Saravanan666/pypi-org huggingface dataset:
Package: quantfactortest
# 请直接使用pip install quantfactortest安装。安装的时候有一个额外的包叫做baostock，它是用来获取指数收益率的（只有日频才有），其目的是用来计算超额收益率（在后面会有详细的说明）。本单因子测试分为分钟级别（可以是1分钟、5分钟、10分钟不等）以及日级别（每天）。
首先对日级别的做出参数上的解释。## 日级别：
使用的时候请先传入必要的参数：使用far=analyze_daily_factor(factor,price,quantiles=5,periods=[1],neutralize=0,demeaned=False,show_IC_line=True,show_IC_heat=True,show_test_line=True,show_test_heat=True)factor、pricefactor以及price是一个n*m的DataFrame的格式，请保持二者的index为datatime格式（或者为时间的str格式，程序会自动转换成datatime的格式）；请保持二者的columns一致，比如000001.sh在factor里面是第一列，那么请让它在price里面也是第一列。factor是用来传递每一个标的在每一日的因子值，price是用来传递每一个标的在每一日的价值，这个价值由你来确定，可以是开盘价，收盘价、平均价等等。那么我们为什么要传入price而不是直接的收益率呢？是为了得到不同收益周期的收益率，详情请参照periods的解释。quantiles默认值为5，为分组回测时分组的组数。periods默认值为[1]。表示我们的收益率的计算周期是1天，也就是用次日的价值/当日的价值-1。如果是[1,2,3,5,10]，那么程序会依次得到这不同收益周期的全部结果。在这里需要额外阐述清楚一个重要的概念。在进行分组回测的时候，假如我们选择的是收益周期为2，如果周一该标的价值10元，周三价值11元，涨幅为10%，但是我们分组回测的收益其实为10%/2=5%。为什么会这样？因为我们还要分一半的资金给到周二时候的股票，周三时候的资金就用的是周一的另一半资金（此时假设已经拿了回来）。neutralize默认值为0，其实就是不做中性化，也就是不减去指数的超额收益。指数的数据来源于baostock，链接为：http://baostock.com/baostock/index.php/%E6%8C%87%E6%95%B0%E6%95%B0%E6%8D%AE如果想要减去某一具体的指数，请把neutralize=某指数的代码，如neutralize=’sh.000001’综合指数，例如：sh.000001 上证指数，sz.399106 深证综指 等；规模指数，例如：sh.000016 上证50，sh.000300 沪深300，sh.000905 中证500，sz.399001 深证成指等；一级行业指数，例如：sh.000037 上证医药，sz.399433 国证交运 等；二级行业指数，例如：sh.000952 300地产，sz.399951 300银行 等；策略指数，例如：sh.000050 50等权，sh.000982 500等权 等；成长指数，例如：sz.399376 小盘成长 等；价值指数，例如：sh.000029 180价值 等；主题指数，例如：sh.000015 红利指数，sh.000063 上证周期 等；基金指数，例如：sh.000011 上证基金指数 等；债券指数，例如：sh.000012 上证国债指数 等；demeaned默认值为False，就是是否减去每一日的所有股票的平均收益（不是根据市值加权平均）。如在周一的三只股票的收益为1%，2%，3%，如果demeaned=True，那么最后的结果将会为-1%，0%，1%。show_IC_line,show_IC_heat,show_test_line,show_test_heat默认值均为True。
show_IC_line：是否展示IC值的折线图，数据量过大的时候（尤其是分钟级别）画图缓慢，不作折线图可以节省时间。
show_IC_heat：是否展示IC值的月度图。
show_test_line：是否展示分组回测结果的折线图。
show_test_heat：是否展示分组回测结果的热力月度图，因为该结果图片太多，设置忽略可以净化荧幕。
设置为True是展示，设置为False是不展示。## 接下来将会解释结果：请使用far.get_all(percentiles=[0.2,0.4,0.6,0.8],buy=[5],sell=[1],gap=10)这里有额外的四个参数，我们将会在涉及到它们的结果中具体阐述用法。首先会输出一个有关于因子描述的dataframe，它会包含每一个分位的因子的情况。percentiles的参数就是为了更具体地展示某一个位置因子的大小。接下来会展示IC（以及RankIC）的值以及IC（以及RankIC）的变化折线图以及月度的IC值，帮助我们来判断是否近期失效以及因子在各个月之间的稳定性；gap就是在作图的时候画的时间滚动平均线的周期，默认为10（2周）。分组回测的相关情况。然后会展示复利收益率各分组的折线图、累加收益率各分组的折线图；某一组的复利（以及累加）月度收益图，其目的是为了判断时间轮动（季节轮动）等。然后就和buy以及sell有关，buy=[5]和sell=[1]意味着我们做多第五分位和做空第一分位，资金的分配都是平均的。默认值是做多最大分位和做空最小分位。然后是该策略的复利收益率的折线图各分位以及各个周期的换手率，帮助我们判断策略的实现难度以及各种手续费。因子的自相关性系数，辅助判断第四条。## 以上就是日级别的回测，分钟级别的回测仅有稍微的变动，我将只阐述不一样的变化。
far=analyze_minute_factor(factor,price,minute,quantiles=5,periods=[240],demeaned=False,trade_day=[],trade_time=[],show_IC_line=True,show_IC_heat=True,show_test_line=True,)minute，请输入您的时间间隔。假如是5分钟，那么请输入5。periods，默认值为240。假设您的时间间隔为5，那么建议不要将交易周期低于240/5=48，因为240分钟=4小时是A股日内的交易时间，A股日内不允许买卖，所以请将periods设置的间隔大于240/minute因为baostock没有分钟级别的指数数据，所以我在此删掉了neutralize。trade_day和trade_time是用来计算收益率的。我要详细地表明它和periods的区别。如果我的minute是5，periods为48，那么我在周一的9:35的股票将会在周二的9:35再次交易，周一的9：40的股票将会在周二的9：40再次交易……如果trade_day为[1],trade_time为[‘15:00’]，这意味着我统一在次日的15：00进行交易。也就是不管我是在周一的9:35买的还是10：00买的，我都会统一在周二的15：00进行交易来计算收益率。在最终的结果中，将不会再产生月度收益率图标，所以没有show_test_heat这个参数。
Package: quantfns
