DEV Community: Emil Nielsen

Optimizing Trading Strategies with Dynamic Execution Scheduling

Emil Nielsen — Mon, 06 Jul 2026 18:05:40 +0000

Optimizing Trading Strategies with Dynamic Execution Scheduling

In the ever-evolving landscape of financial markets, adapting to changes swiftly and efficiently is crucial. At the heart of this adaptability lies the concept of dynamic execution scheduling under market probability drift. This approach draws a parallel with dynamic scheduling in computer systems, optimizing performance by rearranging instructions dynamically.

Understanding Dynamic Execution Scheduling

Dynamic execution scheduling involves the real-time adjustment of trading strategies in response to fluctuating market probabilities. Much like a dynamic scheduler in computing that rearranges tasks to minimize execution stalls, this strategy seeks to optimize trade executions, even amidst the uncertainties of market drifts.

According to research, dynamic scheduling in computer systems allows for handling dependencies that are unknown at compile time, which is akin to adjusting trading strategies when market conditions shift unexpectedly. By employing out-of-order execution, traders can significantly reduce penalties caused by data stalls, optimizing their performance during volatile market conditions.

The Role of AI and Machine Learning

The integration of AI and machine learning into dynamic execution scheduling has been a game-changer for financial markets. These technologies facilitate the development of adaptive algorithms that can respond to market changes in real time.

Adaptive Algorithms for Real-Time Response

Adaptive algorithms are designed to monitor market conditions continuously and adjust execution strategies promptly. This real-time adaptability ensures that trading strategies remain optimal, even as market probabilities drift. The ability to predict and react to market volatility allows traders to maintain a competitive edge.

Parallels with Dynamic Scheduling in Computing

Dynamic scheduling in computing, as detailed in sources like this, involves rearranging tasks to reduce stalls, optimizing the overall system performance. Similarly, in financial markets, dynamic execution scheduling aims to minimize losses and maximize gains by adjusting trading strategies in response to market probability drifts.

Case Studies and Examples

For instance, the use of dynamic scheduling algorithms in real-time tasks with precedence constraints, as explored in this study, highlights the effectiveness of this approach in managing complex dependencies. This methodology, when applied to trading strategies, ensures that all market variables are considered, and execution plans are optimized accordingly.

Conclusion

Dynamic execution scheduling under market probability drift offers a sophisticated and responsive approach to trading in volatile financial markets. By leveraging AI and adaptive algorithms, traders can ensure their strategies are not only robust but also optimized for performance amidst the uncertainties of market changes.

dynamic #ai #trading #finance

Building a Reliable Stop-Loss System for an Automated Trading Bot

Emil Nielsen — Thu, 02 Jul 2026 06:48:32 +0000

One of the most important parts of any automated trading bot isn't entering trades—it's managing risk after a position has been opened. A stop-loss system is only effective if it continuously monitors orders and reacts correctly to every stage of the order lifecycle.

In this article, I'll walk through the stop-loss architecture I've been implementing in Raven, my no-code framework for building automated prediction market strategies. If you prefer a video walkthrough, you can watch the full implementation here:

📺 Video: https://www.youtube.com/watch?v=wc9XaGDV-J4

Why Monitoring Orders Matters

Placing an order does not mean you have an active position.

When an order is submitted, it simply enters the order book where it waits to be matched. Until that happens, there is no live position to manage.

This distinction is essential when building reliable trading systems.

Instead of assuming an order will immediately execute, Raven tracks every state transition and only activates position management once the exchange confirms the order has been matched.

The Order Lifecycle

The architecture separates order management into several stages.

1. Order Placement

The bot submits an order to the exchange.

At this point, the order is simply waiting on the order book.

When a placement event arrives through the exchange's user event stream, the order is added to the Pending Order Tracker.

Its only responsibility is to monitor orders waiting to be matched.

2. Order Match

Once the exchange reports that the order has been matched, it is removed from the Pending Order Tracker and transferred into the Position Tracker.

This is the point where the bot considers the position to be live.

Only now do we begin tracking profit and loss, confirmations, updates, and stop-loss conditions.

3. Position Tracking

The Position Tracker monitors:

Position updates
Order confirmations
Position lifecycle events
Profit & Loss (P&L)
Stop-loss conditions

When the position becomes fully confirmed, the stop-loss logic is activated.

