DEV Community: Emil Nielsen

Building a No-Code Trading Bot Builder for Prediction Markets (Raven)

Emil Nielsen — Fri, 19 Jun 2026 09:56:36 +0000

Today I worked on Raven, a no-code trading bot builder for prediction markets. The goal is to improve usability, fix some bugs in the beta system, and experiment with a new strategy creation experience.

Raven is inspired by tools like n8n, but instead of automating APIs, it lets users visually design and deploy trading strategies for platforms like Polymarket.

Why Raven Exists

Right now, creating a trading strategy in Raven requires connecting multiple nodes visually.

While powerful, this approach can feel overwhelming for new users:

You must understand how nodes interact
You essentially "program visually"
Even simple strategies require multiple components

This is great for advanced users, but not ideal for beginners.

So the question became:

How do we keep the power of a node-based system while making it more accessible?

Introducing a Strategy Creation Wizard

To solve this, I’m working on a step-by-step strategy creation wizard.

Instead of starting with a blank canvas full of nodes, users go through guided steps:

Step 1: Select Venue

Choose where to trade:

Polymarket (and potentially others later)

Step 2: Market Scope

Define what kind of market strategy you want:

Single market
Event-based selection
Recurring series

Step 3: Market Selection

Users choose or paste a market (e.g. Polymarket URL), and the system extracts relevant options automatically.

This is one of the harder UX problems today, so simplifying input here is a big focus.

Step 4: Entry Strategy

This is where the actual trading logic is defined.

Users can select templates like:

Crossover strategy
Mean reversion
Market making
Custom strategy

And configure parameters like:

Position size
EMA periods
Price offsets

Step 5: Exit & Risk Management

Users define how trades close:

Exit on reverse signal
Exit on target conditions
Max hold time
Stop-loss / take-profit rules

We also introduce lifecycle hooks, such as:

Order filled
Order partially filled
Order failed or rejected
Timeout events

These allow post-trade automation like hedging or follow-up actions.

The Final Idea: From Wizard → Canvas

After completing the wizard:

A full strategy is generated automatically
It is converted into a node-based canvas
Users can still fine-tune everything manually

This keeps both worlds:

Beginner-friendly onboarding
Advanced flexibility for power users

Current Engineering Work

While building this, I also worked on several backend issues:

1. Stalled Bots Problem

Some bots:

Remain marked as “running”
But stop producing updates
Or lose WebSocket state

This required debugging heartbeat tracking and event queue behavior.

A possible improvement:

Auto-detect stalled bots
Restart or mark them as failed after a timeout window

2. S3 Session Loading Bug

I investigated an issue where:

A bot session shows as running
But loading historical data from S3 fails or returns incorrect state

This pointed to inconsistencies between:

Live session state
Archived session storage

3. P&L Calculation Issues

Another important bug:

Some users had incorrect leaderboard balances
P&L was not always consistent with closed trades

The system should compute:

P&L = sum(all closed trades in session)

But some cases still relied on snapshots instead of full recomputation.

This is being fixed by validating:

Trade history integrity
Session carry-over logic between bots in a season
Leaderboard correctness

Idea: Telegram Bot Monitoring

One interesting idea that came up:

Instead of only using the web UI, users could:

Monitor bots via Telegram
Start/stop bots remotely
Get status updates and alerts

The UI would still be used for building strategies, but Telegram becomes a lightweight control layer.

Real Trading vs Simulation

One major observation:

Simulation differs from real execution in one key way:

Simulation fills are instant
Real markets (e.g. Polymarket) have delays of seconds

That delay affects:

Stop-loss behavior
Take-profit accuracy
Market timing strategies

This is unavoidable and part of real-world trading systems.

Testing a Polymarket Bot Strategy

I also tested a simple strategy setup involving:

Market selection
Signal comparison
Entry condition triggers

Goal: verify that signals behave consistently across live execution.

Relevant Resources

Here are some useful references if you’re interested in similar systems:

Polymarket trading bot example:
Polymarket Trading Bot (BTC/ETH) GitHub
YouTube walkthrough of a similar bot system:
Trading Bot Tutorial Video
My YouTube channel (updates & builds):
Development Channel

Closing Thoughts

Raven is still in beta, and a lot of the current work is about:

Reducing friction for new users
Stabilizing long-running bots
Improving correctness of financial state (P&L, leaderboards)
Making strategy design more intuitive

The long-term goal is simple:

Make building and testing trading strategies as easy as dragging nodes and answering a few structured questions.

Building a Faster Polymarket Trading Bot: Why 5–10 WebSocket Connections Matter

Emil Nielsen — Wed, 17 Jun 2026 13:51:41 +0000

Polymarket trading has become increasingly competitive. In fast-moving prediction markets—especially short-duration markets such as Bitcoin Up/Down contracts—milliseconds can determine whether a trade captures value or misses an opportunity entirely.

A recent experiment analyzing nearly 9 million Polymarket events across 18 WebSocket connections revealed something many bot developers have suspected: running a single WebSocket connection is not enough for optimal market data quality.

