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cmqd-1.0.4
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مشاهده بیشترتوضیحات
Python bindings for the ClearMacro API
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cmqd-1.0.4 |
| نام | cmqd |
| نسخه کتابخانه | 1.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | ClearMacro |
| ایمیل نویسنده | support@clearmacro.com |
| آدرس صفحه اصلی | https://github.com/cmqd/cm-api-python-sdk |
| آدرس اینترنتی | https://pypi.org/project/cmqd/ |
| مجوز | MIT |
# ClearMacro <!-- omit in toc -->
The ClearMacro Python library provides convenient access to the ClearMacro API from applications written in the Python language. It includes a pre-defined set of methods that make it simple to start interacting with the API.
- [Installation](#installation)
- [Examples](#examples)
- [Full Python Docstrings](#full-python-docstrings)
## Installation
To use the package, run:
```sh
pip install --upgrade cmqd
```
## Examples
Selection of examples; a reference point of getting started.
```
// Instantiate a client
>>> from clearmacro import Client
>>> config = {'url': '<HOST HERE>', 'username': '<EMAIL HERE>', 'password': '<PASSWORD HERE>'}
>>> client = Client(**config)
// Get the catalogue
>>> catalogue = client.get_signals_catalogue()
// Consume it as a DataFrame
>>> client.json_to_df(catalogue)
signalId name description universe category
0 8 Crossborder Flow Crossborder Flow Economics Credit
1 9 Crossborder Private Liquidity Crossborder Private Liquidity Economics Credit
2 10 Crossborder Policy Sector Crossborder Policy Sector Economics Credit
3 11 Crossborder Monetized Savings Crossborder Monetized Savings Economics Inflation
...
```
```
// Get the markets for which a certain signal is available.
>>> markets_for_my_signal = client.get_markets_for_signal('Bond Valuation Score IHS')
// Consume as DataFrame
>>> client.json_to_df(markets_for_my_signal)
classId name marketCategory
0 86 Australia Country
1 87 Austria Country
2 88 Belgium Country
3 89 Brazil Country
4 90 Canada Country
...
```
```
// Request a certain signal series
>>> series = client.get_signal_series(signal='Crossborder Flow', market='US', research_type='Back-test Level', last_date_only=False, start_date='2016-1-1', end_date='2020-10-16')
// Consume it as a DataFrame
>>> client.json_to_df(series)
dateTimes values
0 2016-01-29T23:59:59.99 5.485577
1 2016-02-29T23:59:59.99 5.974441
2 2016-03-31T23:59:59.99 6.388535
3 2016-04-29T23:59:59.99 6.714286
4 2016-05-31T23:59:59.99 6.340190
5 2016-06-30T23:59:59.99 5.599369
6 2016-07-29T23:59:59.99 4.792453
7 2016-08-31T23:59:59.99 3.793103
8 2016-09-30T23:59:59.99 3.207812
9 2016-10-31T23:59:59.99 3.046729
10 2016-11-30T23:59:59.99 3.375776
11 2016-12-30T23:59:59.99 3.981424
...
// If there is no such series, get an exception which can be handled:
>>> client.get_signal_series('Random signal', 'Random market', 'Random research type')
ValueError: Invalid input.
```
```
// Request data with a certain frequency e.g. weekly data on Wednesdays.
>>> wedDataSeries = client.get_signal_series('Country Valuation Score TR', 'Germany', 'Back-test Momentum', frequency_key = 'W-WED')
// Consume it as a DataFrame
>>> client.json_to_df(wedDataSeries)
dateTimes values
0 1995-02-15T23:59:59.99 4.449885
1 1995-02-22T23:59:59.99 5.507832
2 1995-03-01T23:59:59.99 5.042825
3 1995-03-08T23:59:59.99 6.214902
4 1995-03-15T23:59:59.99 6.647544
...
```
## Full Python Docstrings
Documentation for each method of the `Client` class:
```
get_signals_catalogue()
"""
Function to retrieve the list of all signals.
Returns:
JSON list of signal objects.
"""
```
```
get_all_markets()
"""
Function to retrieve the list of all markets.
Returns:
JSON list of market objects.
"""
```
```
get_signal_universes()
"""
Function to retrieve the signal universes.
Returns:
JSON list of universe objects.