This module has been created to provide functions that are useful in pricing and risk management of fixed-income securities. The goal is to break-down complex quantitative financial calculations into easy-to-understand functions as much as possible.To begin with, there are two functions:duration: calculates the Macaulay and Modified Durations.
bondprice: provides an estimated price for a security for a given basis point change.More functions will be added periodically.
Package: quant-friend
Quant Friend背景和目的在量化回测环境，经常需要购置和试用一些临时的计算资源。这个项目的目的是简化在这过程中的重复操作。一键购置模块TBC汇报模块这个模块需要被回测应用引入，在相关计算结束后，讲结果汇报给特定关注者，同时通知计算资源供应商，要求停止服务。email可以显式调用fromquant_friendimportconfig_email_senderconfig_email_sender('sender@my.com','smtp.my.com',465,True,'sender','password')进行邮箱注册，也可以通过设置环境变量，自动注册。smtp_sendersmtp_hostsmtp_portsmtp_sslsmtp_usersmtp_password删除主机可以显式调用fromquant_friendimportregister_ucloudregister_ucloud(region="",private_key="",public_key="",)进行注册，也可以通过设置环境变量，自动注册。任意uc-开头的环境变量都会被自动读取。fromquant_friendimportlist_and_delete_host# 删除所有主机list_and_delete_host('ucloud')# 按业务组删除主机list_and_delete_host('ucloud',Tag="你的业务组")
Package: quantglobal
Quantitative Global - v1 APIThis package allows access to data provided byQuantitative Global Indices.InstallationUse the package managerpipto install quantglobal.pipinstallquantglobalUsageimportQuantGlobalasqgdata=qg.download(key,strategy,underlying,from_date,**end_date)Valid parameterskeythe email address associated with your account (e.g., 'your_address@ email.com')strategy'dca' (Dual Class Arbitrage)'pt' (Pairs Trading)'pt_extended' (Pairs Trading w/ Extended Data)underlyingFor Dual Class Arbitrage ('dca'), the valid underlying parameters are:'google','zillow','fox','news'For Pairs Trading (both 'pt' and 'pt_extended'), the valid underlying parameters are:'communications','consumer_discretionary','consumer_staples','energy','financials','healthcare','industrials','basic_materials','technology','utilities'from_date / end_date (optional)date in YYYY-mm-dd formatSample RequestimportQuantGlobalasqgdata=qg.download(key='authenticated_user@email.com',strategy='dca',underlying='google',from_date='2022-11-25')Output:datetimeGOOG Cumulative ReturnsGOOGL Cumulative ReturnsSpreadGOOG Intraday PerformanceGOOGL Intraday Performance2022-11-25 09:30:0010010000.00%0.00%2022-11-25 09:31:00100.08100.130.050.08%0.13%2022-11-25 09:32:00100.24100.270.030.24%0.27%
Package: quantgov
BranchBuild StatusMasterDevThe QuantGov library is a companion to theQuantGov Platform. It provides an easy way to start a new project
using thequantgov startset of commands, and also provides a set of
classes and functions often used in the QuantGov framework.To install the library, usepip install quantgov.A tutorial of the library is available athttps://quantgov.github.io/quantgov-tutorial/pages/intro.html.How to ContributeCheck for open issues or open a fresh issue to start a discussion around a feature idea or a bug.Forkthe repositoryon GitHub to start making your changes to thedevbranch (or branch off of it).Write a test which shows that the bug was fixed or that the feature works as expected.Send a pull request and bug the maintainer until it gets merged and published. Make sure to add yourself toAUTHORS.
