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Help Docs Sponsors Log in Register Search PyPI Search histdata-fetcher 0.1.0 Download free historical Forex tick and
1-minute bar data from HistData.com into pandas. pip install histdata-fetcher Copy PIP instructions Description Download
files Release history histdata-fetcher Download free historical Forex data from HistData.com straight into a pandas
DataFrame — 1-minute OHLC bars or raw bid/ask ticks, for any of the ~66 instruments the site publishes. HistData.com
has no official API. This library drives the same request flow the download page itself uses, fetches the per-period zip
files concurrently, unpacks and parses them, and hands back one tidy, sorted DataFrame. from histdata_fetcher import
fetch_data result = fetch_data("EUR/USD", "2024-01-01", "2024-03-31", "1min") print(result.data.head())
print(f"{len(result.data):,} bars -> {result.output_path}") datetime open high low close volume 0 2024-01-01 17:00:00
1.10441 1.10448 1.10441 1.10448 0.0 1 2024-01-01 17:01:00 1.10450 1.10453 1.10444 1.10444 0.0 Install pip install
histdata-fetcher Requires Python 3.9+. Pulls in pandas, requests, and pyarrow (for the default Parquet output). Usage
Fetch data from histdata_fetcher import fetch_data result = fetch_data( pair="EURUSD", # "EUR/USD", "eur-usd" etc. all
work start_date="2024-01-01", # str, datetime.date, or datetime.datetime end_date="2024-06-30", # inclusive
timeframe="1min", # "1min" (M1 bars) or "tick" (raw bid/ask) output_format="parquet", # "parquet", "csv", or None to
skip writing output_path=None, # defaults to ./<PAIR>_<tf>_<start>_<end>.parquet max_workers=8,
# zip files downloaded concurrently ) fetch_data returns a FetchResult: Attribute Meaning .data the combined
pandas.DataFrame, sorted by datetime .output_path Path written, or None if nothing was written .fetched_periods period
labels that downloaded, e.g. ["2024", "2025-01"] .failed_periods FailedPeriod records (label, start, end, reason) .ok
True when .data is non-empty Periods the site has no data for are reported, not raised — a gap in the middle of a long
range will not abort the whole pull: result = fetch_data("XAUUSD", "2005-01-01", "2024-12-31", "1min",
output_format=None) for f in result.failed_periods: print(f"{f.period_label}: {f.reason}") To work purely in memory,
pass output_format=None. List available instruments from histdata_fetcher import get_available_pairs catalog =
get_available_pairs("1min", resolve_end_date=False) # one HTTP request print(len(catalog)) # 66
print(catalog["EURUSD"].start_date) # 2000-05-01 resolve_end_date=True (the default) additionally resolves each pair's
most recent published period, which costs one request per pair. Use resolve_end_date=False when you only need the pair
list and start dates. Data notes These are properties of HistData's data, not of this client — worth knowing before
you build on it. Timestamps are EST without DST. Per HistData's FAQ, every timestamp is Eastern Standard Time (UTC−5)
year-round, with no daylight-savings shift. This library leaves them tz-naive, exactly as published. Localize them
yourself if you need UTC: df["datetime"] = df["datetime"].dt.tz_localize("Etc/GMT+5").dt.tz_convert("UTC") Volume is
always 0. HistData does not publish volume for forex/CFD data. The column is kept so the schema matches the source
files. Ticks share timestamps, and are not de-duplicated. Tick timestamps are at best millisecond-resolution, so
genuinely distinct quotes routinely land on the same timestamp. Worse, the resolution is not stable over time —
measured on EURUSD, about 4% of rows in June 2026 share a timestamp, rising to ~50% in July and August 2026, where
HistData publishes whole-second timestamps (milliseconds always 000). There is no unique key, so tick rows are returned
exactly as published, in published order; treating datetime as unique will silently throw away real market data.
1-minute bars are de-duplicated on datetime, since one bar per minute is a true unique key. File granularity differs by
timeframe. 1-minute data is served as one zip per year for elapsed years and one per month for the current year; tick
data is monthly only. The client works this out for you — a request that spans both simply produces a mix, visible in
.fetched_periods. Schemas timeframe="1min" column dtype datetime datetime64 (EST, tz-naive) open / high / low / close
float64 volume float64 (always 0) timeframe="tick" column dtype datetime datetime64 (EST, tz-naive, millisecond
resolution) bid / ask float64 volume float64 (always 0) Sizing your requests Tick data is large: one month of EURUSD
ticks is roughly 1.4 million rows (~9 MB compressed). Pulling several years of ticks in one call will hold all of it in
memory before writing. For big historical pulls, loop a year at a time and write each to its own file. Errors Exception
Raised when PairNotAvailableError the pair isn't offered for that timeframe PeriodUnavailableError a single period
failed (caught internally; surfaces via .failed_periods) HistDataError base class for the above; also raised if the site
layout can no longer be parsed ValueError bad arguments — unknown timeframe, start > end, range entirely before the
pair's first data A start_date earlier than the pair's first published month is clamped forward, and an end_date in the
future is clamped to today; both log a warning. Logging import logging logging.basicConfig(level=logging.INFO)
logging.getLogger("histdata_fetcher").setLevel(logging.DEBUG) Development git clone
https://github.com/Njenjo/histdata-fetcher cd histdata-fetcher pip install -e ".[dev]" pytest pytest runs the offline
suite against an in-process fake of the site — no network needed. The live end-to-end tests are opt-in: pytest -m
network Stability This library scrapes an HTML download flow rather than a documented API, so a redesign of histdata.com
can break it. Parsing failures raise HistDataError with a clear message rather than returning silently wrong data. If
the pair list or download form stops parsing, please open an issue. Legal The data belongs to HistData.com and is
provided under their terms of use — free for personal and educational use, with redistribution restrictions. This
library is an unaffiliated client that automates the public download flow; you are responsible for using it within those
terms. Please keep max_workers modest and don't hammer the site. License MIT — see LICENSE. Project links Homepage
Issues Source Key dates PyPI data Data sourced directly from PyPI's database. Released: Aug 22, 2026 Latest release 1
maintainer PyPI data Data sourced directly from PyPI's database. njenjo Credits Author: householddude License MIT
License (MIT License) Requires Python >=3.9 Provides Extra dev Tags backtesting forex fx histdata historical-data
market-data ohlc pandas tick-data trading Classifiers Development Status 4 - Beta Intended Audience Developers Financial
and Insurance Industry License OSI Approved :: MIT License Operating System OS Independent Programming Language Python
:: 3 Python :: 3.9 Python :: 3.10 Python :: 3.11 Python :: 3.12 Python :: 3.13 Topic Office/Business :: Financial ::
Investment Scientific/Engineering :: Information Analysis Typing Typed Report project as malware Download files Download
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