Quant Research for Prop Trading - Equities
Factor Storage
Stores factors data from across research teams into a centralized library — handling high-frequency and low frequency factors across equities, futures, options and more. Flexible partitioning by time × security or time × factor name supports both high-throughput batch writes and fast cross-sectional and time-series queries across the full market and long historical periods.
Benchmarked at hundreds of GB ingested in minutes and full-market single-factor updates in seconds.
Featured Tutorials & Best Practices:
Mid-to-High Frequency Factor Computation
A Turing-complete scripting language, vectorized functions, and streaming engines support cross-sectional, time-series, industry, and cross-asset factor logic — unifying microstructure, technical, style, and fundamental factors in a single framework.
Team logic can be encapsulated as UDFs and shared across strategy groups. Built-in libraries including WorldQuant 101 Alpha, Technical Analysis Indicator Library, and Cross-Sectional Asset Pricing Factor provide a head start on research.
Featured Tutorials & Best Practices:
- Best Practices for Factor Computation
- Cases for Factor Implementation: General Financial Factors, ETF Options Implied Volatility and Greeks, Daily Factors of Mutual Fund
- Factor Calculation Solutions Compared:
- Python + HDF5 vs. DolphinDB
- Python + File Storage vs. DolphinDB
Unified Stream-Batch Factor Development
DolphinDB's unified stream-batch architecture closes the gap between research and production. Factor logic developed and validated on historical data deploys directly to live data streams — same codebase, same computation logic, guaranteed consistency between backtest and live factor values. One unified stack replaces the traditional Python-for-research / C++-for-production split, cutting deployment cycles by 80%+.
Historical batch backfill and real-time incremental updates share the same execution framework — researchers backfill and validate new factors on historical data after market close, then monitor live factor output intraday. Full cycle from idea to live deployment in hours.
Featured Tutorials & Best Practices:
- Quick Guide to a Unified Batch-Stream Computing Platform
- Calculating High-Frequency Factors in Real-Time
- Real-time scenarios include: Real-time Rank of Price Rate of Change
Multi-Factor Modeling
DolphinDB aligns selected factors with market, financial, position, trading, and risk data by time and security in a single step — generating feature tables and label sets ready for modeling.
- Multi-Factor Framework: Linear weighting, cross-sectional regression, and IC-weighted models run directly in-database, producing scores or expected returns for stock selection and timing.
- Risk Factors & Portfolio Optimization: A built-in risk model framework supports factor exposure analysis, return attribution, and portfolio optimization under position, industry/style neutrality, and exposure constraints.
- ML & Deep Learning: XGBoost, AI Dataloader, LibTorch, and a built-in ML framework enable large-scale model training and real-time inference — all within DolphinDB.
Featured Tutorials & Best Practices:
- XGBoost Plugin Application: Feature Engineering for Stock Volatility Prediction
- AI Dataloader and LibTorch Tools: DolphinDB Application in Deep Learning: Real-Time Stock Volatility Prediction
Automated Factor Discovery
DolphinDB's Shark GPLearn is a genetic algorithm-based framework that uses symbolic regression to automatically generate Alpha expressions from massive historical data — purpose-built for institutions with large-scale factor libraries.
Compared to open-source gplearn, Shark GPLearn adds GPU acceleration, integrates 2,000+ built-in DolphinDB operators, and natively handles three-dimensional panel data (time × stock × factor) — dramatically compressing the generate-screen-backtest iteration cycle.
Request a free trial → https://dolphindb.com/product#shark
Featured Tutorials & Best Practices:
Factor Lab
A fully integrated platform for the entire factor lifecycle — from data management and research to evaluation and strategy backtesting. Every factor's computation logic, data dependencies, historical performance, and applied strategies are automatically tracked, with full lineage tracing and impact analysis.
Teams share a unified view of IC, IC_IR, long-short returns, and other key metrics across the full factor library — enabling performance comparison, portfolio optimization, and collaborative research without duplicated effort.
Request a free trial → https://dolphindb.com/product#starfish
Featured Tutorials & Best Practices:
Mid-to-High Frequency Backtesting Framework
An integrated event-driven engine combining data replay, simulated matching, and backtesting in one. Historical data streams simultaneously to the matching engine and strategy callbacks — strategies generate signals, the engine enforces risk checks, and approved orders flow into the matching engine for execution simulation, with account state updated in real time.
Built on a C++ core and covering equities, options, futures, interbank bonds, and digital assets, DolphinDB delivers up to tens of times the performance of Python-based frameworks like Backtrader and MetaTrader4 at full-market, mid-to-high frequency scale.
Featured Tutorials & Best Practices:
- Quick Start with DolphinDB Backtesting
- Performance and Feature Comparison of DolphinDB Backtesting Framework and Other Backtesting Products
Order Matching Simulation
DolphinDB's Matching Engine Simulator accurately simulates real-market order execution — accepting live snapshot or tick data and strategy orders (limit, market, cancellations), building a virtual order book on price-time priority, and dynamically computing fill price, quantity, and timestamp for each order.
Configurable latency and fill ratio parameters support evaluation across different market conditions. Multiple matching algorithms are available, from proportional fills based on last price and best quote, to execution simulation layered with order book depth and interval trade details.
The result: a more accurate bridge between idealized backtest returns and real-world achievable performance — with precise quantification of slippage, liquidity constraints, and market impact.
Featured Tutorials & Best Practices:
Native Python & C++ Embedding
Swordfish enables direct in-process calls to DolphinDB's high-performance computation engine from Python and C++ applications. Existing Python research scripts and C++ strategy programs gain DolphinDB's stream computation and distributed processing capabilities without rewriting core business logic, delivering millisecond-level data processing and large-scale parallel computation.
The in-process architecture preserves existing code assets and development workflows while significantly improving performance — minimizing migration cost and technical risk.
AI-Powered Investment Research
DolphinDB's AI research system brings large language models into the quantitative research workflow, letting analysts run complex research tasks through natural language.
Specialized agents handle the full research lifecycle: the Research Reproduction Agent parses PDF research reports, extracts factor logic, and generates executable code automatically; the Factor Iteration Agent recommends parameter tuning directions and factor combination strategies based on historical performance; the Portfolio Management Agent answers natural language queries on portfolio returns, performance attribution, and factor exposure; the Stock Screening Agent combines price-volume data, fundamental factors, and investment rationale to generate ranked stock universes with supporting analysis.