A timeseries library to make your workflow easier
Project description
Funtime - Funguana's Time-series database library for your trading bot
Funtime
is a mongodb based time series library. It was created because we ran into the problem of inputting data that may have its schema change and querying it quickly.
We found arctic
to be a good library, yet it lacked straightforward pythonic querying methods. We added a layer on top for our own purposes.
Both funtime
and arctic
use mongodb as the main database
What makes Funtime
better?
The single thing that makes funtime better for timeseries information is that it has more clear querying methods. It should be noted that it still has arctic as the foundation. Meaning the speed of it is extremely fast. This works a lot like tickstore, yet it's easier to get your hands on and use than tickstore
It is a layer on time of arctic. We added the following:
- An easy way to add and filter data with a given timestamp/datetime
- Easy conversions to both a
pandas
anddask
dataframe - A choice to get information by a filtration type. This allows the user to be highly flexible with how they want to get information
Window
- A window query is a query that gets information two dates. They must be valid.Before
- Gets every record before a certain timeAfter
- Gets every record after a certain time
How does it work?
All forms of time data is stored as a epoch timestamp. This is to make querying easier and faster than if we were to use a full datetime object. We do conversions within the library from datetime objects into epoch timestamps.
This increases the insert time, yet reduces the querying time. Numbers are easier to filter than date objects. The biggetst tradeoff is in ease of use.
Example:
from funtime import Store
import mimesis # this is used to seed data for our test
import time
# Create a library and access the store you want
store = Store().create_lib("hello.World").get_store()
# store the data with a timestamp
store['hello.World'].store({
"type": "price",
"currency": "ETH_USD",
"timestamp": time.time(),
"candlestick": {
"open": 1234,
"close": 1234.41,
"other": "etc"
}
})
# Query general information. It returns a generator
runs = store['hello.World'].query({
"type": "price"
})
# Check for results
for r in runs:
print(r)
# Even get information with complex time queries
runs2 = store['hello.World'].query_time(time_type="before", start=time.time(), query_type="price")
# Check for results
for r in runs:
print(r)
Key Things to Remember
The query functions return a generator to handle or pandas dask items you need to use our complementry Object
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