market data without submitting real orders.

Record every signal, expected fill, simulated fill, rejected order, and risk-limit decision. Compare the simulated prices with the prices that were actually available when the signal occurred.

This helps identify problems that historical testing may not reveal, such as delayed signals, stale data, API interruptions, and unrealistic assumptions about order execution.

Keep paper-trading results separate from backtest results. They test different things.

Neither guarantees future profitability, but together they provide a more useful basis for deciding whether a strategy deserves further testing.

Build the research process around reproducibility

Every backtest should preserve enough information for another developer to understand how the result was produced.

Record the dataset version, time window, strategy parameters, fee assumptions, execution model, excluded markets, and software version.

If a result changes after a code update, you should be able to determine whether the strategy changed, the data changed, or the simulator changed.

This is especially relevant when developing automated trading infrastructure. Dexoryn Labs publishes Polymarket trading and copy-trading tools, and evaluating such systems requires attention to data quality, execution assumptions, position tracking, and risk controls alongside the strategy itself.

The engineering objective is not to make every backtest look profitable. It is to make the result explainable and repeatable.

Final thoughts

A useful Polymarket backtesting engine does more than calculate returns from historical prices.

It preserves the timing of information, distinguishes signals from fills, models realistic execution costs, tracks positions correctly, and tests the strategy on unseen data.

Historical prices are a starting point, not a complete simulation of a live market.

Build the test so that its assumptions are visible. Then challenge those assumptions before trusting the result.

That is how backtesting becomes a research tool instead of a performance chart.

How to Build Risk Controls for a Polymarket Trading Bot

Dexoryn — Sun, 04 Oct 2026 18:53:45 +0000

A trading bot does not become safer because it is automated.

In fact, automation can make a bad strategy much more dangerous because software can execute the same mistake hundreds of times without hesitation.

For a Polymarket trading bot, the most important question is therefore not only:

When should the bot trade?

It is also:

When should the bot refuse to trade?

A useful risk layer can control position size, market exposure, price movement, liquidity, duplicate signals, and the number of positions open at the same time.

This article shows how to build those controls in Python.

Why a trading bot needs a risk layer

Imagine a strategy produces this signal:

signal = {
"market": "example-market",
"side": "BUY",
"price": 0.42,
"size": 100
}

Without a risk layer, the execution system might immediately submit it.

That creates a problem.

The strategy may not know that:

The portfolio already has significant exposure.
The market moved from $0.42 to $0.48.
Available liquidity is too low.
The bot already processed the same signal.
The market is no longer active.
The proposed position is too large.
The account has reached its daily loss limit.

The risk engine exists between the strategy and execution.

It acts as a gatekeeper.

Start with a maximum position size

The simplest control is a maximum amount that the bot can allocate to one trade.

MAX_TRADE_SIZE = 50

Then:

def check_trade_size(size):
return size <= MAX_TRADE_SIZE

If the strategy requests:

size = 35

the trade passes.

If it requests:

size = 100

the risk layer rejects it.

This sounds basic, but hard limits are valuable because they protect against unexpected strategy behavior.

Position sizing should be separate from risk limits

Your strategy might calculate a position size based on a target wallet, signal strength, or portfolio allocation.

That does not mean the strategy should have final authority over how much money can be traded.

For example:

def calculate_size(target_trade):
return target_trade * 0.05

Then the risk layer applies its own limit:

def apply_max_size(size):
return min(size, MAX_TRADE_SIZE)

This gives you two separate concepts.

Sizing logic decides what the strategy wants.

Risk logic decides what the system allows.

That separation makes the application easier to control.

Add a maximum portfolio exposure

A bot can make every individual trade look reasonable while creating an unreasonable portfolio.

Suppose your maximum capital allocation is $500.

The bot already has:

Market A: $100
Market B: $120
Market C: $80
Market D: $100

Current exposure is:

$400

The strategy now wants to open another $150 position.

The trade itself might be acceptable.

The portfolio is not.

A simple exposure check can prevent it:

MAX_EXPOSURE = 500

def check_exposure(current_exposure, new_trade):
return (
current_exposure + new_trade
<= MAX_EXPOSURE
)

This is an important distinction.

Trade risk and portfolio risk are not the same thing.

Add a per-market limit

You should also prevent the bot from accumulating too much exposure in a single market.

MAX_MARKET_EXPOSURE = 100

Then:

def check_market_exposure(
current_market_exposure,
new_trade
):
return (
current_market_exposure + new_trade
<= MAX_MARKET_EXPOSURE
)

This becomes especially useful when a strategy receives several signals related to the same market.

Without a market-level limit, repeated signals can quietly build a large position.

Don't assume different markets mean different risks

A more advanced system should also consider correlated exposure.

For example, a bot could hold positions in several markets that are all affected by the same underlying event.

The markets are technically different.

The economic risk may not be.

This is where portfolio-level risk becomes more important than simply counting the number of open positions.

A system with ten small positions is not necessarily safer than a system with three.

It depends on what those positions represent.

Add a price-movement check

This is especially important for copy trading.

Suppose the target trader entered at:

$0.40

Your bot receives the signal when the market is:

$0.46

Blindly copying the trade may no longer make sense.

You can define a maximum acceptable price difference:

MAX_PRICE_DEVIATION = 0.03

Then:

def price_is_valid(
target_price,
current_price
):
deviation = abs(
current_price - target_price
)

return deviation <= MAX_PRICE_DEVIATION

If the deviation is too large, the bot skips the trade.

The important principle is that a signal can become invalid after it is generated.

Check liquidity before executing

Price movement is only one part of execution.

The bot should also consider available liquidity.

Suppose the best ask is $0.42.

That sounds attractive.

But perhaps only 10 shares are available there.

Your bot wants to buy 200.

The actual average execution price could be considerably higher.

A simple liquidity check might be:

MIN_LIQUIDITY = 100

def check_liquidity(available_liquidity):
return available_liquidity >= MIN_LIQUIDITY

A more sophisticated implementation should estimate the expected average execution price for the intended order size.

That connects the risk engine directly to the order book.

Prevent duplicate trades

Event-driven systems need idempotency.

Consider this sequence:

Trade detected
Trade processed
Order submitted
Same event received again
Second order submitted

You now have twice the intended position.

