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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-in-trading/
课程评论：没有评论
课程名称：AI算法交易：买卖信号[Python] 课程概述： 欢迎参加这门全面的交易课程，使用机器学习和人工智能生成买卖信号。基于AI的交易机器人正在迅速崛起，其市场份额持续增长。不仅大型量化金融机构如MLQ AI、Kavout、QuantAI和Precision Alpha等正在利用人工智能进行交易，零售交易者也在使用这一强大的工具来寻找市场的优势。因此，将机器学习融入到算法交易机器人中变得至关重要。 任何交易系统的基础是买卖信号的生成，而这取决于可靠且准确的价格预测。这就是机器学习和人工智能展现其优势的地方。在本课程中，我们将下载不同资产类别的市场数据，并应用多种机器学习算法。这些算法是数据科学和交易中广泛使用的，包括基于概率的算法、深度学习、人工神经网络、决策树等。随后，我们将利用这些算法进行价格预测并生成信号。 **Python实践** 课程中的每一步都包含Python编码部分。首先会解释直觉，然后开发代码以实现该想法，并使用机器学习库进行编程。 **数据来源探索（市场数据）** 任何机器学习项目的第一步是获取数据。不同的市场数据提供商有不同的数据捕获方式。 **特征与目标** 在设计任何机器学习模型之前，需要明确我们的模型期望预测什么。在交易术语中，它是关注趋势、波动性还是收益的买卖信号。此外，直接将原始数据（OHLC）提供给模型，会使价格运动的预测变得困难。因此，设计有助于信号生成的特征是必不可少的。 **机器学习模型** 使用多种类型的机器学习模型在不同资产类别中创建信号。机器学习算法种类繁多且仍在增加，了解和实施大类算法使我们能够探索和应用其他变体。我们不仅在Python中实现这些模型，还探索不同的训练方法和超参数调优。使用在数据科学社区广泛使用的知名Python包。在使用任何程序包或算法之前，我们会首先讲解直觉并解释该模型背后的思路，使用简单的术语，避免复杂的数学公式，确保能够充分理解模型。 本课程旨在为希望在算法交易中应用AI和机器学习的学习者提供实用的技能和知识。
Welcome to one of the most comprehensive trading courses using Machine learning and AI to generate buy/sell signalAI based trading bots are on the rise and their share of the market has been growing rapidly. Not only big trading quant financial institutions such as MLQ AI, Kavout, QuantAI, Precision Alpha, etc are using artificial intelligence for trading but also retail traders have been using this powerful tool to find the edge to the market. This makes having machine learning in your algorithmic trading bot a must.The backbone of any trading setup is buy and sell signal generation, and this comes from having a reliable and correct price prediction. That is where machine learning and artificial intelligence can shine.In this course, different asset classes' market data are downloaded, and different types of machine learning algorithms are applied to those types of data. Those algorithms are the ones widely used in the data science and trading. They include probability based, deep learning, artificial neural networks, decision trees, etc. Then, we use those algorithms to predict price and generate signals.Hands on With PythonEvery step in this course has coding sections with python. First, the intuition is explained then we develop some code to implement that idea using machine learning packages.Exploring Data Sources (Market Data)The very first step in any machine learning project is having access to data. Different market data providers have different ways to capture data.Features and TargetsBefore designing any machine learning model, it needs to be clear that what we expect our model to predict. In trading terminology, is it a trend, volatility, return that the buy/sell signal is focusedAlso, giving raw data (OHLC) to the model, makes it difficult to predict any price movement. Designing the features that can contribute to signal generation is the must.Machine Learning ModelsUsing different types of ML models that can create signals in different asset classes. There are countless number of ML algorithms, and they are still growing. Knowing and implementing big category of those algorithms enable us to explore and implement all other variations.We only not implement those models in Python but also, we explore different ways of training them and tuning hyper parameters. We use well-known python packages that widely used in data science community.Before implementing and using any package or algorithm, we first go through intuition and explain the idea behind that model. we use simple terms and avoid going through complicated Math formula and good enough to diagnose the model.
