Algorithmic Trading
At Quod Financial, our goal is to grow your trading through a wide range of easily customisable strategies and tools, so you stay in control. Quod Algos take data-driven decisions in real time to reposition orders and achieve the best outcomes across changing market conditions.
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.
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.
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.
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.
Migration
Start with out-of-the-box algos and progressively customize, without replacing your entire trading stack.
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.
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)
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.
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.
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.
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.
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):
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
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
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
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
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
Auctions and market phases
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
Pegging and price-tracking
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
Hybrid execution
External / Internal Combination: Combines external executions with internal Quod benchmark algorithms, managing internal and external quantities simultaneously
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
How it works
Execution algorithms operate inside the trading workflow to automate decisions while keeping controls explicit
- Set objectives and constraints (benchmark, participation, price bands, policies)
- Slice and execute using strategy logic and configured parameters
- Adapt in real time using market signals (liquidity, volatility, hit ratio, latency/rejects, phase) and progress vs benchmark
- Measure and refine using analytics and TCA-ready workflows, then tune parameters and behaviours over time
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
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
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
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
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
Migration (adopt 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.
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
Works with Quod modules
Algorithmic Trading is natively integrated within Quod’s platform and can be used alongside
- Order Management System (OMS)
- Execution Management System (EMS)
- Smart Order Routing (SOR)
- Automated Trading
- TCA and Best Execution Reporting
It can also be upsold via custom algo development and third-party integrations as needs evolve.
Algo
Including
Description
Liquidity Seeking
A real-time algorithm which reacts to market events such as market data, execution and a set of other criteria to dynamically update the decision tree to seek liquidity on lit and not-lit (dark) venues. An example is Quod Smart Order Routing Algorithm.
Adaptive Behaviour
Creates different combinations of “solutions” to execute the available liquidity and chooses the best one. Adapts its decision-making
based on market events.
Statistical Behaviour
Integrates real-time / near-time statistical analysis to enrich the adaptive decision making process.
Sniping
Takes a set of predefined triggers such as the bid/offer trigger price and quantity/child order slice to hunt for liquidity.
Dark Pool
Maximizes the executed quantity by dividing the order over the different dark pools, either by spreading it equally or by placing it sequentially.
Lit&Dark
Combines liquidity seeking and optimal executions across lit and dark pools.
VWAP
A benchmarking algorithm that slices an order according to the historical volume reparation over a certain time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Multi-Venue VWAP Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Machine Learning Volume Curve Prediction (Equity)
Forecasts the VWAP volume curve on multiple instruments using Machine Learning approach using order books' states, volume profiles, etc ...
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
TWAP
A slicing algorithm that submits equal size slices over a certain time horizon to get an average execution price as close as possible to the time-weighted average price during the same time horizon. Every slice benefits from the adaptive execution that takes into account the current market conditions and the aggressivity of the algorithm.
Real-Time Aggressivity Adjustment
Based on the execution progress and the position in the slice time window, the child order’s type/time-in-force is adjusted in order to always allow full execution of the slice.
Machine Learning Aggressivity Optimisation (FX)
Intelligence able to know when the TWAP should aggress based on how much time and quantity is left for the current slice, the TWAP algorithm's optimal execution curve and an execution probability estimated from several market indicators (liquidity, volume profile, special events, ...). The optimal execution probability is fed by the Machine Learning Peg Offset (see below).
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum / Maximum Participation
Any child can be restrained to a maximum or/and a minimum participation cap. The theoretical quantity (without any constraint) is calculated and compared with the participation quantity.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Participation (Percentage of Volume - PoV)
A participation algorithm which aims to execute a given percentage of visible liquidity in the market in order to limit market impact. Integrates real-time / near-time analysis to enrich the adaptive decision making process.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Reactive Trading
Reacts to real-time trade volumes and places aggressive orders by comparing the already executed quantity to the overall volume that was traded on the considered markets.
Anticipative Trading
Anticipates future market trades by looking at the visible volume placed on the various order books and placing passive orders against it.
Auction Trading
Balances impact and disperses the orders into and during the auctions. The algorithm phase management detects different market phases, with distinct parameters (e.g. move intra-day from trading to an intra-day auction phase). Either based on a direct execution or on a specific auction algorithm (see below Auction Volume Percentage).
Minimum Participation
When the executed quantity gets lower than the minimum participation cap, the algorithm goes "super-aggressive" until it rises back to an acceptable participation. Includes also an improvement of the average execution price.
Would Price
Ability to define a price at which the order can be completed immediately (up to a defined percentage of the order). When a Would opportunity occurs, the algorithm trades aggressively whenever the price is available.
Auction Volume Percentage
An algorithm which places orders during auctions to ensure a target participation rate against an estimated auction volume. The algorithm phase management detects different market phases, with distinct parameters.
Auction Volume Construction
Auction volume is built based on the estimated auction volume (either historical or predicted) and the indicative auction volume.
Machine Learning Auction Volume Prediction (Equity)
Forecasts the auction volume using Machine Learning approach using order books' states, volume profiles, etc ...
Optimal Placement / Queue Management
It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Would Price
Triggers a slice which is placed at a price defined by the user as the most favourable possible price. Includes adjustment and rebalance between active slice and Would slice depending on its marketability.
