Implementation

Agent-oriented design

Modular AI Agents: Each agent (Strategy, Execution, Risk, etc.) is encapsulated as a service or microservice with a clear responsibility. Agents can be specialized LLMs, RL-based systems, or traditional ML models wrapped in an agent interface, depending on the task at hand

Event-Driven Coordination: Agents communicate with each other via a messaging layer (e.g., using Zenoh for sub-millisecond pub/sub where possible). This allows for decentralized but orchestrated decision-making.

Continuous Learning Loop: Agents constantly incorporate real-time market data, risk metrics, and performance feedback, enabling the system to evolve and self-improve without manual intervention.

Infrastructure Leverage: Our existing infrastructure—data ingestion via Mage and Bytewax, low-latency streaming via RedPanda, and compute clusters in Hetzner (SLURM, S3, JuiceFS)—serves as the “toolbox” these agents can call upon.

Objective: Integrate these prototypes into a cohesive system using an agent framework like ROS or Python's Scikit-Multiagent, or another

Separation of concerns

• Data Layer: Managed in ClickHouse (hosted in Hetzner) as the central “Datahouse,” with additional real-time data streams from RedPanda and Market Data Adapter Nodes. Potentially vector-database for agent knowledge retrieval

• DevOps Layer: Automated provisioning and deployment (Ansible + GitHub Actions + Proxmox for dev environments), with trade boxes collocated on AWS & Alibaba in various regions for direct cross-connect to exchanges and cross-connectivity between them using BSO/Avelacom solutions

• AI/ML Layer: Specialized agents (hosted in our Rust-based core SDK environment or containerized) that rely on the data pipeline for insights and the DevOps pipeline for continuous integration and deployment. They use existing infrastructure as 'tools' or 'plugins'

• Observability & Monitoring: Loki for logging, Grafana dashboards, plus Bytewax’s streaming analytics provide real-time system health and performance insights.

• LLM Ops & MLOps: We’ll likely adopt Helicone or Phi-Data or similar for LLM ops, while more general ML workflow (model training, serving, metadata tracking) might be managed with a combination of Neptune, an ML experiment tracker, or a fully fledged MLOps platform (e.g., MLflow, Vertex AI, or a self-hosted solution integrated with our SLURM cluster).

• Coordination: a central mechanism that routes tasks to the appropriate AI agent and unifies the entire system.

Feedback Loop

Agents do not work in silos; they continuously feedback information to each other. For example, the Risk agent can instruct the Execution agent to reduce position size, or the Research agent might request additional data ingestion from the Data agent.

Core AI Agents and their responsibilities

1. Strategy Research Agent

   1. Tasks: Use our backtesting modules (SLURM cluster in Hetzner, S3/ JuiceFS for data) to develop, refine, and validate new trading strategies. Objectives: Discovering, developing, and improving trading strategies.

   2. Data Access: Reads historical data from ClickHouse or Postgres DB, orchestrated by Mage pipelines and Bytewax for real-time processing. Can also access raw data from binary files

   3. Consider STORM (Stanford Oval) for research

   4. ML Tools:

      1. Recommended Setup: A container-based ML environment on our HPC cluster with frameworks like PyTorch or TensorFlow.

      2. Experiment Tracking: Neptune or MLflow integrated with our HPC environment to log metrics and experiments.

      3. Feature Store or Database: Pulls feature sets, cleans data, and engineers new features.

      4. Hyperparameter Tuning: Could manage or orchestrate big parallel experiments (e.g., via Ray or Spark).

      5. Outputs:

         1. Proposes new alpha signals, factor models, or strategy improvements to the “Strategy Coordinator” (or to a dedicated “Approval” flow).

         2. Informs the Risk agent of expected drawdowns, volatility, etc. for the new strategies.

2. Execution Agent

   1. Tasks: Sends orders through our centrally unified SDK in Rust, leveraging collocated trade servers and low-latency cross-connects from AWS (or Alibaba)

   2. Data Inputs: Real-time market data streaming from RedPanda, Market Data Adapter Nodes (Websocket, Zenoh)

   3. Risk Constraints: Adjusts position sizing or halts trades based on signals from the Risk Management Agent.

   4. Observability: Publishes real-time execution metrics to Loki, visualized in Grafana.

3. Risk Management Agent

   1. Tasks: Monitors exposures, PnL, and real-time volatility across strategies. Tracks and controls market risk, credit risk, liquidity risk, etc. in real time.

   2. Data Layer: Pulls data from ClickHouse (positions, historical correlations) and real-time feeds from RedPanda.

   3. Integration: Instructs the Execution Agent to reduce or close positions if thresholds are breached; logs anomalies to Loki for subsequent review in Grafana.

   4. Communication with Strategy Research Agent: Feeds back realized risk metrics to refine strategies or highlight “risky” ones.

   5. ML Tools: Could run an anomaly detection model (e.g., an autoencoder or streaming ML approach in Bytewax) to identify outlier behaviour.

