jaydotkay Goto Github PK
Name: jke
Type: User
Name: jke
Type: User
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
An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
This tool is designed to update data for Amibroker
A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, NeurIPS 2020 DRL workshop.
Hands-On Machine Learning for Algorithmic Trading, published by Packt
Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio management and optimization.
A KMS System
LSTM model for Nifty - trained and tested from last 15 years (with all technical indicators and other key parameters)
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
Using python and scikit-learn to make stock predictions
Socket.IO server implemented on Java. Realtime java framework
The spider crawls moneycontrol.com and economictimes.com to fetch news of input companies and also scores and classifies the companies to raise an early warning signal
Predicting NIFTY_50 index price movement with LSTM Keras
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
Python Library to get publicly available data on NSE website ie. stock quotes, historical data, live indices
Realtime Data From National Stock Exchange (India)
NSE Nifty Derivatives OI analysis using Python and Excel.
NSE Nifty Option chain analysis on the web page.
A trading robot with different candlestic patterns and Fibonacci Ratios
Library to extract publicly available real-time and historical data from NSE website.
The NSE has a website which displays the option chain in near real-time. This program retrieves this data from the NSE site and then generates useful analysis of the Option Chain for the specified Index or Stock. It also continuously refreshes the Option Chain and visually displays the trend in various indicators useful for Technical Analysis.
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies.
A Python-based stock screener to find stocks with potential breakout probability from NSE India.
Just Announced - "Learn Spring Security OAuth":
:chart_with_upwards_trend: A web based stock forecaster in Django with predictive analysis
Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Different Types of Stock Analysis in Python, R, Matlab, Excel, Power BI
Crowd-sourced stock analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
Redcarpetup.com assignment Intern Task
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.