Create an identifier for every processed event:

def create_event_id(trade):
return (
f"{trade['wallet']}-"
f"{trade['market']}-"
f"{trade['timestamp']}-"
f"{trade['side']}"
)

Then maintain processed events:

processed_events = set()

def is_new_event(event_id):
if event_id in processed_events:
return False

processed_events.add(event_id)
return True

For production software, this state should be stored durably rather than relying only on an in-memory set.

Otherwise a restart can cause the application to forget what it already processed.

Add a maximum number of open positions

Another useful control is a maximum number of simultaneous positions.

MAX_OPEN_POSITIONS = 10

Then:

def can_open_position(open_positions):
return len(open_positions) < MAX_OPEN_POSITIONS

This prevents a strategy from expanding indefinitely when many signals arrive at once.

It is particularly useful for bots monitoring multiple wallets or markets.

Add a cooldown

Sometimes a strategy can generate repeated signals for the same market.

A cooldown can prevent the bot from repeatedly entering within a short period.

COOLDOWN_SECONDS = 60

A simple implementation:

last_trade_time = {}

def cooldown_finished(market, now):
previous = last_trade_time.get(market)

if previous is None:
    return True

return (
    now - previous
    >= COOLDOWN_SECONDS
)

This is not appropriate for every strategy.

A high-frequency strategy may require very different behavior.

The point is that the control should be intentional rather than accidental.

Add a daily loss limit

A bot should also have a point where it stops trading.

For example:

MAX_DAILY_LOSS = 50

Then:

def daily_limit_reached(realized_pnl):
return realized_pnl <= -MAX_DAILY_LOSS

If the limit is reached:

if daily_limit_reached(realized_pnl):
disable_trading()

This prevents a malfunctioning or poorly performing strategy from continuing to accumulate losses simply because it continues receiving signals.

Use a central risk engine

Once you have several rules, putting them into random parts of the code becomes difficult to maintain.

A dedicated class is cleaner:

class RiskEngine:

def approve(self, trade, portfolio, market):

    if trade.size > MAX_TRADE_SIZE:
        return False

    if portfolio.exposure + trade.size > MAX_EXPOSURE:
        return False

    if market.exposure + trade.size > MAX_MARKET_EXPOSURE:
        return False

    if not market.active:
        return False

    if not price_is_valid(
        trade.target_price,
        market.current_price
    ):
        return False

    if not check_liquidity(
        market.available_liquidity
    ):
        return False

    return True

The strategy can then ask:

if risk_engine.approve(
trade,
portfolio,
market
):
execute_trade(trade)

This architecture creates a clear boundary.

The strategy proposes.

The risk engine decides.

The execution layer acts.

Every rejection should be logged

A risk engine is only useful if you can understand what it is doing.

Instead of:

return False

without any explanation, record the reason.

For example:

return {
"approved": False,
"reason": "Maximum market exposure reached"
}

Then your logs might contain:

13:02:14 | BUY | APPROVED
13:03:21 | BUY | REJECTED | price moved
13:04:07 | BUY | REJECTED | exposure limit
13:05:43 | BUY | REJECTED | insufficient liquidity

This makes debugging and strategy evaluation much easier.

Dry-run mode should use the same risk engine

One of the best ways to test a trading bot is to run the entire decision process without sending real orders.

The important part is that dry-run mode should not bypass risk controls.

It should go through exactly the same checks:

approved = risk_engine.approve(
trade,
portfolio,
market
)

if not approved:
log_rejection()
elif DRY_RUN:
log_simulated_trade()
else:
execute_trade()

This allows you to observe how the system would behave before real execution.

Current open-source Polymarket bot implementations use dry-run configurations and position/exposure controls for this type of testing.

What happens when the API fails?

Risk management is not only about market conditions.

Software failures matter too.

Imagine the bot cannot retrieve the latest market state.

Should it trade anyway?

Usually, the safer answer is no.

You can implement a fail-closed approach:

market = get_market()

if market is None:
return reject(
"Market state unavailable"
)

The same principle can apply when:

The order book is stale.
Authentication fails.
The WebSocket disconnects.
Position data cannot be retrieved.
The local state is inconsistent.
The market status is unknown.

A trading bot should not assume that missing information means everything is fine.

Risk controls should be configurable

Avoid scattering constants throughout your code.

Instead:

RISK_CONFIG = {
"max_trade_size": 50,
"max_exposure": 500,
"max_market_exposure": 100,
"max_open_positions": 10,
"max_price_deviation": 0.03,
"max_daily_loss": 50
}

This makes it easier to test different configurations.

You can also keep strategy configuration separate from infrastructure configuration.

For example:

strategy_config
risk_config
execution_config
api_config

That separation becomes increasingly useful as the project grows.

Risk management is not a guarantee of profit

This distinction is important.

A risk engine can prevent certain trades.

It cannot make a bad strategy profitable.

It can reduce exposure.

It cannot predict the future.

It can reject trades when liquidity is poor.

It cannot guarantee good execution.

It can stop trading after a loss threshold.

It cannot guarantee that the remaining capital will be profitable.

Risk management exists to control how the system can lose, not to guarantee that it will win.

A practical decision function

The final decision can remain simple:

def evaluate_trade(
trade,
portfolio,
market
):

checks = [
    check_trade_size(trade.size),
    check_exposure(
        portfolio.exposure,
        trade.size
    ),
    check_market_exposure(
        market.exposure,
        trade.size
    ),
    market.active,
    price_is_valid(
        trade.target_price,
        market.current_price
    ),
    check_liquidity(
        market.available_liquidity
    ),
    can_open_position(
        portfolio.open_positions
    )
]

return all(checks)

The individual rules can become much more sophisticated later.

The architecture does not need to become complicated immediately.

How this applies to Polymarket copy trading

For copy trading, the risk engine becomes particularly important because the bot is responding to someone else's actions.

The target wallet generates a signal.

Your system still needs to determine:

Whether the market is active
Whether the price has moved
Whether sufficient liquidity exists
How much to copy
Whether your portfolio has enough capacity
Whether the event is new
Whether the trade fits your risk limits

That is why a copy-trading bot should never be designed as:

copy_everything(target_wallet)

A better abstraction is:

observe_target()
evaluate_signal()
check_risk()
execute_if_allowed()

The difference is small in code.

It is enormous in system behavior.

Final thoughts

The most important component of an automated trading system may be the code that prevents it from trading.