The Position Monitor

The Position Monitor continuously watches every live position.

Its responsibilities include:

Start Monitoring

Registers a newly opened position and begins listening for updates.

Handle Price Changes

The monitor subscribes to events published through the event bus.

Whenever market prices or user events arrive, the position state is updated.

Calculate Position P&L

Every update recalculates the current profit or loss.

This value drives both:

Take-profit logic
Stop-loss logic

Generate Exit Actions

If the unrealized loss exceeds the configured stop-loss threshold, the Position Monitor generates an exit action.

Rather than placing the order directly, it publishes the action to the execution layer, keeping the architecture modular and easy to extend.

Current Stop-Loss Implementation

The current implementation exits positions using Fill-or-Kill (FOK) orders.

A Fill-or-Kill order must execute immediately in full. If sufficient liquidity isn't available, the order is automatically cancelled.

While this works well for testing, Raven will eventually allow users to choose between multiple execution strategies depending on their own trading preferences.

The goal is to make every aspect of execution configurable rather than hardcoded.

A Real Trading Example

To validate the stop-loss system, I tested it using real trades.

The objective wasn't to make money—it was to verify that the system behaves correctly under live market conditions.

In one example, I bought five shares at 0.55.

The Position Monitor immediately began tracking the position and calculating its P&L.

The stop-loss threshold for this test was approximately 10%.

When the position crossed that threshold, the bot generated an exit signal and submitted a Fill-or-Kill sell order.

Unfortunately, the market moved almost instantly.

Although the order was submitted at the best bid, liquidity disappeared before the exchange could fill it.

The order was therefore cancelled.

This perfectly demonstrates why monitoring execution is so important.

Submitting an order doesn't guarantee that it will execute.

Automatic Recovery

Instead of assuming the stop-loss had worked, the system continued monitoring the order status.

When it detected that the Fill-or-Kill order had been cancelled, it immediately submitted another exit order using the updated market price.

The second order executed successfully.

By the time the position closed, however, the loss had grown to around 20%, even though the stop-loss threshold had originally been 10%.

While that wasn't the desired trading outcome, it validated the architecture.

The system correctly detected the failed execution, kept monitoring the position, and successfully exited on the next opportunity.

Building for Real Markets

One lesson becomes obvious when building trading infrastructure:

Markets don't always behave the way we'd like.

Liquidity disappears.

Orders fail.

Prices move between the moment you decide to exit and the moment your order reaches the exchange.

A robust trading system must continuously monitor these events and respond intelligently when execution doesn't go as planned.

Simply submitting a stop-loss order isn't enough.

Building Raven

Everything described here is being built into Raven, a no-code strategy builder for prediction markets.

Instead of writing hundreds or even thousands of lines of Python, users will be able to visually construct automated trading strategies using connected nodes.

The goal is to make sophisticated prediction market automation accessible while still giving advanced users full control over execution, risk management, and strategy design.

I'm documenting the entire development process publicly on my YouTube channel, where I share architecture decisions, implementation details, and live coding sessions.

📺 Follow the development here: https://www.youtube.com/channel/UCY8IodKR5XCx3yJNGRZi0Sg

Open Source Resources

If you're interested in learning more about automated prediction market trading, I've also open-sourced a trading bot that demonstrates many of the concepts discussed throughout these articles.

💻 GitHub Repository:

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

TypeScript bot for Polymarket CLOB V2 5-minute BTC and ETH Up/Down markets. It monitors Chainlink strike/spot vs order books, enters momentum-aligned positions late in each epoch, optionally completes the opposite leg for a boxed pair, and manages exits with configurable risk rules.

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

Feel free to explore the codebase, experiment with it, or use it as inspiration for your own trading systems.

Final Thoughts

Building a reliable stop-loss system involves far more than triggering a sell order once losses reach a predefined percentage.

It requires continuously tracking the order lifecycle, monitoring position state, responding to exchange events, and handling failed executions gracefully.

By separating responsibilities between a Pending Order Tracker, Position Tracker, Position Monitor, and a dedicated execution layer, Raven provides a resilient architecture designed for the realities of live prediction markets.

As development continues, the framework will become increasingly flexible, allowing traders to configure execution behaviour, risk management, and strategy logic without writing code.

