Feature store example (stocks)
Contents
Feature store example (stocks)#
This notebook demonstrates the following:
Generate features and feature-sets
Build complex transformations and ingest to offline and real-time data stores
Fetch feature vectors for training
Save feature vectors for re-use in real-time pipelines
Access features and their statistics in real-time
Note
By default, this demo works with the online feature store, which is currently not part of the Open Source MLRun default deployment.
In this section
Get started#
Install the latest MLRun package and restart the notebook.
Setting up the environment and project:
import mlrun
mlrun.set_environment(project="stocks")
> 2021-05-23 09:04:04,507 [warning] Failed resolving version info. Ignoring and using defaults
> 2021-05-23 09:04:07,033 [warning] Unable to parse server or client version. Assuming compatible: {'server_version': '0.6.4-rc3', 'client_version': 'unstable'}
('stocks', 'v3io:///projects/{{run.project}}/artifacts')
Create sample data for demo#
import pandas as pd
quotes = pd.DataFrame(
{
"time": [
pd.Timestamp("2016-05-25 13:30:00.023"),
pd.Timestamp("2016-05-25 13:30:00.023"),
pd.Timestamp("2016-05-25 13:30:00.030"),
pd.Timestamp("2016-05-25 13:30:00.041"),
pd.Timestamp("2016-05-25 13:30:00.048"),
pd.Timestamp("2016-05-25 13:30:00.049"),
pd.Timestamp("2016-05-25 13:30:00.072"),
pd.Timestamp("2016-05-25 13:30:00.075")
],
"ticker": [
"GOOG",
"MSFT",
"MSFT",
"MSFT",
"GOOG",
"AAPL",
"GOOG",
"MSFT"
],
"bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
"ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
}
)
trades = pd.DataFrame(
{
"time": [
pd.Timestamp("2016-05-25 13:30:00.023"),
pd.Timestamp("2016-05-25 13:30:00.038"),
pd.Timestamp("2016-05-25 13:30:00.048"),
pd.Timestamp("2016-05-25 13:30:00.048"),
pd.Timestamp("2016-05-25 13:30:00.048")
],
"ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
"price": [51.95, 51.95, 720.77, 720.92, 98.0],
"quantity": [75, 155, 100, 100, 100]
}
)
stocks = pd.DataFrame(
{
"ticker": ["MSFT", "GOOG", "AAPL"],
"name": ["Microsoft Corporation", "Alphabet Inc", "Apple Inc"],
"exchange": ["NASDAQ", "NASDAQ", "NASDAQ"]
}
)
import datetime
def move_date(df, col):
max_date = df[col].max()
now_date = datetime.datetime.now()
delta = now_date - max_date
df[col] = df[col] + delta
return df
quotes = move_date(quotes, "time")
trades = move_date(trades, "time")
View the demo data#
quotes
| time | ticker | bid | ask | |
|---|---|---|---|---|
| 0 | 2021-05-23 09:04:07.013574 | GOOG | 720.50 | 720.93 |
| 1 | 2021-05-23 09:04:07.013574 | MSFT | 51.95 | 51.96 |
| 2 | 2021-05-23 09:04:07.020574 | MSFT | 51.97 | 51.98 |
| 3 | 2021-05-23 09:04:07.031574 | MSFT | 51.99 | 52.00 |
| 4 | 2021-05-23 09:04:07.038574 | GOOG | 720.50 | 720.93 |
| 5 | 2021-05-23 09:04:07.039574 | AAPL | 97.99 | 98.01 |
| 6 | 2021-05-23 09:04:07.062574 | GOOG | 720.50 | 720.88 |
| 7 | 2021-05-23 09:04:07.065574 | MSFT | 52.01 | 52.03 |
trades
| time | ticker | price | quantity | |
|---|---|---|---|---|
| 0 | 2021-05-23 09:04:07.041766 | MSFT | 51.95 | 75 |
| 1 | 2021-05-23 09:04:07.056766 | MSFT | 51.95 | 155 |
| 2 | 2021-05-23 09:04:07.066766 | GOOG | 720.77 | 100 |
| 3 | 2021-05-23 09:04:07.066766 | GOOG | 720.92 | 100 |
| 4 | 2021-05-23 09:04:07.066766 | AAPL | 98.00 | 100 |
stocks
| ticker | name | exchange | |
|---|---|---|---|
| 0 | MSFT | Microsoft Corporation | NASDAQ |
| 1 | GOOG | Alphabet Inc | NASDAQ |
| 2 | AAPL | Apple Inc | NASDAQ |
Define, infer and ingest feature sets#
import mlrun.feature_store as fstore
from mlrun.feature_store.steps import *
from mlrun.features import MinMaxValidator
Build and ingest simple feature set (stocks)#
# add feature set without time column (stock ticker metadata)
stocks_set = fstore.FeatureSet("stocks", entities=[fstore.Entity("ticker")])
fstore.ingest(stocks_set, stocks, infer_options=fstore.InferOptions.default())
| name | exchange | |
|---|---|---|
| ticker | ||
| MSFT | Microsoft Corporation | NASDAQ |
| GOOG | Alphabet Inc | NASDAQ |
| AAPL | Apple Inc | NASDAQ |
Build an advanced feature set - with feature engineering pipeline#
