Strategy投资策略
Steady growth and risk management
稳健增长与风险管理
The research approaches below describe methods our team studies and applies. They are descriptions of methodology, not offers, recommendations or predictions of results. 以下研究方法是我们团队所研究并应用的方法。 它们是对方法论的描述，而非要约、建议或对结果的预测。
Research approaches研究方法
Four approaches we study.
我们研究的四类方法。
Analysing news and public opinion
新闻与舆情分析
We use natural-language and machine-learning techniques to analyse news flow and public sentiment, with the aim of identifying market trends and shifts early. The objective is better-informed decisions and more responsive risk management. 我们运用自然语言处理与机器学习技术分析新闻流与公众情绪， 以期尽早识别市场趋势与转折。目标在于作出更充分知情的决策， 以及更具响应性的风险管理。
Index enhancement strategy
指数增强策略
We analyse market indices with the aim of improving portfolio characteristics relative to a benchmark, using quantitative techniques to identify anomalies and rebalance holdings dynamically as conditions change. 我们分析市场指数，力求相对基准改善组合特征， 并运用量化技术识别异常，随市场条件变化动态调整持仓。
High-frequency trading
高频交易
We research high-frequency approaches that seek to capture short-lived price differences across venues and instruments, using automated execution and adaptive algorithms designed to operate through varying levels of market volatility. 我们研究高频方法， 以捕捉不同交易场所与标的之间转瞬即逝的价差， 并采用自动化执行与自适应算法，使其能够在不同波动水平下运行。
Options volatility strategy
期权波动率策略
We study options-based approaches that position around volatility metrics rather than directional forecasts, with the aim of managing exposure through periods of market uncertainty. 我们研究基于期权的方法， 围绕波动率指标而非方向性预测来建立仓位， 以期在市场不确定时期管理风险敞口。
Risk management风险管理
Risk is a design constraint, not an afterthought.
风险是设计约束，
而非事后补救。
Every approach we research is evaluated on the losses it can produce as carefully as on the returns it might generate. A strategy that performs well on average but fails badly in stress is not one we will run. 我们研究的每一种方法， 对其可能造成的亏损的评估，与对其可能带来的收益的评估同样审慎。 一项平均表现良好、却在压力时期严重失效的策略，我们不会采用。
No risk framework eliminates the possibility of loss. Quantitative methods rely on historical data and modelling assumptions that may not hold in future market conditions. 没有任何风险框架能够消除亏损的可能。 量化方法依赖历史数据与建模假设， 而这些假设在未来的市场环境中未必依然成立。
- Diversification. Exposure spread across instruments, venues and time horizons rather than concentrated in a single view. 分散化。 风险敞口分散于不同标的、交易场所与时间维度，而非集中于单一观点。
- Position sizing. Allocation governed by defined limits rather than conviction alone. 仓位规模。 配置由明确的限额约束，而非仅凭信心决定。
- Drawdown controls. Predefined thresholds that reduce exposure when losses accumulate. 回撤控制。 预设阈值，在亏损累积时降低风险敞口。
- Ongoing validation. Approaches re-tested as market regimes change; strategies are retired when they stop working. 持续验证。 随市场状态变化重新检验；当策略失效时予以退役。
Important information. The strategies described on this page are presented for informational purposes to explain our research methodology. They are not recommendations, offers or solicitations, and they are not tailored to the objectives, financial situation or needs of any particular person. 重要提示。 本页所述策略仅用于说明我们的研究方法论，供参考之用。 它们并非建议、要约或要约邀请， 亦未针对任何特定人士的目标、财务状况或需求作出调整。
All investing involves risk, including possible loss of principal. Quantitative and systematic approaches depend on historical data, models and assumptions that may prove incorrect or may cease to be valid as markets change. Techniques such as high-frequency trading, derivatives and options involve additional risks, including leverage, liquidity and execution risk, and are not suitable for all investors. No strategy assures a profit or protects against loss. See our full disclosures. 一切投资均含有风险，可能损失本金。 量化与系统化方法依赖历史数据、模型与假设， 这些可能被证明有误，或随市场变化而不再成立。 高频交易、衍生品与期权等技术涉及额外风险， 包括杠杆风险、流动性风险与执行风险，并非适合所有投资者。 没有任何策略能够确保盈利或防止亏损。 请参阅我们的完整披露与条款。
Want the detail behind the method?
想了解方法背后的细节？
Tell us what you are trying to achieve and we will walk you through how we work. 告诉我们您希望实现什么， 我们会向您详细说明我们的工作方式。