Kvants Whitepaper
  • Kvants
    • Introducing Kvants
      • Foreword from our CEO
      • Our Mission
      • Market Research
      • Market Opportunity
      • AI Driven Quantitative Trading Models
        • AI in Quantitative Trading Models
        • Advancements in Predictive Analytics
        • Sophisticated Pattern Recognition Techniques
        • Enhanced Market Trend Forecasting in AI-Driven Quantitative Trading Models
        • Real-time Adaptability and Decision Making in AI-Driven Quantitative Trading Models
      • Introducing a new way to generate Alpha.
    • Kvants - Investment Enabled Marketplace for AI-Driven Quant Trading Strategies
      • Kvants App (Kvants Plus+)
      • Selecting an AI-driven Quantitative Trading Strategy
        • Kvants Robo Advisor
        • Quant Strategy Onboarding Due-Diligence Process
      • Stake a $KVAI Tier
      • Investing via Centralized Exchanges
        • API Trading
        • Connecting your exchange
          • Binance
          • OKX
          • KuCoin
          • ByBit
          • Kraken
          • HTX
          • MEXC
      • Investing via DeFi Quant Vaults
        • Funding Smart Contract
          • Strategy Pools
          • Strategy Vaults
        • Funds Operation Smart Contract
        • Strategy Connector (Database + Smart Contract)
        • Mid-Layer Risk Management Server for External Quant Strategies
        • Advantages of DeFi Vaults for Investors
      • Democratizing the world of Quantitative Finance
      • Kvants+ Pro - Securitized Semi-Fungible Tokens
        • Regulatory Compliance
          • Legal opinion for functioning as an Investment Enabled Marketplace
          • BVI Incubator Fund Characteristics
          • UAE Regulations for Marketing Foreign Investment Funds
      • How Kvants AI Works: A Simplified Overview
    • $KVAI Token
      • Tokenomics
      • Token Economy
    • The DAO
    • Roadmap
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  1. Kvants
  2. Kvants - Investment Enabled Marketplace for AI-Driven Quant Trading Strategies
  3. Investing via DeFi Quant Vaults

Mid-Layer Risk Management Server for External Quant Strategies

The mid-layer server will be deployed on external trading strategies to protect our investors against malicious trading signals executed by external quant funds that could have an ill-intent.

The mid-layer is added as a risk parameter to protect the user's funds in omnichannel and single-chain environments.

Such as the dYdX perpetual futures from malicious or out-of-the-ordinary trade signals that might be sent by quant hedge funds, incorporating a "connector container" as a mid-layer offers additional risk management and control. This connector container is an intermediary between third-party trading strategies and the dYdX account where the user's assets are held. The primary purpose of this mid-layer is to introduce enhanced risk management parameters, allowing quantitative analysts Kvants to fine-tune trading strategies according to specific risk profiles.

Here is how this system operates in a technical context:

Integration with Third-Party Strategies

  • The trading server, already connected to the dYdX account via API keys, hosts the connector container.

  • This container interfaces with third-party trading algorithms or strategies. These strategies are developed externally but are intended to operate on the assets held in the dYdX account.

Risk Management Functionalities

  • Within the connector container, kvants can specify various risk management parameters. These include setting maximum drawdown limits, defining stop-loss thresholds, or adjusting leverage levels.

  • The container is programmed to continuously monitor and enforce these parameters, ensuring the trading activities align with the predefined risk tolerance.

Execution of Trades

  • When a third-party strategy generates a trade signal, this signal passes through the connector container.

  • The container validates the trade against the set risk parameters. If a trade violates these parameters, it can be automatically modified or rejected, thus adding a crucial layer of risk control.

Technical Implementation

  • The connector container might be implemented as a microservice or a set of smart contracts. It can be programmed in languages such as Python for algorithmic logic and Solidity for smart contract interaction.

  • The interface with third-party strategies and the dYdX account would likely involve API calls and smart contract functions. For instance, a Python script could analyze trade signals against risk parameters and call a smart contract to execute compliant trades.

Technical Implementation KvantsAI

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Last updated 1 year ago