Python库 | frds-0.6.0.tar.gz

 # FRDS - Financial Research Data Services  [](https://github.com/psf/black) [`frds`](https://github.com/mgao6767/frds/) aims to provide the simplest way to compute [a collection of major academic measures](#supported-measures) used in the finance literature. [Getting started](https://frds.io/getting-started/) by checking out this notebook. ## Example usage ### Import We start by importing relevant modules. Specifically, we import the `Funda` class from the `frds.data.wrds.comp` library since the demo uses only the Fundamentals Annual dataset from Compustat via WRDS. We next import the `setup` and `load` functions from `frds.io.wrds`, which are used to configure WRDS credentials and data management for WRDS datasets. ```Python from frds.data.wrds.comp import Funda from frds.io.wrds import setup, load ``` ### (Optional) Setup Then, set WRDS credentials in case later we need to download from WRDS. ```Python setup(username='username', password='password', save_credentials=True) ``` ### Load data We now download the `Funda` (Fundamentals Annual) dataset and assign it to the variable `FUNDA`. ```Python FUNDA = load(Funda, use_cache=True, obs=100) ``` ### Compute Let's now compute a few metrics to showcase how easy it is. ```Python import numpy as np import pandas as pd from frds.measures.corporate import roa pd.DataFrame( { # We can calculate metrics on the go "Fyear": FUNDA.FYEAR, "Tangibility": FUNDA.PPENT / FUNDA.AT, "Firm_Size": np.log(FUNDA.AT), "MTB": FUNDA.PRCC_F * FUNDA.CSHO / FUNDA.CEQ, # Or we can use the built-in measures available in FRDS: "ROA_v1": roa(FUNDA), "ROA_v2": roa(FUNDA, use_lagged_total_assets=True) } ).dropna().head(10) ``` The result would be a nice `pd.DataFrame`: <table border="0" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th></th> <th>Fyear</th> <th>Tangibility</th> <th>Firm_Size</th> <th>MTB</th> <th>ROA_v1</th> <th>ROA_v2</th> </tr> <tr> <th>GVKEY</th> <th>DATADATE</th> <th></th> <th></th> <th></th> <th></th> <th></th> <th></th> </tr> </thead> <tbody> <tr> <th rowspan="3" valign="top">001000</th> <th>1970-12-31</th> <td>1970</td> <td>0.265351</td> <td>3.510052</td> <td>2.319803</td> <td>0.056143</td> <td>0.065408</td> </tr> <tr> <th>1971-12-31</th> <td>1971</td> <td>0.260450</td> <td>3.378611</td> <td>2.054797</td> <td>0.004705</td> <td>0.004126</td> </tr> <tr> <th>1976-12-31</th> <td>1976</td> <td>0.426061</td> <td>3.652890</td> <td>0.899635</td> <td>0.088996</td> <td>0.947310</td> </tr> <tr> <th rowspan="2" valign="top">001001</th> <th>1984-12-31</th> <td>1984</td> <td>0.781644</td> <td>2.789139</td> <td>1.492970</td> <td>0.069958</td> <td>0.080441</td> </tr> <tr> <th>1985-12-31</th> <td>1985</td> <td>0.567439</td> <td>3.676174</td> <td>3.102697</td> <td>0.065223</td> <td>0.158357</td> </tr> <tr> <th rowspan="3" valign="top">001002</th> <th>1970-12-31</th> <td>1970</td> <td>0.181825</td> <td>2.619000</td> <td>0.499715</td> <td>0.035490</td> <td>0.032331</td> </tr> <tr> <th>1971-12-31</th> <td>1971</td> <td>0.207127</td> <td>2.495104</td> <td>0.827517</td> <td>0.065660</td> <td>0.058009</td> </tr> <tr> <th>1972-12-31</th> <td>1972</td> <td>0.166369</td> <td>2.752131</td> <td>0.561460</td> <td>0.057285</td> <td>0.074074</td> </tr> <tr> <th rowspan="2" valign="top">001003</th> <th>1983-12-31</th> <td>1983</td> <td>0.030015</td> <td>2.143472</td> <td>2.311034</td> <td>0.123109</td> <td>0.186435</td> </tr> <tr> <th>1984-12-31</th> <td>1984</td> <td>0.051450</td> <td>2.109122</td> <td>1.138268</td> <td>0.046960</td> <td>0.138214</td> </tr> </tbody> </table> ## Built-in Measures Check the [built-in measures and documentation](https://frds.io/api/measures/). ## Note This library is still under development and breaking changes may be expected.