df = market.fetch_market(md_request=md_request)
print(df)
folder = 's3://type_your_s3_bucket_here'
# Save to disk in a format friendly for reading later (ie. s3://bla_bla_bla/backtest.fx.tick.dukascopy.NYC.EURUSD.parquet)
# Here it will automatically generate the filename from the folder we gave
# and the MarketDataRequest we made (altenatively, we could have just given the filename directly)
IOEngine().write_time_series_cache_to_disk(folder,
df,
engine='parquet',
md_request=md_request)
md_request.data_engine = folder + '/*.parquet'
df = market.fetch_market(md_request)
print(df)
# Or we could have just read it directly using
df = IOEngine().read_time_series_cache_from_disk(folder,
df,
engine='parquet',
md_request=md_request)
# We can try this using daily data
import os
quandl_api_key = os.environ['QUANDL_API_KEY']
market = Market(market_data_generator=MarketDataGenerator())
df = market.fetch_market(md_request=md_request)
print(df)
folder = "../tests/"
# Save to disk in a file name format friendly for reading later via
# MarketDataRequest (ie. ../tests/backtest.fx.daily.quandl.NYC.parquet)
IOEngine().write_time_series_cache_to_disk(folder, df,
engine="parquet",
md_request=md_request)
md_request.data_engine = "../tests/*.parquet"
df = market.fetch_market(md_request)
print(df)
if run_example == 4:
# In this case we are saving predefined tick data tickers to disk, and
# then reading back using the MarketDataRequest interface
from findatapy.util.dataconstants import DataConstants
from findatapy.market.ioengine import IOEngine
md_request = MarketDataRequest(
start_date="01 Jan 2021",
finish_date="05 Jan 2021",
category="fx",
freq='daily',
quandl_api_key=quandl_api_key
)
market = Market(market_data_generator=MarketDataGenerator())
df = market.fetch_market(md_request=md_request)
print(df)
folder = '../tests/'
# Save to disk in a file name format friendly for reading later via MarketDataRequest (ie. ../tests/backtest.fx.daily.quandl.NYC.parquet)
IOEngine().write_time_series_cache_to_disk(folder, df, engine='parquet', md_request=md_request)
md_request.data_engine = '../tests/*.parquet'
df = market.fetch_market(md_request)
print(df)
if run_example == 4:
# In this case we are saving predefined tick data tickers to disk, and then reading back using the MarketDataRequest interface
from findatapy.util.dataconstants import DataConstants
from findatapy.market.ioengine import IOEngine
md_request = MarketDataRequest(
start_date='01 Jan 2021',
finish_date='05 Jan 2021',
category='fx',
data_source='dukascopy',
