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Resources for developers using Python for scientific computing and quantitative analysis

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Quant Trading

Algorithmic Trading Using Logistic Regression handsoffinvesting.com

Published September 20, 2020 under Machine Learning

Algorithmic Trading Using Logistic Regression

In order to implement an algorithmic trading strategy though, you have to first narrow down a list of stocks that you want to analyze. This walk-through provides an automated process (using python and logistic regression) for determining the best stocks to algo-trade.

I will dive deeper into the logic and code below, but here is a high-level overview of the process:

  1. Import the historical data of every stock using yahoo finance.
  2. Pull in over 32 technical indicators for each stock using the technical analysis library.
  3. Perform a logistic regression on each stock using 5, 30, and 60 day observation time periods.
  4. Interpret the results.

Algorithmic Trading, Quant Trading

Stock Analysis in Python deepnote.com

Published July 13, 2020 under Trading

Stock Analysis in Python

It’s easy to get carried away with the wealth of data and free open-source tools available for data science. After spending a little bit of time with the quandl financial library and the prophet modeling library, I decided to try some simple stock data exploration. Several days and 1000 lines of Python later, I ended up with a complete stock analysis and prediction tool. Although I am not confident (or foolish) enough to use it to invest in individual stocks, I learned a ton of Python in the process and in the spirit of open-source, want to share my results and code so others can benefit.

Quant Trading, Time Series Analysis

Designing an energy arbitrage strategy with linear programming https://www.steveklosterman.com

Published May 12, 2020 under Python

Designing an energy arbitrage strategy with linear programming

The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage system must also be considered. For example, batteries have limited capacity, limited rate of charging, and are not 100% efficient in that not all of the energy used to charge a battery will be available later for discharge.

Quant Trading, Trading

Empyrical: Common financial risk and performance metrics in Python github.io

Published February 11, 2020 under Quant Finance

Lots of quantitative risk metrics for analyzing your backtest and trading performance. Created by Quantopian for their popular Zipline backtesting framework, this library works totally independently.

Finance, Python, Quant Trading

Exploring Stock Price Movements After Major Events medium.com

Published May 19, 2019 under Trading

This post will go through the process of gathering and cleaning this data followed by an exploratory analysis examining price trends and the impact of events on prices using data from the IEX API and scraped events from financial news sites.

API, IEX, Python, Quant Trading

Time Series Analysis with Pandas dataquest.io

Published February 2, 2019 under Python

Quant Trading, Time Series Analysis

Setting Up Alpaca API with Python medium.com

Published November 29, 2018 under Trading

API, Quant Trading

A feature-rich Python framework for backtesting and trading backtrader.com

Published November 23, 2018 under Trading

Algorithmic Trading, Backtesting, Quant Trading

Using Quadratic Discriminant Analysis To Optimize An Intraday Momentum Strategy

Published June 17, 2018 under Trading

Python, Quant Trading

Kalman Filter Techniques And Statistical Arbitrage In China’s Futures Market quantinsti.com

Published June 17, 2018 under Trading

Python, Quant Trading

K-Means Clustering For Pair Selection In Python – Historic Problem of Pair Selection (3 of 3) interactivebrokers.com

Published June 17, 2018 under Trading

Python, Quant Trading

K-Means Clustering For Pair Selection In Python – Heatmaps and ADF Tests (2 of 3) interactivebrokers.com

Published June 17, 2018 under Trading

Python, Quant Trading

K-Means Clustering For Pair Selection In Python – Historic Problem of Pair Selection (1 of 3) interactivebrokers.com

Published June 17, 2018 under Trading

Python, Quant Trading

Value at Risk Estimation Error geodesicanalytics.wordpress.com

Published June 4, 2017 under Python

Python, Quant Trading, VaR

Python Backtesting Libraries For Quant Trading Strategies robusttechhouse.com

Published January 23, 2016 under Python

Python, Quant Trading, Trading

Scott Sanderson on Algorithmic Trading Podcast pythonpodcast.com

Published January 13, 2016 under Blogs

Blogs, Podcast, Python, Quant Trading

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