Our Comprehensive Approach

Covering the Full Trading Lifecycle

Planning Best Execution

A key change as a result of MiFID II is how firms plan, monitor and prove Best Execution.

To help clients plan for and ensure Best Execution, we’ve built a platform and customised approach that identifies a wide range of order characteristics and processes that can benefit from different levels of our Best Execution Automation detailed below.

ADAPTIVE

Significantly sized orders with no unusual characteristics.

The usual approach with these types of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?

Using a quantitative model from a third-party in an automated fashion can help with planning and proving Best Execution. We’ve found that adaptive automation benefits most from a model-driven approach to planning participation – nothing unusual is happening, so the models typically hold.

LOW TOUCH

Little to no impact orders that need to get done quickly.

These low touch orders have very little discretion from the trader, so automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?

The traditional approach is to send low touch orders to several brokers over a set period and use the results to establish who is best at handling the flow. This works well for clients who don’t have a large number of low touch orders. For those that have a many low touch orders, FlexTrade adopts a proprietary scatter algorithm that selects a pre-defined broker and low touch algorithm to randomise a specified portion of the order flow. This creates a feedback loop with continuous improvement as orders are weighted across multiple brokers and algorithms depending on performance.

HIGH TOUCH

Providing all the information you need to add value at the right time.

When a trader is dealing with a high touch order they may look across chat channels, IOIs, news and pre-trade analysis. Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?

Our High Touch automation is a quantitative, data-driven approach that ensures the trader has all the information they require to add value when the time is right. Importantly, any order categorisation is recalculated in real-time to ensure the order hasn't popped out of a bucket due to an incoming alert or signal.

FLEXALGOWHEEL

Select your trading objective and strategy rather than the broker and algorithm.

The most common uses of this automation approach are to avoid inherent trader bias when routing an order, consolidate complex algorithms into a single set of parameters and to align execution objectives with the broker algorithm.

How do you ensure Best Execution?

The FlexAlgoWheelTM allows you to select the trading objective and strategy rather than the broker and algorithm. Once the objective and strategy have been selected the FlexAlgoWheelTM selects the broker and algorithm to align with those objectives. You have full visibility over where the order has gone, but the selection is automated. Over time, this automated process becomes a feedback loop to ensure continuous performance improvement. Other benefits includes:
  • Quantitative performance metrics fed into the order routing decision
  • Open architecture removes limitations on the number of characteristics that can be monitored
  • Software approach allows the FlexAlgoWheelTM to evolve with time, adding non-traditional parameters to drive it
These are orders where you have no particular view.
Effectively, it’s a significantly sized order but nothing of note is appearing in your platform, such as alerts, and there are no unusual characteristics. The usual approach with these kinds of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?
Using a quantitative model from a 3rd party in an automated fashion can assist with planning and proving Best Execution. These types of orders benefit most from a model-driven approach to planning participation – nothing unusual is happening, so the models should hold.

The most common use case of automation.
Orders which constitute a low average daily volume, with no alerts out on the stock, can be classified as low touch. These low touch orders have very little discretion from the trader. They are exhaust flow where the added value of trader intervention is minimal.

Here, as the orders have little to no market impact, the Best Execution approach is to get the trade done quickly. Automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?
The traditional route to do this is to hold a bake off process with a handful of low touch brokers over a set period and use the results of that to establish who is best at handling that flow.

This works well for clients who don’t have a huge number of low touch orders, however FlexTrade adopts a proprietary scatter algorithm which randomly selects a pre-defined broker and low touch algorithm, to randomise a specified portion (e.g. 5%) of the low touch flow.

This enables the creation of a feedback loop, with continuous improvement on low touch flow as orders are weighted across multiple brokers and algorithms depending on performance.

While not obvious for automation, some elements of the best execution workflow can be automated if dictated in a prescriptive fashion.
For example, when a trader is dealing with a high touch order, they may look across chat channels, IOIs, news and pre-trade analysis.
Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?
Our Best Execution automation approach ensures the trader has all the information they require to add value when the time is right.
Importantly, any order categorisation is recalculated in real-time, to ensure the order hasn’t popped out of a bucket due to an incoming alert or signal.