Package: quantifiles
QuantifilesWelcome to Quantifiles, a PyQt5 application designed for viewing dataset files generated byQuantify-core. With Quantifiles, you can easily browse and visualize the contents of your data directory, including the ability to view a plot of the data and browse snapshots.InstallationInstall from PyPIpipinstallquantifilesInstall from sourceTo install Quantifiles from source, first clone the repository:gitclonehttps://gitlab.com/dcrielaard/quantifiles.gitcdquantifilesThen, install the application in a virtual environment using pip:pipinstall-e.Install with pipenvIf you prefer to use pipenv for managing your virtual environment, you can install Quantifiles with the following command:pipenvinstallgit+https://gitlab.com/dcrielaard/quantifiles.git#egg=quantifilesUsageYou can launch the application by running the following command in your terminal:quantifiles[--datadirDATADIR][--liveplotting][--loglevelLOGLEVEL]If you don't specify the data directory, you can still access it by selecting File->Open in the application.Alternatively, you can also use the executable file located in the Scripts folder, which will be generated upon installation.To start the application from within Python, run:fromquantifilesimportquantifilesquantifiles()# This will start the application, optionally you can pass the data directory as an argument.Tip:The plots can be copied to the clipboard by right-clicking on them.ContributingWe welcome contributions to Quantifiles! If you have an idea for a feature, or if you encounter a bug, pleaseopen an issueor submit a pull request.Feel free to dive in!LicenseThis project is licensed under the terms of the BSD 2-Clause License. See the LICENSE file for more details.
Package: quantify-core
quantify-coreQuantify is a Python-based data acquisition framework focused on Quantum Computing and
solid-state physics experiments.
The framework consists ofquantify-core(git|docs)
andquantify-scheduler(git|docs).
It is built on top ofQCoDeSand is a spiritual successor ofPycQED.quantify-coreis the core module that contains all basic functionality to control experiments. This includes:A framework to control instruments.A data-acquisition loop.Data storage and analysis.Parameter monitoring and live visualization of experiments.Overview and CommunityFor a general overview of Quantify and connecting to its open-source community, seequantify-os.org.
Quantify is maintained by the Quantify Consortium consisting of Qblox and Orange Quantum Systems.The software is free to use under the conditions specified in thelicense.
Package: quantify-qblox
Quantify QbloxQuantify Qblox is the Qblox hardware backend for Quantify, which is a Python-based data acquisition framework focused on Quantum Computing and solid-state physics experiments.The framework consists ofquantify-core(git|docs)
andquantify-scheduler(git|docs).
It is built on top ofQCoDeSand is a spiritual successor ofPycQED.
Package: quantify-scheduler
quantify-schedulerQuantify is a Python-based data acquisition framework focused on Quantum Computing and
solid-state physics experiments.
The framework consists ofquantify-core(git|docs)
andquantify-scheduler(git|docs).
It is built on top ofQCoDeSand is a spiritual successor ofPycQED.quantify-scheduleris a Python module for writing quantum programs featuring a hybrid gate-pulse control model with explicit timing control.
