Saravanan666/pypi-org huggingface dataset:
Package: alphaess
alphaessThis Python library logs in to cloud.alphaess.com and retrieves data on your Alpha ESS inverter, photovoltaic panels, and battery if you have one.UsageCreate a new Alpha ESS instance, log in, retrieve a list of Alpha ESS systems and request energy statistics of one of those Alpha ESS systems.APICurrently this package uses an API that I reverse engineered the API from the Alpha ESS web app. This is an internal API subject to change at any time by Alpha ESS.NoteAs of AlphaCloud V5.0.0.2 (2022-10-16) Alpha ESS introduced anti-crawl efforts to make retrieving data more difficult for packages like this one.To be good internet citizens, it is advised that your polling frequency for any AlphaCloud endpoints are 10 seconds at a minimum.MethodsThere are four public methods in this moduleauthenticate(username, password)- attempts to authenticate to the ALpha ESS API with a username and password combination, returns True or False depending on successful authentication or notgetdata()- having successfully authenticated attempts to get statistical energy data on all registered Alpha ESS systems - will return None if there are issues retrieving data from the Alpha ESS API.setbatterycharge(serial, enabled, cp1start, cp1end, cp2start, cp2end, chargestopsoc)- having successfully authenticated set battery grid charging settings for the SN.setbatterydischarge(serial, enabled, dp1start, dp1end, dp2start, dp2end, dischargecutoffsoc)- having successfully authenticated set battery discharge settings for the SN.
Package: alphaessopenapi
alphaessThis Python library uses the Alpha ESS Open API to retrieve data on your Alpha ESS inverter, photovoltaic panels, and battery if you have one.  This library is principally intended for use by my Home Assistant integration [https://github.com/CharlesGillanders/homeassistant-alphaESS]How to use1. Sign up for an open API accountRegister athttps://open.alphaess.com/for a (free) account to get your Developer ID (AppID) and Developer Secret (AppSecret).Once registered, add your battery/inverter to the developer account via the web UI.NoteTo be good internet citizens, it is advised that your polling frequency for any AlphaCloud endpoints are 10 seconds at a minimum.MethodsThere are public methods in this module that duplicate the AlphaESS OpenAPI and provide wrappers forhttps://openapi.alphaess.com/api/getEssListhttps://openapi.alphaess.com/api/getLastPowerDatahttps://openapi.alphaess.com/api/getOneDayPowerBySnhttps://openapi.alphaess.com/api/getOneDateEnergyBySnhttps://openapi.alphaess.com/api/getChargeConfigInfohttps://openapi.alphaess.com/api/updateChargeConfigInfohttps://openapi.alphaess.com/api/getDisChargeConfigInfohttps://openapi.alphaess.com/api/updateDisChargeConfigInfoAll of the above are documented athttps://open.alphaess.com/developmentManagement/apiList(Registration required)getdata() - Attempts to get statistical energy data for use in Home Assistant for all registered Alpha ESS systems - will return None if there are issues retrieving data from the Alpha ESS API.authenticate - Attempts to usehttps://openapi.alphaess.com/api/getEssListto validate authentication to the ALpha ESS API - will return True or False.setbatterycharge (serial, enabled, dp1start, dp1end, dp2start, dp2end, chargecutoffsoc)Parameters:chargecutoffsoc(float) % to stop charging from the grid atenabled(bool) True to charge from the grid, False do notdp1start(datetime.time) The start time of charging period 1 (the minutes must be one of :00, :15, :30, :45)dp1end(datetime.time) The end time of charging period 1 (the minutes must be one of :00, :15, :30, :45)dp2start(datetime.time) The start time of charging period 2 (the minutes must be one of :00, :15, :30, :45)dp2end(datetime.time) The end time of charging period 2 (the minutes must be one of :00, :15, :30, :45)serial(str) The serial number of the battery/inverter.setbatterydischarge (serial, enabled, dp1start, dp1end, dp2start, dp2end, dischargecutoffsoc)Parameters:dischargecutoffsoc(float) % to stop discharging from the battery atenabled(bool) True to discharge from the battery, False do notdp1start(datetime.time) The start time of charging period 1 (the minutes must be one of :00, :15, :30, :45)dp1end(datetime.time) The end time of charging period 1 (the minutes must be one of :00, :15, :30, :45)dp2start(datetime.time) The start time of charging period 2 (the minutes must be one of :00, :15, :30, :45)dp2end(datetime.time) The end time of charging period 2 (the minutes must be one of :00, :15, :30, :45)serial(str) The serial number of the battery/inverter.