A strategy can generate opportunities.

An API can submit orders.

A WebSocket can provide real-time information.

But without a risk layer, all of those capabilities can work against you.

For a Polymarket bot, start with simple controls:

Maximum trade size.

Maximum portfolio exposure.

Maximum market exposure.

Price-deviation limits.

Liquidity checks.

Duplicate-event protection.

Open-position limits.

Daily loss limits.

Fail-closed behavior.

Then test those controls in dry-run mode before connecting live execution.

The objective is not to build a bot that trades as often as possible.

It is to build one that knows when not to trade.

Polymarket APIs Explained: What Developers Actually Need to Build Trading Bots

Dexoryn — Sat, 03 Oct 2026 07:03:14 +0000

If you want to build a trading bot for Polymarket, you do not need one giant API that does everything.

Polymarket exposes several developer interfaces, and each one has a different responsibility. The Gamma API is used to discover markets and retrieve market metadata. The CLOB API handles prices, order books, and trading. The Data API provides information about positions and activity. WebSocket connections provide real-time updates.

Understanding that separation is one of the first things a developer should get right before building a trading bot.

What APIs does Polymarket provide?

Polymarket's current developer documentation describes several integration surfaces:

Gamma API for discovering events and markets
CLOB API for prices, order books, and orders
Data API for positions, activity, and market participation
CLOB Market WebSocket for real-time market updates
CLOB User WebSocket for authenticated order and trade updates
RTDS for real-time reference prices, comments, and trade activity

Polymarket also provides official SDKs for supported environments. The documentation recommends using the SDKs when possible because they provide a unified interface and handle common concerns such as pagination, errors, and wallet setup.

The important part is knowing which interface belongs in which part of your application.

Gamma API: finding the market you want

Before a bot can trade a market, it needs to know what market exists.

That is where the Gamma API comes in.

The current Gamma API endpoint is:

https://gamma-api.polymarket.com

It provides access to market and event metadata.

For example, a simple request can retrieve active markets:

curl -G "https://gamma-api.polymarket.com/markets" \
--data-urlencode "closed=false" \
--data-urlencode "limit=5"

This is useful for applications that need to discover markets dynamically instead of hard-coding market IDs.

A market-discovery service might use this information to build an internal object such as:

market = {
"id": "...",
"question": "...",
"active": True,
"token_ids": [...]
}

Your trading logic can then work with this normalized representation instead of repeatedly parsing raw API responses.

CLOB API: where trading actually happens

The CLOB API is the most important interface for a trading bot.

CLOB stands for Central Limit Order Book.

The current endpoint is:

https://clob.polymarket.com

Polymarket documents the CLOB API as the interface for reading prices and order books and for placing and managing orders.

This means a trading bot will generally interact with the CLOB when it needs to answer questions such as:

What is the current price?

What does the order book look like?

What orders do I currently have?

Can I place an order?

What happened to my order?

That makes the CLOB the core execution interface.

Why the order book matters

A bot should not treat the displayed market price as the price it will automatically receive.

An order book contains available bids and asks at different prices and sizes.

For example:

ASKS

0.51 120 shares
0.52 350 shares
0.54 900 shares

BIDS

0.49 200 shares
0.48 500 shares
0.46 800 shares

Suppose your bot wants to buy 500 shares.

There may only be 120 shares available at 0.51.

The remaining quantity may need to execute at higher prices.

That means the bot needs to think about execution price, not simply the latest displayed price.

For copy trading, this becomes even more important because the target trader may have entered before your bot detected the trade.

Data API: understanding wallet activity

The Data API serves a different purpose.

The current endpoint is:

https://data-api.polymarket.com

Polymarket documents it as an interface for analyzing positions, activity, and market participation.

This becomes useful when building applications around trader behavior.

For example, a wallet-monitoring application could use data from the Data API to examine:

wallet = {
"address": "0x...",
"positions": [...],
"activity": [...]
}

From there, an application could calculate metrics such as:

Recent trading activity
Markets traded
Position exposure
Historical behavior
Trading frequency

This is particularly useful when evaluating wallets for copy trading.

A wallet with a large historical profit is not automatically a good copy target. You also need to understand what produced that performance and whether the behavior can realistically be reproduced.

WebSockets: when polling is not enough

REST APIs are useful when you need to request information.

But trading systems often need to know when something changes.

That is where WebSockets become useful.

Polymarket currently provides a public market WebSocket:

wss://ws-subscriptions-clob.polymarket.com/ws/market

and an authenticated user WebSocket:

wss://ws-subscriptions-clob.polymarket.com/ws/user

The market stream can provide updates involving order books, price changes, last trade prices, tick-size changes, and other market events.

A simplified Python structure could look like:

async def monitor_market(client, token_id):

stream = await client.subscribe([
    {
        "topic": "market",
        "tokenIds": [token_id]
    }
])

async for event in stream:
    print(event)

The exact implementation depends on the current SDK you choose, but the architectural idea is simple:

REST is useful for retrieving state.

WebSockets are useful for reacting to changes in state.

That distinction becomes important when building bots that need timely information.

What can a market WebSocket actually tell you?

Polymarket's current market stream includes several event types.

A book event can contain bids and asks:

{
"type": "book",
"payload": {
"tokenId": "...",
"bids": [
{
"price": "0.08",
"size": "33343.4"
}
],
"asks": [
{
"price": "0.09",
"size": "163939.58"
}
]
}
}

Price-change events can include the updated price, size, side, best bid, and best ask.

The stream can also provide last-trade-price events and best-bid/ask information.

This means a bot can maintain a local view of the market instead of constantly asking the API:

"What is the price now?"

That approach can become inefficient when monitoring many markets.

Authentication is different from public market data

One of the most important concepts for new developers is that reading public market information and submitting trades are not the same thing.

Polymarket's current CLOB authentication uses two layers.

The first layer involves a wallet signature using EIP-712 typed data.

The second layer uses API credentials and HMAC-SHA256 signatures for private CLOB requests.

The documentation describes the authentication flow as establishing wallet control first and then using API credentials for authenticated requests.

That distinction matters because a bot that only reads public prices does not need the same credentials as a bot that places orders.

Never put private keys directly into your code

This sounds obvious, but it is one of the easiest mistakes to make when experimenting with trading bots.

Do not write:

PRIVATE_KEY = "your-real-private-key"

inside a Git repository.