Why the Polymarket "MicroStrategy Sells Bitcoin by May 31, 2026" Market Is So Controversial

Emil Nielsen — Tue, 30 Jun 2026 16:32:58 +0000

Why the Polymarket "MicroStrategy Sells Bitcoin by May 31, 2026" Market Is So Controversial

One of the most disputed Polymarket prediction markets at the moment is "Will MicroStrategy (Strategy) sell any Bitcoin by May 31, 2026?" Despite evidence that the company sold Bitcoin during the specified period, the market is currently expected to resolve "No," leaving many traders confused and frustrated.

The market has already gone through multiple disputes. Two proposed resolutions have both concluded with "No," and the market is now awaiting a final review.

Why Many Traders Believe the Correct Answer Is "Yes"

The controversy began after Strategy disclosed in an SEC filing that it had sold 32 BTC between May 26 and May 31, 2026.

On the surface, this appears to satisfy the market's condition. The market description states:

"This market will resolve to Yes if MicroStrategy sells any of its Bitcoin by 11:59 p.m. ET on May 31, 2026."

Since the sale occurred within that time window, many participants naturally assumed the market should resolve to Yes.

The Key Issue: When the Sale Was Confirmed

The confusion comes from additional resolution criteria that many traders either overlooked or argue were added after the market launched.

The market specifies that its primary resolution sources are:

Official information from Strategy (formerly MicroStrategy)
On-chain data
A consensus of credible reporting

It also includes an important clarification:

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

This is where the dispute begins.

Although the Bitcoin sale took place before May 31, Strategy did not publicly disclose the transaction until June 1, 2026, when it filed an SEC Form 8-K. The filing announced that the company had sold 32 BTC during the period from May 26 through May 31.

Because the confirmation itself arrived after the market deadline, Polymarket currently appears to interpret the market as resolving No, even though the underlying transaction occurred before the cutoff.

Why Traders Are Upset

Many users argue that the market title and primary description strongly imply that the timing of the sale itself should determine the outcome—not the timing of its public disclosure.

Others point to the additional sentence regarding confirmation and argue that it fundamentally changes how the market should be interpreted.

Some members of the community have also suggested that this clarification may have been added after the market was created. While the market description shows it was updated after launch, it remains unclear exactly when the sentence regarding confirmation timing was introduced.

As of now, there has been no official public explanation from Polymarket addressing these concerns.

A Tool to Monitor Polymarket Opportunities

For traders interested in following Polymarket markets more closely, there are several open-source tools available. One example is the Polymarket Arbitrage Trading Bot, an open-source project that monitors Polymarket markets and identifies potential arbitrage opportunities using the platform's Central Limit Order Book (CLOB) API. It is intended for educational and research purposes and can be found on GitHub:

[Polymarket Arbitrage Trading Bot (GitHub)].

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

Waiting for the Final Review

The market remains under final review after multiple disputes.

If Polymarket maintains its current interpretation, the market will resolve No, based on the fact that official confirmation of the sale occurred on June 1 rather than by the May 31 deadline.

If reviewers instead focus on the actual date of the Bitcoin transaction, the market could still resolve Yes.

Until Polymarket issues its final decision—or provides further clarification—the market remains one of the platform's most controversial prediction markets, highlighting how seemingly small differences in resolution wording can have significant financial consequences for traders.

Polymarket’s New Fee Structure: Why Many Trading Bots May No Longer Be Profitable

Emil Nielsen — Wed, 24 Jun 2026 16:51:25 +0000

Polymarket has recently introduced a fee structure on its 15-minute crypto prediction markets, and the implications for algorithmic traders are significant. Many high-frequency trading strategies that previously relied on capturing tiny price inefficiencies may now face serious profitability challenges.

In this article, we'll break down how the new fee model works, what the Maker Rebate Program means for liquidity providers, and why many automated trading bots may need to adapt.

Understanding the New Polymarket Fees

The fee system primarily affects traders participating in the 15-minute crypto markets.

There are two types of participants:

Takers

A taker removes liquidity from the order book by executing immediately against existing orders.

Examples:

Market orders
Aggressive limit orders that execute instantly

Takers pay a fee based on the market price.

