BigQuant
BigQuant is an AI-centric quantitative investment trading platform that provides financial data, factor libraries, visual development and backtesting transactions. It targets quants and quantitative investors, with a focus on machine learning strategies.
Updated: 2026-10-26
What is BigQuant?
BigQuant is an quantitative investment trading platform with artificial intelligence at its core. Its official website states that it "empowers investment with AI, providing investors with enterprise-level AI platforms, big data for quantitative investment, AI research and development tools, a Quant growth system, and a community." Unlike ordinary platforms that only offer backtesting, BigQuant integrates machine learning, PB-level financial data, a massive factor library, and a visual development environment, enabling quants and quantitative investors to use AI for strategy development at a low threshold. The official website claims that many leading domestic financial institutions have chosen BigQuant, and it provides free training and case studies. Its goal is to allow quants to apply machine learning to live trading strategies with a lower threshold.
Who is it for?
- Quants and quantitative teams who develop stock and futures strategies and hope to use machine learning to improve their results, so as to enhance computing power and execution efficiency.
- Research-oriented investors who need PB-level data and factor libraries can shorten data preparation time and focus on strategy research and development.
- Quantitative developers who prefer visual development and also know Python, balancing ease of use and flexibility.
- Beginners who want to systematically learn AI quantitative trading, imitate cases to build their own strategies, and have a growth system to support them and a community to accompany them.
Key features
6Artificial Intelligence Core Platform
An enterprise-level AI infrastructure with automated machine learning and hyperparameter optimization, improving the efficiency of quantitative processing.
PB-level quantitative data
Proprietary standard financial data plus new investment big data, supporting data customization to meet diverse needs.
Massive Factor Library
2000+ basic factors, and AI mines more derivative factors to spot opportunities ahead of others.
Factor Construction and Analysis
Create factors with an expression engine and conduct comprehensive analysis of factors to discover effective factors more quickly.
Visual Development Environment
Seamless integration of modular visualization and Python code versions, improving the efficiency of strategy development.
Advanced Optimization
Hyperparameter search, portfolio optimization, rolling training, attribution analysis, as well as simulation and connection to live trading.
How to use BigQuant
- 1First understand the process using the visualization module and case source code, then transition to custom Python strategies step by step.
- 2Combine factor analysis with attribution to avoid overfitting, and conduct out-of-sample validation of backtest results to prevent inflated performance metrics.
- 3Before connecting to live trading, confirm the brokerage and data authorization, control capital and risk exposure, and roll out in batches.
- 4The official data scope, factor library and billing may be adjusted; please check the official website and documentation before commercial use.
- 5When getting started, it is recommended to first use the free quota to run through the core workflow, confirm that the output quality meets expectations, and then evaluate whether it is worth upgrading to a paid plan.
Traffic & popularity
Data month Sep 2026
BigQuant receives about 340.8K visits per month and ranks #91,966 worldwide.
Monthly visits
12 monthsTraffic sources
- Direct50.4%
- Search34.0%
- Referrals7.2%
- Social5.6%
- Mail1.6%
- Other1.2%
Top countries
- China52.2%
- United States30.6%
- United Kingdom3.3%
- Taiwan, China3.1%
- Australia3.0%
Estimated from third-party traffic data.
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