Welcome to Accern Python!

A python library to consume Accern’s V4 REST API for Titan streaming/historical data.

Overview

Accern is a fast-growing NYC startup that is disrupting the way quantitative hedge funds can gain a competitive advantage using news and social media data. It currently has the world’s largest financial news coverage, covering over 1 billion public news websites, blogs, financial documents, and social media websites. Furthermore, Accern derives proprietary analytics from each news story to help quantitative hedge funds make accurate trading decisions.

Accern consolidates multiple news data feeds into one to help drastically reduce costs of both small and large hedge funds. With Accern proprietary data filters, we are able to deliver relevant articles to clients with a 99 percent accuracy rate. Accern’s delivery mechanism is a RESTful API where it delivers derived analytics from news articles, including the original article URLs so quantitative hedge funds can derive their own analytics in-house from the relevant articles.

The Accern library for Python helps users get fast, flexible data structures from Accern’s V4 Titan streaming/historical data.

Installation

Install from PyPI

Accern python can be installed via pip From PyPI.

pip install accern

Getting started

This is a short tutorial on how to use accern python library for new users. You can see complex usage in API.

  1. Contact support@accern.com. and inquire about an Accern API token.
  2. To quickly start using the Accern API, create an API instance and pass your token:
from accern import API
token = 'YOUR TOKEN'
Client = API(token)
  1. Pass params to get filtered data and make an API request.
schema = {
    'filters': {
        'entity_ticker': 'AAPL'
    }
}
resp = Client.request(schema)
  1. Specify the fields that your are looking for in the data and filter the results.
schema = {
    'select': [
        {
            'field': 'entity_industry'
        }, {
            'field': 'entity_ticker'
        }, {
            'field': 'entity_relevance'
        }
    ]
}
resp = Client.request(schema)

API

Schema Class/ Object

Accern Python Library allows user to filter and select Accern data in 3 different ways: REST API, streaming and historical data request. schema object is used in these three methods to convey user’s idea of how to slice data.

Schema class here will provide some help functions and validate functions to help user build a right one.

Get Fields

Get available fields in the data.

from accern import Schema
print Schema.get_fields()
Get field options

For field type and available options.

Schema.get_options('event_group')
Build your schema
schema = {
    'select': [
        {
            'field': 'entity_ticker',
            'alias': 'ticker'
        }
    ],
    'filters': {
        'entity_sentiment': [
            [70, 100],
            [-100, -70]
        ]
    }
}
Validate schema

After drafting a schema, validate it along with the method you want to use.

Schema.validate_schema(method='api', schema=schema)
Select and add alias

To select only a few fields or rename the fields, pass the alias of the fields you want to schema.

schema = {
    'select': [
        {
            'field': 'entity_ticker',
            'alias': 'ticker'
        }
    ]
}

response = Client.request(schema)

The alias for the selected fields is optional and you can select multiple fields.

schema = {
    'select': [
        {
            'field': 'entity_ticker',
            'alias': 'ticker'
        }, {
            'field': 'harvested_at'
        }
    ]
}

response = Client.request(schema)

If you want to filter the data, here is a list of available fields to filter by:

Parameter Description
entity_competitors A list of ticker symbols of entity’s competitors.
entity_country The parent country of the entity.
entity_industry The industry in which the entity is listed.
entity_region The region where the entity is traded.
entity_sector The sector that the entity belongs to.
entity_ticker The traded ticker symbols of the extracted entity.
entity_type The type of entity, such as public equity, commodity, cryptocurrency, etc.
event Financial events extracted from the stories.
event_group The broader financial events category.
from The eariliest timestamp allowed in the returned data.
last_id The last id before the return data.
story_type Where the stories are published and their mode of access.
story_group_exposure The level of exposure for stories within a story group.

For the full list and available values, you can use the helper function menthioned above.

Pass the filters to schema. The value can be a single value or an array of values.

schema = {
    'filters': {
        'entity_industry': ['Apparel', 'Food Chains'],
        'event': 'Accident'
    }
}

response = Client.request(schema)

A list of filter examples is available at Cookbook

REST APIs

To start with, we import the following:

from accern import API
Create API Instance

Create an API instance

Client = API()
Authenticate REST API Client

Authenticate your account when using the API token. Pass it into the REST Client and the library will pass it to every request. An API request without a token will fail.

