Nasdaq Cloud Data Service (NCDS)

Nasdaq Cloud Data Service (NCDS) provides a modern and efficient method of delivery for realtime exchange data and other financial information. Data is made available through a suite of APIs, allowing for effortless integration of data from disparate sources, and a dramatic reduction in time to market for customer-designed applications. The API is highly scalable, and robust enough to support the delivery of real-time exchange data.

Items To Note

Connecting to the API requires credentials, which are provided by the Nasdaq Data Operations team during an on-boarding process
This sample code only connects to one topic (NLSCTA); during on-boarding process, you will receive a topic list that you're entitled to.
See https://github.com/Nasdaq/NasdaqCloudDataService-SDK-Java for our officially support Java-based SDK.

Getting Started
Using the SDK

Getting Started

Python version support

The SDK currently supports Python 3.9 and above

Get the SDK

The source code is currently hosted on GitHub at: https://github.com/Nasdaq/NasdaqCloudDataService-SDK-Python

Clone the repository: git clone https://github.com/Nasdaq/NasdaqCloudDataService-SDK-Python.git
Move into the directory cd NasdaqCloudDataService-SDK-Python
Install the library and its dependencies from local source with pip install -e .

Optional: to use the Jupyter notebook provided,

Download Jupyter notebook using either pip pip3 install notebook or conda conda install -c conda-forge notebook
To run the notebook, use the command jupyter notebook and the Notebook Dashboard will open in your browser
Select the file python_sdk_examples.ipynb

Retrieving certificates

Run ncdssdk_client/src/main/python/ncdsclient/NCDSSession.py with arguments, which takes the path where the certificate should be installed.

For example: python3.9 ncdssdk_client/src/main/python/ncdsclient/NCDSSession.py -opt INSTALLCERTS -path /my/trusted/store/ncdsinstallcerts

Stream configuration

Replace example stream properties in the file kafka-config.json (https://github.com/Nasdaq/NasdaqCloudDataService-SDK-Python/blob/master/ncdssdk_client/src/main/python/resources/kafka-config.json) with provided values during on-boarding.

Required kafka configuration

"bootstrap.servers": {streams_endpoint_url}:9094
"ssl.ca.location": ca.crt

For optional consumer configurations see: https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md

Client Authentication configuration

Replace example client authentication properties in the file client-authentication-config.json (https://github.com/Nasdaq/NasdaqCloudDataService-SDK-Python/blob/master/ncdssdk_client/src/main/python/resources/client-authentication-config.json) with valid credentials provided during on-boarding.

oauth.token.endpoint.uri: https://{auth_endpoint_url}/auth/realms/demo/protocol/openid-connect/token
oauth.client.id: client
oauth.client.secret: client-secret

Create NCDS Session Client

How to run:

-opt -- Provide the operation you want to perform \n" +
  "        * TOP - View the top nnn records in the Topic/Stream\n" +
  "        * SCHEMA - Display the Schema for the topic\n" +
  "        * METRICS - Display the Metrics for the topic\n" +
  "        * TOPICS - List of streams available on Nasdaq Cloud DataService\n" +
  "        * GETMSG - Get one example message for the given message name\n" +
  "        * INSTALLCERTS - Install certificate to keystore\n" +
  "        * CONTSTREAM   - Retrieve continuous stream  \n" +
  "        * FILTERSTREAM  - Retrieve continuous stream filtered by symbols and/or msgtypes \n" +
  "        * HELP - help \n" +
"-topic -- Provide topic for selected option         --- REQUIRED for TOP,SCHEMA,METRICS,GETMSG,CONTSTREAM and FILTERSTREAM \n" +
"-symbols -- Provide symbols comma separated list    --- OPTIONAL for FILTERSTREAM" +
"-msgnames -- Provide msgnames comma separated list  --- OPTIONAL for FILTERSTREAM" +
"-authprops -- Provide Client Properties File path   --- For using different set of Client Authentication Properties \n" +
"-kafkaprops -- Provide Kafka Properties File path   --- For using different set of Kafka Properties \n" +
"-n -- Provide number of messages to retrieve        --- REQUIRED for TOP \n" +
"-msgName -- Provide name of message based on schema --- REQUIRED for GETMSG \n" +
"-path -- Provide the path for key store             --- REQUIRED for INSTALLCERTS \n" +
"-timestamp -- Provide timestamp in milliseconds     --- OPTIONAL for TOP, CONTSTREAM and FILTERSTREAM\n"

