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DEVELOPMENT-ROADMAP.md
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377
DEVELOPMENT-ROADMAP.md
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# 📋 Stock Bot Development Roadmap
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*Last Updated: June 2025*
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## 🎯 Overview
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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.
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## 🏗️ Architecture Philosophy
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```
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Raw Data → Clean Data → Insights → Strategies → Execution → Monitoring
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```
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Our approach prioritizes:
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- **Data Quality First**: Clean, validated data is the foundation
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- **Incremental Complexity**: Start simple, add sophistication gradually
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- **Monitoring Everything**: Observability at each layer
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- **Fault Tolerance**: Graceful handling of failures and data gaps
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---
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## 📊 Phase 1: Data Foundation Layer (Current Focus)
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### 1.1 Data Service & Providers ✅ **In Progress**
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**Current Status**: Basic structure in place, needs enhancement
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**Core Components**:
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- `apps/data-service` - Central data orchestration service
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- Provider implementations:
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- `providers/yahoo.provider.ts` ✅ Basic implementation
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- `providers/quotemedia.provider.ts` ✅ Basic implementation
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- `providers/proxy.provider.ts` ✅ Proxy/fallback logic
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**Immediate Tasks**:
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1. **Enhance Provider Reliability**
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```typescript
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// libs/data-providers (NEW LIBRARY NEEDED)
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interface DataProvider {
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getName(): string;
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getQuote(symbol: string): Promise<Quote>;
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getHistorical(symbol: string, period: TimePeriod): Promise<OHLCV[]>;
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isHealthy(): Promise<boolean>;
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getRateLimit(): RateLimitInfo;
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}
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```
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2. **Add Rate Limiting & Circuit Breakers**
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- Implement in `libs/http` client
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- Add provider-specific rate limits
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- Circuit breaker pattern for failed providers
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3. **Data Validation Layer**
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```typescript
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// libs/data-validation (NEW LIBRARY NEEDED)
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- Price reasonableness checks
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- Volume validation
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- Timestamp validation
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- Missing data detection
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```
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4. **Provider Registry Enhancement**
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- Dynamic provider switching
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- Health-based routing
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- Cost optimization (free → paid fallback)
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### 1.2 Raw Data Storage
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**Storage Strategy**:
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- **QuestDB**: Real-time market data (OHLCV, quotes)
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- **MongoDB**: Provider responses, metadata, configurations
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- **PostgreSQL**: Processed/clean data, trading records
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**Schema Design**:
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```sql
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-- QuestDB Time-Series Tables
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raw_quotes (timestamp, symbol, provider, bid, ask, last, volume)
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raw_ohlcv (timestamp, symbol, provider, open, high, low, close, volume)
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provider_health (timestamp, provider, latency, success_rate, error_rate)
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-- MongoDB Collections
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provider_responses: { provider, symbol, timestamp, raw_response, status }
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data_quality_metrics: { symbol, date, completeness, accuracy, issues[] }
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```
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**Immediate Implementation**:
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1. Enhance `libs/questdb-client` with streaming inserts
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2. Add data retention policies
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3. Implement data compression strategies
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---
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## 🧹 Phase 2: Data Processing & Quality Layer
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### 2.1 Data Cleaning Service ⚡ **Next Priority**
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**New Service**: `apps/processing-service`
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**Core Responsibilities**:
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1. **Data Normalization**
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- Standardize timestamps (UTC)
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- Normalize price formats
