histdatacom


Namehistdatacom JSON
Version 0.78.4 PyPI version JSON
download
home_pagehttps://github.com/dmidlo/histdata.com-tools
SummaryA Multi-threaded/Multi-Process command-line utility and python package that downloads currency exchange rates from Histdata.com. Imports to InfluxDB. Can be used in Jupyter Notebooks.
upload_time2022-12-14 08:39:10
maintainer
docs_urlNone
authorDavid Midlo
requires_python>=3.10.0
licenseMIT License
keywords finance data datascience histdata.com scraper influxdb currency exchange forex fx etl
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage
            # histdata.com-tools

A Multi-threaded/Multi-Process command-line utility and python package that downloads currency exchange rates from Histdata.com. Imports to InfluxDB. Can be used in Jupyter Notebooks. Works on MacOS, Linux & Windows Systems.
**Requires Python3.10+**

**NEW:** Expanded API support!!!

[![Downloads](https://pepy.tech/badge/histdatacom)](https://pepy.tech/project/histdatacom) ![PyPI - License](https://img.shields.io/pypi/l/histdatacom) ![PyPI](https://img.shields.io/pypi/v/histdatacom) ![PyPI - Status](https://img.shields.io/pypi/status/histdatacom)

---

- [histdata.com-tools](#histdatacom-tools)
- [Disclaimer](#disclaimer)
- [Usage](#usage)
  - [Show the Help and Options](#show-the-help-and-options)
  - [Basic Use](#basic-use)
  - [Available Formats](#available-formats)
    - [CSV Dialect and Format Specifications](#csv-dialect-and-format-specifications)
  - [Date Ranges](#date-ranges)
    - ['Start' & 'Now' Keywords](#start-now-keywords)
  - [Multiple Datasets](#multiple-datasets)
  - [CPU Utilization](#cpu-utilization)
  - [Import to InfluxDB](#import-to-influxdb)
    - [influxdb.yaml](#influxdbyaml)
  - [API - Other Scripts, Modules, & Jupyter Support](#api-other-scripts-modules-jupyter-support)
    - [CLI Automation](#cli-automation)
    - [Jupyter and External Scripts](#jupyter-and-external-scripts)
    - [Full Script Example](#full-script-example)
- [Setup](#setup)
  - [TLDR for all platforms](#tldr-for-all-platforms)
  - [Vanilla Python Setup](#vanilla-python-setup)
    - [Vanilla MacOS and Linux](#vanilla-macos-and-linux)
    - [Vanilla Windows Powershell](#vanilla-windows-powershell)
  - [Anaconda Setup](#anaconda-setup)
    - [Anaconda MacOS and Linux](#anaconda-macos-and-linux)
    - [Anaconda Windows using the Anaconda Prompt](#anaconda-windows-using-the-anaconda-prompt)
  - [Data Table Installation Options](#datatable-installation-options)
- [Roadmap](#roadmap)

---

## 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/](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!](https://www.buymeacoffee.com/dmidlo)*

---

## 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](https://github.com/dmidlo/histdata.com-tools/issues).*

### Show the help and options

```txt
histdatacom -h
```

```txt
histdatacom -h
usage: histdatacom [-h] [-A] [-U] [--by BY] [--version] [-V] [-D] [-X] [-p PAIR [PAIR ...]] [-f FORMAT [FORMAT ...]] [-t TIMEFRAME [TIMEFRAME ...]] [-s START_YEARMONTH] [-e END_YEARMONTH] [-I] [-d] [-b BATCH_SIZE] [-c CPU_UTILIZATION]
                   [--data-directory DATA_DIRECTORY]

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.

Config:
  -p PAIR [PAIR ...], --pairs PAIR [PAIR ...]
                        space separated currency pairs. e.g. -p eurusd usdjpy ...
  -f FORMAT [FORMAT ...], --formats FORMAT [FORMAT ...]
                        space separated formats. -f metatrader ascii ninjatrader metastock
  -t TIMEFRAME [TIMEFRAME ...], --timeframes TIMEFRAME [TIMEFRAME ...]
                        space separated Timeframes. -t tick-data-quotes 1-minute-bar-quotes
  -s START_YEARMONTH, --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 END_YEARMONTH
                        set a start 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 BATCH_SIZE
                        (integer) influxdb write_api batch size. defaults to 5000

System:
  -c CPU_UTILIZATION, --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/"

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.
```

---

### Basic Use

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

```sh
histdatacom -p eurusd -f metatrader -s now
```

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

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

---

#### 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

- *For Detailed specifications for the CSVs that the histdata.com repo provides see [histdata.com_data_specs.md](https://github.com/dmidlo/histdata.com-tools/blob/main/histdata.com_data_specs.md)*

##### To download 1-minute-bar-quotes for both metastock and excel

```sh
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.

```txt
histdatacom -p udxusd -f ascii -t tick-data-quotes -s 2011
```

##### 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

```txt
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

```txt
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

```txt
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.

```txt
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

```txt
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`

```sh
histdatacom -c medium -p udxusd -f metatrader -s 2015-04 -e 2016-04
```

---

### Import to InfluxDB

To import data to an influxdb instance, 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).

- 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

```txt
histdatacom -I -p eurusd -f ascii -t tick-data-quotes -s start -e now
```

#### influxdb.yaml

```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

```shell
curl "https://raw.githubusercontent.com/dmidlo/histdata.com-tools/main/influxdb.sample.yaml" --output influxdb.yaml
```

---

### API - Other Scripts, Modules, & Jupyter Support

histdatacom also has an API to allow developers and to integrate the package into their own projects.  It can be used in one of two ways; The first being a simple interface to automate CLI interaction. The second is as an interface to work with the data directly in a notebook environment like Jupyter Notebooks.

---

#### CLI Automation

##### First import the required modules

```python
import histdatacom
from histdatacom.options import Options
```

##### Create and Initialize a new options object to pass parameters to histdatacom

```python
options = Options()
```

##### Configure for CLI automation

To automate the CLI, simply include one of the boolean behavior flags: `options.validate_urls`, `options.download_data_archives`, `options.extract_csvs`, and `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.

```python
# options.validate_urls = True
# options.download_data_archives = True  # implies validate
options.extract_csvs = True  # implies validate and download
# 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 = "now"
options.cpu_utilization = 100
```

- when a behavior flag is included, `histdatacom` assumes it is being used for `CLI` automation **exclusively** and does **not** provide a return value.

at present, calling from another script or module is limited to using the `__name__=="__main__"` idiom.

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

***Jupyter may be used normally***

```python
histdatacom(options)  # (Jupyter)
```

---

#### Jupyter and External Scripts

As opposed to the `CLI` interface, one may wish to load data from histdata.com and work with it interactively (e.g. in a Jupyter notebook), or as part of a larger pipeline.  To that end, histdatacom provides an option to specify a return type.

- return types can be:

  - A `datatable` Frame
  - a `pandas` dataframe
  - in Apache `arrow` in-memory format

- *to use `pandas` or `arrow` formats you must install the required packages*
  - `pip install pandas`
  - `pip install pyarrow`

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

##### Import the required modules

```python
import histdatacom
from histdatacom.options import Options
```

##### Initialize a new options object to pass parameters to histdatacom

```python
options = Options()
```

##### Jupyter & External Script Options

```python
options.api_return_type = "pandas"  # "datatable", "pandas", or "arrow"
options.formats = {"ascii"}  # Must be {"ascii"}
options.timeframes = {"tick-data-quotes"}  # can be tick-data-quotes or 1-minute-bar-quotes
options.pairs = {"eurusd"}
options.start_yearmonth = "2021-04"
options.end_yearmonth = "now"
options.cpu_utilization = "high"
```

- This example uses just one pair/instrument/symbol `eurusd` and just one timeframe `tick-data-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

```python
data = histdatacom(options)  # (Jupyter)

print(data)
print(type(data))
```

```text
              datetime      bid      ask  vol
0         1617253200478  1.17243  1.17244    0
1         1617253206261  1.17246  1.17248    0
2         1617253206362  1.17247  1.17249    0
3         1617253206946  1.17247  1.17250    0
4         1617253207121  1.17249  1.17250    0
...                 ...      ...      ...  ...
18648493  1650664783081  1.07968  1.08042    0
18648494  1650664783182  1.07968  1.08039    0
18648495  1650664790108  1.07964  1.08032    0
18648496  1650664790958  1.07947  1.08032    0
18648497  1650664794462  1.07947  1.08032    0

[18648498 rows x 4 columns]
<class 'pandas.core.frame.DataFrame'>
```

- When specifying more than one pair/symbol/instrument or timeframe, the api will return an ***list of dictionaries*** with references to the timeframe, pair, records used to create the data, and the merged data itself.

```python
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 = "now"
options.cpu_utilization = "75"
```

```python
data = histdatacom(options)  # (Jupyter)

print(data)
print(type(data))
```

```txt
[
  {
    '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'>
```

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

```txt
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'>
```

at present, calling from another script or module is limited to using the `__name__=="__main__"` idiom.

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

***Jupyter may be used normally***

```python
histdatacom(options)  # (Jupyter)
```

##### Full Script Example

```python
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_datatable_frame(pair, start=None, end=None):
    options = Options()
    options.api_return_type = "datatable"
    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 the latest version of datatable

- **this is a temporary fix until the datatable team updates PyPi. [See this issue](https://github.com/h2oai/datatable/issues/3268) for more details*

check out the section: [Data Table Installation Options](#datatable-installation-options) to either:

- [install a build wheel from Datatable's Appveyor CI/CD pipeline](#install-from-appveyor), or;
- [build from source](#build-from-source)

---

#### Install histdatacom

```sh
pip install histdatacom
```

to install latest development version

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

---

#### Vanilla MacOS and Linux

##### Create a new project directory and change to it

```bash
mkdir myproject && cd myproject && pwd
```

##### Create a Python Virtual Environment and activate it

```bash
python -m venv venv && source venv/bin/activate
```

##### Confirm Python Path and Version

```bash
which python && python --version
```

##### Build the latest version of datatable

follow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)

##### Install the histdata.com-tools package from PyPi

```bash
pip install histdatacom
```

##### Run `histdatacom` to view help message and Options

```bash
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

```powershell
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.

```powershell
[Environment]::SetEnvironmentVariable("Path", "$env:Path;C:\Program Files\Python310")
```

##### Change the Execution Policy to Unrestricted

```powershell
Set-ExecutionPolicy Unrestricted -Force
```

##### Create a new directory and change to it

```powershell
New-Item -Path ".\" -Name "myproject" -ItemType "directory"; Set-Location .\myproject\
```

##### Create a Virtual Environment and activate it

```powershell
python -m venv venv; .\venv\Scripts\Activate.ps1
```

##### Confirm Path and Version

```powershell
Get-Command python | select Source; python --version
```

##### Build the latest version of datatable

follow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)

##### Install histdata.com-tools package from PyPi

```powershell
pip install histdatacom
```

##### Run `histdatacom` to view help message

```powershell
histdatacom -h
```

---

#### Anaconda Setup

---

##### Anaconda MacOS and Linux

###### Create a Project Directory and Change to it

```shell
mkdir myproject && cd myproject && pwd
```

###### Create a `Python 3.10` Anaconda environment with `conda` and activate it

```shell
conda create -n py310 python=3.10 && conda activate py310
```

###### Check Python Path and Version

```shell
which python && python --version
```

###### Build the latest version of datatable

follow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)

###### Install histdatacom package from PyPi

```shell
pip install histdatacom
```

###### Run histdatacom package to view help message

```shell
histdatacom -h
```

---


##### Anaconda Windows using the Anaconda Prompt

###### Create a Directory and Change to it

```shell
mkdir myproject && cd myproject && echo %cd%
```

###### Create a `Python 3.10` Anaconda environment with `conda` and activate it

```shell
conda create -n py310 python=3.10 && conda activate py310
```

###### Check Python Path and Version

```shell
where python && python --version
```

###### Build the latest version of datatable

follow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)

###### Install histdatacom package from PyPi

```shell
pip install histdatacom
```

###### Run histdatacom package to view help message

```shell
histdatacom -h
```

---

### Datatable Installation Options

---

#### Install from Appveyor

Build wheels are pre-compiled versions of datatable, and would easily be the preferred route of installation while we wait for the datatable team to provide an official Python 3.10 package on PyPi.  The only drawback is documenting the procedure as the wheel's URL expires monthly thus this documentation could go out of date rather quickly...

##### Activate Python Environment if you're using one

refer to the **Create a Python Virtual Environment and activate it** steps outlined for your platform

- [Vanilla MacOS and Linux](#vanilla-macos-and-linux)
- [Vanilla Windows Powershell](#vanilla-windows-powershell)
- [Anaconda MacOS and Linux](#anaconda-macos-and-linux)
- [Anaconda Windows using the Anaconda Prompt](#anaconda-windows-using-the-anaconda-prompt)

##### Get the Build Wheel's URL for your platform

To find the latest build wheels for datatable, go to dataable's [Appveyor CI/CD Instance](https://ci.appveyor.com/project/h2oops/datatable):

- Select the Platform you're installing for:
  - ![image](https://user-images.githubusercontent.com/1161295/175226383-5211e4f9-9718-4f0b-9c00-713067f62f02.png)
- Select `"Artifacts"` and right/option-click on the filename that contains `cp310`. e.g. `dist\datatable-1.1.0a2157-cp310-cp310-win_amd64.whl`
- Select `"Copy Link Address"` from your browser's context menu to copy the wheel's URL
  - ![image](https://user-images.githubusercontent.com/1161295/175226442-ffcf8370-31bb-426c-a8e9-09ab29db91e0.png)

##### Install datatable using pip with the wheel's URL from Appveyor

e.g. `pip install {https://APPVEYOR DATATABLE BUILD WHEEL URL.whl}`

---

#### Build from Source

- You will need a C++ compiler installed to build datatable from source

---

##### MacOS XCode Command Line Tools

- For **MacOS**, run `xcode-select --install` from your terminal and confirm the prompts for download and installation of the xcode command-line tools.

---

##### Windows MSVC C++ Compiler

- For **Windows**, you need to download and install the [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/) and choose the option `Desktop Development with C++`, then select install.

---

###### Launch the Visual Studio command line environment (for Windows only)

- Open either a `powershell`, `cmd`, or `Anaconda Prompt` terminal
  - the setup scripts for the VS CLI environments are located in the `.\Common7\Tools\` directory of your Visual Studio installation directory
    - e.g. `"C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\Tools\"`
- Run the VS CLI environment setup script
  - for **Powershell**:
    - `PS> "C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\Tools\Launch-VsDevShell.ps1"`
  - for **CMD** and **Anaconda Prompt**:
    - `> "C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\Tools\LaunchDevCmd.bat"`

---

###### Tell the datatable setup where to find the MSVC C++ compiler

- for **Powershell**:
  - `PS> $env:DT_MSVC_PATH="$env:VSINSTALLDIR"+"VC\Tools\MSVC\"`
- for **CMD** and **Anaconda Prompt**:
  - `set DT_MSVC_PATH=%VSINSTALLDIR%VC\Tools\MSVC\`

---

###### Return to Your Project's Directory

The Visual Studio command line environment setup scripts change your directory, you'll need to find your way back to your project's directory.  I like to use the variable `%USERPROFILE%` to save myself some typing:

*e.g.* `> cd %USERPROFILE%\Documents\projects\myproject`

---

##### Activate Python Environment if you're using one

refer to the **Create a Python Virtual Environment and activate it** steps outlined for your platform

- [Vanilla MacOS and Linux](#vanilla-macos-and-linux)
- [Vanilla Windows Powershell](#vanilla-windows-powershell)
- [Anaconda MacOS and Linux](#anaconda-macos-and-linux)
- [Anaconda Windows using the Anaconda Prompt](#anaconda-windows-using-the-anaconda-prompt)

---

##### Install datatable

```shell
pip install git+https://github.com/h2oai/datatable
```

---

## Roadmap

- [~~Add Support for Anaconda~~](https://github.com/dmidlo/histdata.com-tools/issues/28)
- [Implement MyPy static typing checking](https://github.com/dmidlo/histdata.com-tools/issues/16)
- [Implement UnitTesting with PyTest](https://github.com/dmidlo/histdata.com-tools/issues/9)
- [Create Binary Distributions](https://github.com/dmidlo/histdata.com-tools/issues/10)
  - See about packaging for different operating systems
    - deb/rpm packaging
    - NuGet/Chocolatey
    - MacPorts/Homebrew
- [docker image](https://github.com/dmidlo/histdata.com-tools/issues/11)
- [Create Down-sampling to Standard Candlestick Timeframes](https://github.com/dmidlo/histdata.com-tools/issues/18)
- [Fix terminate on ctrl-c multiprocessing KeyboardInterupt](https://github.com/dmidlo/histdata.com-tools/issues/15)
- [Look at replacing beautifulsoup with html parser](https://github.com/dmidlo/histdata.com-tools/issues/19)
- [Refactor to make use of globals more readable](https://github.com/dmidlo/histdata.com-tools/issues/14)
- [add -v -vv and -vvv flags](https://github.com/dmidlo/histdata.com-tools/issues/13)
- [Change Record statuses to Enum](https://github.com/dmidlo/histdata.com-tools/issues/20)
- [Add -S —set-status flag](https://github.com/dmidlo/histdata.com-tools/issues/21)
- [Create a central place for exceptions](https://github.com/dmidlo/histdata.com-tools/issues/22)
- Add the ability to import an order book to influxdb
- Add a --reset-cache flag to reset all or specified year-month range

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/dmidlo/histdata.com-tools",
    "name": "histdatacom",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.10.0",
    "maintainer_email": "",
    "keywords": "finance,data,datascience,HistData.com,scraper,influxdb,currency exchange,forex,fx,etl",
    "author": "David Midlo",
    "author_email": "dmidlo@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/49/04/fb40838082ffd5b716162f647db5170fae6c471e6c0a212ff8ecb4d5edf4/histdatacom-0.78.4.tar.gz",
    "platform": null,
    "description": "# histdata.com-tools\n\nA Multi-threaded/Multi-Process command-line utility and python package that downloads currency exchange rates from Histdata.com. Imports to InfluxDB. Can be used in Jupyter Notebooks. Works on MacOS, Linux & Windows Systems.\n**Requires Python3.10+**\n\n**NEW:** Expanded API support!!!\n\n[![Downloads](https://pepy.tech/badge/histdatacom)](https://pepy.tech/project/histdatacom) ![PyPI - License](https://img.shields.io/pypi/l/histdatacom) ![PyPI](https://img.shields.io/pypi/v/histdatacom) ![PyPI - Status](https://img.shields.io/pypi/status/histdatacom)\n\n---\n\n- [histdata.com-tools](#histdatacom-tools)\n- [Disclaimer](#disclaimer)\n- [Usage](#usage)\n  - [Show the Help and Options](#show-the-help-and-options)\n  - [Basic Use](#basic-use)\n  - [Available Formats](#available-formats)\n    - [CSV Dialect and Format Specifications](#csv-dialect-and-format-specifications)\n  - [Date Ranges](#date-ranges)\n    - ['Start' & 'Now' Keywords](#start-now-keywords)\n  - [Multiple Datasets](#multiple-datasets)\n  - [CPU Utilization](#cpu-utilization)\n  - [Import to InfluxDB](#import-to-influxdb)\n    - [influxdb.yaml](#influxdbyaml)\n  - [API - Other Scripts, Modules, & Jupyter Support](#api-other-scripts-modules-jupyter-support)\n    - [CLI Automation](#cli-automation)\n    - [Jupyter and External Scripts](#jupyter-and-external-scripts)\n    - [Full Script Example](#full-script-example)\n- [Setup](#setup)\n  - [TLDR for all platforms](#tldr-for-all-platforms)\n  - [Vanilla Python Setup](#vanilla-python-setup)\n    - [Vanilla MacOS and Linux](#vanilla-macos-and-linux)\n    - [Vanilla Windows Powershell](#vanilla-windows-powershell)\n  - [Anaconda Setup](#anaconda-setup)\n    - [Anaconda MacOS and Linux](#anaconda-macos-and-linux)\n    - [Anaconda Windows using the Anaconda Prompt](#anaconda-windows-using-the-anaconda-prompt)\n  - [Data Table Installation Options](#datatable-installation-options)\n- [Roadmap](#roadmap)\n\n---\n\n## Disclaimer\n\n**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*\n\n*If you choose to use this tool, it is **strongly** suggested that you head over to [http://www.histdata.com/download-by-ftp/](http://www.histdata.com/download-by-ftp/) and sign up to help support their traffic costs.*\n\n*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!](https://www.buymeacoffee.com/dmidlo)*\n\n---\n\n## Usage\n\n**Note #1**\nThe number one rule when using this tool is to be **MORE** specific with your input to limit the size of your request.\n\n**Note #2**\n*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.*\n\n*It is likely the default behavior will be modified from its current state to discourage unnecessarily large requests.*\n\n**please submit feature requests and bug reports using this repository's [issue tracker](https://github.com/dmidlo/histdata.com-tools/issues).*\n\n### Show the help and options\n\n```txt\nhistdatacom -h\n```\n\n```txt\nhistdatacom -h\nusage: histdatacom [-h] [-A] [-U] [--by BY] [--version] [-V] [-D] [-X] [-p PAIR [PAIR ...]] [-f FORMAT [FORMAT ...]] [-t TIMEFRAME [TIMEFRAME ...]] [-s START_YEARMONTH] [-e END_YEARMONTH] [-I] [-d] [-b BATCH_SIZE] [-c CPU_UTILIZATION]\n                   [--data-directory DATA_DIRECTORY]\n\noptions:\n  -h, --help            show this help message and exit\n\nMode:\n  -V, --validate_urls   Check generated list of URLs as valid download locations\n  -D, --download_data_archives\n                        download specified pairs/formats/timeframe and create data files\n  -X, --extract_csvs    histdata.com delivers zip files. Use the -X flag to extract them.\n\nConfig:\n  -p PAIR [PAIR ...], --pairs PAIR [PAIR ...]\n                        space separated currency pairs. e.g. -p eurusd usdjpy ...\n  -f FORMAT [FORMAT ...], --formats FORMAT [FORMAT ...]\n                        space separated formats. -f metatrader ascii ninjatrader metastock\n  -t TIMEFRAME [TIMEFRAME ...], --timeframes TIMEFRAME [TIMEFRAME ...]\n                        space separated Timeframes. -t tick-data-quotes 1-minute-bar-quotes\n  -s START_YEARMONTH, --start_yearmonth START_YEARMONTH\n                        set a start year and month for data. e.g. -s 2000-04 or -s 2015-00\n  -e END_YEARMONTH, --end_yearmonth END_YEARMONTH\n                        set a start year and month for data. e.g. -e 2020-00 or -e 2022-04\n\nInfluxdb:\n  -I, --import_to_influxdb\n                        import data to influxdb instance. Use influxdb.yaml to configure.\n  -d, --delete_after_influx\n                        delete data files after upload to influxdb\n  -b BATCH_SIZE, --batch_size BATCH_SIZE\n                        (integer) influxdb write_api batch size. defaults to 5000\n\nSystem:\n  -c CPU_UTILIZATION, --cpu_utilization CPU_UTILIZATION\n                        \"low\", \"medium\", \"high\". High uses all available CPUs OR integer percent 1-200\n  --data-directory DATA_DIRECTORY\n                        Directory Used to save data. default is \"./data/\"\n\nInfo:\n  -A, --available_remote_data\n                        list data retrievable from histdata.com\n  -U, --update_remote_data\n                        update list of data retrievable from histdata.com\n  --by BY               With -A, -U, to sort --by [pair_asc, pair_dsc, start_asc, start_dsc]\n  --version             return current version of histdatacom.\n```\n\n---\n\n### Basic Use\n\n#### Download and extract the current month's available EURUSD data for metatrader 4/5into the default data directory ./data\n\n```sh\nhistdatacom -p eurusd -f metatrader -s now\n```\n\n#### include the `-D` flag to download but NOT extract to csv\n\n```sh\nhistdatacom -D -p usdcad -f metastock -s now\n```\n\n---\n\n#### Available Formats\n\nThe formats available are:\n\n||\n|-----------|\n|metatrader|\n|metastock|\n|ninjatrader|\n|excel|\n|ascii|\n\n histdata.com provides different resolutions of time\n depending on the format.\n\n The following format/timeframe combinations are available:\n\n|||\n|------------------|:-----------:|\n|1-minute-bar-quotes|all formats|\n|tick-data-quotes |ascii|\n|tick-last-quotes|ninjatrader|\n|tick-bid-quotes|ninjatrader|\n|tick-ask-quotes|ninjatrader|\n\n##### CSV Dialect and Format Specifications\n\n- *For Detailed specifications for the CSVs that the histdata.com repo provides see [histdata.com_data_specs.md](https://github.com/dmidlo/histdata.com-tools/blob/main/histdata.com_data_specs.md)*\n\n##### To download 1-minute-bar-quotes for both metastock and excel\n\n```sh\nhistdatacom -p usdjpy -f metastock excel -s now \n```\n\n---\n\n#### Date Ranges\n\ndate ranges are for year and month and can be specified in the following ways:\n | [ -._] |\n|-------|\n|2022-04|\n|\"2202 04\"|\n|2202.04|\n|2202_04|\n\n##### to fetch a single year's data, leave out the month\n\n- 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.\n\n```txt\nhistdatacom -p udxusd -f ascii -t tick-data-quotes -s 2011\n```\n\n##### to fetch a single month's data, include a month, but do not use the `-e, --end_yearmonth` flag\n\n- if you're requesting 1-minute-bar-quotes for any\n    year except the current year, you will receive the\n    the whole year's data\n- this example leaves out the `-p --pair` flag, and will\n    fetch data for all 66 available instruments\n\n```txt\nhistdatacom -f metatrader -s 2012-07\n```\n\n#### `Start` & `Now` Keywords\n\nyou may have noticed that two special year-month keywords exist\n `start` and `now`\n\n- `start` may only be used with the `-s --start_yearmonth`\n   flag and the `-e --end_yearmonth` flag **must** be specified\n   to indicate a range of data\n\n```txt\nhistdatacom -p audusd -f metatrader -s start -e 2008-12\n```\n\n- `now` used alone will return the current year-month\n- when used with as `-s now` it will return the most current month's data\n\n```txt\nhistdatacom -p frxeur -f ninjatrader -s now\n```\n\nin the above example, no `-t --timeframe` flag was specified. This will return all time resolutions available for the specified format(s)\n\n`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.\n\n```txt\nhistdatacom -p xagusd -f ascii -1-minute-bar-quotes -s 2019-04 -e now\n```\n\n---\n\n##### Multiple Datasets\n\n##### multiple datasets can be requested in one command\n\nthis example with use the `-e --end_yearmonth` flag to request a range of data for multiple instruments.\n\n- note: Large requests like these are to be avoided. remember to sign up with histdata.com to help them pay for network costs\n\n```txt\nhistdatacom -p eurusd usdcad udxusd -f metatrader -s start -e 2017-04\n```\n\n---\n\n##### CPU Utilization\n\nOne can set a cap on CPU Utilization with `-c --cpu_utilization`\n\n- available levels are, `\"low\"`,`\"medium\"`,`\"high\"`\n- **OR**\n- integer percent 1-200\n  eg. `-c 100` is equal to `-c high`\n\n```sh\nhistdatacom -c medium -p udxusd -f metatrader -s 2015-04 -e 2016-04\n```\n\n---\n\n### Import to InfluxDB\n\nTo import data to an influxdb instance, 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).\n\n- ascii is the only format accepted for influxdb import.\n- all histdata.com datetime data is in EST (Eastern Standard Time) with no adjustments for daylight savings.\n- 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)\n- this tool converts histdata.com ESTnoDST to UTC Epoch milli-second timestamps as part of the import-to-influx process\n\n```txt\nhistdatacom -I -p eurusd -f ascii -t tick-data-quotes -s start -e now\n```\n\n#### influxdb.yaml\n\n```yaml\n# a sample influxdb.yaml file.\ninfluxdb:\n  org: influx_org\n  bucket: data_bucket\n  url: influx_server_api_url\n  token: influx_user_token\n```\n\n##### Download influxdb.yaml to your project's directory\n\n```shell\ncurl \"https://raw.githubusercontent.com/dmidlo/histdata.com-tools/main/influxdb.sample.yaml\" --output influxdb.yaml\n```\n\n---\n\n### API - Other Scripts, Modules, & Jupyter Support\n\nhistdatacom also has an API to allow developers and to integrate the package into their own projects.  It can be used in one of two ways; The first being a simple interface to automate CLI interaction. The second is as an interface to work with the data directly in a notebook environment like Jupyter Notebooks.\n\n---\n\n#### CLI Automation\n\n##### First import the required modules\n\n```python\nimport histdatacom\nfrom histdatacom.options import Options\n```\n\n##### Create and Initialize a new options object to pass parameters to histdatacom\n\n```python\noptions = Options()\n```\n\n##### Configure for CLI automation\n\nTo automate the CLI, simply include one of the boolean behavior flags: `options.validate_urls`, `options.download_data_archives`, `options.extract_csvs`, and `options.import_to_influxdb`\n\n- Each behavior flag implies the use of the preceding flags.\n  - histdatacom is an ETL pipeline (extract, transform, load) and each step depends on the preceding steps in the pipeline.\n  - For the `CLI`, the order of operations are:\n    - validate urls\n    - download zip files from histdata.com\n    - extract the csv from the zip archive\n    - transform the ESTnoDST datetime to UTC Epoch `AND` upload to InfluxDB.\n\n```python\n# options.validate_urls = True\n# options.download_data_archives = True  # implies validate\noptions.extract_csvs = True  # implies validate and download\n# options.import_to_influxdb = True  # implies validate, download, and extract\noptions.formats = {\"ascii\"}\noptions.timeframes = {\"tick-data-quotes\"}\noptions.pairs = {\"eurusd\"}\noptions.start_yearmonth = \"2021-04\"\noptions.end_yearmonth = \"now\"\noptions.cpu_utilization = 100\n```\n\n- when a behavior flag is included, `histdatacom` assumes it is being used for `CLI` automation **exclusively** and does **not** provide a return value.\n\nat present, calling from another script or module is limited to using the `__name__==\"__main__\"` idiom.\n\n```python\nif __name__==\"__main__\": \n   histdatacom(options)\n```\n\n***Jupyter may be used normally***\n\n```python\nhistdatacom(options)  # (Jupyter)\n```\n\n---\n\n#### Jupyter and External Scripts\n\nAs opposed to the `CLI` interface, one may wish to load data from histdata.com and work with it interactively (e.g. in a Jupyter notebook), or as part of a larger pipeline.  To that end, histdatacom provides an option to specify a return type.\n\n- return types can be:\n\n  - A `datatable` Frame\n  - a `pandas` dataframe\n  - in Apache `arrow` in-memory format\n\n- *to use `pandas` or `arrow` formats you must install the required packages*\n  - `pip install pandas`\n  - `pip install pyarrow`\n\n- ***All datetime is returned as milliseconds since January 1, 1970 (midnight UTC/GMT)***\n\n##### Import the required modules\n\n```python\nimport histdatacom\nfrom histdatacom.options import Options\n```\n\n##### Initialize a new options object to pass parameters to histdatacom\n\n```python\noptions = Options()\n```\n\n##### Jupyter & External Script Options\n\n```python\noptions.api_return_type = \"pandas\"  # \"datatable\", \"pandas\", or \"arrow\"\noptions.formats = {\"ascii\"}  # Must be {\"ascii\"}\noptions.timeframes = {\"tick-data-quotes\"}  # can be tick-data-quotes or 1-minute-bar-quotes\noptions.pairs = {\"eurusd\"}\noptions.start_yearmonth = \"2021-04\"\noptions.end_yearmonth = \"now\"\noptions.cpu_utilization = \"high\"\n```\n\n- This example uses just one pair/instrument/symbol `eurusd` and just one timeframe `tick-data-quotes`.  When the api is called with this 'one-one` specificity, the api will directly return the requested data.\n- Regardless of the specified start_yearmonth and end_yearmonth, the resultant data will be sorted and merged into a single dataset.\n\n##### Pass the options to histdatacom and assign the return to a variable\n\n```python\ndata = histdatacom(options)  # (Jupyter)\n\nprint(data)\nprint(type(data))\n```\n\n```text\n              datetime      bid      ask  vol\n0         1617253200478  1.17243  1.17244    0\n1         1617253206261  1.17246  1.17248    0\n2         1617253206362  1.17247  1.17249    0\n3         1617253206946  1.17247  1.17250    0\n4         1617253207121  1.17249  1.17250    0\n...                 ...      ...      ...  ...\n18648493  1650664783081  1.07968  1.08042    0\n18648494  1650664783182  1.07968  1.08039    0\n18648495  1650664790108  1.07964  1.08032    0\n18648496  1650664790958  1.07947  1.08032    0\n18648497  1650664794462  1.07947  1.08032    0\n\n[18648498 rows x 4 columns]\n<class 'pandas.core.frame.DataFrame'>\n```\n\n- When specifying more than one pair/symbol/instrument or timeframe, the api will return an ***list of dictionaries*** with references to the timeframe, pair, records used to create the data, and the merged data itself.\n\n```python\noptions.api_return_type = \"pandas\"\noptions.formats = {\"ascii\"}\noptions.timeframes = {\"1-minute-bar-quotes\"}\noptions.pairs = {\"eurusd\",\"usdcad\"}\noptions.start_yearmonth = \"2021-01\"\noptions.end_yearmonth = \"now\"\noptions.cpu_utilization = \"75\"\n```\n\n```python\ndata = histdatacom(options)  # (Jupyter)\n\nprint(data)\nprint(type(data))\n```\n\n```txt\n[\n  {\n    'timeframe': 'M1', \n    'pair': 'EURUSD', \n    'records': [<histdatacom.records.Record object ...>, ...],\n    'data':    \n                    datetime     open     high      low    close  vol\n      0       1609711200000  1.22396  1.22396  1.22373  1.22395    0\n      1       1609711260000  1.22387  1.22420  1.22385  1.22395    0\n      2       1609711320000  1.22396  1.22398  1.22382  1.22382    0\n      3       1609711380000  1.22383  1.22396  1.22376  1.22378    0\n      4       1609711440000  1.22378  1.22385  1.22296  1.22347    0\n      ...               ...      ...      ...      ...      ...  ...\n      484172  1650664440000  1.07976  1.08014  1.07976  1.08014    0\n      484173  1650664500000  1.08013  1.08021  1.07997  1.08000    0\n      484174  1650664560000  1.08000  1.08000  1.07956  1.07968    0\n      484175  1650664620000  1.07980  1.07980  1.07958  1.07968    0\n      484176  1650664680000  1.07980  1.07986  1.07963  1.07963    0\n\n      [484177 rows x 6 columns]\n  }, \n  {\n    'timeframe': 'M1', \n    'pair': 'USDCAD',\n    'records': [<histdatacom.records.Record object ...>, ...],\n    'data':                \n                    datetime     open     high      low    close  vol\n      0       1609711200000  1.27136  1.27201  1.27136  1.27201    0\n      1       1609711260000  1.27207  1.27241  1.27207  1.27220    0\n      2       1609711320000  1.27211  1.27219  1.27211  1.27219    0\n      3       1609711380000  1.27212  1.27261  1.27212  1.27261    0\n      4       1609711440000  1.27268  1.27268  1.27261  1.27261    0\n      ...               ...      ...      ...      ...      ...  ...\n      483946  1650664440000  1.27121  1.27132  1.27114  1.27131    0\n      483947  1650664500000  1.27129  1.27137  1.27102  1.27106    0\n      483948  1650664560000  1.27107  1.27114  1.27098  1.27101    0\n      483949  1650664620000  1.27105  1.27105  1.27091  1.27091    0\n      483950  1650664680000  1.27091  1.27097  1.27073  1.27097    0\n\n      [483951 rows x 6 columns]\n  }\n]\n\n<class 'list'>\n```\n\n```python\nprint(data[0]['timeframe'], data[0]['pair'])\nprint(data[0]['data'])\nprint(type(data[0]['data']))\n```\n\n```txt\nM1 EURUSD\n               datetime     open     high      low    close  vol\n0       20210103 170000  1.22396  1.22396  1.22373  1.22395    0\n1       20210103 170100  1.22387  1.22420  1.22385  1.22395    0\n2       20210103 170200  1.22396  1.22398  1.22382  1.22382    0\n3       20210103 170300  1.22383  1.22396  1.22376  1.22378    0\n4       20210103 170400  1.22378  1.22385  1.22296  1.22347    0\n...                 ...      ...      ...      ...      ...  ...\n484172  20220422 165400  1.07976  1.08014  1.07976  1.08014    0\n484173  20220422 165500  1.08013  1.08021  1.07997  1.08000    0\n484174  20220422 165600  1.08000  1.08000  1.07956  1.07968    0\n484175  20220422 165700  1.07980  1.07980  1.07958  1.07968    0\n484176  20220422 165800  1.07980  1.07986  1.07963  1.07963    0\n\n[484177 rows x 6 columns]\n<class 'pandas.core.frame.DataFrame'>\n```\n\nat present, calling from another script or module is limited to using the `__name__==\"__main__\"` idiom.\n\n```python\nif __name__==\"__main__\": \n   histdatacom(options)\n```\n\n***Jupyter may be used normally***\n\n```python\nhistdatacom(options)  # (Jupyter)\n```\n\n##### Full Script Example\n\n```python\nimport histdatacom\nfrom histdatacom.options import Options\nfrom histdatacom.fx_enums import Pairs\n\ndef import_pair_to_influx(pair, start, end):\n    data_options = Options()\n\n    data_options.import_to_influxdb = True  # implies validate, download, and extract\n    data_options.delete_after_influx = True\n    data_options.batch_size = \"2000\"\n    data_options.cpu_utilization = \"high\"\n\n    data_options.pairs = {f\"{pair}\"}# histdata_and_oanda_intersect_symbs\n    data_options.start_yearmonth = f\"{start}\"\n    data_options.end_yearmonth = f\"{end}\"\n    data_options.formats = {\"ascii\"}  # Must be {\"ascii\"}\n    data_options.timeframes = {\"tick-data-quotes\"}  # can be tick-data-quotes or 1-minute-bar-quotes\n    histdatacom(data_options)\n\ndef get_available_range_data(pairs):\n    range_options = Options()\n    range_options.pairs = pairs\n    range_options.available_remote_data = True\n    range_options.by = \"start_dsc\"\n    range_data = histdatacom(range_options)  # (Jupyter)\n    return range_data\n\ndef print_one_datatable_frame(pair, start=None, end=None):\n    options = Options()\n    options.api_return_type = \"datatable\"\n    options.pairs = {f\"{pair}\"}\n    options.start_yearmonth = \"201501\"\n    options.formats = {\"ascii\"}\n    options.timeframes = {\"tick-data-quotes\"}\n    return histdatacom(options)\n\ndef main():\n    histdata_symbs = Pairs.list_keys()\n    \n    # Oanda Symbols:\n    oanda_symbs = {\"audcad\",\"audchf\",\"audhkd\",\"audjpy\",\"audsgd\",\"audusd\",\"cadhkd\",\"cadjpy\",\"cadsgd\",\n    \"chfhkd\",\"chfjpy\",\"euraud\",\"eurcad\",\"eurchf\",\"eurgbp\",\"eurhkd\",\"eurjpy\",\"eursgd\",\"eurusd\",\"gbpaud\",\n    \"gbpcad\",\"gbpchf\",\"gbphkd\",\"gbpjpy\",\"gbpsgd\",\"gbpusd\",\"hkdjpy\",\"sgdchf\",\"sgdhkd\",\"sgdjpy\",\"usdcad\",\n    \"usdchf\",\"usdhkd\",\"usdjpy\",\"usdsgd\",\"audnzd\",\"cadchf\",\"chfzar\",\"eurczk\",\"eurdkk\",\"eurhuf\",\"eurnok\",\n    \"eurnzd\",\"eurpln\",\"eursek\",\"eurtry\",\"eurzar\",\"gbpnzd\",\"gbppln\",\"gbpzar\",\"nzdcad\",\"nzdchf\",\"nzdhkd\",\n    \"nzdjpy\",\"nzdsgd\",\"nzdusd\",\"tryjpy\",\"usdcnh\",\"usdczk\",\"usddkk\",\"usdhuf\",\"usdmxn\",\"usdnok\",\"usdpln\",\n    \"usdsar\",\"usdsek\",\"usdthb\",\"usdtry\",\"usdzar\",\"zarjpy\"}\n\n    histdata_and_oanda_intersect_symbs = histdata_symbs & oanda_symbs\n\n    pairs_data = get_available_range_data(histdata_and_oanda_intersect_symbs)\n    for pair in pairs_data:\n        start = pairs_data[pair]['start']\n        end = pairs_data[pair]['end']\n        \n        import_pair_to_influx(pair, start, end)\n\nif __name__ == '__main__':\n    main()\n```\n\n---\n\n## Setup\n\n### TLDR for all platforms\n\n---\n\n#### Install the latest version of datatable\n\n- **this is a temporary fix until the datatable team updates PyPi. [See this issue](https://github.com/h2oai/datatable/issues/3268) for more details*\n\ncheck out the section: [Data Table Installation Options](#datatable-installation-options) to either:\n\n- [install a build wheel from Datatable's Appveyor CI/CD pipeline](#install-from-appveyor), or;\n- [build from source](#build-from-source)\n\n---\n\n#### Install histdatacom\n\n```sh\npip install histdatacom\n```\n\nto install latest development version\n\n```sh\npip install git+https://github.com/dmidlo/histdata.com-tools.git\n```\n\n---\n\n#### Vanilla MacOS and Linux\n\n##### Create a new project directory and change to it\n\n```bash\nmkdir myproject && cd myproject && pwd\n```\n\n##### Create a Python Virtual Environment and activate it\n\n```bash\npython -m venv venv && source venv/bin/activate\n```\n\n##### Confirm Python Path and Version\n\n```bash\nwhich python && python --version\n```\n\n##### Build the latest version of datatable\n\nfollow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)\n\n##### Install the histdata.com-tools package from PyPi\n\n```bash\npip install histdatacom\n```\n\n##### Run `histdatacom` to view help message and Options\n\n```bash\nhistdatacom -h\n```\n\n---\n\n#### Vanilla Windows Powershell\n\n##### Launch a Powershell Terminal\n\n- Run as Administrator (right-click on shortcut and click Run as Admin...)\n\n##### Make sure python3.10 is in your system's executable path\n\n```powershell\npython --version\n```\n\n- should be already set if you clicked the checkbox when installing python 3.10\n- If not, you can run the following.\n  - you will need to relaunch powershell as admin.\n\n```powershell\n[Environment]::SetEnvironmentVariable(\"Path\", \"$env:Path;C:\\Program Files\\Python310\")\n```\n\n##### Change the Execution Policy to Unrestricted\n\n```powershell\nSet-ExecutionPolicy Unrestricted -Force\n```\n\n##### Create a new directory and change to it\n\n```powershell\nNew-Item -Path \".\\\" -Name \"myproject\" -ItemType \"directory\"; Set-Location .\\myproject\\\n```\n\n##### Create a Virtual Environment and activate it\n\n```powershell\npython -m venv venv; .\\venv\\Scripts\\Activate.ps1\n```\n\n##### Confirm Path and Version\n\n```powershell\nGet-Command python | select Source; python --version\n```\n\n##### Build the latest version of datatable\n\nfollow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)\n\n##### Install histdata.com-tools package from PyPi\n\n```powershell\npip install histdatacom\n```\n\n##### Run `histdatacom` to view help message\n\n```powershell\nhistdatacom -h\n```\n\n---\n\n#### Anaconda Setup\n\n---\n\n##### Anaconda MacOS and Linux\n\n###### Create a Project Directory and Change to it\n\n```shell\nmkdir myproject && cd myproject && pwd\n```\n\n###### Create a `Python 3.10` Anaconda environment with `conda` and activate it\n\n```shell\nconda create -n py310 python=3.10 && conda activate py310\n```\n\n###### Check Python Path and Version\n\n```shell\nwhich python && python --version\n```\n\n###### Build the latest version of datatable\n\nfollow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)\n\n###### Install histdatacom package from PyPi\n\n```shell\npip install histdatacom\n```\n\n###### Run histdatacom package to view help message\n\n```shell\nhistdatacom -h\n```\n\n---\n\n\n##### Anaconda Windows using the Anaconda Prompt\n\n###### Create a Directory and Change to it\n\n```shell\nmkdir myproject && cd myproject && echo %cd%\n```\n\n###### Create a `Python 3.10` Anaconda environment with `conda` and activate it\n\n```shell\nconda create -n py310 python=3.10 && conda activate py310\n```\n\n###### Check Python Path and Version\n\n```shell\nwhere python && python --version\n```\n\n###### Build the latest version of datatable\n\nfollow the instructions from [Install the latest version of datatable](#install-the-latest-version-of-datatable)\n\n###### Install histdatacom package from PyPi\n\n```shell\npip install histdatacom\n```\n\n###### Run histdatacom package to view help message\n\n```shell\nhistdatacom -h\n```\n\n---\n\n### Datatable Installation Options\n\n---\n\n#### Install from Appveyor\n\nBuild wheels are pre-compiled versions of datatable, and would easily be the preferred route of installation while we wait for the datatable team to provide an official Python 3.10 package on PyPi.  The only drawback is documenting the procedure as the wheel's URL expires monthly thus this documentation could go out of date rather quickly...\n\n##### Activate Python Environment if you're using one\n\nrefer to the **Create a Python Virtual Environment and activate it** steps outlined for your platform\n\n- [Vanilla MacOS and Linux](#vanilla-macos-and-linux)\n- [Vanilla Windows Powershell](#vanilla-windows-powershell)\n- [Anaconda MacOS and Linux](#anaconda-macos-and-linux)\n- [Anaconda Windows using the Anaconda Prompt](#anaconda-windows-using-the-anaconda-prompt)\n\n##### Get the Build Wheel's URL for your platform\n\nTo find the latest build wheels for datatable, go to dataable's [Appveyor CI/CD Instance](https://ci.appveyor.com/project/h2oops/datatable):\n\n- Select the Platform you're installing for:\n  - ![image](https://user-images.githubusercontent.com/1161295/175226383-5211e4f9-9718-4f0b-9c00-713067f62f02.png)\n- Select `\"Artifacts\"` and right/option-click on the filename that contains `cp310`. e.g. `dist\\datatable-1.1.0a2157-cp310-cp310-win_amd64.whl`\n- Select `\"Copy Link Address\"` from your browser's context menu to copy the wheel's URL\n  - ![image](https://user-images.githubusercontent.com/1161295/175226442-ffcf8370-31bb-426c-a8e9-09ab29db91e0.png)\n\n##### Install datatable using pip with the wheel's URL from Appveyor\n\ne.g. `pip install {https://APPVEYOR DATATABLE BUILD WHEEL URL.whl}`\n\n---\n\n#### Build from Source\n\n- You will need a C++ compiler installed to build datatable from source\n\n---\n\n##### MacOS XCode Command Line Tools\n\n- For **MacOS**, run `xcode-select --install` from your terminal and confirm the prompts for download and installation of the xcode command-line tools.\n\n---\n\n##### Windows MSVC C++ Compiler\n\n- For **Windows**, you need to download and install the [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/) and choose the option `Desktop Development with C++`, then select install.\n\n---\n\n###### Launch the Visual Studio command line environment (for Windows only)\n\n- Open either a `powershell`, `cmd`, or `Anaconda Prompt` terminal\n  - the setup scripts for the VS CLI environments are located in the `.\\Common7\\Tools\\` directory of your Visual Studio installation directory\n    - e.g. `\"C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\\"`\n- Run the VS CLI environment setup script\n  - for **Powershell**:\n    - `PS> \"C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\Launch-VsDevShell.ps1\"`\n  - for **CMD** and **Anaconda Prompt**:\n    - `> \"C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\LaunchDevCmd.bat\"`\n\n---\n\n###### Tell the datatable setup where to find the MSVC C++ compiler\n\n- for **Powershell**:\n  - `PS> $env:DT_MSVC_PATH=\"$env:VSINSTALLDIR\"+\"VC\\Tools\\MSVC\\\"`\n- for **CMD** and **Anaconda Prompt**:\n  - `set DT_MSVC_PATH=%VSINSTALLDIR%VC\\Tools\\MSVC\\`\n\n---\n\n###### Return to Your Project's Directory\n\nThe Visual Studio command line environment setup scripts change your directory, you'll need to find your way back to your project's directory.  I like to use the variable `%USERPROFILE%` to save myself some typing:\n\n*e.g.* `> cd %USERPROFILE%\\Documents\\projects\\myproject`\n\n---\n\n##### Activate Python Environment if you're using one\n\nrefer to the **Create a Python Virtual Environment and activate it** steps outlined for your platform\n\n- [Vanilla MacOS and Linux](#vanilla-macos-and-linux)\n- [Vanilla Windows Powershell](#vanilla-windows-powershell)\n- [Anaconda MacOS and Linux](#anaconda-macos-and-linux)\n- [Anaconda Windows using the Anaconda Prompt](#anaconda-windows-using-the-anaconda-prompt)\n\n---\n\n##### Install datatable\n\n```shell\npip install git+https://github.com/h2oai/datatable\n```\n\n---\n\n## Roadmap\n\n- [~~Add Support for Anaconda~~](https://github.com/dmidlo/histdata.com-tools/issues/28)\n- [Implement MyPy static typing checking](https://github.com/dmidlo/histdata.com-tools/issues/16)\n- [Implement UnitTesting with PyTest](https://github.com/dmidlo/histdata.com-tools/issues/9)\n- [Create Binary Distributions](https://github.com/dmidlo/histdata.com-tools/issues/10)\n  - See about packaging for different operating systems\n    - deb/rpm packaging\n    - NuGet/Chocolatey\n    - MacPorts/Homebrew\n- [docker image](https://github.com/dmidlo/histdata.com-tools/issues/11)\n- [Create Down-sampling to Standard Candlestick Timeframes](https://github.com/dmidlo/histdata.com-tools/issues/18)\n- [Fix terminate on ctrl-c multiprocessing KeyboardInterupt](https://github.com/dmidlo/histdata.com-tools/issues/15)\n- [Look at replacing beautifulsoup with html parser](https://github.com/dmidlo/histdata.com-tools/issues/19)\n- [Refactor to make use of globals more readable](https://github.com/dmidlo/histdata.com-tools/issues/14)\n- [add -v -vv and -vvv flags](https://github.com/dmidlo/histdata.com-tools/issues/13)\n- [Change Record statuses to Enum](https://github.com/dmidlo/histdata.com-tools/issues/20)\n- [Add -S \u2014set-status flag](https://github.com/dmidlo/histdata.com-tools/issues/21)\n- [Create a central place for exceptions](https://github.com/dmidlo/histdata.com-tools/issues/22)\n- Add the ability to import an order book to influxdb\n- Add a --reset-cache flag to reset all or specified year-month range\n",
    "bugtrack_url": null,
    "license": "MIT License",
    "summary": "A Multi-threaded/Multi-Process command-line utility and python package that downloads currency exchange rates from Histdata.com. Imports to InfluxDB. Can be used in Jupyter Notebooks.",
    "version": "0.78.4",
    "split_keywords": [
        "finance",
        "data",
        "datascience",
        "histdata.com",
        "scraper",
        "influxdb",
        "currency exchange",
        "forex",
        "fx",
        "etl"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "e2e7fa972ef5cd322d06d1919aff1bcf",
                "sha256": "63b175f75f840c12035edff476fa2d5c4dd6c858a3696c2c16829aa0bb6108cb"
            },
            "downloads": -1,
            "filename": "histdatacom-0.78.4-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "e2e7fa972ef5cd322d06d1919aff1bcf",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": ">=3.10.0",
            "size": 49661,
            "upload_time": "2022-12-14T08:39:08",
            "upload_time_iso_8601": "2022-12-14T08:39:08.689421Z",
            "url": "https://files.pythonhosted.org/packages/2a/60/2e4b8cfafdd2584a730ba1f9b7f3042158d74766cb211fb1f3f48bc10a36/histdatacom-0.78.4-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "813b6355b67fb32ba1dd6c9f392b8724",
                "sha256": "6f152839622b05e21b331827c60bce9d6d3c88e542d6e69baa4c0857c15878a2"
            },
            "downloads": -1,
            "filename": "histdatacom-0.78.4.tar.gz",
            "has_sig": true,
            "md5_digest": "813b6355b67fb32ba1dd6c9f392b8724",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.10.0",
            "size": 61126,
            "upload_time": "2022-12-14T08:39:10",
            "upload_time_iso_8601": "2022-12-14T08:39:10.745429Z",
            "url": "https://files.pythonhosted.org/packages/49/04/fb40838082ffd5b716162f647db5170fae6c471e6c0a212ff8ecb4d5edf4/histdatacom-0.78.4.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2022-12-14 08:39:10",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "github_user": "dmidlo",
    "github_project": "histdata.com-tools",
    "travis_ci": false,
    "coveralls": true,
    "github_actions": false,
    "lcname": "histdatacom"
}
        
Elapsed time: 0.03912s