608 lines
23 KiB
Markdown
608 lines
23 KiB
Markdown
# Simplified Crypto Trading Bot Platform: Product Requirements Document (PRD)
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**Version:** 1.0
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**Date:** May 30, 2025
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**Author:** Vasily
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**Status:** Draft
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## Executive Summary
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This PRD outlines the development of a simplified crypto trading bot platform that enables strategy testing, development, and execution without the complexity of microservices and advanced monitoring. The goal is to create a functional system within 1-2 weeks that allows for strategy testing while establishing a foundation that can scale in the future. The platform addresses key requirements including data collection, strategy execution, visualization, and backtesting capabilities in a monolithic architecture optimized for internal use.
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## Current Requirements & Constraints
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- **Speed to Deployment**: System must be functional within 1-2 weeks
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- **Scale**: Support for 5-10 concurrent trading bots
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- **Architecture**: Monolithic application instead of microservices
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- **User Access**: Internal use only initially (no multi-user authentication)
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- **Infrastructure**: Simplified deployment without Kubernetes/Docker Swarm
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- **Monitoring**: Basic logging for modules
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## System Architecture
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### High-Level Architecture
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The platform will follow a monolithic architecture pattern to enable rapid development while providing clear separation between components:
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### Data Flow Architecture
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```
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OKX Exchange API (WebSocket)
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↓
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Data Collector → OHLCV Aggregator → PostgreSQL (market_data)
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↓ ↓
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[Optional] Raw Trade Storage Redis Pub/Sub → Strategy Engine (JSON configs)
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↓ ↓
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Files/Database (raw_trades) Signal Generation → Bot Manager
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↓
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PostgreSQL (signals, trades, bot_performance)
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↓
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Dashboard (REST API) ← PostgreSQL (historical data)
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↑
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Real-time Updates ← Redis Channels
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```
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**Data Processing Priority**:
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1. **Real-time**: Raw data → OHLCV candles → Redis → Bots (primary flow)
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2. **Historical**: OHLCV data from PostgreSQL for backtesting and charts
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3. **Advanced Analysis**: Raw trade data (if stored) for detailed backtesting
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### Redis Channel Design
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```python
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# Real-time market data distribution
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MARKET_DATA_CHANNEL = "market:{symbol}" # OHLCV updates
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BOT_SIGNALS_CHANNEL = "signals:{bot_id}" # Trading decisions
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BOT_STATUS_CHANNEL = "status:{bot_id}" # Bot lifecycle events
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SYSTEM_EVENTS_CHANNEL = "system:events" # Global notifications
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```
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### Configuration Strategy
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**PostgreSQL for**: Market data, bot instances, trades, signals, performance metrics
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**JSON files for**: Strategy parameters, bot configurations (rapid testing and parameter tuning)
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```json
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// config/strategies/ema_crossover.json
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{
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"strategy_name": "EMA_Crossover",
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"parameters": {
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"fast_period": 12,
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"slow_period": 26,
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"risk_percentage": 0.02
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}
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}
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// config/bots/bot_001.json
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{
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"bot_id": "bot_001",
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"strategy_file": "ema_crossover.json",
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"symbol": "BTC-USDT",
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"virtual_balance": 10000,
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"enabled": true
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}
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```
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### Error Handling Strategy
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**Bot Crash Recovery**:
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- Monitor bot processes every 30 seconds
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- Auto-restart crashed bots if status = 'active'
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- Log all crashes with stack traces
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- Maximum 3 restart attempts per hour
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**Exchange Connection Issues**:
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- Retry with exponential backoff (1s, 2s, 4s, 8s, max 60s)
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- Switch to backup WebSocket connection if available
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- Log connection quality metrics
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**Database Errors**:
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- Continue operation with in-memory cache for up to 5 minutes
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- Queue operations for retry when connection restored
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- Alert on prolonged database disconnection
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**Application Restart Recovery**:
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- Read bot states from database on startup
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- Restore active bots to 'active' status
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- Resume data collection for all monitored symbols
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### Component Details and Functional Requirements
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1. **Data Collection Module**
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- Connect to exchange APIs (OKX initially) via WebSocket
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- Aggregate real-time trades into OHLCV candles (1m, 5m, 15m, 1h, 4h, 1d)
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- Store OHLCV data in PostgreSQL for bot operations and backtesting
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- Send real-time candle updates through Redis
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- Optional: Store raw trade data for advanced backtesting
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**FR-001: Unified Data Provider Interface**
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- Support multiple exchanges through standardized adapters
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- Real-time OHLCV aggregation with WebSocket connections
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- Primary focus on candle data, raw data storage optional
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- Data validation and error handling mechanisms
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**FR-002: Market Data Processing**
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- OHLCV aggregation with configurable timeframes (1m base, higher timeframes derived)
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- Technical indicator calculation (SMA, EMA, RSI, MACD, Bollinger Bands) on OHLCV data
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- Data normalization across different exchanges
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- Time alignment following exchange standards (right-aligned candles)
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2. **Strategy Engine**
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- Provide unified interface for all trading strategies
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- Support multiple strategy types with common parameter structure
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- Generate trading signals based on market data
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- Log strategy performance and signals
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- Strategy implementation as a class.
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**FR-003: Strategy Framework**
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- Base strategy class with standardized interface
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- Support for multiple strategy types
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- Parameter configuration and optimization tools (JSON for the parameters)
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- Signal generation with confidence scoring
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**FR-004: Signal Processing**
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- Real-time signal calculation and validation
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- Signal persistence for analysis and debugging
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- Multi-timeframe analysis capabilities
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- Custom indicator development support
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3. **Bot Manager**
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- Create and manage up to 10 concurrent trading bots
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- Configure bot parameters and associated strategies
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- Start/stop individual bots
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- Track bot status and performance
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**FR-005: Bot Lifecycle Management**
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- Bot creation with strategy and parameter selection
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- Start/stop/pause functionality with state persistence
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- Configuration management
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- Resource allocation and monitoring (in future)
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**FR-006: Portfolio Management**
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- Position tracking and balance management
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- Risk management controls (stop-loss, take-profit, position sizing)
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- Multi-bot coordination and conflict resolution (in future)
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- Real-time portfolio valuation (in future)
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5. **Trading Execution**
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- Simulate or execute trades based on configuration
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- Stores trade information in database
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**FR-007: Order Management**
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- Order placement with multiple order types (market, limit, stop)
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- Order tracking and status monitoring (in future)
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- Execution confirmation and reconciliation (in future)
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- Fee calculation and tracking (in future)
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**FR-008: Risk Controls**
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- Pre-trade risk validation
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- Position limits and exposure controls (in future)
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- Emergency stop mechanisms (in future)
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- Compliance monitoring and reporting (in future)
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4. **Database (PostgreSQL)**
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- Store market data, bot configurations, and trading history
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- Optimized schema for time-series data without complexity
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- Support for data querying and aggregation
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**Database (JSON)**
