Build and backtest scalable trading strategies using real time and historical tick data

Using kdb Insights Enterprise to stream live with historical tick data to develop interactive analytics; powering faster, and better in-the-moment decision-making, provides the following benefits.

benefit	description
Live and historical view	To maximize opportunity, you need both real time and historical views of your market share. Use kdb Insights Enterprise to stream live tick data alongside historical data for the big picture view of the market.
React in real time	React to market events as they happen; not minutes or hours later, to increase market share through efficient execution.
Aggregate multiple sources	Aggregate your liquidity sourcing for a better understanding of market depth and trading opportunities.

Financial dataset

With kdb Insights Enterprise you can work with both live and historical data. The example in this page provides a real-time price feed alongside historical trade data. The real-time price feed calculates two moving averages. A cross between these two moving averages establishes a position (or trade exit) in the market.

Build a database

Select Build a Database under Discover kdb Insights Enterprise of the Overview page.
Enter a Name for the database. For example equities.

Define the database schema in the Schema Settings tab. There are two options for setting up the schema:

Column Input - this is best for small data sets.
Code View - this is useful for large schema tables, allowing you enter the schema in JSON. For this example it is quicker if you use the Code View method.

You can try out each of these options by selecting the relevant tab below.

Column InputCode View

Define schemas for three data tables named as: trade, close, and analytics. Column descriptions are optional and not required here.

tradecloseanalytics

column data type

timestamp timestamp

price float

volume integer

column data type

Date date

Time timestamp

Open float

High float

Low float

Close float

AdjClose float

Volume long

AssetCode symbol

column data type

timestamp timestamp

vwap float

twap float

open float

high float

low float

close float

For more information on kdb+ data types.
Select the Advanced toggle, located above and to the right of the field grid.
Update the columns below for the timestamp fields in each table:

setting value

RDB Attribute sorted

IDB Attribute sorted

HDB Attribute sorted
In the right-hand Table Properties panel, update the following settings:

tradecloseanalytics

setting value

Type partitioned

Partition Column timestamp

Real-time Column Sort timestamp

Intraday Column Sort timestamp

Historical Column Sort timestamp

setting value

Type splayed

Real-time Column Sort Time

Intraday Column Sort Time

Historical Column Sort Time

setting value

Type partitioned

Partition Column timestamp

Real-time Column Sort timestamp

Intraday Column Sort timestamp

Historical Column Sort timestamp

Click

Replace the JSON in the code editor with:

equities schema

Paste the JSON code into the code editor:

[
    {
        "columns": [
        {
            "attrMem": "sorted",
            "attrOrd": "sorted",
            "attrDisk": "sorted",
            "description": "KXI Assembly Schema Column",
            "name": "timestamp",
            "type": "timestamp",
            "compound": false,
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "price",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "volume",
            "type": "int",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        }
        ],
        "description": "KXI Assembly Schema",
        "name": "trade",
        "primaryKeys": [],
        "prtnCol": "timestamp",
        "sortColsDisk": ["timestamp"],
        "sortColsMem": ["timestamp"],
        "sortColsOrd": ["timestamp"],
        "type": "partitioned"
    },
    {
        "columns": [
        {
            "attrDisk": "",
            "attrMem": "",
            "attrOrd": "",
            "description": "KXI Assembly Schema Column",
            "name": "Date",
            "type": "date",
            "compound": false,
            "foreign": ""
        },
        {
            "attrDisk": "sorted",
            "attrMem": "sorted",
            "attrOrd": "sorted",
            "description": "KXI Assembly Schema Column",
            "name": "Time",
            "type": "timestamp",
            "compound": false,
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "Open",
            "type": "float",
            "compound": false,
            "attrDisk": "",
            "attrMem": "",
            "attrOrd": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "High",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "Low",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "Close",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "AdjClose",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "Volume",
            "type": "long",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "AssetCode",
            "type": "symbol",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        }
        ],
        "description": "KXI Assembly Schema",
        "name": "close",
        "primaryKeys": [],
        "sortColsDisk": ["Time"],
        "sortColsMem": ["Time"],
        "sortColsOrd": ["Time"],
        "type": "splayed",
        "prtnCol": "Time"
    },
    {
        "columns": [
        {
            "attrMem": "sorted",
            "attrOrd": "sorted",
            "attrDisk": "sorted",
            "description": "KXI Assembly Schema Column",
            "name": "timestamp",
            "type": "timestamp",
            "compound": false,
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "vwap",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "twap",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "open",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "high",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "low",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        },
        {
            "description": "KXI Assembly Schema Column",
            "name": "close",
            "type": "float",
            "compound": false,
            "attrMem": "",
            "attrOrd": "",
            "attrDisk": "",
            "foreign": ""
        }
        ],
        "description": "KXI Assembly Schema",
        "name": "analytics",
        "primaryKeys": [],
        "prtnCol": "timestamp",
        "sortColsDisk": ["timestamp"],
        "sortColsMem": ["timestamp"],
        "sortColsOrd": ["timestamp"],
        "type": "partitioned"
    }
]