This control model allows quantum gate and pulse-level descriptions to be combined in a clearly defined and hardware-agnostic way.quantify-scheduleris designed to allow experimentalists to easily define complex experiments. It produces synchronized pulse schedules
that are distributed to control hardware, after compiling these schedules into control-hardware specific executable programs.Hardware/driver compatibilityQbloxquantify-schedulerqblox-instrumentsCluster firmwarev0.18.10.11.20.6.20.11.10.6.10.11.00.6.0v0.18.00.11.20.6.20.11.10.6.10.11.00.6.0v0.17.10.11.20.6.20.11.10.6.10.11.00.6.0v0.17.00.11.20.6.20.11.10.6.10.11.00.6.0v0.16.10.11.20.6.20.11.10.6.10.11.00.6.0v0.16.00.11.20.6.20.11.10.6.10.11.00.6.0v0.15.00.10.x0.5.00.9.00.4.0v0.14.00.10.x0.5.00.9.00.4.0v0.13.00.10.x0.5.00.9.00.4.0Zurich Instrumentszhinst==21.8.20515,zhinst-qcodes==0.1.4,zhinst-toolkit==0.1.5Overview and CommunityFor a general overview of Quantify and connecting to its open-source community, seequantify-os.org.
Quantify is maintained by the Quantify Consortium consisting of Qblox and Orange Quantum Systems.The software is free to use under the conditions specified in thelicense.
Package: quantile-data-kit
Quantile Data Kit 🔍Publish to pypiHow to deploy a new version of the QDK?Update the package version insetup.py.Run the Makefilemake publishComponentsThere are four types of base components in the QDK.LoadComponent. Takes nothing as input and outputs a DataFrame.TransformComponent. Takes a DataFrame as input and outputs a DataFrame.TrainingComponent. Takes data and a model as input and outputs a trained model.InferenceComponent. Takes data and a model as input and ouputs prediction data.Adding a new component?Adding a new component to the QDK requires the following steps:Type of component:Decide which type of the four components above you are adding.Add component:Once you decide which type of component you are adding, add in the corresponding folder (e.g.qdk/loader) a new Python file that inherits from the parent component. In this file you can optionally overwriteinput_defs,output_defsandconfig_schema. When adding a new component, you are required to add a classmethod with the same name as thecompute_functionattribute on the parent class. The keys in theconfig_schemaare injected into the parameters of the compute function. Lastly, you need to import the new component toqdk/__init__.py. This allows you to import it from top-level.Write tests: To continuously check the robustness of the components, we highly encourage you to add tests usingpytest. The tests can be added atqdk/tests. Reminder to prefix the folder, files and functions withtest_. One is able to test the components using either VScode testing or the terminal (e.g. withpytest -s qdk/tests/test_loaders).
Package: quantile-forest
quantile-forestquantile-forestoffers a Python implementation of quantile regression forests compatible with scikit-learn.Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation[1]. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn variants.Example of fitted model predictions and prediction intervals on California housing data (code)Quick StartInstall quantile-forest fromPyPIusingpip:pipinstallquantile-forestUsagefromquantile_forestimportRandomForestQuantileRegressorfromsklearnimportdatasetsX,y=datasets.fetch_california_housing(return_X_y=True)qrf=RandomForestQuantileRegressor()qrf.fit(X,y)y_pred=qrf.predict(X,quantiles=[0.025,0.5,0.975])DocumentationAn installation guide, API documentation, and examples can be found in thedocumentation.References[1]N. Meinshausen, "Quantile Regression Forests", Journal of Machine Learning Research, 7(Jun), 983-999, 2006.http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdfCitationIf you use this package in academic work, please consider citinghttps://joss.theoj.org/papers/10.21105/joss.05976:@article{Johnson2024,doi={10.21105/joss.05976},url={https://doi.org/10.21105/joss.05976},year={2024},publisher={The Open Journal},volume={9},number={93},pages={5976},author={Reid A. Johnson},title={quantile-forest: A Python Package for Quantile Regression Forests},journal={Journal of Open Source Software}}
Package: quantile_regression_demo
OVERVIEWMost of us are familiar with the charts that pediatricians use that show
percentiles of weight and height as a function of age; generating such a chart
from a small sample of data requires quantile regression or similar methods.