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- Handle split/dividend adjustments
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2. **Quality Checks**
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- Outlier detection (price spikes, volume anomalies)
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- Gap filling strategies
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- Cross-provider validation
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3. **Data Enrichment**
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- Calculate derived metrics (returns, volatility)
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- Add technical indicators
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- Market session classification
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**Library Enhancements Needed**:
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```typescript
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// libs/data-frame (ENHANCE EXISTING)
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class MarketDataFrame {
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// Add time-series specific operations
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fillGaps(strategy: GapFillStrategy): MarketDataFrame;
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detectOutliers(method: OutlierMethod): OutlierReport;
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normalize(): MarketDataFrame;
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calculateReturns(period: number): MarketDataFrame;
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}
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// libs/data-quality (NEW LIBRARY)
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interface QualityMetrics {
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completeness: number;
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accuracy: number;
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timeliness: number;
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consistency: number;
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issues: QualityIssue[];
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}
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```
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### 2.2 Technical Indicators Library
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**Enhance**: `libs/strategy-engine` or create `libs/technical-indicators`
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**Initial Indicators**:
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- Moving averages (SMA, EMA, VWAP)
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- Momentum (RSI, MACD, Stochastic)
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- Volatility (Bollinger Bands, ATR)
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- Volume (OBV, Volume Profile)
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```typescript
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// Implementation approach
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interface TechnicalIndicator<T = number> {
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name: string;
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calculate(data: OHLCV[]): T[];
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getSignal(current: T, previous: T[]): Signal;
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}
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```
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---
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## 🧠 Phase 3: Analytics & Strategy Layer
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### 3.1 Strategy Engine Enhancement
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**Current**: Basic structure exists in `libs/strategy-engine`
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**Enhancements Needed**:
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1. **Strategy Framework**
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```typescript
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abstract class TradingStrategy {
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abstract analyze(data: MarketData): StrategySignal[];
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abstract getRiskParams(): RiskParameters;
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backtest(historicalData: MarketData[]): BacktestResults;
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}
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```
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2. **Signal Generation**
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- Entry/exit signals
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- Position sizing recommendations
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- Risk-adjusted scores
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3. **Strategy Types to Implement**:
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- Mean reversion
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- Momentum/trend following
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- Statistical arbitrage
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- Volume-based strategies
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### 3.2 Backtesting Engine
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**New Service**: Enhanced `apps/strategy-service`
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**Features**:
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- Historical simulation
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- Performance metrics calculation
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- Risk analysis
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- Strategy comparison
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---
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## ⚡ Phase 4: Execution Layer
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### 4.1 Portfolio Management
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**Enhance**: `apps/portfolio-service`
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**Core Features**:
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- Position tracking
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- Risk monitoring
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- P&L calculation
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- Margin management
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### 4.2 Order Management
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**New Service**: `apps/order-service`
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**Responsibilities**:
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- Order validation
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- Execution routing
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- Fill reporting
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- Trade reconciliation
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### 4.3 Risk Management
