January 2020
Beginner to intermediate
432 pages
11h 24m
English
Content preview from Python for Finance Cookbook
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How to do it...
Execute the following steps to decompose the gold prices time series using Facebook's Prophet and create forecasts for one year ahead.
- Import the libraries and authenticate with Quandl:
import pandas as pdimport seaborn as snsimport quandlfrom fbprophet import ProphetQUANDL_KEY = '{key}' # replace {key} with your own API key quandl.ApiConfig.api_key = QUANDL_KEY
- Download the daily gold prices and rename the columns:
df = quandl.get(dataset='WGC/GOLD_DAILY_USD', start_date='2000-01-01', end_date='2005-12-31')df.reset_index(drop=False, inplace=True)df.rename(columns={'Date': 'ds', 'Value': 'y'}, inplace=True)
- Split the series into the training and test sets:
train_indices = df.ds.apply(lambda x: x.year) < 2005df_train ...
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