# 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 approximately 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 FastAPI or Flask for API services, 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.

### Database Design

The database schema emphasizes simplicity while supporting essential trading operations. Core tables include raw_market_data for exchange data storage, candles for OHLCV aggregation, strategies for algorithm definitions, bots for instance management, signals for trading decisions, trades for execution records, and bot_portfolio for performance tracking.

## 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. 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 to retrieve market information including order books, trades, and candlestick data. Raw data is stored in PostgreSQL while processed information is published through Redis channels for real-time distribution to active trading bots.

### Signal Generation and Execution

Strategies subscribe to relevant data streams and generate trading signals based on configured algorithms. The bot manager validates signals against portfolio constraints and executes simulated or live trades according to bot configurations.

## 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 2+ bots for 72+ hours.

### 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. Clear component separation, comprehensive 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.