Package: alphafed
AlphaMed简体中文 |EnglishAlphaMed 是一个基于区块链技术的去中心化联邦学习解决方案，旨在使医疗机构能够在保证其医疗数据隐私和安全的同时，实现多机构联合建模。医疗机构可以在本地节点实现模型的训练，并支持以匿名的身份将加密的参数共享至聚合节点，从而实现更安全、可信的联邦学习。相比于传统的联邦学习，AlphaMed 平台不仅能够确保只有合法的且经过许可的参与者才能加入网络，同时支持节点的匿名化的参与联合建模。同时，区块链的共识算法能够确保网络中的节点得到一直的决策，恶意的参与者或者数据投毒等攻击将被拒绝，从而保证了联邦学习更好的安全性。在联邦学习的过程中，各个参与方都受到智能合约的约束，并且所有的事件、操作都将被记录在区块链的分布式账本上，可追溯、可审计，使得联合机器学习的安全性和隐私保护能力极大的提升。开始构建第一个联邦学习任务
Package: alpha-filter
and greateralpha-filterWhen parsing, sometimes it is necessary to reduce the number of requests to the server, for example, our script collects links from pagination to the product every day, and then parses each product separately. But what to do if some time ago we already parsed these goods, why do it twice. alpha-filter will help filter out those ads that have already been read, and will return only new ones.Getting startingpipinstallalpha-filterUsagefromalphafilterimportfilter_ads,mark_as_processed,is_processed>>>first_parsing_urls=["https://www.example.com/1","https://www.example.com/2"]>>>new,old=filter_ads(first_parsing_urls)>>>new["https://www.example.com/1","https://www.example.com/2"]>>>old[]second_parsing_urls=first_parsing_urls# second parsing same with first>>>new,old=filter_ads(second_parsing_urls)>>>new[]>>>old[]>>>third_parsing_urls=["https://www.example.com/2","https://www.example.com/3"]>>>new,old=filter_ads(third_parsing_urls)>>>new["https://www.example.com/3"]>>>old["https://www.example.com/1"]Also you can mark your urls for some purposes>>>urls_for_mark=["https://www.example.com/2","https://www.example.com/3"]>>>mark_as_processed(urls_for_mark)>>>is_processed("https://www.example.com/2")True>>>is_processed("https://www.example.com/4")FalseIt uses a fast sqlite database to store urls. The database file ('ads.db') will be created in the root directoryWarning!!! this package has no protection against sql injection, do not use it for the external interface
Package: alphafoldmodel
AlphafoldModelis a package to parse Alphafold PDB structures and PAE Matrices into interactive Python objects.The package contains the following classes:AlphafoldModel: Parses the Alphafold model PDB file alongside its JSON PAE matrix into an interactive Python object. It allows declarative queries for a model's local PAE and plDDT metrics.ModelPDB:ModelPDBis the base class that carries the PDB parsing functionalities as well as residue-based nearest-neighbour search methods.ModelPAE:ModelPAEis the base class that carries the PAE parsing functionalities forAlphafoldModel.LoadedKDTree:LoadedKDTreeis a wrapper class which provides an interface over thescipy.spatial.KDTreeclass to instead store and return arbitrary objects with coordinate information.Explore the source file in:src/AlphafoldModel/alphafoldmodelInstallingpip install alphafoldmodel
Package: alphagens
AlphaGensAn agent-environment based backtesting framework.Event-Driven backtestingclassBuyAndHold(BaseStrategy):def__init__(self,engine,broker,account):super().__init__(engine,broker,account)defbefore_trading_end(self):ifself.current_date==self.trade_dates[0]:self.account.order_target_pct_to(pd.Series(1,index=["000001"]))quick startclassBuyAndHold(BaseAgent):def__init__(self,data):super().__init__(data)self.i=-1deftake_action(self,state,date):self.buffer.append(state)self.i+=1ifself.i==0:target_positions=np.array([0.2foriinrange(5)])returntarget_positionselse:returnNO_ACTIONwhileTrue:try:print(f"current date is{env.current_date}")action=agent.take_action(state,env.current_date)next_state,reward,truncated,terminated,info=env.step(action)state=next_statereturns.append(reward)exceptIndexError:break
Package: alphagradient
