Pinned Repositories
argcheck
A powerful (and blazing fast) argument checker and function overloading system for Lua or LuaJIT
BotBuilder
The Microsoft Bot Builder SDK is one of three main components of the Microsoft Bot Framework. The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services.
bulbea
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
caffe
Caffe: a fast open framework for deep learning.
chartpy
Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more
CNTK
Computational Network Toolkit (CNTK)
Data-Analysis
Data Science Using Python
Data-Science--Cheat-Sheet
Cheat Sheets
deep-visualization-toolbox
free-programming-books
:books: Freely available programming books
ana2s007's Repositories
ana2s007/chartpy
Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more
ana2s007/findatapy
Python library to download market data via Bloomberg, Quandl, Yahoo etc.
ana2s007/finmarketpy
Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)
ana2s007/Wildfire-Risk-Management-Pipeline
Wildfire Risk Management Platform
ana2s007/apd-core
Core repo for
ana2s007/awesome-deep-reinforcement-learning-in-finance
🔬 A collection for those AI (RL / DL / SL / Evoluation / Genetic Algorithm) used in financial market. otherwise, we add Technology Analysis / Alpha Research / Arbitrage and other useful strategies tools & docs in quantitative finance market.
ana2s007/awesome-notebooks
Ready to use data science templates, organized by tools to jumpstart your projects in minutes. 😎 published by the Naas community.
ana2s007/awesome-public-datasets
A topic-centric list of HQ open datasets. PR ☛☛☛
ana2s007/awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
ana2s007/best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
ana2s007/cube.js
📊 Cube.js - Open Source Analytics Framework
ana2s007/dagster
A data orchestrator for machine learning, analytics, and ETL.
ana2s007/dodrio
Exploring attention weights in transformer-based models with linguistic knowledge.
ana2s007/dojo
This is a repository for immersive learning, meditation or software development.
ana2s007/fastai-v3
Starter app for fastai v3 model deployment on Render
ana2s007/FinanceHub
Resources for Quantitative Finance
ana2s007/graph4nlp_demo
This repo is to present various code demos on how to use our Graph4NLP library.
ana2s007/MarketAnalysis
Portfolio Theory, Options Theory, & Quant Finance
ana2s007/mljar-supervised
Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning :rocket:
ana2s007/PGPortfolio
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
ana2s007/pyoperant
python package for operant conditioning
ana2s007/pyreadstat
Python package to read sas, spss and stata files into pandas data frames. It is a wrapper for the C library readstat.
ana2s007/PySyft
A library for encrypted, privacy preserving machine learning
ana2s007/quant-trading
Python quantitative trading strategies including MACD, Pair Trading, Heikin-Ashi, London Breakout, Awesome, Dual Thrust, Parabolic SAR, Bollinger Bands, RSI, Pattern Recognition, CTA, Monte Carlo, Options Straddle
ana2s007/Riskfolio-Lib
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
ana2s007/Sentiment-Analysis-AutoML
ana2s007/SMAP
Julia and Python programs that implement some of the tools described in my book "Stochastic Methods in Asset Pricing" (SMAP), MIT Press 2017 (e.g., the method for computing the price of American call options and the construction of the early exercise premium in the Black-Scholes-Merton framework from section 18.4 in SMAP).
ana2s007/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.
ana2s007/tf-quant-finance
High-performance TensorFlow library for quantitative finance.
ana2s007/urnng