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Coinvestor Launches New Algorithmic Trading Strategy for Global Investors

Coinvestor, a leading global digital asset investment platform, has launched three new quantitative strategies that showcase advancements in algorithmic trading. This release allows Coinvestor to provide a broader range of investment opportunities and consistent returns to global investors.

The first strategy, ZOLBO ($ZLB), developed by Alchemi Lab, is an AI-based trading strategy that utilizes probabilistic deep learning models to analyze and process online data. This extensively tested and validated strategy has become the longest-running and most reliable approach at Alchemi Lab, focusing on trading opportunities driven by statistically significant relative price volatility.

The second strategy, Helios Finance Alpha ($HFA), developed by Helios Finance, incorporates the MartinGale probability theory, popularized by Larry Williams. It features a reverse hedge mode for risk management and employs an algorithm that captures even the smallest price movements. The objective is to lower the average cost of positions and achieve rapid profit realization.

Lastly, Eclipse Momentum ($ECM), developed by Levroxy, is an algorithmic trading strategy that leverages artificial intelligence to identify patterns and predict price movements in specific market conditions. With a focus on altcoins excluding Bitcoin and Ethereum, it aims to generate profits for investors. The initial round of investment for these three strategies is now open until July 19 on Coinvestor's official website.

Coinvestor is a digital asset investment platform headquartered in Singapore. The platform has secured funding from prominent venture capitalists, including SoftBank and KB Investment. Committed to establishing a reliable digital asset investment infrastructure, Coinvestor is actively strengthening partnerships with experts in algorithmic trading, research, and community engagement.

This article does not necessarily reflect the opinions of the editors or management of EconoTimes

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