Algorithmic Trading
Stable Quants for Every Portfolio
Stabilize your portfolio. Whether you're managing personal wealth, institutional funds, or retirement savings, anyone can easily achieve steady growth through our robust quantitative algorithms designed for long-term reliability.
Multi-Asset Portfolio Optimization
5010 Renaissance Quant Version 2.0
for algorithmic traders
5010 Renaissance Quant Model is an advanced quantitative solution that performs automated trading in the Bitcoin futures market. This model has undergone systematic validation using our proprietary backtesting engine 'Stratify'. We conducted testing across 15 symbols, with optimization performed through over 1,000 different parameter combinations per individual symbol. Through this comprehensive validation process, we selected the best-performing single strategy and 4 core symbols, achieving enhanced stability and profitability compared to previous models.
Algorithm Enhancement
Selective Entry Points through Time Decay Logic
The existing Renaissance Quant 1.0 version lacked a proprietary algorithm that could effectively capture both short-term and long-term trends. Therefore, Renaissance Quant 2.0 has been enhanced to incorporate both short-term and long-term trends even when reflecting long-term data, through features such as Time Decay Logic. Additionally, we have introduced Confluence Zones to prevent indiscriminate trading. To prevent our clients' valuable assets from declining due to excessive trading fees from opening numerous positions, we have added probabilistic filtering functionality for all trading entry points based on our R&D results.
Portfolio Diversification
Multiple Asset Management via Stratify
for backtesters
To address the limitation of Renaissance Quant 1.0, which only supported Bitcoin futures trading, version 2.0 has been updated to support multiple symbols in the algorithm. We developed and tested our proprietary backtesting system Stratify on 15 assets, and adopted cutting-edge portfolio management algorithms to construct portfolios of individual strategies. Starting with minimizing the variance of return distributions through fundamental algorithms like MPT (Modern Portfolio Theory), we adopted the optimal portfolio construction algorithm based on test results from various state-of-the-art algorithms using Stratify.