Why We Use Apache Beam For Our Systematic Trading Data Pipeline

Excerpt

Apache Beam is a unified programming model for batch and streaming data processing jobs. It comes with support for many runners such as Spark, Flink, Google Dataflow and many more (see here for all runners).

You can define your pipelines in Java, Python or Go. At this time the Java SDK is more mature with support for more database connections, but Python is being rapidly developed and comes at a close second, Go is still in its early stages of development.

In the Robot Wealth batch data pipeline, we rely on Beam (on the Google Dataflow runner) for:

• Downloading data from various APIs
• Loading it to Google Storage
• Transforming and enriching the data
• Calculating features
• Loading it to Big Query
• Data integrity checks

This gives us a scalable data pipeline which is also cost-efficient, because you only pay for Beam when you are using it.

Here is how our batch data pipeline currently looks:

The great thing about running Beam on Google Cloud is how seamlessly everything works together. In fact, every connection between the technologies used in the pipeline has native support in Beam.

The most appealing features that make Beam the right choice for our data pipeline are

• Autoscaling
• GCP Integration
• Easy to maintain codebase.

Apache Beam in Action in a Trading Workflow

Let’s take a look at Beam in action.

In the following code block, we will be defining a data integrity check pipeline that logs an error if OHLC data is faulty.

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
import logging
def price_integrity(row):
“””A function that checks the integrity of a OHLC row,
High must be the largest or equal data point in the row and Low must be the smallest or equal
Arguments:
row {dict} — A dict with Keys being the column name and values being the column value
Returns:
bool — True if row has errors False if row is good
“””

#Checks that the low(high) price is the lowest(highest) or equall
low_error = not(round(float(row[‘low’]),3) <= round(float(row['high']),3) and round(float(row['low']),3)<= round(float(row['open']),3) and round(float(row['low']),3) <= round(float(row['close']),3))
high_error = not(round(float(row[‘high’]),3) >= round(float(row[‘low’]),3) and round(float(row[‘high’]),3) >= round(float(row[‘open’]),3) and round(float(row[‘high’]),3) >= round(float(row[‘close’]),3))
if low_error or high_error:
row.update({‘error’:True})
logging.error(‘Integrity Check error uploading to datastore ….’)
return row
else:
row.update({‘error’:False})
return row

def run():
options = PipelineOptions()
p = beam.Pipeline(options=options)
PriceCheck = (
p
| ‘Mimic BigQuery Read’ >> beam.Create([{‘ticker’:’RW’,’open’:10,’high’:11,’low’:8,’close’:10},
< {'ticker':'RW','open':10,'high':11,'low':8,'close':11},
{‘ticker’:’RW’,’open’:11,’high’:11,’low’:8,’close’:9},
{‘ticker’:’RW’,’open’:10,’high’:12,’low’:8,’close’:10},
{‘ticker’:’RW’,’open’:10,’high’:11,’low’:8,’close’:17}])
| ‘Row Integrity Check’ >> beam.Map(price_integrity)
| ‘Print Results’ >> beam.Map(print)
)
p.run()
run()

Visit Robot Wealth to read the full article and download additional programming code: https://robotwealth.com/why-we-use-apache-beam-for-our-trading-data-pipeline/?pa=CEFCE36499