# python 2
try:
from urllib.request import Request, urlopen
# Python 3
except ImportError:
from urllib2 import Request, urlopen
import pandas as pd
import time
import datetime
import numpy as np
import re
import json
from bs4 import BeautifulSoup
from pytrends.request import TrendReq
class Cryptory():
def __init__(self, from_date, to_date=None, ascending=False,
fillgaps=True, timeout=10.0):
"""Initialise cryptory class
Parameters
----------
from_date : the starting date (as string) for the returned data;
required format is %Y-%m-%d (e.g. "2017-06-21")
to_date : the end date (as string) for the returned data;
required format is %Y-%m-%d (e.g. "2017-06-21")
Optional. If unspecified, it will default to the current day
to_date : binary. Determines whether the returned dataframes are
ordered by date in ascending or descending order
(defaults to False i.e. most recent first)
fillgaps : binary. When data does not exist (e.g. weekends for stocks)
should the rows be filled in with the previous available data
(defaults to True e.g. Saturday stock price will be same as Friday)
fillgaps : float. The max time allowed (in seconds) to pull data from a website
If exceeded, an timeout error is returned. Default is 10 seconds.
"""
self.from_date = from_date
# if to_date provided, defaults to current date
if to_date is None:
self.to_date = datetime.date.today().strftime("%Y-%m-%d")
else:
self.to_date = to_date
self.ascending = ascending
self.fillgaps = fillgaps
self.timeout = timeout
self._df = pd.DataFrame({'date':pd.date_range(start=self.from_date, end=self.to_date)})
def extract_reddit_metrics(self, subreddit, metric, col_label="", sub_col=False):
"""Retrieve daily subscriber data for a specific subreddit scraped from redditmetrics.com
Parameters
----------
subreddit : the name of subreddit (e.g. "python", "learnpython")
metric : the particular subscriber information to be retrieved
(options are limited to "subscriber-growth" (daily change),
'total-subscribers' (total subscribers on a given day) and
'rankData' (the position of the subreddit on reddit overall)
'subscriber-growth-perc' (daily percentage change in subscribers))
col_label : specify the title of the value column
(it will default to the metric name with hyphens replacing underscores)
sub_col : whether to include the subreddit name as a column
(default is False i.e. the column is not included)
Returns
-------
pandas Dataframe
"""
if metric not in ['subscriber-growth', 'total-subscribers', 'rankData', 'subscriber-growth-perc']:
raise ValueError(
"Invalid metric: must be one of 'subscriber-growth', " +
"'total-subscribers', 'subscriber-growth-perc', 'rankData'")
url = "http://redditmetrics.com/r/" + subreddit
if metric == 'subscriber-growth-perc':
metric_name = 'total-subscribers'
else:
metric_name = metric
try:
parsed_page = urlopen(url, timeout=self.timeout).read()
parsed_page = parsed_page.decode("utf8")
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
if metric == 'rankData':
start_segment = parsed_page.find(metric)
else:
start_segment = parsed_page.find("element: '"+metric_name+"'")
if start_segment != -1:
start_list = parsed_page.find("[", start_segment)
end_list = parsed_page.find("]", start_list)
parsed_page = parsed_page[start_list:end_list + 1]
else:
return ValueError("Could not find that subreddit")
parsed_page = parsed_page.replace("'", '"')
parsed_page = parsed_page.replace('a', '\"subscriber_count\"')
parsed_page = parsed_page.replace('y', '\"date\"')
output = json.loads(parsed_page)
output = pd.DataFrame(output)
output['date'] = pd.to_datetime(output['date'], format="%Y-%m-%d")
if metric == 'subscriber-growth-perc':
output['subscriber_count'] = output['subscriber_count'].pct_change()
output = output[(output['date']>=self.from_date) & (output['date']<=self.to_date)]
output = output.sort_values(by='date', ascending=self.ascending).reset_index(drop=True)
if sub_col:
output['subreddit'] = subreddit
if col_label != "":
output = output.rename(columns={'subscriber_count': label})
else:
output = output.rename(columns={'subscriber_count': metric.replace("-","_")})
