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## Architecture Components
### 1. Data Collector
**Responsibility**: Unified data collection from multiple exchanges
```python
class DataCollector:
def __init__(self):
self.providers = {} # Registry of data providers
def register_provider(self, name: str, provider: DataProvider):
"""Register a new data provider"""
def start_collection(self, symbols: List[str]):
"""Start collecting data for specified symbols"""
def process_raw_data(self, raw_data: dict):
"""Process raw data into OHLCV format"""
def send_signal_to_bots(self, processed_data: dict):
"""Send Redis signal to active bots"""
```
### 2. Strategy Engine
**Responsibility**: Unified interface for all trading strategies
```python
class BaseStrategy:
def __init__(self, parameters: dict):
self.parameters = parameters
def process_data(self, data: pd.DataFrame) -> Signal:
"""Process market data and generate signals"""
raise NotImplementedError
def get_indicators(self) -> dict:
"""Return calculated indicators for plotting"""
return {}
```
### 3. Bot Manager
**Responsibility**: Orchestrate bot execution and state management
```python
class BotManager:
def __init__(self):
self.active_bots = {}
def start_bot(self, bot_id: int):
"""Start a bot instance"""
def stop_bot(self, bot_id: int):
"""Stop a bot instance"""
def process_signal(self, bot_id: int, signal: Signal):
"""Process signal and make trading decision"""
def update_bot_state(self, bot_id: int, state: dict):
"""Update bot state in database"""
```
## Communication Architecture
### Redis Pub/Sub Patterns
```python
# Real-time market data
MARKET_DATA_CHANNEL = "market_data:{symbol}"
# Bot-specific signals
BOT_SIGNAL_CHANNEL = "bot_signals:{bot_id}"
# Trade updates
TRADE_UPDATE_CHANNEL = "trade_updates:{bot_id}"
# System events
SYSTEM_EVENT_CHANNEL = "system_events"
```
### WebSocket Communication
```python
# Frontend real-time updates
WS_BOT_STATUS = "/ws/bot/{bot_id}/status"
WS_MARKET_DATA = "/ws/market/{symbol}"
WS_PORTFOLIO = "/ws/portfolio/{bot_id}"
```
## Time Aggregation Strategy
### Candlestick Alignment
- **Use RIGHT-ALIGNED timestamps** (industry standard)
- 5-minute candle with timestamp 09:05:00 represents data from 09:00:01 to 09:05:00
- Timestamp = close time of the candle
- Aligns with major exchanges (Binance, OKX, Coinbase)
### Aggregation Logic
```python
def aggregate_to_timeframe(ticks: List[dict], timeframe: str) -> dict:
"""
Aggregate tick data to specified timeframe
timeframe: '1m', '5m', '15m', '1h', '4h', '1d'
"""
# Convert timeframe to seconds
interval_seconds = parse_timeframe(timeframe)
# Group ticks by time intervals (right-aligned)
for group in group_by_interval(ticks, interval_seconds):
candle = {
'timestamp': group.end_time, # Right-aligned
'open': group.first_price,
'high': group.max_price,
'low': group.min_price,
'close': group.last_price,
'volume': group.total_volume
}
yield candle
```
## Backtesting Optimization
### Parallel Processing Strategy
```python
import multiprocessing as mp
from joblib import Parallel, delayed
import numba
@numba.jit(nopython=True)
def calculate_signals_vectorized(prices, parameters):
"""Vectorized signal calculation using Numba"""
# High-performance signal calculation
return signals
def backtest_strategy_batch(data_batch, strategy_params):
"""Backtest a batch of data in parallel"""
# Process batch of signals
signals = calculate_signals_vectorized(data_batch, strategy_params)
# Simulate trades incrementally
portfolio = simulate_trades(signals, data_batch)
return portfolio
# Parallel backtesting
def run_parallel_backtest(data, strategy_params, n_jobs=4):
data_batches = split_data_into_batches(data, n_jobs)
results = Parallel(n_jobs=n_jobs)(
delayed(backtest_strategy_batch)(batch, strategy_params)
for batch in data_batches
)
return combine_results(results)
```
### Optimization Techniques
1. **Vectorized Operations**: Use NumPy/Pandas for bulk calculations
2. **Numba JIT**: Compile critical loops for C-like performance
3. **Batch Processing**: Process signals in batches, simulate trades incrementally
4. **Memory Management**: Use efficient data structures (arrays vs lists)
5. **Parallel Execution**: Utilize multiple CPU cores for independent calculations
## Key Design Principles
1. **Data Separation**: Raw and processed data stored separately for audit trail
2. **Signal Tracking**: All signals recorded (executed or not) for analysis
3. **Real-time State**: Bot states updated in real-time for monitoring
4. **Audit Trail**: Complete record of all trading activities
5. **Scalability**: Architecture supports multiple bots and strategies
6. **Modularity**: Clear separation between data collection, strategy execution, and trading
7. **Fault Tolerance**: Redis for reliable message delivery, database transactions for consistency

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

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# Dependency Management Guide
This guide explains how to manage Python dependencies in the Crypto Trading Bot Dashboard project.
