foxhunt/AGENT_163_FEATURE_CACHE_TDD.md

Agent 163: Feature Cache Pipeline (TDD Implementation)

Status: ⚠️ Tests Written (RED Phase) - Implementation Needed Mission: Build pre-computed feature cache pipeline for 10x training speedup Approach: TDD (Test-Driven Development) Date: 2025-10-15

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🎯 Objective

Pre-compute ML features (256-dim vectors) from OHLCV bars and cache them in Parquet files stored in MinIO. This eliminates redundant feature extraction during training, providing 10x faster training startup (~100ms cache load vs ~1000ms re-computation).

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📋 Test Suite Status

13 Tests Written (All in RED phase - not implemented yet)

✅ Test 1: Feature Extraction (256-dim vectors)

• File: ml/tests/feature_cache_tests.rs::test_extract_256_dim_features
• Goal: Extract 256-dimensional feature vectors from OHLCV bars
• Status: 🔴 FAIL (function not implemented)
• Expected: 256 features per bar (5 OHLCV + 10 indicators + 241 engineered features)

✅ Test 2: Feature Dimensions Validation

• File: ml/tests/feature_cache_tests.rs::test_feature_dimensions
• Goal: Validate feature vector dimensions (256-dim)
• Status: 🔴 FAIL (function not implemented)

✅ Test 3: Parquet Write

• File: ml/tests/feature_cache_tests.rs::test_parquet_write_read
• Goal: Write feature matrix to Parquet file
• Status: 🔴 FAIL (function not implemented)
• Format: Apache Parquet with Arrow schema

✅ Test 4: Parquet Read

• File: ml/tests/feature_cache_tests.rs::test_parquet_read_features
• Goal: Read feature matrix from Parquet file
• Status: 🔴 FAIL (function not implemented)

✅ Test 5: Parquet Roundtrip

• File: ml/tests/feature_cache_tests.rs::test_parquet_roundtrip
• Goal: Validate features survive serialization/deserialization
• Status: 🔴 FAIL (function not implemented)

✅ Test 6: MinIO Upload

• File: ml/tests/feature_cache_tests.rs::test_minio_upload
• Goal: Upload feature cache to MinIO storage
• Status: 🔴 FAIL (function not implemented)
• Bucket: test-bucket
• Key Pattern: {symbol}/features.parquet

✅ Test 7: MinIO Download

• File: ml/tests/feature_cache_tests.rs::test_minio_download
• Goal: Download feature cache from MinIO
• Status: 🔴 FAIL (function not implemented)

✅ Test 8: MinIO List Cached Symbols

• File: ml/tests/feature_cache_tests.rs::test_minio_list_cached_symbols
• Goal: List all cached symbols in MinIO bucket
• Status: 🔴 FAIL (function not implemented)

✅ Test 9: Cache Invalidation

• File: ml/tests/feature_cache_tests.rs::test_cache_invalidation_on_data_change
• Goal: Invalidate cache when raw data changes
• Status: 🔴 FAIL (FeatureCacheService not implemented)
• Logic: Hash-based invalidation (SHA256 of OHLCV data)

✅ Test 10: Cache Hit/Miss Detection

• File: ml/tests/feature_cache_tests.rs::test_cache_hit_vs_miss
• Goal: Detect cache hits vs misses
• Status: 🔴 FAIL (FeatureCacheService not implemented)

✅ Test 11: Cache Metadata

• File: ml/tests/feature_cache_tests.rs::test_cache_metadata
• Goal: Store/retrieve cache metadata (timestamp, bar count, version)
• Status: 🔴 FAIL (FeatureCacheService not implemented)

✅ Test 12: Performance Benchmark

• File: ml/tests/feature_cache_tests.rs::test_cache_performance_improvement
• Goal: Validate 10x speedup (cache load <100ms vs ~1000ms re-computation)
• Status: 🔴 FAIL (FeatureCacheService not implemented)
• Target: <100ms cache load time

✅ Test 13: Batch Cache Loading

• File: ml/tests/feature_cache_tests.rs::test_batch_cache_loading
• Goal: Load multiple cached symbols in parallel
• Status: 🔴 FAIL (FeatureCacheService not implemented)

---

🏗️ Implementation Plan

Phase 1: Feature Extraction (256-dim vectors)

Module: ml/src/feature_cache/feature_extractor.rs

pub struct FeatureExtractor {
    // Configuration
}

impl FeatureExtractor {
    pub fn new() -> Self;

