Replayable Event Streams for Trading Infrastructure: Building a Recoverable Polymarket Trading Bot
A Polymarket trading bot is more than a strategy that receives market data and sends orders. Once a bot is running continuously, it becomes a distributed system with market-data streams, strategy decisions, risk checks, order execution, position updates, and failures happening asynchronously.
That creates a difficult engineering problem:
What happens when the bot crashes halfway through a trading session?
If the only thing you have is the bot's current in-memory state, you may not know exactly what happened before the crash.
Which market data did it receive?
Which signal did it generate?
Why did it place the order?
Was the order accepted?
Was it partially filled?
What position did the bot believe it had?
A replayable event stream provides an answer.
Instead of treating events as temporary messages, we persist important state transitions so the trading system can reconstruct what happened later.
This article explains how to design replayable event streams for automated trading infrastructure and how the architecture can be applied to a Python-based Polymarket trading bot.
What Is a Replayable Event Stream?
An event stream is an ordered sequence of events representing changes that happened inside a system.
For a trading bot, events might look like:
MarketDiscovered
MarketDataReceived
OrderBookUpdated
SignalGenerated
RiskApproved
OrderSubmitted
OrderAccepted
OrderPartiallyFilled
OrderFilled
PositionUpdated
MarketResolved
PnLCalculated
Instead of only storing the current state:
Position = 100
Cash = $4,500
we preserve the events that produced that state:
09:30:01 MarketDiscovered
09:30:02 OrderBookUpdated
09:30:05 SignalGenerated
09:30:05 RiskApproved
09:30:06 OrderSubmitted
09:30:06 OrderFilled
09:30:07 PositionUpdated
The current state can then be reconstructed by replaying those events.
EVENT STREAM
│
▼
┌────────────────────────┐
│ MarketDiscovered │
│ OrderBookUpdated │
│ SignalGenerated │
│ RiskApproved │
│ OrderSubmitted │
│ OrderFilled │
│ PositionUpdated │
└────────────┬───────────┘
│
replay
│
▼
┌────────────────────────┐
│ CURRENT STATE │
│ Position: 100 │
│ Exposure: $63 │
│ P&L: +$8.20 │
└────────────────────────┘
This concept is closely related to event sourcing.
The event history becomes the source of truth, while the current state becomes a projection of that history.
Why Replayability Matters for Trading Bots
Trading systems have a property that ordinary applications often don't:
State has financial consequences.
If an e-commerce application loses an in-memory variable, the result may be a temporary error.
If a trading bot loses its position state, it can potentially place an incorrect order.
Imagine this sequence:
Bot receives market data
↓
Strategy generates BUY signal
↓
Risk engine approves
↓
Order submitted
↓
Process crashes
↓
Bot restarts
After restarting, the bot needs to know:
Did the order actually execute?
If the answer isn't available, the bot could accidentally submit another order.
Replayable events give the system a historical record from which it can reconstruct the state.
The Architecture
A simple architecture looks like this:
POLYMARKET
│
▼
┌───────────────┐
│ Market Data │
│ WebSocket/API │
└───────┬───────┘
│
▼
┌───────────────┐
│ Event Builder │
└───────┬───────┘
│
▼
┌──────────────────────┐
│ EVENT LOG / STREAM │
│ │
│ MarketUpdated │
│ SignalGenerated │
│ OrderSubmitted │
│ OrderFilled │
│ PositionUpdated │
└──────────┬───────────┘
│
┌──────────┼───────────┐
│ │ │
▼ ▼ ▼
Strategy Risk Execution
State State State
│ │ │
└──────────┼───────────┘
▼
┌───────────────┐
│ Position / │
│ P&L State │
└───────────────┘
The important design principle is:
Persist important state transitions before depending on them.
The bot should not rely exclusively on variables sitting in RAM.
Event Sourcing vs Traditional State
A traditional trading bot might do this:
position = 0
def buy(quantity):
global position
position += quantity
This works until the process crashes.
After restarting:
position
might be:
0
even though the actual account has a position.
An event-sourced design records the transition:
{
"event": "ORDER_FILLED",
"side": "BUY",
"quantity": 100
}
Then state is reconstructed:
position = 0
for event in events:
if event["event"] == "ORDER_FILLED":
if event["side"] == "BUY":
position += event["quantity"]
else:
position -= event["quantity"]
Now the state can be rebuilt from history.
