Quant-engine Architecture

Overview

Quant Engine is a quantitative research and trading platform bridging research and execution. Built on a hybrid C++23/Python core that processes market data at 210 million rows/second, bypassing the Global Interpreter Lock (GIL) for low-latency feature computation and inference.

Key Features

• Zero-Copy C++ Engine: Custom pybind11 extension for volatility and fractional differentiation (450x faster than Pandas).
• GenAI Alpha: Real-time sentiment analysis using Google Gemini (LLM) to filter quantitative signals based on macro-news.
• Hierarchical ML: Meta-labeling strategy (XGBoost) trained on Dollar Bars to optimize Precision over Recall.
• Self-Healing Infra: Dockerized architecture with automated "Cold Start" pipelines that ingest, transform, and retrain models on deployment.
• Full Execution Loop: Connected to Alpaca Markets for paper trading execution.

Tech Stack

• Core: Python 3.11, C++23 (OpenMP), SQL.
• Data: Polars, Parquet (Snappy), PostgreSQL.
• Ops: Docker Compose, GitHub Actions (CI/CD), MLflow.
• Frontend: React, TypeScript, TanStack Query.

Performance

• Precision: 63% (Out-of-sample).
• Latency: < 100ms (End-to-End Inference).

System Architecture

High-Level Design

Inference Request Flow

Market Data Ingestion Pipeline

Overview

This pipeline handles the ETL (Extract, Transform, Load) process for historical market data. It scrapes the current S&P 500 constituents and ingests daily OHLCV data into a high-performance columnar store.

Architecture Decisions

1. Polars vs Pandas

Used Polars for the transformation layer due to its Rust-based query engine and Lazy API.

• Problem: Pandas loads all data into RAM (Eager execution), which causes OOM (Out of Memory) errors when processing 500+ high-resolution files on standard hardware.
• Solution: Polars optimizes memory usage via zero-copy data sharing and threaded execution, allowing us to process datasets larger than available RAM.

2. Storage Format (Parquet + Snappy)

Data is stored as partitioned .parquet files with Snappy compression.

• Why Parquet: Columnar storage allows specific features (e.g., "Close" price) to be queried without scanning the entire dataset (I/O optimization).
• Why Snappy: A compression algorithm optimized for high-throughput read speeds, essential for minimizing latency during model training iterations.

3. Feature Engineering (Microstructure)

• Dollar Volume: Calculated as Close * Volume.
• Theory: This metric enables downstream transformation from standard Time Bars into Dollar Bars (sampling by constant value exchanged). This aligns with Lopez de Prado's findings (Advances in Financial Machine Learning, 2018) that sampling by information flow (market activity) rather than chronological time reduces statistical noise (heteroscedasticity) and improves model convergence.

4. Labeling (Triple Barrier Method)

Instead of fixed-time horizon labeling (which falls victim to noise), we implemented Lopez de Prado's Triple Barrier Method:

• Upper Barrier: Dynamic Profit Take based on rolling volatility (+2σ).
• Lower Barrier: Dynamic Stop Loss (-2σ).
• Vertical Barrier: Expiration limits (10 bars). Result: This converts the regression problem ("predict price") into a classification problem ("predict barrier hit"), which statistically improves ML model convergence.

5. Preliminary Backtest Results (XGBoost)

Training on Dollar Bars (Threshold: $5B) with Triple Barrier Labeling. Metric: Out-of-Sample Precision (Long Only).

Ticker	Precision	Status
GOOGL	89.74%	✅ Strong Alpha (Momentum Regime)
AAPL	59.62%	✅ Alpha
MSFT	56.90%	✅ Alpha
NVDA	50.30%	⚠️ Neutral / High Volatility
AMZN	46.34%	❌ No Signal

Average Precision: 60.58%

6. Meta-Labeling without external features

Implemented hierarchical modeling (Meta-Labeling). The primary model predicts direction, and a secondary model filters signals based on the probability of primary success, reducing false positives.

AVERAGE PRECISION: 63.16%

7. Advanced Feature Engineering: Fractional Differentiation

Standard Machine Learning models fail on financial data because prices are Non-Stationary (statistical properties like mean and variance change over time). However, standard transformations like Log Returns ($d=1$) make data stationary but destroy Memory (long-term correlations and trends).

We implemented Fractional Differentiation (based on Advances in Financial Machine Learning, Lopez de Prado, Chapter 5) to find the optimal trade-off.

• Method: Fixed Fraction Differentiation (FFD).
• Parameter: $d=0.4$ (Retains memory while passing the ADF Stationarity test).
• Optimization: Dynamic windowing with a weight threshold of $1e-3$ to balance precision with data availability.

Visual Verification

• Top (Blue): Raw Price (Non-Stationary, Drifting).
• Middle (Purple): Fractional Diff (Stationary, but retains the structural "Shape" of market regimes).
• Bottom (Grey): Log Returns (Stationary, but pure noise/memory-less).

GenAI Integration: Real-time financial news sentiment analysis using Google Gemini (LLM) to augment quantitative signals.

Performance Engineering (C++23)

To overcome the Global Interpreter Lock (GIL) constraints in Python, the core volatility engine was rewritten in C++23 using pybind11 and OpenMP.

Optimization Strategy

1. Zero-Copy Memory Access: Implemented py::array_t with unchecked<1> proxies to read Numpy memory directly without duplication.
2. Parallel Execution: Utilized #pragma omp parallel for to distribute sliding window calculations across CPU cores.
3. Instruction Set: Compiled with -O3 -march=native to leverage AVX instructions.

Benchmark Results (50 Million Rows)

Implementation	Time	Throughput
Pure Python	~22.0s	2.2M rows/s
Pandas (Vectorized)	~1.5s	33M rows/s
QuantEngine (C++23)	0.238s	210M rows/s

> Result: 6x speedup over Pandas and 90x speedup over Python loops.

C++ Optimization Benchmark (Fractional Differentiation)

Comparing the custom C++23 OpenMP implementation against optimized Python/Numpy.

Dataset Size	Python (Numpy)	QuantEngine (C++23)	Speedup
10,000	64.60 ms	14.30 ms	4.5x
10,000,000	62,122 ms (62s)	138 ms (0.13s)	450x

> Architecture: Zero-Copy memory access via pybind11, OpenMP parallelization, and GIL release management.

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Built by Ricardo Gobbi. Licensed under GPLv3.