Click Apply to apply the JSON to setup the database schema.

Click Save to save the new database and its schema. The resources summary screen is displayed.
Click Save & Deploy. The database runs through the deployment process.
Hover over the icon beside the database name in the left hand menu or over the Getting Ready status for the database in the Databases screen to see the progress of each of the processes. This can take several minutes depending on the resources that are available in your deployment. When successful, the database listed in the left-hand menu shows a green tick and is active for use.

Database warnings

Once the database is active some warnings are displayed in the Issues pane of the Database Overview page, these are expected and can be ignored.

Ingest live data

The live data feed uses Apache Kafka. Apache Kafka is an event streaming platform whose data is easily consumed and published by kdb Insights Enterprise.

Select Import on the Overview page.
Select the Kafka node and complete the connection details as follows:

setting value

Broker* kafka.trykdb.kx.com:443

Topic* spx

Offset* End

Use TLS Unchecked

Use Schema Registry Unchecked
Expand the Advanced parameters section and tick the Use Advanced Kafka Options checkbox. Click [+] to add the following key value-pairs:

key value

sasl.username demo

sasl.password demo

sasl.mechanism SCRAM-SHA-512

security.protocol SASL_SSL
Click Next.
Event data on Kafka is of type JSON. Select a JSON Decoder node and click Next.
Incoming data is converted to a type compatible with a kdb Insights Enterprise database using a schema. The schema is already defined as part of database creation.
1. Leave the value of Data Format. It is set to Any by default.
2. Click the Load Schema icon , select equities from the Database dropdown and trade from the Table dropdown and click Load and then click Next.
The Writer step defines which database and table the data is written to. Use the database and table defined earlier,see here.

setting value

Database* Name as defined earlier see here.

Table* trade

Write Direct to HDB Unchecked

Deduplicate Stream Checked

Set Timeout Value Unchecked
Click Open Pipeline to open a view of the pipeline in the pipeline template.

Modify pipeline

At this stage the pipeline is setup to convert data from an incoming JSON format on the Kafka feed, to a kdb+ friendly dictionary. Another node is required to convert the data from a kdb+ dictionary to a kdb+ table before deployment. This is done using enlist in a Map node.

In the pipeline template, click and drag the Map Function node into the pipeline template workspace.
Disconnect the Decoder and Transform nodse by right-clicking on the join and clicking Remove Edge.
Insert the Map function between the Decoder and Transform nodes by dragging-and-connecting the edge points of the Decoder node to the Map node, and from the Map node to the Transform node, as shown below.

Click on the Map function node and replace the code in the code editor with the following. You have the choice of using either Python or q or UDF code:

pythonq

import pandas as pd
import pykx as kx
def func(data):
    dataDict = data.pd()
    vals,keys = list(dataDict.values()), list(dataDict.keys())
    newDf = pd.DataFrame(vals,index=keys)
    return kx.K(newDf.transpose())

{[data]
    enlist data
 }

Click Apply to save the details to the node.
Enter a unique Name in the top left of the workspace, for example, trade.
Click Save & Deploy.

