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توضیحات

A program for financial portfolio management, analysis and optimisation

ویژگی	مقدار
سیستم عامل	-
نام فایل	FinQuant-0.2.2
نام	FinQuant
نسخه کتابخانه	0.2.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Frank Milthaler
ایمیل نویسنده	f.milthaler@gmail.com
آدرس صفحه اصلی	https://github.com/fmilthaler/FinQuant
آدرس اینترنتی	https://pypi.org/project/FinQuant/
مجوز	MIT

<img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/images/finquant-logo.png" width="45%"> <a href="https://GitHub.com/fmilthaler/FinQuant/stargazers/"> <img src="https://img.shields.io/github/stars/fmilthaler/FinQuant.svg?style=social&label=Star&maxAge=2592000" alt='pypi'> </a> <a href="https://pypi.org/project/FinQuant"> <img src="https://img.shields.io/badge/pypi-v0.2.2-brightgreen.svg?style=popout" alt='pypi'> </a> <a href="https://travis-ci.org/fmilthaler/FinQuant"> <img src="https://travis-ci.org/fmilthaler/FinQuant.svg?style=popout?branch=master" alt='travis'> </a> <a href="http://finquant.readthedocs.io/"> <img src="https://img.shields.io/readthedocs/finquant.svg?style=popout" alt="docs"> </a> <a href="https://GitHub.com/fmilthaler/FinQuant/graphs/contributors/"> <img src="https://img.shields.io/github/contributors/fmilthaler/FinQuant.svg?style=popout" alt="contributors"> </a> <a href="https://github.com/fmilthaler/FinQuant/issues"> <img src="https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=popout" alt="contributions"> </a> <a href="https://github.com/fmilthaler/FinQuant/blob/master/LICENSE.txt"> <img src="https://img.shields.io/github/license/fmilthaler/FinQuant.svg?style=popout" alt="license"> </a> # FinQuant *FinQuant* is a program for financial **portfolio management, analysis and optimisation**. This README only gives a brief overview of *FinQuant*. The interested reader should refer to its [documentation](https://finquant.readthedocs.io "FinQuant Documentation"). ## Table of contents - [Motivation](#Motivation) - [Installation](#Installation) - [Portfolio Management](#Portfolio-Management) - [Returns](#Returns) - [Moving Averages](#Moving-Averages) - [Portfolio Optimisation](#Portfolio-Optimisation) - [Efficient Frontier](#Efficient-Frontier) - [Monte Carlo](#Monte-Carlo) - [Examples](#Examples) - [Building a portfolio with data from web](#Building-a-portfolio-with-data-from-web) - [Building a portfolio with preset data](#Building-a-portfolio-with-preset-data) - [Analysis of a portfolio](#Analysis-of-a-portfolio) - [Optimisation of a portfolio](#Optimisation-of-a-portfolio) ## Motivation Within a few lines of code, *FinQuant* can generate an object that holds your stock prices of your desired financial portfolio, analyses it, and can create plots of different kinds of *Returns*, *Moving Averages*, *Moving Average Bands with buy/sell signals*, and *Bollinger Bands*. It also allows for the optimisation based on the *Efficient Frontier* or a *Monte Carlo* run of the financial portfolio within a few lines of code. Some of the results are shown here. ### Automatically generating an instance of `Portfolio` `finquant.portfolio.build_portfolio` is a function that eases the creating of your portfolio. See below for one of several ways of using `build_portfolio`. ``` from finquant.portfolio import build_portfolio names = ['GOOG', 'AMZN', 'MCD', 'DIS'] start_date = '2015-01-01' end_date = '2017-12-31' pf = build_portfolio(names=names, start_date=start_date, end_date=end_date) ``` `pf` is an instance of `finquant.portfolio.Portfolio`, which contains the prices of the stocks in your portfolio. Then... ``` pf.data.head(3) ``` yields ``` GOOG AMZN MCD DIS Date 2015-01-02 524.81 308.52 85.783317 90.586146 2015-01-05 513.87 302.19 84.835892 89.262380 2015-01-06 501.96 295.29 84.992263 88.788916 ``` ### Portfolio properties Nicely printing out the portfolio's properties ``` pf.properties() ``` Depending on the stocks within your portfolio, the output looks something like the below. ``` ---------------------------------------------------------------------- Stocks: GOOG, AMZN, MCD, DIS Time window/frequency: 252 Risk free rate: 0.005 Portfolio expected return: 0.266 Portfolio volatility: 0.156 Portfolio Sharpe ratio: 1.674 Skewness: GOOG AMZN MCD DIS 0 0.124184 0.087516 0.58698 0.040569 Kurtosis: GOOG AMZN MCD DIS 0 -0.751818 -0.856101 -0.602008 -0.892666 Information: Allocation Name 0 0.25 GOOG 1 0.25 AMZN 2 0.25 MCD 3 0.25 DIS ---------------------------------------------------------------------- ``` ### Cumulative Return ``` pf.comp_cumulative_returns().plot().axhline(y = 0, color = "black", lw = 3) ``` yields <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/images/cumulative-return.svg?sanitize=true" width="60%"> ### Band Moving Average (Buy/Sell Signals) ``` from finquant.moving_average import compute_ma, ema # get stock data for disney dis = pf.get_stock("DIS").data.copy(deep=True) spans = [10, 50, 100, 150, 200] ma = compute_ma(dis, ema, spans, plot=True) ``` yields <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/images/ma-band-buysell-signals.svg?sanitize=true" width="60%"> ### Bollinger Band ``` from finquant.moving_average import plot_bollinger_band # get stock data for disney dis = pf.get_stock("DIS").data.copy(deep=True) span=20 plot_bollinger_band(dis, sma, span) ``` yields <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/images/bollinger-band.svg?sanitize=true" width="60%"> ### Portfolio Optimisation ``` # performs and plots results of Monte Carlo run (5000 iterations) opt_w, opt_res = pf.mc_optimisation(num_trials=5000) # plots the results of the Monte Carlo optimisation pf.mc_plot_results() # plots the Efficient Frontier pf.ef_plot_efrontier() # plots optimal portfolios based on Efficient Frontier pf.ef.plot_optimal_portfolios() # plots individual plots of the portfolio pf.plot_stocks() ``` <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/images/ef-mc-overlay.svg?sanitize=true" width="60%"> ## Installation As it is common for open-source projects, there are several ways to get hold of the code. Choose whichever suits you and your purposes best. ### Dependencies *FinQuant* depends on the following Python packages: - python>=3.5.0 - numpy>=1.15 - pandas>=0.24 - matplotlib>=1.5.1 - quandl>=3.4.5 - yfinance>=0.1.43 - scipy>=1.2.0 - pytest>=2.8.7 ### From PyPI *FinQuant* can be obtained from PyPI ```pip install FinQuant``` ### From GitHub Get the code from GitHub: ```git clone https://github.com/fmilthaler/FinQuant.git``` Then inside `FinQuant` run: ```python setup.py install``` Alternatively, if you do not wish to install *FinQuant*, you can also download/clone it as stated above, and then make sure to add it to your ``PYTHONPATH``. ## Portfolio Management This is the core of *FinQuant*. `finquant.portfolio.Portfolio` provides an object that holds prices of all stocks in your portfolio, and automatically computes the most common quantities for you. To make *FinQuant* an user-friendly program, that combines data analysis, visualisation and optimisation, the object provides interfaces to the main features that are provided in the modules in `./finquant/`. To learn more about the object, please read through the [documentation](https://finquant.readthedocs.io/en/latest/ "FinQuant Documentation"), docstring of the module/class, and/or have a look at the examples. `finquant.portfolio.Portfolio` also provides a function `build_portfolio` which is designed to automatically generate an instance of `Portfolio` for the user's convenience. For more information on how to use `build_portfolio`, please refer to the [documentation](https://finquant.readthedocs.io/en/latest/ "FinQuant Documentation"), its `docstring` and/or have a look at the examples. ## Returns Daily returns of stocks are often computed in different ways. *FinQuant* provides three different ways of computing the daily returns in `finquant.returns`: 1. The cumulative return: <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/tex/738645698dc3073b4bb52a0c078ae829.svg?invert_in_darkmode&sanitize=true" align=middle width=194.52263655pt height=46.976899200000005pt/> 2. Percentage change of daily returns: <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/tex/27215e5f36fd0308b51ab510444edf0d.svg?invert_in_darkmode&sanitize=true" align=middle width=126.07712039999997pt height=48.84266309999997pt/> 3. Log Return: <img src="https://raw.githubusercontent.com/fmilthaler/finquant/master/tex/ef37c00ad58fe657a64041c3093e0640.svg?invert_in_darkmode&sanitize=true" align=middle width=208.3327686pt height=57.53473439999999pt/> In addition to those, the module provides the function `historical_mean_return(data, freq=252)`, which computes the historical mean of the daily returns over a time period `freq`. ## Moving Averages The module `finquant.moving_average` allows the computation and visualisation of Moving Averages of the stocks listed in the portfolio is also provided. It entails functions to compute and visualise the - `sma`: Simple Moving Average, and - `ema`: Exponential Moving Average. - `compute_ma`: a Band of Moving Averages (of different time windows/spans) including Buy/Sell