We take the quantitative, data driven approach to everything we do to improve performance quantitatively, through defined experiments.

The concept of a broker wheel has been around for a long time.
The original use cases were mostly to:

  • Avoid inherent trader bias when routing an order
  • Abstract complex broker algorithms into a single set of parameters
  • Align execution objectives with broker algorithm

How do you ensure Best Execution?
The FlexAlgoWheelTM allows traders to select the trading objective and strategy, rather than the broker and algorithm.

Once the objective and strategy have been selected, behind the scenes, the FlexAlgoWheelTM selects the broker and algorithm to align with those objectives. The trader has full visibility over where the order has gone, but the actual selection is based on automation.

This automation can start as random, but with time should become a feedback loop to ensure continuous improvement in performance.

Defining what constitutes performance as well as ensuring the broker understands the objective fully are key elements when defining the FlexAlgoWheelTM.

Quantitative performance metrics can then feed into the decision as to where the order should be routed.

Having an open architecture approach means there are no limitations on the number of characteristics which can be monitor.

For example, you may want to use a Liquidity Seeking algorithm for as long as you can until a liquidity spike alert comes into the platform.

Software approach also means the FlexAlgoWheelTM can evolve with time, adding non-traditional parameters to drive it.

ADAPTIVE

These are orders where you have no particular view.
Effectively, it’s a significantly sized order but nothing of note is appearing in your platform, such as alerts, and there are no unusual characteristics.

The usual approach with these kinds of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?
Using a quantitative model from a 3rd party in an automated fashion can assist with planning and proving Best Execution.
These types of orders benefit most from a model-driven approach to planning participation – nothing unusual is happening, so the models should hold.

LOW TOUCH

The most common use case of automation.
Orders which constitute a low average daily volume, with no alerts out on the stock, can be classified as low touch. These low touch orders have very little discretion from the trader. They are exhaust flow where the added value of trader intervention is minimal.

Here, as the orders have little to no market impact, the Best Execution approach is to get the trade done quickly. Automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?
The traditional route to do this is to hold a bake off process with a handful of low touch brokers over a set period and use the results of that to establish who is best at handling that flow.

This works well for clients who don’t have a huge number of low touch orders, however FlexTrade adopts a proprietary scatter algorithm which randomly selects a pre-defined broker and low touch algorithm, to randomise a specified portion (e.g. 5%) of the low touch flow.

This enables the creation of a feedback loop, with continuous improvement on low touch flow as orders are weighted across multiple brokers and algorithms depending on performance.

HIGH TOUCH

While not obvious for automation, some elements of the best execution workflow can be automated if dictated in a prescriptive fashion.
For example, when a trader is dealing with a high touch order, they may look across chat channels, IOIs, news and pre-trade analysis.
Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?
Our Best Execution automation approach ensures the trader has all the information they require to add value when the time is right.
Importantly, any order categorisation is recalculated in real-time, to ensure the order hasn’t popped out of a bucket due to an incoming alert or signal.

We take the quantitative, data driven approach to everything we do to improve performance quantitatively, through defined experiments.

FLEXALGOWHEEL

The concept of a broker wheel has been around for a long time.
The original use cases were mostly to:

  • Avoid inherent trader bias when routing an order
  • Abstract complex broker algorithms into a single set of parameters
  • Align execution objectives with broker algorithm

How do you ensure Best Execution?
The FlexAlgoWheel allows traders to select the trading objective and strategy, rather than the broker and algorithm.
Once the objective and strategy have been selected, behind the scenes, the FlexAlgoWheel selects the broker and algorithm to align with those objectives. The trader has full visibility over where the order has gone, but the actual selection is based on automation.

This automation can start as random, but with time should become a feedback loop to ensure continuous improvement in performance.
Defining what constitutes performance as well as ensuring the broker understands the objective fully are key elements when defining the FlexAlgoWheel.

Quantitative performance metrics can then feed into the decision as to where the order should be routed.
Having an open architecture approach means there are no limitations on the number of characteristics which can be monitor.
For example, you may want to use a Liquidity Seeking algorithm for as long as you can until a liquidity spike alert comes into the platform.
Software approach also means the FlexAlgoWheel can evolve with time, adding non-traditional parameters to drive it.

