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4ab17c5 Jan 6, 2018
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@bmoscon @jamesblackburn
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from __future__ import print_function
################################################
# Getting started
################################################
# Install Arctic
# pip install git+https://github.com/manahl/arctic.git
# That's it(!)
# Run MongoDB - https://www.mongodb.org/downloads
# $ mkdir /tmp/pydata-demo
# $ mongod --dbpath /tmp/pydata-demo
from datetime import datetime
import time
import ystockquote
from arctic import Arctic
import collections
import pandas as pd
import pprint
################################################
# Loading data
################################################
def get_stock_history(ticker, start_date, end_date):
data = ystockquote.get_historical_prices(ticker, start_date, end_date)
df = pd.DataFrame(collections.OrderedDict(sorted(data.items()))).T
df = df.convert_objects(convert_numeric=True)
return df
################################################
# VersionStore: Storing and updating stock data
################################################
arctic = Arctic('localhost')
# Create a VersionStore library
arctic.delete_library('jblackburn.stocks')
arctic.initialize_library('jblackburn.stocks')
arctic.list_libraries()
stocks = arctic['jblackburn.stocks']
# get some prices
aapl = get_stock_history('aapl', '2015-01-01', '2015-02-01')
aapl
# store them in the library
stocks.write('aapl', aapl, metadata={'source': 'YAHOO'})
stocks.read('aapl').data['Adj Close'].plot()
stocks.read('aapl').metadata
stocks.read('aapl').version
# Append some more prices - imagine doing this once per period
aapl = get_stock_history('aapl', '2015-02-01', '2015-03-01')
stocks.append('aapl', aapl)
stocks.read('aapl').data
# Reading different versions of the symbol
stocks.list_symbols()
stocks.list_versions('aapl')
# Read the different versions separately
stocks.read('aapl', as_of=1).data.ix[-1]
stocks.read('aapl', as_of=2).data.ix[-1]
# And we can snapshot all items in the library
stocks.snapshot('snap')
stocks.read('aapl', as_of='snap').data.ix[-1]
#################################
# Dealing with lots of data
#################################
#NSYE library
lib = arctic['nyse']
def load_all_stock_history_NYSE():
# Data downloaded from BBG Open Symbology:
#
nyse = pd.read_csv('/users/is/jblackburn/git/arctic/howtos/nyse.csv')
stocks = [x.split('/')[0] for x in nyse['Ticker']]
print(len(stocks), " symbols")
for i, stock in enumerate(stocks):
try:
now = datetime.now()
data = get_stock_history('aapl', '1980-01-01', '2015-07-07')
lib.write(stock, data)
print("loaded data for: ", stock, datetime.now() - now)
except Exception as e:
print("Failed for ", stock, str(e))
# load_all_stock_history_NYSE()
print(len(lib.list_symbols()), " NYSE symbols loaded")
def read_all_data_from_lib(lib):
start = time.time()
rows_read = 0
for s in lib.list_symbols():
rows_read += len(lib.read(s).data)
print("Symbols: %s Rows: %s Time: %s Rows/s: %s" % (len(lib.list_symbols()),
rows_read,
(time.time() - start),
rows_read / (time.time() - start)))
read_all_data_from_lib(lib)
# Symbols: 1315 Rows: 11460225 Rows/s: 2,209,909