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Simplified Crypto Trading Bot Platform: Product Requirements Document
Executive Summary
This simplified PRD addresses the need for a rapid-deployment crypto trading bot platform designed for internal testing and strategy development. The platform eliminates microservices complexity in favor of a monolithic architecture that can be functional within 1-2 weeks while supporting 5-10 concurrent bots. The system focuses on core functionality including data collection, strategy execution, backtesting, and visualization without requiring advanced monitoring or orchestration tools.
System Architecture Overview
The platform follows a streamlined monolithic design that consolidates all components within a single application boundary. This approach enables rapid development while maintaining clear separation between functional modules for future scalability.The architecture consists of six core components working together: Data Collection Module for exchange connectivity, Strategy Engine for unified signal generation, Bot Manager for concurrent bot orchestration, PostgreSQL database for data persistence, Backtesting Engine for historical simulation, and Dashboard for visualization and control.
Simplified Technical Stack
Core Technologies
The platform utilizes a Python-based technology stack optimized for rapid development. The backend employs Python 3.10+ with Dash framework (including built-in Flask server for REST APIs), PostgreSQL 14+ with TimescaleDB extension for time-series optimization, and Redis for real-time pub/sub messaging. The frontend leverages Dash with Plotly for interactive visualization and bot control interfaces, providing a unified full-stack solution.
Database Design
The database schema emphasizes simplicity while supporting essential trading operations. The core approach separates frequently-accessed OHLCV market data from optional raw tick data for optimal performance. Core tables include market_data for OHLCV candles used by bots, bots for instance management with JSON configuration references, signals for trading decisions, trades for execution records, and bot_performance for portfolio tracking. Raw trade data storage is optional and can be implemented later for advanced backtesting scenarios.
Development Methodology
Two-Week Implementation Timeline
The development follows a structured three-phase approach designed for rapid deployment. Phase 1 (Days 1-5) establishes foundational components including database setup, data collection implementation, and basic visualization. Phase 2 (Days 6-10) completes core functionality with backtesting engine development, trading logic implementation, and dashboard enhancement. Phase 3 (Days 11-14) focuses on system refinement, comprehensive testing, and deployment preparation.
Strategy Implementation Example
The platform supports multiple trading strategies through a unified interface design. Strategy parameters are stored in JSON files, making it easy to test different configurations without rebuilding code. A simple moving average crossover strategy demonstrates the system's capability to generate buy and sell signals based on technical indicators. This example strategy shows how the system processes market data, calculates moving averages, generates trading signals, and tracks portfolio performance over time. The visualization includes price movements, moving average lines, signal markers, and portfolio value progression.
Backtesting and Performance Analysis
Strategy Validation Framework
The backtesting engine enables comprehensive strategy testing using historical market data. The system calculates key performance metrics including total returns, Sharpe ratios, maximum drawdown, and win/loss ratios to evaluate strategy effectiveness.
Portfolio Management
The platform tracks portfolio allocation and performance throughout strategy execution. Real-time monitoring capabilities show the distribution between cryptocurrency holdings and cash reserves.
Simplified Data Flow
Real-Time Processing
The data collection module connects to exchange APIs (starting with OKX) to retrieve market information via WebSocket connections. Instead of storing all raw tick data, the system focuses on aggregating trades into OHLCV candles (1-minute, 5-minute, hourly, etc.) which are stored in PostgreSQL. Processed OHLCV data is published through Redis channels for real-time distribution to active trading bots. Raw trade data can optionally be stored for advanced backtesting scenarios.
Signal Generation and Execution
Trading strategies subscribe to relevant OHLCV data streams and generate trading signals based on configured algorithms stored in JSON files for easy parameter testing. The bot manager validates signals against portfolio constraints and executes simulated trades with realistic fee modeling. The system includes automatic crash recovery - bots are monitored and restarted if they fail, and the application can restore active bot states after system restarts.
Future Scalability Considerations
Microservices Migration Path
While implementing a monolithic architecture for rapid deployment, the system design maintains clear component boundaries that facilitate future extraction into microservices. API-first design principles ensure internal components communicate through well-defined interfaces that can be externalized as needed.
Authentication and Multi-User Support
The current single-user design can be extended to support multiple users through role-based access control implementation. Database schema accommodates user management tables and permission structures without requiring significant architectural changes.
Advanced Monitoring Integration
The simplified monitoring approach can be enhanced with Prometheus and Grafana integration when scaling requirements justify the additional complexity. Current basic monitoring provides foundation metrics that can be extended to comprehensive observability systems.
Technical Implementation Details
Time Series Data Management
The platform implements proper time aggregation aligned with exchange standards to ensure accurate candle formation. Timestamp alignment follows right-aligned methodology where 5-minute candles from 09:00:00-09:05:00 receive the 09:05:00 timestamp.
Performance Optimization
Database indexing on timestamp and symbol fields ensures efficient time-series queries. Connection pooling prevents database connection leaks while prepared statements optimize query execution. Memory management includes proper cleanup of data objects after processing to maintain system stability.
Success Metrics and Validation
Development Milestones
Platform success is measured through specific deliverables including core functionality completion within 14 days, system stability maintenance at 99% uptime during internal testing, successful backtesting of at least 3 different strategies, and concurrent operation of 5+ bots for 72+ hours to demonstrate the platform's scalability within its target range.
Strategy Testing Capabilities
The system enables comprehensive strategy validation through historical simulation, real-time testing with virtual portfolios, and performance comparison across multiple algorithms. Backtesting results provide insights into strategy effectiveness before live deployment.
Conclusion
This simplified crypto trading bot platform balances rapid development requirements with future scalability needs. The monolithic architecture enables deployment within 1-2 weeks while maintaining architectural flexibility for future enhancements. The OHLCV-focused data approach optimizes performance by avoiding unnecessary raw data storage, while JSON-based configuration files enable rapid strategy parameter testing without code changes.
Clear component separation, streamlined database design, and strategic technology choices create a foundation that supports both immediate testing objectives and long-term platform evolution. The platform's focus on essential functionality without unnecessary complexity ensures teams can begin strategy testing quickly while building toward more sophisticated implementations as requirements expand. This approach maximizes development velocity while preserving options for future architectural evolution and feature enhancement.