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Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf
Performance analysis of predictive (alpha) stock factors
some attention implements
An example attention network with simple dataset.
By PyTorch
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Python Backtesting library for trading strategies
BackTrader多因子回测框架 (Multi-factors backtesting framework for BackTrader)
A Python implementation of global optimization with gaussian processes.
TensorFlow code and pre-trained models for BERT
Keras implementation of BERT with pre-trained weights
Google AI 2018 BERT pytorch implementation
Batch normalized LSTM for tensorflow
Scalable, event-driven, deep-learning-friendly backtesting library
My Final Project for Basic Algorithm and Programming
Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.
copper price(time series) prediction using bpnn and lstm
Portfolio optimization and simulation in Python
Playing trading games with deep reinforcement learning
This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.
Portfolio optimization with deep learning.
Deep Learning Book Chinese Translation
Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Materials related to the Medium article "Predicting Stock Prices with Echo State Networks".
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.