financial-machine-learning | curated list of practical financial machine learning tools

by firmai Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | financial-machine-learning Summary

financial-machine-learning is a Python library. financial-machine-learning has no bugs, it has no vulnerabilities, it has build file available and it has medium support. You can download it from GitHub.

A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.

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Quality

Security

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Support

financial-machine-learning has a medium active ecosystem.

It has 3430 star(s) with 894 fork(s). There are 206 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 8 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of financial-machine-learning is current.

Quality

financial-machine-learning has 0 bugs and 0 code smells.

Security

financial-machine-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

financial-machine-learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

financial-machine-learning does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

financial-machine-learning releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed financial-machine-learning and discovered the below as its top functions. This is intended to give you an instant insight into financial-machine-learning implemented functionality, and help decide if they suit your requirements.

Search for new repo by category .
Generate a wiki per category .
Parse README . md file
Returns a Pandas DataFrame containing all of the given terms .
Searches for a given search term .
Returns a dictionary of repo attributes .
Search for new repo and append new repo .
Get repo status .
Search new repo by category .
Convert a list of repos .

Get all kandi verified functions for this library.

financial-machine-learning Key Features

No Key Features are available at this moment for financial-machine-learning.

financial-machine-learning Examples and Code Snippets

Welcome to OpenQuant,Project Organization

Python

Lines of Code : 44

License : Permissive (MIT)

Copy

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third part

Fracdiff: Super-fast Fractional Differentiation,How to use,Scikit-learn API

Python

Lines of Code : 26

License : Permissive (BSD-3-Clause)

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from fracdiff.sklearn import Fracdiff

X = ...  # 2d time-series with shape (n_samples, n_features)

f = Fracdiff(0.5)
X = f.fit_transform(X)

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from skl

Fracdiff: Super-fast Fractional Differentiation,How to use,Fractional differentiation

Python

Lines of Code : 16

License : Permissive (BSD-3-Clause)

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import numpy as np
from fracdiff import fdiff

a = np.array([1, 2, 4, 7, 0])
fdiff(a, 0.5)
# array([ 1.       ,  1.5      ,  2.875    ,  4.6875   , -4.1640625])
np.array_equal(fdiff(a, n=1), np.diff(a, n=1))
# True

a = np.array([[1, 3, 6, 10], [0, 5

Community Discussions

Trending Discussions on financial-machine-learning

Translating a Python Pandas line to R:

QUESTION

Translating a Python Pandas line to R:

Asked 2019-Sep-27 at 15:20

I am following a blog post here and I am getting a little stuck on one part regarding the translation from Python pandas to R…

In the part of the blog:

Tick Bars

The author has the line:

data_tick_grp = data.reset_index().assign(grpId=lambda row: row.index // num_ticks_per_bar)

I understand that data is the "data frame" -
reset_index not sure what this is.
assing(grpId =…) - creating a new variable grpId
lambda row: - not sure what this does.
row.index - is this the same as row_number?
\\ - is this the same as floor() in R?
num_ticks_per_bar is calculated as.

total_ticks = len(data) num_ticks_per_bar = total_ticks / num_time_bars num_ticks_per_bar = round(num_ticks_per_bar, -3) # round to the nearest thousand

Which I understand it as:

...

ANSWER

Answered 2019-Sep-26 at 23:27

Usually, Pandas best translates to base R:

reset_index same as resetting row.names for sequential numbering data.frame(..., row.names = NULL)
assign(grpId =…) same as assigning a column in place such as with transform, within or dplyr's mutate
lambda row this is required inside assign to reference data frame, here aliased as row
row.index is same as row number (remember Python is 0-index unlike R)
// is the integer division which in R one can be wrapped with as.integer or floor after division

Altogether, consider below adjustment to translate Pandas line:

Source https://stackoverflow.com/questions/58123865

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install financial-machine-learning

You can download it from GitHub.
You can use financial-machine-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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