A few examples:

Get first 100 records for given stream

python3.9 ncdssdk_client/src/main/python/ncdsclient/NCDSSession.py -opt TOP -n 100 -topic NLSCTA

Get all available streams

python3.9 ncdssdk_client/src/main/python/ncdsclient/NCDSSession.py -opt TOPICS

Using the SDK

Below are several examples for how to access data using the SDK. A Jupyter notebook with this same code and information is provided in the file python_sdk_examples.ipnyb

To run these examples, you will need the import and configuration dictionaries below. Replace the config information with your credentials.

from ncdssdk import NCDSClient

security_cfg = {
    "oauth.token.endpoint.uri": "https://{auth_endpoint_url}/auth/realms/demo/protocol/openid-connect/token",
    "oauth.client.id": "client",
    "oauth.client.secret": "client-secret"
}
kafka_cfg = {
    "bootstrap.servers": "{streams_endpoint_url}:9094",
    "ssl.ca.location": "ca.crt",
    "auto.offset.reset": "earliest"
}

Getting list of data stream available

List all available data stream for the user

ncds_client = NCDSClient(security_cfg, kafka_cfg)
topics = ncds_client.list_topics_for_client()
print("Data set topics:")
for topic_entry in topics:
print(topic_entry)

Example output:

List of streams available on Nasdaq Cloud Data Service:
GIDS
NLSUTP
NLSCTA

Getting schema for the stream

This method returns the schema for the stream in Apache Avro format (https://avro.apache.org/docs/current/spec.html)

ncds_client = NCDSClient(security_cfg, kafka_cfg)
topic = "NLSCTA"
schema = ncds_client.get_schema_for_topic(topic)
print(schema)

Example output:

[ {
"type" : "record",
"name" : "SeqAdjClosingPrice",
"namespace" : "com.nasdaq.equities.trades.applications.nls.messaging.binary21",
"fields" : [ {
  "name" : "SoupPartition",
  "type" : "int"
}, {
  "name" : "SoupSequence",
  "type" : "long"
}, {
  "name" : "trackingID",
  "type" : "long"
}, {
  "name" : "msgType",
  "type" : "string"
}, {
  "name" : "symbol",
  "type" : "string"
}, {
  "name" : "securityClass",
  "type" : "string"
}, {
  "name" : "adjClosingPrice",
  "type" : "int"
} ],
"version" : "1"
}, {...
} .......
.... ]

Get first 10 messages of the stream

ncds_client = NCDSClient(security_cfg, kafka_cfg)
topic = "NLSCTA"
records = ncds_client.top_messages(topic)
for i in range(0, 10):
    print("key: ", records[i].key())
    print("value: ", str(records[i].value()))

Example output:

