resample="15s",
resample_how="last_dropna"
)
market = Market(market_data_generator=MarketDataGenerator())
df = market.fetch_market(md_request=md_request)
print(df)
# the second time we call it, if we have Redis installed, we will fetch
# from memory, so it will be quicker
# also don"t need to run the resample operation again
# need to specify cache_algo_return
md_request.cache_algo = "cache_algo_return"
df = market.fetch_market(md_request)
print(df)
if run_example == 3:
# In this case we are saving predefined daily tickers to disk, and then
# reading back
from findatapy.util.dataconstants import DataConstants
from findatapy.market.ioengine import IOEngine
import os
quandl_api_key = DataConstants().quandl_api_key # change with your own
# Quandl API key!
# in the config file, we can use keywords 'open', 'high', 'low', 'close' and 'volume' for alphavantage data
# download equities data from alphavantage
md_request = MarketDataRequest(
start_date="01 Jan 2002", # start date
finish_date="05 Feb 2017", # finish date
data_source='alphavantage', # use alphavantage as data source
tickers=[
'Apple', 'Citigroup', 'Microsoft', 'Oracle', 'IBM', 'Walmart',
'Amazon', 'UPS', 'Exxon'
], # ticker (findatapy)
fields=['close'], # which fields to download
vendor_tickers=[
'aapl', 'c', 'msft', 'orcl', 'ibm', 'wmt', 'amzn', 'ups', 'xom'
], # ticker (alphavantage)
vendor_fields=['Close'], # which alphavantage fields to download
cache_algo='internet_load_return')
logger.info("Load data from alphavantage directly")
df = market.fetch_market(md_request)
logger.info(
"Loaded data from alphavantage directly, now try reading from Redis in-memory cache"
)
md_request.cache_algo = 'cache_algo_return' # change flag to cache algo so won't attempt to download via web
df = market.fetch_market(md_request)
logger.info("Read from Redis cache.. that was a lot quicker!")
freq='intraday',
resample='15s',
resample_how='last_dropna'
)
market = Market(market_data_generator=MarketDataGenerator())
df = market.fetch_market(md_request=md_request)
print(df)
# the second time we call it, if we have Redis installed, we will fetch from memory, so it will be quicker
# also don't need to run the resample operation again
# need to specify cache_algo_return
md_request.cache_algo = 'cache_algo_return'
df = market.fetch_market(md_request)
print(df)
if run_example == 3:
# In this case we are saving predefined daily tickers to disk, and then reading back
from findatapy.util.dataconstants import DataConstants
from findatapy.market.ioengine import IOEngine
import os
quandl_api_key = DataConstants().quandl_api_key # change with your own Quandl API key!
md_request = MarketDataRequest(
category='fx',
# and "volume" for alphavantage data
# Download equities data from yahoo
md_request = MarketDataRequest(
start_date="01 Jan 2002", # start date
finish_date="05 Feb 2017", # finish date
data_source="yahoo", # use alphavantage as data source
tickers=["Apple", "Citigroup", "Microsoft", "Oracle", "IBM", "Walmart",
"Amazon", "UPS", "Exxon"], # ticker (findatapy)
fields=["close"], # which fields to download
vendor_tickers=["aapl", "c", "msft", "orcl", "ibm", "wmt", "amzn",
"ups", "xom"], # ticker (yahoo)
vendor_fields=["Close"], # which yahoo fields to download
cache_algo="internet_load_return")
logger.info("Load data from yahoo directly")
df = market.fetch_market(md_request)
print(df)
logger.info(
"Loaded data from yahoo directly, now try reading from Redis "
"in-memory cache")
md_request.cache_algo = "cache_algo_return" # change flag to cache algo
# so won"t attempt to download via web
df = market.fetch_market(md_request)
print(df)
logger.info("Read from Redis cache.. that was a lot quicker!")