Your API tokens carry many privileges. Don’t share your secret API tokens in any public spaces like Github, client-side code, etc.

To authenticate, pass it through the constructor or assign your YOUR TOKEN to the API instance.

token = 'YOUR TOKEN'
Client = API(token)

or

token = 'YOUR TOKEN'
Client.token = token

If token is not passed or invalid, an AuthenticateError will be raised

Request Data

The request method will send a GET request to retrieve data. The response will be the most recent 100 documents.

response = Client.request()

A response example.

{
    "first_id": 2714394,
    "last_id": 2742321,
    "total": 100,
    "signals": [
        {
            "id": 2742321,
            "signal_id": "3c78ab00-c751-4e37-8e16-669bad2c7135",
            "story_id": "5a283895a656f23864041346",
            "new_story_group": false,
            "overall_author_republish_score": "0.0",
            "overall_author_timeliness_score": "2.6805",
            "overall_source_republish_score": "0.0",
            "overall_source_timeliness_score": "2.5707",
            "story_group_sentiment_avg": "16.8",
            "story_group_sentiment_stdev": "27.1",
            "story_sentiment": null,
            "templated_story_score": null,
            "author_id": 1265989,
            "source_id": 22210,
            "story_group_count": 4,
            "story_group_traffic_sum": 395006047,
            "story_traffic": 939796,
            "story_group_exposure": "high",
            "story_group_id": "e956870a-10a6-45f7-b6c1-aca6f9425886",
            "story_source": "bloog.pl",
            "story_type": "blog",
            "harvested_at": "2017-12-06T18:36:13.007Z",
            "entity_sentiment": null,
            "event_sentiment": null,
            "entity_name": "Apple Inc.",
            "entity_ticker": "AAPL",
            "entity_exchange": "NASDAQ",
            "entity_relevance": "100.0",
            "entity_country": "United States",
            "entity_indices": [
                "S&P 500",
                "Russell 1000",
                "Russell 3000",
                "Wilshire 5000",
                "BARRON'S 400",
                "NASDAQ 100"
            ],
            "entity_industry": "Computer Manufacturing",
            "entity_region": "North America",
            "entity_sector": "Technology",
            "entity_competitors": [
                "005930",
                "005935",
                "6758",
                "2357",
                "HPQ",
                "MSFT",
                "IBM",
                "CSCO",
                "NOKIA",
                "MSI"
            ],
            "entity_type": "US_EQUITY",
            "entity_composite_figi": "BBG000B9XRY4",
            "entity_exch_code": "UW",
            "entity_figi": "BBG000B9Y5X2",
            "entity_market_sector": "Equity",
            "entity_security_description": "AAPL",
            "entity_security_type": "Common Stock",
            "entity_share_class_figi": "BBG001S5N8V8",
            "entity_unique_id": "EQ0010169500001000",
            "entity_unique_id_fut_opt": null,
            "entity_author_republish_score": "0.0",
            "entity_author_timeliness_score": "0.01",
            "entity_source_republish_score": "0.0",
            "entity_source_timeliness_score": "0.009",
            "event": "Product Development - General",
            "event_group": "Product Development",
            "event_relevance": "100.0",
            "event_author_republish_score": "0.0013",
            "event_author_timeliness_score": "42.7356",
            "event_source_republish_score": "0.0013",
            "event_source_timeliness_score": "42.6185",
            "event_impact_pct_change_avg": "0.0073",
            "event_impact_pct_change_stdev": "0.0715",
            "event_impact_pos": "55.9012",
            "event_impact_neg": "44.0988",
            "event_impact_gt_mu_add_sigma": "0.0915",
            "event_impact_lt_mu_sub_sigma": "0.0",
            "event_impact_gt_mu_pos_add_sigma_pos": "0.0",
            "event_impact_lt_mu_neg_sub_sigma_neg": "0.0",
            "event_impact_gt_mu_pos_add_2sigma_pos": "0.0",
            "event_impact_lt_mu_neg_sub_2sigma_neg": "0.0",
            "event_impact_gt_1pct_pos": "5.3065",
            "event_impact_lt_1pct_neg": "3.2022"
        },
        ...
    ]
}

To select only a few fields or filter some fields, build your schema and pass it to the function.

schema = {
    'select': [
        {
            'field': 'entity_ticker',
            'alias': 'ticker'
        }
    ]
}

response = Client.request(schema)

Streaming API Client

Stream Listener

Create a StreamListener and to handle streaming data.

from accern import Stream

myStreamListener = StreamListener()

Override the on_data function if you want to handle the data yourself.