Monitoring Best Execution

Our flexible framework for order categorisation and definition allows multiple input parameters to be analysed and orders monitored through Best Execution alerting.

Designed to work seamlessly into the trader’s workflow, our alerting system gives traders the opportunity to do what they would normally do, if they had all the time in the world to dedicate to an order.

This is pure exception management – our clients work thousands of orders a day, our alerting framework keeps them informed and in full control of all their orders.

FlexTrade’s Alerting Framework

TRANSACTIONAL ORDER DATA

Captures all relevant information around orders, executions and FIX tag data.

  • FlexAlgoWheelTM: Ensuring execution is in line with objectives
  • Order Status Updates: Monitoring algorithm goals
  • Intelligent Blotter: Automated switching based on market conditions

TRADITIONAL MARKET DATA

Brings together a wide range of market data to provide a full picture of liquidity.

  • IOIs: Proprietary quality scoring and support for actionable IOIs
  • Block Trades: Real-time alerting and highlighting
  • Outliers: Monitor orders to any metric including sector, region, index or representative basket

MARKET INTELLIGENCE

Integration allow traders to enrich data with market context.

  • OTAS: Alerts for insider dealings, EPS momentum, valuation, etc.
  • Dataminr: social media aggregation and alerting
  • RSRCHX: Indicates when new research is published
  • Symphony: Inject messages and leverage bots to identify structure data to map back into a standardised format
  • Voice: Capture call information, including counterparty and timestamps

Proving Best Execution

Best Execution across asset classes relies on a blended approach using TCA and trade lifecycle reconstruction.

FlexTCA captures real-time and post-trade analysis for proving Best Execution in global equities and FX.

Proving Best Execution in fixed income relies on capturing all relevant events during the trade lifecycle, including voice and chat. Tracking transactional information, market data and intelligence ensures “all sufficient steps” taken can be reconstructed on demand.

 

GLOBAL EQUITIES & FX

FlexTCA captures real-time and post-trade analysis for proving Best Execution in global equities and FX.

FIXED INCOME

Proving Best Execution in fixed income relies on capturing all relevant events during the trade lifecycle, including voice and chat. Tracking transactional information, market data and intelligence ensures “all sufficient steps” taken can be reconstructed on demand.

 
 

PLANNING BEST EXECUTION

A key change as a result of MiFID II is how firms plan, monitor and prove best execution.

To help clients plan for and ensure best execution, we’ve built a platform and customised approach that identifies a wide range of order characteristics and processes that can benefit from different levels of our Best Execution Automation detailed below.

Significantly sized orders with no unusual characteristics.
The usual approach with these types of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?
Using a quantitative model from a third-party in an automated fashion can help with planning and proving Best Execution. We’ve found that adaptive automation benefits most from a model-driven approach to planning participation – nothing unusual is happening, so the models typically hold.
Little to no impact orders that need to get done quickly.
These low touch orders have very little discretion from the trader, so automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?
The traditional approach is to send to low touch to several brokers over a set period and use the results to establish who is best at handling the flow. This works well for clients who don’t have a large number of low touch orders. For those that have a many low touch orders, FlexTrade adopts a proprietary scatter algorithm that selects a pre-defined broker and low touch algorithm to randomise a specified portion of the order flow. This creates a feedback loop with continuous improvement as orders are weighted across multiple brokers and algorithms depending on performance.
Providing all the information you need to add value at the right time.
When a trader is dealing with a high touch order they may look across chat channels, IOIs, news and pre-trade analysis. Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?
Our High Touch automation is a quantitative, data-driven approach that ensures the trader has all the information they require to add value when the time is right. Importantly, any order categorisation is recalculated in real-time to ensure the order hasn't popped out of a bucket due to an incoming alert or signal.
Select your trading objective and strategy rather than the broker and algorithm.
The most common uses of this automation approach are to avoid inherent trader bias when routing an order, consolidate complex algorithms into a single set of parameters and to align execution objectives with the broker algorithm.