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**New Library**: `libs/risk-engine`
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**Risk Controls**:
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- Position limits
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- Drawdown limits
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- Correlation limits
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- Volatility scaling
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---
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## 📚 Library Improvements Roadmap
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### Immediate (Phase 1-2)
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1. **`libs/http`** ✅ **Current Priority**
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- [ ] Rate limiting middleware
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- [ ] Circuit breaker pattern
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- [ ] Request/response caching
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- [ ] Retry strategies with exponential backoff
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2. **`libs/questdb-client`**
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- [ ] Streaming insert optimization
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- [ ] Batch insert operations
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- [ ] Connection pooling
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- [ ] Query result caching
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3. **`libs/logger`** ✅ **Recently Updated**
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- [x] Migrated to `getLogger()` pattern
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- [ ] Performance metrics logging
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- [ ] Structured trading event logging
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4. **`libs/data-frame`**
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- [ ] Time-series operations
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- [ ] Financial calculations
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- [ ] Memory optimization for large datasets
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### Medium Term (Phase 3)
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5. **`libs/cache`**
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- [ ] Market data caching strategies
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- [ ] Cache warming for frequently accessed symbols
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- [ ] Distributed caching support
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6. **`libs/config`**
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- [ ] Strategy-specific configurations
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- [ ] Dynamic configuration updates
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- [ ] Environment-specific overrides
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### Long Term (Phase 4+)
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7. **`libs/vector-engine`**
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- [ ] Market similarity analysis
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- [ ] Pattern recognition
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- [ ] Correlation analysis
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---
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## 🎯 Immediate Next Steps (Next 2 Weeks)
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### Week 1: Data Provider Hardening
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1. **Enhance HTTP Client** (`libs/http`)
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- Implement rate limiting
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- Add circuit breaker pattern
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- Add comprehensive error handling
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2. **Provider Reliability** (`apps/data-service`)
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- Add health checks for all providers
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- Implement fallback logic
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- Add provider performance monitoring
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3. **Data Validation**
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- Create `libs/data-validation`
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- Implement basic price/volume validation
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- Add data quality metrics
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### Week 2: Processing Foundation
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1. **Start Processing Service** (`apps/processing-service`)
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- Basic data cleaning pipeline
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- Outlier detection
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- Gap filling strategies
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2. **QuestDB Optimization** (`libs/questdb-client`)
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- Implement streaming inserts
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- Add batch operations
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- Optimize for time-series data
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3. **Technical Indicators**
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- Start `libs/technical-indicators`
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- Implement basic indicators (SMA, EMA, RSI)
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---
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## 📊 Success Metrics
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### Phase 1 Completion Criteria
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- [ ] 99.9% data provider uptime
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- [ ] <500ms average data latency
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- [ ] Zero data quality issues for major symbols
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- [ ] All providers monitored and health-checked
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### Phase 2 Completion Criteria
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- [ ] Automated data quality scoring
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- [ ] Gap-free historical data for 100+ symbols
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- [ ] Real-time technical indicator calculation
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- [ ] Processing latency <100ms
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### Phase 3 Completion Criteria
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- [ ] 5+ implemented trading strategies
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- [ ] Comprehensive backtesting framework
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- [ ] Performance analytics dashboard
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---