   6. Might have a domain-specific rules engine that triggers escalations to a human or an override agent.

4. Data Ingestion & Preprocessing Agent

   1. Primary Responsibility: Ensures data is well-ingested, cleaned, normalized, and stored in our database or data lake.

   2. Tasks: Automates the entire ETL flow from exchange data, on-chain sources, etc.

   3. Pipelines: Mage and Bytewax pipelines feed data into ClickHouse.

   4. Quality Control: Flags missing or corrupted data in real time, raising alerts through Zenoh to the Orchestrator Agent or Grafana alerts. Quality Checks: Statistical checks for outliers, missing fields, suspicious patterns.

   5. Observability: Pipeline metrics captured via Bytewax/Grafana dashboards for throughput and latency.

   6. Outputs: High-quality data sets for the Strategy Research agent or real-time data streams for the Execution agent.

5. Monitoring & Alerting Agent

   1. Tasks: Oversees the health of all agent modules, resource usage in HPC (SLURM) clusters, data pipelines, and DevOps environment (AWS, Alibaba).

   2. Tools: Uses Loki + Grafana for real-time logs and dashboards, triggers alerts (via Zenoh or Slack/Email integrations).

   3. Could also be partly rule-based or ML-driven (e.g., anomaly detection for system metrics).

   4. Notifies dev/ops or relevant AI agents when issues arise (e.g., exchange connectivity problems).

6. Reporting Agent

   1. System-wide reporting to stakeholders

   2. Performance Evaluation Agent: Assesses the performance of individual agents and the overall system using metrics like Sharpe ratio, return on investment (ROI), and capital efficiency.

7. Orchestrator (SuperAgent) - PM (portfolio manager) + a separate Judge Agent with scoring

   1. Primary Responsibility: Observes the entire system’s health, including latencies, error rates, data consistency, and keeps an audit log. Sits at the center, routes tasks among the other agents, ensures concurrency, and resolves resource conflicts.

   2. Tasks: Coordinates workflow across all specialized agents, handles concurrency, and resolves resource conflicts. Could serve as an “executive function,” deciding which agent to call for a given sub-task.

   3. Maintains the bigger picture: overall portfolio objectives (Sharpe ratio, capital allocation, etc.).

   4. Implementation: It is an advanced LLM-based system (managed by Helicone/Phi-Data for usage and logs) plus a rule-based system in Rust that delegates tasks to agents in a microservices approach. Might incorporate specialized logic or a specialized LLM to parse tasks at a high level, then delegate to each specialized agent.

   5. Connectivity: Communicates across agents via Zenoh for speed and resilience, references data from the rest of the stack as needed.

Each agent will be implemented as a separate module, using a programming language like Python. We'll use libraries and frameworks that are well-suited for the specific task each agent is responsible for. For example:

1. Execution Agent: Implemented using our SDK to interact with exchanges.

2. Strategy Agent: Implemented using machine learning libraries like scikit-learn, TensorFlow, or PyTorch to develop and improve trading strategies.

3. Risk Management Agent: Implemented using libraries like pandas, NumPy, and SciPy to analyze risk metrics and make decisions.

Knowledge Graph:

The Knowledge Graph will be implemented as a graph database like Neo4j or Amazon Neptune. This will allow us to store complex relationships between entities like markets, instruments, strategies, risk models, etc.

The Knowledge Graph will contain nodes representing:

1. Markets: Nodes representing different markets (e.g., cryptocurrency exchanges).

2. Instruments: Nodes representing tradable instruments (e.g., cryptocurrencies).

3. Strategies: Nodes representing trading strategies.

4. Risk Models: Nodes representing risk models used by the Risk Management Agent.

Edges between nodes will represent relationships like:

1. Market-Instrument Relationship: Edges connecting markets to instruments traded on those markets.

2. Strategy-Risk Model Relationship: Edges connecting strategies to risk models used by those strategies.

Data Flow & Lifecycle, Mapped to our Stack

1. Data Ingestion

   1. Real-Time Feeds: Market Data Adapter Nodes ingest raw data → Bytewax handles streaming transformations → RedPanda for message queuing → ClickHouse for storage.

   2. Batch Processes: Mage pipelines pull data into ClickHouse or Postgres for backtesting/training archives.

2. Strategy Development & Backtesting

   1. Strategy Research Agent runs large-scale backtests on the Hetzner HPC environment via SLURM.

   2. Leverages S3 + JuiceFS for distributed storage of historical data.

   3. Logs experiment metrics (e.g., PnL, drawdown, hyperparameters) to Neptune or a similar ML experiment tracker.

   4. Validated strategies get “approved” and published to a strategy repository.

3. Live Strategy Deployment

   1. Orchestrator Agent “promotes” validated strategies to live trading.

   2. Execution Agent runs on AWS/Alibaba trade boxes, calling our central Rust SDK to place orders. The Execution agent implements trades in real time, referencing the live risk constraints from the Risk agent.