By default, it returns the raw data.

class MyStreamListener(StreamListener):
    def on_data(self, data):
        df = json.loads(data), orient='columns')
        print (df.head())
Authenticate Stream Client
from accern import StreamClient
token = 'YOUR TOKEN'

stream = StreamClient(myStreamListener, token)
Filter and select with schema
from accern import StreamClient, StreamListener

schema = {
    'select': [
        {
            'field': 'entity_ticker',
            'name': 'ticker'
        },
        {
            'field': 'harvested_at',
            'name': 'hour'
        }
    ],
    'filters': {
        'entity_ticker': [
            'AAPL', 'AMZN'
        ]
    }
}
stream = StreamClient(MyStreamListener(), token, schema)
stream.performs()

Historical Data Request

Accern Historical Batch data request is available for user who has the permission to create a job(data request). The data can be downloaded when the job request is finished.

Create Historical Instance

Create an Historical instance

from accern import HistoricalClient
token = 'YOUR TOKEN'
Client = HistoricalClient(token)
Set up a job

name, description, filters and select are required field to create a job schema. For more detail of how to work with filters and select, please refer to the Schema.

schema = {
    'name': 'test',
    'description': 'request 2017 November data',
    'filters': [
        {
            'harvested_at': [
                ['2017-11-01 00:00:00', '2017-11-30 23:59:59']
            ],
            'entity_sentiment': [
                [-100, 50]
            ]
        }
    ],
    'select': [
        {'field': 'entity_sentiment'},
        {'field': 'entity_ticker'},
        {'field': 'event'},
        {'field': 'harvested_at'}
    ]
}
Check your job history
resp = Client.get_jobs()

If you pass a job id to the get_jobs function, you will get the information of that job.

job_id = 'YOUR JOB ID'
resp = Client.get_jobs(job_id)
Select and Aggregations

You can add minute, hour, day, week, or month aggregation function to the field harvested_at. The alias field should match the function name you choose.

schema = {
    'name': 'Month',
    'description': 'Month Sentiment data',
    'select': [
        {
            'field': 'harvested_at',
            'alias': 'month',
            'function': 'month'
        }
    ]
}

The aggregation function will group signals based on the time interval you choose. If your data will contain other fields, an aggregation function should be given. Otherwise, an API error will occur.

schema = {
    'name': 'Month',
    'description': 'Month Sentiment data',
    'filters': [
        {
            'harvested_at': [
                ['2012-08-01 00:00:00', '2017-11-30 00:00:00']
            ],
            'entity_sentiment': [
                [-100, 50]
            ],
            'entity_ticker': [
                'AAPL',
                'AMZN'
            ]
        }
    ],
    'select': [
        {
            'field': 'entity_sentiment',
            'function': 'sum'
        },
        {
            'field': 'entity_ticker',
            'function': 'group'
        },
        {
            'field': 'harvested_at',
            'alias': 'month',
            'function': 'month'
        }
    ]
}

A full list of the available aggregation functions can be found at Aggregation function

Examples

REST: Request with filters

from accern import API
TOKEN = 'YOUR TOKEN'
Client = API(TOKEN)

schema = {
    'select': [
        {
            'field': 'entity_ticker',
            'name': 'ticker'
        },
        {
            'field': 'harvested_at',
            'name': 'time'
        }
    ],
    'filters': {
        'entity_ticker': [
            "AAPL", "GOOG"
        ]
    }
}

resp = Client.request(schema)

REST: Get one week data for restaurants entities

from accern import API
from datetime import datetime
import pandas as pd
restaurants = [
    'DPZ', 'SONC', 'MCD', 'CMG', 'BWLD', 'DNKN', 'TXRH', 'PZZA',
    'EAT', 'SHAK', 'CAKE', 'YUM', 'SBUX', 'WEN', 'JACK', 'PLAY', 'DFRG',
    'TACO', 'DENN', 'HABT', 'LOCO', 'WING', 'BLMN', 'PBPB', 'RRGB', 'FRGI',
    'FOGO', 'DRI'
]

schema = {
    'select': [
        {
            'field': 'entity_ticker'
        }, {
            'field': 'entity_sentiment'
        }, {
            'field': 'harvested_at'
        }, {
            'field': 'entity_relevance'
        }
    ],
    'filters': {
        'entity_relevance': [70, 100],
        'entity_ticker': restaurants,
        'harvested_at': ['2017-12-01 00:00:00', '2017-12-07 00:00:00']
    }
}

