Usage Examples#
DataQuery Download Interface#
Downlading using OAuth credentials#
To download data from JP Morgan DataQuery, you can use the JPMaQSDownload Object together with your OAuth authentication credentials (default):
import pandas as pd
from macrosynergy.download import JPMaQSDownload
with JPMaQSDownload(
client_id="<dq_client_id>",
client_secret="<dq_client_secret>"
) as downloader:
data = downloader.download(tickers="EUR_FXXR_NSA",
start_date="2022-01-01")
assert isinstance(data, pd.DataFrame) and not data.empty
assert data.shape[0] > 0
data.info()
Downloading using Certificate and Private Key Pair#
Alternatively, you can also the certificate and private key pair, to access DataQuery as:
import pandas as pd
from macrosynergy.download import JPMaQSDownload
with JPMaQSDownload(
oauth=False,
username="<dq_username>",
password="<dq_password>",
crt="<path_to_dq_certificate>",
key="<path_to_dq_key>"
) as downloader:
data = downloader.download(tickers="EUR_FXXR_NSA",
start_date="2022-01-01")
assert isinstance(data, pd.DataFrame) and not data.empty
assert data.shape[0] > 0
data.info()
Both of the above example will download a snippet of example data from the premium JPMaQS dataset of the daily timeseries of EUR FX excess returns.
Using the API you can also access a panel of tickers from different countries like so.
import pandas as pd
from macrosynergy.download import JPMaQSDownload
cids = ['EUR','GBP','USD']
xcats = ['FXXR_NSA','EQXR_NSA']
tickers = [cid+"_"+xcat for cid in cids for xcat in xcats]
with JPMaQSDownload(
client_id="<dq_client_id>",
client_secret="<dq_client_secret>"
) as downloader:
data = downloader.download(tickers=tickers,
start_date="2022-01-01")
assert isinstance(data, pd.DataFrame) and not data.empty
assert data.shape[0] > 0
data.info()
Connecting via a proxy server#
Since a lot of institutions use a proxy server to connect to the internet; the JPMaQSDownload object can be configured to use a proxy server.
It is also possible to use a proxy server with the Dataquery interface. Here’s an example:
import pandas as pd
from macrosynergy.download import JPMaQSDownload
cids = ['EUR','GBP','USD']
xcats = ['FXXR_NSA','EQXR_NSA']
tickers = [cid+"_"+xcat for cid in cids for xcat in xcats]
oauth_proxy="https://secureproxy.example.com:port"
proxy = {"https": oauth_proxy}
## or proxy = {"http": "http://proxy.example.com:port"}
with JPMaQSDownload(
client_id = "<dq_client_id>",
client_secret = "<dq_client_secret>",
proxy = proxy
) as downloader:
data = downloader.download(tickers = tickers, start_date="2022-01-01")
assert isinstance(data, pd.DataFrame) and not df.empty
or,
...
proxies = {
"http": "http://proxy.example.com:port",
"https": "https://secucreproxy.example.com:port",
}
with JPMaQSDownload(
client_id = "<dq_client_id>",
client_secret = "<dq_client_secret>",
proxy = proxies
) as downloader:
data = downloader.download(tickers = tickers)
...
Simulated/Mock Data#
In order to use the rest of the package without access to the API you can simulate quantamental data using the management sub-package.
from macrosynergy.management.simulate import make_qdf
cids = ['AUD', 'GBP', 'NZD', 'USD']
xcats = ['FXXR_NSA', 'FXCRY_NSA', 'FXCRR_NSA', 'EQXR_NSA', 'EQCRY_NSA', 'EQCRR_NSA',
'FXWBASE_NSA', 'EQWBASE_NSA']
df_cids = pd.DataFrame(index=cids, columns=['earliest', 'latest', 'mean_add',
'sd_mult'])
df_cids.loc['AUD'] = ['2000-01-01', '2022-03-14', 0, 1]
df_cids.loc['GBP'] = ['2001-01-01', '2022-03-14', 0, 2]
df_cids.loc['NZD'] = ['2002-01-01', '2022-03-14', 0, 3]
df_cids.loc['USD'] = ['2000-01-01', '2022-03-14', 0, 4]
df_xcats = pd.DataFrame(index=xcats, columns=['earliest', 'latest', 'mean_add',
'sd_mult', 'ar_coef', 'back_coef'])
df_xcats.loc['FXXR_NSA'] = ['2010-01-01', '2022-03-14', 0, 1, 0, 0.2]
df_xcats.loc['FXCRY_NSA'] = ['2010-01-01', '2022-03-14', 1, 1, 0.9, 0.2]
df_xcats.loc['FXCRR_NSA'] = ['2010-01-01', '2022-03-14', 0.5, 0.8, 0.9, 0.2]
df_xcats.loc['EQXR_NSA'] = ['2010-01-01', '2022-03-14', 0.5, 2, 0, 0.2]
df_xcats.loc['EQCRY_NSA'] = ['2010-01-01', '2022-03-14', 2, 1.5, 0.9, 0.5]
df_xcats.loc['EQCRR_NSA'] = ['2010-01-01', '2022-03-14', 1.5, 1.5, 0.9, 0.5]
df_xcats.loc['FXWBASE_NSA'] = ['2010-01-01', '2022-02-01', 1, 1.5, 0.8, 0.5]
df_xcats.loc['EQWBASE_NSA'] = ['2010-01-01', '2022-02-01', 1, 1.5, 0.9, 0.5]
data = make_qdf(df_cids, df_xcats, back_ar=0.75)
The management sub-package can also be used to check which data is available in the dataframe.