Makers

A maker adds liquidity to the order book by placing limit orders that remain available for other traders.

If another trader later fills that resting order, the maker may qualify for a rebate through Polymarket's Maker Rebate Program.

Why Fees Matter

The fee curve is particularly interesting because fees are highest when contracts trade around $0.50.

At the midpoint price:

Effective fee rate can reach approximately 1.56%
A $50 trade value may incur around $0.78 in fees

This is a major consideration for trading bots targeting small percentage gains.

Many market-making and arbitrage strategies are designed to capture profits of roughly 1% per trade. A fee structure that can exceed those gains fundamentally changes the economics of these systems.

The Maker Rebate Program

To encourage liquidity, Polymarket introduced a rebate mechanism.

According to Polymarket:

Place orders that add liquidity to the book and get filled by another trader.

Eligible makers receive rebates distributed daily in USDC.

The rebate pool is funded directly from taker fees collected in eligible markets.

What Changed?

Initially, the Maker Rebate Program reportedly distributed:

100% of collected fees back to makers

However, a later adjustment reduced this to:

20% rebate distribution

This means Polymarket now retains approximately 80% of collected fees while distributing the remaining portion to liquidity providers.

For traders who built strategies around collecting maker rebates, this change significantly impacts expected returns.

A Real-World Observation

Several traders observed that certain high-frequency accounts dramatically reduced activity around the same period the fee changes were introduced.

One account frequently discussed in the community appeared to stop operating shortly after the fee structure became active.

While correlation does not prove causation, it raises an interesting question:

Did the new fee structure eliminate the edge that powered these bots?

When profit margins are already thin, even small fee increases can completely erase expected returns.

The Unknown Variable: Order Resting Time

One area that remains unclear is how Polymarket determines rebate eligibility in practice.

Questions include:

How long must an order remain on the book before qualifying as maker liquidity?
What happens if an order is filled almost immediately?
Is there a minimum resting time requirement?
How exactly are rebates calculated across different order sizes?

These details are important because they directly affect strategy design.

The Mystery of "C"

Another interesting point is the fee documentation's reference to a variable called C.

The fee charts show multiple curves:

C = 10
C = 50
C = 100

However, documentation does not clearly explain:

What C represents
Whether it relates to position size
Whether it corresponds to share quantity
How larger values impact fee calculations

Understanding this variable is essential for accurately modeling trading costs.

If anyone has a definitive explanation of what C represents, it would be valuable information for the trading community.

What This Means for Bot Developers

Bot developers should now carefully evaluate:

1. Net Profit After Fees

Gross profitability is no longer enough.

Strategies should be backtested using the actual fee schedule.

2. Maker vs Taker Behavior

Reducing taker executions may become critical.

Providing liquidity could improve performance if rebates remain meaningful.

3. Fill Probability

A resting order that never executes generates no revenue.

Balancing execution probability with maker rebates becomes a key optimization challenge.

4. Market Selection

Currently, fees only apply to:

15-minute crypto markets

Future expansion of the fee model to other markets could further impact automated trading strategies.

Open-Source Trading Bot for Research

If you're interested in studying or building Polymarket trading systems, this open-source project is a useful starting point:

GitHub Repository:

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

The project demonstrates how automated trading can be implemented for Polymarket BTC and ETH markets and can serve as a learning resource for developers exploring prediction-market automation.

Watch the Full Analysis

For a detailed walkthrough of the fee structure, maker rebates, and potential impacts on trading bots, watch the full YouTube video:

https://www.youtube.com/watch?v=7HXoCMMXr-8

Follow for More Trading Bot Research

I regularly analyze:

Polymarket trading strategies
Prediction market opportunities
Algorithmic trading systems
Market-making techniques
Crypto automation tools

Subscribe to my YouTube channel:

https://www.youtube.com/channel/UCY8IodKR5XCx3yJNGRZi0Sg

Final Thoughts

Polymarket's new fee structure introduces a major shift for automated traders.

While maker rebates can offset some costs, the reduction from 100% rebate distribution to 20% significantly changes the economics of many strategies.

For traders relying on small spreads and frequent executions, profitability may now depend heavily on liquidity provision, execution quality, and careful fee optimization.

The days of ignoring transaction costs on prediction markets may be over.