Designing a Trading Event
A good event should contain enough information to understand what happened without depending on hidden application state.
For example:
from dataclasses import dataclass
from datetime import datetime
from typing import Any
@dataclass
class TradingEvent:
event_id: str
event_type: str
timestamp: datetime
market_id: str
sequence: int
data: dict[str, Any]
An order event could look like:
event = TradingEvent(
event_id="evt-1001",
event_type="ORDER_FILLED",
timestamp=datetime.utcnow(),
market_id="BTC-UP-DOWN-5M",
sequence=18291,
data={
"order_id": "order-123",
"side": "BUY",
"price": 0.63,
"quantity": 100
}
)
This structure gives every event:
- A unique identifier
- An event type
- A timestamp
- A market
- An ordering sequence
- Event-specific data
Event IDs and Idempotency
One of the most important concepts in event-driven trading systems is idempotency.
Suppose the bot receives:
ORDER_FILLED
order_id = 123
quantity = 100
Then the same event is accidentally processed twice.
Without protection:
Position = 0
First event → +100
Second event → +100
Position = 200
But the real position is only 100.
Every event should therefore have a unique identifier.
processed_events = set()
def process_event(event):
if event.event_id in processed_events:
return
apply_event(event)
processed_events.add(event.event_id)
In production, the processed-event record should be persisted rather than stored only in memory.
Sequence Numbers Matter
Timestamps alone aren't always enough to determine ordering.
Consider:
Event A timestamp = 09:30:01.120
Event B timestamp = 09:30:01.118
Network timing can make events arrive out of order.
A sequence number provides an explicit ordering mechanism:
Sequence 1001 → MarketUpdated
Sequence 1002 → SignalGenerated
Sequence 1003 → RiskApproved
Sequence 1004 → OrderSubmitted
Sequence 1005 → OrderFilled
The event stream can then be replayed deterministically.
events = sorted(events, key=lambda event: event.sequence)
for event in events:
apply_event(event)
This is especially useful when multiple components produce events asynchronously.
Building a Simple Event Store in Python
For a prototype, SQLite is enough to demonstrate the architecture.
import sqlite3
import json
class EventStore:
def __init__(self, database="events.db"):
self.connection = sqlite3.connect(database)
self.connection.execute("""
CREATE TABLE IF NOT EXISTS events (
sequence INTEGER PRIMARY KEY AUTOINCREMENT,
event_id TEXT UNIQUE,
event_type TEXT NOT NULL,
market_id TEXT,
timestamp TEXT NOT NULL,
payload TEXT NOT NULL
)
""")
self.connection.commit()
def append(self, event):
self.connection.execute(
"""
INSERT INTO events (
event_id,
event_type,
market_id,
timestamp,
payload
)
VALUES (?, ?, ?, ?, ?)
""",
(
event["event_id"],
event["event_type"],
event["market_id"],
event["timestamp"],
json.dumps(event["payload"])
)
)
self.connection.commit()
Now an event can be persisted:
store = EventStore()
store.append({
"event_id": "evt-1001",
"event_type": "ORDER_FILLED",
"market_id": "BTC-UP-DOWN-5M",
"timestamp": "2026-08-08T09:32:18Z",
"payload": {
"side": "BUY",
"price": 0.63,
"quantity": 100
}
})
This is obviously not a production event store, but it demonstrates the core idea.
Replaying the Event Stream
Now we can rebuild a portfolio state.
def replay(events):
state = {
"cash": 10000.0,
"positions": {},
"realized_pnl": 0.0
}
for event in events:
event_type = event["event_type"]
payload = event["payload"]
if event_type == "ORDER_FILLED":
market = event["market_id"]
quantity = payload["quantity"]
price = payload["price"]
if payload["side"] == "BUY":
state["positions"][market] = (
state["positions"].get(market, 0)
+ quantity
)
state["cash"] -= quantity * price
elif payload["side"] == "SELL":
state["positions"][market] = (
state["positions"].get(market, 0)
- quantity
)
state["cash"] += quantity * price
return state
The important part is that the current state is derived from historical events.
Event Replay After a Crash
Imagine the bot crashes at:
09:32:18
The event store contains:
09:30:01 MarketDiscovered
09:30:02 OrderBookUpdated
09:30:05 SignalGenerated
09:30:05 RiskApproved
09:30:06 OrderSubmitted
09:30:07 OrderFilled
09:30:08 PositionUpdated
When the bot restarts:
DATABASE
│
▼
┌─────────────────┐
│ Historical │
│ Events │
└────────┬────────┘
│
▼
REPLAY
│
▼
┌─────────────────┐
│ Reconstructed │
│ State │
└────────┬────────┘
│
▼
RESUME
The bot doesn't need to guess what happened.