(When working with a large enough sample of data, one can bin the data, i.e.,
divide the x-axis into intervals and calculate percentiles independently for
each interval.  But, this approach uses the data inefficiently and is unworkable
when sample sizes are small).Quantiles and percentiles are the same except for a factor of 100, e.g., the
30th percentile is the 0.3 quantile.This Python script demonstrates that one can perform quantile regression using
only Python, NumPy, and SciPy.  The only other dependency is on matplotlib,
which is used to plot the data and the quantile estimates.DETAILSIn detail, the script does the following:(1) Model parameters are assigned.  (Currently, these are hardwired into the
code).(2) The program generates an artificial bivariate sample of data (x, y) as
follows:x is generated by drawing from a distribution that is uniform on [x_min, x_max], where x_min and x_max are currently 0 and 1, respectively.y is then generated according to a normal distribution having mean -0.5 + x and standard deviation 1.0 + 0.5 * x.(All of this can be changed, e.g., one could choose to make the mean of y quadratic in x).(3) The code defines an objective function based on the tilted absolute value
function (see references for motivation).(4) The SciPy optimization package is then used to optimize (minimize) the
objective function.(5) Using the matplotlib module, the code plots a scatter diagram of the data
with an overlay of percentile lines.
Package: quantile-scatter
quantile_scatter下の方に日本語の説明がありますOverviewVisualization tool that makes it easier to get scatter plots right.The number of uniform data is divided into intervals on the x-axis, and the quantile points for each interval are displayed.Usageimportquantile_scatter# dummy datax_ls=[(4*random.random()-2)**3for_inrange(1000)]y_ls=[math.sin(x)+random.random()*0.5forxinx_ls]# plot [quantile_scatter]quantile_scatter.plot(x=x_ls,# x-listy=y_ls,# y-listmin_bin_ratio=1/20,# Ratio of the smallest group (the number of records in the smallest group as a percentage of the total)ile_ls=[0.25,0.5,0.75])Advanced UsageOption argument ofquantile_scatter.plot()function:mean=True# Also draw the "mean"show=False# Do not show the graph and only return the data to be displayed (useful for saving the graph or drawing with something other than matplotlib)missing_values=[None]# The specification that x contains a missing value of "None". Internally, the value is included in the statistics as "missing".概要散布図を正しく把握しやすくする可視化ツール均一データ数の横軸区間に分け、各区間の分位点を表示する説明は執筆中です使用例importquantile_scatter# ダミーデータx_ls=[(4*random.random()-2)**3for_inrange(1000)]y_ls=[math.sin(x)+random.random()*0.5forxinx_ls]# 分位点散布図の描画 [quantile_scatter]quantile_scatter.plot(x=x_ls,# 横軸数値リストy=y_ls,# 縦軸数値リストmin_bin_ratio=1/20,# 最小グループ割合 (最も小さいグループのレコード数が全体に占める割合)ile_ls=[0.25,0.5,0.75]# どこの分位点を出すか)発展的な利用方法quantile_scatter.plot()関数のoption引数mean=True# 「平均」も描画するshow=False# グラフ表示せず、表示対象データのみを返却 (グラフを保存したい場合や、matplotlib以外で描画したい場合などに有効)missing_values=[None]# xにNoneという欠損値が含まれるという指定。内部的には"missing"という値として集計に含められる。
Package: quantile-transformer-tf
[![Build Status](https://travis-ci.com/yandexdataschool/QuantileTransformerTF.svg?branch=master)](https://travis-ci.com/yandexdataschool/QuantileTransformerTF) [![DOI](https://zenodo.org/badge/156366202.svg)](https://zenodo.org/badge/latestdoi/156366202)# QuantileTransformerTF
Tensorflow implementation ofsklearn.preprocessing.QuantileTransformer. Transform only - please use the sklearn to fit.## InstallationThe releases are published on PyPi` pip install quantile_transformer_tf `To install from the source` python setup.py install `## Usage
Please see the docstrings andtest.pyPlease cite as [![DOI](https://zenodo.org/badge/156366202.svg)](https://zenodo.org/badge/latestdoi/156366202)
Package: quantimpy
quantimpyWelcome to QuantImPy, a Python library for scientific image processing.This code performs morphological operations on Numpy arrays and can compute the
Minkowski functionals and functions.This code is inspired by and partly based on theQuantIm
libraryC/C++ library for scientific
image processing.Documentation can be found onGithub.ioThis library is available onpypi.organd
can be installed using pip:pip install quantimpyIf you use this package in your research, please cite it as:Arnout M.P. Boelens, and Hamdi A. Tchelepi,QuantImPy: Minkowski functionals
and functions with Python, SoftwareX, Volume 16, 2021, 100823, ISSN 2352-7110,
doi:10.1016/j.softx.2021.100823
Package: quantiphyse-dce
DCE plugin for QuantiphyseThis plugin provides modelling tools for DCE-MRI. Currently
two implementations are provided - one based on the Tofts-Orton
model and one using the Fabber Bayesian model fitting tool which
supports both measured and population AIFsQuantiphyse and quantiphyse-fabber must be installed. To
install the DCE plugin from PyPi use:pip install quantiphyse-dceThe plugin will then be available from within Quantiphyse
Package: quantiphyse-deeds
Quantiphyse plugin for the DEEDS registration methodDEEDS is a fully deformable registration method developed by Matthias Heinrich.