Ingest historical data

Where Kafka is used to feed live data to kdb Insights Enterprise, historic data is kept on object storage. Object storage is ideal storage for unstructured data, eliminating the scaling limitations of traditional file storage. Limitless scale makes it the default storage of the cloud with Amazon, Google and Microsoft all employing object storage as their primary storage.

Historicaltrade data is maintained on Microsoft Azure Blob Storage.

Select Import on the Overview page.

Select Microsoft Azure Storage and complete the properties. Fields marked with * are required.

setting	value
MS URI*	ms://kxevg/close_spx.csv
Account*	kxevg
Tenant	Not applicable
File Mode*	Binary
Offset*	0
Chunking*	Auto
Chunk Size*	1MB
Use Watching	Unchecked
Use Authentication	Unchecked

Click Next.
The historic data is stored as a csv file in the cloud. Select CSV decoder and keep the default settings.
Click Next.
Next, transform the data into a format compatible with a kdb Insights Enterprise database with a schema.
1. Leave Data Format unchanged as Any.
2. Click the Load Schema icon , select the database created earlier, see here. Select the close table schema.
Click Load to apply the schema.
Click Next.
Define the following settings:

setting value

Database Name as defined in the section Build a database

Table close

Write Direct to HDB Unchecked

Deduplicate Stream Checked

Set Timeout Value Unchecked
Click Open Pipeline to open a view of the pipeline.
Enter a unique Name for the pipeline in the top left of the workspace. For example, close.
Click Save & Deploy.

Deploy pipelines

You can now deploy the pipelines which will read the data from its source, transform it to a kdb+ compatible format, and write it to the database.

Select each of the pipelines in turn and click on Save & Deploy in the top panel.
Check the progress of the pipelines under the Running Pipelines panel of the Overview tab, which may take several minutes. When the trade pipeline reaches a Status=Running, and the close pipeline reaches a Status=Finished, the data is loaded.

Query the data

Data queries are run in the Query tab; this is accessible from the [+] of the ribbon menu, or Query on the Overview page.

Click the SQL tab and enter the following command to retrieve a count of streaming events in the trade table:
```
SELECT COUNT(*) FROM trade
```
Define the Output Variable as t.
Click Get Data to execute the query. Re-run the query to get an updated value. The results are displayed in the lower part of the Query screen, as shown below.
Right-click in the Console and click Clear to clear the results.
In the SQL tab, replace the existing code with:
```
SELECT * FROM close
```
Define the Output Variable as t.
Click Get Data to execute the query. The results are displayed in the Console as illustrated in the example below which shows end-of-day S&P historic prices.

Create powerful analytics

Using the data, a trade signal is created to generate a position in the market (S&P).

Replace the text in the code editor with the following query:
```
SELECT * FROM trade
```
Set the Output Variable to t.
Click Get Data to execute the query to get the trade data into a variable t for use in the rest of this section.
In the output section, toggle between Console, Table or Visual for different views of the data; for example, a chart of prices over time. Click Get Data again after changing tabs. The following screenshot shows an SQL query against streaming trade data, viewed in the console.

Long and short position simple moving averages

This strategy calculates two simple moving averages, a fast short time period moving average, and a slow long time period moving average.

When the fast line crosses above the slow line, we get a 'buy' trade signal, with a 'sell' signal on the reverse cross.

This strategy is also an always-in-the-market signal, so when a signal occurs, it not only exits the previous trade, but opens a new trade in the other direction; going long (buy low - sell high) or short (sell high - buy back low) when the fast line crosses above or below the slow line.

Enter the following code to create a variable analytics in the scratchpad to calculate the fast and slow moving average.

// taking the close price we calculate two moving averages:
// 1) shortMavg on a window of 10 sec and
// 2) longMavg on a window of 60
analytics : select timestamp,
                price,
                shortMavg:mavg[10;price],
                longMavg:mavg[60;price]
                from t

Click Run Scratchpad.

