A command-line utility and Python ETL package that downloads currency exchange rates from Histdata.com with a default Temporal orchestration runtime and optional InfluxDB/Jupyter integrations.

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  • License Expression: MIT
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  • Tags finance , data , datascience , HistData.com , scraper , influxdb , currency exchange , forex , fx , etl
  • Requires: Python >=3.10.0
  • Provides-Extra: arrow , influx , pandas , jupyter , temporal , all , test , lint , release , dev
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Project description

histdata.com-tools

A command-line utility and Python ETL package for HistData.com currency exchange rate archives. The local Temporal orchestration runtime is the default execution engine for durable planning, downloads, extraction, cache builds, imports, job telemetry, and live Rich progress, while normal PyPI artifacts stay lean by provisioning the pinned Temporal executable through a verified first-run cache.

Data-quality checks cover ZIP/file inventory, CSV/XLSX ingestion, timestamp continuity, OHLC bars, tick and spread behavior, symbol/domain calendars, modeling readiness, and orchestration provenance with JSON reports and CI-friendly exit policies.

InfluxDB imports, Jupyter tooling, and optional pandas/Arrow return formats are available through extras.

Works on macOS, Linux, and Windows. Requires Python 3.10+

Downloads PyPI - License PyPI PyPI - Status

Disclaimer

*I am in no way affiliated with histdata.com or its maintainers. Please use this application in a way that respects the hard work and resources of histdata.com

If you choose to use this tool, it is strongly suggested that you head over to http://www.histdata.com/download-by-ftp/ and sign up to help support their traffic costs.

If you find this tool helpful and would like to support future development, I'm in need of caffeine, feel free to buy me coffee!

Usage

Note #1 The number one rule when using this tool is to be MORE specific with your input to limit the size of your request.

Note #2 histdatacom is a very powerful tool and has the capability to fetch the entire repository housed on histdata.com. This is NEVER necessary. If you are using this tool to fetch data for your favorite trading application, do not download data in all available formats.

It is likely the default behavior will be modified from its current state to discourage unnecessarily large requests.

*please submit feature requests and bug reports using this repository's issue tracker.

Show the help and options

histdatacom -h

usage: histdatacom [-h] [-A] [-U] [--by BY] [--version] [-V] [-D] [-X] [-C]
                   [--config PATH] [-p PAIR [PAIR ...]]
                   [--pair-groups GROUP [GROUP ...]] [-f FORMAT [FORMAT ...]]
                   [-t TIMEFRAME [TIMEFRAME ...]] [-s START_YEARMONTH]
                   [-e END_YEARMONTH] [-I] [-d] [-b BATCH_SIZE]
                   [-c CPU_UTILIZATION] [--data-directory DATA_DIRECTORY] [-v]
                   [--orchestration-start] [--no-orchestration-start]
                   [--submit-only] [--no-overlap]
                   [--schedule-key SCHEDULE_KEY] [--keep-runtime]
                   [--no-keep-runtime] [--request-json-out PATH]
                   [--request-bundle-out PATH] [--quality] [--repo-quality]
                   [--quality-preflight] [--repo-quality-columns]
                   [--quality-target PATH [PATH ...]]
                   [--quality-checks GROUP [GROUP ...]]
                   [--quality-report PATH] [--quality-preflight-report PATH]
                   [--quality-preflight-markdown]
                   [--quality-preflight-markdown-report PATH]
                   [--quality-preflight-validation-report PATH]
                   [--quality-preflight-run-validation]
                   [--quality-preflight-evidence PATH]
                   [--quality-preflight-evidence-max-age-seconds SECONDS]
                   [--quality-preflight-evidence-stale-ok]
                   [--quality-preflight-sample-size COUNT]
                   [--quality-profile PATH] [--quality-fail-on SEVERITY]
                   [--quality-max-errors COUNT] [--quality-max-warnings COUNT]

options:
  -h, --help            show this help message and exit

Mode:
  -V, --validate_urls   Check generated list of URLs as valid download
                        locations
  -D, --download_data_archives
                        download specified pairs/formats/timeframe and create
                        data files
  -X, --extract_csvs    histdata.com delivers zip files. Use the -X flag to
                        extract them.
  -C, --build-cache, --cache-only, --build_cache
                        build canonical Polars .data caches and remove
                        transient ZIP/CSV sources after each cache is ready

Config:
  --config PATH         read recurrent-run defaults from a YAML file; explicit
                        CLI flags override configured values
  -p, --pairs PAIR [PAIR ...]
                        space separated currency pairs. e.g. -p eurusd usdjpy
                        ...
  --pair-groups, --instrument-groups, --symbol-groups GROUP [GROUP ...]
                        named instrument groups to union with --pairs. Common
                        groups: majors, minors, crosses, exotics, major-
                        triangles, metals, commodities, indices
  -f, --formats FORMAT [FORMAT ...]
                        space separated formats. -f metatrader ascii
                        ninjatrader metastock
  -t, --timeframes TIMEFRAME [TIMEFRAME ...]
                        space separated Timeframes. -t tick-data-quotes
                        1-minute-bar-quotes
  -s, --start_yearmonth START_YEARMONTH
                        set a start year and month for data. e.g. -s 2000-04
                        or -s 2015-00
  -e, --end_yearmonth END_YEARMONTH
                        set an end year and month for data. e.g. -e 2020-00 or
                        -e 2022-04

Influxdb:
  -I, --import_to_influxdb
                        import data to influxdb instance. Use influxdb.yaml to
                        configure.
  -d, --delete_after_influx
                        delete data files after upload to influxdb
  -b, --batch_size BATCH_SIZE
                        (integer) influxdb write_api batch size. defaults to
                        5000

System:
  -c, --cpu_utilization CPU_UTILIZATION
                        "low", "medium", "high". High uses all available CPUs
                        OR integer percent 1-200
  --data-directory DATA_DIRECTORY
                        Directory Used to save data. default is "./data/"
  -v, --verbose         increase logging verbosity; repeat as -vv for debug
                        and -vvv for trace

Orchestration:
  --orchestration-start
                        start the local orchestration runtime only when no
                        healthy runtime is running
  --no-orchestration-start
                        submit only when a healthy orchestration runtime is
                        already running
  --submit-only         submit the orchestration job without waiting for its
                        result
  --no-overlap          refuse submission when an active matching scheduled
                        job already exists in this runtime workspace
  --schedule-key SCHEDULE_KEY
                        stable logical key used by --no-overlap for scheduled
                        jobs
  --keep-runtime        leave a runtime started by this command running after
                        the job completes
  --no-keep-runtime     stop a runtime started by this command after waited
                        jobs complete
  --request-json-out PATH
                        write the resolved RunRequest JSON payload to PATH
                        without submitting work; use '-' for stdout
  --request-bundle-out PATH
                        write a scheduled-run bundle JSON payload to PATH
                        without submitting work; use '-' for stdout

Data quality:
  --quality             run offline data-quality assessment against local
                        datasets without contacting HistData.com
  --repo-quality        run offline data-quality assessment and write bounded
                        quality summary metadata back to the local .repo file
  --quality-preflight   benchmark a deterministic sample of existing .data
                        caches before running a cache-scale quality battery
  --quality-target, --quality-path PATH [PATH ...]
                        local file or directory to assess; supports
                        directories, HistData ZIP archives, CSV files, XLSX
                        payloads, and .data cache files
  --quality-checks GROUP [GROUP ...]
                        quality check groups to run; defaults to all.
                        Supported: all, inventory, ingestion, time, bars,
                        ticks, domain, modeling, provenance
  --quality-report PATH
                        write the full machine-readable JSON quality report to
                        PATH
  --quality-preflight-report PATH
                        write the publish-safe JSON quality preflight report
                        to PATH
  --quality-preflight-markdown
                        print the publish-safe Markdown quality preflight
                        evidence report to stdout
  --quality-preflight-markdown-report PATH
                        write the publish-safe Markdown quality preflight
                        evidence report to PATH
  --quality-preflight-validation-report PATH
                        merge validation command status from a
                        closure/readiness JSON report into quality preflight
                        evidence; use 'latest' to discover the newest
                        compatible report under .histdatacom/closure-readiness
  --quality-preflight-run-validation
                        run bounded quality preflight validation commands
                        before rendering evidence
  --quality-preflight-evidence PATH
                        use a saved quality preflight JSON report as evidence
                        before a large cache-backed --quality run
  --quality-preflight-evidence-max-age-seconds SECONDS
                        maximum age for saved quality preflight evidence;
                        defaults to 86400
  --quality-preflight-evidence-stale-ok
                        allow matching quality preflight evidence even when
                        its generated_at_utc timestamp is stale
  --quality-preflight-sample-size COUNT
                        number of cache-size quantile targets to benchmark;
                        defaults to 4
  --quality-profile PATH
                        read a JSON quality profile with rule thresholds,
                        severities, and modeling assumptions
  --quality-fail-on SEVERITY
                        exit non-zero when configured thresholds are exceeded
                        for error, warning, or never. Defaults to error
  --quality-max-errors COUNT
                        maximum error findings allowed before quality mode
                        exits non-zero; defaults to 0
  --quality-max-warnings COUNT
                        maximum warning findings allowed before quality mode
                        exits non-zero when --quality-fail-on warning is
                        selected; defaults to 0

Info:
  -A, --available_remote_data
                        list data retrievable from histdata.com
  -U, --update_remote_data
                        update list of data retrievable from histdata.com
  --by BY               With -A, -U, to sort --by [pair_asc, pair_dsc,
                        start_asc, start_dsc]
  --version             return current version of histdatacom.
  --repo-quality-columns
                        include stored data-quality status columns in -A/-U
                        repository table output

Commands:
  analytics   Run offline data analytics operations
  cleanup     Remove transient source artifacts
  groups      List instrument groups and major triangles
  jobs        Inspect and control orchestrated work
  quality     Inspect local data quality evidence
  runtime     Inspect and manage the orchestration runtime

Run `histdatacom analytics --help` for analytics commands.
Run `histdatacom cleanup --help` for cleanup commands.
Run `histdatacom groups --help` for group discovery commands.
Run `histdatacom jobs --help` for job telemetry commands.
Run `histdatacom quality --help` for quality commands.

Maintainers: this help excerpt is generated from ArgParser.format_help() at a fixed width. After changing public CLI flags, run:

python scripts/sync_readme_cli_help.py
python -m pytest tests/unit/test_readme_help_sync.py

For repeatable issue closure evidence, run the local readiness helper from dev after implementation work is complete:

python scripts/closure_readiness.py \
  --issue 274 \
  --commit-readiness \
  --commit-message "feat(scope): describe the change" \
  --commit-path path/to/changed-file.py \
  --acceptance-test '*=tests/unit/test_changed_behavior.py'
python scripts/closure_readiness.py \
  --issue 274 \
  --closure-verification \
  --infer-commit-paths \
  --commit-message "feat(scope): describe the change" \
  --acceptance-test '*=tests/unit/test_changed_behavior.py'
python scripts/closure_readiness.py --issue 274 --push-readiness
python scripts/closure_readiness.py --issue 274 --issue-audit
python scripts/closure_readiness.py --issue 274 --workflow
python scripts/closure_readiness.py \
  --issue 274 \
  --run-gates \
  --rerun-standalone-formatter-mutations
python scripts/closure_readiness.py \
  --summarize-report .histdatacom/closure-readiness/closure-274.json
python scripts/closure_readiness.py --open-issue-audit
python scripts/closure_readiness.py --open-issue-audit --json
python scripts/closure_readiness.py --issue 274 --workflow --close-issue
python scripts/closure_readiness.py \
  --issue 274 \
  --execute-workflow \
  --pre-mutation-gates \
  --rerun-formatter-mutations \
  --commit-message "feat(scope): describe the change" \
  --commit-path path/to/changed-file.py \
  --acceptance-test '*=tests/unit/test_changed_behavior.py'

The helper checks branch/upstream alignment, dirty and untracked files, linked GitHub issue state, lingering pytest/pre-commit/Temporal/histdatacom tool processes before and after gates, transient ZIP/CSV/XLS/XLSX source artifacts under data/, README help synchronization, git diff --check, main help smoke output, pytest, and pre-commit. Reports are publish-safe JSON and Markdown with a GitHub-ready close comment block. --commit-readiness validates the current change scope and candidate Commitizen message without running git add, git commit, or git push; use repeated --commit-path flags to declare the intended file scope and catch unrelated dirty files. When paired with --issue, the same report-only mode also accepts the --acceptance-* evidence flags and prints commit readiness plus acceptance coverage in one human or JSON payload. --push-readiness reports whether a clean dev branch with local commits ahead of origin/dev is ready to push. Default issue-scoped reports are local outputs under .histdatacom/closure-readiness/; the helper verifies those paths are gitignored before writing them and blocks closure if that safety check drifts. --closure-verification is the one-shot non-mutating readiness mode for issue work: it validates commit scope and message, acceptance coverage, focused pytest commands supplied through --acceptance-test, full closure gates, optional TestPyPI/simple-registry preflight, final git status, GitHub CLI/auth state, issue readback, local workflow policy, source-artifact cleanliness, and owned process health, then prints the exact --execute-workflow command when ready. Use repeated --commit-path flags for an explicit scope or --infer-commit-paths to record the current dirty worktree as the intended scope while warning on broad or ambiguous inferred sets. Explicit report paths still work, but the report marks whether they may dirty the current worktree. --workflow performs the cheap precheck first, stops before expensive gates when local state is blocked, writes safe default reports, and enforces the dev branch workflow. Use --close-issue only when ready to close; it remains an explicit opt-in action and reads back the final issue state after closing. --execute-workflow is the explicit mutating mode: it validates the declared paths and Commitizen message, runs targeted git add, commits, checks push readiness, pushes to the expected upstream, runs closure gates, closes the issue, and writes bounded execution evidence plus full ignored logs. --open-issue-audit is the non-mutating whole-queue triage mode: it reads the live open GitHub issue set, local branch/upstream/worktree state, bounded source signals, and recent issue context, then classifies and ranks the next suggested action in compact human output or stable JSON. Add --pre-mutation-gates to run the same closure gate battery before the first git add; the workflow blocks staging, commit, and push if those gates fail or rewrite files, and records the result separately from the post-push closure gates. Gate-induced file rewrites are reported with changed paths, responsible gates, and whether the mutation appears to be formatter/tool output. The default behavior stays conservative: formatter rewrites still block until the required focused verification and full gate rerun are complete. For standalone --run-gates reports, add --rerun-standalone-formatter-mutations only when you want that one-time formatter/tool-only rerun performed automatically. Add --rerun-formatter-mutations only when you want the workflow to perform that one-time formatter/tool-only rerun automatically before staging. Successful execution prints a compact closeout with final issue, branch, commit, acceptance, report-path, runtime/process health, and the slowest workflow phases. Use --json for the same compact closeout as a stable scriptable payload; use --full-json only when stdout needs the full execution evidence object. Long workflow runs also stream bounded phase progress to stderr so JSON stdout stays parseable; add --quiet-progress when automation should suppress live progress while retaining phase timing in the saved evidence report. Issue closure reports parse issue checklists or Acceptance criteria bullets into acceptance coverage evidence. Attach criterion-specific or shared evidence with --acceptance-status, --acceptance-file, --acceptance-test, --acceptance-report, or --acceptance-note using KEY=VALUE; KEY can be ac-001, a criterion number, slug, hash, or * for all criteria. Automatic issue close refuses missing required criteria unless --acceptance-missing-ok is supplied, and the override reason is recorded with --acceptance-override-reason. Default behavior remains report-only unless this flag is present. Add --release-preflight only during publishing work; normal issue closure records the TestPyPI local simple-registry preflight as not-applicable.

Basic Use

Download and extract the current month's available EURUSD data for metatrader 4/5into the default data directory ./data

histdatacom -p eurusd -f metatrader -s now

include the -D flag to download but NOT extract to csv

histdatacom -D -p usdcad -f metastock -s now

include the -C flag to build internal Polars caches and discard ZIP/CSV sources

histdatacom -C -p eurusd -f ascii -t tick-data-quotes -s 2024-01 -e 2024-03

Cache-only mode validates and downloads the selected HistData archives, builds canonical .data cache files, and removes transient ZIP/CSV sources after each cache is ready. It is intentionally limited to cache-capable ASCII M1 and tick quote datasets, and it does not merge the caches into memory.

clean up transient source artifacts without removing internal caches

histdatacom cleanup sources --data-directory data
histdatacom cleanup sources --data-directory data --apply
histdatacom cleanup status --data-directory data --pair-groups majors -f ascii -t T

Cleanup mode is a dry run unless --apply is present. It removes downloaded ZIP, CSV, XLS, and XLSX source artifacts while preserving internal .data caches. Use cleanup status to inspect cache counts, pending source cleanup, disk pressure, runtime state, and offline workflow snapshots for a symbol or instrument group without shelling out to find, df, ps, or raw Temporal commands. Add --json for the stable scriptable payload.

Configuration Files

Use --config PATH to keep recurrent command options in a YAML file. The file may use a histdatacom: root section or a bare mapping. Keys match the public CLI option names without leading dashes. Explicit CLI flags are parsed after the file and override configured scalar and list values.

histdatacom:
  download_data_archives: true
  extract_csvs: true
  pairs:
    - eurusd
    - gbpusd
  # Or use named groups instead of listing every symbol:
  # instrument_groups: [majors, metals]
  formats:
    - ascii
  timeframes:
    - 1-minute-bar-quotes
  start_yearmonth: 2022-01
  end_yearmonth: 2022-03
  data_directory: /data/histdata
  request_bundle_out: requests/eurusd-cache-bundle.json
  request_json_out: requests/eurusd-cache.json
  cpu_utilization: medium
  orchestration_start: true
  orchestration_wait_result: false
  no_overlap: true
  schedule_key: eurusd-cache
  verbosity: 1

Run it with:

histdatacom --config recurrent.yaml

Config files can also express offline data-quality runs:

histdatacom:
  quality: true
  data_directory: data/
  quality_checks:
    - inventory
    - ingestion
  quality_report: reports/quality.json
  quality_fail_on: error

The routed commands use scoped sections in the same file:

histdatacom:
  analytics:
    command: feed-regimes
    target: data/ASCII/T/eurusd
    bucket: month
    report: reports/eurusd-feed-regimes.json
    json: true
  jobs:
    command: submit
    request_bundle: requests/eurusd-cache-bundle.json
    submit_only: true
    json: true
  cleanup:
    command: status
    data_directory: data/
    pair_groups:
      - majors
    json: true
  runtime:
    command: status
    json: true

Run scoped commands with the same flag:

histdatacom --config recurrent.yaml analytics
histdatacom cleanup --config recurrent.yaml
histdatacom jobs --config recurrent.yaml
histdatacom runtime --config recurrent.yaml

Pair-list presets and shared instrument lists are tracked separately from this full command snapshot surface.

For recurrent low-disk cache-building jobs, set build_cache: true instead of download_data_archives / extract_csvs. The option accepts the same dataset selectors as the CLI and leaves only the internal .data cache artifacts for supported ASCII M1 and tick quote datasets.

Available Formats

The formats available are:
metatrader
metastock
ninjatrader
excel
ascii

histdata.com provides different resolutions of time depending on the format.

The following format/timeframe combinations are available:
1-minute-bar-quotes all formats
tick-data-quotes ascii
tick-last-quotes ninjatrader
tick-bid-quotes ninjatrader
tick-ask-quotes ninjatrader
CSV Dialect and Format Specifications
To download 1-minute-bar-quotes for both metastock and excel
histdatacom -p usdjpy -f metastock excel -s now

Date Ranges

date ranges are for year and month and can be specified in the following ways:

[ -._]
2022-04
"2202 04"
2202.04
2202_04
to fetch a single year's data, leave out the month
  • note: unless you're fetching data for the current year, tick data types will fetch 12 files for each month of the year, 1-minute-bar-quotes will fetch a single OHLC file with the whole year's data.
histdatacom -p udxusd -f ascii -t tick-data-quotes -s 2011

Use named instrument groups for common baskets:

histdatacom --pair-groups majors exotics -f ascii -t tick-data-quotes -s 2022

Use the major triangle basket when preparing data for cross-instrument quality analytics:

histdatacom --pair-groups major-triangles -f ascii -t tick-data-quotes -s 2022

major-triangles covers the USD, EUR, JPY, GBP, CAD, CHF, AUD, and NZD instruments needed by the data-quality triangular comparison rule: 28 downloadable instruments supporting 56 oriented relationships such as AUDCHF / CADCHF ~= AUDCAD. It excludes exotics, metals, commodities, and indices.

Select one oriented triangle by naming all three symbols in the relationship:

histdatacom --pair-groups triangle-eurgbp-eurusd-gbpusd -f ascii -t tick-data-quotes -s 2022

Individual triangle group names use the pattern triangle-{direct}-{numerator}-{denominator}. For example, triangle-eurgbp-eurusd-gbpusd downloads eurgbp, eurusd, and gbpusd for the rule EURUSD / GBPUSD ~= EURGBP.

Discover available groups without inspecting the source:

histdatacom groups list
histdatacom groups list --triangles
histdatacom groups show major-triangles
histdatacom groups show triangle-eurgbp-eurusd-gbpusd
histdatacom groups list --triangles --json

histdatacom groups list shows broad baskets such as majors and major-triangles. Add --triangles to list each individual major triangle group with its readable relationship rule.

to fetch a single month's data, include a month, but do not use the -e, --end_yearmonth flag
  • if you're requesting 1-minute-bar-quotes for any year except the current year, you will receive the the whole year's data
  • this example leaves out the -p --pair flag, and will fetch data for all 66 available instruments
histdatacom -f metatrader -s 2012-07

Start & Now Keywords

you may have noticed that two special year-month keywords exist start and now

  • start may only be used with the -s --start_yearmonth flag and the -e --end_yearmonth flag must be specified to indicate a range of data
histdatacom -p audusd -f metatrader -s start -e 2008-12
  • now used alone will return the current year-month
  • when used with as -s now it will return the most current month's data
histdatacom -p frxeur -f ninjatrader -s now

in the above example, no -t --timeframe flag was specified. This will return all time resolutions available for the specified format(s)

now when used with the -e --end_yearmonth flag is intended to be the end of a range. Rather, if the flags were to be -s 2019-04 -e now the request would return data from April 2019-04 to the present.

histdatacom -p xagusd -f ascii -1-minute-bar-quotes -s 2019-04 -e now
Multiple Datasets
multiple datasets can be requested in one command

this example with use the -e --end_yearmonth flag to request a range of data for multiple instruments.

  • note: Large requests like these are to be avoided. remember to sign up with histdata.com to help them pay for network costs
histdatacom -p eurusd usdcad udxusd -f metatrader -s start -e 2017-04
CPU Utilization

One can set a cap on CPU Utilization with -c --cpu_utilization

  • available levels are, "low","medium","high"
  • OR
  • integer percent 1-200 eg. -c 100 is equal to -c high
histdatacom -c medium -p udxusd -f metatrader -s 2015-04 -e 2016-04

Import to InfluxDB

To import data to an influxdb instance, install the Influx extra and use the -I --import_to_influxdb flag along with an influxdb.yaml file in the current working directory (where ever you are running the command from).

pip install "histdatacom[influx]"
  • ascii is the only format accepted for influxdb import.
  • all histdata.com datetime data is in EST (Eastern Standard Time) with no adjustments for daylight savings.
  • Influxdb does not adjust for timezone and all datetime data is recorded as UTC epoch timestamps (nano-seconds since midnight 00:00, January, 1st, 1970)
  • this tool converts histdata.com ESTnoDST to UTC Epoch milli-second timestamps as part of the import-to-influx process
histdatacom -I -p eurusd -f ascii -t tick-data-quotes -s start -e now

Docker-backed InfluxDB Smoke

When Docker is available, contributors can run a disposable InfluxDB v2 smoke without a user-managed influxdb.yaml:

python scripts/smoke_influx_docker.py

The smoke starts influxdb:2.7-alpine, writes representative HistData M1 and tick line-protocol batches through the real InfluxBatchWriter, queries the bucket, reports the field count, and removes the container. It is intentionally not part of default pytest because it depends on Docker and a pullable InfluxDB image.

influxdb.yaml

# a sample influxdb.yaml file.
influxdb:
  org: influx_org
  bucket: data_bucket
  url: influx_server_api_url
  token: influx_user_token
Download influxdb.yaml to your project's directory
curl "https://raw.githubusercontent.com/dmidlo/histdata.com-tools/main/influxdb.sample.yaml" --output influxdb.yaml

Data Quality Assessments

histdatacom --quality runs offline checks against datasets that are already on disk. It does not contact HistData.com or InfluxDB; it submits a local Temporal orchestration DataQualityWorkflow that runs CPU/file activities. Successful default runs use a scratch report and delete it after validation; pass --quality-report PATH when a durable detailed JSON report is needed. Use it after downloading or extracting data, before trusting local ZIP, CSV, or cache artifacts for import, modeling, or backtesting.

histdatacom --quality --quality-target data/ --quality-report reports/quality.json

The command prints a human summary, source-artifact cleanliness, and scratch report cleanup status. If no --quality-target is passed, quality mode uses the configured data directory. Targets can be plain HistData CSV files, extracted Excel .xlsx payloads, HistData ZIP archives, directories containing those files, or the canonical .data cache file.

Use --repo-quality when the same quality run should also update the local repo helper file with bounded per-instrument quality summaries:

histdatacom --repo-quality --quality-target data/ --quality-report reports/quality.json

The .repo quality metadata stores summary counts, status, checked groups, formats/timeframes/periods, and report artifact references. Detailed findings stay in the JSON quality report on disk. Ordinary -A and -U repository list/update commands do not run quality checks. To display stored quality columns in repository output, use:

histdatacom -A --repo-quality-columns

Cache-Scale Quality Preflight

Use --quality-preflight before a large cache-backed quality battery. It scans existing canonical .data caches, selects a deterministic cache-size quantile sample, runs the selected quality checks against that bounded sample, measures rows/sec and bytes/sec, and compares the extrapolated runtime with the Temporal data_quality activity budget.

histdatacom --quality-preflight \
  --quality-target data \
  --pair-groups major-triangles \
  -f ascii -t tick-data-quotes \
  --quality-checks ticks \
  --quality-preflight-report reports/major-triangles-tick-preflight.json \
  --quality-preflight-markdown-report reports/major-triangles-tick-preflight.md

The console output is human-readable. The optional --quality-preflight-report PATH file is publish-safe JSON with target counts, cache bytes, sampled paths, row counts, throughput, ETA range, sample quality summary, generated timestamp, package version, preflight policy inputs, no-target diagnostics, and a decision section that says whether the full battery is safe, warned, failed, or has no matching targets. Safe and warned decisions include the next histdatacom --quality ... command for the same target scope. Use --quality-preflight-markdown-report PATH to write the matching copy/paste-safe Markdown evidence report for GitHub issue updates, release handoffs, or operator notes. That Markdown includes command/config summary, package version, cache inventory, benchmark sample, ETA/rate, Temporal budget, source-artifact cleanliness, POSIX disk headroom, validation commands, and the explicit runtime-cleanup disposition for the local preflight run. Use --quality-preflight-markdown when stdout should be the Markdown report instead of the compact console summary. Use --quality-preflight-sample-size COUNT to tune the bounded sample.

Validation rows stay not-run by default so ordinary quality preflights do not run repository gates. For release notes or GitHub issue evidence, pass --quality-preflight-validation-report PATH to merge command status from a closure/readiness JSON report. Use --quality-preflight-validation-report latest to resolve the newest compatible JSON report under .histdatacom/closure-readiness without running gates. Pass --quality-preflight-run-validation to run only the bounded local validation bundle: focused quality-preflight tests and git diff --check. Full pytest, pre-commit, publishing, and GitHub issue closure remain explicit closure/release workflow responsibilities.

When launching a large cache-backed --quality run, pass the saved report with --quality-preflight-evidence PATH. If no matching evidence is available, the CLI prints a warning and suggested preflight command before continuing without prompting. Evidence must match the target root, filters, current package version, Temporal data_quality budget, cache target count, and cache byte inventory. Evidence also has to be fresh by default; use --quality-preflight-evidence-max-age-seconds SECONDS to change the 86400-second window, or pass --quality-preflight-evidence-stale-ok to explicitly bypass the age check while still enforcing scope, version, policy, and cache-inventory matches.

Inspect saved evidence directly when you need a non-interactive answer before a large run:

histdatacom quality evidence \
  --evidence reports/major-triangles-tick-preflight.json \
  --target data \
  --pair-groups major-triangles \
  -f ascii -t tick-data-quotes \
  --quality-checks ticks

The command exits 0 only when the evidence is accepted for the current cache scope. Use --json for automation. Rejections distinguish stale evidence, package-version drift, Temporal policy drift, target/filter drift, and cache inventory count, byte, or fingerprint changes. Add --quality-preflight-evidence-stale-ok only when you intentionally want to bypass the age window while still enforcing the other checks.

Full-Dataset Quality Campaigns

Full HistData.com quality campaigns should run in bounded symbol/format/timeframe slices from an environment with a verified Temporal executable: an explicit override, an offline/private bundled artifact, a verified runtime cache entry, or a resolver-provisioned first-run download. Do not run the full repository surface as one accumulating local scrape.

For each slice, run download/extract first, then run --repo-quality so .repo keeps bounded findings and the detailed JSON report path. Normal campaign execution keeps the generated cache artifacts. For low-disk cache-building campaigns, use --build-cache; it builds canonical .data files and removes the transient ZIP/CSV sources as each cache completes. Run cleanup only after --repo-quality succeeds, and never remove .repo or published quality reports.

For interrupted cache builds or older local source artifacts, use histdatacom cleanup sources to inspect removable ZIP, CSV, XLS, and XLSX files, then repeat with --apply when the report is expected. The cleanup command preserves internal .data cache files. Use histdatacom cleanup status first when an operator needs the cache count, pending cleanup count, disk pressure, runtime state, and durable workflow status in one report.

histdatacom -D -X -p eurusd -f ascii -t M1 --data-directory /Volumes/histdata/data
histdatacom --repo-quality \
  --quality-target /Volumes/histdata/data/ASCII/M1/eurusd \
  --quality-report /Volumes/histdata/reports/eurusd-ascii-m1-quality.json \
  --data-directory /Volumes/histdata/data
histdatacom --build-cache -p eurusd -f ascii -t M1 --data-directory /Volumes/histdata/data

Quality Targets and Check Groups

Quality groups are composable. all is the default and cannot be combined with specific groups in the same command.

histdatacom --quality --quality-target data/ --quality-checks inventory ingestion
histdatacom --quality --quality-target data/DAT_ASCII_EURUSD_M1_201202.csv --quality-checks time bars
histdatacom --quality --quality-target data/DAT_ASCII_EURUSD_T_201202.zip --quality-checks ticks domain

Supported groups:

Group Scope
inventory ZIP integrity, filename metadata, expected coverage manifest
ingestion text readability, line endings, delimiter/header checks, schema and typed parsing, row-count anomalies
time EST-no-DST to UTC normalization, month boundaries, ordering, duplicates, granularity, gaps, cross-file continuity
bars M1 bid OHLC integrity, positive prices, precision, outliers, tick-to-M1 reconstruction
ticks tick bid/ask ordering, spread, duplicate/stale/burst/one-sided quote behavior, spread regimes
domain symbol metadata, quote conventions, calendar/session tags, cross-instrument consistency
modeling advisory modeling-readiness checks for bid-only bars, leakage risk, spread-cost assumptions, target horizon feasibility
provenance optional orchestration manifest/status lineage checks for artifact paths, sizes, checksums, cache metadata, stale caches, and orphan files

provenance checks are only applied when a local orchestration .histdatacom/manifest-status.sqlite3 store is available. Explicit --quality-checks provenance runs without a store return a clean info finding that records the missing store; ordinary file-only quality runs are not failed by the absence of orchestration provenance data.

Quality Profiles

Use --quality-profile PATH to load a versioned JSON profile that tunes rule thresholds, severities, precision profiles, gap/session tolerance, tick microstructure profiles, cross-instrument tolerance, and modeling-readiness assumptions. The report metadata includes the active quality_profile source, name, configured rule IDs, and configured modeling-assumption keys.

Strict CI profiles can promote warnings to errors or tighten thresholds:

{
  "schema_version": "histdatacom.quality-profile.v1",
  "name": "strict-ci",
  "rules": {
    "ingestion.ascii.row_count": {
      "min_row_count": 100,
      "tiny_severity": "error"
    },
    "time.ascii.gaps": {
      "tolerance": {
        "suspicious_gap_ms": 300000
      },
      "warning_severity": "error"
    }
  }
}

Exploratory research profiles can loosen market-anomaly thresholds and record modeling assumptions without changing global defaults:

{
  "schema_version": "histdatacom.quality-profile.v1",
  "name": "exploratory-research",
  "rules": {
    "bars.ascii.m1_outliers": {
      "thresholds_by_asset_class": {
        "fx": {
          "max_open_jump_ratio": 0.01
        }
      }
    },
    "ticks.ascii.microstructure": {
      "session_name": "rollover",
      "thresholds_by_symbol_session": {
        "EURUSD:rollover": {
          "one_sided_run_length": 4
        }
      }
    }
  },
  "modeling_assumptions": {
    "ask_side_execution_model": true,
    "current_bar_action_timing": "after_bar_close",
    "spread_cost_model": "fixed_session_profile",
    "target_horizon_minutes": 5
  }
}

histdatacom --quality \
  --quality-target data/ \
  --quality-profile profiles/strict-ci.json \
  --quality-fail-on warning \
  --quality-report reports/quality.json

Format support is explicit in every discovered target's quality_support metadata. The current quality boundary is:

Format Timeframes Quality support
ascii M1, T Deep parser-level checks for ZIP, CSV, and canonical .data cache artifacts
metatrader M1 Inventory-only: filename, ZIP integrity, and expected member checks
ninjatrader M1, T_LAST, T_BID, T_ASK Inventory-only: filename, ZIP integrity, and expected member checks
metastock M1 Inventory-only: filename, ZIP integrity, and expected member checks
excel M1 Inventory-only for ZIPs and extracted .xlsx workbook payloads

Inventory-only targets emit a warning with code HISTDATA_FORMAT_INVENTORY_ONLY; they are intentionally not reported as deeply clean. Recognized formats used with unsupported timeframes emit HISTDATA_FORMAT_UNSUPPORTED.

HistData-specific assumptions are reported directly in findings:

  • ASCII M1 rows are bid-based OHLCV bars.
  • ASCII tick rows include bid and ask values.
  • HistData timestamps are interpreted as fixed EST with no daylight-saving adjustment and normalized to UTC.
  • M1 volume is not treated as automatically meaningful or required for market-quality decisions.

Clean and Failing Examples

A focused ingestion run against a clean M1 CSV reports a clean file and writes a machine-readable report:

histdatacom --quality \
  --quality-target data/DAT_ASCII_EURUSD_M1_201202.csv \
  --quality-checks ingestion \
  --quality-report reports/quality-clean.json

Data quality assessment
checks: ingestion
status: clean
targets: 1 clean: 1 warning: 0 failed: 0
findings: 1 info: 1 warning: 0 error: 0
report: /path/to/reports/quality-clean.json

Clean files
- csv: /path/to/data/DAT_ASCII_EURUSD_M1_201202.csv (findings=1, warnings=0, errors=0)

Warning files
- none

Failed files
- none

The JSON report includes deterministic top-level summary fields:

{
  "schema_version": "histdatacom.quality-report.v1",
  "summary": {
    "error_count": 0,
    "finding_count": 1,
    "info_count": 1,
    "max_severity": "info",
    "rule_count": 3,
    "status": "clean",
    "target_count": 1,
    "warning_count": 0
  }
}

The report payload is a public automation contract. Compatibility expectations and the golden-fixture update workflow are documented in docs/data-quality/report-compatibility.md.

A malformed M1 CSV fails ingestion and exits nonzero by default because --quality-fail-on error with --quality-max-errors 0 is the default policy:

histdatacom --quality \
  --quality-target data/bad/ \
  --quality-checks ingestion \
  --quality-report reports/quality-failing.json

Data quality assessment
checks: ingestion
status: failed
targets: 1 clean: 0 warning: 0 failed: 1
findings: 2 info: 1 warning: 0 error: 1
report: /path/to/reports/quality-failing.json

Clean files
- none

Warning files
- none

Failed files
- csv: /path/to/data/bad/DAT_ASCII_EURUSD_M1_201202_BAD.csv (findings=2, warnings=0, errors=1)

The detailed report carries row and field context for automation and manual investigation:

{
  "schema_version": "histdatacom.quality-report.v1",
  "summary": {
    "error_count": 1,
    "finding_count": 2,
    "max_severity": "error",
    "status": "failed",
    "target_count": 1,
    "warning_count": 0
  },
  "rule_results": [
    {
      "rule_id": "ingestion.ascii.schema",
      "findings": [
        {
          "code": "ASCII_ROW_FIELD_COUNT_INVALID",
          "severity": "error",
          "location": {
            "row_number": 2
          }
        }
      ]
    }
  ]
}

Warning, Error, and Exit Policy

Quality findings use three severities:

  • info: informational summaries and profiles.
  • warning: suspicious data, domain assumptions, or modeling-readiness risks that should be reviewed but do not block ingestion by default.
  • error: hard defects such as corrupt ZIP archives, unreadable files, schema violations, parse failures, invalid OHLC relationships, or negative spreads.

Target status rolls up from findings: any error makes a target failed; warnings without errors make it warning; otherwise it is clean.

Reviewed source-data defects are documented under docs/data-quality/known-data-defects.md. These records explain known vendor anomalies for future batch interpretation, but they do not downgrade quality severities or silence repo-quality failures.

The default process exit policy fails on any error:

histdatacom --quality --quality-target data/

To make warnings fail CI, opt in explicitly:

histdatacom --quality \
  --quality-target data/ \
  --quality-fail-on warning \
  --quality-max-warnings 0

To generate advisory reports without failing a job, disable quality exits:

histdatacom --quality \
  --quality-target data/ \
  --quality-fail-on never \
  --quality-report reports/quality.json

For CI/offline use, run against checked-in fixtures or downloaded artifacts in a workspace cache. The command needs only local filesystem access; network access, HistData.com availability, Temporal, and InfluxDB are not required.

Data Analytics

Data analytics operations describe market-data behavior for downstream feature engineering, dashboards, and modeling decisions. They are separate from histdatacom --quality: analytics reports do not produce clean/warning/failed statuses and do not downgrade repository quality metadata.

Feed-Regime Detection

histdatacom analytics feed-regimes profiles local ASCII tick artifacts by month or year, then segments long histories into feed-behavior eras such as sparse, transitional, and dense periods. The report includes tick density, inter-arrival intervals, quote update cadence, zero-change runs, spread statistics, quiet-gap counts, regime boundaries, and summary metadata.

histdatacom analytics feed-regimes \
  --target data/ASCII/T/eurusd \
  --bucket month \
  --report reports/eurusd-feed-regimes.json

Use --json to print the full machine-readable payload to stdout:

histdatacom analytics feed-regimes --target data/ --json

Use these outputs to choose modeling windows, session filters, feature normalization strategies, or dashboard annotations. Treat surprising regimes as research signals; run histdatacom --quality separately when you need readability, timestamp consistency, ZIP integrity, or pass/fail validation.

Orchestration Runtime

The production default is the local Temporal orchestration runtime for CLI and API runs. Default requests submit a RunRequest to the runtime and start the local service and worker fleet when no healthy runtime is running.

The foreground rollback runtime has been removed after its release-window deprecation period. --foreground is no longer a valid CLI flag, and API code that sets options.use_orchestration = False raises a clear ValueError. If the runtime cannot be started or contacted, CLI calls exit nonzero with a clear error and API calls raise OrchestrationUnavailableError; the runtime never silently falls back to a local foreground execution path.

Runtime Model and Install Surface

The base install includes the Temporal Python SDK because orchestration is the default runtime:

pip install histdatacom

histdatacom[temporal] is available for environments that want to make the runtime dependency explicit, but it does not change the default runtime contract: base installs include the Temporal SDK needed by clients and workers.

The runtime stores Temporal process state, SQLite history, logs, and runtime manifests under a per-user, per-workspace runtime directory. Downloaded ZIP files, extracted CSV/XLSX files, cache IPC files, and merged API artifacts stay under the existing HistData data-directory policy.

Record status metadata is manifest-only for new writes. Normal CLI/API paths update .histdatacom/manifest-status.sqlite3 under the relevant data or runtime status root and no longer create new hidden .meta files beside records. Existing .meta files remain readable as migration inputs; successful imports write the manifest row and remove the legacy file, while missing or corrupt legacy files are reported without blocking manifest-backed operation.

Source distributions and universal wheels include orchestration metadata, CLI entry points, runtime defaults, and third-party notices. The accepted V1.0 packaging design keeps normal PyPI and TestPyPI artifacts metadata-only and provisions the pinned Temporal executable through a verified runtime cache on first use. See Temporal Binary Provisioning for the production design. Release preflight hardening for that non-bundled path is tracked by #251.

Metadata-only artifacts resolve the Temporal executable from an explicit operator override, an offline/private bundle, a verified per-user cache entry, or a pinned first-run download. Bundled executable wheels remain an offline/private distribution path, not the normal PyPI release path. The executable and the Python Temporal SDK are separate concerns: base installs provide the SDK, while the runtime resolver owns executable availability.

Default orchestration submissions are built from resolved runtime context and RunRequest payloads exposed by histdatacom.orchestration. New automation work should use the orchestration facade instead of importing the private runtime implementation package directly. Legacy helper surfaces now accept explicit argument dictionaries rather than ambient parser state; parser globals are not part of runtime selection.

Binary Provisioning and PyPI Packaging

The binary provisioning design is intentionally modeled like the HistData repository file: a small package-owned index pins the allowed remote Temporal artifacts by version, platform, URL, checksum, size, and provenance metadata. Normal PyPI artifacts stay below upload limits because they ship the index and not the binary.

The runtime resolver prefers explicit operator overrides, then verified private/offline bundles, then a verified per-user cache, and finally a first-run download when network provisioning is allowed. HISTDATACOM_TEMPORAL_EXECUTABLE sets a process-wide explicit executable, HISTDATACOM_TEMPORAL_CACHE_DIR sets an alternate cache root, and HISTDATACOM_TEMPORAL_OFFLINE=1 disables first-run network provisioning. Offline environments fail with instructions to pre-seed the cache, install an offline/private bundle, or pass an explicit executable.

Public Orchestration API Boundary

New GUI and automation integrations should submit work through the public orchestration surface:

  • histdatacom.Options passed to histdatacom.main(options) or histdatacom(options)
  • histdatacom.orchestration.contracts.RunRequest
  • histdatacom jobs ... for job telemetry and control
  • histdatacom.orchestration.client job-control helpers for submit, inspect, list, cancel, resume, progress, and artifact polling
  • histdatacom.orchestration.telemetry helpers for job status, progress, logs, results, and artifacts

Do not build new validate/download/extract/cache/import automation by importing Repo, Scraper, Api.validate_caches, Api.merge_caches, or Influx.import_data directly. Those direct side-effect methods remain as compatibility helpers for existing callers and emit LegacyHelperSideEffectWarning when used. Temporal activities continue to call the lower-level histdatacom.activity_stages functions and related adapter objects directly; those stage helpers are the supported worker boundary, not the GUI or automation boundary.

Maintainer Runtime Diagnostics

The normal user path does not require process lifecycle commands. Maintainers can inspect and manage the local runtime through the lower-level lifecycle CLI:

histdatacom runtime doctor --json
histdatacom runtime status --json
histdatacom runtime start
histdatacom runtime start --executable /path/to/temporal
histdatacom runtime stop

status and doctor report component health for the server and each worker lane: orchestration, network, cpu-file, and influx.

Use --workspace or HISTDATACOM_RUNTIME_WORKSPACE for cron, service managers, GUI launchers, and other contexts where the current working directory may not be stable.

Job Telemetry and Automation

Submit a job through the default orchestration runtime:

histdatacom -p eurusd -f ascii -t 1-minute-bar-quotes -s now

Interactive waited CLI runs render a live Rich progress view while the Temporal job is running; piped output and API calls keep the machine-readable result path.

Submit without waiting for completion:

histdatacom --submit-only -p eurusd -f ascii -t 1-minute-bar-quotes -s now

The JSON control surface supports job inspection and future GUI polling:

histdatacom jobs list --json
histdatacom --request-bundle-out run.json --no-overlap --schedule-key eurusd-cache --build-cache -p eurusd -f ascii -t tick-data-quotes -s now
histdatacom jobs preflight --bundle run.json --json
histdatacom jobs list --schedule-key eurusd-cache --active --json
histdatacom jobs progress histdatacom-<request-id> --watch
histdatacom jobs progress histdatacom-<request-id> --json
histdatacom jobs artifacts histdatacom-<request-id> --json
histdatacom jobs cancel histdatacom-<request-id> --reason "operator stop"

Use --request-bundle-out PATH to export a scheduled-run bundle from ordinary CLI options plus --no-overlap --schedule-key <key> without starting Temporal, submitting work, downloading archives, or mutating job state. Use --request-bundle-out - to print the bundle to stdout. That payload can be passed directly to jobs preflight --bundle and jobs submit --bundle; the bundled schedule metadata is applied automatically. Explicit jobs flags override the bundle when needed: --schedule-key <key> replaces the bundled key, --no-overlap enables the guard, and --allow-overlap disables a bundled or request-level guard for a deliberate one-off run.

Use --request-json-out PATH when a lower-level raw RunRequest is needed. Raw request payloads still work with jobs preflight --request-json and jobs submit --request-json; put --no-overlap --schedule-key <key> on those jobs commands when schedule identity should be applied at preflight/submit time. Allowed preflights exit 0; blocked preflights exit 75 and include the blocking job in JSON output. Use jobs list --schedule-key <key> --active to inspect the non-terminal job that would block a scheduled --no-overlap submission. Fingerprint-only scheduled runs can be matched with --schedule-fingerprint sha256:.... jobs inspect --json includes a stable schedule_identity object with the schedule key or fingerprint, active/terminal state, and whether the job blocks duplicate submissions.

Omit --json on jobs progress for the Rich terminal progress view; add --watch to live-refresh it until the job reaches a terminal state. The Rich view includes a bounded operational health panel with runtime/component/PID state, POSIX disk headroom, cache inventory, source-artifact cleanup counts, active workflow counts, and ETA/rate information when progress metadata is available.

  • histdatacom --version stays local and does not require orchestration.
  • -A, -U, -V, -D, -X, -C, and -I keep their existing option semantics before an orchestration request is submitted.
  • --foreground has been removed and is rejected by the CLI.
  • --orchestration-start starts the server and worker lane fleet only when no healthy runtime is running.
  • --no-orchestration-start requires an already-running healthy runtime and fails clearly instead of starting one.
  • --submit-only submits a job and returns job metadata instead of waiting for cache artifacts or workflow results.
  • Waited orchestration -A / -U repository requests keep the output contract: API calls return the available-data dictionary, and CLI calls render the repository table.
  • --build-cache / options.build_cache builds canonical .data cache files for cache-capable ASCII datasets, removes transient ZIP/CSV sources after each cache is ready, and does not merge caches into memory.
  • API calls with options.api_return_type return the requested polars, pandas, or arrow object after a completed orchestration job by materializing cache artifacts on disk.
  • If orchestration is unavailable, CLI calls exit nonzero with a clear error and API calls raise OrchestrationUnavailableError.
  • -v emits high-level orchestration lifecycle logs; -vv adds worker, workflow, and activity detail; -vvv enables trace-level package logging and Temporal SDK/HTTP debug logging. Workflow and activity logs use Temporal's logger adapters so workflow replay does not duplicate normal workflow log lines. Log metadata is bounded to job/stage/status fields, and credential-like keys such as tokens, passwords, and secrets are redacted.

Orchestration-backed API calls use the same public Options object and runtime defaults:

options.orchestration_wait_result = True
options.api_return_type = "polars"

Set options.orchestration_wait_result = False to submit a job and receive job metadata instead of a materialized API return object. Set options.orchestration_start = False when a caller requires a pre-started runtime. options.use_orchestration = False is not supported.

Cron Setup and Examples

Cron jobs should run from a stable project directory, use a predictable runtime workspace, and write logs outside the package tree. Use the same workspace for every scheduled histdatacom, histdatacom runtime, histdatacom jobs, and histdatacom cleanup command that should share runtime state.

A crontab header can make those assumptions explicit:

SHELL=/bin/sh
PATH=/usr/local/bin:/usr/bin:/bin
HISTDATACOM_PROJECT=/srv/histdatacom
HISTDATACOM_DATA=/srv/histdatacom/data
HISTDATACOM_LOG_DIR=/var/log/histdatacom
HISTDATACOM_RUNTIME_WORKSPACE=/srv/histdatacom

Use --no-overlap with a stable --schedule-key for scheduled submissions that must not run twice in the same runtime workspace. The application checks persisted job state before submission and exits nonzero when an active matching job already exists. A shell wrapper or flock can still be useful as an outer defense when available, but it is no longer the only overlap protection. The examples below append logs and use --submit-only for scheduled data/cache work so cron records the job metadata quickly; inspect progress later with histdatacom jobs ....

histdatacom --request-bundle-out run.json --no-overlap --schedule-key eurusd-cache --build-cache --data-directory "$HISTDATACOM_DATA" -p eurusd -f ascii -t tick-data-quotes -s now
histdatacom jobs preflight --bundle run.json --json
histdatacom jobs list --schedule-key eurusd-cache --active --json

Direct CLI submissions that are not driven by a serialized RunRequest still use the submit-time guard:

histdatacom --submit-only --no-overlap --schedule-key eurusd-cache --build-cache --data-directory "$HISTDATACOM_DATA" -p eurusd -f ascii -t tick-data-quotes -s now

# Submit a serialized EURUSD cache bundle only when preflight allows it.
15 1 * * 1-5 cd "$HISTDATACOM_PROJECT" && histdatacom jobs preflight --bundle run.json --json >> "$HISTDATACOM_LOG_DIR/eurusd-cache-preflight.jsonl" 2>&1 && histdatacom jobs submit --start --submit-only --bundle run.json --json >> "$HISTDATACOM_LOG_DIR/eurusd-cache.log" 2>&1

# Optional outer shell lock for hosts that provide flock.
15 1 * * 1-5 cd "$HISTDATACOM_PROJECT" && flock -n /tmp/histdatacom-eurusd.lock sh -c 'histdatacom jobs preflight --bundle run.json --json >> "$HISTDATACOM_LOG_DIR/eurusd-cache-preflight.jsonl" 2>&1 && histdatacom jobs submit --start --submit-only --bundle run.json --json >> "$HISTDATACOM_LOG_DIR/eurusd-cache.log" 2>&1'

Source cleanup can stay in dry-run mode until the reported paths are expected; add --apply only when the cleanup policy is understood for that data root.

# Record cache/source cleanup status each morning.
30 6 * * * cd "$HISTDATACOM_PROJECT" && histdatacom cleanup status --data-directory "$HISTDATACOM_DATA" --pair-groups majors -f ascii -t tick-data-quotes --json >> "$HISTDATACOM_LOG_DIR/cleanup-status.jsonl" 2>&1

# Remove transient ZIP/CSV/XLS/XLSX sources while preserving .data caches.
45 6 * * 0 cd "$HISTDATACOM_PROJECT" && flock -n /tmp/histdatacom-cleanup.lock histdatacom cleanup sources --data-directory "$HISTDATACOM_DATA" --apply >> "$HISTDATACOM_LOG_DIR/source-cleanup.log" 2>&1

Runtime health and maintenance jobs should use the same stable workspace as the scheduled submissions:

# Emit runtime health for monitoring.
*/15 * * * * histdatacom runtime --workspace "$HISTDATACOM_RUNTIME_WORKSPACE" status --json >> "$HISTDATACOM_LOG_DIR/runtime-status.jsonl" 2>&1

# Prune runtime logs and persisted status metadata weekly.
10 3 * * 0 histdatacom runtime --workspace "$HISTDATACOM_RUNTIME_WORKSPACE" maintenance --json >> "$HISTDATACOM_LOG_DIR/runtime-maintenance.jsonl" 2>&1

Runtime User and Maintainer Docs

See Temporal Orchestration User Guide for submit, observe, cancel, retry, resume, artifacts, and user troubleshooting workflows. See Temporal Orchestration Runtime Runbook for maintainer lifecycle commands, runtime path layout, port policy, worker lanes, SQLite persistence, maintenance, and low-level diagnostics. See Temporal Workflow Topology for workflow, activity, task queue, and testing boundaries. See Temporal Orchestration Performance Baseline for lane sizing and benchmark policy.

API - Other Scripts, Modules, & Jupyter Support

histdatacom exposes one Python API entry point for scripts, applications, and notebooks:

import histdatacom
from histdatacom.options import Options

options = Options()
result = histdatacom(options)

The same Options object supports two common API paths:

  • submit CLI-shaped ETL work from a script or application, usually for validate/download/extract/import jobs that do not return a dataframe.
  • request dataframe/table results for interactive work in Jupyter or for larger Python programs that need to consume the data directly.

API calls use the orchestration runtime by default. A missing runtime is started when needed unless options.orchestration_start = False is set. The copyable examples live under samples/; pytest executes those samples in hermetic mode without contacting HistData.com or starting a Temporal runtime.

  • samples/api_quickstart.py
  • samples/notebooks/api_quickstart.ipynb

Script and Application Automation

First import the required modules
import histdatacom
from histdatacom.options import Options
Create and Initialize a new options object to pass parameters to histdatacom
options = Options()
Configure automation options

To submit the same ETL work a user would normally request from the CLI, set one of the boolean behavior flags: options.validate_urls, options.download_data_archives, options.extract_csvs, options.build_cache, or options.import_to_influxdb.

  • Each behavior flag implies the use of the preceding flags.
    • histdatacom is an ETL pipeline (extract, transform, load) and each step depends on the preceding steps in the pipeline.
    • For the CLI, the order of operations are:
      • validate urls
      • download zip files from histdata.com
      • extract the csv from the zip archive
      • transform the ESTnoDST datetime to UTC Epoch AND upload to InfluxDB.
# options.validate_urls = True
# options.download_data_archives = True  # implies validate
options.extract_csvs = True  # implies validate and download
# options.build_cache = True  # implies validate/download; leaves only .data caches
# options.import_to_influxdb = True  # implies validate, download, and extract
options.formats = {"ascii"}
options.timeframes = {"tick-data-quotes"}
options.pairs = {"eurusd"}
options.start_yearmonth = "2021-04"
options.end_yearmonth = "2021-05"
options.cpu_utilization = "medium"

  • Automation requests submit through orchestration by default and start a missing runtime when needed. Set options.orchestration_wait_result = False when the caller only needs job metadata, set options.orchestration_start = False when a caller requires a pre-started runtime. options.use_orchestration = False is rejected because the foreground runtime has been removed.

  • New automation should not call legacy helper classes directly for validate/download/extract/cache/import work. Direct side-effect helper methods warn because they bypass durable orchestration status, cancellation, retry/resume, and worker-lane routing.

  • When an ETL behavior flag is included without api_return_type, the call submits work and does not return dataframe data.

Use the normal Python __name__ == "__main__" guard for executable scripts:

if __name__ == "__main__":
    histdatacom(options)

Jupyter and External Scripts

For notebooks and data-consuming Python programs, set options.api_return_type. The completed orchestration job materializes cache artifacts and returns a dataframe or table.

  • return types can be:

    • a polars dataframe
    • a pandas dataframe
    • a pyarrow table

  • polars is installed with histdatacom.

  • to use pandas or arrow return formats, install the optional extras

    • pip install "histdatacom[pandas]"
    • pip install "histdatacom[arrow]"

  • to use InfluxDB imports or notebook tooling, install the corresponding extras

    • pip install "histdatacom[influx]"
    • pip install "histdatacom[jupyter]"

  • All datetime is returned as milliseconds since January 1, 1970 (midnight UTC/GMT)

Import the required modules
import histdatacom
from histdatacom.options import Options
Initialize a new options object to pass parameters to histdatacom
options = Options()
Jupyter & External Script Options
options.api_return_type = "polars"  # "polars", "pandas", or "arrow"
options.formats = {"ascii"}  # Must be {"ascii"}
options.timeframes = {"1-minute-bar-quotes"}  # can be tick-data-quotes or 1-minute-bar-quotes
options.pairs = {"eurusd"}
# Or choose named baskets with options.pair_groups = {"majors", "major-triangles"}
# Or one triangle with options.pair_groups = {"triangle-eurgbp-eurusd-gbpusd"}
options.start_yearmonth = "2021-04"
options.end_yearmonth = "2021-05"
options.cpu_utilization = "medium"
  • This example uses just one pair/instrument/symbol eurusd and just one timeframe 1-minute-bar-quotes. When the api is called with this 'one-one` specificity, the api will directly return the requested data.
  • Regardless of the specified start_yearmonth and end_yearmonth, the resultant data will be sorted and merged into a single dataset.
Pass the options to histdatacom and assign the return to a variable
data = histdatacom(options)  # (Jupyter)

print(type(data))
print(data.shape)

<class 'polars.dataframe.frame.DataFrame'>
(rows depend on the requested period, 6)
  • When specifying more than one pair/symbol/instrument or timeframe, the API returns a list of dictionaries with references to the timeframe, pair, records used to create the data, and the merged data itself.
options.api_return_type = "pandas"
options.formats = {"ascii"}
options.timeframes = {"1-minute-bar-quotes"}
options.pairs = {"eurusd","usdcad"}
options.start_yearmonth = "2021-01"
options.end_yearmonth = "2021-02"
options.cpu_utilization = "medium"

data = histdatacom(options)  # (Jupyter)

print(data)
print(type(data))

[
  {
    'timeframe': 'M1',
    'pair': 'EURUSD',
    'records': [<histdatacom.records.Record object ...>, ...],
    'data':
                    datetime     open     high      low    close  vol
      0       1609711200000  1.22396  1.22396  1.22373  1.22395    0
      1       1609711260000  1.22387  1.22420  1.22385  1.22395    0
      2       1609711320000  1.22396  1.22398  1.22382  1.22382    0
      3       1609711380000  1.22383  1.22396  1.22376  1.22378    0
      4       1609711440000  1.22378  1.22385  1.22296  1.22347    0
      ...               ...      ...      ...      ...      ...  ...
      484172  1650664440000  1.07976  1.08014  1.07976  1.08014    0
      484173  1650664500000  1.08013  1.08021  1.07997  1.08000    0
      484174  1650664560000  1.08000  1.08000  1.07956  1.07968    0
      484175  1650664620000  1.07980  1.07980  1.07958  1.07968    0
      484176  1650664680000  1.07980  1.07986  1.07963  1.07963    0

      [484177 rows x 6 columns]
  },
  {
    'timeframe': 'M1',
    'pair': 'USDCAD',
    'records': [<histdatacom.records.Record object ...>, ...],
    'data':
                    datetime     open     high      low    close  vol
      0       1609711200000  1.27136  1.27201  1.27136  1.27201    0
      1       1609711260000  1.27207  1.27241  1.27207  1.27220    0
      2       1609711320000  1.27211  1.27219  1.27211  1.27219    0
      3       1609711380000  1.27212  1.27261  1.27212  1.27261    0
      4       1609711440000  1.27268  1.27268  1.27261  1.27261    0
      ...               ...      ...      ...      ...      ...  ...
      483946  1650664440000  1.27121  1.27132  1.27114  1.27131    0
      483947  1650664500000  1.27129  1.27137  1.27102  1.27106    0
      483948  1650664560000  1.27107  1.27114  1.27098  1.27101    0
      483949  1650664620000  1.27105  1.27105  1.27091  1.27091    0
      483950  1650664680000  1.27091  1.27097  1.27073  1.27097    0

      [483951 rows x 6 columns]
  }
]

<class 'list'>

print(data[0]['timeframe'], data[0]['pair'])
print(data[0]['data'])
print(type(data[0]['data']))

M1 EURUSD
               datetime     open     high      low    close  vol
0       20210103 170000  1.22396  1.22396  1.22373  1.22395    0
1       20210103 170100  1.22387  1.22420  1.22385  1.22395    0
2       20210103 170200  1.22396  1.22398  1.22382  1.22382    0
3       20210103 170300  1.22383  1.22396  1.22376  1.22378    0
4       20210103 170400  1.22378  1.22385  1.22296  1.22347    0
...                 ...      ...      ...      ...      ...  ...
484172  20220422 165400  1.07976  1.08014  1.07976  1.08014    0
484173  20220422 165500  1.08013  1.08021  1.07997  1.08000    0
484174  20220422 165600  1.08000  1.08000  1.07956  1.07968    0
484175  20220422 165700  1.07980  1.07980  1.07958  1.07968    0
484176  20220422 165800  1.07980  1.07986  1.07963  1.07963    0

[484177 rows x 6 columns]
<class 'pandas.core.frame.DataFrame'>

The notebook/API path is covered by pytest and pre-commit through the hermetic samples/notebooks/api_quickstart.ipynb execution test. The checked-in snippets.ipynb file remains an exploratory example and is not executed by default because it can request live HistData.com data.

Full Script Example
import histdatacom
from histdatacom.options import Options
from histdatacom.fx_enums import Pairs

def import_pair_to_influx(pair, start, end):
    data_options = Options()

    data_options.import_to_influxdb = True  # implies validate, download, and extract
    data_options.delete_after_influx = True
    data_options.batch_size = "2000"
    data_options.cpu_utilization = "high"

    data_options.pairs = {f"{pair}"}# histdata_and_oanda_intersect_symbs
    data_options.start_yearmonth = f"{start}"
    data_options.end_yearmonth = f"{end}"
    data_options.formats = {"ascii"}  # Must be {"ascii"}
    data_options.timeframes = {"tick-data-quotes"}  # can be tick-data-quotes or 1-minute-bar-quotes
    histdatacom(data_options)

def get_available_range_data(pairs):
    range_options = Options()
    range_options.pairs = pairs
    range_options.available_remote_data = True
    range_options.by = "start_dsc"
    range_data = histdatacom(range_options)  # (Jupyter)
    return range_data

def print_one_polars_frame(pair, start=None, end=None):
    options = Options()
    options.api_return_type = "polars"
    options.pairs = {f"{pair}"}
    options.start_yearmonth = "201501"
    options.formats = {"ascii"}
    options.timeframes = {"tick-data-quotes"}
    return histdatacom(options)

def main():
    histdata_symbs = Pairs.list_keys()

    # Oanda Symbols:
    oanda_symbs = {"audcad","audchf","audhkd","audjpy","audsgd","audusd","cadhkd","cadjpy","cadsgd",
    "chfhkd","chfjpy","euraud","eurcad","eurchf","eurgbp","eurhkd","eurjpy","eursgd","eurusd","gbpaud",
    "gbpcad","gbpchf","gbphkd","gbpjpy","gbpsgd","gbpusd","hkdjpy","sgdchf","sgdhkd","sgdjpy","usdcad",
    "usdchf","usdhkd","usdjpy","usdsgd","audnzd","cadchf","chfzar","eurczk","eurdkk","eurhuf","eurnok",
    "eurnzd","eurpln","eursek","eurtry","eurzar","gbpnzd","gbppln","gbpzar","nzdcad","nzdchf","nzdhkd",
    "nzdjpy","nzdsgd","nzdusd","tryjpy","usdcnh","usdczk","usddkk","usdhuf","usdmxn","usdnok","usdpln",
    "usdsar","usdsek","usdthb","usdtry","usdzar","zarjpy"}

    histdata_and_oanda_intersect_symbs = histdata_symbs & oanda_symbs

    pairs_data = get_available_range_data(histdata_and_oanda_intersect_symbs)
    for pair in pairs_data:
        start = pairs_data[pair]['start']
        end = pairs_data[pair]['end']

        import_pair_to_influx(pair, start, end)

if __name__ == '__main__':
    main()

Setup

TLDR for all platforms

Install histdatacom

pip install histdatacom

Polars is installed by default. To request optional API return formats:

pip install "histdatacom[pandas]"
pip install "histdatacom[arrow]"

InfluxDB import and notebook support are optional:

pip install "histdatacom[influx]"
pip install "histdatacom[jupyter]"
pip install "histdatacom[all]"

histdatacom[temporal] remains available for explicit runtime installs, but the Temporal Python SDK is part of the base package dependency set because orchestration is the default runtime.

to install latest development version

pip install git+https://github.com/dmidlo/histdata.com-tools.git

Developer Setup

Use a project virtual environment for local development. Do not install developer tooling into the user-local Python environment.

python -m venv venv
source venv/bin/activate
PYTHONNOUSERSITE=1 python -m pip install -e ".[dev]"
PYTHONNOUSERSITE=1 pre-commit install --install-hooks

On Windows, use the same project-local environment contract with PowerShell:

py -m venv venv
.\venv\Scripts\Activate.ps1
$env:PYTHONNOUSERSITE = "1"
python -m pip install -e ".[dev]"
pre-commit install --install-hooks

The local Git hooks are designed to run from normal git commit and git push commands after setup, even when the shell has not activated the virtual environment. Hook wrappers resolve developer tools from HISTDATACOM_DEV_VENV, the active VIRTUAL_ENV, ./venv, or ./.venv in that order. Keep the project virtual environment in place after installing the hooks; do not rely on user-local Python packages to satisfy histdatacom, coverage, or other release gates.

The dependency surfaces are split by purpose:

  • .[test] installs pytest, coverage, pandas, pyarrow, InfluxDB support, notebook execution support, and test-only support around the base Temporal SDK dependency.
  • .[lint] installs pre-commit and direct lint/type/doc hygiene tools.
  • .[release] installs build and publish tooling.
  • .[dev] is the aggregate local contributor environment with test, lint, release, and optional integration dependencies.

The dev, lint, test, and release extras pin direct developer tools where reproducibility matters. Runtime dependencies keep compatibility lower bounds rather than lock-file pins because histdatacom is a published PyPI library. The active lint baseline is Black, Ruff, mypy, generic file checks, Pyroma, ShellCheck, Commitizen, and the local CLI/coverage smoke hooks. The previous flake8 plugin stack was intentionally replaced with Ruff so local installs and hook behavior do not drift independently.

Release Operator Path

Tagged releases and manual release runs should build the normal metadata-only sdist and universal wheel for PyPI/TestPyPI. The V1.0 provisioning design moves Temporal executable availability into a verified first-run resolver backed by a packaged artifact index and a per-user cache. Release preflight should prove the normal wheel is under the upload-size gate and that a clean install can provision or locate the pinned runtime through the resolver.

The existing bundled platform-wheel tooling remains useful for offline/private artifacts and emergency operator recovery, but those artifacts are not the default PyPI path. The GitHub Release workflow builds them only for explicit build-only dry runs: set include_bundled_platform_wheels=true with release_target=build-only, and set bundled_platform_wheel_size_confirmed=true only after confirming the private/offline purpose and artifact-size policy.

For bundled platform-wheel release dry runs, Linux and macOS remain worker-starting runtime smoke gates. Windows currently verifies installability, bundled runtime metadata, Temporal executable version, and CLI entry points, and collects layered startup diagnostics. Until #314's native Windows worker-start blocker is fixed, the Windows bundled runtime gate is install/CLI-only.

Use release_target=build-only for metadata-only dry runs, release_target=testpypi for the first publish rehearsal, and release_target=pypi only after setting testpypi_dry_run_confirmed=true. The final histdatacom-dist artifact contains only the metadata-only universal wheel and source distribution; JSON build and checksum reports are uploaded separately as release reports. Bundled platform-wheel artifacts are uploaded separately only for the explicit private/offline build-only path and are not consumed by TestPyPI/PyPI publish jobs.

If runtime provisioning fails after release, prefer yanking the affected package only when the Python artifact itself is wrong. Bad or unreachable Temporal runtime artifacts should be handled by fixing the artifact index in a patch release, while explicit executable overrides and pre-seeded caches remain operator recovery paths.

Coverage Policy

Coverage is enforced as a conservative total-project ratchet. The initial threshold is set in .coveragerc from the current baseline so CI catches real coverage regressions without blocking modernization work on unrelated low-legacy modules. Future test work should raise fail_under when the baseline improves; do not lower it unless a PR explains the production risk and links the follow-up issue.

CI runs pytest through pytest-cov, enforces the .coveragerc threshold, and uploads coverage.xml plus the htmlcov/ report for every Python and OS matrix leg. The first-pass gate is total-only. Per-package or domain thresholds belong with the broader testing work tracked in issues #9 and #68.

The live Temporal runtime smoke is not collected by default pytest because it requires a real Temporal executable and starts local worker processes. Bundled platform-wheel release smoke uses scripts/smoke_runtime_install.py --hermetic-runtime-smoke, which submits a local-only dataset-planning workflow with an explicit worker config and does not contact HistData.com. Bundled platform-wheel release smoke also runs scripts/smoke_runtime_install.py --default-routing-runtime-smoke, which starts the runtime with non-default worker routing and submits without an explicit worker config so the installed package must resolve the running frontend, namespace, and queues from persisted runtime state. Run scripts/smoke_runtime_install.py --quality-runtime-smoke to exercise the installed histdatacom --quality console command against clean and dirty local M1 fixtures through the packaged DataQualityWorkflow without contacting HistData.com or InfluxDB. Run scripts/smoke_runtime_install.py --live-runtime-smoke separately when an operator intentionally wants external HistData.com URL-validation coverage. These commands fail on shutdown leaks: stop exceptions, missing stop status, persistent stopping status, or known remaining runtime PIDs.

Vanilla MacOS and Linux

Create a new project directory and change to it
mkdir myproject && cd myproject && pwd
Create a Python Virtual Environment and activate it
python -m venv venv && source venv/bin/activate
Confirm Python Path and Version
which python && python --version
Install the histdata.com-tools package from PyPi
pip install histdatacom
Run histdatacom to view help message and Options
histdatacom -h

Vanilla Windows Powershell

Launch a Powershell Terminal
  • Run as Administrator (right-click on shortcut and click Run as Admin...)
Make sure python3.10 is in your system's executable path
python --version
  • should be already set if you clicked the checkbox when installing python 3.10
  • If not, you can run the following.
    • you will need to relaunch powershell as admin.
[Environment]::SetEnvironmentVariable("Path", "$env:Path;C:\Program Files\Python310")
Change the Execution Policy to Unrestricted
Set-ExecutionPolicy Unrestricted -Force
Create a new directory and change to it
New-Item -Path ".\" -Name "myproject" -ItemType "directory"; Set-Location .\myproject\
Create a Virtual Environment and activate it
python -m venv venv; .\venv\Scripts\Activate.ps1
Confirm Path and Version
Get-Command python | select Source; python --version
Install histdata.com-tools package from PyPi
pip install histdatacom
Run histdatacom to view help message
histdatacom -h

Anaconda Setup

Anaconda MacOS and Linux
Create a Project Directory and Change to it
mkdir myproject && cd myproject && pwd
Create a Python 3.10 Anaconda environment with conda and activate it
conda create -n py310 python=3.10 && conda activate py310
Check Python Path and Version
which python && python --version
Install histdatacom package from PyPi
pip install histdatacom
Run histdatacom package to view help message
histdatacom -h
Anaconda Windows using the Anaconda Prompt
Create a Directory and Change to it
mkdir myproject && cd myproject && echo %cd%
Create a Python 3.10 Anaconda environment with conda and activate it
conda create -n py310 python=3.10 && conda activate py310
Check Python Path and Version
where python && python --version
Install histdatacom package from PyPi
pip install histdatacom
Run histdatacom package to view help message
histdatacom -h

Roadmap

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  • License Expression: MIT
    SPDX License Expression
  • Tags finance , data , datascience , HistData.com , scraper , influxdb , currency exchange , forex , fx , etl
  • Requires: Python >=3.10.0
  • Provides-Extra: arrow , influx , pandas , jupyter , temporal , all , test , lint , release , dev
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