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- Store strategy parameters and bot onfiguration in JSON in the beginning for simplicity of editing and testing
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5. **Backtesting Engine**
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- Run simulations on historical data using vectorized operations for speed
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- Calculate performance metrics
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- Support multiple timeframes and strategy parameter testing
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- Generate comparison reports between strategies
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**FR-009: Historical Simulation**
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- Strategy backtesting on historical market data
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- Performance metric calculation (Sharpe ratio, drawdown, win rate, total return)
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- Parameter optimization through grid search (limited combinations for speed) (in future)
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- Side-by-side strategy comparison with statistical significance
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**FR-010: Simulation Engine**
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- Vectorized signal calculation using pandas operations
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- Realistic fee modeling (0.1% per trade for OKX)
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- Look-ahead bias prevention with proper timestamp handling
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- Configurable test periods (1 day to 24 months)
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6. **Dashboard & Visualization**
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- Display real-time market data and bot status
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- Show portfolio value progression over time
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- Visualize trade history with buy/sell markers on price charts
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- Provide simple bot control interface (start/stop/configure)
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**FR-011: Dashboard Interface**
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- Real-time bot monitoring with status indicators
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- Portfolio performance charts (total value, cash vs crypto allocation)
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- Trade history table with P&L per trade
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- Simple bot configuration forms for JSON parameter editing
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**FR-012: Data Visualization**
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- Interactive price charts with strategy signal overlays
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- Portfolio value progression charts
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- Performance comparison tables (multiple bots side-by-side)
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- Fee tracking and total cost analysis
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### Non-Functional Requirements
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1 Performance Requirements
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**NFR-001: Latency**
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- Market data processing: <100ms from exchange to database
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- Signal generation: <500ms for standard strategies
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- API response time: <200ms for 95% of requests
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- Dashboard updates: <2 seconds for real-time data
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**NFR-002: Scalability**
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- Database queries scalable to 1M+ records per table
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- Horizontal scaling capability for all services (in future)
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2. Reliability Requirements
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**NFR-003: Availability**
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- System uptime: 99.5% excluding planned maintenance
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- Data collection: 99.9% uptime during market hours
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- Automatic failover for critical services
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- Graceful degradation during partial outages
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**NFR-004: Data Integrity**
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- Zero data loss for executed trades
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- Transactional consistency for all financial operations
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- Regular database backups with point-in-time recovery
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- Data validation and error correction mechanisms
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3. Security Requirements
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**NFR-005: Authentication & Authorization** (in future)
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**NFR-006: Data Protection**
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- End-to-end encryption for sensitive data (in future)
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- Secure storage of API keys and credentials
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- Regular security audits and penetration testing (in future)
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- Compliance with financial data protection regulations (in future)
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## Technical Implementation
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### Database Schema
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The database schema separates frequently-accessed OHLCV data from raw tick data to optimize performance and storage.
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```sql
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-- OHLCV Market Data (primary table for bot operations)
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CREATE TABLE market_data (
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id SERIAL PRIMARY KEY,
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exchange VARCHAR(50) NOT NULL DEFAULT 'okx',
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(5) NOT NULL, -- 1m, 5m, 15m, 1h, 4h, 1d
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timestamp TIMESTAMPTZ NOT NULL,
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open DECIMAL(18,8) NOT NULL,
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high DECIMAL(18,8) NOT NULL,
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low DECIMAL(18,8) NOT NULL,
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close DECIMAL(18,8) NOT NULL,
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volume DECIMAL(18,8) NOT NULL,