Define a feature set with custom data processing and time aggregation functions:
# create a new feature set
quotes_set = fstore.FeatureSet("stock-quotes", entities=[fstore.Entity("ticker")])
Define a custom pipeline step (python class)
class MyMap(MapClass):
def __init__(self, multiplier=1, **kwargs):
super().__init__(**kwargs)
self._multiplier = multiplier
def do(self, event):
event["multi"] = event["bid"] * self._multiplier
return event
Build and show the transformatiom pipeline
Use storey stream processing classes along with library and custom classes:
quotes_set.graph.to("MyMap", multiplier=3)\
.to("storey.Extend", _fn="({'extra': event['bid'] * 77})")\
.to("storey.Filter", "filter", _fn="(event['bid'] > 51.92)")\
.to(FeaturesetValidator())
quotes_set.add_aggregation("ask", ["sum", "max"], "1h", "10m", name="asks1")
quotes_set.add_aggregation("ask", ["sum", "max"], "5h", "10m", name="asks5")
quotes_set.add_aggregation("bid", ["min", "max"], "1h", "10m", name="bids")
# add feature validation policy
quotes_set["bid"] = fstore.Feature(validator=MinMaxValidator(min=52, severity="info"))
# add default target definitions and plot
quotes_set.set_targets()
quotes_set.plot(rankdir="LR", with_targets=True)
Test and show the pipeline results locally (allow to quickly develop and debug)
fstore.preview(
quotes_set,
quotes,
entity_columns=["ticker"],
timestamp_key="time",
options=fstore.InferOptions.default(),
)
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.013574 args={'min': 52, 'value': 51.95}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.020574 args={'min': 52, 'value': 51.97}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.031574 args={'min': 52, 'value': 51.99}
| asks1_sum_1h | asks1_max_1h | asks5_sum_5h | asks5_max_5h | bids_min_1h | bids_max_1h | time | bid | ask | multi | extra | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ticker | |||||||||||
| GOOG | 720.93 | 720.93 | 720.93 | 720.93 | 720.50 | 720.50 | 2021-05-23 09:04:07.013574 | 720.50 | 720.93 | 2161.50 | 55478.50 |
| MSFT | 51.96 | 51.96 | 51.96 | 51.96 | 51.95 | 51.95 | 2021-05-23 09:04:07.013574 | 51.95 | 51.96 | 155.85 | 4000.15 |
| MSFT | 103.94 | 51.98 | 103.94 | 51.98 | 51.95 | 51.97 | 2021-05-23 09:04:07.020574 | 51.97 | 51.98 | 155.91 | 4001.69 |
| MSFT | 155.94 | 52.00 | 155.94 | 52.00 | 51.95 | 51.99 | 2021-05-23 09:04:07.031574 | 51.99 | 52.00 | 155.97 | 4003.23 |
| GOOG | 1441.86 | 720.93 | 1441.86 | 720.93 | 720.50 | 720.50 | 2021-05-23 09:04:07.038574 | 720.50 | 720.93 | 2161.50 | 55478.50 |
| AAPL | 98.01 | 98.01 | 98.01 | 98.01 | 97.99 | 97.99 | 2021-05-23 09:04:07.039574 | 97.99 | 98.01 | 293.97 | 7545.23 |
| GOOG | 2162.74 | 720.93 | 2162.74 | 720.93 | 720.50 | 720.50 | 2021-05-23 09:04:07.062574 | 720.50 | 720.88 | 2161.50 | 55478.50 |
| MSFT | 207.97 | 52.03 | 207.97 | 52.03 | 51.95 | 52.01 | 2021-05-23 09:04:07.065574 | 52.01 | 52.03 | 156.03 | 4004.77 |
# print the feature set object
print(quotes_set.to_yaml())
kind: FeatureSet
metadata:
name: stock-quotes
spec:
entities:
- name: ticker
value_type: str
features:
- name: asks1_sum_1h
value_type: float
aggregate: true
- name: asks1_max_1h
value_type: float
aggregate: true
- name: asks5_sum_5h
value_type: float
aggregate: true
- name: asks5_max_5h
value_type: float
aggregate: true
- name: bids_min_1h
value_type: float
aggregate: true
- name: bids_max_1h
value_type: float
aggregate: true
- name: bid
value_type: float
validator:
kind: minmax
severity: info
min: 52
- name: ask
value_type: float
- name: multi
value_type: float
- name: extra
value_type: float
partition_keys: []
timestamp_key: time
source:
path: None
targets:
- name: parquet
kind: parquet
- name: nosql
kind: nosql
graph:
states:
MyMap:
kind: task
class_name: MyMap
class_args:
multiplier: 3
storey.Extend:
kind: task
class_name: storey.Extend
class_args:
_fn: '({''extra'': event[''bid''] * 77})'
after:
- MyMap
filter:
kind: task
class_name: storey.Filter
class_args:
_fn: (event['bid'] > 51.92)
after:
- storey.Extend
FeaturesetValidator:
kind: task
class_name: mlrun.feature_store.steps.FeaturesetValidator
class_args:
featureset: .
