awesome-algorithmic-trading
A curated list of awesome algorithmic trading frameworks, libraries, software and resources (by joelowj)
fooltrader
quant framework for stock (by foolcage)
Our great sponsors
| awesome-algorithmic-trading | fooltrader | |
|---|---|---|
| 1 | 1 | |
| 684 | 1,124 | |
| - | - | |
| 10.0 | 0.0 | |
| almost 5 years ago | 11 months ago | |
| Python | ||
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-algorithmic-trading
Posts with mentions or reviews of awesome-algorithmic-trading.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-09.
fooltrader
Posts with mentions or reviews of fooltrader.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-09.
What are some alternatives?
When comparing awesome-algorithmic-trading and fooltrader you can also consider the following projects:
financial-machine-learning - A curated list of practical financial machine learning tools and applications.
zvt - modular quant framework.
Stock-Market-Sentiment-Analysis - Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka