Gate Products

Gate Prediction Market is an event trading product system built around major global events. Users can make predictions and trade future outcomes across different themes, including sports, crypto markets, macro events, and social trends. Unlike the traditional “fixed odds” model, prediction markets are closer to “probability pricing markets,” where event outcomes continuously change in response to market trading behavior.

Gate Prediction Market is a new type of event trading product built around trending events, probability trading, and market sentiment. Its core logic is to let users trade and evaluate future event outcomes through a mechanism where “prices reflect expectations.” As on-chain prediction markets develop rapidly, prediction markets are evolving from traditional prediction tools into infrastructure for information aggregation and probability pricing.

Gate Pre-IPOs is designed to let users access pre-IPO investment opportunities through a digital asset platform. At its core, it standardizes and digitizes what has traditionally been a private equity process. In the first SpaceX (SPCX) offering, users can subscribe using USDT or GUSD and receive SPCX allocations that are distributed fully unlocked upon completion. This lowers the barriers typically associated with primary market investing and allows broader participation through digital assets.

Over the past week, markets saw heightened volatility driven by recurring geopolitical tensions and stronger than expected economic data. Shifting signals from the United States and Iran pushed oil prices higher, with WTI rising more than 7% on the week. Solid retail sales, ISM readings, and nonfarm payrolls supported a rebound in equities, while precious metals reached new highs. Crypto assets moved in line with improving risk appetite, with Bitcoin approaching the $70k resistance level. On the flow side, BTC ETFs recorded modest weekly net inflows of about $22.3 million, an improvement from the prior week but still limited, while ETH ETFs continued to see net outflows. Quarter end rebalancing and holiday effects led to quick reversals after initial inflows. On chain and trading activity concentrated in oil and precious metals, DEX structure reshuffled, and Meteora volumes increased significantly. Stablecoin supply remained elevated, with USDC edging lower while DAI and USDS absorbed incremental flows, and A

Smart Leverage and futures trading are both common ways to gain leveraged exposure, but they differ significantly in their mechanisms and risk structures. Futures trading relies on a margin system, allowing users to set their own leverage levels, but it comes with liquidation risk. In contrast, Smart Leverage requires no margin and does not involve forced liquidation, as positions are managed through dynamic leverage and automated risk controls. Overall, futures trading offers greater flexibility but carries higher risk, making it more suitable for experienced traders, while Smart Leverage is simpler to use and better suited for beginners or users who want to reduce operational complexity.

Gate Smart Leverage is a trading tool that automatically creates and manages leveraged positions, allowing users to gain leveraged exposure without margin requirements or manual operations. Its core mechanism consists of three parts. First, it establishes underlying leveraged positions through derivatives markets. Second, it dynamically adjusts leverage based on market fluctuations to manage risk exposure. Finally, it incorporates automated risk control to avoid forced liquidation. Unlike leveraged ETF tokens, Smart Leverage does not use fixed multiples. Instead, it adapts to market conditions, offering greater flexibility in volatile or complex environments, though this also introduces uncertainty in returns.

Smart Leverage and ETF leveraged tokens are both margin-free leveraged products, but they differ fundamentally in how they operate. ETF leveraged tokens use fixed multipliers, such as 3x, and rely on rebalancing to maintain leverage, making them more suitable for trending markets. Smart Leverage, by contrast, uses dynamic leverage that automatically adjusts positions based on market volatility, making it better suited for choppy or complex conditions. In terms of risk, ETFs are more prone to volatility decay, while Smart Leverage exhibits path-dependent returns. Neither is inherently superior, the key lies in matching the tool to the market environment and trading strategy.

Smart Leverage is a trading tool built on dynamic leverage and automated risk control. Its effectiveness depends heavily on market conditions and how it is used. In trending markets, it can amplify gains by following the trend. In ranging markets, its dynamic rebalancing helps reduce risk. For short-term trading, it improves capital efficiency. It can also be applied to hedging strategies to lower portfolio volatility. However, it is not suitable for long-term holding or highly uncertain market conditions. Its core principle is simple: match the right scenario with the right strategy.

Although Smart Leverage requires no margin and eliminates liquidation risk, it does not mean it is risk-free. Its core risks stem from the uncertainty introduced by its dynamic leverage mechanism, along with exposure to market volatility, path dependency, and performance decay in choppy conditions. In extreme market scenarios, net asset value can still fluctuate sharply. At the same time, limited user control over leverage reduces strategic flexibility. In essence, Smart Leverage does not reduce risk, it reshapes it, making it more suitable for strategic use once its mechanics are fully understood.

Gate Smart Leverage is an automated leveraged trading tool that requires no margin and carries no liquidation risk. It allows users to gain amplified exposure through a system that automatically builds and manages leveraged positions, delivering a trading experience similar to spot trading. Unlike traditional futures trading, it relies on dynamic leverage and automated risk controls to adjust positions, reducing complexity and the likelihood of liquidation. Compared with leveraged ETF tokens, Smart Leverage does not use fixed multiples but instead adapts to market conditions, making it better suited for volatile or complex markets. However, returns are not amplified by a fixed multiplier, and market risk and uncertainty still remain.

Gate ETF, or leveraged tokens, are crypto financial instruments that “tokenize” leveraged trading. They allow users to gain amplified price exposure through spot trading without needing margin or a futures account. As the crypto derivatives market evolves, these products are increasingly used to improve capital efficiency and simplify risk management in trending markets.

Gate Skills Hub is a crypto skill management and distribution platform within the Gate AI ecosystem. It connects AI agents with blockchain data, trading capabilities, and automated strategies, enabling them to execute complex Web3 tasks and operate autonomously. Through standardized skill interfaces and modular design, Gate Skills Hub allows AI agents to invoke different types of on-chain and market capabilities, making it possible to build more scalable intelligent systems.

Gate Skills Hub is a skill invocation and execution platform within the Gate AI ecosystem. It connects AI Agents with on-chain data, trading capabilities, and automated task systems. Through modular skill architecture, it packages different functions into callable capabilities, enabling AI Agents to execute complex Web3 tasks and build automated workflows.

Gate Skills Hub is a crypto skill management platform within the Gate AI ecosystem. It organizes capabilities such as trading operations, on-chain interactions, data analysis, and automated scheduling into distinct skill types, forming a structured and modular system.

Gate Research: This paper focuses on the practical need for trade review in the crypto market. Based on the OpenClaw framework and Gate MCP capabilities, it develops an AI investment advisory system that automates the entire process from data ingestion and metric analysis to report generation. By introducing an agent-based architecture and modular tool invocation, the system enables AI not only to understand trading data but also to perform analysis and support decision-making, producing review reports that are both interpretable and actionable. Overall, this approach validates the potential of the “LLM + MCP + Agent” paradigm in financial scenarios, offering a feasible path for the engineering implementation of AI-driven investment assistance and laying the foundation for future evolution toward more intelligent and quantitatively driven decision systems.