marketstore

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Published: Jun 19, 2018 License: Apache-2.0

README

MarketStore

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Introduction

MarketStore is a database server optimized for financial timeseries data. You can think of it as an extensible DataFrame service that is accessible from anywhere in your system, at higher scalability.

It is designed from the ground up to address scalability issues around handling large amounts of financial market data used in algorithmic trading backtesting, charting, and analyzing price history with data spanning many years, and granularity down to tick-level for the all US equities or the exploding crypto currencies space. If you are struggling with managing lots of HDF5 files, this is perfect solution to your problem.

The batteries are included with the basic install - you can start pulling crypto price data from GDAX and writing it to the db with a simple plugin configuration.

MarketStore enables you to query DataFrame content over the network at as low latency as your local HDF5 files from disk, and appending new data to the end is two orders of magnitude faster than DataFrame would be. This is because the storage format is optimized for the type of data and use cases as well as for modern filesystem/hardware characteristics.

MarketStore is production ready! At Alpaca it has been used in production for years in serious business. If you encounter a bug or are interested in getting involved, please see the contribution section for more details.

Install

Docker

If you want to get started right away, you can bootstrap a marketstore db instance using our latest docker image.

docker run -p 5993:5993 alpacamarkets/marketstore:v2.1.2
Source

MarketStore is implemented in Go (with some CGO), so you can build it from source pretty easily. You need Go 1.9+ and dep.

go get -u github.com/alpacahq/marketstore

and then in the repo directory, install dependencies using

make configure

then compile and install the project binaries using

make install

Optionally, you can install the project's included plugins using

make plugins

Usage

Run it:

marketstore

To learn how to format a proper db query, please see this

Configuration

In order to run MarketStore, a YAML config file is needed. A default file (mkts.yml) is included in the repo. The path to this file is passed in to the launcher binary with the -config flag, or by default it finds a file named mkts.yml in the directory it is running from.

Options
Flag Type Description
root_directory string Allows the user to specify the directory in which the MarketStore database resides
listen_port int Port that MarketStore will serve through
timezone string System timezone by name of TZ database (e.g. America/New_York)
log_level string Allows the user to specify the log level (info
queryable bool Allows the user to run MarketStore in polling-only mode, where it will not respond to query
stop_grace_period int Sets the amount of time MarketStore will wait to shutdown after a SIGINT signal is received
wal_rotate_interval int Frequency (in mintues) at which the WAL file will be trimmed after being flushed to disk
stale_threshold int Threshold (in days) by which MarketStore will declare a symbol stale
enable_add bool Allows new symbols to be added to DB via /write API
enable_remove bool Allows symbols to be removed from DB via /write API
triggers slice List of trigger plugins
bgworkers slice List of background worker plugins
Example mkts.yml
root_directory: /project/data/mktsdb
listen_port: 5993
log_level: info
queryable: true
stop_grace_period: 0
wal_rotate_interval: 5
stale_threshold: 5
enable_add: true
enable_remove: false

Clients

After starting up a MarketStore instance on your machine, you're all set to be able to read and write tick data.

Python

pymarketstore is the standard python client.

In [1]: import pymarketstore as pymkts

## query data

In [2]: param = pymkts.Params('BTC', '1Min', 'OHLCV', limit=10)

In [3]: cli = pymkts.Client()

In [4]: reply = cli.query(param)

In [5]: reply.first().df()
Out[5]:
                               Open      High       Low     Close     Volume
Epoch
2018-01-17 17:19:00+00:00  10400.00  10400.25  10315.00  10337.25   7.772154
2018-01-17 17:20:00+00:00  10328.22  10359.00  10328.22  10337.00  14.206040
2018-01-17 17:21:00+00:00  10337.01  10337.01  10180.01  10192.15   7.906481
2018-01-17 17:22:00+00:00  10199.99  10200.00  10129.88  10160.08  28.119562
2018-01-17 17:23:00+00:00  10140.01  10161.00  10115.00  10115.01  11.283704
2018-01-17 17:24:00+00:00  10115.00  10194.99  10102.35  10194.99  10.617131
2018-01-17 17:25:00+00:00  10194.99  10240.00  10194.98  10220.00   8.586766
2018-01-17 17:26:00+00:00  10210.02  10210.02  10101.00  10138.00   6.616969
2018-01-17 17:27:00+00:00  10137.99  10138.00  10108.76  10124.94   9.962978
2018-01-17 17:28:00+00:00  10124.95  10142.39  10124.94  10142.39   2.262249

