DataFrame Server for Financial Timeseries Data
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README.md

MarketStore

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Introduction

MarketStore is a database server optimized for financial time-series 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. The image comes pre-loaded with the default mkts.yml file and declares the VOLUME /data, as its root directory. To run the container with the defaults:

docker run -i -p 5993:5993 alpacamarkets/marketstore:latest

If you want to run a custom mkts.yml with your instance, you can create a new container, load your mkts.yml file into it, then run it.

docker create --name mktsdb -p 5993:5993 alpacamarkets/marketstore:latest
docker cp mkts.yml mktsdb:/etc/mkts.yml
docker start -i mktsdb

Open a session with your running docker instance using

marketstore connect --url localhost:5993

Source

MarketStore is implemented in Go (with some CGO), so you can build it from source pretty easily. You need Go 1.11+ as it uses go mod to manage dependencies.

go get -u github.com/alpacahq/marketstore

and then in the repo directory, install dependencies using

make vendor

then compile and install the project binaries using

make install

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

make plugins

Usage

You can list available commands by running

marketstore

or

$GOPATH/bin/marketstore

depending on your GOPATH.

You can create a new configuration file named mkts.yml, populated with defaults by running:

$GOPATH/bin/marketstore init

and then start the marketstore server with:

$GOPATH/bin/marketstore start

The output will look something like:

example@alpaca:~/go/bin/src/github.com/alpacahq/marketstore$ marketstore
I0619 16:29:30.102101    7835 log.go:14] Disabling "enable_last_known" feature until it is fixed...
I0619 16:29:30.102980    7835 log.go:14] Initializing MarketStore...
I0619 16:29:30.103092    7835 log.go:14] WAL Setup: initCatalog true, initWALCache true, backgroundSync true, WALBypass false:
I0619 16:29:30.103179    7835 log.go:14] Root Directory: /example/go/bin/src/github.com/alpacahq/marketstore/project/data/mktsdb
I0619 16:29:30.144461    7835 log.go:14] My WALFILE: WALFile.1529450970104303654.walfile
I0619 16:29:30.144486    7835 log.go:14] Found a WALFILE: WALFile.1529450306968096708.walfile, entering replay...
I0619 16:29:30.244778    7835 log.go:14] Beginning WAL Replay
I0619 16:29:30.244861    7835 log.go:14] Partial Read
I0619 16:29:30.244882    7835 log.go:14] Entering replay of TGData
I0619 16:29:30.244903    7835 log.go:14] Replay of WAL file /example/go/bin/src/github.com/alpacahq/marketstore/project/data/mktsdb/WALFile.1529450306968096708.walfile finished
I0619 16:29:30.289401    7835 log.go:14] Finished replay of TGData
I0619 16:29:30.340760    7835 log.go:14] Launching rpc data server...
I0619 16:29:30.340792    7835 log.go:14] Initializing websocket...
I0619 16:29:30.340814    7835 plugins.go:14] InitializeTriggers
I0619 16:29:30.340824    7835 plugins.go:42] InitializeBgWorkers

Configuration

In order to run MarketStore, a YAML config file is needed. A default file (mkts.yml) can be created using marketstore init. The path to this file is passed in to the start command with the --config flag, or by default it finds a file named mkts.yml in the directory it is running from.

Options

Var 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

Default mkts.yml

root_directory: data
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. Make sure that in another terminal, you have marketstore running

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

Connect to a marketstore instance with

// For a local db-
marketstore connect --dir <path>
// For a server-
marketstore connect --url <address>

and run commands through the sql session.

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