Search Results for "git:/git.code.sf.net/p/docfetcher/code"
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Showing 26 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
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AI Hedge Fund
An AI Hedge Fund Team
This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions. Its structure is educational: intended more as a proof-of-concept than a ready-to-use financial product, giving learners insight into the mechanics of quantitative finance automation. ... -
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AIQuant
AI-powered platform for quantitative trading
...Stock trading strategies: large models, factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, high-frequency trading, etc. Resource summary: network-wide resource summary, practical cases, paper interpretation, and code implementation. -
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Yahoo! Finance market data downloader
Yahoo! Finance market data downloader
...finance. yfinance aimed to offer a temporary fix to the problem by scraping the data from Yahoo! Finance and returning a the data in the same format as pandas_datareader's get_data_yahoo(), thus keeping the code changes in existing software to a minimum. The latest version of yfinance is a complete re-write of the libray, offering a reliable method of downloading historical market data from Yahoo! Finance, up to 1 minute granularity, with a more Pythonic way. The Ticker() module allows you get market and metadata for security, using a Pythonic way. -
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NeuralProphet
A simple forecasting package
NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in... -
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NautilusTrader
A high-performance algorithmic trading platform
NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the parity challenge of keeping the Python research/backtest environment, consistent with the production live trading environment. NautilusTraders design, architecture and implementation philosophy holds software correctness and safety at the highest level, with the aim of supporting Python native, mission-critical, trading system backtesting and live deployment workloads. -
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VisiData
A terminal spreadsheet multitool for discovering and arranging data
...VisiData supports tsv, CSV, SQLite, JSON, xlsx (Excel), hdf5, and many other formats. Requires Linux, OS/X, or Windows (with WSL). Hundreds of other commands and options are also available; see the documentation. Code in the stable branch of this repository, including the main vd application, loaders, and plugins, is available for use and redistribution under GPLv3. VisiData is a free, open-source tool that lets you quickly open, explore, summarize, and analyze datasets in your computer’s terminal. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources. -
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Google Spreadsheets Python
Google Sheets Python API
...Google has deprecated it in favor of google-auth. If you’re still using oauth2client credentials, the library will convert these to google-auth for you, but you can change your code to use the new credentials to make sure nothing breaks in the future. If you familiar with the Jupyter Notebook, Google Colaboratory is probably the easiest way to get started using gspread. -
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alpha_vantage
A python wrapper for Alpha Vantage API for financial data.
...This module implements a python interface to the free API provided by Alpha Vantage. You can have a look at all the API calls available in their API documentation. For code-less access to the APIs, you may also consider the official Google Sheet Add-on or the Microsoft Excel Add-on by Alpha Vantage. To get data from the API, simply import the library and call the object with your API key. Next, get ready for some awesome, free, realtime finance data. Your API key may also be stored in the environment variable ALPHAVANTAGE_API_KEY. ... -
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Cryptocheck
Monitors balances of your cryptocurrency addresses
...It continuously monitors balances, records history data and sends all the data to your Cryptocheck desktop application. Supported cryptocurrencies: https://sourceforge.net/p/cryptocheck/wiki/Home/#supported-cryptocurrencies For more details about Cryptocheck and how to use it, see wiki: https://sourceforge.net/p/cryptocheck/wiki I am open to add other cryptocurrencies on your request. -
Gen AI apps are built with MongoDB AtlasMongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
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Financial Calculator
Professional financial tools: 16 sections with text and visual reports
...Whether you’re a student, small business owner, or finance professional, Financial Calculator brings professional-grade precision to your desktop — no installation required. Perform calculations for Bank, Barcode, Building, Buy & Sell, Commodity, Dividing Money, Inflation, Interest, Internet Usage, Invoice, Loan, Password, Phone, QR Code, Stock Market, and Tax. Unlock the full version with a small cryptocurrency payment (as low as $0.01). -
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Financial Calculator
Professional financial tools: 16 sections with text and visual reports
...Whether you’re a student, small business owner, or finance professional, Financial Calculator brings professional-grade precision to your desktop — no installation required. Perform calculations for Bank, Barcode, Building, Buy & Sell, Commodity, Dividing Money, Inflation, Interest, Internet Usage, Invoice, Loan, Password, Phone, QR Code, Stock Market, and Tax. Unlock the full version with a small cryptocurrency payment (as low as $0.01). -
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club
Software for small organisations like clubs.
