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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advances-in-momentum-trading-strategies/
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课程名称：动量交易策略的进展 课程概述：动量交易策略的进展是一门全面深入的课程，旨在为研究生及经验丰富的专业人士提供学习机会。该课程将理论、实践应用与前沿研究相结合，使参与者掌握在不同市场条件下动量交易的复杂性。 课程内容包括： 1. **趋势跟踪投资的百年证据**：探讨趋势跟踪策略在百年间的历史表现与方法论，涵盖危机时期及不同经济环境下的表现。 2. **动量转折点**：揭示动量交易中的转折点概念，学习动态与静态策略的区别，以及噪声和持续性对信号质量的影响。 3. **快速与慢速趋势**：深入分析趋势分析中的速度变化(窗口期)理论与应用，了解风险管理的角色及S&P 500统计数据在动量策略中的作用。 4. **波动性目标的头寸规模**：理解波动性目标对不同资产类别头寸规模的影响，以及此方法为何有效。 5. **深度动量网络（时间序列动量策略）**：学习如何利用深度神经网络增强时间序列动量策略，包括交易信号的构建与性能评估。 6. **结合变更点检测的高级深度动量网络**：探讨在深度动量网络中集成变更点检测的方法论及结果。 7. **使用学习排名的交叉动量策略**：获取构建交叉系统策略的见解，使用学习排名（LTR），并实现Python库下的LambdaMart。 8. **有利于策略的市场条件**：分析不同市场条件下的投资策略，如套利、动量和价值投资，同时学习信号与投资组合的构建。 9. **通过自注意力的上下文感知LTR增强交叉策略**：理解如何利用上下文感知模型和变换器架构提升交叉动量策略中的排名。 为什么选择这门课程？无论您是一名专注于金融工程、机器学习、应用数学的研究生，还是专业的量化交易员或分析师，这门课程都将提升您对动量交易策略的理解与应用。它不仅仅是一门课程，更是您在动态交易世界未来发展的投资。
Advances in Momentum Trading Strategies is a comprehensive and in-depth course designed for graduate-level students and seasoned professionals. This course offers a unique blend of theory, practical application, and cutting-edge research, enabling participants to master the intricacies of momentum trading across various market conditions.Course Sections:A Century of Evidence on Trend-Following Investing: Explore the historical performance and methodology of trend-following strategies over a century, including during crises and different economic environments.Momentum Turning Points: Unravel the concept of turning points in momentum trading. Learn about dynamic versus static strategies, and the impact of noise and persistence on signal quality.Trending Fast and Slow: Delve into the theory and application of varying speed (window periods) in trend analysis. Discover the role of risk management and the statistics of S & P 500 in momentum strategies.Position Sizing: Volatility Targeting: Understand the impact of volatility targeting on position sizing across asset classes, and why this approach is effective.Deep Momentum Networks (Time Series Momentum Strategies): Learn about enhancing time-series momentum strategies using deep neural networks, including the construction of trading signals and performance evaluation.Advanced Deep Momentum Networks with Change Point Detection: Explore the integration of change point detection in deep momentum networks, examining methodology and results.Cross-Sectional Momentum Strategies with Learning to Rank: Gain insights into building cross-sectional systematic strategies using Learning to Rank (LTR), including Python library implementation for LambdaMart.Market Conditions that Favor Strategies: Analyze various investment strategies like carry, momentum, and value in different market conditions. Learn about signal and portfolio construction.Enhancing Cross-Sectional Strategies by Context-Aware LTR with Self-Attention: Understand how to enhance ranking in cross-sectional momentum strategies using context-aware models and transformer architecture.Why This Course?Whether you're a graduate student specializing in financial engineering, machine learning, applied mathematics, or a professional quant trader or analyst, this course will elevate your understanding and application of momentum trading strategies. It's not just a course; it's an investment in your future in the dynamic world of trading.
