def test_free_fx_tick_download():
# First we can do it by defining all the vendor fields, tickers etc.
# so we bypass the configuration file
md_request = MarketDataRequest(start_date="05 Dec 2016",
finish_date="07 Dec 2016",
fields=["bid"],
vendor_fields=["bid"],
freq="tick",
data_source="dukascopy",
tickers=["EURUSD"],
vendor_tickers=["EURUSD"])
md_request.data_source = "dukascopy"
df = market.fetch_market(md_request).resample("1min").mean()
assert df is not None
def test_free_fx_tick_download():
# first we can do it by defining all the vendor fields, tickers etc. so we bypass the configuration file
md_request = MarketDataRequest(start_date='05 Dec 2016',
finish_date='07 Dec 2016',
fields=['bid'],
vendor_fields=['bid'],
freq='tick',
data_source='fxcm',
tickers=['EURUSD'],
vendor_tickers=['EURUSD'])
df_tick = market.fetch_market(md_request)
df = df_tick.resample('1min').mean()
md_request.data_source = 'dukascopy'
df1 = market.fetch_market(md_request).resample('1min').mean()
assert df is not None
assert df1 is not None
df = df.resample('1s').mean()
print(df.tail(n=60))
if run_example == 6 or run_example == 0:
####### FXCM (and compare with DukasCopy) examples
# let's download data for end of 2016/start 2017 for EUR/USD - the raw data has bid/ask, if we specify close, we calculate
# it as the average
# first we can do it by defining all the vendor fields, tickers etc. so we bypass the configuration file
md_request = MarketDataRequest(start_date='01 Dec 2016',
finish_date='07 Dec 2016',
fields=['bid'],
vendor_fields=['bid'],
freq='tick',
data_source='fxcm',
tickers=['EURUSD'],
vendor_tickers=['EURUSD'])
df_tick = market.fetch_market(md_request)
df = df_tick.resample('1min').mean()
md_request.data_source = 'dukascopy'
df1 = market.fetch_market(md_request).resample('1min').mean()
df1.columns = [c + '_dk' for c in df1.columns]
df = df1.join(df)
print(df_tick.tail(n=100))
print(df.tail(n=100))
vendor_tickers=fx_vendor_tickers, # Ticker (Quandl)
vendor_fields=['close'])
# Fill in your own API keys for Quandl and FRED here
# md_request.QUANDL_API_KEY = "TYPE HERE"
# md_request.FRED_API_KEY = "TYPE HERE"
df_fx = market.fetch_market(md_request=md_request)
##### Download deposit rate data
rates_tickers = ['USD3M', 'CAD3M', 'EUR3M', 'AUD3M', 'CHF3M', 'SEK3M', 'GBP3M', 'NOK3M', 'JPY3M', 'NZD3M']
rates_vendor_tickers = ['IR3TIB01USM156N', 'IR3TIB01CAM156N', 'IR3TIB01EZM156N', 'IR3TIB01AUM156N', 'IR3TIB01CHM156N',
'IR3TIB01SEM156N', 'IR3TIB01GBM156N', 'IR3TIB01NOM156N', 'IR3TIB01JPM156N', 'IR3TIB01NZM156N']
md_request.data_source = 'alfred'
md_request.tickers = rates_tickers
md_request.vendor_tickers = rates_vendor_tickers
df_rates = market.fetch_market(md_request=md_request)
df_rates = df_rates.resample('BM').last()
df = df_fx.join(df_rates, how='left')
df = df.fillna(method='ffill')
df.to_csv("fx_rates.csv")
##### Download US stocks data
equities_tickers = ['TWTR', 'GOOG', 'FB', 'AAPL', 'NFLX', 'AMZN', 'TSLA', 'S&P500']
equities_vendor_tickers = ['TWTR', 'GOOG', 'FB', 'AAPL', 'NFLX', 'AMZN', 'TSLA', '^GSPC']
md_request.data_source = 'yahoo'
cache_algo='cache_algo_return',
abstract_curve=FXSpotCurve(
construct_via_currency='USD',
depo_tenor='ON'))
df_tot = market.fetch_market(md_request=md_request)
df_tot.columns = [x + '-tot-cuemacro' for x in df_tot.columns]
df_tot = df_tot.tz_localize(pytz.utc)
df_tot.index = df_tot.index + pd.Timedelta(
hours=22) # Roughly NY close 2200 GMT
md_request.abstract_curve = None
# Get intraday spot data
md_request.freq = 'tick'
md_request.data_source = 'dukascopy'
df_intraday_spot = market.fetch_market(md_request=md_request)
df_intraday_spot = pd.DataFrame(
df_intraday_spot.resample('1min').last().dropna())
# Get Bloomberg calculated total return indices (for spot)
md_request.category = 'fx-tot'
md_request.freq = 'daily'
md_request.data_source = 'bloomberg'
df_bbg_tot = market.fetch_market(md_request)
df_bbg_tot.columns = [x + '-bbg' for x in df_bbg_tot.columns]
df_bbg_tot = df_bbg_tot.tz_localize(pytz.utc)
df_bbg_tot.index = df_bbg_tot.index + pd.Timedelta(
hours=22) # Roughly NY close 2200 GMT
if run_example == 6 or run_example == 0:
####### FXCM (and compare with DukasCopy) examples
# let"s download data for end of 2016/start 2017 for EUR/USD - the
# raw data has bid/ask, if we specify close, we calculate
# it as the average
# first we can do it by defining all the vendor fields, tickers etc.
# so we bypass the configuration file
md_request = MarketDataRequest(start_date="01 Dec 2016",
finish_date="07 Dec 2016",
fields=["bid"],
vendor_fields=["bid"],
freq="tick",
data_source="fxcm",
tickers=["EURUSD"],
vendor_tickers=["EURUSD"])
df_tick = market.fetch_market(md_request)
df = df_tick.resample("1min").mean()
md_request.data_source = "dukascopy"
df1 = market.fetch_market(md_request).resample("1min").mean()
df1.columns = [c + "_dk" for c in df1.columns]
df = df1.join(df)
print(df_tick.tail(n=100))
print(df.tail(n=100))
