US Long Term Mutual Fund Monthly Net New Cash Flow


Files Size Format Created Updated License Source
2 85kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Investment Company Institute (ICI)

Data Files


This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d) Date of data collection
Total_Long_Term_Cash_Flows 2 integer Total long term net cash flows
Total_Equity 3 integer Total equity funds
Total_Domestic_Equity 4 integer Total domestic equity
Large_Cap_Equity 5 integer Large-cap funds invest primarily in companies with large market capitalizations, which are generally more than $5 billion or in companies with both medium and large market capitalizations.
Mid_Cap_Equity 6 integer Mid-cap funds invest primarily in companies with medium market capitalizations, generally ranging from $1 billion to $7 billion or in companies with both small and medium market capitalizations.
Small_Cap_Equity 7 integer Small-cap funds invest primarily in companies with small market capitalizations of up to $2 to 2.5 billion.
Multi_Cap_Equity 8 integer Multi-cap funds invest in companies of all market capitalizations or are not limited to companies within specific market capitalizations.
Other_Equity 9 integer Other domestic equity funds seek capital appreciation by investing in companies in related fields or by employing alternative strategies, such as long/short, market neutral, leveraged, or inverse strategies.
Total_World_Equity 10 integer Total world equity funds
Developed_Markets_Equity 11 integer Developed market funds invest primarily in equity securities traded worldwide, including funds with a global, international, or regional focus. Also included in this category are funds investing in companies traded worldwide, including emerging markets, while employing a long/short, market neutral, leveraged, or inverse strategy.
Emerging_Markets_Equity 12 integer Emerging market funds invest primarily in companies based in less-developed countries of the world.
Hybrid_Funds 13 integer Hybrid funds invest in a mix of equity and debt securities.
Total_Bond 14 integer Total bond funds
Total_Taxable_Bond 15 integer Total taxable bond funds
Investment_Grade_Taxable_Bond 16 integer Investment grade bond funds seek current income by investing primarily (80 percent) in investment grade debt securities.
High_Yield_Taxable_Bond 17 integer High-yield bond funds seek current income by investing two-thirds or more of their portfolios in lower-rated corporate bonds (Baa or lower by Moody’s and BBB or lower by Standard and Poor’s rating services) and floating rate securities.
Government_Taxable_Bond 18 integer Government bond funds pursue an objective of high current income by investing in taxable bonds issued, or backed, by the U.S. government, and include mortgage-backed securities.
Multisector_Taxable_Bond 19 integer Multisector bond funds seek to provide high current income for their shareholders by investing predominantly in a combination of domestic fixed-income securities, including mortgage-backed securities and high-yield bonds, may invest up to 25 percent in bonds issued by foreign companies and governments, and funds pursing long/short, market neutral, leveraged, and inverse strategies.
Global_Taxable_Bond 20 integer Global tacable bond or world bond funds seek current income by investing in debt securities worldwide.
Municipal_Bond 21 integer Municipal bond funds invest in municipal bonds of a single state or a national mix of municipal bonds.


This is a preview version. There might be more data in the original version.

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Import into your tool

If you are using R here's how to get the data you want quickly loaded:


json_file <- ""
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = ""

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]

data = package.resources[0].read()

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = ''

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = ''

package =
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data =
puts data