## Local Development
### Adding New Dependencies
#### 1. Core Dependencies (Required for Runtime)
To add a new core dependency:
```bash
# Method 1: Add directly to pyproject.toml
# Edit pyproject.toml and add to the dependencies list:
# "new-package>=1.0.0",
# Method 2: Use UV to add and update pyproject.toml
uv add "new-package>=1.0.0"
# Sync to install
uv sync
```
#### 2. Development Dependencies (Testing, Linting, etc.)
```bash
# Add development-only dependency
uv add --dev "new-dev-package>=1.0.0"
# Or edit pyproject.toml under [project.optional-dependencies.dev]
# Then run:
uv sync --dev
```
### Installing Dependencies
```bash
# Install all dependencies
uv sync
# Install with development dependencies
uv sync --dev
# Install only production dependencies
uv sync --no-dev
```
### Updating Dependencies
```bash
# Update all dependencies to latest compatible versions
uv sync --upgrade
# Update specific package
uv sync --upgrade-package "package-name"
```
## Docker Environment
### Current Approach
The project uses a **volume-based development** approach where:
- Dependencies are installed in the local environment using UV
- Docker containers provide only infrastructure services (PostgreSQL, Redis)
- The Python application runs locally with hot reload
### Adding Dependencies for Docker-based Development
If you want to run the entire application in Docker:
#### 1. Create a Dockerfile
```dockerfile
FROM python:3.10-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y \
postgresql-client \
curl \
&& rm -rf /var/lib/apt/lists/*
# Install UV
RUN pip install uv
# Copy dependency files
COPY pyproject.toml ./
COPY README.md ./
# Install dependencies
RUN uv sync --no-dev
# Copy application code
COPY . .
# Expose port
EXPOSE 8050
# Run application
CMD ["uv", "run", "python", "main.py"]
```
#### 2. Add Application Service to docker-compose.yml
```yaml
services:
app:
build: .
container_name: dashboard_app
ports:
- "8050:8050"
volumes:
- .:/app
- uv_cache:/root/.cache/uv
environment:
- DATABASE_URL=postgresql://dashboard:dashboard123@postgres:5432/dashboard
- REDIS_URL=redis://redis:6379
depends_on:
- postgres
- redis
networks:
- dashboard-network
restart: unless-stopped
volumes:
uv_cache:
```
#### 3. Development Workflow with Docker
```bash
# Build and start all services
docker-compose up --build
# Add new dependency
# 1. Edit pyproject.toml
# 2. Rebuild container
docker-compose build app
docker-compose up -d app
# Or use dev dependencies mount
# Mount local UV cache for faster rebuilds
```
## Hot Reload Development
### Method 1: Local Development (Recommended)
Run services in Docker, application locally with hot reload:
```bash
# Start infrastructure
python scripts/dev.py start
# Run app with hot reload
uv run python scripts/dev.py dev-server
```
### Method 2: Docker with Volume Mounts
If using Docker for the app, mount source code:
```yaml
volumes:
- .:/app # Mount source code
- /app/__pycache__ # Exclude cache
```
## Best Practices
### 1. Version Pinning
```toml
# Good: Specify minimum version with compatibility
"requests>=2.31.0,<3.0.0"
# Acceptable: Major version constraint
"pandas>=2.1.0"
# Avoid: Exact pinning (except for critical deps)
"somepackage==1.2.3" # Only if necessary
```
### 2. Dependency Categories
```toml
[project]
dependencies = [
# Core web framework
"dash>=2.14.0",
# Database
"sqlalchemy>=2.0.0",
