energyquantified
Energy Quantified Time series API client.
Decision gist · record as of 2026-08-14
Yes, if you have an Energy Quantified account and need programmatic access to energy market time series. The package is actively maintained, has low install friction, carries a permissive license, and integrates well with standard data analysis tools. No known vulnerabilities. Requires Python 3.10+ and a valid API key.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and a valid Energy Quantified API key (account creation required).
- Low friction install with five lightweight runtime dependencies.
- The package is actively maintained with a recent release; last commit was 2026-07-22.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-02-04 (191 days) · last repo commit 2026-07-22 · 34 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 228,608 downloads/mo, #9,149 on PyPI
Alternatives
Verify before relying
pip install energyquantified
from energyquantified import EnergyQuantified
from datetime import date, timedelta
eq = EnergyQuantified(api_key='your_api_key')
curves = eq.metadata.curves(q='de wind production actual')
timeseries = eq.timeseries.load(
curves[0],
begin=date.today() - timedelta(days=10),
end=date.today()
)
pd_df = timeseries.to_pandas_dataframe()- Whether the API key requirement and trial account limitations affect typical use cases
- Performance characteristics when handling large time series datasets
- Specific data series coverage and update frequency from Energy Quantified's database
What it is and what it does
energyquantified is a Python client for Energy Quantified's time series API, designed to fetch energy market data directly into your Python environment. It handles authentication, metadata caching, rate-limiting, and automatic retries, and provides full-text search across thousands of data series covering forecasts, OHLC data, and period-based metrics. The library integrates seamlessly with pandas and polars for downstream analysis.
You initialize a client with an API key, search for data series by name or attributes, load time series within a date range, and convert results to pandas or polars DataFrames. It supports timezone handling, unit conversions, and aggregation options. Requires Python 3.10 or later and an active Energy Quantified account (trial users get 30 days of history).
Use it for
- Fetch historical wind and solar production data for energy market analysis or forecasting models
- Build dashboards that pull live energy prices and forecasts into pandas DataFrames for reporting
- Retrieve OHLC and SRMC data for backtesting trading strategies on energy commodities
- Aggregate multi-series energy data across regions and resolutions for research or compliance reporting
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have an Energy Quantified account and need programmatic access to energy market time series.
The package is actively maintained, has low install friction, carries a permissive license, and integrates well with standard data analysis tools. No known vulnerabilities. Requires Python 3.10+ and a valid API key.
Install
energyquantified on PyPI
Before you install
Low friction install with five lightweight runtime dependencies. The package is actively maintained with a recent release; last commit was 2026-07-22.
Requires Python 3.10 or later and a valid Energy Quantified API key (account creation required).
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install energyquantified
from energyquantified import EnergyQuantified
from datetime import date, timedelta
eq = EnergyQuantified(api_key='your_api_key')
curves = eq.metadata.curves(q='de wind production actual')
timeseries = eq.timeseries.load(
curves[0],
begin=date.today() - timedelta(days=10),
end=date.today()
)
pd_df = timeseries.to_pandas_dataframe()Verify before relying
- Whether the API key requirement and trial account limitations affect typical use cases
- Performance characteristics when handling large time series datasets
- Specific data series coverage and update frequency from Energy Quantified's database
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagespython-dateutilpytzrequeststzlocalwebsocket-client |
| Maintenance | Actively maintained 191 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 228,608 / month, #9,149 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Office/Business :: FinancialTopic :: Software Development :: Libraries :: Python Modules |
Evidence: energyquantified-0.15.1-py3-none-any.whl
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