It rebuilds its internal state.
Snapshots Make Replay Faster
There is one problem with event replay.
Imagine the bot has processed:
50 million events
Replaying all of them every time the application starts would be expensive.
The solution is snapshots.
For example:
Events 1 ──────────────── 10,000
│
▼
Snapshot
│
Events 10,001 ─────────── 20,000
│
▼
Snapshot
Instead of replaying everything:
Start
↓
Load latest snapshot
↓
Replay only new events
↓
Current state
For example:
snapshot = load_latest_snapshot()
state = snapshot.state
events = load_events_after(
snapshot.sequence
)
for event in events:
apply_event(state, event)
This provides the benefits of event sourcing without requiring full replay every time.
What Events Should a Polymarket Bot Store?
Not every piece of information needs to become an event.
High-value state transitions should be persisted.
I would start with:
Market Events
MARKET_DISCOVERED
MARKET_STARTED
MARKET_RESOLVED
Market Data Events
ORDERBOOK_UPDATED
PRICE_UPDATED
LIQUIDITY_CHANGED
Strategy Events
SIGNAL_GENERATED
SIGNAL_REJECTED
STRATEGY_ENABLED
STRATEGY_DISABLED
Risk Events
RISK_CHECK_PASSED
RISK_CHECK_FAILED
POSITION_LIMIT_REACHED
CIRCUIT_BREAKER_TRIGGERED
Execution Events
ORDER_SUBMITTED
ORDER_ACCEPTED
ORDER_REJECTED
ORDER_CANCELLED
ORDER_PARTIALLY_FILLED
ORDER_FILLED
Portfolio Events
POSITION_UPDATED
BALANCE_UPDATED
PNL_UPDATED
This gives you a useful event history without turning every internal function call into an event.
Don't Store Only Orders
A common mistake is creating an event stream containing only execution events.
For example:
ORDER_SUBMITTED
ORDER_FILLED
ORDER_CANCELLED
That tells you what the bot did.
It doesn't tell you why.
A better event stream includes the strategy decision:
ORDERBOOK_UPDATED
↓
SIGNAL_GENERATED
↓
RISK_CHECK_PASSED
↓
ORDER_SUBMITTED
↓
ORDER_FILLED
Now you can reconstruct the complete decision path.
Recording the Strategy Decision
Suppose a strategy detects:
Market price = $0.63
Model probability = 71%
Estimated edge = 8%
Instead of logging only:
BUY
store:
{
"event_type": "SIGNAL_GENERATED",
"payload": {
"side": "UP",
"market_price": 0.63,
"model_probability": 0.71,
"edge": 0.08,
"strategy": "momentum"
}
}
Now you can later ask:
Did signals with an estimated 8% edge actually make money?
That makes your event stream useful for quantitative research.
Replayable Events as a Debugging Tool
Suppose a trader reports:
"The bot bought the wrong side."
Without an event history, debugging could take hours.
With replayable events:
09:31:20
OrderBookUpdated
09:31:20
SignalGenerated
side=UP
probability=0.71
09:31:20
RiskApproved
09:31:21
OrderSubmitted
side=UP
09:31:21
OrderFilled
side=UP
You can reproduce the exact sequence.
Now you can determine whether the problem was:
- market data
- probability model
- signal generation
- risk logic
- order routing
- position management
This is significantly more powerful than reading application logs.
Deterministic Replay
The ideal replay system should produce the same state from the same event sequence.
For example:
events = load_events()
state_a = replay(events)
state_b = replay(events)
assert state_a == state_b
If identical event streams produce different states, you may have hidden nondeterminism.
Common sources include:
- current time
- random numbers
- external API calls
- mutable global state
- unordered collections
- asynchronous side effects
A deterministic replay system should minimize these dependencies.
Separate Events From External Side Effects
There is an important architectural rule:
Replaying an event should not accidentally place another real order.
Imagine replaying:
ORDER_SUBMITTED
If the event handler directly calls the exchange API during replay, the bot could submit the order again.
That would be extremely dangerous.
Instead, separate:
EVENT
↓
STATE TRANSITION
from:
DECISION
↓
EXTERNAL SIDE EFFECT
For example:
def apply_event(state, event):
"""
Pure state transition.
