LAMA: The Brokerage Firm’s Framework for Staying Ahead of the Curve

Brokerage firms are constantly under pressure to stay ahead of the competition. They need to make sure that they are using the latest technology and techniques to provide their clients with the best possible service. With constant advancements of technologies and integrations used by these brokerage systems, technical issues do arise.

With the increase in volumes of trading with retail trading, the National Stock Exchange of India (NSE) – one of the largest stock exchanges of the world, needed a system to prevent technical incidents and outages across different market participants.

To address these technical operational incidents Securities and Exchange Board of India (SEBI) formed a working group. This SEBI working group proposed proactive monitoring and problem detection system – Log Analytics and Monitoring Application (LAMA).

What is LAMA?

LAMA is an API based performance data gathering and early warning system that is designed to help brokerage firms identify and address potential problems before they cause an outage. It collects data from the broker’s trading systems and uses machine learning to identify patterns that could indicate a problem. If LAMA detects a potential problem, it will generate an alert so that the brokerage firm and exchange can take preventive corrective action. It is a cost-effective way to prevent outages and reduce the risk of financial losses.

NSE India has adopted LAMA and LAMA reporting is a requirement for brokerage firms. There are heavy penalties for non-compliance with LAMA. Non-compliance is NOT an option.

LAMA Key Metrics
Application Hardware Database Network
Log Monitoring CPU Replication Status Bandwidth
Request/Second – throughput Memory Replication Queue Size Packet Error Count
Response Time Disk Replication Bandwidth
Trading API Failure Count Uptime Replication Latency
Client Authentication Failure Count
Historical Requests/Second
Historical Response Time

Reporting to Exchange

All the LAMA Key metrics details reporting is to be done at every 5 minutes for 24 hours on all trading days.

LAMA compliance challenges

Time-consuming:  Time consuming manual posting of data to LAMA is not even an option because of the frequency and the extent of data reporting requirement.

Inadequate monitoring data:  Most brokerage firms don’t have adequate monitoring on all the required systems to be able to report required data to NSE at required intervals.

How Applicare helps?

Applicare provides an out of the box implementation of LAMA that can be deployed in any brokerage firm’s environment in a matter of hours. With our current banking and brokerage customers, Applicare has been reporting data to NSE since NSE LAMA has gone live. We have worked closely with our customers, NSE LAMA team and our partner TA3S in developing and fine tuning Applicare LAMA implementation.

Applicare is scalable to meet the needs of any stock broker, regardless of their size. This means that it can be used by both large and small stock brokers.

The Applicare is an asset for any organization that wants to improve its IT operations. It helps you in identifying and troubleshooting problems, improve performance, and make better decisions about your IT infrastructure. It is built from ground up by administrators and developers who faced these challenges every day and wanted to automate the repetitive tasks.

Applicare provides full stack observability, self-detects problems and becomes foundation for self healing.


Applicare Agent

The Applicare agent is a key component of the Applicare monitoring solution. The agent is easy to install and use. It collects data from your virtual machines, systems and applications, and then sends it to the Applicare controller for processing and analysis.

Applicare Controller

The Applicare controller posts the data to LAMA API. All the posted data is saved for the historical purpose. Historical records can be used to identify patterns and trends that can help brokerage firms make better decisions. By analyzing them, brokerage firms can identify patterns that may indicate a problem with their trading system.

LAMA Key Metrics Dashboard


















LAMA API Failure Notifications

If there was a failure while posing the key metrics to the LAMA API gateway, the Applicare notification service will notify the brokerage firms with its efficient logging mechanism. The notification service can be customized to meet the specific needs of the customers.







Brokerage firms are responsible for the financial losses of their clients. If an outage occurs, the firm may be liable for the losses incurred by their clients. Applicare can help protect the firm’s reputation by preventing outages and keeping their trading systems running smoothly. Applicare is known for its flexibility and ease of use, which allows it to be customized to meet the specific needs of each customer. Applicare makes the LAMA data posting process hassle-free and user-friendly for the brokerage firms.

Getting started with Applicare is easy. Simply register and get access to free on prem or in cloud Applicare.



How to monitor Microservices?

Microservices are being used every where and for good reasons. They do provide you with many benefits especially improved focus and cutting the time to market. Microservices do bring complexities too. Monitoring microservices is complex because of simply the number of them. Monitoring a user transaction requires monitoring many microservices. Correlating the data from them to identify the root cause manually is a nightmare especially in a complex environment with 100s or 1000s of microservices. This post is not about the pros and cons of microservices but is about how to monitor them easily using Applicare.

We are using an example of two Springboot java microservices – Users and Departments – a user belongs to a department. Every time we go and look up a user using Users microservice it makes a call to Departments microservice to get the department details. I would use Applicare service flow to demonstrate the architecture of our sample.


To  enable monitoring on each microservice all we need to do is to add Applicare agent arguments to the start of those microservices along with making Applicare agent binaries available at runtime. We will cover building Docker image of microservice with Applicare agent pre deployed  and running that image in Kubernetes environment in a future blog post. It is super easy.

Once these Users and Departments microservices are started with Applicare agent, they connect and start reporting data to Applicare controller. Once you login into an Applicare controller and download single agent, it includes information about the controller to report the data back. This controller can be on prem, in cloud  or our from SAAS offering.

You get details on infra performance


along with cumulative performance of each microservice and performance of each instance.


Applicare automatically ties the data together when Users microservice makes a call to Departments microservice for looking up the user’s department details.


No more struggling with monitoring of microservices and correlating the data. With Applicare you see complete user transaction details that may span across multiple microservices.

Getting started with Applicare is easy. Simply register to get access to free on prem or in cloud Applicare controller. Remember single agent is downloaded from the controller and deployed in your environment.

Have fun with microservices monitoring and instantly finding root cause 🙂