from macrosynergy.management import check_availability
filt_na = (data['cid'] == 'USD') & (data['real_date'] < '2015-01-01')
data_filt.loc[filt_na, 'value'] = np.nan
check_availability(df=data_filt, xcats=xcats, cids=cids)
You can also use the built-in utility functions to reshape the data depending on the dates or tickers of your choice.
from macrosynergy.management import reduce_df
data_reduced = reduce_df(data, xcats=xcats[:-1], cids=cids[0],
start='2012-01-01', end='2018-01-31')
Panel#
Basket#
The basket class is used to calculate the returns and carries of financial contracts using various methods, a basket is created as so.
from macrosynergy.panel.basket import Basket
black = {'AUD': ['2010-01-01', '2013-12-31'], 'GBP': ['2010-01-01', '2013-12-31']}
contracts = ['AUD_FX', 'AUD_EQ', 'NZD_FX', 'GBP_EQ', 'USD_EQ']
gdp_figures = [17.0, 17.0, 41.0, 9.0, 250.0]
basket_1 = Basket(
df=data, contracts=contracts_1, ret="XR_NSA", cry=["CRY_NSA", "CRR_NSA"],
blacklist=black
)
basket_1.make_basket(weight_meth="equal", max_weight=0.55, basket_name="GLB_EQUAL")
Using the basket class you have access to the methods such as visulasing the weights associated with each contract, or returning the weight or basket.
basket_1.return_basket()
basket_1.return_weights()
basket_1.weight_visualiser(basket_name="GLB_EQUAL")
You can also calculate and visualise the following and more with built-in functions.
from macrosynergy.panel.historic_vol import historic_vol
data_historic = historic_vol(
data, cids=cids, xcat='FXXR_NSA', lback_periods=21, lback_meth='ma', half_life=11,
remove_zeros=True)
from macrosynergy.panel.make_zn_scores import make_zn_scores
z_mean = make_zn_scores(data, xcat='FXXR_NSA', sequential=True, cids=cids,
blacklist=black, iis=False, neutral='mean',
pan_weight=0.5, min_obs=261, est_freq="w")
z_median = make_zn_scores(data, xcat='FXXR_NSA', sequential=True, cids=cids,
blacklist=black, iis=False, neutral='median',
pan_weight=0.5, min_obs=261, est_freq="d")
from macrosynergy.panel.return_beta import return_beta
benchmark_return = "USD_FXXR_NSA"
data_hedge = return_beta(df=data, xcat='FXXR_NSA', cids=cids,
benchmark_return=benchmark_return, start='2010-01-01',
end='2020-10-30',
blacklist=black, meth='ols', oos=True,
refreq='w', min_obs=24, hedged_returns=True)
print(df_hedge)
beta_display(df_hedge=df_hedge, subplots=False)
view_timelines(data, xcats=['FXXR_NSA','FXCRY_NSA'], cids=cids[0],
size=(10, 5), title='AUD Return and Carry')
Signal#
Signal Return Relations#
The SignalReturnRelations class analyses and visualises signal and return series.
from macrosynergy.signal.signal_return import SignalReturnRelations
srn = SignalReturnRelations(data, ret="EQXR_NSA", sig="EQCRY_NSA", rival_sigs=None,
sig_neg=True, cosp=True, freq="M", start="2002-01-01")
srn.summary_table()
In the creation of the class you can also indicate rival signals for basic relational statistics.
r_sigs = [ "EQCRR_NSA"]
srn = SignalReturnRelations(data, "EQXR_NSA", sig="EQCRY_NSA", rival_sigs=r_sigs,
sig_neg=True, cosp=True, freq="M", start="2002-01-01")
df_sigs = srn.signals_table(sigs=['EQCRY_NSA_NEG', 'EQCRR_NSA_NEG'])
df_sigs_all = srn.signals_table()
Using the class you can plot accuracy bars between returns and signals.
srn.accuracy_bars(type="signals", title="Accuracy measure between target return, EQXR_NSA,"
" and the respective signals, ['EQCRY_NSA_NEG', "
" 'EQCRR_NSA_NEG'].")
PnL#
Naive pnl#
The NaivePnL class computes Pnls with limited signal options and disregarding transaction costs.
from macrosynergy.pnl.naive_pnl import NaivePnL
pnl = NaivePnL(data, ret="EQXR_NSA", sigs=["CRY", "GROWTH"], cids=cids,
start="2000-01-01", bms=["EUR_EQXR_NSA", "USD_EQXR_NSA"])
You can then make the pnl and see a list of key pnl statistics.
pnl.make_pnl(
sig="GROWTH", sig_op="zn_score_pan", sig_neg=True, rebal_freq="monthly",
vol_scale=5, rebal_slip=1, min_obs=250, thresh=2)
df_eval = pnl.evaluate_pnls(
pnl_cats=["PNL_GROWTH_NEG"], start="2015-01-01", end="2020-12-31")