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trades_count INTEGER, -- number of trades in this candle
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created_at TIMESTAMPTZ DEFAULT NOW(),
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UNIQUE(exchange, symbol, timeframe, timestamp)
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);
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CREATE INDEX idx_market_data_lookup ON market_data(symbol, timeframe, timestamp);
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CREATE INDEX idx_market_data_recent ON market_data(timestamp DESC) WHERE timestamp > NOW() - INTERVAL '7 days';
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-- Raw Trade Data (optional, for detailed backtesting only)
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CREATE TABLE raw_trades (
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id SERIAL PRIMARY KEY,
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exchange VARCHAR(50) NOT NULL DEFAULT 'okx',
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symbol VARCHAR(20) NOT NULL,
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timestamp TIMESTAMPTZ NOT NULL,
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type VARCHAR(10) NOT NULL, -- trade, order, balance, tick, books
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data JSONB NOT NULL, -- response from the exchange
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created_at TIMESTAMPTZ DEFAULT NOW()
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) PARTITION BY RANGE (timestamp);
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CREATE INDEX idx_raw_trades_symbol_time ON raw_trades(symbol, timestamp);
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-- Monthly partitions for raw data (if using raw data)
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-- CREATE TABLE raw_trades_y2024m01 PARTITION OF raw_trades
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-- FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');
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-- Bot Management (simplified)
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CREATE TABLE bots (
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id SERIAL PRIMARY KEY,
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name VARCHAR(100) NOT NULL,
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strategy_name VARCHAR(50) NOT NULL,
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symbol VARCHAR(20) NOT NULL,
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timeframe VARCHAR(5) NOT NULL,
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status VARCHAR(20) NOT NULL DEFAULT 'inactive', -- active, inactive, error
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config_file VARCHAR(200), -- path to JSON config
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virtual_balance DECIMAL(18,8) DEFAULT 10000,
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current_balance DECIMAL(18,8) DEFAULT 10000,
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last_heartbeat TIMESTAMPTZ,
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created_at TIMESTAMPTZ DEFAULT NOW(),
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updated_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Trading Signals (for analysis and debugging)
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CREATE TABLE signals (
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id SERIAL PRIMARY KEY,
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bot_id INTEGER REFERENCES bots(id),
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timestamp TIMESTAMPTZ NOT NULL,
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signal_type VARCHAR(10) NOT NULL, -- buy, sell, hold
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price DECIMAL(18,8),
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confidence DECIMAL(5,4),
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indicators JSONB, -- technical indicator values
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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CREATE INDEX idx_signals_bot_time ON signals(bot_id, timestamp);
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-- Trade Execution Records
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CREATE TABLE trades (
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id SERIAL PRIMARY KEY,
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bot_id INTEGER REFERENCES bots(id),
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signal_id INTEGER REFERENCES signals(id),
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timestamp TIMESTAMPTZ NOT NULL,
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side VARCHAR(5) NOT NULL, -- buy, sell
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price DECIMAL(18,8) NOT NULL,
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quantity DECIMAL(18,8) NOT NULL,
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fees DECIMAL(18,8) DEFAULT 0,
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pnl DECIMAL(18,8), -- profit/loss for this trade
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balance_after DECIMAL(18,8), -- portfolio balance after trade
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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CREATE INDEX idx_trades_bot_time ON trades(bot_id, timestamp);
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-- Performance Snapshots (for plotting portfolio over time)
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CREATE TABLE bot_performance (
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id SERIAL PRIMARY KEY,
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bot_id INTEGER REFERENCES bots(id),
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timestamp TIMESTAMPTZ NOT NULL,
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total_value DECIMAL(18,8) NOT NULL, -- current portfolio value
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cash_balance DECIMAL(18,8) NOT NULL,
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crypto_balance DECIMAL(18,8) NOT NULL,
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total_trades INTEGER DEFAULT 0,
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winning_trades INTEGER DEFAULT 0,
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total_fees DECIMAL(18,8) DEFAULT 0,
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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CREATE INDEX idx_bot_performance_bot_time ON bot_performance(bot_id, timestamp);
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```
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**Data Storage Strategy**:
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- **OHLCV Data**: Primary source for bot operations, kept indefinitely, optimized indexes
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- **Raw Trade Data**: Optional table, only if detailed backtesting needed, can be partitioned monthly
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- **Alternative for Raw Data**: Store in compressed files (Parquet/CSV) instead of database for cost efficiency
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**MVP Approach**: Start with OHLCV data only, add raw data storage later if advanced backtesting requires it.