Use environment variables or another secure secret-management mechanism instead.

For example:

import os

private_key = os.environ["POLYMARKET_PRIVATE_KEY"]

Then keep the actual value outside your source code.

The same principle applies to API credentials.

A trading bot has a much higher security requirement than a normal data-analysis script because compromised credentials can have financial consequences.

How the APIs fit together in a trading bot

Consider a bot that monitors a Polymarket wallet and copies its trades.

Different parts of the application can use different interfaces.

Market discovery

Use the Gamma API to find the relevant market and retrieve its metadata.

Wallet analysis

Use the Data API and other appropriate public data sources to understand activity and positions.

Market monitoring

Use the CLOB market WebSocket to keep current market information available.

Execution

Use the CLOB API when the strategy decides that an order should be submitted.

Account monitoring

Use authenticated order and trade updates to track what happened after an order was submitted.

This separation makes the application easier to maintain.

Instead of building one enormous trading function, you can create independent components:

market_data.py
wallet_data.py
strategy.py
risk.py
execution.py
portfolio.py

Each component has a defined responsibility.

REST polling vs WebSockets

The choice between REST and WebSockets depends on what your application needs.

REST is useful when:

You need an initial snapshot
You are loading historical information
You are requesting specific market data
You are checking account state
You do not need continuous updates

WebSockets are useful when:

You need continuous market updates
You are monitoring order books
You need to react to price changes
You are building real-time execution logic
You are monitoring multiple active markets

In practice, a serious bot often uses both.

For example, the application can retrieve an initial market snapshot through an API and then keep its state updated through a WebSocket stream.

A simple architecture for a Python bot

A practical project could start with something like:

polymarket_bot/
│
├── main.py
├── config.py
├── market_data.py
├── wallet.py
├── strategy.py
├── risk.py
├── execution.py
├── portfolio.py
└── tests/

The responsibilities can remain straightforward.

"market_data.py" handles market information.

"wallet.py" handles trader or account data.

"strategy.py" determines whether an opportunity meets your rules.

"risk.py" decides whether the proposed trade is allowed.

"execution.py" communicates with the trading interface.

"portfolio.py" tracks positions and exposure.

This structure is much easier to expand than putting everything into "main.py".

What should you build first?

Do not start with live order execution.

Start with market discovery.

Then build a small program that retrieves market data.

After that, add order-book monitoring.

Then build your strategy logic.

Then add simulated execution.

Only after those components behave correctly should you consider connecting live trading.

This development sequence makes debugging considerably easier because you can determine whether a problem comes from your data, strategy, risk logic, or execution layer.

How Dexoryn uses these concepts

Dexoryn Labs currently maintains public Polymarket trading and copy-trading repositories.

Its Python trading-bot repository uses real-time wallet/trade monitoring, configurable sizing, dry-run mode, persistent state, slippage controls, and position limits.

The newer copy-trading repository also exposes a broader application structure with multiple target wallets, a dashboard, WebSocket detection, dry-run execution, and persistent state.

The interesting part from a developer perspective is not simply the existence of a bot.

It is how these individual API capabilities become application features.

Real-time feeds become trade detection.

Market data becomes execution context.

Authentication becomes secure order submission.

Position data becomes portfolio management.

Risk controls sit between the strategy and execution layer.

That is where an API becomes a trading system.

Common mistakes when working with Polymarket APIs

Treating every API as interchangeable

They are not.

Gamma, CLOB, Data API, and WebSockets solve different problems.

Building around outdated tutorials

Polymarket's developer stack evolves.

The current documentation includes migration guidance for older Data API versions and older SDKs.

Always check the current documentation before copying an old code example into a new project.

Polling everything

Constantly requesting the same information through REST can be wasteful when a real-time stream is available.

Ignoring the order book

A market price is not enough to understand execution quality.

Liquidity, spread, available size, and price movement all matter.

Going live too early

A bot that works perfectly in a dry run can behave very differently when real orders, partial fills, price movement, network failures, and authentication errors enter the picture.

Final thoughts

Polymarket's API ecosystem is easier to understand once you stop thinking of it as one API.

The Gamma API helps you discover markets.

The CLOB API gives your application access to prices, order books, and trading.

The Data API provides wallet and position information.

WebSockets keep applications informed about real-time changes.

Together, these interfaces provide the building blocks for everything from market dashboards and analytics tools to automated trading systems.

But the API is only the infrastructure.

The difficult engineering work is deciding what information matters, turning that information into a strategy, controlling risk, and making sure the system behaves correctly when the market does something unexpected.

That is where a Polymarket bot stops being an API script and starts becoming a real software system.

How Polymarket Copy Trading Bots Work: Wallet Monitoring, Trade Detection, and Execution

Dexoryn — Fri, 02 Oct 2026 11:11:44 +0000

A Polymarket copy trading bot monitors a target wallet, detects relevant trading activity, decides whether the trade should be copied, calculates the appropriate position size, and submits an order under predefined risk constraints.

The difficult engineering problem is not simply detecting that another wallet traded.

It is turning that event into a reliable execution workflow without blindly reproducing every transaction.

This post breaks down the architecture behind a Polymarket copy trading system and the technical problems developers need to consider when building one.

What Is a Polymarket Copy Trading Bot?

A copy trading bot uses another trader's observable activity as an input for automated trading.

A simplified workflow looks like this:

Target Wallet
↓
Activity Monitoring
↓
Trade Detection
↓
Copy Filters
↓
Position Sizing
↓
Risk Checks
↓
Order Execution
↓
Position Monitoring

Each stage solves a different problem.

The monitoring layer determines what happened.

The decision layer determines whether the event should be copied.

The execution layer turns that decision into an order.

Polymarket provides developer infrastructure for accessing market data and interacting with its trading system, including its Central Limit Order Book (CLOB). Its documentation provides APIs and SDKs for developers building applications around the platform.

Monitoring the Target Wallet

The first requirement is identifying activity from the wallet you want to follow.

The system needs to continuously observe the target wallet and identify relevant events.

Conceptually:

while True:
events = get_wallet_activity(target_wallet)

for event in events:
    if is_new(event):
        process_event(event)

A production implementation needs considerably more than this.

It needs to handle:

Duplicate events
Temporary connection failures
Missing events
Reconnection
Event ordering
Persistent state
Rate limits
Monitoring multiple wallets

The bot also needs a way to remember what it has already processed.

