Pinned Repositories
linfa
A Rust machine learning framework.
ndarray
ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
alacritty
A cross-platform, OpenGL terminal emulator.
anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
arrayvec
A vector with a fixed capacity. (Rust)
omscs-notes-notes
The raw markdown notes for OMSCS Notes.
probability
Probabilistic reasoning and statistical analysis in TensorFlow
tensorflow
An Open Source Machine Learning Framework for Everyone
stokhos's Repositories
stokhos/async-book_zh-cn
Asynchronous Programming in Rust, Chinese translation
stokhos/avellaneda-stoikov
Avellaneda-Stoikov HFT market making algorithm implementation
stokhos/bayesian-machine-learning
Notebooks related to Bayesian methods for machine learning
stokhos/blitz-bayesian-deep-learning
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
stokhos/comp-phys
Computational Physics
stokhos/cpsc-4770_6770
Distritbuted and Cluster Computing
stokhos/cvxportfolio
Portfolio optimization and simulation in Python
stokhos/cvxstoc
Disciplined convex stochastic programming. For the cvxstoc home page, please see:
stokhos/git-manual
:octocat: git command reference manual 🦋
stokhos/gruvbox
A simplified and optimized Gruvbox colorscheme for Vim
stokhos/HFT
High Frequency Market Making
stokhos/interactive-coding-challenges
Continually updated, interactive, test-driven Python coding interview challenges (algorithms and data structures).
stokhos/Leetcode_Solutions
Repo for leetcodes
stokhos/limit-order-book
A C++ and Python implementation of the limit order book.
stokhos/linfa
A Rust machine learning framework.
stokhos/lru_cache
A C++ implementation of a LRU cache
stokhos/machine-learning-asset-management
Machine Learning in Asset Management (by @firmai)
stokhos/ndarray
ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
stokhos/optimization
different algorithms to find population close to the optimal fitness, including genetic algorithm, differential evolution algorithm, PSO, firefly algorithm, cuckoo search algorithm and whale optimization algorithm in C++.
stokhos/PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
stokhos/Python
All Algorithms implemented in Python
stokhos/Riskfolio-Lib
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
stokhos/rl-sketchpad
Collection of Deep Reinforcement Learning Jupyter Notebooks. Each notebook is self-contained and presents single algorithm. These include DP, MC, TD, SARSA, Q-Learning and DQNs.
stokhos/RL-Stock
📈 如何用深度强化学习自动炒股
stokhos/sapling
A highly experimental code editor where you edit code, not text.
stokhos/scientific_computing
various project using python or spark
stokhos/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).
stokhos/statarb
stokhos/tensorflow
An Open Source Machine Learning Framework for Everyone
stokhos/universal-portfolios
Collection of algorithms for online portfolio selection