The Financial Trading Value Chain: Who Takes a Share of Every Trade?

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Core thesis

A stock trade may look extremely simple:

Buy → Hold → Sell

But behind every transaction sits a multi-layer financial technology and commercial ecosystem:

Exchange → Clearing → Broker → Algorithm Vendor → Financial Infrastructure → End Customer

Every layer has its own business model.

Exchanges charge market infrastructure fees. Brokers charge commissions and service fees. Algorithm vendors monetize trading technology. Financial infrastructure companies sell software, hardware, and services. Institutions and retail investors ultimately bear the transaction costs and keep the residual investment return.

To understand the economics of modern capital markets, one must therefore look beyond the stock price itself:

Where does the money go after a trade happens? Who creates value? Who captures it? And who ultimately bears the risk?

1. The Full Journey of a Trade

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A typical A-share transaction can be viewed as:

                     End Customer
                Institution / Retail
                         |
                         v
                 Broker Trading System
                         |
              +----------+----------+
              |                     |
        Manual Trading          Algorithmic Trading
              |                     |
              |              T+0 / VWAP / TWAP
              |                / SOR / ML
              |                     |
              +----------+----------+
                         |
                         v
                  Core Trading System
                         |
                         v
                 Risk / Account Layer
                         |
                         v
                      Exchange
                         |
                         v
                Clearing & Settlement

From a value-chain perspective, the same transaction can be divided into several layers:

Layer 1 — Exchange / Clearing Infrastructure
        ↓
Layer 2 — Broker
        ↓
Layer 3 — Algorithm & Technology Vendors
        ↓
Layer 4 — Financial IT Infrastructure
        ↓
Layer 5 — Institutions and Retail Investors

The most important distinction is:

The end customer bears the economic risk, while most upstream participants monetize infrastructure, technology, transaction services, or access.

2. How Much Does a Trade Actually Cost?

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Using the fee structure in the source material as an illustrative example, suppose an investor buys RMB 10,000 of A-shares.

Using this simplified example:

 id="4a5n8g"
Buy-side cost
≈ RMB 1.44 – 3.44

Sell-side cost
≈ RMB 6.44 – 8.44

These figures are illustrative. Actual investor costs depend on current regulations, broker pricing, minimum charges, instrument type, and account arrangements.

The important economic lesson is:

Transaction cost is not one fee. It is a stack of fees.

3. What Changes When T+0 Algorithmic Services Are Added?

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A traditional retail brokerage account may have:

Commission
≈ 0.01% – 0.03%

A specialized algorithmic service may carry a higher commercial fee structure.

The difference is not simply the algorithm itself.

It reflects a package:

Client
  |
  v
Higher Service Fee
  |
  +---- Broker
  |
  +---- Algorithm Provider
  |
  +---- Infrastructure

The economic model therefore changes from:

Trading access

to:

Trading as a technology service.

4. The Five-Layer Financial Trading Value Chain

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A simplified ecosystem looks like this:

┌───────────────────────────────────────────┐
│              END CUSTOMERS                │
│ Institutions / Quant Funds / Retail       │
└───────────────────┬───────────────────────┘
                    │
                    │ Trading Fees
                    ▼
┌───────────────────────────────────────────┐
│                  BROKER                   │
│ Brokerage / PB / Algorithmic Services     │
└───────────────────┬───────────────────────┘
                    │
                    │ Technology Procurement
                    ▼
┌───────────────────────────────────────────┐
│       ALGORITHM & TECHNOLOGY VENDORS      │
│ T+0 / SOR / VWAP / TWAP / ML / AI         │
└───────────────────┬───────────────────────┘
                    │
                    │ Software / Hardware
                    ▼
┌───────────────────────────────────────────┐
│        FINANCIAL IT INFRASTRUCTURE        │
│ Trading Core / LDP / FPGA / Message Bus   │
└───────────────────┬───────────────────────┘
                    │
                    ▼
┌───────────────────────────────────────────┐
│       EXCHANGE / CLEARING SYSTEMS         │
│ Matching / Registration / Settlement      │
└───────────────────────────────────────────┘

Each layer captures a different form of economic value.

5. Layer One: Exchanges and Clearing Institutions

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The Infrastructure Monopoly

#

Exchanges do not need to predict whether investors will make or lose money.

Their business model is:

Trading Activity
       ×
Infrastructure Fee
       =
Exchange Revenue

The key characteristics are:

• Structural market position
• Stable infrastructure demand
• Revenue linked to trading activity
• Limited exposure to the investment outcome of individual customers

This leads to a useful conceptual distinction:

Investors speculate on the market. Exchanges monetize the existence of the market.

6. The Clearing Layer

#

Clearing and settlement institutions sit even deeper in the infrastructure stack.

Their job is to ensure:

Trade
  ↓
Registration
  ↓
Clearing
  ↓
Settlement

This infrastructure is fundamentally different from a trading strategy.

A quant strategy may fail.

A brokerage strategy may fail.

A clearing system must simply work.

That creates a unique economic characteristic:

Mission-critical infrastructure tends to monetize reliability rather than investment performance.

7. Layer Two: Brokers — The Central Commercial Hub

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Brokers are the most complex layer in the value chain.

They simultaneously operate as:

Customer Interface
+
Trading Channel
+
Core Trading Platform
+
Risk Manager
+
Algorithm Service Provider
+
Prime Broker

This makes the broker the central commercial hub between the investor and the market.

8. Broker Revenue Stream One: Traditional Brokerage

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The classic formula is:

Trading Value
      ×
Commission Rate
      =
Brokerage Revenue

The long-term problem is obvious:

Commission Rates
        ↓
        ↓
        ↓
Compression

As competition increases, pure transaction-channel economics become increasingly difficult.

This creates pressure for brokers to monetize:

• Algorithmic execution
• Wealth management
• Financing
• Prime brokerage
• Institutional services
• Data
• Technology

9. Broker Revenue Stream Two: Algorithmic Services

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Algorithmic services create a new commercial layer.

Traditional trading:

Client
  ↓
Broker
  ↓
Exchange

Algorithmic trading:

Client
  ↓
Algorithm
  ↓
Broker Infrastructure
  ↓
Exchange

The broker is no longer selling only access.

It is selling:

Technology-enhanced execution.

This creates room for service differentiation and potentially higher pricing.

10. Broker Revenue Stream Three: Prime Brokerage

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Institutional clients require much more than a trading account.

They may need:

• Execution
• Algorithmic trading
• Risk management
• Clearing
• Financing
• Data
• APIs
• Portfolio reporting
• Compliance infrastructure

Therefore:

Retail Brokerage

        ↓

Prime Brokerage / Institutional Platform

represents a significant increase in commercial value per client.

11. Layer Three: Algorithm Vendors

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Algorithm vendors occupy an unusual position.

They may not own:

• The customer relationship
• The brokerage account
• The exchange membership

But they own:

The trading logic.

Typical products include:

• T+0 strategies
• VWAP
• TWAP
• Smart Order Routing
• Quantitative strategy platforms
• Machine-learning execution
• AI-enhanced execution

12. Why Revenue Sharing Is Attractive to Algorithm Vendors

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Traditional enterprise software can be sold through:

License
+
Maintenance

Algorithmic trading software has a different economic profile.

Once the technology is integrated:

Algorithm
   ↓
More Adoption
   ↓
More Trading Volume
   ↓
More Revenue Share

This creates:

Usage-based monetization.

The vendor participates in the economic success of adoption.

13. The Software Economics of Algorithm Vendors

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Algorithm businesses often have:

High Upfront R&D Cost
        ↓
Low Marginal Delivery Cost

Once a strategy or execution engine is mature, serving another customer can be much cheaper than developing the technology from scratch.

Therefore:

Algorithm IP
×
Customer Base
×
Trading Volume
=
Scalable Revenue

This is why algorithm vendors can have attractive economics once they reach sufficient scale.

14. The Main Risk for Algorithm Vendors

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The same model creates a major risk.

If:

Vendor Revenue
∝
Trading Volume

then:

Trading Activity ↓
       ↓
Client Usage ↓
       ↓
Vendor Revenue ↓

This means algorithm vendors are highly sensitive to:

• Market activity
• Client adoption
• Broker distribution
• Strategy performance
• Regulatory changes

A strong technology product without sufficient distribution may therefore remain commercially small.

15. Layer Four: Financial IT Infrastructure — The “Picks and Shovels”

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There is another layer that is often overlooked:

Financial technology infrastructure vendors.

These companies provide:

• Core trading systems
• Ultra-low-latency gateways
• Message buses
• FPGA acceleration
• Risk engines
• Market-data systems
• LDP-style low-latency platforms

Representative technology routes in the Chinese financial IT ecosystem include:

Kingdom
HARE + KOCA-LDP

        ↓

Hundsun
LDP + RCM + PTrade

        ↓

HuRui
Ultra-Low-Latency Trading Platforms

The business logic is:

Sell the infrastructure that allows everyone else to compete.

16. Why Infrastructure Vendors Resemble “Picks and Shovels”

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The analogy is simple.

Gold miners:

Quant Funds
Brokers
Institutional Traders

Pick-and-shovel suppliers:

Trading Infrastructure Vendors

The trader may:

• Win
• Lose
• Shut down a strategy
• Change the model

But infrastructure may still generate revenue through:

• Software licenses
• Implementation fees
• Maintenance
• Upgrades
• Support
• Hardware

Therefore:

Infrastructure revenue can be less dependent on whether a particular trading strategy succeeds.

17. Kingdom, Hundsun and HuRui: Different Infrastructure Positions

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The three vendors can be viewed as representing different layers of the financial infrastructure stack.

This leads to an important market principle:

The more algorithmic trading expands, the more important the underlying infrastructure becomes.

18. Layer Five: The End Customer

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At the bottom of the value chain are:

• Quantitative funds
• Public funds
• Insurance institutions
• Brokerage proprietary desks
• High-net-worth clients
• Retail investors

They ultimately bear the transaction costs.

A simplified institutional equation is:

Gross Investment Return

-

Commission

-

Taxes

-

Algorithm Cost

-

Market Data

-

Infrastructure

=

Net Investment Return

For retail:

Investment Return

-

Commission

-

Taxes

-

Slippage

=

Net Return

The economic question is therefore:

Can the investment strategy generate enough return to cover the entire financial technology stack?

19. Institutional vs Retail Cost Structures

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Institutional Trading

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Institutions may pay for:

• Tick data
• Order-book data
• Quant researchers
• Execution algorithms
• Low-latency infrastructure
• FPGA
• Co-location
• Risk systems

The structure becomes:

High Cost
+
High Trading Frequency
+
High Technology Investment

Retail Trading

#

Typical setup:

Mobile App
+
Broker Platform
+
Standard Market Data

Infrastructure costs are much lower.

But the investor also has:

• Less data
• Less computing
• Less execution sophistication
• Less institutional risk control

This creates two very different economic ecosystems.

20. The Real Question: Who Is “Taking the Money”?

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The transaction can be viewed as:

                     Customer Capital
                           |
                           v
                    Trade Occurs
                           |
          +----------------+----------------+
          |                |                |
          v                v                v
     Government         Exchange         Broker
          |                |                |
       Taxes          Infrastructure     Commission
                           |
                           v
                    Algorithm Vendor
                           |
                           v
                  Financial Infrastructure
                           |
                           v
                    Customer Net Return

The key insight is:

The cost is a chain, not a single number.

