We provide Python development platform built upon the TongsQuant C++ Core, designed to
facilitate efficient backtesting and live trading of quantitative strategies.
Seamless Transition from Backtesting to Live Trading:
Utilizing a unified framework, strategies can be backtested and then directly deployed for live trading. The backtesting process incorporates transaction fees and slippage, ensuring realistic performance assessments.
Offline Deployment:
With dependency limited to a single shared object file (libpytongs.so), deployment is straightforward. Users maintain control over their exchange API keys, enhancing security, and strategy code remains confidential.
High Development and Execution Efficiency:
The platform integrates Python and C++, allowing for rapid strategy development in Python while leveraging the performance of C++ for core operations. This architecture enables swift backtesting; for instance, processing over 500,000 data points in minutes typically completes within seconds.
Proven Live Trading Capabilities:
pytongs has been validated through extended live trading, offering features such as seamless switching between multiple exchanges, flexible order sizing, comprehensive position management, profit monitoring, and a robust logging mechanism.
Data-driven quantitative research. Seamless automated trade execution on our proprietary system. Comprehensive post-trade analysis.
Data is the foundation of our work. Every bit and byte is meticulously sanitized and cross-validated before research begins.
We welcome fresh ideas and new perspectives. However, before adopting any strategy or predictive model, we eliminate human bias and ensure the algorithm is fully verifiable.
Understanding comes from analysis. That’s why we prioritize bridging the gap between simulation and real-world execution, making adjustments as necessary.
Countless third-party trading solutions exist, but none met our needs. So we built our own—and we’re constantly refining it. From start to finish, we analyze every aspect of our trading process, making it faster, more reliable, and endlessly scalable.
We developed highly accurate, in-house market simulators to efficiently research and validate predictive models. Additionally, we continuously refine our simulations by analyzing real market trading results to enhance their accuracy.
Our risk management system ensures safer trading operations and is fully integrated into our in-house platform. Every order is executed within predefined risk parameters and monitored in real-time.
Based on
quantitative research. Automated trade execution on our pytongs system.
Full tracking log
system.
Market data is analyzed and processed in mere microseconds thanks to our C++ core. High-frequency trading creates significant challenges, requiring our engineering team to constantly balance data processing, algorithm complexity, and hardware limitations.
In long-term investing, such as portfolio management, we concentrate on systematically developing dependable prediction models grounded in rigorous statistical analysis. We consistently utilize diverse data sources, maintained and organized with precision – an essential element for creating more resilient portfolio strategies.
Our market-making strategies play a key role in stabilizing financial markets by ensuring liquidity. Precision and speed in order execution are crucial for success in this space. Partnering with multiple exchanges worldwide, we actively contribute to a more efficient trading environment with ample liquidity.
As a quantitative trading firm, we develop automated systems powered by data-driven analysis to generate consistent and sustainable returns. Our engineering teams focus on what humans excel at—designing algorithmic decision-making processes to navigate the speed and complexity of modern financial markets in ways no human trader ever could.