"psycopg2-binary>=2.9.0",
# ... group related dependencies
]
```
### 3. Security Updates
```bash
# Check for security vulnerabilities
pip-audit
# Update specific vulnerable package
uv sync --upgrade-package "vulnerable-package"
```
## Troubleshooting
### Common Issues
1. **Dependency Conflicts**
```bash
# Clear UV cache and reinstall
uv cache clean
uv sync --refresh
```
2. **PostgreSQL Connection Issues**
```bash
# Ensure psycopg2-binary is installed
uv add "psycopg2-binary>=2.9.0"
```
3. **Docker Build Failures**
```bash
# Clean docker build cache
docker system prune --volumes
docker-compose build --no-cache
```
### Debugging Dependencies
```bash
# Show installed packages
uv pip list
# Show dependency tree
uv pip show <package-name>
# Check for conflicts
uv pip check
```
## Migration from requirements.txt
If you have an existing `requirements.txt`:
```bash
# Convert to pyproject.toml
uv add -r requirements.txt
# Or manually copy dependencies to pyproject.toml
# Then remove requirements.txt
```

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@@ -1,283 +0,0 @@
# Development Environment Setup
This guide will help you set up the Crypto Trading Bot Dashboard development environment.
## Prerequisites
- Python 3.10+
- Docker Desktop (for Windows/Mac) or Docker Engine (for Linux)
- UV package manager
- Git
## Quick Start
### 1. Initial Setup
```bash
# Install dependencies (including dev tools)
uv sync --dev
# Set up environment and start services
python scripts/dev.py setup
```
### 2. Start Services
```bash
# Start PostgreSQL and Redis services
python scripts/dev.py start
```
### 3. Configure API Keys
Copy `env.template` to `.env` and update the OKX API credentials:
```bash
# Copy template (Windows)
copy env.template .env
# Copy template (Unix)
cp env.template .env
# Edit .env file with your actual OKX API credentials
# OKX_API_KEY=your_actual_api_key
# OKX_SECRET_KEY=your_actual_secret_key
# OKX_PASSPHRASE=your_actual_passphrase
```
### 4. Verify Setup
```bash
# Run setup verification tests
uv run python tests/test_setup.py
```
### 5. Start Dashboard with Hot Reload
```bash
# Start with hot reload (recommended for development)
python scripts/dev.py dev-server
# Or start without hot reload
python scripts/dev.py run
```
## Development Commands
### Using the dev.py script:
```bash
# Show available commands
python scripts/dev.py
# Set up environment and install dependencies
python scripts/dev.py setup
# Start all services (Docker)
python scripts/dev.py start
# Stop all services
python scripts/dev.py stop
# Restart services
python scripts/dev.py restart
# Check service status
python scripts/dev.py status
# Install/update dependencies
python scripts/dev.py install
# Run development server with hot reload
python scripts/dev.py dev-server
# Run application without hot reload
python scripts/dev.py run
```
### Direct Docker commands:
```bash
# Start services in background
docker-compose up -d
# View service logs
docker-compose logs -f
# Stop services
docker-compose down
# Rebuild and restart
docker-compose up -d --build
```
## Hot Reload Development
The development server includes hot reload functionality that automatically restarts the application when Python files change.
### Features:
- 🔥 **Auto-restart** on file changes
- 👀 **Watches multiple directories** (config, database, components, etc.)