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## 🚨 Risk Mitigation
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### Data Risks
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- **Provider Failures**: Multi-provider fallback strategy
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- **Data Quality**: Automated validation and alerting
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- **Rate Limits**: Smart request distribution
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### Technical Risks
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- **Scalability**: Horizontal scaling design
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- **Latency**: Optimize critical paths early
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- **Data Loss**: Comprehensive backup strategies
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### Operational Risks
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- **Monitoring**: Full observability stack (Grafana, Loki, Prometheus)
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- **Alerting**: Critical issue notifications
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- **Documentation**: Keep architecture docs current
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---
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## 💡 Innovation Opportunities
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### Machine Learning Integration
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- Predictive models for data quality
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- Anomaly detection in market data
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- Strategy parameter optimization
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### Real-time Processing
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- Stream processing with Kafka/Pulsar
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- Event-driven architecture
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- WebSocket data feeds
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### Advanced Analytics
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- Market microstructure analysis
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- Alternative data integration
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- Cross-asset correlation analysis
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---
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*This roadmap is a living document that will evolve as we learn and adapt. Focus remains on building solid foundations before adding complexity.*
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**Next Review**: End of June 2025
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@ -19,112 +19,205 @@ export const proxyProvider: ProviderConfig = {
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operations: {
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'fetch-and-check': async (payload: { sources?: string[] }) => {
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const { proxyService } = await import('./proxy.tasks');
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const { queueManager } = await import('../services/queue.service');
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await queueManager.drainQueue();
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const proxies = await proxyService.fetchProxiesFromSources();
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const proxiesCount = proxies.length;
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// Get the actual proxies to create individual jobs
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if (proxiesCount > 0) {
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try {
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const { queueManager } = await import('../services/queue.service');
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if (proxies && proxies.length > 0) {
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// Calculate delay distribution over 24 hours
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const totalDelayMs = 24 * 60 * 60 * 1000; // 24 hours in milliseconds
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const delayPerProxy = Math.floor(totalDelayMs / proxies.length);
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if (proxiesCount === 0) {
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logger.info('No proxies fetched, skipping job creation');
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return { proxiesFetched: 0, batchJobsCreated: 0 };
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}
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try {
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// Optimized batch size for 800k proxies
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const batchSize = 200; // Process 200 proxies per batch job
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const totalBatches = Math.ceil(proxies.length / batchSize);
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const totalDelayMs = 24 * 60 * 60 * 1000; // 24 hours
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const delayPerBatch = Math.floor(totalDelayMs / totalBatches);
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logger.info('Creating proxy validation batch jobs', {
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totalProxies: proxies.length,
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batchSize,
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totalBatches,
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delayPerBatch: `${(delayPerBatch / 1000 / 60).toFixed(2)} minutes`,
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estimatedCompletion: '24 hours'
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});
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// Process batches in chunks to avoid memory issues
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const batchCreationChunkSize = 50; // Create 50 batch jobs at a time
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let batchJobsCreated = 0;
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for (let chunkStart = 0; chunkStart < totalBatches; chunkStart += batchCreationChunkSize) {
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const chunkEnd = Math.min(chunkStart + batchCreationChunkSize, totalBatches);
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// Create batch jobs in parallel for this chunk
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const batchPromises = [];
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for (let i = chunkStart; i < chunkEnd; i++) {
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const startIndex = i * batchSize;
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const endIndex = Math.min(startIndex + batchSize, proxies.length);
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const batchProxies = proxies.slice(startIndex, endIndex);
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const delay = i * delayPerBatch;
