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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