Example Order Book Imbalance Algorithm

algorithmic trading backtest and optimization examples using order book imbalances. (bitcoin, cryptocurrency, bitmex, binance futures, market making)

MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

Python-based implementations of algorithms for learning on imbalanced data.

PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous. Several mobile-friendly models are provided

Study of data imbalance and asynchronous aggregation algorithm on Federated Learning system (using PySyft)

Our implementations of the Multi-class Imbalance learning algorithms (for the KBS paper)

Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.

Zooming Memory Based Initialization (ZoMBI) algorithm for discovery of optima within challenging needle-in-a-haystack (extreme data imbalance) datasets.

A MATLAB implementation of Adaptive k-Nearest Neighbor Algorithms called Ada-kNN and Ada-kNN2 (alongside a global weighting scheme for handling class imbalance).

By learning and using prediction for failures, it is one of the important steps to improve the reliability of the cloud computing system. Furthermore, gave the ability to avoid incidents of failure and costs overhead of the system. It created a wonderful opportunity with the breakthroughs of machine learning and cloud storage that utilize generated huge data that provide pathways to predict when the system or hardware malfunction or fails. It can be used to improve the reliability of the system with the help of insights of using statistical analysis on the workload data from the cloud providers. This research will discuss regarding job usage data of tasks on the large “Google Cluster Workload Traces 2019” dataset, using multiple resampling techniques such as “Random Under Sampling, Random Oversampling and Synthetic Minority Oversampling Technique” to handle the imbalanced dataset. Furthermore, using multiple machine learning algorithm which is for traditional machine learning algorithm are “Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier and Extreme Gradient Boosting Classifier” while deep learning algorithm using “Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)” for job failure prediction between imbalanced and balanced dataset. Then, to have a comparison of imbalanced and balanced in terms of model accuracy, error rate, sensitivity, f – measure, and precision. The results are Extreme Gradient Boosting Classifier and Gradient Boosting Classifier is the most performing algorithm with and without imbalanced handling techniques. It showcases that SMOTE is the best method to choose from for handling imbalanced data. The deep learning model of LSTM and Gated Recurrent Unit may be not the best for the in terms of accuracy, based on the ROC Curve its better than the XGBoost Classifier and Gradient Boosting Classifier.

Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). :sunflower:

There are some reproduced algorithms for learning from imbalanced data, including over-sampling,under-sampling and boosting

Classification project to pinpoint potential loan customers from an imbalanced dataset. Emphasizes on penalized and tree-based algorithms, optimizing for both recall and precision to enhance campaign efficacy and conversion rates.

Apply 7 common Machine Learning Algorithms to detect fraud, while dealing with imbalanced dataset

A comprehensive and unified platform for evaluating DAL algorithms across CV and NLP tasks, supporting 21 algorithms, 10 datasets, and realistic scenarios including open-set and class-imbalanced settings.

Taherkhani, A, Cosma, G, McGinnity, M (2020) AdaBoost-CNN: an adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning, Neurocomputing, 404, pp.351-366, ISSN: 0925-2312. DOI: 10.1016/j.neucom.2020.03.064.

Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.

Problem Statement Amazon is an online shopping website that now caters to millions of people everywhere. Over 34,000 consumer reviews for Amazon brand products like Kindle, Fire TV Stick and more are provided. The dataset has attributes like brand, categories, primary categories, reviews.title, reviews.text, and the sentiment. Sentiment is a categorical variable with three levels "Positive", "Negative“, and "Neutral". For a given unseen data, the sentiment needs to be predicted. You are required to predict Sentiment or Satisfaction of a purchase based on multiple features and review text. picture Dataset Snapshot picture Project Task: Week 1 Class Imbalance Problem: Perform an EDA on the dataset. a) See what a positive, negative, and neutral review looks like. b) Check the class count for each class. It’s a class imbalance problem. Convert the reviews in Tf-Idf score. Run multinomial Naive Bayes classifier. Everything will be classified as positive because of the class imbalance. Project Task: Week 2 Tackling Class Imbalance Problem: Oversampling or undersampling can be used to tackle the class imbalance problem. In case of class imbalance criteria, use the following metrices for evaluating model performance: precision, recall, F1-score, AUC-ROC curve. Use F1-Score as the evaluation criteria for this project. Use Tree-based classifiers like Random Forest and XGBoost. Note: Tree-based classifiers work on two ideologies namely, Bagging or Boosting and have fine-tuning parameter which takes care of the imbalanced class. Project Task: Week 3 Model Selection: Apply multi-class SVM’s and neural nets. Use possible ensemble techniques like: XGboost + oversampled_multinomial_NB. Assign a score to the sentence sentiment (engineer a feature called sentiment score). Use this engineered feature in the model and check for improvements. Draw insights on the same. Project Task: Week 4 Applying LSTM: Use LSTM for the previous problem (use parameters of LSTM like top-word, embedding-length, Dropout, epochs, number of layers, etc.) Hint: Another variation of LSTM, GRU (Gated Recurrent Units) can be tried as well. Compare the accuracy of neural nets with traditional ML based algorithms. Find the best setting of LSTM (Neural Net) and GRU that can best classify the reviews as positive, negative, and neutral. Hint: Use techniques like Grid Search, Cross-Validation and Random Search Optional Tasks: Week 4 Topic Modelling: Cluster similar reviews. Note: Some reviews may talk about the device as a gift-option. Other reviews may be about product looks and some may highlight about its battery and performance. Try naming the clusters. Perform Topic Modelling Hint: Use scikit-learn provided Latent Dirchlette Allocation (LDA) and Non-Negative Matrix Factorization (NMF).

