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10k-word-embeddings

Word embeddings learned from 10-K documents

computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

face_analysis-branch_amit

This projects has models for detection human face's in given Image and also extends to identify the other information such as Gender, Emotion, Ethnicity

finance

Here you can find all the quantitative finance algorithms that I've worked on and refined over the past year!

financial-machine-learning

A curated list of practical financial machine learning (FinML) tools and applications in Python.

fun-with-soec

Stochastic Optimization and Evolutionary Computing Algorithm implementation in python

happy-days-with-r

Happy-Days-With-R is a repository of R assignments which I have solved during my master's at Centre for Modeling and Simulation in 2015-16.

interactive-coding-challenges

120+ interactive Python coding interview challenges (algorithms and data structures). Includes Anki flashcards.

mac-setup-script

Will contain script to setup mac for developing environment

multi-label-classifier

Multi-Label Classifier in R

opensignals

sato

Code and data for Sato https://arxiv.org/abs/1911.06311.

sdoosa-algo-trade-python

Algo trade project in python

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

zerodha_live_automate_trading-_using_ai_ml_on_indian_stock_market-using-basic-python

Online trading using Artificial Intelligence Machine leaning with basic python on Indian Stock Market, trading using live bots indicator screener and back tester using rest API and websocket 😊