Without that state management, the same trade could potentially be interpreted as a new event more than once.

Detecting a Trade

Not every wallet event should become a copy order.

The monitoring system first needs to classify the event.

For example:

if event.type == "BUY":
handle_buy(event)

elif event.type == "SELL":
handle_sell(event)

The actual implementation can be more sophisticated because the system may need to understand the market, token, outcome, quantity, price, and transaction context.

The important architectural principle is:

Raw blockchain or market activity should not directly trigger live execution.

There should be a decision layer between observation and execution.

Deciding Whether to Copy the Trade

Suppose the target wallet buys a position.

Should your bot automatically buy it too?

Not necessarily.

The bot can apply filters such as:

def should_copy(trade):
if trade.market in blocked_markets:
return False

if trade.value > MAX_TRADE_SIZE:
    return False

if trade.age_seconds > MAX_TRADE_AGE:
    return False

return True

This allows the follower to define its own rules.

For example, you might only copy:

Specific markets
Specific trade sizes
Recent trades
Specific target wallets
Certain directions
Trades within a maximum price difference

This is one of the most important distinctions between copy trading and simply mirroring transactions.

The follower needs its own constraints.

Position Sizing

The target trader and follower rarely have identical account sizes.

Suppose the target trader buys $1,000 worth of a position.

A follower with $200 available cannot simply reproduce the same position.

Instead, the bot can use a sizing model.

For example:

copy_amount = target_trade.value * COPY_RATIO

copy_amount = min(
copy_amount,
MAX_POSITION_SIZE
)

A system could also use fixed sizing:

copy_amount = 25

Or proportional sizing:

copy_amount = follower_balance * 0.05

The important part is that position sizing should be an explicit part of the architecture.

It should not be an accidental consequence of the target trader's order size.

Checking the Market Before Execution

The target trader's transaction happened at a specific point in time.

Your bot receives that information afterward.

That creates an execution problem.

Imagine:

Target trade:
YES @ $0.42

Bot detects trade:
YES @ $0.42

Current market:
YES @ $0.47

Blindly executing at $0.47 could produce a very different trade.

A better system can check the current market before submitting the order.

For example:

current_price = get_current_price(token)

if abs(current_price - target_price) > MAX_PRICE_DEVIATION:
skip_trade()

The exact rule depends on the strategy.

The principle is more important:

Detection does not automatically mean execution.

Slippage and Liquidity

Slippage is particularly important in automated copy trading.

A target trader may have received a specific price because liquidity was available at the moment their order was executed.

Seconds later, the available liquidity can be different.

For example:

Target execution:

$0.50 → 500 shares available

Follower arrives:

$0.50 → 50 shares available
$0.51 → 100 shares available
$0.53 → 200 shares available

The follower may therefore receive a significantly different average execution price.

A copy-trading system needs to account for this possibility.

Possible controls include:

Maximum acceptable price deviation
Maximum order size
Minimum liquidity
Maximum slippage
Trade expiration
Skip conditions

These controls are more useful than simply trying to execute every detected trade.

Sending the Order

After the trade passes the filters, sizing rules, and risk checks, the execution layer can submit the order.

Conceptually:

order = {
"token_id": token_id,
"side": "BUY",
"price": price,
"size": copy_amount
}

result = submit_order(order)

The actual implementation depends on the trading infrastructure and authentication model being used.

Polymarket's developer documentation provides interfaces for interacting with its CLOB and submitting orders.

A production system also needs to handle execution failures.

For example:

try:
result = submit_order(order)

except Exception as error:
log_execution_error(error)
alert_operator()

The error-handling strategy should be more specific than simply retrying everything.

Blind retries can create another problem: duplicate orders.

Tracking the Result

Submitting an order is not the end of the workflow.

The bot should record what happened.

For example:

Detected trade
↓
Copy approved
↓
Order submitted
↓
Order accepted
↓
Order filled
↓
Position updated

The system should maintain enough state to understand the difference between:

An order that was submitted
An order that was partially filled
An order that was fully filled
An order that failed
An order that was cancelled

This information becomes important when the target trader later changes or closes the position.

Risk Controls Should Be Independent

One mistake in copy trading is assuming that the target trader's risk management should automatically become the follower's risk management.

It should not.

The follower's account needs independent limits.

For example:

if daily_loss >= MAX_DAILY_LOSS:
disable_trading()

if total_exposure >= MAX_TOTAL_EXPOSURE:
reject_trade()

if position_size > MAX_POSITION_SIZE:
reject_trade()

Other controls can include:

Maximum capital per market
Maximum number of simultaneous positions
Maximum trade size
Daily loss limits
Allowed markets
Emergency shutdown
Manual approval mode

These controls provide a boundary around the automation.

A Simple Architecture

Putting the pieces together:

            ┌─────────────────┐
            │ Target Wallet   │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Wallet Monitor  │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Trade Detector  │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Copy Filters    │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Position Sizing │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Risk Engine     │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Order Executor  │
            └────────┬────────┘
                     ↓
            ┌─────────────────┐
            │ Position Monitor│
            └─────────────────┘

This separation is useful because each component can be tested independently.

For example, developers can test trade detection without placing live orders.

They can test position sizing with historical events.

They can test execution logic using a paper-trading or dry-run mode before enabling live trading.

Where Does Dexoryn Fit?

Dexoryn Labs has publicly released Polymarket automation projects, including a Python-based Polymarket trading bot focused on copy trading.

Its public repository describes functionality including target-wallet monitoring, configurable trade sizing, exposure controls, and a dry-run mode for testing the system before live execution.

That makes the project a practical example of the architecture described above.

The important distinction is that the repository is an implementation of a copy-trading system, not evidence that copying a particular trader will produce the same performance.

Execution conditions, market prices, liquidity, timing, and account configuration can all differ.

Copy Trading Is an Event-Processing Problem

At first glance, Polymarket copy trading looks like a trading problem.

From a software-engineering perspective, it is also an event-processing system.

The application continuously receives information, transforms that information into structured events, evaluates those events against rules, and potentially triggers an external action.

That means familiar engineering concerns become important:

State management
Idempotency
Error handling
Observability
Authentication
Rate limits
Persistence
Recovery
Logging
Testing

The trading strategy is only one part of the system.

