Personal CV of Han Bolun

韩博伦毕业于北京大学元培学院, 光华金融方向, 2016年加入中金公司EQ部门成为一名跨境销售交易员。

2017年离开中金公司, 加入上海鑫晟投资管理有限公司开始了量化工程师的职业生涯，专注于短线量化因子挖掘和日内交易。

2022年11月, 我重返校园, 在南洋理工大学攻读应用经济学硕士学位。

2024年，创立周大福新能源（上海）有限责任公司北京分公司，主管A股高频交易。

Graduated from Peking University in 2016, majored in finance and physics, Han Bolun had joined CICC EQ. Department and became a cross-border sales trader.

In 2017, I left CICC and joined the GLOBAL FORTUNE UNITED and started the career as an AI engineer focusing on short-term quantitative factor mining and Intraday trading.

In November 2022, I returned to campus to pursue a Master of Science in Applied Economics at Nanyang Technological University.

In 2024, I co-founded the CREO LLC, and responsible for high-frequency trading in A-shares market.

基本经历

• 2016 年北大毕业，加入中金从事交易。
• 2017 年加入原 EQ 部门领导创业团队，上海鑫晟投资管理有限公司。
• 2017 - 2020 年，从事中低频量化研究，包括 alpha市场中性策略，多因子选股等策略。
• 2020 年开始进行高频量化策略开发：包括因子挖掘，机器学习等。
• 2022 年上线部署量化高频策略。
• 2022-10 至 2023-11 于新加坡南洋理工攻读 MSAE 项目。
• 2024年至今，创立周大福新能源（上海）有限责任公司，任高频量化研究副总裁。

主要工作经历和方法论:

• 中性策略 (alpha) 策略，
  • 持仓周期约5天
  • 基于深度学习
  • 年化约12%，最大回撤约5%。
  • 无市场多头暴露，但有较大行业暴露。
• 日内策略-实盘
  • 基于机器学习，Markov Chain, 设计了不同的regime，并在regime change的时候下单。
  • 应用在自营的股票仓位中。
  • 另外用来给一些票做市值管理。
• 日内策略-研究
  • 基于量价的因子挖掘（股票），采用遗传算法迭代优化。
  • 事件,情绪因子的挖掘（股票），与外部服务商合作。
  • crypto的做市算法，与一些矿主进行的合作。
  • 基于成分的量价以及关系等信息，开发的指数因子。
• 算法交易-实盘
  • 为日内策略和其他自营策略，设计智能算法
  • 基于lv2 transaction 数据
  • 在保证成交量和时限的情况下尽可能追求最佳价格
• 其他量化相关工作
• 实盘的风控等系统
• 场外期权定价系统
• 期权做市算法
• 应用深度学习等算法，对公司其他策略进行优化

Experience:

• Graduated from Peking University in 2016 and subsequently joined CITIC Securities as a trader.
• In 2017, co-founded Global Fortune Management Co., Ltd. with the former EQ department head.
• From 2017 to 2020, focused on medium to low-frequency quantitative research, including alpha market-neutral strategies and multi-factor stock selection strategies.
• Started developing high-frequency quantitative strategies in 2020, involving factor discovery and machine learning.
• Successfully deployed high-frequency strategies in 2022.
• Pursued postgraduate studies in Singapore from October 2022 to November 2023, and have since returned to China.

Main Work Experience and Methodology:

• Alpha Strategies (Market-Neutral):
  • Holding period of approximately 5 days.
  • Utilized deep learning techniques.
  • Achieved an annualized return of about 12% with a maximum drawdown of around 5%.
  • Remained unaffected by overall market long exposure but exhibited significant industry risk exposure.
• Intraday Strategies (Online):
  • Employed machine learning and Markov Chain to design different market regimes and execute trades during regime changes.
  • Applied for proprietary stock position management and market capitalization management for specific stocks.
• Intraday Strategy (Research):
  • Conducted factor mining based on volume-price relationships for stocks using genetic algorithms for iterative optimization.
  • Collaborated with external service providers for event and sentiment factor mining for stocks.
  • Developed market-making algorithms for cryptocurrencies in cooperation with some miners.
  • Created index factors based on constituents, volume-price relationships, and other information.
• Algorithmic Trading in Practice:
  • Designed intelligent algorithms for intraday strategies and other proprietary strategies.
  • Utilized Level-2 transaction data.
  • Strived for optimal prices while ensuring volume and timeliness.
• Other Quantitative-Related Work:
  • Real-time risk control systems for trading.
  • Over-the-counter option pricing systems.
  • Option market-making algorithms.
  • Applied deep learning and other algorithms to optimize the company's other strategies.

last update: Mar. 2024