pyesg:Python的经济情景生成器

<p align="center"> <img src="https://github.com/jason-ash/pyesg/blob/master/docs/images/pyesg.png" width="450" /> </p> <p align="center"> <em>Simulate stocks, interest rates, and other stochastic processes.<br><strong>pyesg</strong> is a lightning fast economic scenario generator for Python.</em> </p> <p align="center"> <a href="https://github.com/tterb/atomic-design-ui/blob/master/LICENSEs" target="_blank"> <img src="https://img.shields.io/apm/l/atomic-design-ui.svg?style=flat-square" alt="License"/> </a> <a href="https://pypi.python.org/pypi/pyesg/" target="_blank"> <img src="https://img.shields.io/pypi/pyversions/pyesg.svg?style=flat-square" alt="Supported python versions"/> </a> <a href="https://badge.fury.io/py/pyesg" target="_blank"> <img src="https://img.shields.io/pypi/v/pyesg?color=light%20green&label=pypi%20package&style=flat-square" alt="Pypi package version"/> </a> <a href="https://github.com/jason-ash/pyesg/actions" target="_blank"> <img src="https://img.shields.io/github/workflow/status/jason-ash/pyesg/pyesg%20CI/master?style=flat-square" alt="Build status"/> </a> <a href="https://coveralls.io/github/jason-ash/pyesg?branch=master" target="_blank"> <img src="https://img.shields.io/coveralls/github/jason-ash/pyesg?style=flat-square" alt="Coverage"/> </a> </p> --- ### What is an economic scenario generator? An economic scenario generator simulates the behavior of unpredictable financial markets like stocks, interest rates, or energy prices. It uses mathematical models called _stochastic processes_ to generate thousands of unique scenarios. What can you do with an economic scenario generator? Here are a few examples: <details> <summary><strong>Simulate an investment portfolio</strong></summary> * Generate 1,000 correlated, daily stock prices * Construct a portfolio from the stocks * Plot the value of the portfolio over time, and calculate metrics like high, low, max drawdown, etc. </details> <details> <summary><strong>Choose a mortgage</strong></summary> * Generate 1,000 30-year, monthly scenarios of mortgage rates * Calculate the monthly payment for different loans under each scenario: - 30-year fixed (lock in the rate today for all 30 years) - 15-year fixed (lock in the rate today and pay over 15 years) - 10-year adjustable-rate (fixed rate for 10 years, then variable rate for 20 years - etc. * Determine which loan fits the best with your budget </details> <details> <summary><strong>Plan for retirement</strong></summary> * Generate 1,000 30-year, monthly scenarios of stock market data * Generate 1,000 30-year, monthly scenarios of bond fund data * Track your portfolio balance as a mix of investments in stocks and bonds * Each month, simulate a withdrawal from your portfolio and the growth from your investments * Adjust the withdrawal up or down and identify the types of markets that lead to the best and worst outcomes </details> <details> <summary><strong>Protect your business</strong></summary> * **Banks**: test whether loan portfolios are resilient to drops in interest rates and higher-than-expected customer refinancing * **Insurers**: test whether assets are sufficient to cover liabilities under a wide range of bond market scenarios * **Investment managers**: test the impact of market declines on revenue from account-based fees * **Small Businesses**: model customer demand correlated with the stock market to plan for potential downturns </details> ## Key Features of **_pyesg_** - Built on **numpy** for lightning fast scenario generation: 10,000+ scenarios in milliseconds - Dozens of stochastic processes available out of the box - Equity: Geometric Brownian Motion / Black Scholes, Heston - Interest rate: Vasicek, Cox-Ingersoll-Ross, Hull-White - Complete implementation of the American Academy of Actuaries <a href="https://www.actuary.org/content/economic-scenario-generators" target="_blank">stochastic interest rate generator</a> that is **200 times faster** than the Excel version - Curve interpolation functions like Nelson-Siegel and Svensson models - Hundreds of unit tests and excellent <a href="https://coveralls.io/github/jason-ash/pyesg?branch=master" target="_blank">code coverage</a> - Open source software, free to use, copy, or modify under MIT license ## Coming soon - More models! - Fit stochastic models using historical data - Built-in plotting methods - Scenario summary reports ## Installing **_pyesg_** **_pyesg_** is available on <a href="https://pypi.org/project/pyesg/" target="_blank">PyPI</a>, and can be installed with pip. ``` pip install pyesg ``` ## Usage All models in **_pyesg_** are created and used nearly identically. First, create a model with its required parameters. ```python import pyesg # create a new model with the required parameters model = pyesg.GeometricBrownianMotion(mu=0.05, sigma=0.2) ``` Generate scenarios by calling the `<model>.scenarios` method. The example below, with 10,000 daily scenarios (2,520,000 values) took just **160 milliseconds** to run! <details> <summary>Under the hood: <code>model.scenarios()</code></summary> This method generates scenarios, given the following arguments: - `x0` : the value or array of values each scenario starts with. For example, you might pass `100.0` to model a security with a current value of 100, or `[100.0, 90.0]` to model two scenarios with start values of 100 and 90. - `dt` : the length of each time step in the projection, in years. For example, you might pass `1/12` to model mothly timesteps, `1/52` to model weekly timesteps, `1/252` to model daily (trading day) timesteps, or `1` to model annual timesteps. - `n_scenarios` : the number of scenarios you want to generate. Often you can generate 10,000 scenarios in fractions of a second. - `n_steps` : the number of timesteps for each scenario. In combination with `dt`, this argument determines the total length of time a projection covers. For example, with `dt=1/52` and `n_steps=104`, the projection will cover two years with weekly timesteps. With `dt=1/12` and `n_steps=360`, the projection will cover 30 years with monthly timesteps. - `random_state` : an optional field that may be provided if you want to be able to reproduce pseudo-random numbers for each batch of scenarios. For example, `<model>.scenarios(..., random_state=123)` will generate the exact same batch of scenarios every time. </details> ```python import pyesg # instantiate a new model with the required parameters model = pyesg.GeometricBrownianMotion(mu=0.05, sigma=0.2) # prepare the arguments to generate scenarios. Here we'll # generate 10,000 scenarios with 252 daily (trading day) # time steps, for a one-year projection in total. x0 = 100.0 # the start value of our process dt = 1/252 # the length of each timestep in years n_scenarios = 10000 # the number of scenarios to generate n_steps = 252 # the number of time steps per scenario random_state = 123 # optional random_state for reproducibility # now we generate the scenarios; this outputs a numpy array. It will # have shape (10000, 253), which represents (scenarios, timesteps). # There are 253 timesteps because the initial value is included to start results = model.scenarios(x0, dt, n_scenarios, n_steps, random_state) # array([[100. , 98.65207527, 97.12924873, ..., 111.3500094 , # 112.00479028, 113.12444153], # [100. , 101.27637842, 100.8971646 , ..., 61.8709475 , # 63.00222064, 62.22126261], # [100. , 100.37636067, 99.32267874, ..., 141.66969149, # 140.38291993, 138.91659076], # ..., # [100. , 99.42484152, 97.68732205, ..., 139.9306172 , # 139.52301459, 139.05345463], # [100. , 100.75304745, 102.09894601, ..., 115.66615197, # 116.16385992, 118.06267759], # [100. , 101.24269853, 101.73381851, ..., 8