return output
def extract_coinmarketcap(self, coin, coin_col=False):
"""Retrieve basic historical information for a specific cryptocurrency from coinmarketcap.com
Parameters
----------
coin : the name of the cryptocurrency (e.g. 'bitcoin', 'ethereum', 'dentacoin')
coin_col : whether to include the coin name as a column
(default is False i.e. the column is not included)
Returns
-------
pandas Dataframe
"""
try:
output = pd.read_html("https://coinmarketcap.com/currencies/{}/historical-data/?start={}&end={}".format(
coin, self.from_date.replace("-", ""), self.to_date.replace("-", "")))[0]
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
output = output.assign(Date=pd.to_datetime(output['Date']))
for col in output.columns:
if output[col].dtype == np.dtype('O'):
output.loc[output[col]=="-",col]=0
output[col] = output[col].astype('int64')
output.columns = [re.sub(r"[^a-z]", "", col.lower()) for col in output.columns]
if coin_col:
output['coin'] = coin
return output
def extract_bitinfocharts(self, coin, metric="price", coin_col=False, metric_col=False):
"""Retrieve historical data for a specific cyrptocurrency scraped from bitinfocharts.com
Parameters
----------
coin : the code of the cryptocurrency (e.g. 'btc' for bitcoin)
full range of available coins can be found on bitinfocharts.com
metric : the particular coin information to be retrieved
(options are limited to those listed on bitinfocharts.com
including 'price', 'marketcap', 'transactions' and 'sentinusd'
coin_col : whether to include the coin name as a column
(default is False i.e. the column is not included)
metric_col : whether to include the metric name as a column
(default is False i.e. the column is not included)
Returns
-------
pandas Dataframe
"""
if coin not in ['btc', 'eth', 'xrp', 'bch', 'ltc', 'dash', 'xmr', 'btg', 'etc', 'zec',
'doge', 'rdd', 'vtc', 'ppc', 'ftc', 'nmc', 'blk', 'aur', 'nvc', 'qrk', 'nec']:
raise ValueError("Not a valid coin")
if metric not in ['transactions', 'size', 'sentbyaddress', 'difficulty', 'hashrate', 'price',
'mining_profitability', 'sentinusd', 'transactionfees', 'median_transaction_fee',
'confirmationtime', 'marketcap', 'transactionvalue', 'mediantransactionvalue',
'tweets', 'activeaddresses', 'top100cap']:
raise ValueError("Not a valid bitinfocharts metric")
new_col_name = "_".join([coin, metric])
parsed_page = Request("https://bitinfocharts.com/comparison/{}-{}.html".format(metric, coin),
headers = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11'})
try:
parsed_page = urlopen(parsed_page, timeout=self.timeout).read()
parsed_page = parsed_page.decode("utf8")
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
start_segment = parsed_page.find("new Dygraph")
if start_segment != -1:
start_list = parsed_page.find('[[', start_segment)
end_list = parsed_page.find(']]', start_list)
parsed_page = parsed_page[start_list:end_list]
else:
raise ValueError("Could not find the appropriate text tag in the scraped page")
parsed_page = parsed_page.replace('new Date(', '')
parsed_page = parsed_page.replace(')', '')
parsed_page = parsed_page.replace('null', '0')
parsed_page = parsed_page.replace('["', '{"date":"')
parsed_page = parsed_page.replace('",', '","{}":'.format(new_col_name))
parsed_page = parsed_page.replace('],', '},')
parsed_page = parsed_page + '}]'
output = json.loads(parsed_page)
output = pd.DataFrame(output)
output['date'] = pd.to_datetime(output['date'], format="%Y-%m-%d")
output = output[(output['date']>=self.from_date) & (output['date']<=self.to_date)]
# for consistency, put date column first
output = output[['date', new_col_name]]
if coin_col:
output['coin'] = coin
if metric_col:
output['metric'] = metric
return output.sort_values(by='date', ascending=self.ascending).reset_index(drop=True)
def extract_poloniex(self, coin1, coin2, coin1_col=False, coin2_col=False):
"""Retrieve the historical price of one coin relative to another (currency pair) from poloniex
Parameters
----------
coin1 : the code of the denomination cryptocurrency
(e.g. 'btc' for prices in bitcoin)
coin2 : the code for the coin for which prices are retrieved
