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65. Ultra-Scale Backtest Data Lake

Overview

The Ultra-Scale Backtest Data Lake provides centralized, high-performance management of massive backtest datasets (tick, Level2, event streams) for millions of strategies. It supports efficient storage, fast retrieval, metadata management, compression, and distributed scalability—enabling institutional-grade research and machine learning.

Architecture & Module Breakdown

Module Description
Data Lake Storage Efficient storage and retrieval of raw/results
Metadata Manager Manages run parameters, environment, version
Query Optimizer Fast index/partition-based data retrieval
Version Controller Manages versioned backtest data
API Upload, download, and query endpoints
Frontend Data lake browser and smart query panel

Microservice Directory

services/backtest-data-lake-center/
├── src/
│   ├── main.py
│   ├── storage/data_lake_storage.py
│   ├── metadata/metadata_manager.py
│   ├── optimizer/query_optimizer.py
│   ├── versioner/version_controller.py
│   ├── api/data_lake_api.py
│   ├── config.py
│   └── requirements.txt
├── Dockerfile

Core Component Design

1. Data Lake Storage 

import pyarrow.parquet as pq

class DataLakeStorage:
    def save_backtest_data(self, strategy_id, data):
        pq.write_table(data, f"/data_lake/{strategy_id}.parquet")

    def load_backtest_data(self, strategy_id):
        return pq.read_table(f"/data_lake/{strategy_id}.parquet")

2. Metadata Manager 

class MetadataManager:
    def save_metadata(self, strategy_id, metadata):
        db.insert("backtest_metadata", strategy_id=strategy_id, **metadata)

    def query_metadata(self, filters):
        return db.query("backtest_metadata", filters)

3. Query Optimizer 

class QueryOptimizer:
    def optimize_query(self, query_conditions):
        # Precompute partition keys, indexes
        return fast_query_engine(query_conditions)

4. Version Controller 

class VersionController:
    def manage_versions(self, strategy_id, version, data):
        save_to_versioned_bucket(strategy_id, version, data)

5. API Example 

from fastapi import APIRouter

router = APIRouter()

@router.post("/data_lake/upload")
async def upload_backtest_data(strategy_id: str, data: bytes):
    return data_lake_storage.save_backtest_data(strategy_id, data)

@router.get("/data_lake/download/{strategy_id}")
async def download_backtest_data(strategy_id: str):
    return data_lake_storage.load_backtest_data(strategy_id)

Frontend Integration

DataLakeBrowserView.tsx - Strategy backtest data browser - Version list/history comparison - Smart query (by strategy, time, market, params) - Metadata snapshot panel

Implementation Roadmap

  • Phase 1: Core storage, metadata, and API
  • Phase 2: Query optimizer, versioning, and frontend
  • Phase 3: Distributed storage, streaming updates, geo-replication

System Integration

  • Backtest engines store results and metadata in the data lake
  • Research and analytics systems query and download data
  • Supports multi-version, multi-strategy, multi-market research

Business & Technical Value

  • Scale: Petabyte-level, multi-year, multi-strategy data management
  • Performance: High-throughput, compressed, vectorized I/O
  • Traceability: Full metadata and version history for every run
  • Resilience: Distributed, geo-replicated, disaster recovery ready
  • Research Power: Enables massive ML training and strategy research