What Should Developers Test Before Live Execution?

Before allowing a bot to trade with real funds, test the complete pipeline.

A useful progression is:

Historical testing

Feed previously observed trades into the system and check whether the detection and decision logic behaves as expected.

Dry run

Monitor live activity but do not submit real orders.

Small-scale execution

If the system passes testing, use tightly constrained exposure.

Monitoring

Track errors, execution differences, latency, rejected orders, and unexpected positions.

The purpose is not simply to prove that the bot can place an order.

It is to verify that the bot behaves correctly when things go wrong.

Final Thoughts

A Polymarket copy trading bot is more than a script that watches a wallet and sends the same transaction.

A reliable implementation needs several independent layers:

Observe
→ Detect
→ Filter
→ Size
→ Validate
→ Execute
→ Monitor

The most important engineering challenge is the gap between the trade you observe and the trade you can actually execute.

That gap contains timing, liquidity, slippage, risk management, authentication, and software reliability problems.

Understanding those layers is essential before treating automated copy trading as a simple plug-and-play strategy.

Dexoryn's public Polymarket projects provide one practical implementation of this architecture, while Polymarket's developer infrastructure provides the APIs and trading interfaces developers can build around.

The next interesting engineering question is what happens inside each layer: particularly how a bot identifies a target wallet's trade, avoids duplicate events, and decides whether the market is still executable.

Polymarket Trading Bots: How to Build the Decision Engine

Dexoryn — Thu, 01 Oct 2026 08:39:19 +0000

Getting market data is easy. Deciding what to do with it is the difficult part.

A Polymarket trading bot can connect to the API.

It can stream market data.

It can read an order book.

It can calculate prices.

It can even use an AI model to analyze an event.

None of that means the bot knows when it should trade.

The difficult part sits in the middle.

The decision engine.

This is the layer that takes everything the system knows about a market and turns it into a decision:

Do nothing.

Keep monitoring.

Prepare an order.

Enter a position.

Reduce a position.

Exit.

Designing this layer badly can make an otherwise impressive trading system behave like a collection of disconnected scripts.

Let's break down what a proper decision engine needs to do.

Start with a normalized market state

The decision engine shouldn't have to understand raw API responses.

Its first job should be receiving a clean representation of the current market.

For example:

market_state = {
"market_id": "...",
"token_id": "...",
"mid_price": 0.61,
"best_bid": 0.60,
"best_ask": 0.62,
"spread": 0.02,
"depth": 1250,
"volume": 84000,
"timestamp": 1760000000
}

The actual fields will depend on the data sources and implementation, but the architectural principle is important:

Normalize first. Decide second.

Without normalization, strategy code becomes tightly coupled to individual API responses.

That makes the system harder to test and harder to change.

The bot needs its own estimate

Suppose the market is pricing an outcome around 61%.

The bot needs an independent estimate before it can determine whether that price is interesting.

Call the bot's estimated probability:

P(model)

and the market-implied probability:

P(market)

A simplified comparison might look like:

Model probability: 68%
Market price: 61%
Difference: 7%

At first glance, that looks like an opportunity.

But this is where inexperienced trading systems make a mistake.

A difference between two numbers is not automatically a trade.

The bot still needs to ask whether the difference is large enough to survive uncertainty and execution costs.

Probability estimation has uncertainty

Suppose the model estimates:

68%

That doesn't mean the true probability is exactly 68%.

Perhaps the model's historical calibration suggests that estimates around 68% frequently have meaningful error.

The decision engine therefore shouldn't think only in terms of:

68% vs 61%

It should also consider:

How confident are we in the 68% estimate?

This is where calibration becomes important.

A model that consistently predicts 70% events that happen only 55% of the time is not producing useful probabilities, regardless of how sophisticated the model looks.

For a serious system, probability estimates should therefore be evaluated historically.

Expected value is more useful than raw disagreement

Suppose a YES contract costs $0.61.

If the bot estimates a 68% probability of YES, a simplified expected-value calculation can start with:

Expected value = P(win) × payout − cost

For a binary $1 payout:

EV = 0.68 × $1.00 − $0.61

EV = $0.07

That looks attractive.

But it is still incomplete.

The bot hasn't accounted for execution.

Execution changes the calculation

Imagine the bot wants to buy at $0.61.

But the available order book doesn't contain enough size at that price.

The effective entry price might become $0.625.

Now:

EV = 0.68 × $1.00 − $0.625

EV = $0.055

The theoretical edge has already fallen.

If the strategy also has to deal with an eventual exit, adverse movement, partial fills, or other execution effects, the usable edge can become smaller still.

This is why the decision engine shouldn't use:

«Model probability − displayed price»

as its complete trading signal.

It needs to reason about executable price.

Build an edge threshold

A bot shouldn't necessarily trade whenever:

model probability > market price

That condition is too weak.

Instead, the strategy can require a minimum estimated edge.

For example:

if expected_edge < MIN_EDGE:
return "NO_TRADE"

The threshold shouldn't be chosen arbitrarily.

It should come from testing.

If historical analysis shows that tiny estimated edges are unreliable after execution costs, the system can require a larger margin.

This is where backtesting becomes useful.

Add a confidence layer

Now imagine two opportunities.

Market A

Model probability: 68%
Market price: 61%
Estimated edge: 7%
Model confidence: High

Market B

Model probability: 68%
Market price: 61%
Estimated edge: 7%
Model confidence: Low

The numerical edge is identical.

The information quality isn't.

A decision engine can therefore combine:

Estimated edge

with:

Confidence

with:

Execution quality

with:

Risk constraints

That creates a much more realistic decision process.

Don't let one signal control the bot

A common mistake is creating something like:

if signal == "BUY":
place_order()

That is far too simplistic.

A better decision process might look like:

if not market_is_tradeable:
return "NO_TRADE"

if data_is_stale:
return "NO_TRADE"

if estimated_edge < minimum_edge:
return "NO_TRADE"

if confidence < minimum_confidence:
return "NO_TRADE"

if execution_cost > maximum_cost:
return "NO_TRADE"

if risk_limit_reached:
return "NO_TRADE"

return "TRADE"

Notice something interesting.

The majority of the decision engine is actually designed to prevent trades.

That's not a weakness.

A trading system doesn't make money because it trades frequently.

It needs to make decisions that are consistent with its strategy and risk constraints.

