CareerLow

CareerLow's Stars

• ggerganov/llama.cpp

  LLM inference in C/C++

  Language:C++65.8k5493.8k9.4k

• getcursor/cursor

  The AI Code Editor

  23.9k1872k1.5k

• microsoft/qlib

  Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

  Language:Python15.3k2989082.6k

• lukas-blecher/LaTeX-OCR

  pix2tex: Using a ViT to convert images of equations into LaTeX code.

  Language:Python12.1k74269999

• jpmorganchase/python-training

  Python training for business analysts and traders

  Language:Jupyter Notebook6k249131.1k

• quantopian/pyfolio

  Portfolio and risk analytics in Python

  Language:Jupyter Notebook5.6k3034171.8k

• corna/me_cleaner

  Tool for partial deblobbing of Intel ME/TXE firmware images

  Language:Python4.5k188383278

• georgezouq/awesome-ai-in-finance

  🔬 A curated list of awesome LLMs & deep learning strategies & tools in financial market.

  3.4k1213412

• tradytics/eiten

  Statistical and Algorithmic Investing Strategies for Everyone

  Language:Python2.8k9622327

• nautechsystems/nautilus_trader

  A high-performance algorithmic trading platform and event-driven backtester

  Language:Python2k63548465

• VikParuchuri/texify

  Math OCR model that outputs LaTeX and markdown

  Language:Python817141161

• easystats/bayestestR

  :ghost: Utilities for analyzing Bayesian models and posterior distributions

  Language:R5631736355

• google-deepmind/uncertain_ground_truth

  Dermatology ddx dataset, Jax implementations of Monte Carlo conformal prediction, plausibility regions and statistical annotation aggregation from our recent work on uncertain ground truth (TMLR'23 and ArXiv pre-print).

  Language:Python53511233

• valeman/Transformers_And_LLM_Are_What_You_Dont_Need

  The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.

  48414126

• 0xfdf/toraniko

  A multi-factor equity risk model for quantitative trading.

  Language:Python37712361

• sstoikov/microprice

  Language:Jupyter Notebook34810294

• hansbuehler/deephedging

  Implementation of the vanilla Deep Hedging engine

  Language:Jupyter Notebook22513241

• robertmartin8/pValuation

  Quantamental finance research with python

  Language:Jupyter Notebook1317041

• Duane321/mutual_information

  Contains companion code for the Mutual Information YouTube channel

  Language:Jupyter Notebook894240

• wangys96/SVI-Volatility-Surface-Calibration

  SVI volatility surface model and an example of China 50ETF option

  Language:Jupyter Notebook571027

• thedatabeat/SVI

  Language:Python463225

• alex33d/backtest_optimizer

  Optimization of trading strategy hyperparameters with combinatorial cross validation and stress tesing

  Language:Python302111

• sakaki-/efi-install-guide-source

  The MediaWiki source pages for "Sakaki's EFI Install Guide" (as hosted on the Gentoo wiki)

  Language:Shell253113

• wesley1001/factorlab

  FactorLab is a python library which enables the transformation of raw data into informative alpha and risk factors used in the investment algorithm development process.

  Language:Python11204

• sstoikov/tradeimbalance

  Language:TeX7204

• ooples/Stock.Indicators

  Stock Indicators for .NET is a C# library package that produces financial market technical indicators. Send in historical price quotes and get back desired indicators such as moving averages, Relative Strength Index, Stochastic Oscillator, Parabolic SAR, etc. Nothing more. It can be used in any market analysis software using standard OHLCV price quotes for equities, commodities, forex, cryptocurrencies, and others. We had private trading algorithms, machine learning, and charting systems in mind when originally creating this community library.

  Language:C#6002

• robertmartin8/ReasonableDeviations

  my webpage

  Language:HTML62012

• erikfriedrich/Quant-Department

  My contributions to the Quant Department of the Hedge Fund Club e.V. (University of Mannheim)

  Language:Python5101

• mathworks/estimating-option-implied-probability-distributions-for-asset-pricing

  Forecasting the performance of an asset and quantifying the uncertainty associated with such a forecast is a difficult task: one that is frequently made more difficult by a shortage of observed market data. This example illustrates one approach for creating a price forecast based on option price data.

  5402

• ebergel/OpenQ

  The open-source implementation of Q*, achieved in context as a zero-shot reprogramming of the attention mechanism. (synthetic data)

  Language:Python20