Never sends an external order.
"""
if event["event_type"] == "ORDER_FILLED":
state["position"] += event["payload"]["quantity"]
return state
Then live execution is separate:
def execute_order(client, order):
return client.submit_order(order)
This separation makes replay safe.
Replay vs Backtesting
Replay and backtesting are related but different.
Backtesting
Usually asks:
What would the strategy have done historically?
Replay
Asks:
What actually happened inside my production system?
Backtesting may start with:
Historical market data
↓
Strategy
↓
Simulated orders
↓
Hypothetical P&L
Replay uses:
Actual historical events
↓
State reconstruction
↓
Actual historical decisions
↓
Actual execution state
Replay is therefore particularly useful for production debugging.
Replay as an Incident Investigation Tool
Consider this incident:
Bot P&L suddenly dropped.
You could replay the last 30 minutes:
09:00 MarketDataConnected
09:01 SignalGenerated
09:01 OrderFilled
09:02 PositionUpdated
09:05 WebSocketDisconnected
09:06 ReconnectAttempt
09:06 OrderSubmitted
09:06 OrderRejected
09:07 RiskStateUpdated
09:08 PositionMismatchDetected
09:08 CircuitBreakerTriggered
Now the incident has a timeline.
Instead of asking:
"What happened?"
you can inspect the exact event sequence.
Connecting Replayability With Observability
This is where the previous observability architecture becomes even more powerful.
Observability tells you:
Something went wrong.
Replayability tells you:
Exactly how the system reached that state.
Together:
TRADING SYSTEM
│
┌──────────────┼──────────────┐
│ │ │
▼ ▼ ▼
LOGS METRICS EVENTS
│ │ │
│ │ ▼
│ │ REPLAY
│ │ │
└──────────────┼──────────────┘
▼
DEBUG / ANALYZE
This combination is extremely valuable for automated trading infrastructure.
A Practical Python Event Bus
A simple in-process event bus can help organize the architecture.
from collections import defaultdict
class EventBus:
def __init__(self):
self.handlers = defaultdict(list)
def subscribe(self, event_type, handler):
self.handlers[event_type].append(handler)
def publish(self, event):
for handler in self.handlers[event["event_type"]]:
handler(event)
You can then subscribe components:
bus = EventBus()
bus.subscribe(
"ORDER_FILLED",
position_manager.handle
)
bus.subscribe(
"ORDER_FILLED",
metrics.handle
)
bus.subscribe(
"ORDER_FILLED",
audit_log.handle
)
The event itself becomes the shared interface between components.
Event-Driven Trading Architecture
A mature system can evolve into:
MARKET DATA
│
▼
┌──────────────────┐
│ Event Stream │
└────────┬─────────┘
│
┌──────────────────┼──────────────────┐
│ │ │
▼ ▼ ▼
Strategy Risk Analytics
│ │ │
└──────────────────┼──────────────────┘
▼
Order Manager
│
▼
Polymarket API
│
▼
Execution Events
│
▼
Event Stream
│
┌──────────────┼──────────────┐
▼ ▼ ▼
Positions P&L Observability
This architecture allows each subsystem to consume the events it needs.
What About Kafka?
For a small bot, you probably don't need Kafka.
Start with:
SQLite
PostgreSQL
or another durable database.
As throughput and infrastructure complexity increase, technologies such as:
Kafka
Redpanda
NATS JetStream
Redis Streams
can become useful depending on your requirements.
The important concept isn't the specific technology.
The important concept is:
Events should be durable, ordered where necessary, replayable, and independently consumable.
Event Retention
Trading systems can generate a lot of data.
You need a retention strategy.
For example:
Hot data
↓
Recent events
↓
Fast database
Warm data
↓
Historical trading events
↓
Cheap storage
Cold data
↓
Long-term archives
You may also separate high-value state events from raw market-data events.
For example, you might retain:
ORDER_FILLED
SIGNAL_GENERATED
RISK_CHECK
POSITION_UPDATED
for a long time while keeping ultra-high-frequency order-book updates under a shorter retention policy.
Versioning Your Events
Trading infrastructure evolves.
Suppose version 1 uses:
{
"price": 0.63
}
and version 2 adds:
{
"price": 0.63,
"slippage": 0.002
}
Old events still need to be replayable.