TOKEN = 'YOUR TOKEN'
Client = API(TOKEN)

response = Client.request(schema)
############### Get restaurants data ###############
result = pd.DataFrame()
while response['total'] > 0:
    df = pd.DataFrame.from_dict(response['signals'], orient='columns')
    result = result.append(df, ignore_index=True)
    schema['filters']['last_id'] = response['last_id']
    response = Client.request(schema)

result = result.drop_duplicates().reset_index(drop=True)
result.to_csv('restaurants.csv', index=False)

Streaming: Save to csv

from accern import StreamClient, StreamListener
from datetime import datetime
import os
import pandas as pd


class MyStreamListener(StreamListener):
    def on_data(self, raw_data):
        df = pd.DataFrame.from_dict(raw_data, orient='columns')
        print ("%s - Saving %s signals..." % (datetime.now(), len(df)))
        if not os.path.exists('output.csv'):
            df.to_csv('output.csv', encoding='utf-8', index=False)
        else:
            df.to_csv('output.csv', mode='a', header=False, encoding='utf-8', index=False)

TOKEN = 'YOUR TOKEN'
stream = StreamClient(MyStreamListener(), TOKEN)
stream.performs()

Streaming: Create output csv structure and seperate by hour

from accern import StreamListener, StreamClient
from datetime import datetime, timedelta
import os
import pandas as pd
from pymongo import MongoClient

record = datetime.now()
record_time = datetime(year=record.year, month=record.month, day=record.day, hour=record.hour - 1, minute=0, second=0, microsecond=0)

class MyStreamListener(StreamListener):
    def __init__(self):
        self.db = MongoClient().accern

    def on_data(self, raw_data):
        global record_time
        df = pd.DataFrame.from_dict(raw_data, orient='columns')
        time = datetime.now()
        if (time - record_time).seconds / 60 > 60:
            record_time = record_time + timedelta(hours=1)
            df.to_csv('./accern_stream/2017-12-01/%s.csv' % (record_time.strftime('%Y-%m-%dT%H:%M:%S')), index=False, encoding='utf-8')
            print ('Appended %s signals' % (len(df)))
        else:
            df.to_csv('./accern_stream/2017-12-01/%s.csv' % (record_time.strftime('%Y-%m-%dT%H:%M:%S')), index=False, mode='a', header=False, encoding='utf-8')
            print ('Appended %s signals' % (len(df)))
myStreamListener = MyStreamListener()

TOKEN = 'YOUR TOKEN'
Streamer = StreamClient(MyStreamListener(), TOKEN)

if not os.path.exists('./accern_stream'):
    os.mkdir('./accern_stream')
if not os.path.exists('./accern_stream/2017-12-01'):
    os.mkdir('./accern_stream/2017-12-01')

Streamer.performs()

Streaming: Save to mongo

from accern import StreamClient, StreamListener
from datetime import datetime
import json
from pymongo import MongoClient


class MyStreamListener(StreamListener):
    def __init__(self):
        self.db = MongoClient()['accern'] # Replace with your db name

    def on_data(self, raw_data):
        data_json = raw_data
        print ("%s - Saving %s signals..." % (datetime.now(), len(data_json)))
        # Replace with your db, collection names
        self.db['accern']['stream'].insert_many(data_json)
TOKEN = 'YOUR TOKEN'
stream = StreamClient(MyStreamListener(), TOKEN)
stream.performs()

Historical Data: Create one historical job

A full example can be found at Historical Job Example

from accern import HistoricalClient

TOKEN = 'YOUR TOKEN'
Client = HistoricalClient(TOKEN)

schema = {
    'name': 'Daily Sentiment',
    'description': 'Get Daily Sentiment data',
    "select": [
        {
            "field": "entity_ticker",
            "alias": "ticker"
        }, {
            "field": "harvested_at"
        }
    ],
    "filters": [
        {
            "entity_ticker": ["AAPL", "GOOG","MSFT"]
        }
    ]
}
resp = Client.create_job(schema)

