jonny536486040's 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
AutoIT-Scripts
All of my AutoIT Scripts
backtrader
Python Backtesting library for trading strategies
deap
Distributed Evolutionary Algorithms in Python
DeepEvolve
Rapid hyperparameter discovery for neural nets using genetic algorithms
DeepLearningImplementations
Implementation of recent Deep Learning papers
dlib
A toolkit for making real world machine learning and data analysis applications in C++
examples-of-web-crawlers
python有趣的爬虫例子,对新手比较友好,主要爬取淘宝、天猫、微信、豆瓣、QQ等网站。
finplot
Performant and effortless finance plotting for Python
force-directed-layout-algorithms
Force directed layout algorithms for Python
gota
Gota: DataFrames and data wrangling in Go (Golang)
hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
hyperopt-sklearn
Hyper-parameter optimization for sklearn
japonicus
Genetic Algorithm for Gekko Trading Bot.
LSTM-Neural-Network-for-Time-Series-Prediction
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data
LSTM_RNN_Tutorials_with_Demo
LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation using Keras-Tensorflow
Machine-Learning-for-Beginner-by-Python3
为机器学习的入门者提供多种基于实例的sklearn、TensorFlow以及自编函数(AnFany)的ML算法程序。
MachineLearning
Machine learning resources，including algorithm, paper, dataset, example and so on.
ML-Finance
Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.
pmdarima
A package that brings R's beloved auto.arima to Python, making an even stronger case for why Python > R for data science.
pyqtgraph
Fast data visualization and GUI tools for scientific / engineering applications
scipy-lecture-notes
Tutorial material on the scientific Python ecosystem
StockPrediction-SVM
Semester Project for Stock Price Prediction
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
tsfresh
Automatic extraction of relevant features from time series:
tutorials-1
CatBoost tutorials repository
WebGLSamples.github.io
WebGL Samples and Examples