From Brokerage Pricing to Revenue Sharing: The Complete Business Logic of T+0 Algorithmic Trading Services

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Core thesis

The commercial logic of broker-provided T+0 algorithmic trading services can be understood as a three-layer system:

Cost structure → Pricing → Revenue sharing → Competition → Pricing evolution

The broker controls the client relationship, trading account, execution infrastructure, and compliance framework.

The algorithm vendor provides the strategy and execution intelligence.

The client ultimately pays for the service through trading costs.

The economic question is therefore not simply:

“How much does T+0 cost?”

but:

“Where does the money go, who captures the value, and how will the pricing model evolve?”

1. What Is the Customer Actually Buying?

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T+0 algorithmic trading is often described as:

“A broker provides an algorithm that helps investors trade intraday.”

That description is incomplete.

A real T+0 service is closer to:

Existing Position
       |
       v
Real-Time Market Data
       |
       v
T+0 Strategy Engine
       |
       v
Execution Decision
       |
       v
Risk Control
       |
       v
Broker Trading Gateway
       |
       v
Exchange

The service therefore combines:

• Trading strategy
• Real-time market data
• Execution algorithms
• Broker infrastructure
• Risk controls
• Compliance systems
• Client support

The product is not simply “an algorithm.”

It is:

Algorithm + trading infrastructure + broker service

2. The First Layer: How Brokers Price T+0 Services

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According to the industry estimates reflected in the source material, some current broker T+0 services are priced around:

0.028% – 0.035%

or, in Chinese brokerage terminology:

2.8 – 3.5 ten-thousandths

These figures should be treated as industry estimates rather than universal published market tariffs.

The key point is the pricing structure.

A simplified model is:

Customer Commission
        |
        +---- Algorithm Procurement
        |
        +---- Hardware & Connectivity
        |
        +---- Compliance & Operations
        |
        +---- Broker Margin

This is essentially a:

Cost-plus pricing model

3. Cost Layer One: Algorithm Procurement

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If a broker does not build its entire T+0 engine internally, it needs external algorithm capabilities.

The source material mentions vendors such as:

• Kafang Technology
• Yueran Technology
• Xuntou
• Feitu Technology
• Zicheng Technology
• HaoXing Technology

The industry estimates in the source suggest algorithm procurement costs may range from:

0.005% – 0.03%

depending on vendor, service model, contract structure, and implementation.

The important economic distinction is:

External procurement becomes a variable cost embedded in the broker’s commercial model.

4. Cost Layer Two: Hardware, Market Data and Connectivity

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The algorithm itself is only one component.

The broker also needs infrastructure.

A simplified stack:

Market Data
      |
      v
Algorithm Server
      |
      v
Strategy Engine
      |
      v
Risk Engine
      |
      v
Broker Trading Gateway
      |
      v
Exchange

Potential infrastructure costs include:

Market Data

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• Level-2 data
• Tick data
• Real-time feeds
• Historical market data

Compute

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• Strategy servers
• High-availability nodes
• Disaster recovery systems

Network

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• Dedicated connections
• Low-latency gateways
• High-performance NICs
• Potential FPGA acceleration

The important point:

A financial algorithm is not commercially useful unless it can execute reliably through the broker’s production infrastructure.

5. Cost Layer Three: Compliance and Operations

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Financial technology has another major cost that consumer software does not:

Compliance.

A T+0 algorithm service may require:

• Suitability management
• Risk disclosures
• Programmatic trading controls
• Abnormal trading monitoring
• Logging
• Audit trails
• Incident response
• Regulatory reporting

Therefore:

Algorithm
+
Trading System
+
Risk Control
+
Compliance

is the actual product.

This explains why the software license itself cannot be treated as the total service cost.

6. Cost Layer Four: Broker Profit

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The simplified equation is:

Customer Revenue

        -

Algorithm Cost

        -

Infrastructure Cost

        -

Compliance Cost

        =

Broker Contribution Margin

This is the conventional commercial logic behind broker pricing.

The key question then becomes:

Who has the pricing power?

7. Who Controls the Price?

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Pricing power is not evenly distributed.

7.1 Self-developed Brokers

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Brokers with substantial internal algorithm capabilities have greater pricing flexibility.

Their cost structure is more like:

Customer Revenue
       |
       v
Internal Algorithm Team

rather than:

Customer Revenue
       |
       v
External Vendor

The advantage is not “free software.”

The advantage is:

Variable vendor fees become internal fixed R&D costs.

7.2 Procurement-Oriented Brokers

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A broker relying heavily on external vendors faces:

• Vendor fees
• Revenue-sharing requirements
• Contract restrictions
• Higher marginal service costs

That creates less room to reduce client pricing.

7.3 Scale Effects

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Large brokers can spread:

• Infrastructure costs
• Compliance costs
• Engineering costs

across a larger customer base.

This creates a structural advantage:

Scale reduces unit cost.

8. The Second Layer: Revenue Sharing Across the Industry Chain

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The T+0 ecosystem can be simplified into three layers:

Algorithm Vendor

        ↓

Broker

        ↓

End Customer

Each participant owns a different piece of the value chain.

8.1 Algorithm Vendor

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Provides:

• Strategy logic
• Optimization
• Model development
• Algorithm updates
• Technical support

8.2 Broker

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Provides:

• Customer relationship
• Securities account
• Trading gateway
• Market data
• Risk control
• Compliance
• Execution infrastructure

8.3 Customer

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Provides:

• Capital
• Trading volume
• Transaction revenue

9. Why Revenue Sharing Makes Economic Sense

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Traditional enterprise software:

License
+
Maintenance Fee

Algorithmic trading is different.

Its commercial value depends heavily on actual usage.

Therefore:

More Usage
    |
    v
More Trading Activity
    |
    v
More Broker Revenue
    |
    v
More Vendor Revenue

This creates:

Usage-based monetization.

The vendor does not only sell technology.

It participates in the economic upside of adoption.

10. Why Vendors Prefer Trading-Volume Revenue Sharing

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For algorithm vendors, revenue sharing offers several advantages.

10.1 Lower Procurement Friction

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The broker does not have to pay the entire economic value upfront.

10.2 Long-Term Alignment

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If the algorithm performs well:

Better Algorithm

      ↓

Higher Adoption

      ↓

Higher Trading Volume

      ↓

Higher Vendor Revenue

10.3 Strong Switching Costs

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Once an algorithm becomes integrated into:

• Broker systems
• Client workflows
• Risk controls
• Execution infrastructure

replacing it becomes expensive.

This encourages long-term relationships.

11. The Hidden Conflict: Trading Volume vs Net Client Return

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This is the most important economic tension in the entire model.

Suppose:

Vendor Revenue ∝ Trading Volume

and:

Broker Revenue ∝ Trading Volume

while:

Client Objective = Maximize Net Return

Then the incentives are not perfectly aligned.

The client ultimately cares about:

Gross Trading Return

-

Commission

-

Taxes

-

Slippage

-

Market Impact

=

Net Return

Therefore:

High trading volume is valuable to the broker and vendor, but not necessarily to the client.

This is the structural foundation of the “commission farming” debate.

It does not mean every broker or vendor encourages excessive trading.

It means the business model contains an inherent incentive mismatch.

12. The Third Layer: Evolution of the Pricing Model

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The source material describes a four-stage potential evolution.

Stage One: Flat Pricing

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2022–2024

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Typical characteristics:

One Product
        |
One Service Level
        |
One Broad Commission Range

Why?

Because the market was still developing.

The priorities were:

• Customer acquisition
• Market education
• Product adoption

Pricing differentiation remained limited.

13. Stage Two: Tiered Pricing

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2024–2026

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As competition increased, pricing became more sophisticated.

Possible segmentation:

Pricing can now depend on:

• Asset size
• Trading volume
• Service level
• Customization
• Algorithm access

The fundamental transition is:

From one-size-fits-all to customer-segment pricing.