Add a schema version:
{
"event_type": "ORDER_FILLED",
"schema_version": 2,
"payload": {
"price": 0.63,
"quantity": 100,
"slippage": 0.002
}
}
Your replay engine can then support older versions.
This becomes increasingly important as the bot evolves.
Event Streams and the Polymarket API
When implementing this architecture, the external market and trading interfaces should be treated as sources of events and confirmations rather than as your application's state database.
The Polymarket developer documentation should be your primary reference for the current APIs, market-data interfaces, and trading integration details.
A useful architecture is:
Polymarket
│
▼
External Events
│
▼
Normalization
│
▼
Internal Trading Events
│
├── Strategy
├── Risk
├── Execution
├── Portfolio
└── Observability
This creates a clean boundary between external APIs and internal application state.
Using a Python Polymarket Trading Bot as the Practical Layer
For developers who want to move from architecture to implementation, my Python repository is available here:
Benjam1nCup/Polymarket-trading-bot-python-V2
The repository can be used as a practical starting point for experimenting with automated Polymarket strategies and building additional infrastructure around them.
The next architectural step is to add a persistent event layer:
Existing Bot
│
├── Market Data
├── Strategy
├── Risk
├── Execution
└── Position
│
▼
Event Stream
│
┌────┴────┐
▼ ▼
Database Analytics
│
▼
Replay
This turns a trading bot into something closer to a recoverable trading platform.
Recommended Project Structure
A clean Python implementation could look like:
polymarket-bot/
│
├── market_data/
│ ├── websocket.py
│ └── orderbook.py
│
├── strategy/
│ ├── momentum.py
│ └── arbitrage.py
│
├── risk/
│ ├── limits.py
│ └── circuit_breaker.py
│
├── execution/
│ └── order_manager.py
│
├── portfolio/
│ └── positions.py
│
├── events/
│ ├── models.py
│ ├── store.py
│ ├── bus.py
│ └── replay.py
│
├── observability/
│ ├── logging.py
│ ├── metrics.py
│ └── alerts.py
│
└── main.py
The events/ directory becomes the foundation for persistence and replay.
The Most Important Events to Replay
If you're building this system incrementally, don't try to capture everything immediately.
Start with:
1. SIGNAL_GENERATED
2. RISK_CHECK_PASSED
3. RISK_CHECK_FAILED
4. ORDER_SUBMITTED
5. ORDER_ACCEPTED
6. ORDER_REJECTED
7. ORDER_PARTIALLY_FILLED
8. ORDER_FILLED
9. ORDER_CANCELLED
10. POSITION_UPDATED
11. PNL_UPDATED
12. CIRCUIT_BREAKER_TRIGGERED
These events give you a surprisingly complete picture of the trading system.
Testing the Replay Engine
A replay system should be tested aggressively.
For example:
def test_buy_order_updates_position():
events = [
{
"event_type": "ORDER_FILLED",
"payload": {
"side": "BUY",
"quantity": 100,
"price": 0.63
}
}
]
state = replay(events)
assert state["positions"] == 100
Test multiple events:
def test_buy_then_sell():
events = [
{
"event_type": "ORDER_FILLED",
"payload": {
"side": "BUY",
"quantity": 100,
"price": 0.63
}
},
{
"event_type": "ORDER_FILLED",
"payload": {
"side": "SELL",
"quantity": 40,
"price": 0.70
}
}
]
state = replay(events)
assert state["positions"] == 60
Also test duplicate events.
def test_duplicate_event_is_ignored():
event = {
"event_id": "abc",
"event_type": "ORDER_FILLED",
"payload": {
"side": "BUY",
"quantity": 100,
"price": 0.63
}
}
events = [event, event]
state = replay(events)
assert state["positions"] == 100
These tests are extremely valuable because replay correctness directly affects position correctness.
Common Mistakes
1. Treating logs as an event store
Logs are primarily for diagnostics.
They should not automatically become your authoritative trading state.
2. Storing only final positions
Knowing that the position is 100 doesn't tell you how it became 100.
Store the transitions.
3. No event IDs
Without unique IDs, duplicate delivery can create incorrect state.
4. Ignoring ordering
Events should have deterministic ordering semantics where ordering matters.
5. Executing external actions during replay
Replay should be safe.
Replaying historical events must never accidentally submit live orders.
6. No schema version
Your event structure will eventually change.
Version it from the beginning.
7. Making the event stream too large
Don't turn every function call into an event.
Focus on meaningful state transitions.