(e.g. 'eth' for ethereum)
coin1_col : whether to include the coin1 code as a column
(default is False i.e. the column is not included)
coin2_col : whether to include the coin2 code as a column
(default is False i.e. the column is not included)
Returns
-------
pandas Dataframe
"""
from_date = int(time.mktime(time.strptime(self.from_date, "%Y-%m-%d")))
to_date = int(time.mktime(time.strptime(self.to_date, "%Y-%m-%d")))
url = "https://poloniex.com/public?command=returnChartData¤cyPair={}_{}&start={}&end={}&period=86400".format(
coin1.upper(), coin2.upper(), from_date, to_date)
try:
parsed_page = urlopen(url, timeout=self.timeout).read()
parsed_page = parsed_page.decode("utf8")
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
output = json.loads(parsed_page)
if isinstance(output, dict):
if 'error' in list(output.keys()):
raise ValueError("The content of the page was not as it should be")
output = pd.DataFrame(output)
# more intuitive column order
output = output[['date', 'close', 'open', 'high', 'low',
'weightedAverage', 'quoteVolume', 'volume']]
output['date'] = pd.to_datetime(output['date'], unit='s')
output = output.sort_values(by='date', ascending=self.ascending).reset_index(drop=True)
if coin1_col:
output['coin1'] = coin1
if coin2_col:
output['coin2'] = coin2
return output
def get_exchange_rates(self, from_currency="USD", to_currency="EUR",
from_col=False, to_col=False):
"""Retrieve the historical exchange rate between two (fiat) currencies
Parameters
----------
from_currency : the from currency or the currency of denomination (e.g. 'USD')
to_currency : the currency to which you wish to exchange (e.g. 'EUR')
from_col : whether to include the from_currency code as a column
(default is False i.e. the column is not included)
to_col : whether to include the to_currency code as a column
(default is False i.e. the column is not included)
Returns
-------
pandas Dataframe
"""
n_days = (datetime.date.today() -
datetime.datetime.strptime(self.from_date, "%Y-%m-%d").date()).days + 1
url = "https://www.indexmundi.com/xrates/graph.aspx?c1={}&c2={}&days={}".format(
from_currency, to_currency, n_days)
try:
parsed_page = urlopen(url, timeout=self.timeout).read()
parsed_page = parsed_page.decode("utf8")
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
start_segment = parsed_page.find("chart xAxisName")
if start_segment != -1:
start_list = parsed_page.find("<", start_segment)
end_list = parsed_page.find("/></chart>", start_list)
parsed_page = parsed_page[start_list:end_list]
else:
raise ValueError("Could not find the appropriate text tag in the scraped page")
parsed_page = re.sub(r" showLabel='[0-9]'", "", parsed_page)
parsed_page = parsed_page.replace("'", '"')
parsed_page = parsed_page.replace("set ", '')
parsed_page = parsed_page.replace("<", "{")
parsed_page = parsed_page.replace("/>", "},")
parsed_page = parsed_page.replace('label', '\"date\"')
parsed_page = parsed_page.replace('value', '\"exch_rate\"')
parsed_page = parsed_page.replace('=', ':')
parsed_page = parsed_page.replace(' ', ',')
output = json.loads('[' + parsed_page + '}]')
output = pd.DataFrame(output)
output['date'] = pd.to_datetime(output['date'], format="%m/%d/%Y")
output['exch_rate'] = pd.to_numeric(output['exch_rate'], errors='coerce')
if from_col:
output['from_currency'] = from_currency
if to_col:
output['to_currency'] = to_currency
output = self._merge_fill_filter(output)
return output
def get_stock_prices(self, market, market_name=None):
"""Retrieve the historical price (or value) of a publically listed stock or index
Parameters
----------
market : the code of the stock or index (see yahoo finance for examples)
('%5EDJI' refers to the Dow Jones and '%5EIXIC' pulls the Nasdaq index)
market_name : specify an appropriate market name or label (under the market_name column)
the default is None (default is None i.e. the column is not included)
Returns
-------
pandas Dataframe
Notes
-----
This method scrapes data from yahoo finance, so it only works when the historical
data is presented on the site (which is not the case for a large number of stocks/indices).
