Algorithmic Trading Solutions

Build and deploy algorithms to more effectively execute trading strategies

Upcoming Event
November 17 - 18, 2021

DeveloperWeek Austin 2021 – Southwest’s largest developer event

Join us to discuss AI, Microservices, Containers, ML, DevOps and more

Meet Us
Latest ZipRadio
October 8, 2021

Podcast – Conversational AI in Retail Banking and Investing

Soul Machines expert discusses applications of digital people in retail banking

Play Now
On Demand ZipChat
Watch Recording

ZipChat E16: Cloud Application Monitoring with Dynatrace

Chief Technology Strategist at Dynatrace discusses intelligent cloud monitoring

Play Now

Maximum trading volume at minimal risk

Algorithmic trading uses an automated process that follows specific instructions—derived from calculations that weigh market timing, price, quantity, and a variety of trading signals—to maximize the profitability of transactions. An algorithmic trading solution does the heavy lifting for you—making trades UI at faster speeds without veering from the programmed strategy.

Excellarate brings your algorithm dreams to life. Drawing on two decades of experience building custom, innovative FinTech solutions, we work closely with each client—whether it be a technology provider or a firm that trades—to build customized solutions that make it easy to deploy trading algorithms that monitor market conditions, and quickly adapt to fluctuations to achieve maximum revenue.

Why Algorithmic Trading?

Monitor multiple markets simultaneously to
maximize the number of completed trades

Significantly reduce the risk of manual errors during trade execution

Reduce the cost of transaction fees

Avoid significant price changes
with more precise timing

Remove the emotional
aspects of trading

Algorithmic Trading Strategies

Arbitrage

Arbitrage is the price differential when a dual-listed stock is sold at different prices on different sites. An algorithm programmed to detect price differentials can bring in significant profits with low risk.

Trend-Following

Following trends is the most common–and simplest–strategy to follow in trading. It relies little on forecasting or analytics, making it easy to implement.

Index Fund Rebalancing

There are certain times when index funds are rebalanced. This strategy would seek to time trading in order to capitalize on offers just before rebalancing occurs.

Mathematical Miles

This strategy seeks to capitalize on proven mathematics models, such as a Delta-neutral strategy that targets multiple positions with balancing positive and negative deltas, which can even out the response to market movements.

Mean Reversion

Based on the theory that both high and low prices are temporary, with most settling around the mean most of the time, this strategy targets any time the price goes above mean.

Volume-Weighted Average Price (VWAP)

This strategy consists of breaking up a large order into smaller ones using historical stock profiles, in order to get close to the VWAP.

Time-Weighted Average Price (TWAP)

This strategy consists of breaking up a large order into smaller ones using evenly divided time intervals between start and end time, to minimize market impact.

Implementation Shortfall

This strategy consists of breaking up a large order into smaller ones using evenly divided time intervals between start and end time, to minimize market impact.

Other Non-Usual Trading Algorithms

High-tech algorithms are used to detect sell-side algorithms in order to neutralize the potential for sellers to target larger-order buyers.