linxus fs fixes
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ac23b70146
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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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|
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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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||||
|
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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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|
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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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||||
|
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**Next Review**: End of June 2025
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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**:
|
||||
|
||||
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
|
||||
|
|
|
|||
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