How do you ensure Best Execution?
The FlexAlgoWheelTM allows you to select the trading objective and strategy rather than the broker and algorithm. Once the objective and strategy have been selected the FlexAlgoWheelTM selects the broker and algorithm to align with those objectives. You have full visibility over where the order has gone, but the selection is automated. Over time, this automated process becomes a feedback loop to ensure continuous performance improvement. Other benefits includes:
  • Quantitative performance metrics fed into the order routing decision
  • Open architecture removes limitations on the number of characteristics that can be monitored
  • Software approach allows the FlexAlgoWheelTM to evolve with time, adding non-traditional parameters to drive it
These are orders where you have no particular view.
Effectively, it’s a significantly sized order but nothing of note is appearing in your platform, such as alerts, and there are no unusual characteristics. The usual approach with these kinds of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?
Using a quantitative model from a 3rd party in an automated fashion can assist with planning and proving Best Execution. These types of orders benefit most from a model-driven approach to planning participation – nothing unusual is happening, so the models should hold.

The most common use case of automation.
Orders which constitute a low average daily volume, with no alerts out on the stock, can be classified as low touch. These low touch orders have very little discretion from the trader. They are exhaust flow where the added value of trader intervention is minimal.

Here, as the orders have little to no market impact, the Best Execution approach is to get the trade done quickly. Automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?
The traditional route to do this is to hold a bake off process with a handful of low touch brokers over a set period and use the results of that to establish who is best at handling that flow.

This works well for clients who don’t have a huge number of low touch orders, however FlexTrade adopts a proprietary scatter algorithm which randomly selects a pre-defined broker and low touch algorithm, to randomise a specified portion (e.g. 5%) of the low touch flow.

This enables the creation of a feedback loop, with continuous improvement on low touch flow as orders are weighted across multiple brokers and algorithms depending on performance.

While not obvious for automation, some elements of the best execution workflow can be automated if dictated in a prescriptive fashion.
For example, when a trader is dealing with a high touch order, they may look across chat channels, IOIs, news and pre-trade analysis.
Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?
Our Best Execution automation approach ensures the trader has all the information they require to add value when the time is right.
Importantly, any order categorisation is recalculated in real-time, to ensure the order hasn’t popped out of a bucket due to an incoming alert or signal.

We take the quantitative, data driven approach to everything we do to improve performance quantitatively, through defined experiments.

The concept of a broker wheel has been around for a long time.
The original use cases were mostly to:

  • Avoid inherent trader bias when routing an order
  • Abstract complex broker algorithms into a single set of parameters
  • Align execution objectives with broker algorithm

How do you ensure Best Execution?
The FlexAlgoWheelTM allows traders to select the trading objective and strategy, rather than the broker and algorithm.

Once the objective and strategy have been selected, behind the scenes, the FlexAlgoWheelTM selects the broker and algorithm to align with those objectives. The trader has full visibility over where the order has gone, but the actual selection is based on automation.

This automation can start as random, but with time should become a feedback loop to ensure continuous improvement in performance.

Defining what constitutes performance as well as ensuring the broker understands the objective fully are key elements when defining the FlexAlgoWheelTM.

Quantitative performance metrics can then feed into the decision as to where the order should be routed.

Having an open architecture approach means there are no limitations on the number of characteristics which can be monitor.

For example, you may want to use a Liquidity Seeking algorithm for as long as you can until a liquidity spike alert comes into the platform.

Software approach also means the FlexAlgoWheelTM can evolve with time, adding non-traditional parameters to drive it.

ADAPTIVE

These are orders where you have no particular view.
Effectively, it’s a significantly sized order but nothing of note is appearing in your platform, such as alerts, and there are no unusual characteristics.

The usual approach with these kinds of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?
Using a quantitative model from a 3rd party in an automated fashion can assist with planning and proving Best Execution.
These types of orders benefit most from a model-driven approach to planning participation – nothing unusual is happening, so the models should hold.

LOW TOUCH

The most common use case of automation.
Orders which constitute a low average daily volume, with no alerts out on the stock, can be classified as low touch. These low touch orders have very little discretion from the trader. They are exhaust flow where the added value of trader intervention is minimal.

Here, as the orders have little to no market impact, the Best Execution approach is to get the trade done quickly. Automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?
The traditional route to do this is to hold a bake off process with a handful of low touch brokers over a set period and use the results of that to establish who is best at handling that flow.