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### Technology Stack
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The platform will be built using the following technologies:
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- **Backend Framework**: Python 3.10+ with Dash (includes built-in Flask server for REST API endpoints)
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- **Database**: PostgreSQL 14+ (with TimescaleDB extension for time-series optimization)
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- **Real-time Messaging**: Redis (for pub/sub messaging between components)
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- **Frontend**: Dash with Plotly (for visualization and control interface) and Mantine UI components
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- **Configuration**: JSON files for strategy parameters and bot configurations
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- **Deployment**: Docker container setup for development and production
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### API Design
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**Dash Callbacks**: Real-time updates and user interactions
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**REST Endpoints**: Historical data queries for backtesting and analysis
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```python
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# Built-in Flask routes for historical data
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@app.server.route('/api/bot/<bot_id>/trades')
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@app.server.route('/api/market/<symbol>/history')
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@app.server.route('/api/backtest/results/<test_id>')
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```
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### Data Flow
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The data flow follows a simple pattern to ensure efficient processing:
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1. **Market Data Collection**:
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- Collector fetches data from exchange APIs
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- Raw data is stored in PostgreSQL
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- Processed data (e.g., OHLCV candles) are calculated and stored
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- Real-time updates are published to Redis channels
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2. **Signal Generation**:
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- Bots subscribe to relevant data channels and generate signals based on the strategy
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- Signals are stored in database and published to Redis
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3. **Trade Execution**:
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- Bot manager receives signals from strategies
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- Validates signals against bot parameters and portfolio
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- Simulates or executes trades based on configuration
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- Stores trade information in database
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4. **Visualization**:
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- Dashboard subscribes to real-time data and trading updates
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- Queries historical data for charts and performance metrics
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- Provides interface for bot management and configuration
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## Development Roadmap
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### Phase 1: Foundation (Days 1-5)
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**Objective**: Establish core system components and data flow
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1. **Day 1-2**: Database Setup and Data Collection
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- Set up PostgreSQL with initial schema
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- Implement OKX API connector
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- Create data storage and processing logic
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2. **Day 3-4**: Strategy Engine and Bot Manager
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- Develop strategy interface and 1-2 example strategies
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- Create bot manager with basic controls
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- Implement Redis for real-time messaging
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3. **Day 5**: Basic Visualization
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- Set up Dash/Plotly for simple charts
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- Create basic dashboard layout
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- Connect to real-time data sources
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- Create mockup strategies and bots
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### Phase 2: Core Functionality (Days 6-10)
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**Objective**: Complete essential features for strategy testing
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1. **Day 6-7**: Backtesting Engine
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- Get historical data from the database or file (have for BTC/USDT in csv format)
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- Create performance calculation metrics
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- Develop strategy comparison tools
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2. **Day 8-9**: Trading Logic
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- Implement virtual trading capability
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- Create trade execution logic
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- Develop portfolio tracking
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3. **Day 10**: Dashboard Enhancement
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- Improve visualization components
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- Add bot control interface
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- Implement real-time performance monitoring
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### Phase 3: Refinement (Days 11-14)
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**Objective**: Polish system and prepare for ongoing use
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1. **Day 11-12**: Testing and Debugging
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- Comprehensive system testing
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- Fix identified issues
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- Performance optimization
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2. **Day 13-14**: Documentation and Deployment
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- Create user documentation
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- Prepare deployment process
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- Set up basic monitoring
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## Technical Considerations
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### Scalability Path
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While the initial system is designed as a monolithic application for rapid development, several considerations ensure future scalability:
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1. **Module Separation**: Clear boundaries between components enable future extraction into microservices
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2. **Database Design**: Schema supports partitioning and sharding for larger data volumes
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3. **Message Queue**: Redis implementation paves way for more robust messaging (Kafka/RabbitMQ)
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4. **API-First Design**: Internal components communicate through well-defined interfaces
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### Time Aggregation
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Special attention is given to time aggregation to ensure consistency with exchanges:
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```python
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def aggregate_candles(trades, timeframe, alignment='right'):
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"""
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Aggregate trade data into OHLCV candles with consistent timestamp alignment.