Top 10 Records for the Topic: NLSCTA
key: 14600739
value: {"SoupPartition": 0, "SoupSequence": 14600739, "trackingID": 72000000024569, "msgType": "S", "event": "E", "schema_name": "SeqSystemEventMessage"}
key: 14600740
value: {"SoupPartition": 0, "SoupSequence": 14600740, "trackingID": 72900000006514, "msgType": "J", "symbol": "A", "securityClass": "N", "consHigh": 1487799, "consLow": 1466600, "consClose": 1478100, "cosolidatedVolume": 1259303, "consOpen": 1486800, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600741
value: {"SoupPartition": 0, "SoupSequence": 14600741, "trackingID": 72900000006514, "msgType": "J", "symbol": "AA", "securityClass": "N", "consHigh": 378039, "consLow": 366800, "consClose": 368400, "cosolidatedVolume": 6047752, "consOpen": 372000, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600742
value: {"SoupPartition": 0, "SoupSequence": 14600742, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAA", "securityClass": "P", "consHigh": 250400, "consLow": 250101, "consClose": 250250, "cosolidatedVolume": 3121, "consOpen": 250400, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600743
value: {"SoupPartition": 0, "SoupSequence": 14600743, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAAU", "securityClass": "P", "consHigh": 176500, "consLow": 174700, "consClose": 176000, "cosolidatedVolume": 303143, "consOpen": 175000, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600744
value: {"SoupPartition": 0, "SoupSequence": 14600744, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAC", "securityClass": "N", "consHigh": 97900, "consLow": 97500, "consClose": 97500, "cosolidatedVolume": 19787, "consOpen": 97600, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600745
value: {"SoupPartition": 0, "SoupSequence": 14600745, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAC+", "securityClass": "N", "consHigh": 12800, "consLow": 12000, "consClose": 12500, "cosolidatedVolume": 85652, "consOpen": 12300, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600746
value: {"SoupPartition": 0, "SoupSequence": 14600746, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAC=", "securityClass": "N", "consHigh": 100500, "consLow": 99500, "consClose": 100000, "cosolidatedVolume": 74060, "consOpen": 99500, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600747
value: {"SoupPartition": 0, "SoupSequence": 14600747, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAIC", "securityClass": "N", "consHigh": 41850, "consLow": 40600, "consClose": 40600, "cosolidatedVolume": 241597, "consOpen": 41800, "schema_name": "SeqEndOfDayTradeSummary"}
key: 14600748
value: {"SoupPartition": 0, "SoupSequence": 14600748, "trackingID": 72900000006514, "msgType": "J", "symbol": "AAIC-B", "securityClass": "N", "consHigh": 249700, "consLow": 249700, "consClose": 249700, "cosolidatedVolume": 238, "consOpen": 249700, "schema_name": "SeqEndOfDayTradeSummary"}

Get first 10 messages of the stream from given timestamp

This returns the first 10 available messages of the stream given timestamp in milliseconds since the UNIX epoch.

ncds_client = NCDSClient(security_cfg, kafka_cfg)
topic="NLSCTA"
timestamp = 1590084446510
records = ncds_client.top_messages(topic, timestamp)
for i in range(0, 10):
    print("key: ", records[i].key())
    print("value: ", str(records[i].value()))

Example output:

Offset: 105834100
Top 10 Records for the Topic:NLSCTA
key:9362630
value :{"SoupPartition": 0, "SoupSequence": 9362630, "trackingID": 50845551492208, "msgType": "T", "marketCenter": "L", "symbol": "SIVR    ", "securityClass": "P", "controlNumber": "0000A2MLOB", "price": 164797, "size": 1, "saleCondition": "@  o", "cosolidatedVolume": 520174}
key:9362631
value :{"SoupPartition": 0, "SoupSequence": 9362631, "trackingID": 50845557908136, "msgType": "T", "marketCenter": "Q", "symbol": "TJX     ", "securityClass": "N", "controlNumber": "   8358213", "price": 540300, "size": 100, "saleCondition": "@   ", "cosolidatedVolume": 16278768}
key:9362632
value :{"SoupPartition": 0, "SoupSequence": 9362632, "trackingID": 50845565203932, "msgType": "T", "marketCenter": "L", "symbol": "CMI     ", "securityClass": "N", "controlNumber": "0000A2MLOC", "price": 1579900, "size": 100, "saleCondition": "@   ", "cosolidatedVolume": 568622}
key:9362633
value :{"SoupPartition": 0, "SoupSequence": 9362633, "trackingID": 50845565791061, "msgType": "T", "marketCenter": "L", "symbol": "UTI     ", "securityClass": "N", "controlNumber": "0000A2MLOD", "price": 70150, "size": 64, "saleCondition": "@  o", "cosolidatedVolume": 151359}
key:9362634
value :{"SoupPartition": 0, "SoupSequence": 9362634, "trackingID": 50845566628604, "msgType": "T", "marketCenter": "L", "symbol": "UFS     ", "securityClass": "N", "controlNumber": "0000A2MLOE", "price": 203660, "size": 24, "saleCondition": "@  o", "cosolidatedVolume": 664962}
key:9362635
value :{"SoupPartition": 0, "SoupSequence": 9362635, "trackingID": 50845569154140, "msgType": "T", "marketCenter": "L", "symbol": "KR      ", "securityClass": "N", "controlNumber": "0000A2MLOF", "price": 320350, "size": 100, "saleCondition": "@   ", "cosolidatedVolume": 4054473}
key:9362636
value :{"SoupPartition": 0, "SoupSequence": 9362636, "trackingID": 50845577944984, "msgType": "T", "marketCenter": "L", "symbol": "PAGP    ", "securityClass": "N", "controlNumber": "0000A2MLOG", "price": 98350, "size": 100, "saleCondition": "@   ", "cosolidatedVolume": 1557084}
key:9362637
value :{"SoupPartition": 0, "SoupSequence": 9362637, "trackingID": 50845588007117, "msgType": "T", "marketCenter": "L", "symbol": "LUV     ", "securityClass": "N", "controlNumber": "0000A2MLOH", "price": 297413, "size": 4, "saleCondition": "@  o", "cosolidatedVolume": 16791899}
key:9362638
value :{"SoupPartition": 0, "SoupSequence": 9362638, "trackingID": 50845596356365, "msgType": "T", "marketCenter": "L", "symbol": "M       ", "securityClass": "N", "controlNumber": "0000A2MLOI", "price": 54000, "size": 10, "saleCondition": "@  o", "cosolidatedVolume": 39273663}
key:9362639
value :{"SoupPartition": 0, "SoupSequence": 9362639, "trackingID": 50845600594567, "msgType": "T", "marketCenter": "L", "symbol": "TTM     ", "securityClass": "N", "controlNumber": "0000A2MLOJ", "price": 56000, "size": 400, "saleCondition": "@   ", "cosolidatedVolume": 1293244}

Get example message from stream

Print message to the console for given message name.

ncds_client = NCDSClient(security_cfg, kafka_cfg)
topic = "NLSCTA"
print(ncds_client.get_sample_messages(topic, "SeqDirectoryMessage", all_messages=False))

Example output:

{'SoupPartition': 0, 'SoupSequence': 500, 'trackingID': 11578737109589, 'msgType': 'R', 'symbol': 'AMN', 'marketClass': 'N', 'fsi': '', 'roundLotSize': 100, 'roundLotOnly': 'N', 'issueClass': 'C', 'issueSubtype': 'Z', 'authenticity': 'P', 'shortThreshold': 'N', 'ipo': '', 'luldTier': '2', 'etf': 'N', 'etfFactor': 0, 'inverseETF': 'N', 'compositeId': 'BBG000BCT197', 'schema_name': 'SeqDirectoryMessage'}

Get continuous stream

ncds_client = NCDSClient(security_cfg, kafka_cfg)
topic = "NLSCTA"
consumer = ncds_client.ncds_kafka_consumer(topic)
while True:
    messages = consumer.consume(num_messages=1, timeout=5)
    if len(messages) == 0:
        print(f"No Records Found for the Topic: {topic}")
              
    for message in messages:
        print(f"value :" + message.value())