columns: null
after:
- filter
Aggregates:
kind: task
class_name: storey.AggregateByKey
class_args:
aggregates:
- name: asks1
column: ask
operations:
- sum
- max
windows:
- 1h
period: 10m
- name: asks5
column: ask
operations:
- sum
- max
windows:
- 5h
period: 10m
- name: bids
column: bid
operations:
- min
- max
windows:
- 1h
period: 10m
table: .
after:
- FeaturesetValidator
output_path: v3io:///projects/{{run.project}}/artifacts
status:
state: created
stats:
ticker:
count: 8
unique: 3
top: MSFT
freq: 4
asks1_sum_1h:
count: 8.0
mean: 617.9187499999999
min: 51.96
max: 2162.74
std: 784.8779804245735
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 1
- 0
- 0
- 0
- 0
- 0
- 1
- - 51.96
- 157.499
- 263.03799999999995
- 368.57699999999994
- 474.11599999999993
- 579.655
- 685.194
- 790.733
- 896.2719999999999
- 1001.8109999999999
- 1107.35
- 1212.889
- 1318.4279999999999
- 1423.9669999999999
- 1529.5059999999999
- 1635.0449999999998
- 1740.5839999999998
- 1846.1229999999998
- 1951.6619999999998
- 2057.2009999999996
- 2162.74
asks1_max_1h:
count: 8.0
mean: 308.59625
min: 51.96
max: 720.93
std: 341.7989955655851
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 3
- - 51.96
- 85.4085
- 118.857
- 152.3055
- 185.754
- 219.2025
- 252.65099999999998
- 286.0995
- 319.54799999999994
- 352.9964999999999
- 386.44499999999994
- 419.89349999999996
- 453.3419999999999
- 486.7904999999999
- 520.2389999999999
- 553.6875
- 587.136
- 620.5844999999999
- 654.0329999999999
- 687.4815
- 720.93
asks5_sum_5h:
count: 8.0
mean: 617.9187499999999
min: 51.96
max: 2162.74
std: 784.8779804245735
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 1
- 0
- 0
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- 0
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- 1
- - 51.96
- 157.499
- 263.03799999999995
- 368.57699999999994
- 474.11599999999993
- 579.655
- 685.194
- 790.733
- 896.2719999999999
- 1001.8109999999999
- 1107.35
- 1212.889
- 1318.4279999999999
- 1423.9669999999999
- 1529.5059999999999
- 1635.0449999999998
- 1740.5839999999998
- 1846.1229999999998
- 1951.6619999999998
- 2057.2009999999996
- 2162.74
asks5_max_5h:
count: 8.0
mean: 308.59625
min: 51.96
max: 720.93
std: 341.7989955655851
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 3
- - 51.96
- 85.4085
- 118.857
- 152.3055
- 185.754
- 219.2025
- 252.65099999999998
- 286.0995
- 319.54799999999994
- 352.9964999999999
- 386.44499999999994
- 419.89349999999996
- 453.3419999999999
- 486.7904999999999
- 520.2389999999999
- 553.6875
- 587.136
- 620.5844999999999
- 654.0329999999999
- 687.4815
- 720.93
bids_min_1h:
count: 8.0
mean: 308.41125
min: 51.95
max: 720.5
std: 341.59667259325835
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 3
- - 51.95
- 85.3775
- 118.80499999999999
- 152.2325
- 185.65999999999997
- 219.08749999999998
- 252.515
- 285.94249999999994
- 319.36999999999995
- 352.79749999999996
- 386.22499999999997
- 419.6524999999999
- 453.0799999999999
- 486.50749999999994
- 519.935
- 553.3625
- 586.79
- 620.2175
- 653.645
- 687.0725
- 720.5
bids_max_1h:
count: 8.0
mean: 308.42625
min: 51.95
max: 720.5
std: 341.58380276661245
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 3
- - 51.95
- 85.3775
- 118.80499999999999
- 152.2325
- 185.65999999999997
- 219.08749999999998
- 252.515
- 285.94249999999994
- 319.36999999999995
- 352.79749999999996