Historical Data: Check the status of a historical job

from accern import HistoricalClient
import io
import requests
import pandas as pd

token = 'YOUR TOKEN'
Client = HistoricalClient(token)

resp = Client.get_jobs('YOUR JOB ID')
print resp['job']

Appendix

Field Filter Cookbook

Here is a cookbook of how to filter fields by using our REST API.

from accern import API

TOKEN = 'YOUR TOKEN'
Client = API()
Client.token = TOKEN

Filter by single event.

schema = {
    'filters': {
        'event': 'Analyst Ratings'
    }
}

resp = Client.request(schema)

Filter by a list of events.

schema = {
    'filters': {
        'event': ['Analyst Ratings', 'Corporate Actions']
    }
}

Get only entity_type = US_EQUITY.

schema = {
    'filters': {
        'entity_type': 'US_EQUITY'
    }
}

Get only story_type = news.

schema = {
    'filters': {
        'story_type': 'news'
    }
}

Get articles with low story_group_exposure.

schema = {
    'filters': {
        'story_group_exposure': 'low'
    }
}

You can provide multiple fields in the filter (they will be AND’d).

schema = {
    'filters': {
        'event': ['Analyst Ratings', 'Corporate Actions'],
        'story_group_exposure': 'low',
        'story_type': 'news'
    }
}

You can filter date by either from or harvested_at. The time is in UTC.

schema = {
    'filters': {
        'from': '2017-11-01'
    }
}

or

schema = {
    'filters': {
        'harvested_at': ['2017-11-01 00:00:00', '2017-11-31 00:00:00']
    }
}

Field Aggregate Function

Field Functions
author_id count
entity_author_republish_score average, count, max, min, sum
entity_author_timeliness_score average, count, max, min, sum
entity_country group
entity_exch_code group
entity_exchange group
entity_industry group
entity_relevance average, count, max, min, sum
entity_sentiment average, count, max, min, sum
entity_source_timeliness_score average, count, max, min, sum
entity_source_republish_score average, count, max, min, sum
entity_ticker group
entity_type group
event_author_timeliness_score average, count, max, min, sum
event_author_republish_score average, count, max, min, sum
event_group group
event_source_timeliness_score min, max, avg, sum
event_source_republish_score average, count, max, min, sum
event_impact_gt_1pct_pos group
event_impact_gt_mu_add_sigma group
event_impact_gt_mu_pos_add_2sigma_pos average, count, max, min, sum
event_impact_gt_mu_pos_add_sigma_pos average, count, max, min, sum
event_impact_lt_1pct_neg average, count, max, min, sum
event_impact_lt_mu_sub_sigma average, count, max, min, sum
event_impact_lt_mu_sub_add_2sigma_pos average, count, max, min, sum
event_impact_lt_mu_sub_add_sigma_pos average, count, max, min, sum
event_impact_neg average, count, max, min, sum
event_impact_pct_change_avg average, count, max, min, sum
event_impact_pct_change_stdev average, count, max, min, sum
event_impact_pos average, count, max, min, sum
event_relevance average, count, max, min, sum
event_sentiment average, count, max, min, sum
event group
harvested_at minute, hour, day, week, month
overall_author_timeliness_score average, count, max, min, sum
overall_author_republish_score average, count, max, min, sum
overall_source_timeliness_score average, count, max, min, sum
overall_source_republish_score average, count, max, min, sum
story_group_count average, count, max, min, sum
story_group_exposure group
story_group_traffic_sum average, count, max, min, sum
story_traffic average, count, max, min, sum
story_type group
templated_story_score average, count, max, min, sum