This article breaks down the findings and explores how developers can improve the performance of their Polymarket trading bots.

The Experiment

The test focused on Polymarket's 5-minute Bitcoin Up/Down markets and measured:

Event coverage (missed messages)
Latency (data freshness)
Connection reliability
Data gaps
First-seen event performance

The experiment collected:

Nearly 9 million events
Across 18 unique WebSocket connections
Over 51 market rotations
Approximately 4 hours of runtime

Reference video:

YouTube: https://www.youtube.com/watch?v=pT7fu4n8o9Q

Why WebSocket Redundancy Matters

Most trading bots rely on a single WebSocket connection to receive market updates.

The problem?

WebSocket connections occasionally:

Stall
Drop packets
Experience routing delays
Deliver messages at different speeds

Even when multiple connections receive the same event, they often do not arrive simultaneously.

By maintaining multiple concurrent connections and selecting the earliest arriving event, a trading system can significantly improve execution speed and data quality.

Event Coverage Results

The study measured how many market events were missed.

Connections	Miss Rate (PPM)	Coverage
1	~2206	99.77%
2	Similar to 1	Slight improvement
5	~565	Significant improvement
10	Even lower	Best result

Key Takeaway

Moving from 1 connection to 5 connections reduced missed events by roughly 74%.

For algorithmic traders, missing fewer events means:

More accurate market state
Better order timing
Reduced execution risk

Latency Improvements

Latency was measured as the difference between the event timestamp provided by Polymarket and the local receipt time.

Median Latency

Connections	Median Latency
1	67.2 ms
2	63 ms
5	58 ms
10	55.9 ms

While a 10 ms improvement may not sound dramatic, in highly competitive prediction markets it can create a meaningful edge.

95th Percentile Latency

The most interesting improvement occurred in the latency tail.

Connections	P95 Latency
1	522 ms
2	305 ms
5	175 ms
10	154 ms

This shows that redundant connections don't just improve average performance—they dramatically reduce worst-case delays.

Eliminating Data Gaps

One of the biggest risks in automated trading is silent connection degradation.

The test measured the largest observed gap between events.

Largest Data Gap

Connections	Largest Gap
1	~12 seconds
2	4.86 seconds
5	Milliseconds
10	2.8 milliseconds

A single 12-second blind spot can be catastrophic in a fast-moving market.

By running multiple parallel streams, the system effectively eliminated these gaps.

First-Seen Advantage

Another interesting metric was the "first-seen win rate."

This measures which connection receives an event first.

Results showed:

Connections	First-Seen Rate
1	0.49%
2	3.81%
5	28.13%
10	76.57%

The implication is clear:

More connections create more opportunities to receive an event before competitors relying on fewer data streams.

The Engineering Trade-Off

Of course, increasing WebSocket connections is not free.

Developers must consider:

Higher Resource Usage

More connections mean:

More CPU consumption
Higher memory usage
Increased network traffic

Rate Limits

Polymarket infrastructure may impose limits on excessive connection counts.

Running 10 connections per strategy can quickly become problematic if multiple bots are operating simultaneously.

Event Deduplication

A robust architecture must:

Receive identical events from multiple connections.
Deduplicate incoming messages.
Keep only the earliest event.
Forward the winning event to the trading engine.

This requires careful engineering but delivers measurable performance gains.

Open-Source Polymarket Bot Examples

Developers looking to experiment with Polymarket automation can explore several open-source projects.

Polymarket 5-Minute Bot Collection

https://github.com/topics/polymarket-5min-bot

A collection of repositories focused on automated trading strategies for short-duration Polymarket markets.

Polymarket Trading BTC ETH M Bot

https://github.com/nahuelvivas/Polymarket-Trading-BTC-ETH-M-Bot

An example trading bot targeting cryptocurrency prediction markets on Polymarket.

Polymarket Copy Trading

https://github.com/ducksybils/polymarket-copytrading

A copy-trading implementation that mirrors selected trader activity.

Architecture Recommendation

Based on the test results, a practical architecture would be:

Polymarket
      │
 ┌────┼────┐
 WS1 WS2 WS3
 WS4 WS5 WS6
 WS7 WS8 WS9
 WS10
      │
Deduplication Layer
      │
Earliest Event Selector
      │
Market State Engine
      │
Execution Engine

This design allows the system to:

Reduce latency
Improve coverage
Eliminate connection gaps
Increase first-seen probability

Final Thoughts

The data suggests a clear conclusion:

One WebSocket connection is adequate for casual monitoring, but not for serious automated trading.

The largest gains appear when moving from:

1 → 5 connections
5 → 10 connections

The sweet spot for many traders is likely between 5 and 10 concurrent WebSocket connections, balancing performance improvements against infrastructure costs and potential rate limits.

As Polymarket grows and competition intensifies, market participants who invest in low-latency data infrastructure may gain a measurable advantage over traders relying on a single connection.

In prediction markets, information arrives only once. The trader who sees it first often wins.