For more information on DEEDS and related projects see:http://www.mpheinrich.de/software.htmlThis plugin incorporates a version of the DEEDS code with the author's permission. See
LICENSE for copyright information.ReferencesMRF-Based Deformable Registration and Ventilation Estimation of Lung CT.by Mattias P. Heinrich, M. Jenkinson, M. Brady and J.A. Schnabel IEEE Transactions on Medical Imaging 2013, Volume 32, Issue 7, July 2013, Pages 1239-1248http://dx.doi.org/10.1109/TMI.2013.2246577Multi-modal Multi-Atlas Segmentation using Discrete Optimisation and Self-Similaritiesby Mattias P. Heinrich, Oskar Maier and Heinz Handels VISCERAL Challenge@ ISBI, Pages 27-30 2015http://ceur-ws.org/Vol-1390/visceralISBI15-4.pdfMIND: Modality Independent Neighbourhood Descriptor for Multi-modal Deformable Registrationby MP Heinrich, M Jenkinson, M Bhushan, T Matin, F Gleeson, M Brady, JA Schnabel, Medical Image Analysis. vol. 16(7) 2012, pp. 1423-1435For Quantiphyse documentation see:https://quantiphyse.readthedocs.io/
Package: quantiphyse-fabber
Fabber plugin for QuantiphyseQuantiphyse is a visualisation and data analysis tool for volumetric
medical imaging data especially MRI. Seehttps://quantiphyse.orgfor more
information.Fabber is a model fitting tool designed to fit nonlinear parameterised
models to 4D fMRI data such as ASL, CEST, DCE, DSC, etc.To install, either use the setup script or:pip install quantiphyse-fabberThe plugin will then be available from within Quantiphyse
Package: quantiphyse-fsl
FSL plugin for QuantiphyseThis plugin provides a Quantiphyse interface to selected FSL
tools, currently:BETFASTFLIRT/MCFLIRT (via the registration widget)FSL_ANAT (experimental)Atlasses/standard dataQuantiphyse and FSL must be installed. Note that this plugin is
an interface only and contains no FSL code itself!
To install the plugin from PyPi use:pip install quantiphyse-fslThe plugin will then be available from within Quantiphyse
Package: quantiprot
Quantiprotcurrent version: 0.2.5The Quantiprot package is a python package designed to facilitate quantitative analysis of protein sequences.The Quantiprot package is developed and maintained atPolitechnika Wroclawskawith financial support fromNational Science Centregrant no. 2015/17/D/ST6/04054 (Formal linguistics for proteomics - modeling, analysis and hypotheses testing).LicenseThe Quantiprot package is distributed underthe MIT license.CitingIf you publish results obtained using Quantiprot, please cite:B.M. Konopka, M. Marciniak, and W. Dyrka. Quantiprot – a Python package for quantitative analysis of protein sequences. BMC Bioinformatics 18:339, 2017CodeCurrently, the code is hosted byE-SCIENCE.PL.DocumentationMore information on the package andexamplescan be found in theUSER MANUAL.ContactPlease contactwitolddotdyrkaatpwrdotedudotpl.
Package: quantitative-vale-model
quantitative_vale_modelThis package consists in a Simple and profitable quantitative model to user in Brazilian VALE3 (VALE S.A.) StocksInstallpipinstallquantitative-vale-modelImportimportquantitative_vale_modelCall the principal functionsquantitative_vale_model.backtest('%Y-%m-%d')quantitative_vale_model.trade_action()