DocumentationFor now, official documentation is hosted onnathanheidacker.github.ioIntroductionAlphaGradientis a package for creating and backtesting financial algorithms in native python. AlphaGradient implements asset and portfolio-analagous datastructures that interact in intuitive ways, and provides a framework for developing algorithms that utilize these objects in highly parallelized backtests. AlphaGradient is built on top of the industry's most widely adopted libraries, and requires (comparatively) little technical knowhow and time investment to pick up.To be able to use AlphaGradient effectively, you must know...basic financereallybasic pythonWhere other libraries might require you to be a programmer first and and a certified quantitative financial analyst second, AlphaGradient is truly geared towards hobbyist algorithmic traders who want to play around with ideas for financial algorithms. Within minutes, you can have a fully backtestable algorithm using nothing but basic python.AlphaGradient is to the algorithmic trader as console gaming is to the gamer; it gets the job done and comes at a bargain--batteries included. If you have even a passing interest in finance or algorithmic trading, this package makes it as easy as possible to get involved without having a background in computer science.InstallationThe source code is currently hosted on GitHub at:https://github.com/nathanheidacker/alphagradientBinary installers for the latest released version are available at thePython
Package Index (PyPI)# using pip
pip install alphagradientDependenciespandasnumpyaenumyfinancetqdmpathosLicenseAlphaGradient is licensed under theApache LicenseAbout Me and Contact InfoIm a student from Northwestern University in Evanston Illinois studying cognitive science and artificial intelligence. I'm currently seeking employment. If you're interested in my work, feel free to contact me atthis email
Package: alphai
AlphAIAlphAI is a high-level open-source Python toolkit designed for efficient AI development and in-depth GPU profiling. Supporting popular tensor libraries likePyTorchandJax, it optimizes developer operations on GPU servers and integrates seamlessly withAmerican Data Science Labs, offering robust control over remote Jupyter Lab servers and environment runtimes.FeaturesGPU Profiling and Analytics: Advanced GPU profiling capabilities to maximize resource efficiency and performance.Benchmarking Tools: Pythonic, easy-to-use tools for evaluating and comparing model performance.Remote Jupyter Lab Integration: Programmatic management of remote Jupyter Lab servers for enhanced productivity.Local Tensor Model Support: Streamlines the integration and management of tensor models from providers like Hugging Face.Tensor Engine Compatibility: Fully compatible with PyTorch, with upcoming support for Jax and TensorFlow.Quick StartInstallationInstall AlphAI easily using pip:pipinstallalphai# If you'd like to install torch in a Linux machine with CUDA-driverspipinstallalphai[torch]Authentication Pre-requisitesAlthough not strictly required to use the computational functions of the alphai package, it is recommended to create an account atAmerican Data Scienceand generate an API key to make use of your two free remote Jupyter Lab servers.You don't need an API key to use the GPU profiling, benchmarking, and generate modules.Basic UsageHere's a quick example to get started with AlphAI:fromalphaiimportAlphAI# Initialize AlphAIaai=AlphAI(api_key=os.environ.get("ALPHAI_API_KEY"),)# Start remote Jupyter Lab serversaai.start_server()# Upload to your server's file systemaai.upload("./main.py")# Start python kernel and run code remotelycode="print('Hello world!')"aai.run_code(code)Documentation and Detailed UsageFor more documentation and detailed instructions on how to use AlphAI's various features, please refer to ourDocumentation.Working with Tensor ModelsGuidance on integrating and leveraging tensor models.GPU Profiling and AnalyticsComprehensive features for GPU profiling and analytics.Integration with American Data Science LabsDiscover the benefits of integrating AlphAI with American Data Science Labs.System RequirementsPython 3.9+PyTorch (recommnended) or Jax (limited support)Linux OS i.e. Ubuntu 18.04+ContributingWe welcome contributions! Please see ourContribution Guidelinesfor more information.LicenseAlphAI is released under theApache 2.0license.Support and ContactFor support or inquiries about enterprise solutions, contact us atinfo@amdatascience.com.