"""
from_date = int(time.mktime(time.strptime(self.from_date, "%Y-%m-%d")))
# we want the daily data
# this site works off unix time (86400 seconds = 1 day)
to_date = int(time.mktime(time.strptime(self.to_date, "%Y-%m-%d"))) + 86400
url = "https://finance.yahoo.com/quote/{}/history?period1={}&period2={}&interval=1d&filter=history&frequency=1d".format(
market, from_date, to_date)
try:
parsed_page = urlopen(url, timeout=1).read()
parsed_page = parsed_page.decode("utf8")
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
start_segment = parsed_page.find('{\"prices\":')
if start_segment != -1:
start_list = parsed_page.find("[", start_segment)
end_list = parsed_page.find("]", start_list)
parsed_page = parsed_page[start_list:end_list+1]
else:
raise ValueError("Could not find the appropriate text tag in the scraped page")
output = json.loads(parsed_page)
output = pd.DataFrame(output)
output['date'] = pd.to_datetime(output['date'],unit='s').apply(lambda x: x.date())
output['date'] = pd.to_datetime(output['date'])
# dividends mess up the dataframe
if 'amount' in output.columns:
output = output[pd.isnull(output['amount'])]
output = output.drop(columns=['amount', 'data', 'type'])
if market_name is not None:
output['market_name'] = market_name
output = self._merge_fill_filter(output)
return output
def get_oil_prices(self):
"""Retrieve the historical oil price (London Brent crude)
Parameters
----------
Returns
-------
pandas Dataframe
Notes
-----
This site seems to take significantly longer than the others to scrape
If you get timeout errors, then increase the timeout argument when
you initalise the cryptory class
"""
try:
parsed_page = urlopen("https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=D",
timeout=self.timeout).read()
parsed_page = parsed_page.decode("utf8")
except:
# future versions may split out the different exceptions (e.g. timeout)
#return pd.DataFrame({"error":e}, index=[0])
raise
souped_page = BeautifulSoup(parsed_page, 'html.parser')
souped_values = [soups.text for soups in souped_page.findAll("td", {"class": "B3"})]
souped_dates = [datetime.datetime.strptime(
re.sub('\xa0\xa0| to .*','', soups.text), '%Y %b-%d')
for soups in souped_page.findAll("td", {"class": "B6"})]
output = []
for i in range(5):
output.append(pd.DataFrame({"date":[date + datetime.timedelta(days=i)
for date in souped_dates],
"oil_price":souped_values[i::5]}))
output = pd.concat(output)
output.loc[output['oil_price']=="",'oil_price']=np.nan
output['oil_price'] = pd.to_numeric(output['oil_price'])
output = self._merge_fill_filter(output)
return output
def get_metal_prices(self):
"""Retrieve the historical price of gold, silver, platinum and palladium
Parameters
----------
Returns
-------
pandas Dataframe
"""
current_year = datetime.datetime.now().year
from_year = datetime.datetime.strptime(self.from_date, "%Y-%m-%d").year
to_year = datetime.datetime.strptime(self.to_date, "%Y-%m-%d").year
if to_year is None:
to_year = current_year
output = []
for i in range(from_year, to_year+1):
if i==current_year:
output.append(pd.read_html("http://www.kitco.com/gold.londonfix.html")[-1])
else:
output.append(pd.read_html("http://www.kitco.com/londonfix/gold.londonfix"+
str(i)[-2:]+".html")[-1])
output = pd.concat(output).dropna()
output.columns = ['date', 'gold_am', 'gold_pm','silver', 'platinum_am',
'platinum_pm', 'palladium_am', 'palladium_pm']
output = output.assign(date=pd.to_datetime(output['date']))
for col in output.select_dtypes(include=['object']):
output.loc[output[col]=="-",col]=np.nan
output[col] = output[col].astype('float64')
output = pd.merge(self._df, output, on="date", how="left")
if self.fillgaps:
for old_val, new_val in zip(['gold_am', 'gold_pm', 'platinum_am', 'platinum_pm',
'palladium_am', 'palladium_pm'],
['gold_pm', 'gold_am', 'platinum_pm', 'platinum_am',
'palladium_pm', 'palladium_am']):
output.loc[output[old_val].isnull(), old_val]= output.loc[output[old_val].isnull(),
new_val]
output = output.fillna(method='ffill')
output = output.sort_values(by='date', ascending=self.ascending).reset_index(drop=True)
output = output[(output['date']>=self.from_date) & (output['date']<=self.to_date)]
return output
def get_google_trends(self, kw_list, trdays=250, overlap=100,
cat=0, geo='', tz=360, gprop='', hl='en-US',
sleeptime=1, isPartial_col=False,
from_start=False, scale_cols=True):
"""Retrieve daily google trends data for a list of search terms
Parameters
----------