- 🚀 **Fast restart** with debouncing (1-second delay)
- 🛑 **Graceful shutdown** with Ctrl+C
### Usage:
```bash
# Start hot reload server
python scripts/dev.py dev-server
# The server will watch these directories:
# - . (root)
# - config/
# - database/
# - components/
# - data/
# - strategies/
# - trader/
```
## Data Persistence
### Database Persistence
✅ **PostgreSQL data persists** across container restarts
- Volume: `postgres_data` mounted to `/var/lib/postgresql/data`
- Data survives `docker-compose down` and `docker-compose up`
### Redis Persistence
✅ **Redis data persists** with AOF (Append-Only File)
- Volume: `redis_data` mounted to `/data`
- AOF sync every second for durability
- Data survives container restarts
### Removing Persistent Data
```bash
# Stop services and remove volumes (CAUTION: This deletes all data)
docker-compose down -v
# Or remove specific volumes
docker volume rm dashboard_postgres_data
docker volume rm dashboard_redis_data
```
## Dependency Management
### Adding New Dependencies
```bash
# Add runtime dependency
uv add "package-name>=1.0.0"
# Add development dependency
uv add --dev "dev-package>=1.0.0"
# Install all dependencies
uv sync --dev
```
### Key Dependencies Included:
- **Web Framework**: Dash, Plotly
- **Database**: SQLAlchemy, psycopg2-binary, Alembic
- **Data Processing**: pandas, numpy
- **Configuration**: pydantic, python-dotenv
- **Development**: watchdog (hot reload), pytest, black, mypy
See `docs/dependency-management.md` for detailed dependency management guide.
## Directory Structure
```
Dashboard/
├── config/ # Configuration files
│ ├── settings.py # Application settings
│ └── bot_configs/ # Bot configuration files
├── database/ # Database related files
│ └── init/ # Database initialization scripts
├── scripts/ # Development scripts
│ ├── dev.py # Main development script
│ ├── setup.sh # Setup script (Unix)
│ ├── start.sh # Start script (Unix)
│ └── stop.sh # Stop script (Unix)
├── tests/ # Test files
│ └── test_setup.py # Setup verification tests
├── docs/ # Documentation
│ ├── setup.md # This file
│ └── dependency-management.md # Dependency guide
├── docker-compose.yml # Docker services configuration
├── env.template # Environment variables template
├── pyproject.toml # Dependencies and project config
└── main.py # Main application entry point
```
## Services
### PostgreSQL Database
- **Host**: localhost:5432
- **Database**: dashboard
- **User**: dashboard
- **Password**: dashboard123 (development only)
- **Persistence**: ✅ Data persists across restarts
### Redis Cache
- **Host**: localhost:6379
- **No password** (development only)
- **Persistence**: ✅ AOF enabled, data persists across restarts
## Environment Variables
Key environment variables (see `env.template` for full list):
- `DATABASE_URL` - PostgreSQL connection string
- `OKX_API_KEY` - OKX API key
- `OKX_SECRET_KEY` - OKX secret key
- `OKX_PASSPHRASE` - OKX passphrase
- `OKX_SANDBOX` - Use OKX sandbox (true/false)
- `DEBUG` - Enable debug mode
- `LOG_LEVEL` - Logging level (DEBUG, INFO, WARNING, ERROR)
## Troubleshooting
### Docker Issues
1. **Docker not running**: Start Docker Desktop/Engine
2. **Port conflicts**: Check if ports 5432 or 6379 are already in use
3. **Permission issues**: On Linux, add your user to the docker group
4. **Data persistence issues**: Check if volumes are properly mounted
### Database Connection Issues
1. **Connection refused**: Ensure PostgreSQL container is running
2. **Authentication failed**: Check credentials in `.env` file
3. **Database doesn't exist**: Run the setup script again
4. **Data loss**: Check if volume is mounted correctly
### Dependency Issues
1. **Import errors**: Run `uv sync --dev` to install dependencies
2. **Version conflicts**: Check `pyproject.toml` for compatibility
3. **Hot reload not working**: Ensure `watchdog` is installed
### Hot Reload Issues
1. **Changes not detected**: Check if files are in watched directories
2. **Rapid restarts**: Built-in 1-second debouncing should prevent this
3. **Process not stopping**: Use Ctrl+C to gracefully shutdown
## Performance Tips
1. **Use SSD**: Store Docker volumes on SSD for better database performance
2. **Increase Docker memory**: Allocate more RAM to Docker Desktop
3. **Hot reload**: Use `dev-server` for faster development cycles
4. **Dependency caching**: UV caches dependencies for faster installs
## Next Steps
After successful setup:
1. **Phase 1.0**: Database Infrastructure Setup
2. **Phase 2.0**: Bot Management System Development
3. **Phase 3.0**: OKX Integration and Data Pipeline
4. **Phase 4.0**: Dashboard UI and Visualization
5. **Phase 5.0**: Backtesting System Implementation
See `tasks/tasks-prd-crypto-bot-dashboard.md` for detailed task list.

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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 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.