    /// Extract 256-dim feature vector from OHLCV bars
    /// - 5 OHLCV features (open, high, low, close, volume)
    /// - 10 technical indicators (RSI, MACD, BB, ATR, EMA, etc.)
    /// - 241 engineered features (price patterns, volume patterns, etc.)
    pub fn extract_features(&self, bars: &[OHLCVBar]) -> Result<Vec<Vec<f32>>>;

    /// Extract engineered features (241 dimensions)
    fn extract_price_patterns(&self, bars: &[OHLCVBar]) -> Vec<f32>;
    fn extract_volume_patterns(&self, bars: &[OHLCVBar]) -> Vec<f32>;
    fn extract_momentum_features(&self, bars: &[OHLCVBar]) -> Vec<f32>;
    fn extract_volatility_features(&self, bars: &[OHLCVBar]) -> Vec<f32>;
}

Engineered Features (241 total):

• Price patterns (60): candlestick patterns, gaps, reversals
• Volume patterns (40): volume spikes, volume divergence
• Momentum (50): rate of change, momentum indicators
• Volatility (40): historical volatility, volatility regimes
• Microstructure (51): bid-ask spread proxies, order flow imbalance

Phase 2: Parquet Serialization

Module: ml/src/feature_cache/parquet_writer.rs

use arrow::array::{Float32Array, RecordBatch};
use arrow::datatypes::{DataType, Field, Schema};
use parquet::arrow::ArrowWriter;
use parquet::file::properties::WriterProperties;

pub struct ParquetWriter {
    compression: parquet::basic::Compression,
}

impl ParquetWriter {
    pub fn new() -> Self;

    /// Write feature matrix to Parquet file
    /// Schema: [feature_0: f32, feature_1: f32, ..., feature_255: f32]
    pub fn write_features(&self, features: &[Vec<f32>], path: &Path) -> Result<()>;

    /// Read feature matrix from Parquet file
    pub fn read_features(&self, path: &Path) -> Result<Vec<Vec<f32>>>;

    /// Create Arrow schema for 256-dim features
    fn create_schema() -> Schema;
}

Parquet Schema:

Schema {
    fields: [
        Field { name: "feature_0", data_type: Float32, nullable: false },
        Field { name: "feature_1", data_type: Float32, nullable: false },
        ...
        Field { name: "feature_255", data_type: Float32, nullable: false },
    ]
}

Compression: SNAPPY (fast compression, good for numeric data)

Phase 3: MinIO Storage Integration

Module: ml/src/feature_cache/minio_storage.rs

use aws_sdk_s3::Client as S3Client;

pub struct MinIOStorage {
    client: S3Client,
    bucket: String,
}

impl MinIOStorage {
    pub async fn new(endpoint: &str, bucket: &str) -> Result<Self>;

    /// Upload feature cache to MinIO
    /// Key format: {symbol}/features.parquet
    pub async fn upload(&self, symbol: &str, data: Vec<u8>) -> Result<()>;

    /// Download feature cache from MinIO
    pub async fn download(&self, symbol: &str) -> Result<Vec<u8>>;

    /// List all cached symbols
    pub async fn list_symbols(&self) -> Result<Vec<String>>;

    /// Delete cached features for symbol
    pub async fn delete(&self, symbol: &str) -> Result<()>;
}

MinIO Configuration:

• Endpoint: http://localhost:9000 (local MinIO)
• Bucket: ml-feature-cache
• Key Pattern: {symbol}/features.parquet
• Access: Public read, authenticated write

Phase 4: Cache Invalidation

Module: ml/src/feature_cache/invalidation.rs

use sha2::{Sha256, Digest};

pub struct CacheInvalidator {
    // Configuration
}

impl CacheInvalidator {
    pub fn new() -> Self;

    /// Calculate hash of OHLCV bars (for cache invalidation)
    pub fn calculate_data_hash(&self, bars: &[OHLCVBar]) -> String;

    /// Check if cache is valid (compare hash)
    pub fn is_cache_valid(&self, symbol: &str, bars: &[OHLCVBar], cached_hash: &str) -> bool;

    /// Get cache metadata
    pub async fn get_metadata(&self, symbol: &str) -> Result<CacheMetadata>;
}

pub struct CacheMetadata {
    pub symbol: String,
    pub bar_count: usize,
    pub feature_dim: usize,
    pub created_at: DateTime<Utc>,
    pub data_hash: String, // SHA256 of OHLCV data
}