As the ecosystem evolves, successful bot developers will need to treat fees as a core component of strategy design rather than an afterthought.

Three Major Updates from Polymarket: Rebates, Parlay Trading, and a New Developer SDK

Emil Nielsen — Tue, 23 Jun 2026 20:19:35 +0000

Polymarket has recently introduced a series of updates that could significantly impact traders, developers, and anyone tracking the ecosystem for potential incentive programs or future token distribution.

This breakdown covers three key announcements: a new taker fee rebate system, upcoming combinatorial (parlay-style) markets, and a major overhaul of Polymarket’s developer SDKs.

1. Taker Fee Rebate Program (Up to 50%)

Polymarket is rolling out a tier-based rebate system for taker trades, where users can earn up to 50% fee rebates depending on their 30-day weighted trading volume.

Tier Structure (simplified)

Bronze → minimal volume thresholds
Silver / Gold → mid-tier volume rewards
Platinum / Diamond / Obsidian → highest tiers

At the top end:

$10M+ 30-day volume → up to 50% fee rebate

Key Mechanics

Rebates are based on weighted volume, not raw volume:

Trade size × (1 − entry price) × category weight × bonuses

Example intuition

Trades further out-of-the-money (e.g. 5% probability events) generate higher weighted volume
Category multipliers apply:
- Sports: 1.0
- Politics: 1.3
- Crypto: 2.3
- Geopolitics: free / special handling

This design strongly incentivizes:

Taking directional risk further from midpoint pricing
Trading higher-weighted categories like crypto and politics

Why this matters

There is growing speculation that:

These tiers may later connect to a future POLY token airdrop
“Future rewards” language suggests retroactive incentive design

While unconfirmed, similar systems in crypto have historically preceded token distributions.

2. Combinatorial Markets (Parlay-Style Contracts)

Another major rumored feature is the introduction of combinatorial outcome contracts, similar to sports parlays.

How it would work

Combine multiple outcomes into one position
All legs must be correct for payout
Missing one = position goes to zero
Odds multiply across legs

Example

Team A wins + Player scores + Over X points → one combined bet

Why it matters

This introduces:

Higher leverage-style payoff structures
Increased engagement for sports markets
Potential massive liquidity expansion during events like the World Cup

There are indications that:

A World Cup-focused feature release is planned
Early internal references suggest strong emphasis on sports expansion

If rolled out broadly, this could be one of the biggest structural upgrades to prediction markets in years.

3. New SDK: Unified Python + TypeScript Infrastructure

Polymarket is also releasing a new unified developer SDK (Python + TypeScript), replacing older fragmented tooling.

Key improvements

Single client architecture
Strongly typed market primitives:
- Market
- MarketState
- Outcome
- Resolution
Built-in pagination for gamma markets
Cleaner error handling
Market discovery tools:
- Events
- Tags
- Sports categories
- Search
Real-time data streams (prices + events)

Why developers care

Previously, working with Polymarket APIs required:

Reverse engineering payloads
Handling inconsistent responses
Multiple SDK fragments

Now:

Cleaner abstraction layer
Easier bot development
More reliable data pipelines

This is especially relevant for:

Arbitrage bots
Market-making strategies
Automated trading systems

Useful Resources

Trading Bot (GitHub)

A practical example of automation and trading infrastructure:
[Polymarket Trading Bot (BTC/ETH/M Bot)]

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

Full Video Breakdown

Watch on YouTube

YouTube Channel

Channel Page

Final Thoughts

These updates point to three major strategic directions for Polymarket:

Stronger trading incentives (rebates + tier system)
Expansion into more complex derivatives (parlays / combinatorial markets)
Serious infrastructure scaling for developers and bots (new SDK)

If these features roll out as described, they could significantly increase trading volume and reshape how users interact with prediction markets—especially around high-profile events like the World Cup.

Building Visual Trading Strategies for Prediction Markets with Raven

Emil Nielsen — Mon, 22 Jun 2026 14:57:28 +0000

Prediction markets are becoming increasingly popular as traders look for new ways to express market views beyond traditional financial instruments. While many traders rely on custom scripts and coding expertise, a new approach is emerging: visual strategy builders.

In a recent development update, Raven showcased an MVP focused on making prediction market automation more accessible through a visual node-based strategy builder.