FAQ
Is event sourcing necessary for every trading bot?
No.
A simple prototype can work with normal state management and trade logs.
Replayable events become increasingly valuable when you need:
- automatic recovery
- reliable position reconstruction
- detailed debugging
- auditability
- multi-component architectures
- historical strategy analysis
Should market-data updates be stored as events?
It depends on the system.
For a high-frequency stream, storing every raw update can become expensive.
You may instead store important derived events such as:
ORDERBOOK_UPDATED
SIGNAL_GENERATED
PRICE_THRESHOLD_CROSSED
while keeping raw market data in a separate storage system.
Can SQLite be used in production?
SQLite can be perfectly reasonable for a single-process prototype.
For a continuously running multi-component trading platform, PostgreSQL or a dedicated event-streaming system may be more appropriate.
The architecture should come before the infrastructure choice.
Does replay replace a database of positions?
Not necessarily.
A practical system can use both:
Event Store
↓
Replay
↓
Current Position State
↓
Fast Queries
The event stream provides the history.
The position database provides fast access to current state.
Can replay be used for backtesting?
Yes, but replay and backtesting should remain conceptually separate.
Replay reconstructs what happened.
Backtesting simulates what could have happened.
Both can share the same strategy and state-transition components.
How does replayability help with a Polymarket trading bot?
It provides a recovery mechanism.
If the bot crashes, you can reconstruct:
Market state
↓
Strategy decisions
↓
Risk decisions
↓
Orders
↓
Fills
↓
Positions
↓
P&L
This makes the trading system much easier to operate safely.
Final Thoughts
A trading bot should not depend on whatever happens to exist in memory at the moment the process is running.
For serious automated trading infrastructure, history should be part of the architecture.
Replayable event streams provide that history.
They allow a Polymarket trading bot to reconstruct its state after crashes, investigate unexpected trades, analyze strategy decisions, detect execution problems, and build reliable post-trade research pipelines.
The architecture can be summarized simply:
MARKET DATA
│
▼
TRADING EVENTS
│
┌───────────┼───────────┐
▼ ▼ ▼
STRATEGY RISK EXECUTION
│ │ │
└───────────┼───────────┘
▼
EVENT STORE
│
┌────────┴────────┐
▼ ▼
CURRENT STATE REPLAY
│ │
▼ ▼
TRADING DEBUGGING
SYSTEM RESEARCH
The key idea is simple:
Don't just store what your trading bot knows now. Store the events that explain how it got there.
Once those events are durable and replayable, your trading infrastructure becomes recoverable, testable, auditable, and much easier to improve.
That is a significant step from building a trading script toward building a real trading system.
🤝 Collaboration & Contact
If you’re interested in building trading bots, buy trading bots, collaborating, exploring strategy improvements, or discussing about this system, feel free to reach out.
I’m especially open to connecting with:
Quant traders
Engineers building trading infrastructure
Researchers in prediction markets
Investors interested in market inefficiencies
📌 GitHub Repository
This repo has some Polymarket several bots in this system.
You can explore the full implementation, strategy logic, and ongoing updates about 5 min crypto market here:
Benjam1nCup
/
Polymarket-trading-bot-python-V2
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Polymarket Trading Bot | Polymarket Arbitrage Bot
An open-source and Strong Strategy collection of Polymarket trading bot and Polymarket arbitrage bot in Python for high-performance automated trading on polymarket crypto 5min markets.
Features
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Explosive growth of Polymarket with surging trading volume and new short-term markets
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Increasing dominance of automated bots and AI in 5-minute crypto prediction markets
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Higher profitability potential through advanced arbitrage and market-making strategies
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Stronger edge for Python-based bots with real-time orderbook intelligence and low-latency execution
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Continuous evolution of sniper, ladder, stair, momentum, and copy trading strategies
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Scalable daily profits as prediction markets move toward hundreds of billions in annual volume
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Full future-proof architecture for new features, contracts, and high-frequency trading environments
Included Trading Bots
Designed for arbitrage, directional strategies, and ultra-short-term markets (including 5-minute rounds), this bot framework provides a robust foundation for building and scaling automated trading strategies on Polymarket .
Demo Video
Documentation
Throughout this…
💬 Get in Touch
If you have ideas, questions, or would like to collaborate or want these trading bots, don't hesitate to reach out directly.
Feedback on your repo (based on your description & strategy)
Contact Info
Telegram
https://t.me/BenjaminCup
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