## write data

In [7]: import numpy as np

In [8]: import pandas as pd

In [9]: data = np.array([(pd.Timestamp('2017-01-01 00:00').value / 10**9, 10.0)], dtype=[('Epoch', 'i8'), ('Ask', 'f4')])

In [10]: cli.write(data, 'TEST/1Min/Tick')
Out[10]: {'responses': None}

In [11]: cli.query(pymkts.Params('TEST', '1Min', 'Tick')).first().df()
Out[11]:
                            Ask
Epoch
2017-01-01 00:00:00+00:00  10.0

Command-line

The mkts cli tool included with the project and built with make all allows a user to write/read data to time series buckets. Use the runtest.sh wrapper under cmd/tools/mkts/examples to see some examples of its usage.

This test script will create a bucket, load example tick data from a csv into the bucket, and run a simple query.

The last few lines of output should match the following:

=============================  ==========  ==========  ==========  
                        Epoch  Bid         Ask         Nanoseconds  
=============================  ==========  ==========  ==========  
2016-12-31 02:37:57 +0000 UTC  1.05185     1.05197     139999810   
2016-12-31 02:38:02 +0000 UTC  1.05185     1.05198     389999832   
2016-12-31 02:38:09 +0000 UTC  1.05188     1.052       389999583   
2016-12-31 02:38:09 +0000 UTC  1.05189     1.05201     889999385   
2016-12-31 02:38:10 +0000 UTC  1.05186     1.05197     139999706   
2016-12-31 02:38:10 +0000 UTC  1.05186     1.05192     389999188   
2016-12-31 02:38:10 +0000 UTC  1.05181     1.05189     639999508   
2016-12-31 02:38:10 +0000 UTC  1.05182     1.0519      889999829   
2016-12-31 02:38:11 +0000 UTC  1.05181     1.05189     389999631   
2016-12-31 02:38:18 +0000 UTC  1.0518      1.0519      139999900   
=============================  ==========  ==========  ==========  
Elapsed parse time: 19.523 ms
Elapsed query time: 4.707 ms

Plugins

Go plugin architecture works best with Go1.10+ on linux. For more on plugins, see the plugins package Some featured plugins are covered here -

Streaming

You can receive realtime bars updates through the WebSocket streaming feature. The db server accepts a WebSocket connection on /ws, and we have built a plugin that pushes the data. Take a look at the package for more details.

GDAX Data Feeder

The batteries are included so you can start pulling crypto price data from GDAX right after you install MarketStore. Then you can query DataFrame content over the network at as low latency as your local HDF5 files from disk, and appending new data to the end is two orders of magnitude faster than DataFrame would be. This is because the storage format is optimized for the type of data and use cases as well as for modern filesystem/hardware characteristics.

You can start pulling data from GDAX if you configure the data poller. For more information, see the package

On-Disk Aggregation

This plugin allows you to only worry about writing tick/minute level data. This plugin handles time-based aggregation on disk. For more, see the package

Development

If you are interested in improving MarketStore, you are more than welcome! Just file issues or requests in github or contact oss@alpaca.markets. Before opening a PR please be sure tests pass-

make unittest
Plugins Development

We know the needs and requirements in this space are diverse. MarketStore provides strong core functionality with flexible plug-in architecture. If you want to build your own, look around plugins

Directories

Path Synopsis
cmd
contrib
calendar
Package calendar provides market calendar, with which you can check if the market is open at specific point of time.
Package calendar provides market calendar, with which you can check if the market is open at specific point of time.
ondiskagg
This is a shim package for buiding a plugin module wrapping the importable aggtrigger package.
This is a shim package for buiding a plugin module wrapping the importable aggtrigger package.
ondiskagg/aggtrigger
OnDiskAgg implements a trigger to downsample base timeframe data and write to disk.
OnDiskAgg implements a trigger to downsample base timeframe data and write to disk.
buffile
package buffile helps batch write by writes to the tempobary in-memory buffer under the assumption that many writes come in to the part of single file frequently.
package buffile helps batch write by writes to the tempobary in-memory buffer under the assumption that many writes come in to the part of single file frequently.
stream
Package stream implements websocket interface for streaming in the server core.
Package stream implements websocket interface for streaming in the server core.
bgworker
Package bgworker provides interface for bgworker plugins.
Package bgworker provides interface for bgworker plugins.
trigger
Package trigger provides interface for trigger plugins.
Package trigger provides interface for trigger plugins.
uda
avg
max
min
io
log

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