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agora

📈Financial Markowitz Portfolio Optimization (Bonds, Stocks, Commodities), including classical Efficient Frontier, Utility Function etc.

aialpha

Use unsupervised and supervised learning to predict stocks

aiqfy_master

algo-trading

This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!

algorithmics_trading_analysis_strategy

Designing, developing, testing and deploying technical strategies using Python

arima-lstm-hybrid-corrcoef-predict

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

atoti_notebooks

atoti notebooks gallery

axioma-py

binance-news-sentiment-bot

This is a fully functioning Binance trading bot that takes into account the news sentiment for the top 100 crypto feeds. If you like this project consider donating though the Brave browser to allow me to continuously improve the script.

brownie

A Python-based development and testing framework for smart contracts targeting the Ethereum Virtual Machine.

brownie-strategy-mix

brownie-wrapper-mix

btc-technical-analysis

A project for learning technical trading strategies and data analysis with python

building-python-microservices-with-fastapi

Building Python Microservices with FastAPI, Published by Packt

campisi-fixed-income-performance-attribution-model

Campisi纯债型基金业绩归因模型程序，适用于**市场，需要有Wind的API接口权限

codefromvideo

Source code from the youtube video

cryptofolio

Track gain and loss of your cryptocurrency portfolio.

dash-sample-apps

Open-source demos hosted on Dash Gallery

deep-q-learning

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

deepai

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

financephd

Development space for PhD in Finance

financial-machine-learning-articles

Contains the code for my financial machine learning articles

financial_trading_with_python

Are you fascinated by the financial markets and interested in financial trading? This course will help you to understand why people trade, what the different trading styles are, and how to use Python to implement and test your trading strategies. Start your trading adventure with an introduction to technical analysis, indicators, and signals. You'll learn to build trading strategies by working with real-world financial data such as stocks, foreign exchange, and cryptocurrencies. By the end of this course, you'll be able to implement custom trading strategies in Python, backtest them, and evaluate their performance.

fundamentals

Fundamentals of data exploration, data manipulation, data cleaning, and data analysis

ibtradeqtdev

Trading Software for IB

jpmorgan-virtual-experience-codes

JPMorgan Chase has traders in all the major financial centers and creates a marketplace for asset classes around the globe for our investor clients. Trading teams are organized by asset class: Equities (stocks) Commodities Credit (debt and bonds) Currency & Emerging Markets Public Finance (Government bonds) Interest rates Securitized Products For this project, a trader from the equities team (publicly listed company stocks) has requested functionality be added to their dashboard to allow them to input specific information so they can monitor a new trading strategy. In order to do this, you’ll need to set up your system so you can interface with the relevant financial data feed, make the required calculations and then present this in a way that allows the traders to visualize and analyze this data in real time. The visualization of charts and data analysis our trader’s see is all built on JPMorgan Chase's own open sourced software called Perspective. You’ll learn how to implement this to facilitate the trader’s requested changes and deliver actionable insights. You’ll have to gain an understanding of the user requirements and then build something that meets those requirements. 1: Interface with a stock price data feed Interface with a stock price data feed and set up your system for analysis of the data Financial Data Python Git Basic Programming 2: Use JPMorgan Chase frameworks and tools Implement the Perspective open source code in preparation for data visualization React Typescript Web Applications 3: Display data visually for traders Use Perspective to create the chart for the trader’s dashboard Technical Communication Financial Analysis Web Applications

libormarketmodel

Single and Multi Factor Libor Market Model with Monte Carlo simulations to price a swaption receiver and a zcb option

marketplace

🏛️ Decentraland's NFT Marketplace

panel

A high-level app and dashboarding solution for Python

panel-altair-dashboard

A tutorial on how to use Panel and Altair to create a simple data dashboard app.