Scaling Participation
An algorithm that regularly adapts its participation based on different price levels, in continuous trading and in auctions. The participation adjustment is based on real-market data and optimal slice amendments. The more passive price, the more participation.
Continuous Scaling
Supports different levels of participation in continuous based on price.
Auction Scaling
Supports different levels of participation in the auctions based on price. It allows early entry of auction slices, optimal queue management and frequent order amendments, increasing in frequency towards the expected uncross.
Arrival Price / Implementation Shortfall
An algorithm that regularly adapts its participation based on estimated market impact in order to remain within a given price band. Participation is increased when the probability of high market impact is low and decreased when the probability is high. Adaptation of the participation is based both on historical behaviour and real-time data
Multi-Venue Volume Curve Construction
For all multi-listed instruments, the VWAP volume curve is built based on the historical volume observed on all venues the order is set to participate on, including the MTFs. The calculation then combines the volume for all of these to build the VWAP curve. The calculation can also exclude Auction and Dark volumes if necessary.
Real-Time Market Volume Calculation
Calculates real-time market volume by combining newly received trades at time, previous cumulated volume traded in the market and quantity already executed by the strategy when trades are received.
Participation Adjustment
Decreases participation rate when prices move against arrival price and increases participation rate when price moves in favour of arrival price.
Pegging
An algorithm set to track a given reference price (possibly with a limit price). The "best effort" version conflates market data, reducing the frequency of price updates.
Pegged Order
An order to the bid or ask with or without an offset. The display quantity will float with the bid or ask, up to the ultimate limit price of the order.
Pegged Order with Price
An enhanced pegged order that pegs to the BBO using an offset (in ticks or price) as defined by the users. Additional conditions such as limit price, min/max quantity or a-would-price (which for buy and Above which for sell) are available to hunt for liquidity.
Machine Learning Peg Offset (FX)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
External / Internal Combination
Ability to combine “external” executions with “internal” Quod benchmark algorithms. The latter can manage two quantities simultaneously : an internal “benchmark” quantity managed and sent in lit pools, and an “external” quantity sent to an external broker using their own logic.
Iceberg
A slicing algorithm which randomly sends child orders onto the market, so it is not initially being recognised as such by other market participants.
Pair Trading
A neutral trading strategy enabling traders to profit from virtually any market conditions including uptrend, downtrend or sideways movement.
Direct Trading Mode
Places direct aggressive orders to sell the outperforming instrument and buy the underperforming instrument based on a spread deviation condition.
Participation Trading Mode
Participates at a certain percentage of volume on each instrument to trade passively on the spread. Child orders are triggered based on a spread deviation condition as well as a quantity equivalence condition.
Auto-hedging
Cross-asset class rule-driven autohedger based on position and real-time market data (delta, vega, gamma) for single trades or accrued positions.
Synthetic Order Type
Triggers a market child order whenever the specified market price on any of the order listings is less than or equal to (respectively greater than or equal to) the specified stop price.
Synthetic Stop
A limit sell order for a given instrument which is managed by the system and triggered by falling price.
Synthetic Take Profit
A limit sell order for a given instrument which is managed by the system and triggered by raising price.
Trailing Stop
A Stop-loss order which the stop loss price is set to some fixed percentage below the market price. The market price rises, the stop loss price rises proportionally
Triggering
Releases the order when the Market price (best ask for buy, best bid for sell) reaches a pre-determined value.
Triggering on same instrument
Orders on instrument X triggered by X's market price reaching a pre-determined value.
Triggering on different instrument
Orders on instrument X triggered by Y's market price reaching a pre-determined value.
Synthetic OTO
Triggers an order when another order is fully filled.
Synthetic OCO
Cancels an order when another one is fully filled.
Synthetic Time-In-Force
Synthesises a Good-Till-Date (GTD) or Good Till-Cancel (GTC) order type which is held away from the market and able to execute on a range of venues meeting the trade criteria.
Timed Order
Releases the order at a specific time to the exchange for execution.
Percentage On Close
Aims to emulate an ATC time-in-force by releasing the order when the corresponding trading phase is detected.
Synthetic Block
For some shares in ME markets, foreign investors have a trading limit that can be bypassed when another sells the shares. Trades cannot be identified so for all, orders are created to block the shares.
Machine Learning Applications
Machine Learning enhanced applications to improve our algorithmic strategies.
Machine Learning Clustering (Equity)
Analyses the similarity of different equity instrument via cluster analysis, fully data driven and Machine Learning approach that aims to identify groups of instrument that perform similarly over short periods of time.
Machine Learning Volume Curve Prediction (Equity)
Enchances the pegging mechanism by predicting the optimal execution probability attached to a range of offsets. The goal is to execute orders as passively as possible while being able to execute most of the parent quantity. The prediction is based on a Machine Learning approach using market conditions (order book's states, volatilities, volume profiles, noise variance).
Machine Learning Peg Offset (FX)
Custom
Builds client side algos or uses our native algo API for customising this menu and building your own. Over 150 parameters and unlimited logical decisions give you complete customisation of your strategies
What Teams Think
Expert Testimonial
Group Head of Equity and Derivatives International Trading, DZ BankCEO, Quod Financial
FAQ
Frequently Asked Questions
Multi-Asset Trading Solution
Find Out More