2025-06-09 22:55:51 -04:00

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📋 Stock Bot Development Roadmap

Last Updated: June 2025

🎯 Overview

This document outlines the development plan for the Stock Bot platform, focusing on building a robust data pipeline from market data providers through processing layers to trading execution. The plan emphasizes establishing solid foundational layers before adding advanced features.

🏗️ Architecture Philosophy

Raw Data → Clean Data → Insights → Strategies → Execution → Monitoring

Our approach prioritizes:

Data Quality First: Clean, validated data is the foundation
Incremental Complexity: Start simple, add sophistication gradually
Monitoring Everything: Observability at each layer
Fault Tolerance: Graceful handling of failures and data gaps

📊 Phase 1: Data Foundation Layer (Current Focus)

1.1 Data Service & Providers ✅ In Progress

Current Status: Basic structure in place, needs enhancement

Core Components:

apps/data-service - Central data orchestration service
Provider implementations:
- providers/yahoo.provider.ts ✅ Basic implementation
- providers/quotemedia.provider.ts ✅ Basic implementation
- providers/proxy.provider.ts ✅ Proxy/fallback logic

Immediate Tasks:

Enhance Provider Reliability

// libs/data-providers (NEW LIBRARY NEEDED)
interface DataProvider {
  getName(): string;
  getQuote(symbol: string): Promise<Quote>;
  getHistorical(symbol: string, period: TimePeriod): Promise<OHLCV[]>;
  isHealthy(): Promise<boolean>;
  getRateLimit(): RateLimitInfo;
}

Add Rate Limiting & Circuit Breakers
- Implement in libs/http client
- Add provider-specific rate limits
- Circuit breaker pattern for failed providers

Data Validation Layer

// libs/data-validation (NEW LIBRARY NEEDED)
- Price reasonableness checks
- Volume validation
- Timestamp validation
- Missing data detection

Provider Registry Enhancement
- Dynamic provider switching
- Health-based routing
- Cost optimization (free → paid fallback)

1.2 Raw Data Storage

Storage Strategy:

QuestDB: Real-time market data (OHLCV, quotes)
MongoDB: Provider responses, metadata, configurations
PostgreSQL: Processed/clean data, trading records

Schema Design:

-- QuestDB Time-Series Tables
raw_quotes (timestamp, symbol, provider, bid, ask, last, volume)
raw_ohlcv (timestamp, symbol, provider, open, high, low, close, volume)
provider_health (timestamp, provider, latency, success_rate, error_rate)

-- MongoDB Collections
provider_responses: { provider, symbol, timestamp, raw_response, status }
data_quality_metrics: { symbol, date, completeness, accuracy, issues[] }

Immediate Implementation:

Enhance libs/questdb-client with streaming inserts
Add data retention policies
Implement data compression strategies

🧹 Phase 2: Data Processing & Quality Layer

2.1 Data Cleaning Service ⚡ Next Priority

New Service: apps/processing-service

Core Responsibilities:

Data Normalization
- Standardize timestamps (UTC)
- Normalize price formats
- Handle split/dividend adjustments
Quality Checks
- Outlier detection (price spikes, volume anomalies)
- Gap filling strategies
- Cross-provider validation
Data Enrichment
- Calculate derived metrics (returns, volatility)
- Add technical indicators
- Market session classification

Library Enhancements Needed:

// libs/data-frame (ENHANCE EXISTING)
class MarketDataFrame {
  // Add time-series specific operations
  fillGaps(strategy: GapFillStrategy): MarketDataFrame;
  detectOutliers(method: OutlierMethod): OutlierReport;
  normalize(): MarketDataFrame;
  calculateReturns(period: number): MarketDataFrame;
}

// libs/data-quality (NEW LIBRARY)
interface QualityMetrics {
  completeness: number;
  accuracy: number;
  timeliness: number;
  consistency: number;
  issues: QualityIssue[];
}

2.2 Technical Indicators Library

Enhance: libs/strategy-engine or create libs/technical-indicators

Initial Indicators:

Moving averages (SMA, EMA, VWAP)
Momentum (RSI, MACD, Stochastic)
Volatility (Bollinger Bands, ATR)
Volume (OBV, Volume Profile)

// Implementation approach
interface TechnicalIndicator<T = number> {
  name: string;
  calculate(data: OHLCV[]): T[];
  getSignal(current: T, previous: T[]): Signal;
}