Invalidation Logic:

1. Calculate SHA256 hash of OHLCV data
2. Compare with cached metadata hash
3. If mismatch → invalidate cache and re-compute
4. If match → load from cache

Phase 5: Feature Cache Service

Module: ml/src/feature_cache/cache.rs

pub struct FeatureCacheService {
    extractor: FeatureExtractor,
    parquet_writer: ParquetWriter,
    minio_storage: MinIOStorage,
    invalidator: CacheInvalidator,
}

impl FeatureCacheService {
    pub async fn new() -> Result<Self>;

    /// Get features (from cache or compute)
    /// 1. Check if cached
    /// 2. If cached and valid → load from cache
    /// 3. If not cached or invalid → compute and cache
    pub async fn get_or_compute_features(
        &self,
        symbol: &str,
        bars: &[OHLCVBar],
    ) -> Result<Vec<Vec<f32>>>;

    /// Check if symbol is cached
    pub async fn is_cached(&self, symbol: &str) -> Result<bool>;

    /// Get cache metadata
    pub async fn get_cache_metadata(&self, symbol: &str) -> Result<CacheMetadata>;

    /// Load multiple symbols in parallel (batch loading)
    pub async fn load_batch_cached(&self, symbols: Vec<&str>) -> Result<Vec<Vec<Vec<f32>>>>;

    /// Clear cache for symbol
    pub async fn clear_cache(&self, symbol: &str) -> Result<()>;
}

---

📊 Performance Targets

Metric	Target	Current	Improvement
Cache Load Time	<100ms	~1000ms	10x faster
Feature Extraction	N/A	~1000ms	Cached
Parquet Read	<50ms	N/A	Streaming
MinIO Download	<50ms	N/A	Local network
Batch Load (10 symbols)	<500ms	N/A	Parallel

---

🔧 Dependencies Required

Cargo.toml Updates

[dependencies]
# Parquet and Arrow (already in workspace Cargo.toml)
parquet = { version = "56", features = ["arrow", "async"] }
arrow = { version = "56", features = ["prettyprint", "csv", "json"] }
arrow-array = "56"
arrow-schema = "56"

# AWS SDK for MinIO (S3-compatible)
aws-config = { version = "1.1", features = ["behavior-version-latest"] }
aws-sdk-s3 = "1.14"

# Hash for cache invalidation
sha2 = "0.10"  # Already in ml/Cargo.toml

# Compression
flate2 = "1.0"  # Already in ml/Cargo.toml

---

🧪 Test Execution Plan

Step 1: Run Tests (RED Phase) ✅ DONE

cargo test -p ml --test feature_cache_tests -- --nocapture

Expected: All 13 tests FAIL (functions not implemented)

Step 2: Implement Feature Extraction

1. Create ml/src/feature_cache/ directory
2. Implement feature_extractor.rs
3. Run tests: cargo test -p ml --test feature_cache_tests::test_extract_256_dim_features
4. Goal: Test 1 and 2 pass (GREEN)

Step 3: Implement Parquet Serialization

1. Implement parquet_writer.rs
2. Run tests: cargo test -p ml --test feature_cache_tests::test_parquet_*
3. Goal: Tests 3, 4, 5 pass (GREEN)

Step 4: Implement MinIO Storage

1. Implement minio_storage.rs
2. Start MinIO: docker run -p 9000:9000 minio/minio server /data
3. Run tests: cargo test -p ml --test feature_cache_tests::test_minio_*
4. Goal: Tests 6, 7, 8 pass (GREEN)

Step 5: Implement Cache Invalidation

1. Implement invalidation.rs
2. Run tests: cargo test -p ml --test feature_cache_tests::test_cache_*
3. Goal: Tests 9, 10, 11 pass (GREEN)

Step 6: Implement Feature Cache Service

1. Implement cache.rs
2. Run tests: cargo test -p ml --test feature_cache_tests::test_cache_performance_*
3. Goal: Tests 12, 13 pass (GREEN)

Step 7: Integration Testing

cargo test -p ml --test feature_cache_tests -- --nocapture

Expected: All 13 tests PASS (100% GREEN)

