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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.
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:latestIf 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 mktsdbOpen a session with your running docker instance using
marketstore connect --url localhost:5993MarketStore 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/marketstoreand then in the repo directory, install dependencies using
make vendorthen compile and install the project binaries using
make installOptionally, you can install the project's included plugins using
make pluginsYou 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
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.
| 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 |
| disable_variable_compression | bool | disables the default compression of variable data |
| triggers | slice | List of trigger plugins |
| bgworkers | slice | List of background worker plugins |
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: falseAfter starting up a MarketStore instance on your machine, you're all set to be able to read and write tick data.
pymarketstore is the standard python client. Make sure that in another terminal, you have marketstore running
- query data
import pymarketstore as pymkts
param = pymkts.Params('BTC', '1Min', 'OHLCV', limit=10)
cli = pymkts.Client()
reply = cli.query(param)
reply.first().df()shows
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
import numpy as np
import pandas as pd
data = np.array([(pd.Timestamp('2017-01-01 00:00').value / 10**9, 10.0)], dtype=[('Epoch', 'i8'), ('Ask', 'f4')])
cli.write(data, 'TEST/1Min/Tick')
# Out[10]: {'responses': None}
cli.query(pymkts.Params('TEST', '1Min', 'Tick')).first().df()shows
Ask
Epoch
2017-01-01 00:00:00+00:00 10.0
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.
Go plugin architecture works best with Go1.10+ on linux. For more on plugins, see the plugins package Some featured plugins are covered here -
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.
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
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
If you are interested in improving MarketStore, you are more than welcome! Just file issues or requests in github or contact [email protected]. Before opening a PR please be sure tests pass-
make unittestWe 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