   3. Risk Management Agent monitors real-time trades, updating parameters as needed.

4. Risk & Performance Feedback

   1. Execution data, PnL metrics, risk metrics feed back into ClickHouse/RedPanda.

   2. The Strategy Research Agent uses this feedback loop for continuous learning and optimization.

   3. Alerts and anomalies get posted to Grafana dashboards or triggers from Loki logs.

   4. The Risk agent monitors positions, PnL, volatility. If conditions breach certain thresholds, it directly instructs the Execution agent to reduce exposure or close positions.

5. LLM Involvement & Ops

   1. If we incorporate LLM-based reasoning agents (for, say, strategy parameter interpretation, natural language reporting, or advanced orchestrator logic), you would rely on a solution like Helicone or Phi-Data to track usage, manage deployments, and control costs.

   2. Agents generate code stubs or help with Rust-based automation via Aider for iterative improvements.

   3. Realized performance data is fed back to the Strategy Research agent for post-trade analysis and further improvements.

Technical Highlights & Recommendations

1. Compute & Deployment

   1. Continue leveraging Hetzner HPC for heavy backtesting tasks under SLURM.

   2. Maintain AWS/Alibaba & BSO/Avelacom/Microwave cross-connect for minimal latency to exchanges.

   3. Keep using Tailscale for secure VPN connections between devboxes, HPC cluster, and production trade boxes.

   4. Implement Zenoh connectivity in communication workflow. Use an event-driven microservices approach with specialized agent frameworks (LangChain Agents, Haystack Agents, etc.).

2. ML & Data Science Stack

   1. Use a Model Registry (e.g., MLflow, Weights & Biases) for storing, versioning, and deploying all our AI models.

   2. Model Training & Experimentation: Python-based environment with PyTorch or TensorFlow on HPC cluster.

   3. For RL-based or advanced ML modules, ensure we have the compute infrastructure (GPUs/TPUs) and a robust pipeline for training and deployment.

   4. Tracking & Governance: Neptune or MLflow for experiment metadata; integrate with our SLURM job submissions.

   5. LLM Ops: Helicone or Phi-Data for usage tracking, billing insights, and model version management (especially if you self-host an open-source LLM or rely on external APIs).

3. Observability & Monitoring

   1. Combine Loki and Grafana dashboards to track logs, system health, trading metrics, agent states, etc.

   2. Use Bytewax or a streaming layer for real-time analytics on operational metrics and agent performance.

   3. Provide or store logs of agent reasoning for debugging.

4. Security & Custody

   1. Use Fireblocks for asset custody and treasury management and as a DEFI gateway

   2. Enforce Tailscale for secure distributed development and operational environments.

   3. Secure sign-offs for large or unusual trades with a “human in the loop” or a multi-signature approach.

   4. Implement robust fail-safes: e.g., if the Risk agent fails, freeze trading or revert to a conservative fallback strategy.

   5. Test and stage our agent workflows extensively before production (sandbox environment first).

5. Orchestration & Scheduling

   1. Tools like Airflow, Dagster, or Prefect can coordinate batch tasks (like backtests or nightly data ingestion). We are currently using Mage.ai

   2. For near-real-time or streaming tasks, rely on the messaging layer or streaming frameworks (Kafka Streams, Spark Streaming, Flink, etc.).

• Employ Cursor as the IDE of choice.

• Utilize libraries like Scikit-learn or XGBoost for machine-learning tasks

Closing Thoughts

By weaving in our existing technologies—ClickHouse, RedPanda, Bytewax, Mage, AWS/Alibaba trade boxes, Hetzner HPC, Loki, Grafana, Tailscale, and Fireblocks — we can build a robust AI-first agentic trading stack. Each agent (strategy, execution, risk, data ingestion, etc.) can tap into:

• Real-time data (RedPanda + Bytewax + Market Data Nodes -> ClickHouse / Postrgres DB),

• Large-scale computational resources (Hetzner HPC + SLURM + S3/JuiceFS),

• Automated operational workflows (Ansible + GitHub Actions + Rust-based SDK),

• Observability (Loki + Grafana), 1Token for metrics and risk management API.

• Secure connectivity (Tailscale for secure networking, Fireblocks for custody)

Finally, introducing an Orchestrator Agent—potentially enhanced by LLM-based reasoning under an LLM ops platform—ensures all specialized agents collaborate, scaling our research, risk management, and execution processes to an AI-first future.

By leveraging multiple specialized AI agents—each equipped with the right tools and aligned by a central orchestrator—you can automate nearly the entire trading lifecycle. Over time, the system self-improves through feedback loops: data -> strategy updates -> execution & risk feedback -> new data. This creates a robust framework for an “AI-first” algorithmic trading firm capable of scaling in complexity and sophistication while maintaining a clean separation of concerns and well-defined agent responsibilities.

Competitive Advantage

• Adaptive & Scalable: Our agent-based framework can easily integrate new strategies or data sources—outpacing competitors that rely on static, manual processes.

• Cost-Efficient Execution: Automated decision-making around best execution practices will reduce slippage, improve fill rates, and maximize returns.

• Speed to Market: With multi-agent orchestration, new signals and insights move from research to production with minimal time lag.