Python client for Alpaca's trade API
Les fonctionnalités principales de alpacahq/alpaca-trade-api-python sont : Financial Analytics, Market Data Sources.
Les alternatives open-source à alpacahq/alpaca-trade-api-python incluent : openbb-finance/openbbterminal — OpenBBTerminal is a Python financial data platform and command line interface designed for aggregating and analyzing… cuemacro/findatapy — Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc. ranaroussi/yfinance — This library is a Python-based tool for retrieving historical and real-time financial market data from public sources.… je-suis-tm/quant-trading — This project is a Python financial analytics framework and quantitative trading library. It provides a suite of… edtechre/pybroker — pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for… ricequant/rqalpha — RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides…
This library is a Python-based tool for retrieving historical and real-time financial market data from public sources. It functions as a programmatic interface for downloading stock prices, dividends, financial statements, and corporate calendars, allowing users to perform automated research and analysis on various market assets. The project distinguishes itself by structuring retrieved financial time series directly into tabular data frames, which facilitates mathematical analysis and manipulation of market metrics. It supports efficient data retrieval through multi-threaded batch downloadin
OpenBBTerminal is a Python financial data platform and command line interface designed for aggregating and analyzing market data from diverse APIs. It serves as a quantitative analysis tool for processing stock, crypto, and derivative datasets to identify market trends and build investment strategies. The project utilizes a pluggable financial API framework with an adapter-based architecture, allowing external financial data providers to be integrated as independent modules. This system standardizes information from public and proprietary sources into a unified layer to support cross-asset an
Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.
This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The