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logger.info('Creating individual proxy validation jobs', {
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proxyCount: proxies.length,
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distributionPeriod: '24 hours',
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delayPerProxy: `${(delayPerProxy / 1000 / 60).toFixed(2)} minutes`
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const batchPromise = queueManager.addJob({
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type: 'proxy-batch-validation',
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service: 'proxy',
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provider: 'proxy-service',
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operation: 'process-proxy-batch',
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payload: {
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proxies: batchProxies,
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batchIndex: i,
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totalBatches,
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source: 'fetch-and-check'
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},
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priority: 3
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}, {
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delay: delay,
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jobId: `proxy-batch-${i}-${Date.now()}`
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});
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let queuedCount = 0;
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for (let i = 0; i < proxies.length; i++) {
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const proxy = proxies[i];
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const delay = i * delayPerProxy;
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try {
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await queueManager.addJob({
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type: 'proxy-validation',
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service: 'proxy',
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provider: 'proxy-service',
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operation: 'check-proxy',
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payload: {
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proxy: proxy,
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source: 'fetch-and-check',
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autoTriggered: true,
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batchIndex: i,
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totalBatch: proxies.length
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},
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priority: 3
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}, {
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delay: delay
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});
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queuedCount++;
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// Log progress every 100 jobs
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if ((i + 1) % 100 === 0 || i === proxies.length - 1) {
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logger.info('Proxy validation jobs queued progress', {
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queued: i + 1,
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total: proxies.length,
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percentage: `${((i + 1) / proxies.length * 100).toFixed(1)}%`
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});
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}
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} catch (error) {
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logger.error('Failed to queue proxy validation job', {
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proxy: `${proxy.host}:${proxy.port}`,
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batchIndex: i,
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error: error instanceof Error ? error.message : String(error)
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});
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} }
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logger.info('Proxy validation jobs queuing completed', {
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total: proxies.length,
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||||
successful: queuedCount,
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failed: proxies.length - queuedCount,
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||||
totalDelay: `${(totalDelayMs / 1000 / 60 / 60).toFixed(1)} hours`,
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||||
avgDelayPerJob: `${(delayPerProxy / 1000 / 60).toFixed(2)} minutes`
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||||
});
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||||
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||||
return {
|
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proxiesFetched: proxiesCount,
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||||
jobsQueued: queuedCount,
|
||||
totalDelay: `${(totalDelayMs / 1000 / 60 / 60).toFixed(1)} hours`,
|
||||
avgDelayPerJob: `${(delayPerProxy / 1000 / 60).toFixed(2)} minutes`
|
||||
};
|
||||
} else {
|
||||
logger.warn('No proxies found to create validation jobs', {
|
||||
proxiesFetched: proxiesCount
|
||||
});
|
||||
return {
|
||||
proxiesFetched: proxiesCount,
|
||||
jobsQueued: 0,
|
||||
message: 'No cached proxies found'
|
||||
};
|
||||
batchPromises.push(batchPromise);
|
||||
}
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||||
|
||||
} catch (error) {
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||||
logger.error('Failed to create individual proxy validation jobs', {
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||||
proxiesCount,
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||||
error: error instanceof Error ? error.message : String(error)
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||||
// Wait for this chunk to complete
|
||||
const results = await Promise.allSettled(batchPromises);
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||||
const successful = results.filter(r => r.status === 'fulfilled').length;
|
||||
const failed = results.filter(r => r.status === 'rejected').length;
|
||||
|
||||
batchJobsCreated += successful;
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||||
|
||||
logger.info('Batch chunk created', {
|
||||
chunkStart: chunkStart + 1,
|
||||
chunkEnd,
|
||||
totalChunks: Math.ceil(totalBatches / batchCreationChunkSize),
|
||||
successful,
|
||||
failed,
|
||||
totalCreated: batchJobsCreated,
|
||||
progress: `${((chunkEnd / totalBatches) * 100).toFixed(1)}%`
|
||||
});
|
||||
return {
|
||||
proxiesFetched: proxiesCount,
|
||||
jobsQueued: 0,
|
||||
error: error instanceof Error ? error.message : String(error)
|
||||
}; }
|
||||
