financial-machine-learning VS awesome-algorithmic-trading

Compare financial-machine-learning vs awesome-algorithmic-trading and see what are their differences.

awesome-algorithmic-trading

A curated list of awesome algorithmic trading frameworks, libraries, software and resources (by joelowj)
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financial-machine-learning awesome-algorithmic-trading
111 1
5,533 684
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9.4 10.0
3 days ago almost 5 years ago
Python
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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.

financial-machine-learning

Posts with mentions or reviews of financial-machine-learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-09.

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.

What are some alternatives?

When comparing financial-machine-learning and awesome-algorithmic-trading you can also consider the following projects:

qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

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

zipline - Zipline, a Pythonic Algorithmic Trading Library

quant-trading - Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

Finance - Study resources for quantitative finance

OpenBBTerminal - Investment Research for Everyone, Everywhere.

vectorbt - Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.

deep-finance - Datasets, papers and books on AI & Finance.

FinanceToolkit - Transparent and Efficient Financial Analysis

fooltrader - quant framework for stock