# 📋 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**:

1. **Enhance Provider Reliability**
   ```typescript
   // 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;
   }
   ```

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

3. **Data Validation Layer**
   ```typescript
   // libs/data-validation (NEW LIBRARY NEEDED)
   - Price reasonableness checks
   - Volume validation
   - Timestamp validation
   - Missing data detection
   ```

4. **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**:
```sql
-- 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**:
1. Enhance `libs/questdb-client` with streaming inserts
2. Add data retention policies
3. Implement data compression strategies

---

## 🧹 Phase 2: Data Processing & Quality Layer

### 2.1 Data Cleaning Service ⚡ **Next Priority**

**New Service**: `apps/processing-service`

**Core Responsibilities**:
1. **Data Normalization**
   - Standardize timestamps (UTC)
   - Normalize price formats
   - Handle split/dividend adjustments

2. **Quality Checks**
   - Outlier detection (price spikes, volume anomalies)
   - Gap filling strategies
   - Cross-provider validation

3. **Data Enrichment**
   - Calculate derived metrics (returns, volatility)
   - Add technical indicators
   - Market session classification

**Library Enhancements Needed**:

```typescript
// 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)

```typescript
// 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**:

1. **Strategy Framework**
   ```typescript
   abstract class TradingStrategy {
     abstract analyze(data: MarketData): StrategySignal[];
     abstract getRiskParams(): RiskParameters;
     backtest(historicalData: MarketData[]): BacktestResults;
   }
   ```

2. **Signal Generation**
   - Entry/exit signals
   - Position sizing recommendations
   - Risk-adjusted scores

3. **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)

1. **`libs/http`** ✅ **Current Priority**
   - [ ] Rate limiting middleware
   - [ ] Circuit breaker pattern
   - [ ] Request/response caching
   - [ ] Retry strategies with exponential backoff

2. **`libs/questdb-client`** 
   - [ ] Streaming insert optimization
   - [ ] Batch insert operations
   - [ ] Connection pooling
   - [ ] Query result caching

3. **`libs/logger`** ✅ **Recently Updated**
   - [x] Migrated to `getLogger()` pattern
   - [ ] Performance metrics logging
   - [ ] Structured trading event logging

4. **`libs/data-frame`**
   - [ ] Time-series operations
   - [ ] Financial calculations
   - [ ] Memory optimization for large datasets

### Medium Term (Phase 3)

5. **`libs/cache`**
   - [ ] Market data caching strategies
   - [ ] Cache warming for frequently accessed symbols
   - [ ] Distributed caching support

6. **`libs/config`**
   - [ ] Strategy-specific configurations
   - [ ] Dynamic configuration updates
   - [ ] Environment-specific overrides

### Long Term (Phase 4+)

7. **`libs/vector-engine`**
   - [ ] Market similarity analysis
   - [ ] Pattern recognition
   - [ ] Correlation analysis

---

## 🎯 Immediate Next Steps (Next 2 Weeks)

### Week 1: Data Provider Hardening
1. **Enhance HTTP Client** (`libs/http`)
   - Implement rate limiting
   - Add circuit breaker pattern
   - Add comprehensive error handling

2. **Provider Reliability** (`apps/data-service`)
   - Add health checks for all providers
   - Implement fallback logic
   - Add provider performance monitoring

3. **Data Validation** 
   - Create `libs/data-validation`
   - Implement basic price/volume validation
   - Add data quality metrics

### Week 2: Processing Foundation
1. **Start Processing Service** (`apps/processing-service`)
   - Basic data cleaning pipeline
   - Outlier detection
   - Gap filling strategies

2. **QuestDB Optimization** (`libs/questdb-client`)
   - Implement streaming inserts
   - Add batch operations
   - Optimize for time-series data

3. **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