MalykhN

MalykhN's Stars

• scorpionhiccup/StockPricePrediction

  Stock Price Prediction using Machine Learning Techniques

  Language:Jupyter Notebook1.3k8314420

• MarcosCarreira/DermanPapers

  Notebooks that replicate original quantitative finance papers from Emanuel Derman

  Language:Jupyter Notebook40914058

• deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-

  Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

  Language:Jupyter Notebook3991411121

• rfeinman/pytorch-minimize

  Newton and Quasi-Newton optimization with PyTorch

  Language:Python32292835

• mandli/intro-numerical-methods

  Jupyter notebooks and other materials developed for the Columbia course APMA 4300

  Language:Jupyter Notebook2793844399

• duemig/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).

  Language:Jupyter Notebook25314352

• hydrosquall/tiingo-python

  Python client for interacting with the Tiingo Financial Data API (stock ticker and news data)

  Language:Python25399353

• federicomariamassari/willowtree

  Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.

  Language:Python2406926

• WLM1ke/apimoex

  Сlient for MOEX ISS

  Language:Python10173028

• softhints/Pandas-Tutorials

  Language:Jupyter Notebook321014

• hse-ds/ml-hse-nes

  Курс "Машинное обучение" на совместной программе НИУ ВШЭ и РЭШ

  Language:Jupyter Notebook316026

• QuhiQuhihi/project_Asset_Allocation

  quantitative asset allocation strategy

  Language:Jupyter Notebook19105

• jeromeku/cryptocurrency-derivatives-pricing-and-delta-neutral-volatility-trading

  This project is to download and analyze cryptocurrency option data available on Deribit via a public API. Data are collected on an Ubuntu remote server with the implementation of Python3, Shell and SQLite and are then analyzed locally with Python3.

  Language:Python151015

• captain-pool/levenberg_marquardt

  Torch Implementation of Hessian Free Second Order Optimizer (Levenberg Marquardt) for Dense Models

  Language:Python8111

• Parskatt/torchlm

  Levenberg-Marquardt optimizer in pytorch

  Language:Python7200

• ey3lock3r/Deribit-Options-V2

  Language:Python5204

• greyfin2707/VaR-calculation

  Calculation of Value at Risk using Generalized normal distribution, EGARCH and GARCH + EVT

  Language:Jupyter Notebook5100

• adam-coogan/levmarq-torch

  PyTorch implementation of the Levenberg-Marquardt algorithm.

  Language:Python4200

• congcong009/btc-volatility-by-ARMA-EGARCH

  This is the code notebook for the paper on using Python to study the volatility of BTC in ARIMA-EGARCH model.

  Language:Jupyter Notebook4303

• FrenchCommando/malliavin-ssr

  Language:Python4202

• MarkHendricks/finm-fiderivs-2023

  Language:Jupyter Notebook39

• LorenzoProserpio/MasterThesisRoughVol

  Master Thesis Rough Volatility (Lorenzo Proserpio & Alessandro Nodari)

  Language:Jupyter Notebook2

• amirshamaei/LMTorch

  The Bounded (Clamped) Levenberg-Marquardt Method in Pytorch

  Language:Python1100

• ConnorStoneAstro/torchLM

  Levenberg-Marquardt algorithm variations in pytorch

  Language:Python1100

• Foktavianes/Construction-of-Arbitrage-free-implied-volatility-surface-

  Language:Jupyter Notebook1

• FrenchCommando/cliquet-edgeworth

  Language:Python1200

• MarineBqt/Option-Pricing-In-The-Local-Volatility-Model

  Language:Python1

• RubensYamamoto/PricingHedgingLista2

  Language:Jupyter Notebook1

• RubensYamamoto/PricingHedgingProva2

  Language:Jupyter Notebook1

• Wei-David-Xu/MatlabCode_Implied-Willow-Tree

  Language:MATLAB1