Example output: note that only the first ten messages of the stream are shown in this example

value :{"SoupPartition": 0, "SoupSequence": 1, "trackingID": 7233292771056, "msgType": "S", "event": "O", "schema_name": "SeqSystemEventMessage"}
value :{"SoupPartition": 0, "SoupSequence": 2, "trackingID": 11578719526113, "msgType": "R", "symbol": "A", "marketClass": "N", "fsi": "", "roundLotSize": 100, "roundLotOnly": "N", "issueClass": "C", "issueSubtype": "Z", "authenticity": "P", "shortThreshold": "N", "ipo": "", "luldTier": "1", "etf": "N", "etfFactor": 0, "inverseETF": "N", "compositeId": "BBG000C2V3D6", "schema_name": "SeqDirectoryMessage"}
value :{"SoupPartition": 0, "SoupSequence": 3, "trackingID": 11578719526113, "msgType": "G", "symbol": "A", "securityClass": "N", "adjClosingPrice": 1500300, "schema_name": "SeqAdjClosingPrice"}
value :{"SoupPartition": 0, "SoupSequence": 4, "trackingID": 11578719831656, "msgType": "R", "symbol": "AA", "marketClass": "N", "fsi": "", "roundLotSize": 100, "roundLotOnly": "N", "issueClass": "C", "issueSubtype": "Z", "authenticity": "P", "shortThreshold": "N", "ipo": "", "luldTier": "1", "etf": "N", "etfFactor": 1, "inverseETF": "N", "compositeId": "BBG00B3T3HD3", "schema_name": "SeqDirectoryMessage"}
value :{"SoupPartition": 0, "SoupSequence": 5, "trackingID": 11578719831656, "msgType": "G", "symbol": "AA", "securityClass": "N", "adjClosingPrice": 374400, "schema_name": "SeqAdjClosingPrice"}
value :{"SoupPartition": 0, "SoupSequence": 6, "trackingID": 11578719879872, "msgType": "R", "symbol": "AAA", "marketClass": "P", "fsi": "", "roundLotSize": 100, "roundLotOnly": "N", "issueClass": "Q", "issueSubtype": "I", "authenticity": "P", "shortThreshold": "N", "ipo": "", "luldTier": "2", "etf": "Y", "etfFactor": 1, "inverseETF": "N", "compositeId": "BBG00X5FSP48", "schema_name": "SeqDirectoryMessage"}
value :{"SoupPartition": 0, "SoupSequence": 7, "trackingID": 11578719879872, "msgType": "G", "symbol": "AAA", "securityClass": "P", "adjClosingPrice": 250050, "schema_name": "SeqAdjClosingPrice"}
value :{"SoupPartition": 0, "SoupSequence": 8, "trackingID": 11578719916519, "msgType": "R", "symbol": "AAAU", "marketClass": "P", "fsi": "", "roundLotSize": 100, "roundLotOnly": "N", "issueClass": "Q", "issueSubtype": "I", "authenticity": "P", "shortThreshold": "N", "ipo": "", "luldTier": "1", "etf": "Y", "etfFactor": 1, "inverseETF": "N", "compositeId": "BBG00LPXX872", "schema_name": "SeqDirectoryMessage"}
value :{"SoupPartition": 0, "SoupSequence": 9, "trackingID": 11578719916519, "msgType": "G", "symbol": "AAAU", "securityClass": "P", "adjClosingPrice": 179850, "schema_name": "SeqAdjClosingPrice"}
value :{"SoupPartition": 0, "SoupSequence": 10, "trackingID": 11578719950254, "msgType": "R", "symbol": "AAC", "marketClass": "N", "fsi": "", "roundLotSize": 100, "roundLotOnly": "N", "issueClass": "O", "issueSubtype": "Z", "authenticity": "P", "shortThreshold": "N", "ipo": "", "luldTier": "2", "etf": "N", "etfFactor": 1, "inverseETF": "N", "compositeId": "BBG00YZC2Z91", "schema_name": "SeqDirectoryMessage"}

