Fusion - get started¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
from fusion import Fusion

plt.style.use("bmh")

import pandas as pd import matplotlib.pyplot as plt from fusion import Fusion plt.style.use("bmh")

Establish the connection¶

In [2]:

fusion = Fusion()

fusion = Fusion()

Show the available functionality¶

In [3]:

fusion

fusion

Out[3]:

Fusion object 
Available methods:
+-------------------------+-----------------------------------------------------------------------------------------------+
| catalog_resources       | List the resources contained within the catalog, for example products and datasets.           |
| dataset_resources       | List the resources available for a dataset, currently this will always be a datasetseries.    |
| datasetmember_resources | List the available resources for a datasetseries member.                                      |
| download                | Downloads the requested distributions of a dataset to disk.                                   |
| from_bytes              | Uploads data from an object in memory.                                                        |
| get_events              | Run server sent event listener and print out the new events. Keyboard terminate to stop.      |
| get_fusion_filesystem   | Creates Fusion Filesystem.                                                                    |
| list_catalogs           | Lists the catalogs available to the API account.                                              |
| list_dataset_attributes | Returns the list of attributes that are in the dataset.                                       |
| list_datasetmembers     | List the available members in the dataset series.                                             |
| list_datasets           | Get the datasets contained in a catalog.                                                      |
| list_distributions      | List the available distributions (downloadable instances of the dataset with a format type).  |
| list_products           | Get the products contained in a catalog. A product is a grouping of datasets.                 |
| listen_to_events        | Run server sent event listener in the background. Retrieve results by running get_events.     |
| to_bytes                | Returns an instance of dataset (the distribution) as a bytes object.                          |
| to_df                   | Gets distributions for a specified date or date range and returns the data as a dataframe.    |
| to_table                | Gets distributions for a specified date or date range and returns the data as an arrow table. |
| upload                  | Uploads the requested files/files to Fusion.                                                  |
| default_catalog         | Returns the default catalog.                                                                  |
+-------------------------+-----------------------------------------------------------------------------------------------+

Access function documentation¶

In [4]:

fusion.to_df?

fusion.to_df?

Signature:
fusion.to_df(
    dataset: str,
    dt_str: str = 'latest',
    dataset_format: str = 'parquet',
    catalog: str = None,
    n_par: int = None,
    show_progress: bool = True,
    columns: List = None,
    filters: List = None,
    force_download: bool = False,
    download_folder: str = None,
    dataframe_type: str = 'pandas',
    **kwargs,
) -> pandas.core.frame.DataFrame
Docstring:
Gets distributions for a specified date or date range and returns the data as a dataframe.

Args:
    dataset (str): A dataset identifier
    dt_str (str, optional): Either a single date or a range identified by a start or end date,
        or both separated with a ":". Defaults to 'latest' which will return the most recent
        instance of the dataset.
    dataset_format (str, optional): The file format, e.g. CSV or Parquet. Defaults to 'parquet'.
    catalog (str, optional): A catalog identifier. Defaults to 'common'.
    n_par (int, optional): Specify how many distributions to download in parallel.
        Defaults to all cpus available.
    show_progress (bool, optional): Display a progress bar during data download Defaults to True.
    columns (List, optional): A list of columns to return from a parquet file. Defaults to None
    filters (List, optional): List[Tuple] or List[List[Tuple]] or None (default)
        Rows which do not match the filter predicate will be removed from scanned data.
        Partition keys embedded in a nested directory structure will be exploited to avoid
        loading files at all if they contain no matching rows. If use_legacy_dataset is True,
        filters can only reference partition keys and only a hive-style directory structure
        is supported. When setting use_legacy_dataset to False, also within-file level filtering
        and different partitioning schemes are supported.
        More on https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetDataset.html
    force_download (bool, optional): If True then will always download a file even
        if it is already on disk. Defaults to False.
    download_folder (str, optional): The path, absolute or relative, where downloaded files are saved.
        Defaults to download_folder as set in __init__
    dataframe_type (str, optional): Type
Returns:
    class:`pandas.DataFrame`: a dataframe containing the requested data.
        If multiple dataset instances are retrieved then these are concatenated first.
File:      c:\dev\fusion\fusion\fusion.py
Type:      method

Explore the datasets¶

In [5]:

fusion.list_datasets("FX")

fusion.list_datasets("FX")

