Pinned Repositories
Data-Science--Cheat-Sheet
Cheat Sheets
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
God-Level-Data-Science-ML-Full-Stack
A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI
gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
High-Frequency-Trading-Model-with-IB
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
intraday_nope_research
Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
LLAMARUST
Run inference for Large Language Models on CPU, with Rust 🦀🚀🦙
machine-learning-for-trading
Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading
QuantResearch
Quantitative analysis, strategies and backtests
parandcor's Repositories
parandcor/Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
parandcor/High-Frequency-Trading-Model-with-IB
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
parandcor/LLAMARUST
Run inference for Large Language Models on CPU, with Rust 🦀🚀🦙
parandcor/machine-learning-for-trading
Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading
parandcor/RSI-Analysis
The objective is to understand how many companies are above or below a specific threshold to understand the level of overbought or oversold of the companies within a specific index
parandcor/td-ameritrade-python-api
Unofficial Python API client library for TD Ameritrade
parandcor/awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
parandcor/Awesome-Quant-Machine-Learning-Trading
Quant/Algorithm trading resources with an emphasis on Machine Learning
parandcor/God-Level-Data-Science-ML-Full-Stack
A collection of scientific methods, processes, algorithms, and systems to build stories & models. This roadmap contains 16 Chapters, whether you are a fresher in the field or an experienced professional who wants to transition into Data Science & AI
parandcor/gpt-engineer
Specify what you want it to build, the AI asks for clarification, and then builds it.
parandcor/intraday_nope_research
parandcor/QuantResearch
Quantitative analysis, strategies and backtests
parandcor/ByteTrack
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
parandcor/Deep_Learning_Machine_Learning_Stock
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
parandcor/DupireNN
Neural network local volatility with dupire formula
parandcor/Financial-Risk-Management
Code for Financial Risk Management
parandcor/fromthetransistor
From the Transistor to the Web Browser, a rough outline for a 12 week course
parandcor/julia
The Julia Language: A fresh approach to technical computing.
parandcor/Jupyter-Notebooks
Quantitative Risk Book
parandcor/Papers
My Quant Research Papers (incl. Coding & Excel Examples)
parandcor/PiML-Toolbox
PiML (Python Interpretable Machine Learning) toolbox for model development and validation
parandcor/PyMySQL
Pure Python MySQL Client
parandcor/Python_Option_Pricing
An libary to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options
parandcor/Riskfolio-Lib
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
parandcor/RustBooks
List of Rust books
parandcor/slimevolleygym
A simple OpenAI Gym environment for single and multi-agent reinforcement learning
parandcor/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
parandcor/StudyBook
Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)
parandcor/systematictradingexamples
Examples of code related to book www.systematictrading.org and blog qoppac.blogspot.com
parandcor/tapnet