基于机器学习和深度学习的长短时量化交易算法.zip

[![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url] [![LinkedIn][linkedin-shield]][linkedin-url] <a href="https://www.buymeacoffee.com/lastancientone" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> <!-- MARKDOWN LINKS & IMAGES --> <!-- https://www.markdownguide.org/basic-syntax/#reference-style-links --> [contributors-shield]: https://img.shields.io/github/contributors/LastAncientOne/Deep-Learning-Machine-Learning-Stock.svg?style=for-the-badge [contributors-url]: https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock/graphs/contributors [forks-shield]: https://img.shields.io/github/forks/LastAncientOne/Deep-Learning-Machine-Learning-Stock.svg?style=for-the-badge [forks-url]: https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock/network/members [stars-shield]: https://img.shields.io/github/stars/LastAncientOne/Deep-Learning-Machine-Learning-Stock.svg?style=for-the-badge [stars-url]: https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock/stargazers [issues-shield]: https://img.shields.io/github/issues/LastAncientOne/Deep-Learning-Machine-Learning-Stock.svg?style=for-the-badge [issues-url]: https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock/issues [license-shield]: https://img.shields.io/github/license/LastAncientOne/Deep-Learning-Machine-Learning-Stock.svg?style=for-the-badge [license-url]: LICENSE [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555 [linkedin-url]: https://linkedin.com/in/tin-hang <img src="DL_Title.PNG"> <h1 align="center">Deep Learning and Machine Learning for Stock Predictions</h1> Description: This is a comprehensive study and analysis of stocks using deep learning (DL) and machine learning (ML) techniques. Both machine learning and deep learning are types of artificial intelligence (AI). The objective is to predict stock behavior by employing various machine learning and deep learning algorithms. The focus is on experimenting with stock data to understand how and why certain methods are effective, as well as identifying reasons for their potential limitations. Different stock strategies are explored within the context of machine learning and deep learning. Technical Analysis and Fundamental Analysis are utilized to predict future stock prices using these AI techniques, encompassing both long-term and short-term predictions. Machine learning is a branch of artificial intelligence that involves the development of algorithms capable of automatically adapting and generating outputs by processing structured data. On the other hand, deep learning is a subset of machine learning that employs similar algorithms but with additional layers of complexity, enabling different interpretations of the data. The network of algorithms used in deep learning is known as artificial neural networks, which mimic the interconnectedness of neural pathways in the human brain. Deep learning and machine learning are powerful approaches that have revolutionized the AI landscape. Understanding the fundamentals of these techniques and the commonly used algorithms is essential for aspiring data scientists and AI enthusiasts. Regression, as a fundamental concept in predictive modeling, plays a crucial role in analyzing and predicting continuous variables. By harnessing the capabilities of these algorithms and techniques, we can unlock incredible potential in various domains, leading to advancements and improvements in numerous industries. ### Machine Learning Step-by-Step 1. Collecting/Gathering Data. 2. Preparing the Data - load data and prepare it for the machine learning training. 3. Choosing a Model. 4. Training the Model. 5. Evaluating the Model. 6. Parameter Tuning. 7. Make a Predictions. ### Deep Learning Model Step-by-Step 1. Define the Model. 2. Complie the Model. 3. Fit the Model with training dataset. 4. Make a Predictions. <h3 align="left">Programming Languages and Tools:</h3> <p align="left"> </a> <a href="https://www.python.org" target="_blank"> <img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg" alt="python" width="50" height="50"/> </a> <a href="https://nteract.io/" target="_blank"> <img src="https://avatars.githubusercontent.com/u/12401040?s=200&v=4" alt="Nteract" width="50" height="50"/> </a> <a href="https://anaconda.org/" target="_blank"> <img src="https://www.clipartkey.com/mpngs/m/227-2271689_transparent-anaconda-logo-png.png" alt="Anaconda" width="50" height="50"/> </a> <a href="https://www.spyder-ide.org/" target="_blank"> <img src="https://www.pinclipart.com/picdir/middle/180-1807410_spyder-icon-clipart.png" alt="Spyder" width="50" height="50"/> </a> <a href="https://jupyter.org/" target="_blank"> <img src="https://upload.wikimedia.org/wikipedia/commons/3/38/Jupyter_logo.svg" alt="Jupyter Notebook" width="50" height="50"/> </a> <a href="https://notepad-plus-plus.org/" target="_blank"> <img src="https://logos-download.com/wp-content/uploads/2019/07/Notepad_Logo.png" alt="Notepad++" width="50" height="50"/> </a> </p> ### Three main types of data: Categorical, Discrete, and Continuous variables 1. Categorical variable(Qualitative): Label data or distinct groups. Example: location, gender, material type, payment, highest level of education 2. Discrete variable (Class Data): Numerica variables but the data is countable number of values between any two values. Example: customer complaints or number of flaws or defects, Children per Household, age (number of years) 3. Continuous variable (Quantitative): Numeric variables that have an infinite number of values between any two values. Example: length of a part or the date and time a payment is received, running distance, age (infinitly accurate and use an infinite number of decimal places) ### Data Use 1. For 'Quantitative data' is used with all three centre measures (mean, median and mode) and all spread measures. 2. For 'Class data' is used with median and mode. 3. For 'Qualitative data' is for only with mode. ### Two types of problems: 1. Classification (predict label) 2. Regression (predict values) ### Bias-Variance Tradeoff #### Bias - Bias is the difference between our actual and predicted values. - Bias is the simple assumptions that our model makes about our data to be able to predict new data. - Assumptions made by a model to make a function easier to learn. #### Variance - Variance is opposite of bias. - Variance is variability of model prediction for a given data point or a value that tells us the spread of our data. - If you train your data on training data and obtain a very low error, upon changing the data and then training the same. ### Overfitting, Underfitting, and the bias-variance tradeoff Overfitted is when the model memorizes the noise and fits too closely to the training set. Good fit is a model that learns the training dataset and genernalizes well with the old out dataset. Underfitting is when it cannot establish the dominant trend within the data; as a result, in training errors and poor performance of the model. #### Overfitting: Overfitting model is a good model with the training data that fit or at lease with near each observation; however, the model mist the point and random noise is capture inside the model. The model have low training error and high CV error, low in-sample error and high out-of-sample error, and high variance. 1. High Train Accuracy 2. Low Test Accuracy #### Avoiding Overfitting: 1. Early stopping - stop the training before the model starts learnin