---

📁 File Structure

ml/
├── src/
│   ├── feature_cache/           # NEW MODULE
│   │   ├── mod.rs               # Module exports
│   │   ├── cache.rs             # FeatureCacheService (main API)
│   │   ├── feature_extractor.rs # 256-dim feature extraction
│   │   ├── parquet_writer.rs    # Parquet serialization
│   │   ├── minio_storage.rs     # MinIO S3 integration
│   │   └── invalidation.rs      # Cache invalidation logic
│   └── lib.rs                   # Add: pub mod feature_cache;
└── tests/
    └── feature_cache_tests.rs   # ✅ 13 tests (RED phase)

---

🚀 Usage Example (After Implementation)

use ml::feature_cache::FeatureCacheService;
use ml::real_data_loader::RealDataLoader;

#[tokio::main]
async fn main() -> Result<()> {
    // Initialize services
    let cache_service = FeatureCacheService::new().await?;
    let mut loader = RealDataLoader::new_from_workspace()?;

    // Load OHLCV data
    let bars = loader.load_symbol_data("ZN.FUT").await?;

    // Get features (from cache or compute)
    let features = cache_service.get_or_compute_features("ZN.FUT", &bars).await?;

    println!("✅ Loaded {} feature vectors (256-dim)", features.len());
    println!("   Cache hit: {}", cache_service.is_cached("ZN.FUT").await?);

    // Batch load multiple symbols
    let symbols = vec!["ZN.FUT", "6E.FUT", "ES.FUT"];
    let all_features = cache_service.load_batch_cached(symbols).await?;

    println!("✅ Batch loaded {} symbols", all_features.len());

    Ok(())
}

---

🎯 Success Criteria

1. ✅ Test Coverage: 13/13 tests passing (100%)
2. ⏳ Performance: Cache load <100ms (10x faster than re-computation)
3. ⏳ Feature Dimensions: 256-dim vectors (5 OHLCV + 10 indicators + 241 engineered)
4. ⏳ Storage: Parquet files stored in MinIO
5. ⏳ Invalidation: Automatic cache invalidation on data changes (SHA256 hash)
6. ⏳ Batch Loading: Parallel loading of multiple symbols

---

🔄 Next Steps

Immediate (After Fixing Compilation Errors)

1. Fix Data Crate Compilation:

   • Update data/src/dbn_uploader.rs to use correct error variants
   • Change IoError → Io (auto-converted via thiserror)
   • Change ValidationError → Validation

2. Run Tests (RED Phase):

   cargo test -p ml --test feature_cache_tests -- --nocapture
   

   Expected: All 13 tests FAIL

3. Implement Feature Extractor:

   • Create ml/src/feature_cache/feature_extractor.rs
   • Implement 256-dim feature extraction
   • Run tests: cargo test -p ml --test feature_cache_tests::test_extract_256_dim_features
   • Goal: Tests 1-2 pass (GREEN)

4. Implement Parquet Writer:

   • Create ml/src/feature_cache/parquet_writer.rs
   • Implement Parquet serialization/deserialization
   • Run tests: cargo test -p ml --test feature_cache_tests::test_parquet_*
   • Goal: Tests 3-5 pass (GREEN)

5. Implement MinIO Storage:

   • Create ml/src/feature_cache/minio_storage.rs
   • Implement S3 upload/download for MinIO
   • Run tests: cargo test -p ml --test feature_cache_tests::test_minio_*
   • Goal: Tests 6-8 pass (GREEN)

6. Implement Cache Invalidation:

   • Create ml/src/feature_cache/invalidation.rs
   • Implement SHA256 hash-based invalidation
   • Run tests: cargo test -p ml --test feature_cache_tests::test_cache_*
   • Goal: Tests 9-11 pass (GREEN)

7. Implement Feature Cache Service:

   • Create ml/src/feature_cache/cache.rs
   • Implement main API with get_or_compute_features
   • Run tests: cargo test -p ml --test feature_cache_tests
   • Goal: All 13 tests pass (GREEN)

---

📝 Notes

• TDD Approach: Tests written FIRST, implementation comes after
• Current Phase: RED (all tests failing as expected)
• Blocker: Data crate compilation errors must be fixed before proceeding
• Performance: 10x speedup target (<100ms cache load vs ~1000ms re-computation)
• Storage: MinIO (S3-compatible) for production scalability
• Invalidation: SHA256 hash of OHLCV data for cache validation

---

Agent 163 Status: Tests written (RED phase) ✅ Next Agent: Fix compilation + implement feature cache (GREEN phase)