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SPEC: AlphaPulse Trading System

Purpose:
This document serves as the single source of truth for the roocode agent. Every time a task is requested, the agent will read this file to understand the project context, including all modules, business logic, domain details, and architectural decisions.

1. Project Overview

  • Project Name: AlphaPulse
  • Description:
    A sophisticated algorithmic trading system that combines reinforcement learning, technical analysis, and risk management to execute automated trading strategies across multiple asset classes.
  • Objectives:
    • Develop a robust trading system using state-of-the-art machine learning techniques
    • Implement comprehensive risk management and portfolio optimization
    • Provide real-time market analysis and trade execution capabilities
    • Support both paper trading and live trading environments
  • Target Audience:
    • Quantitative traders and researchers
    • Financial institutions
    • Individual algorithmic traders

2. Domain & Business Context

  • Domain Description:
    The system operates in the financial markets domain, focusing on algorithmic trading using machine learning. It incorporates technical analysis, market data processing, and automated decision-making.

  • Business Objectives & Value:

    • Automate trading decisions using AI/ML techniques
    • Minimize human emotion in trading decisions
    • Optimize portfolio performance through systematic strategies
    • Reduce operational risks through automated processes

  • Key Business Rules:

    • Rule 1: Risk Management - Strict position sizing and stop-loss implementation
    • Rule 2: Data Integrity - Real-time market data validation and cleaning
    • Rule 3: Trade Execution - Systematic entry and exit based on ML model predictions

3. Architectural Overview

  • System Architecture:
    The system follows a modular architecture with clear separation of concerns:

    AlphaPulse
├── Data Pipeline (Market Data Processing)
├── Feature Engineering
├── ML Models (RL/Supervised)
├── Risk Management
├── Portfolio Management
└── Trade Execution

  • Technology Stack:

    • Core: Python 3.12+
    • ML Framework: PyTorch, Stable-Baselines3
    • Data Processing: Pandas, NumPy
    • Technical Analysis: TA-Lib
    • API Integration: REST/WebSocket clients
    • Database: SQL/NoSQL for data persistence

4. Modules & Components

Data Pipeline Module

  • Description: Handles market data acquisition and preprocessing
  • Responsibilities:
    • Real-time data fetching
    • Historical data management
    • Data cleaning and validation
  • Key Business Logic:
    • Data normalization and feature calculation
    • Market data caching and storage
  • Dependencies:
    • Exchange APIs
    • Data providers
    • Storage systems

ML Models Module

  • Description: Implements trading algorithms and models
  • Responsibilities:
    • Model training and evaluation
    • Trading signal generation
    • Performance monitoring
  • Key Business Logic:
    • RL-based trading strategy
    • Technical indicator processing
    • Market state representation
  • Dependencies:
    • Data Pipeline
    • Feature Engineering
    • Risk Management

Risk Management Module

  • Description: Handles risk assessment and position sizing
  • Responsibilities:
    • Position size calculation
    • Stop-loss management
    • Portfolio risk monitoring
  • Key Business Logic:
    • Risk-adjusted position sizing
    • Dynamic stop-loss calculation
    • Portfolio exposure limits
  • Dependencies:
    • Portfolio Management
    • Trade Execution

Trade Execution Module

  • Description: Manages order execution and tracking
  • Responsibilities:
    • Order placement and management
    • Position tracking
    • Trade logging
  • Key Business Logic:
    • Smart order routing
    • Trade execution optimization
    • Position reconciliation
  • Dependencies:
    • Exchange APIs
    • Risk Management
    • Portfolio Management

5. Business Logic & Domain Rules

  • Core Business Processes:

    1. Market Data Processing
      • Real-time data ingestion
      • Feature calculation
      • State representation
    2. Trading Decision Making
      • Model inference
      • Risk assessment
      • Order generation
    3. Trade Execution
      • Order placement
      • Position management
      • Performance tracking

  • Validation & Invariants:

    • Market data must be clean and normalized
    • Risk limits must be enforced at all times
    • Portfolio constraints must be maintained
    • Trade execution must be atomic

6. Data & Domain Models

  • Entity Descriptions: 
    MarketData:
  - timestamp: DateTime
  - prices: DataFrame
  - volumes: DataFrame
  
Position:
  - asset_id: str
  - quantity: Decimal
  - entry_price: Decimal
  - entry_time: int
  - exit_price: Optional[Decimal]
  - exit_time: Optional[int]
  - pnl: Optional[Decimal]
  
PortfolioData:
  - timestamp: DateTime
  - positions: List[Position]
  - equity: Decimal
  - exposure: Decimal

7. Interfaces & Integration

  • Internal APIs:

    • Data Pipeline Interface
    • Model Interface
    • Trade Execution Interface
    • Risk Management Interface

  • External Services:

    • Exchange APIs: For market data and trading
    • Data Providers: For additional market data
    • Storage Services: For data persistence

8. Non-Functional Requirements

  • Performance:

    • Sub-second latency for trading decisions
    • Real-time data processing capability
    • Efficient model inference

  • Scalability & Reliability:

    • Horizontal scaling for data processing
    • Fault tolerance in trade execution
    • Automated recovery mechanisms

  • Security:

    • Secure API key management
    • Encrypted communications
    • Access control and authentication

  • Maintainability:

    • Comprehensive logging
    • Performance monitoring
    • Error tracking and reporting

9. Revision History

Version Date Author Changes
1.0 2025-02-14 Roo Initial specification

10. Glossary & References

Note: Update this document as the project evolves to ensure that the roocode agent always has the most up-to-date context.