"""
```
```
get_categories_for_universe(universe: str)
"""
Function to retrieve the list of categories corresponding to the passed universe.
Args:
universe (str): One of the signal universes. Available options are the "name" fields from the list of universe objects obtained by calling the get_signal_universes method.
Returns:
JSON list of category objects.
"""
```
```
get_categories_for_universe_id(universe_id: int)
"""
Function to retrieve the list of categories corresponding to the passed universe id.
Args:
universe_id (int): One of the signal universes. Available options are the "universeId" fields from the list of universe objects obtained by calling the get_signal_universes method.
Returns:
JSON list of category objects.
"""
```
```
get_signals_for_universe_cat_pair(universe: str, category: str)
"""
Function to retrieve all signals belonging to the given universe, category pair.
Args:
universe (str): One of the signal universes. Available options are the "name" fields from the list of universe objects obtained by calling the get_signal_universes method.
category (str): One of the categories of the above universe. Available options are the "name" fields from the list of category objects obtained by calling get_categories_for_universe(universe).
Returns:
JSON list of signal objects.
"""
```
```
get_signals_for_universe_cat_pair_id(universe_id: int, category_id: int)
"""
Function to retrieve all signals belonging to the given universe, category pair by id.
Args:
universe_id (int): One of the signal universes. Available options are the "universeId" fields from the list of universe objects obtained by calling the get_signal_universes method.
category_id (int): One of the categories of the above universe. Available options are the "categoryId" fields from the list of category objects obtained by calling get_categories_for_universe_id(universe_id).
Returns:
JSON list of signal objects.
"""
```
```
get_market_categories()
"""
Function to retrieve the list of all market categories.
Returns:
JSON list of market category objects.
"""
```
```
get_markets_for_market_cat(market_category: str)
"""
Function to retrieve the list of markets corresponding to the passed market category.
Args:
market_category (str): One of the market categories. Available options are the "name" fields from the list of market category objects obtained by calling the get_market_categories method.
Returns:
JSON list of market objects.
"""
```
```
get_markets_for_market_cat_id(market_category_id: int)
"""
Function to retrieve the list of markets corresponding to the passed market category id.
Args:
market_category_id (int): One of the market categories. Available options are the "marketCategoryId" fields from the list of market category objects obtained by calling the get_market_categories method.
Returns:
JSON list of market objects.
"""
```
```
get_markets_for_signal(signal: str)
"""
Function to retrieve the list of markets corresponding to the passed signal.
Args:
signal (str): One of the signals. Available options are the "name" fields from the list of signals objects obtained by calling the get_signals_catalogue method.
Returns:
JSON list of market objects.
"""
```
```
get_markets_for_signal_id(signal_id: int)
"""
Function to retrieve the list of markets corresponding to the passed signal id.
Args:
signal_id (int): One of the signals. Available options are the "signalId" fields from the list of signals objects obtained by calling the get_signals_catalogue method.
Returns:
JSON list of market objects.
"""
```
```
get_signals_for_market(market: str)
"""
Function to retrieve the list of signals available for the given market.
Args:
market (str): One of the valid markets. Available options are the "name" fields from the list of market objects obtained by calling the get_all_markets method.
Returns:
JSON list of signal objects.
"""
```
```
get_signals_for_market_id(class_id: int)
"""
Function to retrieve the list of signals available for the given market.
Args:
class_id (int): One of the valid markets. Available options are the "classId" fields from the list of market objects obtained by calling the get_all_markets method.
Returns:
JSON list of signal objects.
"""
```
```
get_research_types_for_signal_market_pair(signal: str, market: str)
"""
Function to retrieve the list of research types corresponding to the passed signal and market.
Args:
signal (str): One of the signals. Available options are the "name" fields from the list of signal objects obtained by calling the get_signals_catalogue method.
market (str): One of the valid markets for the above signal. Available options are the "name" fields from the list of market objects obtained by calling the get_markets_for_signal(signal) method.
Returns:
JSON list of research type objects.
"""
```
```
get_research_types_for_signal_market_pair_id(signal_id: int, market_id: int)
"""
Function to retrieve the list of research types corresponding to the passed signal id and market id.