kw_list : list of search terms (max 5)- see pyTrends for more details
trdays : the number of days to pull data for in a search
(the max is around 270, though the website seems to indicate 90)
overlap : the number of overlapped days when stitching two searches together
cat : category to narrow results - see pyTrends for more details
geo : two letter country abbreviation (e.g 'US', 'UK')
default is '', which returns global results - see pyTrends for more details
tz : timezone offset
(default is 360, which corresponds to US CST - see pyTrends for more details)
grop : filter results to specific google property
available options are 'images', 'news', 'youtube' or 'froogle'
default is '', which refers to web searches - see pyTrends for more details
hl : language (e.g. 'en-US' (default), 'es') - see pyTrends for more details
sleeptime : when stiching multiple searches, this sets the period between each
isPartial_col : remove the isPartial column
(default is True i.e. column is removed)
from_start : when stitching multiple results, this determines whether searches
are combined going forward or backwards in time
(default is False, meaning searches are stitched with the most recent first)
scale_cols : google trend searches traditionally returns scores between 0 and 100
stitching could produce values greater than 100
by setting this to True (default), the values will range between 0 and 100
Returns
-------
pandas Dataframe
Notes
-----
This method is essentially a highly restricted wrapper for the pytrends package
Any issues/questions related to its use would probably be more likely resolved
by consulting the pytrends github page
https://github.com/GeneralMills/pytrends
"""
if len(kw_list)>5 or len(kw_list)==0:
raise ValueError("The keyword list can contain at most 5 words")
if trdays>270:
raise ValueError("trdays must not exceed 270")
if overlap>=trdays:
raise ValueError("Overlap can't exceed search days")
stich_overlap = trdays - overlap
from_date = datetime.datetime.strptime(self.from_date, '%Y-%m-%d')
to_date = datetime.datetime.strptime(self.to_date, '%Y-%m-%d')
n_days = (to_date - from_date).days
# launch pytrends request
_pytrends = TrendReq(hl=hl, tz=tz)
# get the dates for each search
if n_days <= trdays:
trend_dates = [' '.join([self.from_date, self.to_date])]
else:
trend_dates = ['{} {}'.format(
(to_date - datetime.timedelta(i+trdays)).strftime("%Y-%m-%d"),
(to_date - datetime.timedelta(i)).strftime("%Y-%m-%d"))
for i in range(0,n_days-trdays+stich_overlap,
stich_overlap)]
if from_start:
trend_dates = trend_dates[::-1]
try:
_pytrends.build_payload(kw_list, cat=cat, timeframe=trend_dates[0],
geo=geo, gprop=gprop)
except:
raise
output = _pytrends.interest_over_time().reset_index()
if len(output)==0:
raise ValueError('search term returned no results (insufficient data)')
for date in trend_dates[1:]:
time.sleep(sleeptime)
try:
_pytrends.build_payload(kw_list, cat=cat, timeframe=date,
geo=geo, gprop=gprop)
except:
raise
temp_trend = _pytrends.interest_over_time().reset_index()
temp_trend = temp_trend.merge(output, on="date", how="left")
# it's ugly but we'll exploit the common column names
# and then rename the underscore containing column names
for kw in kw_list:
norm_factor = np.ma.masked_invalid(temp_trend[kw+'_y']/temp_trend[kw+'_x']).mean()
temp_trend[kw] = temp_trend[kw+'_x'] * norm_factor
temp_trend = temp_trend[temp_trend.isnull().any(axis=1)]
temp_trend['isPartial'] = temp_trend['isPartial_x']
output = pd.concat([output, temp_trend[['date', 'isPartial'] + kw_list]], axis=0, sort=False)
# reorder columns in alphabetical order
output = output[['date', 'isPartial']+kw_list]
if not isPartial_col:
output = output.drop('isPartial', axis=1)
output = output[output['date']>=self.from_date]
if scale_cols:
# the values in each column are relative to other columns
# so we need to get the maximum value across the search columns
max_val = float(output[kw_list].values.max())
for col in kw_list:
output[col] = 100.0*output[col]/max_val
output = output.sort_values('date', ascending=self.ascending).reset_index(drop=True)
return output
def _merge_fill_filter(self, other_df):
output = pd.merge(self._df, other_df, on="date", how="left")
output = output.sort_values(by='date', ascending=self.ascending).reset_index(drop=True)
if self.fillgaps:
if self.ascending:
output = output.fillna(method='ffill')
else:
output = output.fillna(method='bfill')
output = output[(output['date']>=self.from_date) & (output['date']<=self.to_date)]
return output