Package: alphainsight-jc6102
alphainsight_jc6102a Python package designed to facilitate financial data analysis using the Alpha Vantage API.Installation$pipinstallalphainsight_jc6102UsagePlease see Final_Project_Vignettet.ipynb to have more understanding of usage.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensealphainsight_jc6102was created by Jieyuan Chen. It is licensed under the terms of the MIT license.Creditsalphainsight_jc6102was created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: alphainspect
AlphaInspect仿alphalens的单因子分析工具安装pip install -i https://pypi.org/simple --upgrade alphainspect  # 官方源
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade alphainspect  # 国内镜像源使用准备数据。运行data/prepare_data.pydate, asset。必需的两个字段factor因子值。放在因子发生时刻forward return远期收益率。计算收益率后需要回移到开始位置。为何是shift(-n)收益率，而不是shift(n)因子呢？多期收期率。如果移动因子，会导致一个因子就要移动多次因子一般成百上千，全移动要的工作量非常大，而收期率则少很多推荐大家使用expr_codegen和polars_ta等项目运行examples/demo1.py示例弹出简易图表运行examples/demo2.py示例弹出完整图表运行examples/demo3.py示例多进程并行输出HTML网页报表运行examples/demo4.py示例事件图表部分图示累计收益的计算方法参考cum_returns.mdalphainspect与alphalens的不同不自动计算forward_returns。alphalens设periods=(1, 5, 10)，然后内部计算持有1、5、10期数的收益率alphainspect由用户外部生成，用户可以比较同因子，不同交易方式产生的差异。例如：RETURN_OC_1: T+1开盘入场，T+1收盘出场RETURN_CC_1: T+0开盘入场，T+1收盘出场RETURN_OO_1: T+1开盘入场，T+2开盘出场RETURN_OO_5: T+1开盘入场，T+6开盘出场不做去极值、标准化、行业中性化等操作alphalens的各参数要弄懂还是很麻烦的，初学者如绩效达不到要求就得深入研究源代码找原因alphainspect用户在外可以一次性全计算好，如F1_ORG, F1_ZS, F1_NEUT，然后在分别传不同因子进行比较即可资金分配只用等权alphalens有因子加权、多空等设置alphainspect只提供等权一种计算方法，实现简单收益率计算方法不同alphalens多期简单收益率几何平均成1期，然后+1累乘alphainspect由用户提供1期简单收益率，然后根据要求持有或调仓，得到新的权益，循环迭代下去。更精确alphainspect与alphalens的相同数据组织方式相同。都是长表，都是因子不移动，收益率计算，然后后移到与因子产生时间对齐累计收益计算模式类似。每期产生因子，持有时长超过一期，都是将资金分成多份分别入场不考虑滑点和手续费。单因子是用来合成多因子的，因手续费和滑点而错过部分单因子就可惜了，应当在因子合成后的回测阶段才考虑手续费二次开发git --clone https://github.com/wukan1986/alphainspect.git
cd alphainspect
pip install -e .
Package: alphalens-reloaded
Alphalens is a Python library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with theZiplineopen source backtesting library, andPyfoliowhich provides performance and risk analysis of financial portfolios.The main function of Alphalens is to surface the most relevant statistics and plots about an alpha factor, including:Returns AnalysisInformation Coefficient AnalysisTurnover AnalysisGrouped AnalysisGetting startedWith a signal and pricing data creating a factor "tear sheet" is a two step process:importalphalens# Ingest and format datafactor_data=alphalens.utils.get_clean_factor_and_forward_returns(my_factor,pricing,quantiles=5,groupby=ticker_sector,groupby_labels=sector_names)# Run analysisalphalens.tears.create_full_tear_sheet(factor_data)Learn moreCheck out theexample notebooksfor more on how to read and use the factor tear sheet.InstallationInstall with pip:pip install alphalens-reloadedInstall with conda:conda install -c ml4t alphalens-reloadedInstall from the master branch of Alphalens repository (development code):pip install git+https://github.com/stefan-jansen/alphalens-reloadedAlphalens depends on:matplotlibnumpypandasscipyseabornstatsmodelsUsageA good way to get started is to run the examples in aJupyter notebook.To get set up with an example, you can:Run a Jupyter notebook server via:jupyternotebookFrom the notebook list page(usually found athttp://localhost:8888/), navigate over to the examples directory, and open any file with a .ipynb extension.Execute the code in a notebook cell by clicking on it and hitting Shift+Enter.Questions?If you find a bug, feel free to open an issue on ourgithub tracker.ContributeIf you want to contribute, a great place to start would be thehelp-wanted issues.CreditsAndrew CampbellJames ChristopherThomas WieckiJonathan LarkinJessica Stauth (jstauth@quantopian.com)Taso PetridisFor a full list of contributors see thecontributors page.Example Tear SheetsExample factor courtesy ofExtractAlphaPeformance Metrics TablesReturns Tear SheetInformation Coefficient Tear SheetSector Tear Sheet
