Developer Builds Inventory-Balanced Trading Bot for Prediction Market Polymarket

·2 views

A software developer has shared how they built an automated trading strategy for Polymarket, a prediction market platform where users trade on event outcomes like YES or NO. Rather than focusing solely on when to buy or sell, the strategy emphasizes post-trade management, including inventory control, hedging, and order lifecycle. The bot detects price trend reversals and executes trades in small cycles — for example, splitting a 100-share position across five cycles of 20 shares each — to reduce market impact. Protective rules automatically trigger aggressive position closures when hedges fail, markets near settlement, or orders remain unfilled beyond set time limits. The developer concludes that a trading signal alone is insufficient, and that a robust automated system must also handle execution, inventory balancing, and risk control.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

How Developers Can Organize and Share Project Links More Efficiently

Developers working on multiple projects quickly accumulate a large number of URLs, spanning repositories, documentation, dashboards, and public-facing resources. Managing these links through browser bookmarks alone is often insufficient, as bookmarks are designed for personal use rather than organized sharing and tracking. Separating private internal links from public-facing ones is a practical first step toward better link management. Tools like URL shorteners and link management platforms offer features such as custom slugs, click analytics, QR codes, and project-based collections to keep links organized. Grouping shortened links by project rather than managing them individually can make it easier to find, share, and update resources consistently.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Anthropic and OpenAI Launch Opus 5.5 and GPT-6 Sol on the Same Day

On September 22, 2026, Anthropic released Claude Opus 5.5 and OpenAI followed roughly 90 minutes later with GPT-6 Sol and a smaller companion model called Luna, marking an unplanned same-day launch from two leading AI companies. Independent benchmarks from Artificial Analysis showed Opus 5.5 outperforming GPT-6 Sol on quality metrics, including Terminal-Bench 4.0 scores of 52.5% versus 43.9%. However, GPT-6 Sol offered a significantly lower price point, with one developer analysis estimating it to be approximately 47% cheaper than Anthropic's offering. The two companies used different testing conditions in their official announcements, making direct comparisons difficult and highlighting the lack of a shared industry benchmark standard. Analysts note that the competitive focus in 2026 has shifted from raw capability to performance per dollar, with both firms emphasizing real-world task outcomes over leaderboard scores.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How WhatsApp Works: A Step-by-Step System Design Walkthrough

A DEV Community article documents a real adaptive learning session in which two developers, Pedro and João, collaboratively designed a WhatsApp-like messaging system from first principles. Rather than presenting concepts upfront, João guided Pedro through a series of engineering problems, letting solutions emerge organically from each challenge. The session covered key topics including WebSockets for persistent connections, load balancers for distributing traffic across multiple servers, and Redis as a shared state layer to track user-to-server mappings. Each solved problem revealed a new one, such as how to handle stale state in Redis when a user unexpectedly loses connectivity. The article aims to preserve this problem-driven learning process as a reusable educational resource for engineers studying distributed systems.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

1 in 7 Software Dependencies Has No Active Maintainer, Study Finds

An analysis of 8,943 software components found that 13.4%, or 1,202 packages, show signs of having no active maintainer. About 80% of these were flagged through inference — specifically, no release or repository activity in over four years — rather than through an explicit deprecation notice. The study covered 24 public repositories across five ecosystems including npm, PyPI, Maven, Gradle, and Go, with npm showing the highest share of unmaintained packages at 15.1%. Researchers argue the key concern is not whether a package is flawed, but whether a fix would ever arrive if a new vulnerability were discovered. The findings highlight a gap in standard security reporting, which typically assumes an active maintainer exists to issue patches.

0 comments Read more at DEV Community