Why Visual Strategy Builders Matter

Most automated trading systems require users to write code, manage APIs, and handle deployment. This creates a significant barrier for traders who have strong market knowledge but limited programming experience.

Raven takes a different approach by allowing users to create strategies using drag-and-drop nodes. Instead of writing hundreds of lines of code, traders can connect data sources, signals, conditions, and actions visually.

The goal is rapid experimentation and strategy iteration without needing to become a software engineer.

Current Data Sources

The current version supports:

Venue selection
Market selection
Dynamic market filtering
Polymarket integration

The architecture is designed to support additional prediction market venues in the future, opening possibilities such as cross-venue arbitrage strategies.

For example, users can select specific markets like Bitcoin price prediction markets or define broader criteria that automatically discover matching markets.

Trading Signals Available

Raven currently exposes several categories of signals.

Order Book Signals

Users can access:

Best Bid
Best Ask
Mid Price
Spread

These values update in real-time using Polymarket WebSocket data.

Candlestick Data

Strategies can use:

Open
High
Low
Close
Volume

Across multiple timeframes:

1 minute
5 minutes
15 minutes
30 minutes
1 hour
4 hours
1 day

This enables traders to build familiar technical-analysis-based strategies inside prediction markets.

Moving Averages

The platform currently includes:

EMA (Exponential Moving Average)
SMA (Simple Moving Average)
VWAP (Volume Weighted Average Price)

These indicators allow users to implement trend-following and mean-reversion systems visually.

Creating Strategy Logic

The real power comes from combining signals with conditions.

Threshold Conditions

Example:

Buy when the best ask is greater than 0.70
Sell when the spread exceeds a specific value

Signal Comparisons

Users can compare:

Order book signals
Candlestick values
Moving averages

and generate true/false outputs that drive trading decisions.

Formula Nodes

Formula nodes allow traders to create custom expressions by mapping multiple signals into variables.

Example:

(Ask Price + Open Price) < 0.99

This makes it possible to create sophisticated decision rules without writing code.

Code Nodes

For advanced users, code nodes provide additional flexibility and can return:

Boolean values
Numeric values

These outputs can be chained into other conditions and strategy components.

Combining Conditions

Raven supports logical operations such as:

AND

Require multiple conditions to be true before executing a trade.

Example:

Ask price above threshold
Moving average crossover confirmed

OR

Execute when any qualifying condition becomes true.

This allows traders to create layered decision trees visually.

Trade Execution

Once conditions are met, users can trigger actions.

Supported actions include:

Buy Orders

Options include:

Market orders
Limit orders
Best Bid pricing
Best Ask pricing
Midpoint pricing
Custom price offsets

Sell Orders

Similar flexibility is available for exits and profit-taking.

Position Sizing

Strategies can specify:

Fixed amounts
Percentage of capital
Dynamic sizing rules

Lifecycle Automation

One particularly interesting feature is lifecycle hooks.

A strategy can react to events such as:

Order filled
Order cancelled
Order rejected
Order timeout

For example:

Buy position
Wait for fill
Automatically place a profit-taking sell order

This enables fully automated trade management without manual intervention.

Repeatable Strategy Flows

The repeat node allows a strategy to restart after a position closes or after specific lifecycle events occur.

This is useful for continuously scanning markets and re-entering when new opportunities appear.

Inspiration from Existing Polymarket Trading Bots

If you're interested in building automated prediction market strategies today, a great open-source resource is:

Polymarket Trading BTC ETH M Bot:

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

The repository demonstrates how automated trading systems can interact with Polymarket markets and provides valuable insights for traders exploring prediction market automation.

Raven aims to bring many of these capabilities into a visual environment where strategies can be built without extensive coding.

The Future of Prediction Market Automation

Prediction markets are still in their early stages compared to traditional financial markets. As liquidity and participation continue to grow, tools that simplify automation could significantly expand adoption.

Visual strategy builders lower the barrier to entry and allow traders to focus on ideas rather than infrastructure.

Features such as:

Real-time order book analysis
Technical indicators
Automated execution
Lifecycle management
Multi-market support

could make prediction market trading more accessible than ever.