Simplify management of bar, kitchen and memberships of small organisations, that don't need complicated management programs. Made in python, MongoDB is required. Source code: https://github.com/NrtypmForhp/club -
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Robin-Stocks API Library
This is a library to use with Robinhood Financial App
...In addition, it can be used to get real-time ticker information, assess the performance of your portfolio, and can also get tax documents, total dividends paid, and more. The code is simple to use, easy to understand, and easy to modify. With this library, you can view information on stocks, options, and cryptocurrencies in real-time, create your own robo-investor or trading algorithm, and improve your programming skills. The supported APIs are Robinhood, Gemini, and TD Ameritrade. If you are contributing to this project and would like to use automatic testing for your changes, you will need to install pytest and pytest-dotenv. ... -
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OpenOffice.org Utility Library
Library modules for creating ODF documents.
...These documents include many features including: - Create multiple table spreadsheets - Cells with text, numbers, dates, formulas - Ability to use built-in styles - Ability to create automatic styles (ie. bold, italics, underline, font size, font color, background color, etc.) - Set column and row attributes including width and height I am currently hosting the new development code at the following URL: https://github.com/josephcolton/ooolib-python I will be posting releases here on SourceForge as well as GitHub, so you can come to either place for the releases. The newest code will only be available using Git on GitHub. -
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FormaVid
Small Business Appliance
...The appliance is built using scripts so no hidden "monkey business" and you can choose a "compatible base distro" based on your needs: familiarity; performance; security; usability; etc. Please see https://sourceforge.net/p/formavid/code/ci/master/tree/INSTALL.txt for details. The appliance is specially designed to run on the Google Cloud Platform "Free Tier" https://formavid.org/gce and has it's own custom install script https://sourceforge.net/projects/formavid/files/gcloud-gce-deployment/download with instructions https://sourceforge.net/projects/formavid/files/ provided. -
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TA-Lib.git: Technical Analysis Library
Mirror of the TA-Lib project using a Git repository
This project is intended to provide Git access to the code of the original project, TA-Lib, which uses Subversion. It is intended for system integrators wishing to use TA-Lib in their Git-managed project through Git submodules or subtrees. No actual development is being done here; all development happens in the original project. -
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Optopsy
A nimble options backtesting library for Python
...The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. ... -
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abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc. -
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QuickFIX is the worlds first Open Source C++ FIX (Financial Information eXchange) engine, helping financial institutions easily integrate with each other. The SVN repository is now locked. Latest code is hosted at github. https://github.com/quickfix/quickfix
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...The API has been developed by reverse engineering network traffic between FX TradingStation (FXCM's trading platform) and FXCM's servers. The API substitutes client side of FX TradingStation (FXTS) system so FXCM servers treat Python code using this API as regular FXTS session. The API has been developed in time when automated trading was not available to retail traders or access to API was too "expensive" (deposit on account bigger than $100k). Today FXCM API is open to all FXCM traders and more can be found at: https://github.com/FXCMAPI https://www.fxcm.com/trading-services/api-trading
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Technical indicators in Python
Technical indicators in Python
Technical indicators in Python For now there are: RSI - Relative Strength Index, SMA - Simple Moving Average, WMA - Weighted Moving Average, EMA - Exponential Moving Average, BB - Bollinger Bands, Bollinger Bandwidth, %B, ROC and MA envelopes When I can I will add more. If anyone wishes to contribute with new code or corrections/suggestions, feel free. -
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Luciano's Quant Blog
Assorted bits of code for quant development
This repo contains code that I share through my blog. It consists of assorted samples that will be useful to amateur quantitative developers or systematic traders. -
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QuotesViewer is a graphical tool giving you easy and fast access to quotes of all shares on the Euronext stock exchange. Quotes information can be searched and sorted on different criteria, ie. market, ISIN code, mnemonic, name, price, volume.
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A personal accounting project, based on the idea of getting the data models and fundamentals right first. Expect a large number of documentation-only updates before seeing working code.