- 386.22499999999997
- 419.6524999999999
- 453.0799999999999
- 486.50749999999994
- 519.935
- 553.3625
- 586.79
- 620.2175
- 653.645
- 687.0725
- 720.5
time:
count: 8
mean: '2021-05-23 09:04:07.035699200'
min: '2021-05-23 09:04:07.013574'
max: '2021-05-23 09:04:07.065574'
bid:
count: 8.0
mean: 308.42625
min: 51.95
max: 720.5
std: 341.58380276661245
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 3
- - 51.95
- 85.3775
- 118.80499999999999
- 152.2325
- 185.65999999999997
- 219.08749999999998
- 252.515
- 285.94249999999994
- 319.36999999999995
- 352.79749999999996
- 386.22499999999997
- 419.6524999999999
- 453.0799999999999
- 486.50749999999994
- 519.935
- 553.3625
- 586.79
- 620.2175
- 653.645
- 687.0725
- 720.5
ask:
count: 8.0
mean: 308.59
min: 51.96
max: 720.93
std: 341.79037903369954
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 3
- - 51.96
- 85.4085
- 118.857
- 152.3055
- 185.754
- 219.2025
- 252.65099999999998
- 286.0995
- 319.54799999999994
- 352.9964999999999
- 386.44499999999994
- 419.89349999999996
- 453.3419999999999
- 486.7904999999999
- 520.2389999999999
- 553.6875
- 587.136
- 620.5844999999999
- 654.0329999999999
- 687.4815
- 720.93
multi:
count: 8.0
mean: 925.27875
min: 155.85000000000002
max: 2161.5
std: 1024.7514082998375
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
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- 0
- 0
- 3
- - 155.85000000000002
- 256.13250000000005
- 356.415
- 456.6975
- 556.98
- 657.2625
- 757.545
- 857.8275
- 958.11
- 1058.3925
- 1158.6750000000002
- 1258.9575
- 1359.2399999999998
- 1459.5225
- 1559.8049999999998
- 1660.0875
- 1760.37
- 1860.6525000000001
- 1960.935
- 2061.2175
- 2161.5
extra:
count: 8.0
mean: 23748.82125
min: 4000.15
max: 55478.5
std: 26301.95281302916
hist:
- - 4
- 1
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
- 0
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- - 4000.15
- 6574.0675
- 9147.985
- 11721.9025
- 14295.82
- 16869.7375
- 19443.655000000002
- 22017.572500000002
- 24591.49
- 27165.4075
- 29739.325
- 32313.2425
- 34887.16
- 37461.0775
- 40034.995
- 42608.9125
- 45182.83
- 47756.747500000005
- 50330.665
- 52904.582500000004
- 55478.5
preview:
- - asks1_sum_1h
- asks1_max_1h
- asks5_sum_5h
- asks5_max_5h
- bids_min_1h
- bids_max_1h
- time
- bid
- ask
- multi
- extra
- - 720.93
- 720.93
- 720.93
- 720.93
- 720.5
- 720.5
- 2021-05-23T09:04:07.013574
- 720.5
- 720.93
- 2161.5
- 55478.5
- - 51.96
- 51.96
- 51.96
- 51.96
- 51.95
- 51.95
- 2021-05-23T09:04:07.013574
- 51.95
- 51.96
- 155.85000000000002
- 4000.15
- - 103.94
- 51.98
- 103.94
- 51.98
- 51.95
- 51.97
- 2021-05-23T09:04:07.020574
- 51.97
- 51.98
- 155.91
- 4001.69
- - 155.94
- 52.0
- 155.94
- 52.0
- 51.95
- 51.99
- 2021-05-23T09:04:07.031574
- 51.99
- 52.0
- 155.97
- 4003.23
- - 1441.86
- 720.93
- 1441.86
- 720.93
- 720.5
- 720.5
- 2021-05-23T09:04:07.038574
- 720.5
- 720.93
- 2161.5
- 55478.5
- - 98.01
- 98.01
- 98.01
- 98.01
- 97.99
- 97.99
- 2021-05-23T09:04:07.039574
- 97.99
- 98.01
- 293.96999999999997
- 7545.23
- - 2162.74
- 720.93
- 2162.74
- 720.93
- 720.5
- 720.5
- 2021-05-23T09:04:07.062574
- 720.5
- 720.88
- 2161.5
- 55478.5
- - 207.97
- 52.03
- 207.97
- 52.03
- 51.95
- 52.01
- 2021-05-23T09:04:07.065574
- 52.01
- 52.03
- 156.03
- 4004.77
Ingest data into offline and online stores#
This writes to both targets (Parquet and NoSQL).