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Parameters:
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- trades: List of trade dictionaries with timestamp and price
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- timeframe: String representing the timeframe (e.g., '1m', '5m', '1h')
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- alignment: String indicating timestamp alignment ('right' or 'left')
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Returns:
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- Dictionary with OHLCV data
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"""
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# Convert timeframe to pandas offset
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if timeframe.endswith('m'):
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offset = pd.Timedelta(minutes=int(timeframe[:-1]))
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elif timeframe.endswith('h'):
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offset = pd.Timedelta(hours=int(timeframe[:-1]))
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elif timeframe.endswith('d'):
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offset = pd.Timedelta(days=int(timeframe[:-1]))
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# Create DataFrame from trades
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df = pd.DataFrame(trades)
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# Convert timestamps to pandas datetime
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
|
|
|
|
# Floor timestamps to timeframe
|
|
if alignment == 'right':
|
|
df['candle_time'] = df['timestamp'].dt.floor(offset)
|
|
else:
|
|
df['candle_time'] = df['timestamp'].dt.ceil(offset) - offset
|
|
|
|
# Aggregate to OHLCV
|
|
candles = df.groupby('candle_time').agg({
|
|
'price': ['first', 'max', 'min', 'last'],
|
|
'amount': 'sum'
|
|
}).reset_index()
|
|
|
|
# Rename columns
|
|
candles.columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
|
|
|
|
return candles
|
|
```
|
|
|
|
### Performance Optimization
|
|
|
|
For the initial release, several performance optimizations are implemented:
|
|
|
|
1. **Database Indexing**: Proper indexes on timestamp and symbol fields
|
|
2. **Query Optimization**: Prepared statements and efficient query patterns
|
|
3. **Connection Pooling**: Database connection management to prevent leaks
|
|
4. **Data Aggregation**: Pre-calculation of common time intervals
|
|
5. **Memory Management**: Proper cleanup of data objects after processing
|
|
|
|
## User Interface
|
|
|
|
The initial user interface focuses on functionality over aesthetics, providing essential controls and visualizations, minimalistic design.
|
|
|
|
1. **Market Data View**
|
|
- Real-time price charts for monitored symbols
|
|
- Order book visualization
|
|
- Recent trades list
|
|
|
|
2. **Bot Management**
|
|
- Create/configure bot interface
|
|
- Start/stop controls
|
|
- Status indicators
|
|
|
|
3. **Strategy Dashboard**
|
|
- Strategy selection and configuration
|
|
- Signal visualization
|
|
- Performance metrics
|
|
|
|
4. **Backtesting Interface**
|
|
- Historical data selection
|
|
- Strategy parameter configuration
|
|
- Results visualization
|
|
|
|
## Risk Management & Mitigation
|
|
|
|
### Technical Risks
|
|
**Risk:** Exchange API rate limiting affecting data collection
|
|
**Mitigation:** Implement intelligent rate limiting, multiple API keys, and fallback data sources
|
|
|
|
**Risk:** Database performance degradation with large datasets
|
|
**Mitigation:** Implement data partitioning, archival strategies, and query optimization (in future)
|
|
|
|
**Risk:** System downtime during market volatility
|
|
**Mitigation:** Design redundant systems, implement circuit breakers, and emergency procedures (in future)
|
|
|
|
### Business Risks
|
|
**Risk:** Regulatory changes affecting crypto trading
|
|
**Mitigation:** Implement compliance monitoring, maintain regulatory awareness, design for adaptability
|
|
|
|
**Risk:** Competition from established trading platforms
|
|
**Mitigation:** Focus on unique value propositions, rapid feature development, strong user experience
|
|
|
|
### 8.3 User Risks
|
|
**Risk:** User losses due to platform errors
|
|
**Mitigation:** Comprehensive testing, simulation modes, risk warnings, and liability disclaimers
|
|
|
|
## Future Expansion
|
|
|
|
While keeping the initial implementation simple, the design accommodates future enhancements:
|
|
|
|
1. **Authentication System**: Add multi-user support with role-based access
|
|
2. **Advanced Strategies**: Support for machine learning and AI-based strategies
|
|
3. **Multi-Exchange Support**: Expand beyond OKX to other exchanges
|
|
4. **Microservices Migration**: Extract components into separate services
|
|
5. **Advanced Monitoring**: Integration with Prometheus/Grafana
|
|
6. **Cloud Deployment**: Support for AWS/GCP/Azure deployment
|
|
|
|
## Success Metrics
|
|
|
|
The platform's success will be measured by these key metrics:
|
|
|
|
1. **Development Timeline**: Complete core functionality within 14 days
|
|
2. **System Stability**: Maintain 99% uptime during internal testing. System should monitor itself and restart if needed (all or just modules)
|
|
3. **Strategy Testing**: Successfully backtest at least 3 different strategies
|
|
4. **Bot Performance**: Run at least 2 bots concurrently for 72+ hours |