Args:
signal_id (int): One of the signals. Available options are the "signalId" fields from the list of signal objects obtained by calling the get_signals_catalogue method.
market_id (int): One of the valid markets for the above signal. Available options are the "classId" fields from the list of market objects obtained by calling the get_markets_for_signal_id(signal_id) method.
Returns:
JSON list of research type objects.
"""
```
```
get_signal_series(
signal: str,
market: str,
research_type: str,
last_date_only=False,
start_date=None,
end_date=None,
frequency_key=None,
)
"""
Function to retrieve a signal time series.
Args:
signal (str): Options are "name" fields from list of signal objects returned by get_signals_catalogue.
market (str): Options are "name" fields from list of market objects returned by get_markets_for_signal(signal).
research_type (str): Options are "name" fields from list of research type objects returned by get_research_types_for_signal_market_pair(signal, market).
last_date_only (bool, optional): Flag indicating if only the last date of the time series is desired.
start_date (str, optional): Start date of the series in ISO format YYYY-MM-DD.
end_date (str, optional): End date of the series in ISO format YYYY-MM-DD.
frequency_key (str): One of the desired frequencies (None is for all values available, undetermined frequency):
D, (Daily - all days)
WD, (Weekdays)
W_MON, (Weekly data on Mondays)
W_TUE, (Weekly data on Tuesdays)
W_WED, (Weekly data on Wednesdays)
W_THU, (Weekly data on Thursdays)
W_FRI, (Weekly data on Fridays)
W_SAT, (Weekly data on Saturdays)
W_SUN, (Weekly data on Sundays)
M, (Monthly data - end of month)
MS, (Monthly data - start of month)
Q, (Quarterly data - end of quarter)
QS (Quarterly data - start of quarter)
Examples:
>>> client.get_signal_series('Crossborder Flow', 'US', 'Back-test Level', last_date_only=False, start_date='2016-1-1', end_date='2020-10-16')
Returns:
Time series object.
"""
```
```
get_signal_series_id(
signal_id: int,
market_id: int,
research_type_id: int,
last_date_only=False,
start_date=None,
end_date=None,
frequency_key=None
)
"""
Function to retrieve a signal time series by ids.
Args:
signal_id (int): Options are "signalId" fields from list of signal objects returned by get_signals_catalogue.
market_id (int): Options are "classId" fields from list of market objects returned by get_markets_for_signal_id(signal_id).
research_type_id (int): Options are "researchTypeId" fields from list of research type objects returned by get_research_types_for_signal_market_pair_id(signal_id, market_id).
last_date_only (bool): Flag indicating if only the last date of the time series is desired.
start_date (str): Start date of the series in ISO format YYYY-MM-DD.
end_date (str): End date of the series in ISO format YYYY-MM-DD.
frequency_key (str): One of the desired frequencies (None is for all values available, undetermined frequency):
D, (Daily - all days)
WD, (Weekdays)
W_MON, (Weekly data on Mondays)
W_TUE, (Weekly data on Tuesdays)
W_WED, (Weekly data on Wednesdays)
W_THU, (Weekly data on Thursdays)
W_FRI, (Weekly data on Fridays)
W_SAT, (Weekly data on Saturdays)
W_SUN, (Weekly data on Sundays)
M, (Monthly data - end of month)
MS, (Monthly data - start of month)
Q, (Quarterly data - end of quarter)
QS (Quarterly data - start of quarter)
Examples:
>>> client.get_signal_series_id(8, 137, 94, last_date_only=False, start_date='2016-1-1', end_date='2020-10-16')
Returns:
Time series object.
"""
```
```
json_to_df(json)
"""
Function to convert JSON/JSON list to a pandas.DataFrame object.
Args:
json: The JSON to be converted - this is the format all methods return.
Returns:
The data contained in the JSON cast as a DataFrame.
If the data is a time series, the dataframe will be indexed by datetimes and there
will be a 'values' column containing the value corresponding to each DateTimeIndex.
"""
```
نیازمندی
| مقدار | نام |
|---|---|
| >=2.20 | requests[security] |
| >=1.7.1 | pyjwt |
| >=2.20 | requests |
| >=1.1.3 | pandas |
نحوه نصب
نصب پکیج whl cmqd-1.0.4:
pip install cmqd-1.0.4.whl
نصب پکیج tar.gz cmqd-1.0.4:
pip install cmqd-1.0.4.tar.gz