- Python编程基本经验
- 基本熟悉统计学和线性代数
机器学习是人工智能的一个分支，指计算机可以在不被明确编程的情况下学习。
在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决财务的现实问题。Python将被用作编程语言。
参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些团队项目以将所学知识运用到实践中。
在本次培训结束后，参与者将能够：
- 了解机器学习的基本概念
- 了解机器学习在金融领域的应用和使用
- 使用Python机器学习开发自己的算法交易策略
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
介绍
- 统计学习（统计分析）和机器学习之间的区别
- 金融公司采用机器学习技术及招募相关人才
了解不同类型的机器学习
- 有监督学习 vs 无监督学习
- 迭代和评估
- 偏差方差权衡
- 结合有监督学习和无监督学习（半监督学习）
了解机器学习语言和工具集
- 开源 vs 专有系统和软件
- Python vs R vs Matlab
- 库和框架
了解神经网络
了解金融基本概念
- 了解股票交易
- 了解时间序列数据
- 了解金融分析
金融领域机器学习案例研究
- 信号生成和测试
- 特征工程
- 人工智能算法交易
- 数量贸易预测
- 针对资产组合管理的机器人顾问
- 风险管理和欺诈检测
- 保险承保
实践：用于机器学习的Python
- 设置Workspace
- 获取Python机器学习库和包
- 使用Pandas
- 使用Scikit-Learn
将金融数据导入Python
- 使用Pandas
- 使用Quandl
- 与Excel集成
用Python处理时间序列数据
- 探索你的数据
- 可视化你的数据
用Python实现常见的金融分析
- 收益
- 移动窗口
- 波动性计算
- 普通最小二乘回归（OLS）
用Python监督机器学习开发算法交易策略
- 了解动量交易策略
- 了解逆转交易策略
- 实施你的简单的移动平均线（SMA）交易策略
回溯测试（Backtesting）你的机器学习交易策略
- 学习回测的陷阱
- 你的回测器（Backtester）组件
- 使用Python回测工具
- 实现你的简单回测器
改进你的机器学习交易策略
- KMeans
- K最近邻（KNN）算法
- 分类或回归树
- 遗传算法
- 使用多符号投资组合
- 使用风险管理框架
- 使用事件驱动的回测
评估你的机器学习交易策略的表现
- 使用夏普比率（Sharpe Ratio）
- 计算最大跌幅
- 使用复合年增长率（CAGR）
- 衡量收益的分配
- 使用贸易水平指标
- 总结
故障排除
结束语
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