STOCK MARKET PREDICTION WITH MACHINE LEARNING

The premise was simple. Use “big” data analytics and machine learning models to predict the movement of stock prices. However, we had really “dirty” data and our Data Scientists were stuggling to seperate the noise from the signals. We spent a lot of time cleaning the data and introducing good old principles like “how can I run the model somewhere over than a laptop?”. This was a true startup, a bunch of people in a room trying to get stuff working. No red tape, no calling the “helpdesk” to sort out your IT problems (I actually was the helpdesk).

CALCULATE DELTA RISK WITH QUANTLIB

QuantLib is a free and open-source software library for quantitative finance. It provides a wide range of functionality for pricing and risk-managing financial derivatives, including interest rate swaps.

OPTIMIZING STRAGGLERS IN GOOGLE CLOUD DATAFLOW

I’m currently bench-marking Flink against Google Cloud Dataflow using the same Apache Beam pipeline for quantitative analytics. One observation I’ve seen with Flink is the tail latency associated with some shards. 

WHY CRYPTOCURRENCY MATTERS

The point of cryptocurrency is to provide a decentralized, secure, and efficient way to transfer value. Cryptocurrencies are not issued by any central authority, such as a government or bank, and they are not backed by any physical asset. Instead, they are created and maintained by a network of computers that are running a special software program. This software program is designed to verify and record cryptocurrency transactions, and to prevent fraud.

OPTIMIZING LATENCY IN MICROSERVICES

Great talk by by Peter Lawrey regarding latency in micro-services.  https://www.infoq.com/presentations/latency-sensitive-microservices/

APACHE BEAM VS FLINK: CHOOSING THE RIGHT FRAMEWORK

Apache Beam vs. Apache Flink: Choosing the Right Distributed Processing Framework

Apache Beam and Apache Flink are both powerful open-source frameworks for distributed data processing, enabling efficient handling of massive datasets. While they share the common goal of parallel data processing, they differ significantly in their architecture, programming model, and execution strategies. Understanding these differences is crucial for choosing the right tool for your specific needs. This article will help you navigate the decision-making process.

SCALING RISK ANALYTICS ON GOOGLE CLOUD

This one is better explained with the presentation below. If you want to learn how to run quantitative analytics at scale, it’s well worth a watch.

DEVSECOPS VS SRE: KEY DIFFERENCES EXPLAINED

DevSecOps and SRE are two complementary approaches to ensuring the reliability and security of software systems.

BUILDING HIGH-PERFORMANCE TEAMS

Here are some tips on how to build high-performance teams:

CASH EQUITIES: ORDER MANAGEMENT SYSTEM

Built and maintained a client and market side booking service, off order book trade reporting engine and trade manager/repository