Package: alphalens-tej
Alphalens is a Python library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with theZiplineopen source backtesting library, andPyfoliowhich provides performance and risk analysis of financial portfolios.The main function of Alphalens is to surface the most relevant statistics and plots about an alpha factor, including:Returns AnalysisInformation Coefficient AnalysisTurnover AnalysisGrouped AnalysisGetting startedWith a signal and pricing data creating a factor "tear sheet" is a two step process:importalphalens# Ingest and format datafactor_data=alphalens.utils.get_clean_factor_and_forward_returns(my_factor,pricing,quantiles=5,groupby=ticker_sector,groupby_labels=sector_names)# Run analysisalphalens.tears.create_full_tear_sheet(factor_data)Learn moreCheck out theexample notebooksfor more on how to read and use the factor tear sheet.InstallationInstall with pip:pip install alphalens-reloadedInstall with conda:conda install -c ml4t alphalens-reloadedInstall from the master branch of Alphalens repository (development code):pip install git+https://github.com/stefan-jansen/alphalens-reloadedAlphalens depends on:matplotlibnumpypandasscipyseabornstatsmodelsUsageA good way to get started is to run the examples in aJupyter notebook.To get set up with an example, you can:Run a Jupyter notebook server via:jupyternotebookFrom the notebook list page(usually found athttp://localhost:8888/), navigate over to the examples directory, and open any file with a .ipynb extension.Execute the code in a notebook cell by clicking on it and hitting Shift+Enter.Questions?If you find a bug, feel free to open an issue on ourgithub tracker.ContributeIf you want to contribute, a great place to start would be thehelp-wanted issues.CreditsAndrew CampbellJames ChristopherThomas WieckiJonathan LarkinJessica Stauth (jstauth@quantopian.com)Taso PetridisFor a full list of contributors see thecontributors page.Example Tear SheetsExample factor courtesy ofExtractAlphaPeformance Metrics TablesReturns Tear SheetInformation Coefficient Tear SheetSector Tear Sheet
Package: alphalib
Welcome to alphalibA library for your daily data engineering and data science routines.This file will become your README and also the index of your documentation.InstallpipinstallalphalibHow to useIngest from Excel toaccountstable in PostgreSQL# Ingest from Excel to `accounts` table in PostgreSQLexcel_source=file_sources.get(FileSource.Excel,file_path="data/accounts.xlsx")config={'host':'localhost','port':5432,'db':'testdb','user':'user1','password':'userpwd'}pgsql_target=db_targets.get(DatabaseTarget.PostgreSQL,**config)ingest(excel_source,pgsql_target,'accounts')2020-12-12 21:26:59,943 INFO(): {'user_id': INTEGER(), 'username': VARCHAR(length=50), 'password': VARCHAR(length=50), 'email': VARCHAR(length=255), 'created_on': TIMESTAMP(), 'last_login': TIMESTAMP()}



Total records in data/accounts.xlsx - 100
user_id - 100
username - 100
password - 100
email - 100
created_on - 1