Field Value List

entity_sector
entity_industry
entity_sector entity_industry
Basic Industries Agricultural Chemicals
Basic Industries Aluminum
Basic Industries Containers/Packaging
Basic Industries Electric Utilities: Central
Basic Industries Engineering & Construction
Basic Industries Environmental Services
Basic Industries Forest Products
Basic Industries General Bldg Contractors - Nonresidential Bldgs
Basic Industries Home Furnishings
Basic Industries Homebuilding
Basic Industries Major Chemicals
Basic Industries Metal Fabrications
Basic Industries Military/Government/Technical
Basic Industries Mining & Quarrying of Nonmetallic Minerals (No Fuels)
Basic Industries Miscellaneous
Basic Industries Other Specialty Stores
Basic Industries Package Goods/Cosmetics
Basic Industries Paints/Coatings
Basic Industries Paper
Basic Industries Precious Metals
Basic Industries Specialty Chemicals
Basic Industries Steel/Iron Ore
Basic Industries Telecommunications Equipment
Basic Industries Textiles
Basic Industries Water Supply
Capital Goods Aerospace
Capital Goods Auto Manufacturing
Capital Goods Auto Parts:O.E.M.
Capital Goods Automotive Aftermarket
Capital Goods Biotechnology: Laboratory Analytical Instruments
Capital Goods Building Materials
Capital Goods Building Products
Capital Goods Construction/Ag Equipment/Trucks
Capital Goods Containers/Packaging
Capital Goods Electrical Products
Capital Goods Electronic Components
Capital Goods Engineering & Construction
Capital Goods Fluid Controls
Capital Goods Homebuilding
Capital Goods Industrial Machinery/Components
Capital Goods Industrial Specialties
Capital Goods Marine Transportation
Capital Goods Medical Specialities
Capital Goods Metal Fabrications
Capital Goods Military/Government/Technical
Capital Goods Miscellaneous
Capital Goods Ordnance And Accessories
Capital Goods Pollution Control Equipment
Capital Goods Railroads
Capital Goods Specialty Chemicals
Capital Goods Steel/Iron Ore
Capital Goods Tools/Hardware
Capital Goods Wholesale Distributors
Consumer Durables Automotive Aftermarket
Consumer Durables Building Products
Consumer Durables Consumer Electronics/Appliances
Consumer Durables Consumer Specialties
Consumer Durables Containers/Packaging
Consumer Durables Diversified Electronic Products
Consumer Durables Electrical Products
Consumer Durables Electronic Components
Consumer Durables Home Furnishings
Consumer Durables Industrial Machinery/Components
Consumer Durables Industrial Specialties
Consumer Durables Metal Fabrications
Consumer Durables Miscellaneous manufacturing industries
Consumer Durables Office Equipment/Supplies/Services
Consumer Durables Publishing
Consumer Durables Specialty Chemicals
Consumer Durables Telecommunications Equipment
Consumer Non-Durables Apparel
Consumer Non-Durables Beverages (Production/Distribution)
Consumer Non-Durables Consumer Electronics/Appliances
Consumer Non-Durables Consumer Specialties
Consumer Non-Durables Electronic Components
Consumer Non-Durables Environmental Services
Consumer Non-Durables Farming/Seeds/Milling
Consumer Non-Durables Food Chains
Consumer Non-Durables Food Distributors
Consumer Non-Durables Homebuilding
Consumer Non-Durables Meat/Poultry/Fish
Consumer Non-Durables Motor Vehicles
Consumer Non-Durables Package Goods/Cosmetics
Consumer Non-Durables Packaged Foods
Consumer Non-Durables Plastic Products
Consumer Non-Durables Recreational Products/Toys
Consumer Non-Durables Shoe Manufacturing
Consumer Non-Durables Specialty Foods
Consumer Non-Durables Telecommunications Equipment
Consumer Non-Durables Textiles
Consumer Non-Durables Tobacco
Consumer Services Advertising
Consumer Services Automotive Aftermarket
Consumer Services Books
Consumer Services Broadcasting
Consumer Services Building operators
Consumer Services Catalog/Specialty Distribution
Consumer Services Clothing/Shoe/Accessory Stores
Consumer Services Consumer Electronics/Video Chains
Consumer Services Consumer Specialties
Consumer Services Consumer: Greeting Cards
Consumer Services Department/Specialty Retail Stores
Consumer Services Diversified Commercial Services
Consumer Services Electronics Distribution
Consumer Services Farming/Seeds/Milling
Consumer Services Food Chains
Consumer Services Home Furnishings
Consumer Services Homebuilding
Consumer Services Hotels/Resorts
Consumer Services Marine Transportation
Consumer Services Military/Government/Technical
Consumer Services Miscellaneous
Consumer Services Motor Vehicles
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Public Utilities Electric Utilities: Central
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Transportation Aerospace
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Transportation Oil Refining/Marketing
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Transportation Railroads
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