This strategy uses a 10-period moving average for the fast signal, and a 60-period moving average for the slow signal. Streamed updates for the moving average calculations are added to the chart, as illustrated below.

A "fast" and "slow" moving average zoomed in the visual chart view.

Trade position

The crossover of the two fast and slow moving averages creates trades. These can be tracked with a new variable called positions.

In the scratchpad, append the following:

// when shortMavg and long Mavg cross each other we create the position
// 1) +1 to indicate the signal to buy the asset or
// 2) -1 to indicate the signal to sell the asset
positions : select timestamp,
                position:?[shortMavg<longMavg;-1;1],
                return:log[price % prev price]
                from analytics

Click Run Scratchpad. A position analytic to track crossovers between the fast and slow moving averages is displayed in the query visual chart, as shown in the following screenshot.

q/kdb+ functions explained

If any of the above code is new to you don't worry, we have detailed the q functions used above:

?[x;y;z] when x is true return y, otherwise return z
x<y returns true when x is less than y, otherwise return false
log to return the natual logarithm
x%y divides x by y

A position analytic to track crossovers between the "fast" and "slow" moving averages; signals displayed in the query visual chart.

Active versus passive strategy

The current, active strategy is compared to a passive strategy; that is a strategy tracking a major index, like the S&P 500 or a basket of stocks, such as an ETF. We want to know if our strategy performs better.

In the Scratchpad, append the following code, to create a new variable called performance which generates a benchmark to compare strategy performance to.

performance : select timestamp,
                    benchmark: exp sums return,
                    strategy: exp sums return * prev position
                from positions

Click Run Scratchpad.

q/kdb+ functions explained

If any of the above code is new to you don't worry, we have detailed the q functions used above:

exp raise e to a power where e is the base of natual logarithms
sums calculates the cumulative sum
* to multiply
prev returns the previous item in a list

The result is plotted on a chart, as shown below. The active strategy outperforms the passive benchmark.

The active moving average crossover strategy is compared to a passive benchmark; the strategy outperforms the benchmark as illustrated in the chart.

Create a View

Up to now, ad hoc comparative analysis was conducted in the Scratchpad. We can formalize this better by creating a View.

A View can be created by clicking [+] on the ribbon menu and clicking View, or selecting Visualize on the Overview page.

Saving analytics to a database

Before creating a View, we must save the analytics to a database. The quickest way to do this is to modify the data pipeline used by the analytic.

Open the trade pipeline template; select it from the left-hand pipeline menu on the Overview page.
If the pipeline is still running, click Teardown and select Clean up resources after teardown.
From the pipeline template view, add a Split node from the list of Function nodes. Position it between the Map node and the Apply Schema node. Right-click existing links to clear, before reconnecting the point-edges with a drag-and-connect. This leaves one available point-edge in the Split node, as illustrated below.

I want to learn more about the Function Split node
Add a Timer Window node to the pipeline workspace. Connect it to the second point-edge of the Split node, as illustrated in the following screenshot. The Timer Window aggregates incoming data by time.
Click on the Timer Window and set the timer to run every 5 seconds. Properties marked with * are required properties.
Click Apply.

property value

Period 0D00:00:05

Skip Empty Windows Unchecked

Use Count Triggger Unchecked

Accept Dictionaries Checked

I want to learn more about the Window Timer Windows

Next we define the business logic for the analytic in a Map node. Add a Map node from the Functions, and connect it to the Timer Window node. Click on the Map node and replace the code displayed with the following code, as shown below.

