Algorithmic Trading
What is Algorithmic Trading & How to Get Started - In this video, you will learn everything you need to know about how to learn algorithmic trading. After watching this video, you should have a clear idea about what algorithmic trading is and how YOU can become an algorithmic trader.
How to Use Monte Carlo Simulation With geometric Brownian motion (GBM) - One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). For example, to calculate the value at risk (VaR) of a portfolio, we can run a Monte Carlo simulation that attempts to predict the worst likely loss for a portfolio given a confidence interval over a specified time horizon (we always need to specify two conditions for VaR: confidence and horizon).
In this article, we will review a basic MCS applied to a stock price using one of the most common models in finance: geometric Brownian motion (GBM). Therefore, while Monte Carlo simulation can refer to a universe of different approaches to simulation, we will start here with the most basic.
yfinance - Yahoo Finance, lots of free and up-to-date data
Nasdaq Data Link - not much free data
A comprehensive guide to downloading stock prices in Python
Nasdaq Data Link Python Github - A Python library for Nasdaq Data Link's RESTful API
Alpaca - API for stock trading. Trade with algorithms, connect with apps, build services — all with commission-free stock trading API.
Combine Python with realtime stock data and trading with up to 200 requests per every minute per API key. This is a very powerful tool which didn't exist two or three years ago. Alpaca also allows us to buy and sell stocks in the live market in a paper trading account. This paper trading feature lets you test your strategies without ever risking real money on your trades.
investpy docs - python library for investing
Quandl: A Step-by-Step guide - 2021
TA Lib - Technical Analysis Library
TA Lib Github - complicated to install
An Introduction to Python for Trading: Benefits, Strategies, and More - QuantInsti blog, 2021
Investopedia