last_login - 1Ingest from CSV toaccountstable in MySQL# Ingest from CSV to `accounts` table in MySQLcsv_source=file_sources.get(FileSource.CSV,file_path="data/accounts.csv")config={'host':'localhost','port':3306,'db':'testdb','user':'user1','password':'userpwd'}mysql_target=db_targets.get(DatabaseTarget.MySQL,**config)ingest(csv_source,mysql_target,'accounts')2020-12-12 21:35:29,017 INFO(): {'user_id': INTEGER(), 'username': VARCHAR(length=50), 'password': VARCHAR(length=50), 'email': VARCHAR(length=255), 'created_on': TIMESTAMP(), 'last_login': TIMESTAMP()}



Total records in data/accounts.csv - 100
user_id - 100
username - 100
password - 100
email - 100
created_on - 1
last_login - 1
Package: alphamed-federated
AlphaMedAlphaMed 是一个基于区块链技术的去中心化联邦学习解决方案，旨在使医疗机构能够在保证其医疗数据隐私和安全的同时，实现多机构联合建模。医疗机构可以在本地节点实现模型的训练，并支持以匿名的身份将加密的参数共享至聚合节点，从而实现更安全、可信的联邦学习。相比于传统的联邦学习，AlphaMed 平台不仅能够确保只有合法的且经过许可的参与者才能加入网络，同时支持节点的匿名化的参与联合建模。同时，区块链的共识算法能够确保网络中的节点得到一直的决策，恶意的参与者或者数据投毒等攻击将被拒绝，从而保证了联邦学习更好的安全性。在联邦学习的过程中，各个参与方都受到智能合约的约束，并且所有的事件、操作都将被记录在区块链的分布式账本上，可追溯、可审计，使得联合机器学习的安全性和隐私保护能力极大的提升。开始构建第一个联邦学习任务在不同结构的数据源之间构建异构联邦学习任务如果 AlphaMed 平台预置的现有算法依然无法满足业务需要，还可以自行设计联邦学习算法，并使其运行在 AlphaMed 平台之上。这里展示了如果自定义联邦学习算法，并通过 AlphaMed 平台实际执行联邦学习任务。为了帮助开发者调试自己的代码，AlphaMed 平台还提供了一套模拟运行环境，以在本地节点模拟实际运行环境，包括联邦学习运行环境。AlphaMed 平台除面向算法工程师提供了开发联邦学习模型的支持外，还面向模型使用者提供了预训练模型的支持。与传统预训练模型相比，AlphaMed 平台上的预训练模型支持功能更为强大、使用更为方便。在 AlphaMed 平台上，不仅可以寻找并下载心仪的预训练模型，更可以利用私有数据微调、部署预训练模型，使其更加适配于私有的业务数据。AlphaMed 平台预置了一定数量的预训练模型，同时也支持第三方开发者开发上传自己的预训练模型。这里展示了如何设计自己的预训练模型。项目目录说明src/alphafed├── auto_mlAutoML 模块│   └── cvatCVAT 工具├── contractor合约消息工具├── data_channel数据传输工具├── docs说明文档│   ├── auto_mlAutoML 说明文档│   ├── customized_scheduler自定义调度器说明文档│   ├── fed_avgFedAvg 横向联邦说明文档│   ├── hetero_nnHeteroNN 异构联邦说明文档│   ├── mock模拟调试说明文档│   └── tutorialtutorial 示例说明文档，包含所有主要功能├── examples脚本测试代码 / 示例代码├── fed_avgFedAvg 横向联邦模块├── hetero_nnHeteroNN 异构联邦模块│   └── psi隐私求交模块├── secure安全工具├── fs.py文件系统工具├── loggers.py日志工具├── mock.py模拟调试工具└── utils.py其它工具
Package: alphaneural
Machine Learning EvxThis is a simplified version ofalphaneuralpackage used to generate buy and sell signals for crypto and conventional stock markets based on the above article on medium.InstallationInstall alphaneural withpython3 -m pip install alphaneuralUsageIn your python script simply import the module and use as follows:from alphaneural.alphaneural import alpha_param
print(apha_param(df,'mem'))The above methods take OHCLV data and the option to specifiy the file path as 'file' or in memory saved variable 'mem'. This will result in a single parameternamed alpha.Testing an entire dataframeTesting of a dataframe for correct buy, sell signals is as simple as applying the lambda function as follows:import pandas as pd
from alphaneural import alpha_param

df = pd.read_csv('../../../path/to_your.csv')

def getEnterSignal(data,src):
    alpha = alpha_param(data,f'{src}')
    return alpha

mainsig = getEnterSignal(df,'mem')

df['sig'] = df['alpha'].apply(lambda x: 1 if x < mainsig else 0)Alphaneural can be applied to a file as follows:from alphaneural import alpha_param  
alpha = alpha_param('../../../path/to_your.csv','file')WarningThis is not financial advise. Alphaneural is entirely on its preliminary stages. Use it at your own risk.
Package: alphanum
alphanumSimple Python library to generate pseudo-random alphanumeric strings of
arbitrary length. Requires Python 3.5+.Installationalphanum can be obtained from PyPI withpip install alphanum.Alternatively, build it withPoetry:pipinstallpoetry
gitclonehttps://github.com/clpo13/alphanumcdalphanum
poetrybuild
pipinstalldist/alphanum-x.y.z-py3-none-any.whlUsageimportalphanumfoo=alphanum.generate(10)print(foo)LicenseCopyright (c) 2020 Cody Logan. MIT licensed.