After the new Polymarket regulations are announced, how to build a trading bot that is not banned and continues to generate profits?

On February 18, 2026, the decentralized prediction market platform Polymarket quietly removed its long-standing 500-millisecond Taker quote delay and fully implemented a dynamic fee mechanism without prior announcement. This technical adjustment, dubbed a “silent coup” by the community, caused over half of the existing trading bots on the platform to become invalid overnight. The myth of the “one-month $515,000 profit with 99% win rate” delay arbitrage strategy also came to an end as fees exceeded the bid-ask spread.

This change is more than just parameter adjustments; it signifies a shift in the platform’s underlying logic: advantages are moving from Taker (order-consuming) arbitrage to Maker (order-placing) market making and liquidity provision. This article will systematically analyze the background, data implications, public sentiment, and technical implementation to show how, under the new rules, a compliant, efficient, and sustainably profitable Polymarket trading bot can be built.

Policy Background and Timeline

Understanding this new regulation requires placing it within Polymarket’s policy evolution over the past two months. It is not an isolated event but a targeted crackdown on “delay arbitrageurs.”

• Early January 2026: Polymarket suddenly announced a dynamic Taker fee for 15-minute digital currency markets, calculated as Fee = C × 0.25 × (p × (1-p))², where the fee peaks near 50% probability at about 1.56%. Initially, to appease market makers, 100% of the fee was refunded as a rebate to Makers.
• January 11–18, 2026: The platform observed high-frequency bots beginning to exit, with total fees collected decreasing. The platform then adjusted policy, reducing Maker rebate from 100% to 20%, testing market response.
• February 18, 2026: A pivotal turning point. Polymarket implemented two major changes simultaneously: first, removing the long-standing 500ms Taker delay; second, extending the fee mechanism to sports markets like NCAA and Serie A, marking the normalization of fee structures.

The causal chain of these actions is clear: widespread delay arbitrage bots eroded market maker profits → market makers exited, leading to liquidity drought → platform introduced fees to drive out low-quality arbitrageurs → removing delay and reintroducing rebates shifted the ecosystem’s focus back to genuine market makers.

Data and Structural Analysis

The impact of these new rules on the market microstructure is revolutionary. We can understand this through two key data dimensions:

Disappearance of Delay and Order Book Dynamics

Previously, the 500ms delay served as a “safety buffer” for Makers, giving them enough time to withdraw expired quotes when prices moved. Removing the delay means that once a Taker clicks, the trade executes immediately, with no window for cancellation. This implies that if your cancel-and-replace cycle exceeds 200ms, you face serious adverse selection risk—others can eat your stale orders before you can update.

Shift in Pricing Power of Fee Curves

The introduction of dynamic fees altered the cost formula for arbitrageurs. In critical probability ranges (45%-55%), Taker costs spike to 1.56%. For arbitrage bots relying on millisecond bid-ask spreads (usually below 1%), this is fatal.

Data confirms: after fee introduction, Polymarket’s total fee volume halved, directly indicating a mass exodus of high-frequency arbitrage bots. The remaining space is now waiting for a new generation of Maker bots to fill.

Public Sentiment Analysis

Post-announcement, opinions are sharply divided.

Mainstream View 1: The “Money Printer” Era Is Over

The community generally believes that the era of riskless arbitrage based on information asymmetry has ended. The widely circulated “money printer” tutorials (e.g., exploiting Binance and Polymarket spreads) are now obsolete. Most retail traders feel the barrier has been raised, making simple arbitrage impossible.

Mainstream View 2: A Cleanup of “Scientists”

Some market makers and veteran players welcome the move. They see Polymarket’s actions as a cleanup, removing “scientists” (geeks exploiting technical advantages) who only profit from system vulnerabilities, restoring fairness. As analyses suggest, the platform’s role is to provide a fair arena, and these new rules are a patch to that “game.”

Controversy: Redefining Fairness

Some argue that removing delay increases Taker certainty, but the faster cancel-replace cycle now demands (150ms), raising the entry barrier from “coding skills” to “owning low-latency VPS and infrastructure.” Does this constitute a new form of unfairness? Currently, such infrastructure-based unfairness is accepted in HFT circles.

Reality Check on the “Polymarket Bots Suppression” Narrative

The narrative that “Polymarket is fighting bots” needs clarification. The fact is: Polymarket targets specific bots—namely, Taker bots that do not provide liquidity and exploit delay vulnerabilities for predatory arbitrage.

The platform is not anti-bot but selective: through dynamic fees and rebates, it incentivizes market making. The new rules are effectively calling for a new generation of bots—those willing to place bilateral orders, provide depth, and compress cancel-replace cycles within 100ms.

Thus, “bots not banned” does not mean stopping automation but aligning bot behavior with the platform’s long-term interests (liquidity, low slippage). Market makers are now “insiders,” while arbitrageurs are the “targets” for banning.

Industry Impact Analysis

Polymarket’s adjustments could set a precedent for prediction markets and broader DeFi.

A Watershed for Specialization

Bot development will shift from amateur scripting to low-latency system engineering. Languages like Rust, with performance advantages (e.g., zero-allocation hot paths, SIMD JSON parsing via polyfill-rs), will replace Python as the core development language.

Emergence of AI Agents

Notably, on February 19, the day after fee adjustments, Polymarket released CLI tools designed for AI integration. This hints at a future where AI agents participate—beyond human vs. machine, to machine vs. machine. Future bots may incorporate ML pipelines, such as predicting prices over 5 seconds based on real-time order book data, to secure optimal order placements at $0.90–$0.95.

Lessons for Centralized Exchanges

For centralized exchanges like Gate.io, Polymarket’s experiment demonstrates how economic models (tiered fees, rebates) and technical parameters (latency control) can fine-tune market microstructure, combat harmful behaviors, and protect liquidity providers. Such strategies could improve order book health and user experience.

Evolutionary Scenarios

Based on current logic, several future scenarios for Polymarket’s bot ecosystem can be envisioned:

Scenario 1: Dominance of High-Performance Market Makers (Baseline)

Bots focus on low-latency architecture and precise position management. They use WebSocket feeds, earn rebates on bilateral orders, and leverage 5-minute deterministic markets for “time arbitrage.” Market depth improves, spreads narrow.

Scenario 2: Rise of AI-Powered Prediction Models (Optimistic)

With improved CLI tools, AI agents flood in. They no longer rely solely on order book arbitrage but analyze news, on-chain data, and natural language to predict outcomes. Strategies evolve into “speed + intelligence” competitions. ML models predicting 5-second price moves are emerging.

Scenario 3: Arms Race and Regulatory Intervention (Risk)

Latency arms race intensifies—top players colocate servers closer to Polymarket’s matching engine. As prediction markets influence real-world events, risks of insider trading and manipulation grow. Cases like Israeli military personnel using confidential info for bets highlight potential regulatory crackdowns.

Conclusion

Polymarket’s new rules are not the end but a new chapter. For developers, building a “non-bannable” trading bot means adapting to the platform’s evolving logic: abandoning Taker arbitrage maps, embracing Maker market making.

This requires a comprehensive tech upgrade: switching from REST polling to WebSocket streams, embedding dynamic feeRateBps in order signatures, and optimizing cancel-replace cycles within 100ms. Incorporating machine learning for short-term price prediction will be key to capturing alpha.

In this race of rule-driven technological淘汰, survival favors those who understand risk and can provide value—those who build liquidity, not just chase speed.