Separate signal from action

Another important architectural decision is keeping the signal engine separate from the execution engine.

The signal engine might produce:

{
"direction": "YES",
"estimated_probability": 0.68,
"market_price": 0.61,
"estimated_edge": 0.07,
"confidence": 0.82
}

The decision engine then evaluates that signal.

Only after the decision engine approves the trade should the execution system become involved.

This separation makes testing much easier.

You can test:

Did the model generate a reasonable signal?

separately from:

Did the execution engine correctly place the order?

And separately again from:

Did the risk engine allow the position?

Risk should be a hard constraint

Suppose the model discovers an extremely attractive opportunity.

The account already has significant exposure to the same underlying event.

Should the bot simply increase the position?

Not necessarily.

The decision engine needs access to portfolio state.

For example:

portfolio = {
"current_exposure": 420,
"max_exposure": 500,
"available_balance": 1000
}

If taking another $150 position would exceed the maximum exposure, the signal should not override that constraint.

The architecture should make the risk limit stronger than the signal.

A strong signal is not permission to ignore risk.

Give the bot more than BUY and SELL

Real systems often need intermediate states.

For example:

NO_TRADE
WATCH
ANALYZE
READY
ENTER
HOLD
REDUCE
EXIT
COOLDOWN
ERROR

Why?

Because markets change continuously.

A market might look interesting but not yet provide acceptable execution.

The bot can monitor it instead of immediately entering.

That makes the decision engine stateful rather than purely reactive.

State matters

Consider this sequence.

At 10:00:

«Market looks attractive.»

At 10:01:

«Spread widens.»

At 10:02:

«New information changes the model probability.»

At 10:03:

Liquidity improves.

The bot shouldn't treat every update as an entirely new situation.

It needs to understand its current state.

A state machine can help:

If conditions deteriorate, the system can move back to an earlier state instead of forcing a trade.

Log the reason behind every decision

This is one of the most important engineering practices.

Don't log only:

TRADE EXECUTED

Log why.

For example:

Market: ...
Model probability: 0.68
Executable price: 0.615
Estimated edge: 0.065
Confidence: 0.82
Spread: 0.012
Available depth: ...
Portfolio exposure: ...
Decision: ENTER

And when the bot rejects a trade:

Decision: NO_TRADE
Reason: insufficient executable depth

Now you can analyze the system later.

You can discover that perhaps 70% of rejected signals were rejected because of liquidity.

Or perhaps the model generates many signals but most have insufficient edge.

Those observations become engineering data.

The decision engine should be testable without live trading

This is where a lot of bot projects become dangerous.

The decision logic shouldn't require live orders just to test whether it works.

Feed historical market states into the engine.

Give it:

market state
model estimate
order-book conditions
portfolio state

Then record the decision.

This lets you evaluate:

how often signals trigger
how often risk rules reject them
how sensitive results are to thresholds
whether estimated edges survive execution assumptions
whether the strategy behaves differently in different market conditions

Only after this layer is behaving predictably should live execution become the focus.

The decision engine is where the strategy becomes a system

A Polymarket bot isn't simply:

«API + model + order.»

The interesting engineering work happens between those components.

Raw market data needs to become a normalized state.

The model needs to produce an estimate rather than a vague prediction.

The estimate needs to be compared with an executable market price.

The resulting edge needs to survive uncertainty and execution costs.

Risk constraints need to be applied.

Only then should the system decide whether an order deserves to reach the execution layer.

That is what the decision engine does.

And it may be one of the most important parts of the entire bot.

Because the goal of automated trading isn't to build a system that can place orders.

The goal is to build a system that can explain why an order should exist before it places one.

Dexoryn Labs explores the engineering behind automated Polymarket systems, from market data and decision engines to trading bots, AI, execution, and infrastructure.

Stop Polling Polymarket: Build a Real-Time Market Monitor With WebSockets

Dexoryn — Tue, 29 Sep 2026 13:43:50 +0000

If your Polymarket bot keeps asking for the same data every few seconds, you are probably building the wrong data layer.

Polling is easy.

You send a request, wait, send another request, and repeat.

For a small script, that is perfectly fine.

But once you want to monitor several markets or react quickly to order-book changes, constantly asking for the latest state becomes inefficient.

Polymarket provides a better option for this use case: a public WebSocket market channel.

The interesting part is not simply connecting to WebSockets.

It is learning how to turn a stream of market events into a useful local state.

The architecture changes

With polling, your program thinks like this:

Request
Wait
Request again
Wait
Request again

With a WebSocket connection, the server can send market updates as they occur.

Polymarket currently provides a public market WebSocket channel at:

wss://ws-subscriptions-clob.polymarket.com/ws/market

The market channel does not require authentication. It can stream order-book updates, price changes and trade information for subscribed outcome tokens.

That makes it useful for a monitoring system before you even think about automated trading.

Subscribe to a market

You first need the token ID for the outcome you want to monitor.

Once you have it, a minimal Python client can connect to the market channel.

import json
import websocket

TOKEN_ID = "YOUR_TOKEN_ID"

def on_open(ws):
message = {
"assets_ids": [TOKEN_ID],
"type": "market",
"custom_feature_enabled": True
}

ws.send(json.dumps(message))

def on_message(ws, message):
data = json.loads(message)
print(data)

ws = websocket.WebSocketApp(
"wss://ws-subscriptions-clob.polymarket.com/ws/market",
on_open=on_open,
on_message=on_message
)

ws.run_forever()

The important detail is the subscription.

You are telling Polymarket which asset IDs you want updates for rather than repeatedly requesting the entire market state.

What actually comes back?

This is where WebSockets become useful.

The market channel can provide events such as:

book

Updates to the order book.

price_change

Changes caused by orders being placed or cancelled.

last_trade_price

Information about an executed trade.

Polymarket also documents additional market events when custom features are enabled, including best bid and ask updates and market lifecycle events.

Instead of treating every message as something to print, your application should turn these events into state.

For example:

market_state = {
"best_bid": None,
"best_ask": None,
"last_trade": None,
"updated_at": None
}

Then update that state whenever a relevant event arrives.

That gives your program a continuously changing view of the market.

Don't build a strategy yet

This is where I would deliberately slow down.

Once developers see real-time prices, it is tempting to write:

if price < 0.40:
buy()

But a real-time data stream does not create a trading strategy.

