This chapter kicks off Part 2 that illustrates how you can use a range of supervised and unsupervised ML models for trading. We will explain each model's assumptions and use cases before we demonstrate relevant applications using various Python libraries. There are several aspects that many of th...
Evaluating how the burgeoning supply of alternative data can be used for trading Working with alternative data in Python, such as by scraping the internet 04 Financial Feature Engineering: How to research Alpha Factors If you are already familiar with ML, you know that feature engineering is a ...
Intraday FX trading: An evolutionary reinforcement learning approach FX trading via recurrent reinforcement learning Machine Learning for Market Microstructure and High Frequency Trading Git & Code 关于这个主题有一个相关的Git项目:deependersingla/deep_trader 此外,国外有人还贴出了基于Forex的Python实现的完整co...
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and...
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to
Python 2.7 来运行这个项目,以及以下包: -Numpy+MKL -matplotlib -scipy -pandas 是获取所有必需软件包的绝佳资源。 这对于 numpy 尤其重要——标准的“pip intall numpy”命令不包含安装 scipy 所需的所有库。 如果您在尝试安装 scipy 时遇到错误,只需从该站点获取 numpy+mkl 的链接即可。 确保您选择的版本适用...
探索Python中的强化学习:Q-learning 强化学习是一种机器学习方法,用于训练智能体(agent)在与环境的交互中学习如何做出最优决策。Q-learning是强化学习中的一种基于价值函数的方法,用于学习最优策略。本文将详细介绍Q-learning的原理、实现方式以及如何在Python中应用。
可以直接安装Anaconda：Python数据分析与挖掘好帮手—Anaconda，它内置了Python和pip.
Platform(keras/tensorflow/sklearn):Python语言必备,相关的类库,没必要完全重写所有的东西 深度学习 只要神经网络超过两层都可以叫做深度学习, 包括如下两类 CNN for spatial data (空间):当我们的数据依赖某些空间结构的先后顺序时使用 LsTM for temporal data (时间):X有一定的时序关系时 例如隐马可夫模型(HMM) ...
使用Keras后端构建RL智能体的神经网络的代码:(全部代码文末下载,同时有Python) 部分代码展示 state_names_length <-12# just for example a_CustomLayer <- R6::R6Class( "CustomLayer" , inherit = KerasLayer ,public=list( call =function(x, mask = NULL){ ...