Quod’s Algo Trading at a glance
Pre-defined, systematic execution logic embedded within Quod’s multi-asset OMS/EMS, designed to adapt to what is actually happening in the market.
Who it’s for
Buy-side and sell-side trading desks that require controllable, transparent execution at institutional scale.
Problems solved
Provides automation, with an aim to seek liquidity and to reduce market impact, improving speed, control, and best-execution outcomes.
Migration
Start with out-of-the-box algos and progressively customize, without replacing your entire trading stack.
How it works
How it works
Algorithms slice according to different formulas, such as Time, Volume or Predictive Price , with embedded pegging and smart ordering logic to manage orders. It uses configurable rules, real-time market data and venue messages, and calculated or Machine Learning inputs.
Markets covered
Algorithmic execution for Equities, FX Futures and Options and Digital assets, built for fragmented liquidity and multi-venue workflows, with consistent behaviour across regions and venues.
With Quod's Algo Trading you get
Automate your low-touch trades by providing traders with tools for best execution. Backtest trading activity, gain control over transaction costs, and maintain the speed and accuracy of your trades.
Out of the box Algo Trading solution
Choose from 20+ pre-built algorithms or customize your own strategies using a web-based builder, so execution adapts to market conditions without sacrificing governance.
Quant as a Service offering
Leverage Quod’s quant team to backtest, optimise, and evolve strategies—creating a practical improvement loop based on outcomes and market behaviour.
AI/ML powered Algo Suite
Optimise performance with AI/ML enhancements that learn from real time, historical data and curves to strengthen decision-making and execution precision, including:
Machine Learning volume curve prediction (Equity)
Machine Learning aggressivity optimisation (FX)
Machine Learning peg offset (FX)
Liquidity Seeking Algos
Find liquidity faster in fragmented markets with algorithms that adapt to changing conditions, manage risk, and support consistent execution even in volatile environments.
With 150+ parameters, traders can manage behaviour with native support for common order types, phase management, and risk management controls for anti-gaming, runaway algorithms, and price variations.
The algorithms increase their intelligence by gathering data and statistical analysis on liquidity, historical performance, time of day, volatility, hit ratio, preferences, last look, latency, rejects, and more.
This class of algorithms is designed to find liquidity in a fragmented market by implementing complex execution strategies, with different behaviours across Lit, Dark, LPs/Venues, and the internal pool.
Trading and Execution Algos
Achieve trading objectives with precision, whether minimising market impact, hitting VWAP/TWAP targets, following Participation/PoV, or implementing advanced behaviours, with full transparency on execution decisions.
Trading and execution algorithms are designed to achieve a specified objective, including market impact control or generating alpha.
Examples include:
TWAP, VWAP, Participation/PoV
Arrival Price / Implementation Shortfall
Last Look Smoothed (where applicable)
Pair Trading (Alpha)
These strategies can be defined and customised by Quod or by the client. Every decision is clearly outlined and uses real-time market information to adjust behaviour.
Quod Algo Suite for Equities, FX and Futures & Options, and Digital Assets
Below is a representative view of capabilities supported across the algo suite (availability depends on asset class and configuration):
Core execution styles
- Liquidity Seeking / Lit & Dark: Seeks liquidity across lit and not-lit venues using real-time event-driven decisioning
- Dark Pool: Maximises executed quantity across multiple dark pools using sequential or spread logic
- Sniping: Uses predefined triggers (price/size/slice) to hunt for liquidity
- Iceberg: Randomised slicing to reduce detectability
Benchmark execution and participation
- VWAP: Slices based on historical volume distribution; each slice adapts using current conditions and aggressivity controls
- TWAP: Equal-size slicing across a horizon; each slice adapts to current conditions and aggressivity controls
- Participation (PoV): Targets a percentage of market volume, using real-time market volume calculation and reactive/anticipative behaviours
- Arrival Price / Implementation Shortfall: Adjusts participation based on estimated market impact to stay within a price band
Auctions and market phases
Auction Trading / Auction Volume Percentage: Phase management and auction-aware participation, with auction volume construction and queue/placement logic
Pegging and price-tracking
Pegging / Pegged Order with Price: Tracks reference prices with offsets and conditions; ML peg offset enhancements (FX) where relevant
Hybrid execution
External / Internal Combination: Combines external executions with internal Quod benchmark algorithms, managing internal and external quantities simultaneously
How it works
Execution algorithms operate inside the trading workflow to automate decisions while keeping controls explicit
1
Setting
Set objectives and constraints (benchmark, participation, price bands, policies)
2
Slice & Execute
Slice and execute using strategy logic and configured parameters
3
Adapt
Adapt in real time using market signals (liquidity, volatility, hit ratio, latency/rejects, phase) and progress vs benchmark
4
Refine
Measure and refine using analytics and TCA-ready workflows, then tune parameters and behaviours over time
Quod Algorithms Featuring
Quod OMS combines easy configuration, reliable support, and cost-effective operations into one platform. Firms can integrate smoothly, manage orders with precision, and leverage real-time insights across multiple asset classes—all while reducing complexity and operational costs.
Controls and governance
Quod’s algo suite is designed for performance, control, and transparency rather than black-box execution. Desks can govern behaviour through
- 150+ parameters (phases, aggressivity, order types, risk rules, constraints)
- Policy controls (venue preferences, reject handling, latency sensitivity, last look behaviour where applicable)
- Operational safeguards including Emergency Stop Controls / Kill Switch
- Monitoring and review via decision transparency and logs/controls available in the workflow
Integration and connectivity
Quod supports institutional integration patterns so algorithmic trading can operate inside broader trading environments
- Integrate via API or file and connect with 3rd-party systems where required
- Admin APIs and integration interfaces include FIX | C++ | JAVA | .NET | REST
- Workflow support includes DMA (direct and algo) and Care (high-touch and algo); additional workflow modes can be enabled depending on configuration
Migration without replacing your entire stack
Migrating to Quod’s algorithmic trading is straightforward: start with out-of-the-box algos on representative flow, validate outcomes, then progressively customise strategies, controls, and policies, without needing a full-stack replacement in one step.
Works with Quod modules
Algorithmic Trading is natively integrated within Quod’s platform and can be used alongside.
It can also be upsold via custom algo development and third-party integrations as needs evolve.
What Teams Think
Expert Testimonial
Group Head of Equity and Derivatives International Trading, DZ BankCEO, Quod Financial
FAQ
Frequently Asked Questions
Find Out More