Polymarket Trading Bot: How Developers Can Automate Market Analysis Using Real-World Resolution Disputes

Emil Nielsen — Tue, 16 Jun 2026 16:45:52 +0000

The rise of prediction markets has created new opportunities for algorithmic traders. A well-designed Polymarket trading bot can identify pricing inefficiencies, monitor resolution criteria, track social sentiment, and capitalize on market misunderstandings before they are reflected in odds.

One recent example involves the controversial Polymarket market:

"Will MicroStrategy sell any Bitcoin by May 31, 2026?"

This case demonstrates exactly why developers are increasingly building automated systems to analyze prediction markets and exploit information asymmetries.

The MicroStrategy Bitcoin Sale Controversy

According to a June 1 SEC filing from Strategy (formerly MicroStrategy), the company reported that it had sold 32 BTC during the period of May 26–May 31, 2026.

At first glance, the market should clearly resolve to YES because Bitcoin was sold before the specified deadline.

However, Polymarket's resolution notes included additional context stating:

"Confirmation achieved outside of the market's time frame does not qualify."

The complication was that the confirmation of the sale appeared in an SEC filing published on June 1, after the market deadline.

This created a dispute among traders:

Some argued the sale itself occurred before May 31.
Others argued the confirmation arrived after the deadline.
The market entered multiple dispute rounds.
Market pricing implied only about a 1% chance of a YES resolution despite evidence of the sale.

This situation highlights a critical lesson for prediction market traders:

Market rules matter more than common assumptions.

Why This Matters for a Polymarket Trading Bot

Many traders focus only on the headline question.

A sophisticated Polymarket trading bot goes further by analyzing:

Resolution criteria
Market descriptions
Clarifications and updates
SEC filings
On-chain activity
Social sentiment on X
Community discussions
Historical dispute outcomes

When a market becomes controversial, odds can diverge significantly from expected outcomes.

These inefficiencies create opportunities for algorithmic traders.

Features of an Advanced Polymarket Trading Bot

A production-ready bot typically includes several components.

1. Market Monitoring

The bot continuously scans active markets and identifies unusual price movements.

Example triggers:

Large volume spikes
Sudden probability changes
Resolution disputes
Oracle challenges

2. News and Filing Analysis

A bot can monitor:

SEC filings
Company announcements
Press releases
Government disclosures

In the MicroStrategy example, an automated filing monitor could have immediately detected the June 1 disclosure.

3. Social Sentiment Analysis

Many Polymarket traders discuss developments on:

X (Twitter)
Discord
Reddit
Telegram

Natural language processing can help identify emerging consensus before market odds adjust.

4. Resolution Rule Parsing

One of the most overlooked advantages of a Polymarket trading bot is automated rule analysis.

A bot can:

Extract resolution criteria
Detect ambiguous wording
Flag markets with high dispute risk
Estimate alternative resolution outcomes

The MicroStrategy market demonstrates how critical this capability can be.

5. Risk Management

Every trading strategy should include:

Position sizing
Stop-loss rules
Exposure limits
Portfolio diversification

Prediction markets can remain irrational longer than expected.

Open-Source Polymarket Trading Bot Projects

Developers interested in building their own systems can start with several open-source repositories.

Polymarket Trading BTC ETH M Bot

GitHub:
https://github.com/nahuelvivas/Polymarket-Trading-BTC-ETH-M-Bot

Features include:

Automated market monitoring
BTC and ETH-related market trading
API integration examples
Position management logic

Polymarket BTC 15 Minute Trading Bot

GitHub:
https://github.com/aulekator/Polymarket-BTC-15-Minute-Trading-Bot

Features include:

Short-term prediction market strategies
Bitcoin-focused trading
Automated execution
Market data processing

These repositories provide useful starting points for developers looking to create custom trading infrastructure.

Lessons from the MicroStrategy Market

The disputed MicroStrategy market demonstrates why algorithmic analysis is increasingly valuable.

Key takeaways include:

Market resolution depends on exact wording.
Confirmation timing may matter more than event timing.
Traders frequently misprice disputed outcomes.
Information asymmetry creates opportunities.
Automated monitoring can react faster than manual traders.

A well-designed Polymarket trading bot should not simply follow price action. It should understand market mechanics, resolution criteria, and the sources that determine final outcomes.

Video Analysis

For a detailed breakdown of the MicroStrategy Bitcoin sale dispute and its potential impact on Polymarket resolution, watch:

https://www.youtube.com/watch?v=J3myO_Fg6iQ

The video provides an overview of the SEC filing, community reactions, dispute process, and the arguments surrounding the market's final resolution.

Final Thoughts

As prediction markets continue to grow, the edge increasingly belongs to traders who can process information faster and more accurately than the crowd.

Building a Polymarket trading bot that combines market data, rule analysis, SEC filings, social sentiment, and dispute monitoring can provide a significant advantage when trading complex and controversial markets.

The MicroStrategy Bitcoin sale dispute serves as a perfect example of how understanding the fine details of market resolution can be just as important as understanding the underlying event itself.