signals - `plot_bollinger_band`: a Bollinger Band for - `sma`, - `ema`. ## Portfolio Optimisation ### Efficient Frontier An implementation of the Efficient Frontier (`finquant.efficient_frontier.EfficientFrontier`) allows for the optimisation of the portfolio for - `minimum_volatility` Minimum Volatility, - `maximum_sharpe_ratio` Maximum Sharpe Ratio - `efficient_return` Minimum Volatility for a given expected return - `efficient_volatility` Maximum Sharpe Ratio for a given target volatility by performing a numerical solve to minimise/maximise an objective function. Often it is useful to visualise the *Efficient Frontier* as well as the optimal solution. This can be achieved with the following methods: - `plot_efrontier`: Plots the *Efficient Frontier*. If no minimum/maximum Return values are provided, the algorithm automatically chooses those limits for the *Efficient Frontier* based on the minimum/maximum Return values of all stocks within the given portfolio. - `plot_optimal_portfolios`: Plots markers of the portfolios with the Minimum Volatility and Maximum Sharpe Ratio. For reasons of user-friendliness, interfaces to these functions are provided in `finquant.portfolio.Portfolio`. Please have a look at the [documentation](https://finquant.readthedocs.io "FinQuant Documentation"). ### Monte Carlo Alternatively a *Monte Carlo* run of `n` trials can be performed to find the optimal portfolios for - minimum volatility, - maximum Sharpe ratio The approach branded as *Efficient Frontier* should be the preferred method for reasons of computational effort and accuracy. The latter approach is only included for the sake of completeness, and creation of beautiful plots. ## Examples For more information about the project and details on how to use it, please look at the examples provided in `./example`. **Note**: In the below examples, `pf` refers to an instance of `finquant.portfolio.Portfolio`, the object that holds all stock prices and computes its most common quantities automatically. To make *FinQuant* a user-friendly program, that combines data analysis, visualisation and optimisation, the object also provides interfaces to the main features that are provided in the modules in `./finquant/` and are discussed throughout this README. ### Building a portfolio with data from web `./example/Example-Build-Portfolio-from-web.py`: Shows how to use *FinQuant* to build a financial portfolio by downloading stock price data through the Python package `quandl`/`yfinance`. ### Building a portfolio with preset data `./example/Example-Build-Portfolio-from-file.py`: Shows how to use *FinQuant* to build a financial portfolio by providing stock price data yourself, e.g. by reading data from disk/file. ### Analysis of a portfolio `./example/Example-Analysis.py`: This example shows how to use an instance of `finquant.portfolio.Portfolio`, get the portfolio's quantities, such as - Expected Returns, - Volatility, - Sharpe Ratio. It also shows how to extract individual stocks from the given portfolio. Moreover it shows how to compute and visualise: - the different Returns provided by the module `finquant.returns`, - *Moving Averages*, a band of *Moving Averages*, and a *Bollinger Band*. ### Optimisation of a portfolio `./example/Example-Optimisation.py`: This example focusses on the optimisation of a portfolio. To achieve this, the example shows the usage of `finquant.efficient_frontier.EfficientFrontier` for optimising the portfolio, for the - Minimum Volatility - Maximum Sharpe Ratio - Minimum Volatility for a given target Return - Maximum Sharpe Ratio for a given target Volatility. Furthermore, it is also shown how the entire *Efficient Frontier* and the optimal portfolios can be computed and visualised. If needed, it also gives an example of plotting the individual stocks of the given portfolio within the computed *Efficient Frontier*. Also, the optimisation of a portfolio and its visualisation based on a *Monte Carlo* is shown. Finally, *FinQuant*'s visualisation methods allow for overlays, if this is desired. Thus, with only the following few lines of code, one can create an overlay of the *Monte Carlo* run, the *Efficient Frontier*, its optimised portfolios for *Minimum Volatility* and *Maximum Sharpe Ratio*, as well as the portfolio's individual stocks.

نیازمندی

مقدار	نام
-	quandl
-	yfinance
-	numpy
-	pandas
-	scipy
-	matplotlib
-	pytest

زبان مورد نیاز

مقدار	نام
>=3.5	Python

نحوه نصب

نصب پکیج whl FinQuant-0.2.2:

pip install FinQuant-0.2.2.whl

نصب پکیج tar.gz FinQuant-0.2.2:

pip install FinQuant-0.2.2.tar.gz