} else { logger.info('No proxies fetched, skipping job creation');
|
||||
|
||||
// Small delay between chunks to prevent overwhelming Redis
|
||||
if (chunkEnd < totalBatches) {
|
||||
await new Promise(resolve => setTimeout(resolve, 100));
|
||||
}
|
||||
}
|
||||
|
||||
logger.info('All batch jobs creation completed', {
|
||||
totalProxies: proxies.length,
|
||||
batchJobsCreated,
|
||||
totalBatches,
|
||||
avgProxiesPerBatch: Math.floor(proxies.length / totalBatches),
|
||||
estimatedDuration: '24 hours'
|
||||
});
|
||||
|
||||
return {
|
||||
proxiesFetched: 0,
|
||||
jobsQueued: 0,
|
||||
message: 'No proxies fetched'
|
||||
proxiesFetched: proxiesCount,
|
||||
batchJobsCreated,
|
||||
totalBatches,
|
||||
avgProxiesPerBatch: Math.floor(proxies.length / totalBatches)
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
logger.error('Failed to create batch jobs', {
|
||||
proxiesCount,
|
||||
error: error instanceof Error ? error.message : String(error)
|
||||
});
|
||||
throw error;
|
||||
}
|
||||
},
|
||||
|
||||
'process-proxy-batch': async (payload: {
|
||||
proxies: ProxyInfo[],
|
||||
batchIndex: number,
|
||||
totalBatches: number,
|
||||
source: string
|
||||
}) => {
|
||||
const { queueManager } = await import('../services/queue.service');
|
||||
|
||||
logger.info('Processing proxy batch', {
|
||||
batchIndex: payload.batchIndex,
|
||||
batchSize: payload.proxies.length,
|
||||
totalBatches: payload.totalBatches,
|
||||
progress: `${((payload.batchIndex + 1) / payload.totalBatches * 100).toFixed(2)}%`
|
||||
});
|
||||
|
||||
const batchDelayMs = 15 * 60 * 1000; // 15 minutes per batch
|
||||
const delayPerProxy = Math.floor(batchDelayMs / payload.proxies.length);
|
||||
|
||||
logger.info('Batch timing calculated', {
|
||||
batchIndex: payload.batchIndex,
|
||||
proxiesInBatch: payload.proxies.length,
|
||||
batchDurationMinutes: 30,
|
||||
delayPerProxySeconds: Math.floor(delayPerProxy / 1000),
|
||||
delayPerProxyMs: delayPerProxy
|
||||
});
|
||||
|
||||
// Use BullMQ's addBulk for better performance
|
||||
const jobsToCreate = payload.proxies.map((proxy, i) => ({
|
||||
name: 'proxy-validation',
|
||||
data: {
|
||||
type: 'proxy-validation',
|
||||
service: 'proxy',
|
||||
provider: 'proxy-service',
|
||||
operation: 'check-proxy',
|
||||
payload: {
|
||||
proxy: proxy,
|
||||
source: payload.source,
|
||||
batchIndex: payload.batchIndex,
|
||||
proxyIndexInBatch: i,
|
||||
totalBatch: payload.totalBatches
|
||||
},
|
||||
priority: 2
|
||||
},
|
||||
opts: {
|
||||
delay: i * delayPerProxy,
|
||||
jobId: `proxy-${proxy.host}-${proxy.port}-batch${payload.batchIndex}-${Date.now()}-${i}`,
|
||||
removeOnComplete: 3,
|
||||
removeOnFail: 5
|
||||
}
|
||||
}));
|
||||
|
||||
try {
|
||||
const jobs = await queueManager.addBulk(jobsToCreate);
|
||||
|
||||
logger.info('Batch processing completed successfully', {
|
||||
batchIndex: payload.batchIndex,
|
||||
totalProxies: payload.proxies.length,
|
||||
jobsCreated: jobs.length,
|
||||
batchDelay: '15 minutes',
|
||||
progress: `${((payload.batchIndex + 1) / payload.totalBatches * 100).toFixed(2)}%`
|
||||
});
|
||||
|
||||
return {
|
||||
batchIndex: payload.batchIndex,
|
||||
totalProxies: payload.proxies.length,
|
||||
jobsCreated: jobs.length,
|
||||
jobsFailed: 0
|
||||
};
|
||||
} catch (error) {
|
||||
logger.error('Failed to create validation jobs for batch', {
|
||||
batchIndex: payload.batchIndex,
|
||||
batchSize: payload.proxies.length,
|
||||
error: error instanceof Error ? error.message : String(error)
|
||||
});
|
||||
|
||||
return {
|
||||
batchIndex: payload.batchIndex,
|
||||
totalProxies: payload.proxies.length,
|
||||
jobsCreated: 0,
|
||||
jobsFailed: payload.proxies.length
|
||||
};
|
||||
}
|
||||
},
|
||||
'check-proxy': async (payload: {
|
||||
|
||||
'check-proxy': async (payload: {
|
||||
proxy: ProxyInfo,
|
||||
source?: string,
|
||||
batchIndex?: number,
|
||||
proxyIndexInBatch?: number,
|
||||
totalBatch?: number
|
||||
}) => {
|
||||
const { checkProxy } = await import('./proxy.tasks');
|
||||
|
|
@ -132,7 +225,7 @@ export const proxyProvider: ProviderConfig = {
|
|||
logger.debug('Checking individual proxy', {
|
||||
proxy: `${payload.proxy.host}:${payload.proxy.port}`,
|
||||
batchIndex: payload.batchIndex,
|
||||
totalBatch: payload.totalBatch,
|
||||
proxyIndex: payload.proxyIndexInBatch,
|
||||
source: payload.source
|
||||
});
|
||||
|
||||
|
|
@ -148,12 +241,13 @@ export const proxyProvider: ProviderConfig = {
|
|||
return {
|
||||
result: result,
|
||||
batchInfo: {
|
||||
index: payload.batchIndex,
|
||||
batchIndex: payload.batchIndex,
|
||||
proxyIndex: payload.proxyIndexInBatch,
|
||||
total: payload.totalBatch,
|
||||
source: payload.source
|
||||
}
|
||||
};
|
||||
},
|
||||
}
|
||||
},
|
||||
|
||||
scheduledJobs: [
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@ export class QueueService {
|
|||
};
|
||||
|
||||
// Worker configuration
|
||||
const workerCount = parseInt(process.env.WORKER_COUNT || '4');
|
||||
const workerCount = parseInt(process.env.WORKER_COUNT || '5');
|
||||
const concurrencyPerWorker = parseInt(process.env.WORKER_CONCURRENCY || '20');
|
||||
|
||||
this.logger.info('Connecting to Redis/Dragonfly', connection);
|
||||
|
|
@ -180,6 +180,10 @@ export class QueueService {
|
|||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
async addBulk(jobs: any[]) : Promise<any[]> {
|
||||
return await this.queue.addBulk(jobs)
|
||||
}
|
||||
private setupEventListeners() {
|
||||
this.queueEvents.on('completed', (job) => {
|
||||
this.logger.info('Job completed', { id: job.jobId });
|
||||
|
|
@ -396,6 +400,13 @@ export class QueueService {
|
|||
delayed: delayed.length
|
||||
};
|
||||
}
|
||||
|
||||
async drainQueue() {
|
||||
if (!this.isInitialized) {
|
||||
await this.queue.drain()
|
||||
}
|
||||
}
|
||||
|
||||
async getQueueStatus() {
|
||||
if (!this.isInitialized) {
|
||||
throw new Error('Queue service not initialized. Call initialize() first.');
|
||||
|
|
@ -412,12 +423,14 @@ export class QueueService {
|
|||
}
|
||||
};
|
||||
}
|
||||
|
||||
getWorkerCount() {
|
||||
if (!this.isInitialized) {
|
||||
return 0;
|
||||
}
|
||||
return this.workers.length;
|
||||
}
|
||||
|
||||
getRegisteredProviders() {
|
||||
return providerRegistry.getProviders().map(({ key, config }) => ({
|
||||
key,
|
||||
|
|
|
|||
24
libs/data-adjustments/package.json
Normal file
24
libs/data-adjustments/package.json
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
{
|
||||
"name": "@stock-bot/data-adjustments",
|
||||
"version": "1.0.0",
|
||||
"description": "Stock split and dividend adjustment utilities for market data",
|
||||
"type": "module",
|
||||
"main": "dist/index.js",
|
||||
"types": "dist/index.d.ts",
|
||||
"scripts": {
|
||||
"build": "tsc",
|
||||
"test": "bun test",
|
||||
"test:watch": "bun test --watch"
|
||||
},
|
||||
"dependencies": {
|
||||
"@stock-bot/types": "*",
|
||||
"@stock-bot/logger": "*"
|
||||
},
|
||||
"devDependencies": {
|
||||
"typescript": "^5.4.5",
|
||||
"bun-types": "^1.1.12"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"typescript": "^5.0.0"
|
||||
}
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue