findatapy

findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using a unified high level interface. Users can also define their own custom tickers, using configuration files. There is also functionality which is particularly useful for those downloading FX market data. Below example shows how to download AUDJPY data from Quandl (and automatically calculates this via USD crosses).

Contributors for the project are very much welcome, see below!

from findatapy.market import Market, MarketDataRequest, MarketDataGenerator

market = Market(market_data_generator=MarketDataGenerator())

md_request = MarketDataRequest(start_date='year', category='fx', data_source='quandl', tickers=['AUDJPY'])

df = market.fetch_market(md_request)
print(df.tail(n=10))

Here we see how to download tick data from DukasCopy, wih the same API calls and minimal changes in the code.

md_request = MarketDataRequest(start_date='14 Jun 2016', finish_date='15 Jun 2016',
                                   category='fx', fields=['bid', 'ask'], freq='tick', 
                                   data_source='dukascopy', tickers=['EURUSD'])

df = market.fetch_market(md_request)
print(df.tail(n=10))

I had previously written the open source PyThalesians financial library. This new findatapy library has similar functionality to the market data part of that library. However, I've totally rewritten the API to make it much cleaner and easier to use. It is also now a fully standalone package, so you can more easily use it with whatever libraries you have for analysing market data or doing your backtesting (although I'd recommend my own finmarketpy package if you are doing backtesting of trading strategies!).

A few things to note:

Please bear in mind at present findatapy is currently a highly experimental alpha project and isn't yet fully documented
Uses Apache 2.0 licence

Contributors

Contributors are always welcome for finmarketpy, findatapy and chartpy. If you'd like to contribute, have a look at Planned Features for areas we're looking for help on. Or if you have any ideas for improvements to the libriares please let us know too!

Gallery

To appear

Requirements

Major requirements

Required: Python 3.6
Required: pandas, numpy etc.
Recommended: blpapi - Bloomberg Python Open API
Recommended: chartpy - for funky interactive plots (https://github.com/cuemacro/chartpy) and
Recommended: arctic - AHL library for managing time series in MongoDB
Recommended: multiprocessor_on_dill - standard multiprocessing library pickle causes issues (https://github.com/sixty-north/multiprocessing_on_dill)
Recommended: fredapi - ALFRED/FRED has been rewritten and added to the project directly (from https://github.com/mortada/fredapi)

Installation

For detailed installation instructions for chartpy, findatapy & finmarketpy and its associated Python libraries go to https://github.com/cuemacro/finmarketpy/blob/master/INSTALL.md. The tutorial includes details on how to setup your entire Python environment.

You can install the library using the below. After installation:

Make sure you edit the dataconstants class for the correct Eikon API, Quandl API and Twitter API keys etc.
Or you can run set_api_keys.py script to set the API keys via storing in your keyring
Or you can create a datacred.py file which overwrites these keys
Or some of these API keys can be passed via MarketDataRequest on demand

To install via pip (latest release):

pip install findatapy

To install newest repo copy:

pip install git+https://github.com/cuemacro/findatapy.git

Couldn't push MarketDataRequest message

You might often get an error like the below, when you are downloading market data with findatapy, and you don't have Redis installed.

Couldn't push MarketDataRequest

findatapy includes an in-memory caching mechanism, which uses Redis a key/value in-memory store. The idea is that if we do exactly the same data download call with the same parameters of a MarketDataRequest it will check this volatile cache first, before going out to our external data provider (eg. Quandl).

Note, that Redis is usually set up as volatile cache, so once your computer is turned off, this cache will be lost.

If Redis is not installed, this caching will fail and you'll get this error. However, all other functionality aside from the caching will be fine. All findatapy will do is to always go externally to download market data. Redis is available for Linux. There is also an unsupported (older) Windows version available, which I've found works fine, although it lacks some functionality of later Redis versions.

findatapy examples

In findatapy/examples you will find several demos on how to download data from many different sources. Note, for some such as Bloomberg or Eikon, you'll need to have a licence/subscription for it to work.

Release Notes

0.1.15 - findatapy (10 Sep 2020)
0.1.14 - findatapy (25 Aug 2020)
0.1.13 - findatapy (24 Aug 2020)
0.1.12 - findatapy (06 May 2020)

Coding log

10 Sep 2020
- Adding Eikon as a market data source
25 Aug 2020
- Fixes for newer Pandas eg. 1.0.5
- Fixes for ALFRED downloading of economic data
24 Aug 2020
- Removed .ix references (to work with newer Pandas)
06 May 2020
- Amended function to remove points outside FX hours to exclude 1 Jan every year
- RetStats can now resample time series (removed kurtosis)
- Tidy up some code comments
07 Apr 2020
- Bug fix in constants
06 Apr 2020
- Minor changes to ConfigManager
05 Apr 2020
- Added push to cache parameter for MarketDataRequest
04 Apr 2020
- Added timeout for Dukascopy download
14 Mar 2020
- Fixed bug with downloading short intervals of Dukascopy tick data
20 Feb 2020
- Made Redis optional dependency
30 Dec 2019
- Added message about lack of Redis
17 Dec 2019
- Fix issue with Redis cache if two similar elements cached (takes the last now)
16 Dec 2019
- Fix problem with missing Redis dependency when reading from market
04 Dec 2019
- Allow usage on Azure Notebooks, by making keyring dependency optional
03 Nov 2019
- Added script to set API keys with keyring
02 Nov 2019
- Added BoE as a data source
- Removed blosc/msgpack (msgpack deprecated in pandas) and replaced with pyarrow for caching
- Uses keyring library for API keys (unless specified in DataCred)
- Began to add tests for IO and market data download
03 Oct 2019
- Remove API key from cache
- Remove timezone when storing in Arctic (can cause issues with later versions of Pandas)
14 Aug 2019
- Bloomberg downloaders now works with Pandas 0.25
- Fixed Yahoo downloader to work with yfinance (replacing pandas_datareader for Yahoo)
06 Aug 2019
- Adding parameters to MarketDataRequest for user specified API keys (Quandl, FRED & Alpha Vantage)
23 Jul 2019
- Changed some rolling calculations in Calculation class to work with newer pandas
12 Jul 2019
- Fixed issues with DukasCopy downloading when using multi-threading
01 Mar 2019
- Added read/write Parquet
- Added concat dataframes
15 Nov 2018
- Fixed aggregation by hour/day etc. with pandas > 0.23
- Filter data frame columns by multiple keywords
20 Sep 2018 - Fixed bug in ALFRED
25 Jul 2018 - Better timezone handling when filtering by holidays
23 Jul 2018 - Fixed additional bug in filter
27 Jun 2018 - Added note about installing blpapi via pip
23 Jun 2018 - Fixed bug filtering dataframes with timezones
29 May 2018 - Added port
11 May 2018
- Allow filtering of dataframes by user defined holidays
25 Apr 2018
- Added transaction costs by asset
- Fixed bug with Redis caching
21 Apr 2018 - New features
- use CSV/HDF5 files with MarketDataRequest (includes flatfile_example.py)
- allow resample parameter for MarketDataRequest
- added AlphaVantage as a data source
- added fxcmpy as a a data source (unfinished)
20 Apr 2018 - Remove rows where all NaNs for daily data when returning from MarketDataGenerator
26 Mar 2018 - Change logging level for downloading dates of DukasCopy
20 Mar 2018 - Added insert_sparse_time_series in Calculation, and mask_time_series_by_time in Filter.
07 Mar 2018 - Fixed bugs for date_parser.
20 Feb 2018 - Added cryptocurrency data generators and example
22 Jan 2018 - Added function to remove duplicate consecutive data
05 Jan 2018 - Fixed bug when downloading BBG reference data
18 Dec 2017 - Fixed FXCM downloader bug
24 Nov 2017 - Minor bug fixes for DukasCopy downloader
10 Oct 2017 - Added handling of username and password for arctic
26 Aug 2017 - Improved threading for FXCM and DukasCopy downloaders
25 Aug 2017 - Added FXCM downloader (partially finished)
23 Aug 2017 - Improved overwritting of constants by cred file
10 Jul 2017 - Added method for calculation of autocorrelation in Calculations
07 Jun 2017 - Added methods for calendar day seasonality in Calculations
25 May 2017 - Removed unneeded dependency in DataQuality
22 May 2017 - Began to replace pandas OLS with statsmodels
03 May 2017 - Added section for contributors
28 Apr 2017 - Issues with returning weekend data for FX spot fixed
18 Apr 2017 - Fixed FX spot calc
13 Apr 2017 - Fixed issues with FX cross calculations (and refactored)
07 Apr 2017 - Fix issue with returned Quandl labels in returned time series, downloading of Bloomberg tick data
06 Apr 2017 - Fixed issue with not specifying field
13 Mar 2017 - Changed examples to use SwimPool
08 Mar 2017 - Fixed bug with DukasCopy data (was getting wrong month) and added blpapi pre-built
28 Feb 2017 - Added passthrough for BBG overrides via MarketDataRequest
23 Feb 2017 - Added ability to specify tickers with wildcards
21 Feb 2017 - Optimised code to speed up downloading Bloomberg data considerably
17 Feb 2017 - Added switch between multiprocess and multiprocessing on dill libraries in SpeedCache
15 Feb 2017 - Added multiprocessing_example, switched to using multiprocess library and improved SpeedCache (for deletion of keys)
14 Feb 2017 - Speeded up returns statistic computation and created DataQuality class
13 Feb 2017 - Added SwimPool class
12 Feb 2017 - Fixed small filtering bug (for start/finish date) and began adding tests
11 Feb 2017 - Added example to show how to use Redis caching
09 Feb 2017 - Added in-memory caching when loading market data (via Redis)
08 Feb 2017 - Pad columns now returns columns in same order as input
07 Feb 2017 - Added Redis to IOEngine
05 Feb 2017 - Added openpyxl as a dependency
01 Feb 2017 - Added method for aligning left and right dataframes (with fill down) and rolling_corr (to work with pandas <= 0.13)
25 Jan 2017 - Work on stop losses for multiple assets in DataFrame and extra documentation for IOEngine
24 Jan 2017 - Extra method for calculating signal * returns (multiplying matrices)
19 Jan 2017 - Changed examples location in project, added future based variables to Market
18 Jan 2017 - Fixed returning of bid/ask in DukasCopy
16 Jan 2017 - Added override for stop/take profit signals (& allow dynamic levels), speed up for filtering of time series by column
13 Jan 2017 - Added "expiry" for tickers (optional to add), so can handle futures data better when downloading and various bugs fixed for getting Bloomberg reference data fetching
11 Jan 2017 - Added extra documentation and method for assessing stop loss/take profit
10 Jan 2017 - Added better handling for downloading of Bloomberg reference requests
05 Jan 2017 - Fixed fxspotdata_example example, fixed singleton mechanism in ConfigManager
24 Dec 2016 - Added more error handling for Quandl
20 Dec 2016 - Updated deprecated some pandas deprecated methods in Calculations class & various bug fixes
14 Dec 2016 - Bug fixes for DukasCopy downloader (@kalaytan) and added delete ticker from disk (Arctic)
09 Dec 2016 - Speeded up ALFRED/FRED downloader
30 Nov 2016 - Rewrote fredapi downloader (added helped methods) and added to project
29 Nov 2016 - Added ALFRED/FRED as a data source
28 Nov 2016 - Bug fixes on MarketDataGenerator and BBGLowLevelTemplate (@spyamine)
04 Nov 2016 - Added extra field converters for Quandl
02 Nov 2016 - Changed timeouts for accessing MongoDB via arctic
17 Oct 2016 - Functions for filtering time series by period
13 Oct 2016 - Added YoY metric in RetStats, by default pad missing returned columns for MarketDataGenerator
07 Oct 2016 - Add .idea from .gitignore
06 Oct 2016 - Fixed downloading of tick count for FX
04 Oct 2016 - Added arctic_example for writing pandas DataFrames
02 Oct 2016 - Added read/write dataframes via AHL's Arctic (MongoDB), added multi-threaded outer join, speeded up downloading intraday FX
28 Sep 2016 - Added more data types to download for vol
23 Sep 2016 - Fixed issue with downloading events
20 Sep 2016 - Removed deco dependency, fixed issue downloading Quandl fields, fixed issue with setup files
02 Sep 2016 - Edits around Bloomberg event download, fixed issues with data downloading threading
23 Aug 2016 - Added skeletons for ONS and BOE data
22 Aug 2016 - Added credentials file
17 Aug 2016 - Uploaded first code

End of note

olveh/findatapy