t6_Managing the Risk_limit_metrics of Algorithmic Strategies_Sharpe_adjust_trade side_position_share

LIQING LIN

于 2022-01-03 18:08:02 发布

阅读量4.6k

点赞数 1

CC 4.0 BY-SA版权

分类专栏： pythonForFinance2 文章标签：大数据人工智能

本文链接：https://blog.youkuaiyun.com/Linli522362242/article/details/122159869

pythonForFinance2 专栏收录该内容

57 篇文章 ¥9.90 ¥99.00

订阅专栏

超级会员免费看

本文探讨了算法交易中的风险管理，包括交易损失风险、违规风险，如欺诈和填单，以及如何量化风险和建立风险管理算法。了解风险类型和因素至关重要，良好的风险管理能将亏损策略转化为盈利。我们研究了不同风险指标，如止损、最大回撤和夏普比率，并展示了如何动态调整风险以适应市场变化。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

So far, we have built a good understanding of how algorithmic trading works and how we can build trading signals from market data. We also looked into some basic trading strategies, as well as more sophisticated trading strategies, so it may seem like we are in a good place to start trading, right? Not quite. Another very important requirement to be successful at algorithmic trading is understanding risk management and using good risk management practices.

Bad risk management practices can turn any good algorithmic trading strategy into a non profitable one. On the o