Capabilities
Quant Engine:
End-to-End from Data to Decisions
A complete quantitative investment infrastructure covering data processing, factor computation, portfolio construction, risk management, and execution monitoring.
🏗️ Technical Architecture
Data Layer
📡 Data Acquisition & Cleaning
Ingests from 8 data sources including Bloomberg, Reuters, and HKEX API. Automatically handles missing values, outliers, and corporate actions. Daily updates with ≤ 15 min latency.
PythonPandasSQL
Factor Layer
🧬 Factor Computation Engine
Real-time computation of 40+ quant factors across 5 categories (Value, Momentum, Quality, Volatility, Size). Supports custom factor combinations and dynamic weight adjustment.
NumPyCythonZipline
Portfolio Layer
⚖️ Portfolio Optimizer
Mean-variance optimization, risk parity, Black-Litterman model. Supports multi-objective optimization with constraints (sector/stock/factor exposure caps).
cvxpyPyPortfolioOpt
Risk Layer
🛡️ Risk Management System
Real-time VaR/CVaR calculation, stress testing, scenario analysis, factor exposure monitoring. Automatic anomaly alerts triggering manual review.
RiskfolioMonte Carlo
🔬 Core Capabilities
🎯
🎯 Factor-Based Stock Selection
We track 40+ quant factors across 5 categories. Each factor undergoes rigorous IC analysis, stratified backtesting, and robustness checks — only factors passing all tests enter the model.
Value 8Momentum 6
Quality 10Volatility 5
Size 3Custom 8
📡
📡 Signal Generation & Execution
Automated factor screening pipeline runs daily after market close. Signal generation → portfolio optimization → order generation → auto-submission via API, completed within 30 minutes.
Supports IBKR API and multi-broker FIX protocol integration, with TWAP/VWAP algorithmic orders to minimize large-order impact cost.
🏗️
🏗️ Portfolio Optimization & Rebalancing
Black-Litterman framework combined with factor views for portfolio allocation. Quarterly rebalancing with trading cost optimization (only adjusts positions exceeding tolerance bands). Supports tax-loss harvesting.
🛡️
🛡️ Risk Model & Stress Testing
Multi-level risk monitoring: portfolio level (VaR, CVaR, max drawdown), factor level (factor crowding, momentum reversal risk), stock level (event risk scan, liquidity alerts).
Weekly historical stress scenarios (2008 Financial Crisis, 2020 COVID, 2022 Rate Hike Cycle) to ensure portfolio resilience under extreme market conditions.
⏱ Data Update Cadence
Daily 16:30
Daily market data acquisition complete, auto-trigger factor pipeline
Daily 17:00
Four-layer screen complete, daily signals generated
Daily 17:15
Portfolio optimizer computes target weights, generates rebalancing orders
Daily 17:30
Risk metrics updated, anomaly alerts pushed
Every Monday
Weekly factor IC report, portfolio performance attribution
Quarterly Start
Quarterly formal rebalancing, factor weight review & adjustment
💡 What Sets Us Apart: Most quant platforms only provide factor data. SA Quant delivers the complete decision chain — from raw data to final orders, with no black boxes. Clients can inspect each screening layer's logic and elimination reasons.