Watch the Full Demo

You can watch the original walkthrough here:

https://www.youtube.com/watch?v=4smiJ_YAev8

Follow for More Content

For more updates on prediction markets, trading automation, algorithmic strategies, and Raven development, check out my YouTube channel:

https://www.youtube.com/channel/UCY8IodKR5XCx3yJNGRZi0Sg

What do you think is the most important feature for a visual trading strategy builder? Let me know in the comments.

The Hidden Arbitrage Opportunities Emerging Between Polymarket and Kalshi

Emil Nielsen — Sat, 20 Jun 2026 17:06:22 +0000

Prediction markets have evolved far beyond simple betting platforms. While most participants focus on forecasting outcomes, some traders are discovering that the real edge lies in understanding market mechanics, liquidity inefficiencies, and resolution processes.

A recent development that caught my attention is Kalshi's launch of short-duration crypto prediction markets, allowing traders to speculate on 15-minute price movements for Bitcoin, Ethereum, and Solana. These markets closely resemble products already available on Polymarket, opening the door to a fascinating opportunity: cross-platform arbitrage.

Arbitrage Between Polymarket and Kalshi

In theory, prediction markets should be efficient.

For every market:

YES Price + NO Price = $1.00

However, markets are rarely perfectly efficient, especially when liquidity is fragmented across different platforms.

Imagine the following scenario:

Polymarket YES = $0.47
Kalshi NO = $0.50

Total cost = $0.97

Since one of these outcomes must eventually win, a trader can theoretically lock in a guaranteed payout of $1.00 while only spending $0.97, creating a 3% arbitrage opportunity.

As Kalshi and Polymarket continue offering similar crypto markets, monitoring these discrepancies could become increasingly profitable.

The challenge, however, is speed.

By the time a human identifies the opportunity and manually places trades, the market may have already corrected itself.

This is where automation becomes essential.

Why Trading Bots Matter

Successful arbitrage often depends on milliseconds rather than minutes.

A bot can continuously monitor:

Market prices
Liquidity levels
Spread differences
Resolution status
Order book changes

and execute trades automatically when predefined conditions are met.

For traders interested in building automated strategies, this open-source repository provides a useful starting point:

GitHub Repository:

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

The bot focuses on Polymarket's crypto prediction markets and demonstrates how automated trading systems can interact with market data and execute trades programmatically.

While originally designed for market-making and trading strategies, the same infrastructure could potentially be adapted for arbitrage opportunities across multiple platforms.

The "Bond Market" Strategy Nobody Talks About

Perhaps even more interesting than arbitrage is what many experienced Polymarket traders call the "bond market" strategy.

At first glance, these trades look ridiculous.

You might see someone risking:

$100,000 to win $100
$1,000,000 to win $1,000
$7,000,000 to win $15,000

Most people immediately ask:

"Why would anyone risk millions for such tiny returns?"

The answer lies in understanding how market resolution works.

Understanding UMA Resolution

Polymarket relies on UMA's oracle system to determine market outcomes.

The simplified process looks like this:

An outcome is proposed.
A challenge period opens.
Participants may dispute the outcome by posting a bond.
UMA token holders vote on the truth.
After successful validation, the market becomes finalized.

Many traders only pay attention to Polymarket's interface.

However, there can be a delay between:

UMA officially finalizing an outcome
Polymarket displaying the market as fully resolved

This creates a temporary inefficiency.

Where the Alpha Exists

Once UMA has finalized an outcome, some traders argue that the probability of reversal is effectively zero.

Yet during the transition period, Polymarket users may still be willing to sell winning shares below their final payout value because they want immediate liquidity.

For example:

Winning YES share trades at $0.999
Final payout = $1.00

A trader can purchase these shares and wait for final settlement.

The profit per trade appears tiny:

$0.001 per share

But the strategy becomes powerful when scaled.

Example

Investment: $1,000

Return per trade: 0.1%

Profit: $1

Repeat daily:

$1 × 365 days = $365

Annual return:

36.5% APY

The key insight is that the trade isn't dependent on predicting outcomes.

The outcome has already been determined.

The trader is simply providing liquidity during the final resolution phase.

The Importance of Risk Management

This is where many traders misunderstand the strategy.

Buying a market at 99% before the outcome is fully known is completely different from buying a market at 99.9% after UMA has finalized the outcome.

The first trade still contains outcome risk.

