Machine-Learning-and-AI-in-Trading
Here is some of codes generated in Python using Machine Learning and AI for generating prediction in Stock Prices.
Packages Used:
- Talib
- Scikit Learn
- TensorFlow
- Keras
- Pandas
- Numpy and numexpr (Faster Numpy for better performance)
- Visualisation tools like Seaborn and Matplotlib
Still Working... Will keep on updating...
LSTMs have gave me promosing results and now I am exploring advance LSTM like MD-LSTM and MiD-LSTM for better prediction and more feature inclusion.
Here is result one of my project done for Sentiment Analysis on News and LSTM with RNN.
Below is Prediction for NASDAQ using our model. Price is scaled to 1. Trained the model for past 2 years and predicted the results for 200 days in the future.
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PS: These are my own codes which are stricktly experimental. This does not represent firms views. Use these codes at your own risks.
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这是一篇2015年提出的论文，但是我今天看还是对现实的实践具有指导作用。本文从传统软件工程的技术债引出机器学习系统的技术债，并且比较了两者不同。 不同点具体为： 传统的软件技术债都是代码层面的，但是机器学习系统代码层面、系统层面都有。 具体的比较方面如下： 复杂模型侵蚀边界（Complex Models Erode Boundaries） 传统的软件开发，可以用封装和设计模式等方法将整个工程分为多
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A Survey of Deep Learning Techniques Applied to Trading Deep learning has been getting a lot of attention lately with breakthroughs in image classification and speech recognition. However, its applica
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Bernard Marr said: In short, the best answer is that: Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. And, Machine Lea
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Elon Musk Demis Hassabis Fei fei Li Andrew Ng Jeff Dean Andrew Kapathy Mikolov Jurgen Schmidhuber Geof. Hinton GREAT AI Company Netflix Facebook(Meta) Deepmind Google Amazon
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Machine-learning-in-action 个人使用jupyter notebook整理的peter的《机器学习实战》代码，使其更有层次感，更加连贯，也根据自己的代码习惯，加了一些自己的修改，以及注释 这是给自己做的笔记，贴出来，也是希望大家一起学习! 注：原版所有代码点击这里 GitHub整理的资源apachecn/MachineLearning 内容包括： adaBoos
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Lộ trình học Machine Learning, Deep Learning cho người mới bắt đầu Tôi đã từng học Machine Learning trong vòng 2 tháng và tôi tin bạn cũng có thể làm được. Lộ trình sẽ giúp bạn nắm chắc công nghệ này
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学习意味着通过学习或经验获得知识或技能。 基于此，我们可以定义机器学习（ML）如下 - 它可以被定义为计算机科学领域，更具体地说是人工智能的应用，其为计算机系统提供了学习数据和从经验改进而无需明确编程的能力。 基本上，机器学习的主要焦点是允许计算机自动学习而无需人为干预。 现在问题是如何开始和完成这种学习？ 它可以从数据的观察开始。 数据可以是一些示例，指令或一些直接经验。 然后在此输入的基础上，
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Machine Learning This project provides a web-interface,as well as a programmatic-apifor various machine learning algorithms. Supported algorithms: Support Vector Machine (SVM) Support Vector Regressio
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深度学习 我们可以在Personal Computer上完成庞大的任务 深度学习是一种适应于各类问题的万能药 神经网络 神经网络出现于80年代，但当时计算机运行慢，数据集很小，神经网络不适用 现在神经网络回来了，因为能够进行GPU计算，可用使用的数据集也变大 分类 分类的一些讨论可以在这个项目里看到 Machine Learning不仅是Classification！但分类是机器学习的核心。 学会
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Machine Learning Projects This repository contains mini projects in machine learning with jupyter notebook files.Go to the projects folder and see the readme for detailed instructions about the projec