Example syntax to run the client based on this SDK

To list streams available on Nasdaq Cloud Data Service

python3.9 NCDSSession.py -opt TOPICS

To display the schema for the given topic

python3.9 NCDSSession.py -opt SCHEMA -topic NLSCTA

To dump top n records from the given topic

python3.9 NCDSSession.py -opt TOP -n 10 -topic NLSCTA

To use client based specific authorization file instead of using from the resources of client code base

python3.9 NCDSSession.py -opt TOP -n 10 -topic NLSCTA -authprops client-authentication-config.json

To use the specific kafka properties instead of using the kafka properties from the resources of the client base code

python3.9 NCDSSession.py -opt TOP -n 10 -topic NLSCTA -kafkaprops kafka-config.json

To use the specific client based authorization file and specific kafka properties file

python3.9 NCDSSession.py -opt TOP -n 10 -topic NLSCTA -authprops client-authentication-config.json -kafkaprops kafka-config.json

To display a specific message type

python3.9 NCDSSession.py -opt GETMSG -topic NLSCTA -msgname SeqDirectoryMessage

To dump top n records from the given topic from given timestamp in milliseconds since the UNIX epoch

python3.9 NCDSSession.py -opt TOP -n 10 -topic NLSCTA -timestamp 1590084445610

To retrieve a continuous stream of messages from the given topic

python3.9 NCDSSession.py -opt CONTSTREAM -topic NLSCTA

To retrieve a stream of messages from the given topic, filtered by symbols or message names

python3.9 NCDSSession.py -opt FILTERSTREAM -topic NLSCTA -symbols SPCE

Documentation

An addition to the example application, there is extra documentation at the package and class level, which are located in project https://github.com/Nasdaq/NasdaqCloudDataService-SDK-Python/tree/master/ncdssdk/docs

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

Code and documentation released under the Apache License, Version 2.0

I am trying to run the pip install -e . and getting the below error:

#10 15.37   × python setup.py bdist_wheel did not run successfully.
#10 15.37   │ exit code: 1
#10 15.37   ╰─> [45 lines of output]
#10 15.37       running bdist_wheel
#10 15.37       running build
#10 15.37       running build_py
#10 15.37       creating build
#10 15.37       creating build/lib.linux-x86_64-3.9
...
#10 15.37       error: command 'gcc' failed: No such file or directory
#10 15.37       [end of output]
...
#10 15.96   × Running setup.py install for confluent-kafka did not run successfully.
#10 15.96   │ exit code: 1
#10 15.96   ╰─> [45 lines of output]
#10 15.96       running install
#10 15.96       running build
#10 15.96       running build_py
#10 15.96       creating build
#10 15.96       creating build/lib.linux-x86_64-3.9
...
#10 15.96       error: command 'gcc' failed: No such file or directory
#10 15.96       [end of output]
#10 15.96   
#10 15.96   note: This error originates from a subprocess, and is likely not a problem with pip.
#10 15.97 error: legacy-install-failure
#10 15.97 
#10 15.97 × Encountered error while trying to install package.
#10 15.97 ╰─> confluent-kafka
...

The Python version that I am using is 3.9. NOTE: I am running the source code inside a docker container.

Can someone please help me with it?

The steps I have taken to fix the issue but didn't help: I tried installing these pip install wheel setuptools but still, the error exists.

Getting pip installation errors

opened by noorsheikh 1

Fix deserialization issue with a bytes field

Remove the serialization of the avro message into a json string. This is unneeded as the deserialize function is allowed to return any object, and it causes issues when there is an avro field of type bytes, as this is not a valid type for json objects.

opened by ssortman 0
Update Jupyter notebook and README

Adds more documentation to the Jupyter notebook as well as a code block to install dependencies. Updates the link to the Java github repo in the README.

opened by jenniferwang99 0
Integration test top-level and util file

Adds in the top level pytest file containing our integration tests as well as a helper util file for generating and pushing mock messages to topics for testing

opened by jenniferwang99 0
Add documentation for NCDS Python SDK

Adds documentation for the Nasdaq Cloud Data Services Python SDK. Can be viewed by opening docs/build/index.html in your browser.

