- financial-machine-learning VS qlib
- financial-machine-learning VS zvt
- financial-machine-learning VS zipline
- financial-machine-learning VS quant-trading
- financial-machine-learning VS Finance
- financial-machine-learning VS OpenBBTerminal
- financial-machine-learning VS vectorbt
- financial-machine-learning VS deep-finance
- financial-machine-learning VS awesome-algorithmic-trading
- financial-machine-learning VS FinanceToolkit
Financial-machine-learning Alternatives
Similar projects and alternatives to financial-machine-learning
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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.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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
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Finance
Discontinued Study resources for quantitative finance
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OpenBBTerminal
Investment Research for Everyone, Everywhere.
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deep-finance
Discontinued Datasets, papers and books on AI & Finance.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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vectorbt
Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
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awesome-algorithmic-trading
A curated list of awesome algorithmic trading frameworks, libraries, software and resources
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FinanceToolkit
Transparent and Efficient Financial Analysis
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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 th
financial-machine-learning reviews and mentions
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The primary programming language of financial-machine-learning is Python.