# save ingest data and print the FeatureSet spec
df = fstore.ingest(quotes_set, quotes)
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.013574 args={'min': 52, 'value': 51.95}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.020574 args={'min': 52, 'value': 51.97}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.031574 args={'min': 52, 'value': 51.99}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.013574 args={'min': 52, 'value': 51.95}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.020574 args={'min': 52, 'value': 51.97}
info! bid value is smaller than min, key=['MSFT'] time=2021-05-23 09:04:07.031574 args={'min': 52, 'value': 51.99}
Get an offline feature vector for training#
Example of combining features from 3 sources with time travel join of 3 tables with time travel.
Specify a set of features and request the feature vector offline result as a dataframe:
features = [
"stock-quotes.multi",
"stock-quotes.asks5_sum_5h as total_ask",
"stock-quotes.bids_min_1h",
"stock-quotes.bids_max_1h",
"stocks.*",
]
vector = fstore.FeatureVector("stocks-vec", features, description="stocks demo feature vector")
vector.save()
resp = fstore.get_offline_features(vector, entity_rows=trades, entity_timestamp_column="time")
resp.to_dataframe()
| price | quantity | multi | total_ask | bids_min_1h | bids_max_1h | name | exchange | |
|---|---|---|---|---|---|---|---|---|
| 0 | 51.95 | 75 | 155.97 | 155.94 | 51.95 | 51.99 | Microsoft Corporation | NASDAQ |
| 1 | 51.95 | 155 | 155.97 | 155.94 | 51.95 | 51.99 | Microsoft Corporation | NASDAQ |
| 2 | 720.77 | 100 | 2161.50 | 2162.74 | 720.50 | 720.50 | Alphabet Inc | NASDAQ |
| 3 | 720.92 | 100 | 2161.50 | 2162.74 | 720.50 | 720.50 | Alphabet Inc | NASDAQ |
| 4 | 98.00 | 100 | 293.97 | 98.01 | 97.99 | 97.99 | Apple Inc | NASDAQ |
Initialize an online feature service and use it for real-time inference#
service = fstore.get_online_feature_service("stocks-vec")
Request feature vector statistics, can be used for imputing or validation
service.vector.get_stats_table()
| count | mean | min | max | std | hist | unique | top | freq | |
|---|---|---|---|---|---|---|---|---|---|
| multi | 8.0 | 925.27875 | 155.85 | 2161.50 | 1024.751408 | [[4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | NaN | NaN | NaN |
| total_ask | 8.0 | 617.91875 | 51.96 | 2162.74 | 784.877980 | [[4, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,... | NaN | NaN | NaN |
| bids_min_1h | 8.0 | 308.41125 | 51.95 | 720.50 | 341.596673 | [[4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | NaN | NaN | NaN |
| bids_max_1h | 8.0 | 308.42625 | 51.95 | 720.50 | 341.583803 | [[4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... | NaN | NaN | NaN |
| name | 3.0 | NaN | NaN | NaN | NaN | NaN | 3.0 | Alphabet Inc | 1.0 |
| exchange | 3.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | NASDAQ | 3.0 |
Real-time feature vector request
service.get([{"ticker": "GOOG"}, {"ticker": "MSFT"}])
[{'asks5_sum_5h': 2162.74,
'bids_min_1h': 720.5,
'bids_max_1h': 720.5,
'multi': 2161.5,
'name': 'Alphabet Inc',
'exchange': 'NASDAQ',
'total_ask': None},
{'asks5_sum_5h': 207.97,
'bids_min_1h': 51.95,
'bids_max_1h': 52.01,
'multi': 156.03,
'name': 'Microsoft Corporation',
'exchange': 'NASDAQ',
'total_ask': None}]
service.get([{"ticker": "AAPL"}])
[{'asks5_sum_5h': 98.01,
'bids_min_1h': 97.99,
'bids_max_1h': 97.99,
'multi': 293.97,
'name': 'Apple Inc',
'exchange': 'NASDAQ',
'total_ask': None}]
service.close()