Pinned Repositories
algorithmic_trading_book
2 books and related source codes for algorithmic trading.
AutoTrader
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
blog
Build a Jekyll blog in minutes, without touching the command line.
BSR
Free R-Tips is a FREE Newsletter provided by Business Science. It comes with bite-sized code tutorials every week.
Clustering-in-Python
Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.
dlr
fidlr (FInancial Data LoadeR) is an RStudio addin designed to simplify the financial data downloading process
Factor-Model
Financial-Modeling-Prep-API
A brief description on how to use Financial Modeling Prep Api
ibapir
Interactive Brokers api for R
KalmanFilter
alphamindtech's Repositories
alphamindtech/algorithmic_trading_book
2 books and related source codes for algorithmic trading.
alphamindtech/AutoTrader
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
alphamindtech/blog
Build a Jekyll blog in minutes, without touching the command line.
alphamindtech/BSR
Free R-Tips is a FREE Newsletter provided by Business Science. It comes with bite-sized code tutorials every week.
alphamindtech/Clustering-in-Python
Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.
alphamindtech/dlr
fidlr (FInancial Data LoadeR) is an RStudio addin designed to simplify the financial data downloading process
alphamindtech/Factor-Model
alphamindtech/Financial-Modeling-Prep-API
A brief description on how to use Financial Modeling Prep Api
alphamindtech/ibapir
Interactive Brokers api for R
alphamindtech/learnDataScience
code for Data Science From Scratch book
alphamindtech/legitindicators
Collection of indicators that I used in my strategies.
alphamindtech/library
A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
alphamindtech/pyExample
Introductory tutorial for Moonshot demonstrating data collection, universe selection, and backtesting of an end-of-day momentum strategy.
alphamindtech/pylectures1
Learn quantitative finance with this comprehensive lecture series. Adapted from the Quantopian Lecture Series. Uses free sample data.
alphamindtech/pylectures2
Learn how to research fundamental factors using Pipeline, Alphalens, and Sharadar price and fundamental data.
alphamindtech/pylectures3
In-depth walkthrough of Pipeline, an API for filtering and performing computations on large universes of securities. The Pipeline API is part of Zipline but can also be used on a standalone basis.
alphamindtech/qstats
Portfolio analytics for quants, written in Python
alphamindtech/Quant-trading
quant trading algos and other things
alphamindtech/QuantAnalysis
Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau
alphamindtech/quantScientist
A Free Newsletter for Quantitative and Algorithmic Trading, Portfolio Analysis, and Investing
alphamindtech/Resources
https://daya6489.github.io/Data-Science-Resources
alphamindtech/rExample
This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!
alphamindtech/RQuantFX
alphamindtech/RQuantTrader
alphamindtech/ShinyTemplate
Examples for R Shiny app + docker + ShinyProxy deployment
alphamindtech/shinyuieditor
A GUI for laying out a Shiny application that generates clean and human-readable UI code
alphamindtech/stat_learn
Loose collection of Jupyter notebooks, mostly for my blog
alphamindtech/WorldQuant
Code implementation of the Quantigic 101 Formulaic Alphas