🧠 Phase 3: Analytics & Strategy Layer

3.1 Strategy Engine Enhancement

Current: Basic structure exists in libs/strategy-engine

Enhancements Needed:

Strategy Framework

abstract class TradingStrategy {
  abstract analyze(data: MarketData): StrategySignal[];
  abstract getRiskParams(): RiskParameters;
  backtest(historicalData: MarketData[]): BacktestResults;
}

Signal Generation
- Entry/exit signals
- Position sizing recommendations
- Risk-adjusted scores
Strategy Types to Implement:
- Mean reversion
- Momentum/trend following
- Statistical arbitrage
- Volume-based strategies

3.2 Backtesting Engine

New Service: Enhanced apps/strategy-service

Features:

Historical simulation
Performance metrics calculation
Risk analysis
Strategy comparison

⚡ Phase 4: Execution Layer

4.1 Portfolio Management

Enhance: apps/portfolio-service

Core Features:

Position tracking
Risk monitoring
P&L calculation
Margin management

4.2 Order Management

New Service: apps/order-service

Responsibilities:

Order validation
Execution routing
Fill reporting
Trade reconciliation

4.3 Risk Management

New Library: libs/risk-engine

Risk Controls:

Position limits
Drawdown limits
Correlation limits
Volatility scaling

📚 Library Improvements Roadmap

Immediate (Phase 1-2)

libs/http ✅ Current Priority
- Rate limiting middleware
- Circuit breaker pattern
- Request/response caching
- Retry strategies with exponential backoff
libs/questdb-client
- Streaming insert optimization
- Batch insert operations
- Connection pooling
- Query result caching
libs/logger ✅ Recently Updated
- Migrated to getLogger() pattern
- Performance metrics logging
- Structured trading event logging
libs/data-frame
- Time-series operations
- Financial calculations
- Memory optimization for large datasets

Medium Term (Phase 3)

libs/cache
- Market data caching strategies
- Cache warming for frequently accessed symbols
- Distributed caching support
libs/config
- Strategy-specific configurations
- Dynamic configuration updates
- Environment-specific overrides

Long Term (Phase 4+)

libs/vector-engine
- Market similarity analysis
- Pattern recognition
- Correlation analysis

🎯 Immediate Next Steps (Next 2 Weeks)

Week 1: Data Provider Hardening

Enhance HTTP Client (libs/http)
- Implement rate limiting
- Add circuit breaker pattern
- Add comprehensive error handling
Provider Reliability (apps/data-service)
- Add health checks for all providers
- Implement fallback logic
- Add provider performance monitoring
Data Validation
- Create libs/data-validation
- Implement basic price/volume validation
- Add data quality metrics

Week 2: Processing Foundation

Start Processing Service (apps/processing-service)
- Basic data cleaning pipeline
- Outlier detection
- Gap filling strategies
QuestDB Optimization (libs/questdb-client)
- Implement streaming inserts
- Add batch operations
- Optimize for time-series data
Technical Indicators
- Start libs/technical-indicators
- Implement basic indicators (SMA, EMA, RSI)

📊 Success Metrics

Phase 1 Completion Criteria

99.9% data provider uptime
<500ms average data latency
Zero data quality issues for major symbols
All providers monitored and health-checked

Phase 2 Completion Criteria

Automated data quality scoring
Gap-free historical data for 100+ symbols
Real-time technical indicator calculation
Processing latency <100ms

Phase 3 Completion Criteria

5+ implemented trading strategies
Comprehensive backtesting framework
Performance analytics dashboard

🚨 Risk Mitigation

Data Risks

Provider Failures: Multi-provider fallback strategy
Data Quality: Automated validation and alerting
Rate Limits: Smart request distribution

Technical Risks

Scalability: Horizontal scaling design
Latency: Optimize critical paths early
Data Loss: Comprehensive backup strategies

Operational Risks

Monitoring: Full observability stack (Grafana, Loki, Prometheus)
Alerting: Critical issue notifications
Documentation: Keep architecture docs current

💡 Innovation Opportunities

Machine Learning Integration

Predictive models for data quality
Anomaly detection in market data
Strategy parameter optimization

Real-time Processing

Stream processing with Kafka/Pulsar
Event-driven architecture
WebSocket data feeds

Advanced Analytics

Market microstructure analysis
Alternative data integration
Cross-asset correlation analysis

This roadmap is a living document that will evolve as we learn and adapt. Focus remains on building solid foundations before adding complexity.

Next Review: End of June 2025

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