Examples of algorithms dealing with imbalanced data.

Predicted probabilities from machine learning classification algorithms may be used to tackle imbalance data. The study uses the Portuguese bank marketing dataset as a case study, as published in Towards Data Science on Medium.com

We introduce a statistical measure for the imbalance of Lightning Network Nodes and provide a greedy algorithm for nodes to selfishly decrease their imbalance which has positive effects for routing random payments

Credit card frauds cost a lot to the banks as well as the customers. Here we compare various machine learning algorithms to find the best one in detecting credit card frauds. The dataset is highly imbalanced, therefore we use a technique known as SMOTE to generate synthetic data

In this repository you'll find a theoretical introduction to the problem of class imbalance, as well as a notebook with examples about how to use some of the algorithms mentioned in the theoretical guide.

Monitoring the health condition of the rotating machine via extracting features from sensor data/measurement and applying Principal Component Analysis, PCA. Applied K-Nearest Neighbor Algorithm for classifying five faults, Fault 1 - Bearing, Fault 2 - Gear Mesh, Fault 3 - Resonance, Fault 4 - Imbalance, Fault 5 - Misalignment. In addition to this, deisgned MMSE estimator to predict the actual measured data (without noise) of wind turbine blades and implemented CUSUM two-sided test to identifiy if the measured data exceeds the desired threshold and generate an alert. A report was generated highlighting important findings and critically evaluating the results.

A tool combining genetic algorithms and clustering for flexible, high-quality oversampling in imbalanced datasets.

unofficial Pytorch implementation of ML-ROS in Addressing imbalance in multi-label classification Measures and random resampling algorithms

This is a customer churn prediction project using machine learning algorithms like Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost, and Gradient Boosting. The project aims to analyze and predict customer churn in a dataset, using techniques like class weighting and SMOTE to handle class imbalance

A comparative study of ML algorithms for anti-money laundering (AML) detection using the IBM AML dataset. Implemented Decision Trees, Random Forests, XGBoost, LGBM, SGD and SVM to evaluate model performance on imbalanced data with feature engineering techniques.

Epilepsy is the name of a neurological disorder of the human brain, which is characterized by chronic disorders and occurs at random to interrupt the normal function of the brain. The diagnosis and analysis of epileptic seizure is made with the help of Electroencephalography (EEG). In order to detect seizure, it involves the interpretation of long EEG records by the expert physicians, which is time-consuming and need high human efforts. Thus, this study aims to construct an automatic seizure detection system to analyze epileptic EEG signals. The CHB-MIT Scalp EEG recording of patients is used in this work for the experiment purpose. The Welch Fast Fourier Transform is used to convert time domain features to the frequency domain. The statistical features are extracted respectively in the time domain and frequency domain. The ANOVA based feature selection is used to deduct variables. The Random Under-sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE) methods are used to solve the data imbalance problem. Four machine learning algorithms, including decision tree classifier (DTC), extra-decision tree classifier (EDTC), Linear Discriminant Analysis Classifier(LDAC), Quadratic Discriminant Classifier(QDC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Multi-layer Perceptron Classifier (MLPC), and Stochastic Gradient Descent Classifier (SGDC) are used to classify the data. As a result, the performance of the proposed classifier is 99.48% of accuracy, 99.79% of sensitivity, and 99.17% of specificity. The system might be a helpful tool for doctors to make a more reliable and objective analysis of patient EEG records.