Documentation generated with sphinx.

opened by jenniferwang99 0
Adds in config loaders and other helper util files
Implements the authentication config and kafka config loaders

Adds in some helper util files: IsItPyTest.py for checking if a pytest is running, Oauth.py for returning the oauth callback, SeekToMidnight.py to help a consumer seek back to a certain timestamp
opened by jenniferwang99 0
Add in NCDSSession file and file structure
creates file structure for the NCDSSession CLI

includes two helper util functions for printing help messages and validating command line input

adds temp authentication and kafka config files
opened by jenniferwang99 0
Tracking Number Timestamp

In the Nasdaq Basic docs, I am seeing that "TrackingNumber/trackingID" for a quote is composed of the Nasdaq internal tracking number and the Timestamp in nanoseconds from midnight. I need to access the unix timestamp of this quote, and wanted to first see if there was a better way to access this than from manipulating the trackingID?

If not, I would like to confirm that the Timestamp in nanoseconds from midnight is assuming UTC?

Thanks.

opened by lsharples1 2

Fix invalid notebook

I received the following error when trying to run the notebook:

Unreadable Notebook: NasdaqCloudDataService-SDK-Python/python_sdk_examples.ipynb NotJSONError('Notebook does not appear to be JSON: \'{\\n "cells": [\\n {\\n "cell_type": "m...')

After adding the missing comma, I was able to run the notebook with no issue

opened by normand1 0

0.4.0(Dec 13, 2022)

Adding support timeout settings Adding support for number of message settings Removed Consumer Config
Source code(tar.gz)
Source code(zip)
0.2.0(Nov 1, 2021)

Removing certificate pinning
Source code(tar.gz)
Source code(zip)
0.1.0(Nov 1, 2021)

Source code(tar.gz)
Source code(zip)

Nasdaq Cloud Data Service (NCDS) provides a modern and efficient method of delivery for realtime exchange data and other financial information. This repository provides an SDK for developing applications to access the NCDS.

Related tags

Overview

Nasdaq Cloud Data Service (NCDS)

Items To Note

Table of Contents

Getting Started

Python version support

Get the SDK

Retrieving certificates

Stream configuration

Client Authentication configuration

Create NCDS Session Client

Using the SDK

Getting list of data stream available

Getting schema for the stream

Get first 10 messages of the stream

Get first 10 messages of the stream from given timestamp

Get example message from stream

Get continuous stream

Example syntax to run the client based on this SDK

Documentation

Contributing

License

Comments

Releases(0.4.0)

0.4.0(Dec 13, 2022)

0.2.0(Nov 1, 2021)

0.1.0(Nov 1, 2021)

Owner

Nasdaq

Updater for PGCG (Paradox Game Converters Group) converters written in Python.

This bot automaticaly access to giveaway ! You can won free NFT !

PaddleOCR推理的pytorch实现和模型转换

Pyspark sam - Analyze Big Sequence Alignments with PySpark in AWS EMR

TeamDltvideo Bot you can add in your Telegram Group Thanks

Threat Intel Platform for T-POTs

A repo containing toolings and software useful for a DevOps Engineer

Michelle is a Discord Bot coded in Python with Discord.py by Mudit07.

Async client API for the Telegram Group Calls

pymobiledevice fork with more recent coding standards and many more features

BT CCXT Store

Hazard-Nuker - Hazard Nuker With Python

Ulaavi for nuke, helps to keep our stocl elements organised.

A Python library for inserting an reverse shell attached to Telegram in any Python application.

Script to post multiple status(posts) on twitter

An attempt to make a bot that can auto-archive Danganronpa KG RPs on Discord.

My Discord Bot that I used to learn Python. Please disregard the unstructured code!

Code for paper "Adversarial score matching and improved sampling for image generation"

Play Video & Music on Telegram Group Video Chat

An API wrapper around Discord API.