This works well for clients who don’t have a huge number of low touch orders, however FlexTrade adopts a proprietary scatter algorithm which randomly selects a pre-defined broker and low touch algorithm, to randomise a specified portion (e.g. 5%) of the low touch flow.

This enables the creation of a feedback loop, with continuous improvement on low touch flow as orders are weighted across multiple brokers and algorithms depending on performance.

HIGH TOUCH

While not obvious for automation, some elements of the best execution workflow can be automated if dictated in a prescriptive fashion.
For example, when a trader is dealing with a high touch order, they may look across chat channels, IOIs, news and pre-trade analysis.
Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?
Our Best Execution automation approach ensures the trader has all the information they require to add value when the time is right.
Importantly, any order categorisation is recalculated in real-time, to ensure the order hasn’t popped out of a bucket due to an incoming alert or signal.

We take the quantitative, data driven approach to everything we do to improve performance quantitatively, through defined experiments.

FLEXALGOWHEEL

The concept of a broker wheel has been around for a long time.
The original use cases were mostly to:

  • Avoid inherent trader bias when routing an order
  • Abstract complex broker algorithms into a single set of parameters
  • Align execution objectives with broker algorithm

How do you ensure Best Execution?
The FlexAlgoWheel allows traders to select the trading objective and strategy, rather than the broker and algorithm.
Once the objective and strategy have been selected, behind the scenes, the FlexAlgoWheel selects the broker and algorithm to align with those objectives. The trader has full visibility over where the order has gone, but the actual selection is based on automation.

This automation can start as random, but with time should become a feedback loop to ensure continuous improvement in performance.
Defining what constitutes performance as well as ensuring the broker understands the objective fully are key elements when defining the FlexAlgoWheel.

Quantitative performance metrics can then feed into the decision as to where the order should be routed.
Having an open architecture approach means there are no limitations on the number of characteristics which can be monitor.
For example, you may want to use a Liquidity Seeking algorithm for as long as you can until a liquidity spike alert comes into the platform.
Software approach also means the FlexAlgoWheel can evolve with time, adding non-traditional parameters to drive it.

BEST EXECUTION AUTOMATION

What constitutes Best Execution depends on the characteristics of an order. Characteristics could constitute multiple parameters including percentage of average daily volume, liquidity, volatility, portfolio manager instruction, or 3rd party data item.

Defining the order characteristics allows us to infer a process each order must go through to achieve Best Execution according to the workflow.

Some processes are naturally ripe for automation. Orders can be added to certain ‘routes’, which can have automated actions attributed to them which we look at below.

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BEST EXECUTION PLANNING

What constitutes Best Execution depends on the characteristics of an order. Characteristics could constitute multiple parameters including percentage of average daily volume, liquidity, volatility, portfolio manager instruction, or 3rd party data item.

Defining the order characteristics allows us to infer a process each order must go through to achieve Best Execution according to the workflow.

Some processes are naturally ripe for automation. Orders can be added to certain ‘routes’, which can have automated actions attributed to them which we look at below.

 

These are orders where you have no particular view.
Effectively, it’s a significantly sized order but nothing of note is appearing in your platform, such as alerts, and there are no unusual characteristics. The usual approach with these kinds of orders may be to route to a broker VWAP over the day, or participate to a certain level.

How do you ensure Best Execution?
Using a quantitative model from a 3rd party in an automated fashion can assist with planning and proving Best Execution. These types of orders benefit most from a model-driven approach to planning participation – nothing unusual is happening, so the models should hold.

The most common use case of automation.
Orders which constitute a low average daily volume, with no alerts out on the stock, can be classified as low touch. These low touch orders have very little discretion from the trader. They are exhaust flow where the added value of trader intervention is minimal.

Here, as the orders have little to no market impact, the Best Execution approach is to get the trade done quickly. Automation can be applied to take these orders off the trader’s blotter and place them in the market.

How do you ensure Best Execution?
The traditional route to do this is to hold a bake off process with a handful of low touch brokers over a set period and use the results of that to establish who is best at handling that flow.

This works well for clients who don’t have a huge number of low touch orders, however FlexTrade adopts a proprietary scatter algorithm which randomly selects a pre-defined broker and low touch algorithm, to randomise a specified portion (e.g. 5%) of the low touch flow.

