Pinned Repositories
-Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020_practice
AAAI2022-HCM
Source code of AAAI'22 paper: A Hybrid Causal Structure Learning Algorithm for Mixed-type Data
aat
Asynchronous, event-driven algorithmic trading in Python and C++
Adv_Fin_ML_Exercises
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]
Advanced-Deep-Trading
Mostly experiments based on "Advances in financial machine learning" book
aenea
Client-server library for using voice macros from Dragon NaturallySpeaking and Dragonfly on remote/non-windows hosts.
aeron
Efficient reliable UDP unicast, UDP multicast, and IPC message transport
AGI-Papers
Papers and Book to look at when starting AGI 📚
dysts
More than a hundred strange attractors
Mastering-Python-for-Finance-source-codes
Accompanying source codes for my book 'Mastering Python for Finance'.
chetanmehra's Repositories
chetanmehra/ai_for_trading_nanodegree_alpha_research_multi_factor_modeling_project
Modeling a multi-alpha factor stock portfolio. For Udacity's AI for Trading Nanodegree.
chetanmehra/Algorithmic-trading
Deep Learning – Artificial Neural Network Using TensorFlow In Python
chetanmehra/AlgorithmicTrading-MachineLearning
Deep Learning - Neural network (RNN, LSTM & GRU)
chetanmehra/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.
chetanmehra/BayesHMM
Full Bayesian Inference for Hidden Markov Models
chetanmehra/BirdsongGAN_sequencelearning
a generative adversarial network that generates snippets of birdsong spectrogram combined with Bayesian time series models for sequence learning on the snippets
chetanmehra/burst_detection
Detect bursts in batched data using Kleinberg's (2002) algorithm.
chetanmehra/CausalNet
chetanmehra/changepoint
A place for the development version of the changepoint package on CRAN.
chetanmehra/changepoint.online
chetanmehra/eikondataapi
chetanmehra/EnsembleSystemDevelopment
Python modules for building and testing trading system ensembles. Provides a framework for system combination.
chetanmehra/finance_gan
Wasserstein GAN with gradient penalty (WGAN-GP) applied to financial time series.
chetanmehra/High-frequency-Pairs-trading
This is the final project of Statistical Arbitrage course and it aims to apply pairs trading in high frequency data to realize auto-trading
chetanmehra/LeanReportCreator
Create beautiful HTML/PDF reports for sharing your LEAN backtest and live trading results.
chetanmehra/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
chetanmehra/noobs-term
A terminal configuration for noobs
chetanmehra/Pairs-Trading-using-Copula
This project is to apply Copula Function to pair trading strategy both in American stock market.
chetanmehra/RGAN
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
chetanmehra/seq2seq
A general-purpose encoder-decoder framework for Tensorflow
chetanmehra/sklearn-pandas
Pandas integration with sklearn
chetanmehra/StackNet
StackNet is a computational, scalable and analytical Meta modelling framework
chetanmehra/tcav
chetanmehra/telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
chetanmehra/Test-stock-prediction-algorithms
Use deep learning, genetic programming and other methods to predict stock and market movements
chetanmehra/The-Gateway-code-samples
Code samples for The Gateway.
chetanmehra/TimeSeries_Seq2Seq
This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.
chetanmehra/TrustScore
To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).
chetanmehra/TVD_Condat2013
Python implementation of the 1D Total Variation Denoising algorithm A Direct Algorithm for 1D Total Variation Denoising (Sign. Proc. Letters, DOI:10.1109/LSP.2013.2278339) using xtensor and pybind11 to bind c++ and numpy.
chetanmehra/WaveNet-BTC
WaveNet model applied to intra-day Bitcoin exchange forcasting