Pinned Repositories
A-Deep-Learning-Based-Illegal-Insider-Trading-Detection-and-Prediction-Technique-in-Stock-Market
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
AlphaTrading
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.
AutoDataUpdater
This tool is designed to update data for Amibroker
FinRL-Library
A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, NeurIPS 2020 DRL workshop.
Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
Intelligent-Quantitative-Trading
Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio management and optimization.
JKKMS
A KMS System
LongShortMemorymodelNifty
LSTM model for Nifty - trained and tested from last 15 years (with all technical indicators and other key parameters)
Machine-Learning-for-Algorithmic-Trading-Second-Edition
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
MachineLearningStocks
Using python and scikit-learn to make stock predictions
jaydotkay's Repositories
jaydotkay/Intelligent-Quantitative-Trading
Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio management and optimization.
jaydotkay/A-Deep-Learning-Based-Illegal-Insider-Trading-Detection-and-Prediction-Technique-in-Stock-Market
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
jaydotkay/AlphaTrading
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.
jaydotkay/AutoDataUpdater
This tool is designed to update data for Amibroker
jaydotkay/FinRL-Library
A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, NeurIPS 2020 DRL workshop.
jaydotkay/Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
jaydotkay/LongShortMemorymodelNifty
LSTM model for Nifty - trained and tested from last 15 years (with all technical indicators and other key parameters)
jaydotkay/Machine-Learning-for-Algorithmic-Trading-Second-Edition
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
jaydotkay/MachineLearningStocks
Using python and scikit-learn to make stock predictions
jaydotkay/netty-socketio
Socket.IO server implemented on Java. Realtime java framework
jaydotkay/news-sentiment-analysis
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
jaydotkay/NIFTY_50_STOCK_PREDICTION
Predicting NIFTY_50 index price movement with LSTM Keras
jaydotkay/NLP-Models-Tensorflow
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
jaydotkay/nsepy
Python Library to get publicly available data on NSE website ie. stock quotes, historical data, live indices
jaydotkay/OIAnalysis
NSE Nifty Derivatives OI analysis using Python and Excel.
jaydotkay/option_chain_analysis
NSE Nifty Option chain analysis on the web page.
jaydotkay/pynse
Library to extract publicly available real-time and historical data from NSE website.
jaydotkay/Python-NSE-Option-Chain-Analyzer
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.
jaydotkay/qlib
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.
jaydotkay/Screeni-py
A Python-based stock screener to find stocks with potential breakout probability from NSE India.
jaydotkay/spring-security-oauth
Just Announced - "Learn Spring Security OAuth":
jaydotkay/Stock-Forecaster
:chart_with_upwards_trend: A web based stock forecaster in Django with predictive analysis
jaydotkay/Stock-Prediction
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
jaydotkay/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
jaydotkay/Stock_Analysis_For_Quant
Different Types of Stock Analysis in Python, R, Matlab, Excel, Power BI
jaydotkay/stocksight
Crowd-sourced stock analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
jaydotkay/Time-Series-Forecast-NSEPy
Redcarpetup.com assignment Intern Task
jaydotkay/TuringTrader
The Open-Source Backtesting Engine/ Market Simulator by Bertram Solutions.
jaydotkay/tutorials
Just Announced - "Learn Spring Security OAuth":
jaydotkay/udacity_ml_nano_degree