The second trade may simply contain settlement delay risk.

If there remains any possibility of reversal, a single loss can wipe out years of small gains.

This is why understanding the underlying resolution mechanics is critical.

The Whale Strategy

Several large Polymarket traders appear to be exploiting these inefficiencies at scale.

Examples shared publicly include positions such as:

$1 million to earn roughly $1,000
$6 million to earn several thousand dollars
$7 million to earn approximately $15,000

These trades seem absurd until viewed through the lens of annualized returns.

A 0.1% gain executed repeatedly can produce substantial returns on large capital bases.

The strategy resembles fixed-income investing more than traditional speculation.

Instead of predicting the future, participants are harvesting inefficiencies created by market structure.

The Future: Automated Resolution Arbitrage

The most exciting opportunity may be combining both ideas:

Cross-platform arbitrage between Kalshi and Polymarket.
Resolution arbitrage during UMA settlement windows.

An automated system could:

Monitor Kalshi and Polymarket simultaneously.
Detect YES/NO pricing mismatches.
Track UMA resolution events.
Purchase underpriced winning shares.
Execute trades automatically.

As prediction markets continue growing, inefficiencies are likely to become more competitive, making automation increasingly important.

The traders who understand both market mechanics and software development may have a significant edge.

Watch the Full Discussion

YouTube Video:

https://www.youtube.com/watch?v=pGYNN-sEUVw

Follow for More Prediction Market Research

My YouTube Channel:

https://www.youtube.com/channel/UCY8IodKR5XCx3yJNGRZi0Sg

I regularly cover:

Polymarket strategies
Prediction market arbitrage
Trading bots
Crypto market opportunities
Automated trading systems
AI-assisted market analysis

As always, this article is for educational purposes only and should not be considered financial advice. Prediction markets carry risk, and traders should fully understand resolution mechanics, liquidity constraints, and platform-specific rules before deploying capital.

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]
DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket
Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

TypeScript bot for Polymarket CLOB V2 5-minute BTC and ETH Up/Down markets. It monitors Chainlink strike/spot vs order books, enters momentum-aligned positions late in each epoch, optionally completes the opposite leg for a boxed pair, and manages exits with configurable risk rules.

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99
BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4
Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features
- Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
- Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
- High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
- Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
- Paper trading — simulated fills without live CLOB orders
- Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
- Deposit wallet…
View on 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

DextersSlab / Polymarket-Arbitrage-Trading-Bot

Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket

Polymarket Arbitrage Trading Bot (BTC, ETH Momentum Arbitrage trading bot)

You can check this bot pnl with this account.

https://polymarket.com/@9g9g99

BTC-ETH-Momentum-Arbitrage-Bot-9g9g99.mp4

Built with @polymarket/clob-client-v2 and Node.js 20+. See V2_MIGRATION.md for Polymarket exchange upgrade notes.

Features

Dual-market confirmation — BTC and ETH must align before entries (reduces false signals)
Chainlink Data Streams — strike at epoch open + live spot for spot_minus_strike
High-frequency monitor — REST CLOB /book polling (~150ms), merged btc/eth wave logs
Six strategy phases — buy1, buy2, buy3, buy4, risk1, risk2, risk3
Paper trading — simulated fills without live CLOB orders
Optional redeem — gasless redeem via Polymarket builder relayer after epoch end
Deposit wallet…

View on GitHub

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/DextersSlab/Polymarket-Arbitrage-Trading-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.

In this article, we can learn general methods and strategies for the Polymarket 5-minute rise/fall cryptocurrency market trading bot. #polymarket #trading #bot

Emil Nielsen — Mon, 15 Jun 2026 08:23:16 +0000

Benjamin-Cup

Jun 14

How to Build a Polymarket Trading bot: 5-Minute Crypto Up/Down Market Trading Bot in Python

#tutorial #python #automation #architecture

6 min read

In this article, we can learn general methods and strategies for the Polymarket 5-minute rise/fall cryptocurrency market trading bot.

Emil Nielsen — Mon, 15 Jun 2026 08:21:25 +0000

Benjamin-Cup

Jun 14

How to Build a Polymarket Trading bot: 5-Minute Crypto Up/Down Market Trading Bot in Python

#tutorial #python #automation #architecture

6 min read