// the following code is calculating the fields:
// 1) vwap = weighted average price, adjusted by volume, over a given time period
// 2) twap = weighted average price over a given time period
// 3) open = price at which a security first trades when an exchange opens for the day
// 4) high = highest price security is traded at
// 5) low = lowest price security is traded at
// 6) close = price at which a security last trades when an exchange closes for the day
// 7) volume = volume of trades places
{[data]
    select last timestamp,
       vwap:|[1;volume] wavg price,
       twap:last[price]^(next[timestamp]-timestamp) wavg price,
       open:first price,
       high:max price,
       low:min price,
       close: last price
    from update "P"$timestamp,
         volume:|[1;volume]
    from data
    }

kdb+/q functions explained

If any of the above code is new to you don't worry, we have detailed the q functions used above:

first/last to get first/last record
| to return the greater number between 1 and volume
^ to replace any nulls with the previous price
wavg to get weighted average
- to subtract
max to get maximum value
min to get minimum value
$"P" to cast to timestamp datatype

Click Apply to apply these settings to the node.
The data generated by the analytic has to be transformed and written to the database. In the same way you did it for the trade table, connect an Apply Node node and a kdb Insights Database node to the Map node, as shown below. Use the equities database that you created earlier, and the analytics table for the schema and writing to database.
Click Save & Deploy to deploy the modified pipeline. A successful deployment shows Status=Running under Running Pipelines on the Overview page.

Load and review the ready-made view

Create a new View by selecting the plus icon at the top toolbar and select View.
Upload the ready-made View:
1. Click on the link here to display a JSON representation of the ready-made Equities View in a new browser window.
2. Select all the text in the new browser window and copy it into an empty text file using the editor of your choice.
3. Save the file on your machine. The file must be saved with a .json extension.
4. Drag the file over the View, going to the top left-hand corner. When the workspace is highlighted in blue and Drop Dashboards to Import is displayed in the center of the workspace you can drop the file. The ready-made View is displayed in a new tab.

Note that the dashboard has two screens.

The Chart view uses both live and historic data for the big picture in the market.

The top chart in the dashboard plots the price data and VWAP analytic generated by the trade pipeline.

The lower chart plots historic index data ingested by the Azure object storage pipeline.

The following screenshot shows the new View. The top chart is a live feed of price and the calculated VWAP analytic. The lower chart is a time series chart of historic index data.
The Table dashboard view tracks positions in the market.

In the top chart, a green highlight is used if a Long (a buyer) trade, or red if Short (a seller).

A similar highlight is used in the table on the left and in the volume bars of the lower chart.

The following screenshot illustrates the Chart view showing the strategy position in the market. Long trades are marked in green and short trades in red. The Chart view shows the strategy position in the market; long trades are marked in green and short trades in red.

I want to learn more about building views

Next Steps

Learn how to build and run a machine learning model to create stock predictions in real-time on this financial dataset in the next tutorial.
Learn more about kdb+

column	data type
Date	date
Time	timestamp
Open	float
High	float
Low	float
Close	float
AdjClose	float
Volume	long
AssetCode	symbol

column	data type
timestamp	timestamp
vwap	float
twap	float
open	float
high	float
low	float
close	float

setting	value
Type	partitioned
Partition Column	timestamp
Real-time Column Sort	timestamp
Intraday Column Sort	timestamp
Historical Column Sort	timestamp

setting	value
Type	splayed
Real-time Column Sort	Time
Intraday Column Sort	Time
Historical Column Sort	Time

setting	value
Broker*	kafka.trykdb.kx.com:443
Topic*	spx
Offset*	End
Use TLS	Unchecked
Use Schema Registry	Unchecked

key	value
sasl.username	demo
sasl.password	demo
sasl.mechanism	SCRAM-SHA-512
security.protocol	SASL_SSL

setting	value
Database*	Name as defined earlier see here.
Table*	trade
Write Direct to HDB	Unchecked
Deduplicate Stream	Checked
Set Timeout Value	Unchecked

setting	value
Database	Name as defined in the section Build a database
Table	close
Write Direct to HDB	Unchecked
Deduplicate Stream	Checked
Set Timeout Value	Unchecked

property	value
Period	0D00:00:05
Skip Empty Windows	Unchecked
Use Count Triggger	Unchecked
Accept Dictionaries	Checked

setting	value
RDB Attribute	sorted
IDB Attribute	sorted
HDB Attribute	sorted