This enables the creation of a feedback loop, with continuous improvement on low touch flow as orders are weighted across multiple brokers and algorithms depending on performance.

While not obvious for automation, some elements of the best execution workflow can be automated if dictated in a prescriptive fashion.
For example, when a trader is dealing with a high touch order, they may look across chat channels, IOIs, news and pre-trade analysis.
Having all this information populate in the blotter automatically ensures information leakage is not a challenge.

How do you ensure Best Execution?
Our Best Execution automation approach ensures the trader has all the information they require to add value when the time is right.
Importantly, any order categorisation is recalculated in real-time, to ensure the order hasn’t popped out of a bucket due to an incoming alert or signal.

We take the quantitative, data driven approach to everything we do to improve performance quantitatively, through defined experiments.

The concept of a broker wheel has been around for a long time.
The original use cases were mostly to:

  • Avoid inherent trader bias when routing an order
  • Abstract complex broker algorithms into a single set of parameters
  • Align execution objectives with broker algorithm

How do you ensure Best Execution?
The FlexAlgoWheelTM allows traders to select the trading objective and strategy, rather than the broker and algorithm.
Once the objective and strategy have been selected, behind the scenes, the FlexAlgoWheelTM selects the broker and algorithm to align with those objectives. The trader has full visibility over where the order has gone, but the actual selection is based on automation.

This automation can start as random, but with time should become a feedback loop to ensure continuous improvement in performance.
Defining what constitutes performance as well as ensuring the broker understands the objective fully are key elements when defining the FlexAlgoWheelTM.

Quantitative performance metrics can then feed into the decision as to where the order should be routed.
Having an open architecture approach means there are no limitations on the number of characteristics which can be monitor.
For example, you may want to use a Liquidity Seeking algorithm for as long as you can until a liquidity spike alert comes into the platform.
Software approach also means the FlexAlgoWheelTM can evolve with time, adding non-traditional parameters to drive it.

BEST EXECUTION MONITORING

We offer a flexible framework for order categorisation and definition which allows multiple input parameters to be analysed and orders monitored. Once the order is categorised, we can monitor the progress through Best Execution alerting.

Best Execution alerting is not intended to intrude on the trader’s workflow. It is intended to give the trader an opportunity to do what they would normally do if they had all the time in the world to dedicate to a specific order.

We believe in the idea of plan and prove Best Execution with sufficient framework in place to ensure the trader knows what’s happening with all their orders.

This is pure exception management — our clients work thousands of orders a day and our alerting framework keeps them informed and in full control.

 

TRANSACTIONAL

Data related to the order itself: orders, executions, RFQ responses and so on

  • If we understand the order objective from the FlexAlgoWheel, we can monitor transactional information to ensure objectives are being met
  • Order Status Updates (OSUs) from brokers give real-time feedback on what the algorithm is thinking – that way, we can catch issues before execution
  • Orders only just qualified as large-in-scale, which are about to be routed to a dark allocator (where it will be subsequently split to smaller, non-LIS slices) should warn the trader

TRADITIONAL MARKET DATA

  • Analysing IOI data coming into the system, and attributing a quality score to each
  • The score is then built into an alert to show the trader whether it makes more sense to switch execution to be against an IOI instead of routing to a broker
  • Block trades – identifying when a block trade has gone through on the market, which may lead to switching strategy
  • Deviation from sector, region, index etc. – monitoring and alerting based on deviation from a reference point, such as a representative basket

MARKET INTELLIGENCE

FlexTrade’s integration program allows traders to enrich existing data with market context.

  • OTAS – alerts for insider dealings, EPS momentum, valuation etc.
  • RSRCHX – indicates when new research is published
  • Symphony – scrape chats for relevant information

BEST EXECUTION PROOF

FlexTCA offers real-time and post-trade analysis capturing for proving Best Execution in global equities and FX.

Best Execution proof in fixed income relies on our ability to capture all relevant events during the trade lifecycle, including real-world events.

Our ability to track transactional information, market data, along with market intelligence, ensures “all sufficient steps” are taken, can be reconstructed on demand.

Best Execution across the asset classes relies on a blended approach using TCA, and trade lifecycle reconstruction.