It only gives your strategy fresher information.

A better first project is a monitor that answers:

What changed?
When did it change?
How much did it change?
What was the order book doing?
Was there a meaningful spread?

Now you have something you can actually investigate.

Add a simple alert

Once the local state works, you can add a basic research alert.

def check_market(state):
bid = state["best_bid"]
ask = state["best_ask"]

if bid is None or ask is None:
    return

spread = ask - bid

if spread > 0.05:
    print(
        f"Wide spread detected: "
        f"bid={bid:.3f}, ask={ask:.3f}"
    )

This still doesn't trade.

It simply tells you when something deserves attention.

That distinction matters.

A monitoring system observes the market.

A strategy interprets the market.

An execution system acts on the strategy.

Keeping those layers separate makes the whole project easier to understand and debug.

Why this matters for larger projects

Once you monitor multiple markets, the architecture becomes much more interesting.

You can maintain one local state per token:

markets = {
"TOKEN_A": {
"best_bid": None,
"best_ask": None,
"last_trade": None
},
"TOKEN_B": {
"best_bid": None,
"best_ask": None,
"last_trade": None
}
}

Your WebSocket listener updates the state.

Your analysis layer reads the state.

Your alert system decides what deserves attention.

And only later, if you have a tested reason to do so, an execution layer can interact with orders.

That separation is much healthier than putting everything inside one giant trading script.

The bigger lesson

The first version of a Polymarket bot does not need to be a bot.

It can be a market observability system.

Get the data.

Keep the state current.

Record what changes.

Study the behavior.

Then build the strategy.

Polymarket already provides the real-time market feed. The interesting engineering problem is what you do with that stream once it reaches your application.

Don't automate the trade before you understand the data.

Build the monitor first.

Build a Polymarket Market Snapshot Before You Build a Trading Bot

Dexoryn — Mon, 28 Sep 2026 21:34:23 +0000

A small Python tool can tell you more about a market than a simple price feed ever will.

A lot of Polymarket bot projects start with something like this:

if price < 0.40:
buy()

Technically, that is a bot.

It is also not much of a trading system.

The problem is not the "if" statement. The problem is everything it doesn't know.

What market is this?

How much liquidity is available?

What is the spread?

How has the price moved?

When does the market resolve?

Is the market even worth considering?

Before automating orders, build something that can answer those questions.

The idea: create a market snapshot

Polymarket currently exposes several public data sources. Gamma provides market and event metadata, while the CLOB provides order-book and pricing data. Polymarket's own developer material recommends using these sources for market-data workflows.

We can combine them into one simple Python object.

The goal is not to trade.

The goal is to turn this:

Market ID
Price

into something closer to:

Question
Outcome
Price
Liquidity
Volume
Best bid
Best ask
Spread
End date

That is a much better foundation.

Step 1: Find an active market

Start with Gamma.

import requests

url = "https://gamma-api.polymarket.com/markets"

params = {
"active": "true",
"closed": "false",
"limit": 20
}

response = requests.get(url, params=params)
response.raise_for_status()

markets = response.json()

for market in markets[:5]:
print(market["question"])

Gamma's market endpoint exposes fields such as the question, liquidity, volume, active status, end date, outcomes and CLOB token IDs.

Now you have a list of markets.

But you still don't know much about the actual trading conditions.

Step 2: Get the YES token

Polymarket markets expose CLOB token IDs that connect the market metadata to the order book.

import json

market = markets[0]

token_ids = market["clobTokenIds"]

if isinstance(token_ids, str):
token_ids = json.loads(token_ids)

yes_token = token_ids[0]

print("YES token:", yes_token)

This token ID is what we'll use to query the CLOB.

Step 3: Read the order book

Now we can inspect the actual market.

book_url = "https://clob.polymarket.com/book"

response = requests.get(
book_url,
params={"token_id": yes_token}
)

response.raise_for_status()

book = response.json()

print(book)

The CLOB exposes order-book data without authentication for read operations.

This is where the project becomes more interesting.

Instead of treating the displayed probability as the entire market, your program can inspect the available bids and asks.

For example:

bids = book.get("bids", [])
asks = book.get("asks", [])

best_bid = max(
float(order["price"]) for order in bids
) if bids else None

best_ask = min(
float(order["price"]) for order in asks
) if asks else None

print("Best bid:", best_bid)
print("Best ask:", best_ask)

Now your program knows something much more useful than a single price.

It knows what buyers and sellers are actually offering.

Step 4: Calculate the spread

Once you have the best bid and ask, calculate the difference.

if best_bid is not None and best_ask is not None:
spread = best_ask - best_bid

print("Spread:", round(spread, 4))

A market at 60¢ with a tiny spread is a very different environment from a market where buyers are at 55¢ and sellers are at 65¢.

Your bot should know that difference.

Step 5: Turn it into a snapshot

Now combine the information.

snapshot = {
"question": market["question"],
"liquidity": market.get("liquidity"),
"volume": market.get("volume"),
"end_date": market.get("endDate"),
"best_bid": best_bid,
"best_ask": best_ask,
"spread": spread if best_bid and best_ask else None
}

for key, value in snapshot.items():
print(f"{key}: {value}")

You have just created the beginning of a research layer.

No automated orders.

No private keys.

No complicated strategy.

Just better information.

Why build this first?

Because automation makes bad assumptions faster.

If your strategy says:

if price < 0.40:
buy()

the program doesn't know whether the market has enough liquidity.

It doesn't know whether the spread is wide.

It doesn't know whether the market resolves tomorrow or six months from now.

It doesn't know why the price is 40¢.

It simply sees a number and follows an instruction.

A useful trading system needs context before execution.

Where to take it next

Once the snapshot works, you can start adding actual research logic:

The important part is that the strategy comes late.

You first build a reliable view of the market.

Then you decide what information is useful.

Only after that should you think seriously about automation.

Polymarket's current developer stack also provides historical price data through the CLOB, which means the same research layer can eventually compare current conditions with previous market behavior.

That is a much more interesting project than simply writing a bot that buys when a number crosses a threshold.

Your first Polymarket bot doesn't need to trade

It can observe.

It can collect.

It can compare.

It can tell you when a market deserves attention.

And after you understand what the data is actually telling you, you can decide whether automation makes sense.

Build the eyes before you build the hands.