14. Stage Three: Performance-Based Pricing

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A more advanced model could be:

Base Commission

       +

Performance Fee

For example:

Base Fee
+
Share of Value Created Above Benchmark

The economic relationship changes from:

More Trading
        ↓
More Revenue

to:

More Value Created
        ↓
More Revenue

This would produce much stronger alignment.

15. The Difficult Question: What Is “Performance”?

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Performance-based pricing creates a hard technical problem.

What exactly counts as value created?

Possible benchmarks include:

Benchmark A

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Price improvement versus arrival price.

Benchmark B

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Performance versus VWAP.

Benchmark C

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Performance versus TWAP.

Benchmark D

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Risk-adjusted excess return.

Benchmark E

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Portfolio-level incremental return.

The commercial model becomes much more complicated once performance has to be measured objectively.

16. Stage Four: AI-Driven Dynamic Pricing

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The most aggressive future model is dynamic pricing.

Conceptually:

Dynamic Price
=
Base Price

× Market Condition Factor

× Client Profile Factor

× Algorithm Quality Factor

Possible inputs:

• Market volatility
• Client asset size
• Trading behavior
• Algorithm performance
• Expected service cost
• Risk profile

The result would resemble:

• Cloud pricing
• Advertising auctions
• Dynamic insurance pricing

In other words:

Algorithm services could eventually become dynamically priced financial infrastructure.

This is a future-looking scenario, not an established universal market practice.

17. The Deeper Commercial Evolution

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The commercial evolution can therefore be summarized as:

Flat Price

     ↓

Tiered Pricing

     ↓

Performance-Based Pricing

     ↓

Dynamic AI Pricing

And the underlying drivers are:

Competition

+

Technology

+

Regulation

+

Customer Sophistication

18. The Three-Way Negotiation

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The T+0 ecosystem is fundamentally a three-party negotiation.

19. Conflict One: Trading Frequency

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Vendor:

Higher utilization is beneficial.

Broker:

More trading generates more commission revenue.

Client:

More trading is worthwhile only when the incremental return exceeds the additional cost.

This is why transaction economics matter more than raw algorithm performance.

20. Conflict Two: Vendor Revenue Share

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The vendor wants:

Higher Share

The broker wants:

Lower Procurement Cost

The negotiation therefore depends on:

• Algorithm quality
• Brand
• Adoption
• Exclusivity
• Broker scale
• Switching cost

The stronger the algorithm’s perceived differentiation, the stronger the vendor’s bargaining power.

21. Conflict Three: Client Size vs Service Cost

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A large client may demand:

• Lower commissions
• Higher service quality
• Customization
• API access

But the broker also faces:

• Infrastructure cost
• Support cost
• Risk cost
• Compliance cost

The result is:

Client asset size becomes a natural segmentation variable.

22. The Emergence of the “Algorithm Marketplace”

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A logical future evolution is a broker-side algorithm marketplace.

                Broker Platform

                     |

          Algorithm Marketplace

       +------+------+------+------+

       |      |      |      |      |

     VWAP    T+0    ML    AI Algo  SOR

       |      |      |      |      |

    Vendor A  B      C      D      E

The client could:

1. Compare algorithms
2. Review historical performance
3. Compare pricing
4. Select an algorithm
5. Switch providers

This transforms algorithms from:

Individually procured software

into:

A marketplace of financial capabilities.

23. From Software Product to Financial Service Platform

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The commercial evolution can be described as:

Sell Code

   ↓

Sell Algorithms

   ↓

Sell Services

   ↓

Sell Outcomes

   ↓

Sell Platform Access

This is a much broader transformation than a simple commission change.

The product itself evolves:

Software
    ↓
Service
    ↓
Outcome
    ↓
Ecosystem

24. Why “Build + Buy” Is Likely to Win

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The future broker architecture is unlikely to be:

100% Internal

or:

100% Outsourced

A hybrid model is more plausible:

Broker Platform

├── Internal Core
│   ├── Risk
│   ├── Data
│   ├── Execution Framework
│   └── Compliance
│
└── External Algorithm Ecosystem
    ├── VWAP
    ├── T+0
    ├── ML
    └── AI

This combines:

• Control
• Speed of innovation
• Vendor competition
• Internal IP protection

25. The Future Pricing Unit May Be “Value Created”

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Today, a client may ask:

“Is this commission 0.028% or 0.03%?”

Tomorrow, the better question may be:

“How much trading value does this algorithm create per unit of fee?”

For example:

Execution Improvement

-

Algorithm Cost

=

Net Value Created

The commercial model then becomes:

Price per Value Created

instead of:

Price per Trade

This is one of the most important possible transitions in financial technology monetization.

26. T+0 Business Model: The Complete Economic Flywheel

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The entire business model can be represented as:

Algorithm Capability

        ↓

Customer Adoption

        ↓

Trading Volume

        ↓

Broker Revenue

        ↓

Vendor Revenue Share

        ↓

R&D Investment

        ↓

Better Algorithms

        ↓

Better Customer Experience

        ↓

Higher Adoption

This is a financial technology flywheel.

The algorithm vendor gets paid because the technology is used.

The broker gets paid because the infrastructure is used.

The client stays because the service creates net value.

27. The Complete T+0 Business Architecture

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                         Client
                           |
                           |
                    Trading Activity
                           |
                           v
                         Broker
                +----------+----------+
                |                     |
          Trading Infrastructure   Algorithm Service
                |                     |
                |                     v
                |              Algorithm Vendor
                |                     |
                |             +-------+-------+
                |             |               |
                |          Fixed Fee       Revenue Share
                |             |               |
                +-------------+---------------+
                              |
                              v
                       Future Model
                              |
                   Base Fee + Performance
                              |
                              v
                      AI Dynamic Pricing

28. Strategic Implications

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For Clients

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The correct metric is not the headline commission.

It is:

Net Economic Value

=

Trading Return

-

Commission

-

Taxes

-

Slippage

-

Market Impact

A cheaper algorithm that creates less value can be more expensive economically.

For Brokers

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The next competitive layer is likely to include:

• Internal algorithm infrastructure
• Open vendor ecosystems
• Performance attribution
• Segmented pricing
• Data-driven client management
• Compliance automation

The question becomes:

“Which broker creates the highest net value for the client?”

rather than:

“Which broker has the lowest commission?”

For Algorithm Vendors

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The long-term competitive requirements become:

• Better strategies
• Better evidence
• Transparent attribution
• API integration
• Continuous optimization
• Outcome-based pricing

The business gradually shifts from:

Sell Technology

toward:

Prove Economic Value

29. T+0 Pricing Evolution Timeline

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* Future stages are scenario projections based on the source material, not established universal industry standards.

30. Conclusion: T+0 Is Ultimately Selling Economic Value

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The commercial logic of T+0 algorithmic trading can be reduced to one chain:

Cost Structure
      ↓
Broker Pricing
      ↓
Revenue Sharing
      ↓
Customer Adoption
      ↓
Competition
      ↓
Pricing Evolution
      ↓
Value-Based Monetization

The first stage is:

Cost-based pricing

The second stage:

Usage-based revenue sharing

The next potential stage:

Performance-based pricing

The long-term possibility:

Dynamic AI pricing

This is not simply a story about whether a broker charges 0.028% or 0.03%.

It is a story about how the financial industry gradually moves from:

Charging for Transactions

toward:

Charging for Value Created

The broker owns the customer relationship and financial infrastructure.

The algorithm vendor owns the strategy capability.

The client owns the final decision.

The long-term business model will therefore depend on one thing above all:

Can the economic value generated by the algorithm be measured, attributed, and shared transparently?

When that becomes possible, T+0 will no longer be just a trading tool.

It will become a platform for algorithmic financial services.

Appendix: Core Concepts

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Final takeaway

The ultimate evolution of T+0 pricing is not from “high commission” to “low commission.”

It is from:

Paying for trading activity

to:

Paying for measurable trading value.