Out[5]:

	identifier	title	containerType	region	category	coverageStartDate	coverageEndDate	description	status
12	FX_EASIDX	Economic Activity Surprise Index (EASI) FX	Snapshot-Full	EMEA, North America, Emerging Markets, APAC, G...	Economic	2019-01-01	2024-02-01	The Economic Activity Surprise Index is publis...	Subscribed
22	FX_MEAN_IMM	FX Mean Reversion Strategies IMM	Snapshot-Full	EMEA, North America, APAC, Emerging Markets, G...	FX	2019-01-01	2024-02-01	The FX Mean Reversion, IMM dataset from J.P. M...	Subscribed
28	FXO_RR	FX Option Structure | Risk Reversal	Snapshot-Full	EMEA, North America, APAC, Emerging Markets, G...	FX	2019-01-01	2024-02-01	Implied volatility for 10 and 25 delta FX opti...	Subscribed
29	FXO_ST	FX Option Structure | Strangles	Snapshot-Full	EMEA, North America, APAC, Emerging Markets, G...	FX	2019-01-01	2024-02-01	Implied volatility for 10 and 25 delta FX opti...	Subscribed
39	FXO_SP	FX Cash Rate	Snapshot-Full	EMEA, North America, Emerging Markets, APAC, G...	FX	2019-01-01	2024-04-11	This dataset includes FX spot rates for major ...	Subscribed
53	STANDARD_VALUED_HOLDINGS_SUMMARY	Sample: Valued Holdings Summary	Snapshot-Full	Global	Middle Office	NaN	NaN	Provides portfolio level valuation in a specif...	Available
60	FX_ECONOMIC	FX Specialized | Momentum Strategies (Economics)	Snapshot-Full	EMEA, North America, APAC, Emerging Markets, G...	FX	2019-01-01	2024-01-29	Momentum signals in a trend following strategy...	Subscribed
89	FX_JPM_TCI	FX Passive Index	Snapshot-Full	EMEA, North America, APAC, Global	FX	2019-01-01	2024-02-01	FX passive index level and currency sub-indices.	Subscribed
90	FX_MEAN_HFFV	FX Mean Reversion Strategies Hi Freq Fair Value	Snapshot-Full	EMEA, North America, APAC, Global	FX	2019-01-01	2024-02-01	The FX High Frequency Fair Value dataset from ...	Subscribed
126	STANDARD_VALUED_HOLDINGS	Sample: Valued Holdings	Snapshot-Full	Global	Middle Office	NaN	NaN	Provides market value in local currency of the...	Available
147	STANDARD_CLIENT_STANDING_INSTRUCTIONS	Sample: Client Standing Instructions	Snapshot-Full	Global	Custody	NaN	NaN	Standard dataset detailing client standing ins...	Available
148	STANDARD_CLS_FX_TRANSACTIONS	Sample: CLS FX Transactions	Snapshot-Full	Global	Custody	NaN	NaN	Standard dataset detailing all CLS FX trade ac...	Available
182	STANDARD_FUTURE_VALUED_FX_CONTRACTS	Sample: Future Valued FX Contracts	Snapshot-Full	Global	Custody	NaN	NaN	Standard Future Valued FX Contracts dataset di...	Available
210	STANDARD_MARKET_VALUATION_DETAIL_CCY_CONTRACTS	Sample: Market Valuation Detail Currency Contr...	Snapshot-Full	Global	Fund Accounting	NaN	NaN	This dataset exclusively shows all pending spo...	Available
222	FXO_VOL_EOD_G10	FXO End of Day Implied Volatility - G10	Snapshot-Full	Global	FX	2013-01-02	2024-04-11	J.P. Morgan’s FXO End of Day Implied Volatilit...	Subscribed
342	FXO_VOL_EOD_EM	FXO End of Day Implied Volatility - EM	Snapshot-Full	Emerging Markets, Global	FX	2013-01-02	2024-04-11	J.P. Morgan’s FXO End of Day Implied Volatilit...	Subscribed

Display the attributes¶

In [6]:

fusion.list_dataset_attributes("FXO_SP")

fusion.list_dataset_attributes("FXO_SP")

Out[6]:

	source	dataType	description	identifier	isDatasetKey	title
0	Data Query	String	The instrument name	instrument_name	True	Instrument Name
1	Data Query	String	The currency pair	currency_pair	False	Currency Pair
2	Data Query	String	The time period of an investment, agreement or...	term	False	Term
3	Data Query	String	The product identifier	product	False	Product
4	Data Query	String	The snapshot date	date	False	Date
5	Data Query	Double	The spot and forward fx rate	fx_rate	False	FX Rate

Download and load¶

In [7]:

df = fusion.to_df("FXO_SP", "20220101:20221231", columns=["currency_pair", "date", "fx_rate"], filters=[("currency_pair", "=", "GBPUSD")])

df = fusion.to_df("FXO_SP", "20220101:20221231", columns=["currency_pair", "date", "fx_rate"], filters=[("currency_pair", "=", "GBPUSD")])

100%|████████████████████████████████████████████████████████████████████████████████| 258/258 [00:26<00:00,  9.77it/s]

Analyze¶

In [8]:

df.head()

df.head()

Out[8]:

	currency_pair	date	fx_rate
0	GBPUSD	20221118	1.19300
1	GBPUSD	20220310	1.31295
2	GBPUSD	20220322	1.32590
3	GBPUSD	20220318	1.31705
4	GBPUSD	20220103	1.34475

In [9]:

df["date"] = pd.to_datetime(df["date"].astype("str"))
df.sort_values("date").set_index("date").plot(grid=True);

df["date"] = pd.to_datetime(df["date"].astype("str")) df.sort_values("date").set_index("date").plot(grid=True);