174 Open source projects that are alternatives of or similar to py-ecomplexity

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​The Economic Simulation Library provides an extensive collection of tools to develop, test, analyse and calibrate economic and financial agent-based models. The library is designed to take advantage of different computer architectures. In order to facilitate rapid iteration during model development the library can use parallel computation. Econ…

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

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Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).

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AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库

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Dichotomy Tests provides interesting tests that show you how inclined you are toward certain philosophical, psychological or political views. Each test tries to discover your preferred beliefs and will attempt to accurately position you along numerous dichotomic axes.

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AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库

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ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.