Metadata-Version: 2.1
Name: empyrial
Version: 0.2.8
Summary: AI and data-driven quantitative portfolio management library for portfolio risk and performance analysis 投资组合管理
Home-page: https://github.com/ssantoshp/Empyrial
Author: Santosh Passoubady
Author-email: santoshpassoubady@gmail.com
License: MIT
Description: # By Investors, For Investors.
        
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        Empyrial is a Python-based **open-source quantitative investment** library dedicated to **financial institutions** and **retail investors**, officially released in Mars 2021. Already used by **thousands of people working in the finance industry**, Empyrial aims to become an all-in-one platform for **portfolio management**, **analysis**, and **optimization**.
        
        Empyrial **empowers portfolio management** by bringing different financial approaches such as **risk analysis**, **quantitative analysis**, **fundamental analysis**, **factor analysis** and **prediction making**.
        
        With Empyrial, you can easily analyze security or a portfolio with these different approaches and **get the best insights from it**.
        
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        ## Features
        
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        | Feature 📰 | Status |
        | --                      | ------    |
        | Empyrial (backtesting + performance analysis) | :star: [Released](https://github.com/ssantoshp/Empyrial/releases/tag/v0.2.4) on May 30, 2021 |
        | Oracle (prediction lens using several ML models)| :alien: [Beta](https://github.com/ssantoshp/Empyrial/releases/tag/0.2.7) on Jun 1, 2021 | 
        | Fundamental lens | :smile_cat: In development...  |
        | Risk lens | :smile_cat: In development...  | 
        | Alpha lens | :smile_cat: In development... |
        | Sentiment lens | :smile_cat: In development... | 
          
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        Here are the functions available with Empyrial:
        
        - `empyrial` : quantitative and performance analysis of your portfolio | [Quickstart](https://colab.research.google.com/drive/1cj40dDqctfWNrVz_nK-FDhdWPay7fVBF?usp=sharing) | [Documentation](https://github.com/ssantoshp/Empyrial/wiki/Engine)
        
        - `oracle` : prediction generation on your portfolio using several prediction models (Prophet, Auto-ARIMA, Fast Fourier Transform...) | [Quickstart](https://colab.research.google.com/drive/11rMpQqW9Om82wzh71cr5k3vDQSNMZ4V1?usp=sharing)| [Documentation](https://github.com/ssantoshp/Empyrial/wiki/Oracle)
        
        ## Usage
        
        ```py
        from empyrial import empyrial, Engine
        
        portfolio = Engine(
                          start_date= "2020-06-09",
                          portfolio= ["BABA", "RELIANCE.NS", "KO", "^DJI","^IXIC"],
                          weights = [0.2, 0.2, 0.2, 0.2, 0.2],
                          benchmark = ["SPY"]
        )
        
        empyrial(portfolio)
        ```
        
        Output:
        
        ![report](https://user-images.githubusercontent.com/61618641/120065794-8203ef00-c073-11eb-84a8-8dda6908da4c.png)<br/><br /><br />
        
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          ![return](https://user-images.githubusercontent.com/61618641/120065822-afe93380-c073-11eb-915d-8b8b27c6fd38.png)<br /><br /><br />
        
        ![creturn](https://user-images.githubusercontent.com/61618641/120065881-ea52d080-c073-11eb-84a5-11da5dbf0bcb.png)<br /><br /><br />
        
        ![heatmap](https://user-images.githubusercontent.com/61618641/120065930-2ab24e80-c074-11eb-8861-e1996a950774.png)<br /><br /><br />
        
        ![drawdonw](https://user-images.githubusercontent.com/61618641/120065973-6cdb9000-c074-11eb-99cb-f3ee8110576f.png)<br /><br /><br />
        
        ![top](https://user-images.githubusercontent.com/61618641/120065975-6fd68080-c074-11eb-93f9-cbb3f2dd859d.png)<br /><br /><br />
        
        ![rolling](https://user-images.githubusercontent.com/61618641/120065977-74029e00-c074-11eb-92c6-8d0bee2a6234.png)
        
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        Full documentation : https://github.com/ssantoshp/Empyrial
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