21. Friction Cost: The Most Important Concept for Investors

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A professional investor does not calculate only:

Buy Price
+
Sell Price

The full model is:

Exchange Fees
+
Clearing Fees
+
Broker Commission
+
Algorithm Cost
+
Slippage
+
Market Impact
+
Funding Cost
+
Opportunity Cost

Therefore:

Gross Return

-

Friction Costs

=

Net Return

This distinction is fundamental.

A strategy with high gross return can still be commercially unattractive if:

Transaction Cost
+
Infrastructure Cost
>
Alpha

22. Why the Financial Chain Continues to Add Fees

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Financial markets have become technologically more complex.

The old market needed:

Trading Channel

The modern market needs:

Trading Channel
+
Data
+
Execution Algorithms
+
Low Latency
+
Risk Management
+
AI

Every additional capability creates:

New Value
+
New Cost
+
New Commercial Model

This is why financial technology keeps expanding as a service layer.

23. T+0 as a Case Study in Financial Monetization

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A traditional model:

Client
  ↓
Broker
  ↓
Commission

A T+0 model:

Client
  ↓
Higher-Service Commission
  ↓
Broker
  ↓
Algorithm Vendor

The algorithm service therefore becomes a monetization mechanism for technological differentiation.

This is why T+0 should be viewed as:

A financial service business, not simply a trading algorithm.

24. Where Pricing May Go Next

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The current model can evolve from:

Transaction-Based Pricing

toward:

Base Fee
+
Performance Fee

and eventually:

Dynamic AI Pricing

A conceptual model:

Dynamic Service Price
=
Base Price

× Market Condition

× Client Profile

× Algorithm Quality

This would turn algorithmic trading into a more sophisticated financial-services marketplace.

25. The Financial Trading Marketplace of the Future

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Imagine a broker platform with:

                 Algorithm Marketplace

        +---------+---------+---------+---------+
        |         |         |         |         |
       VWAP      T+0       ML        AI       SOR
        |         |         |         |         |
     Vendor A  Vendor B  Vendor C  Vendor D  Vendor E

The customer could:

• Compare algorithms
• Review historical performance
• Compare pricing
• Select a service
• Switch providers

The algorithm stops being a static software package.

It becomes:

A tradable financial technology service.

26. From Software Product to Financial Platform

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The commercial evolution can be summarized as:

Sell Code

   ↓

Sell Algorithm

   ↓

Sell Service

   ↓

Sell Outcome

   ↓

Sell Platform Access

This is a much deeper transformation than simple commission pricing.

It reflects a broader financial technology trend:

Monetization is moving closer to measurable economic value.

27. Why Hybrid “Build + Buy” Models Will Likely Dominate

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Future brokers are unlikely to choose:

100% Build

or:

100% Buy

A hybrid architecture is more practical:

Broker Platform

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

This balances:

• Control
• Innovation
• Vendor competition
• Intellectual property
• Time to market

28. The Ultimate Pricing Unit May Become “Value Created”

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Today’s investor asks:

“Is the commission 0.02% or 0.03%?”

A more sophisticated future question is:

“How much measurable value did this algorithm create per unit of cost?”

Conceptually:

Execution Improvement

-

Algorithm Cost

=

Net Economic Value

The commercial model shifts from:

Price per Trade

toward:

Price per Value Created

This may ultimately become one of the most important changes in financial technology monetization.

29. The Three Fundamental Laws of the Financial Value Chain

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The entire ecosystem can be reduced to three principles.

Law One: Infrastructure Monetizes Activity

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Exchanges and core infrastructure generate revenue because trading occurs.

Law Two: Technology Monetizes Capability

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Brokers, algorithm vendors, and IT vendors monetize:

• access
• execution
• technology
• data
• automation

Law Three: Investors Bear the Residual Risk

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The investor ultimately receives:

Gross Investment Return

-

All Trading Frictions

Whatever remains is the investor’s economic outcome.

This is why financial markets can contain profitable technology businesses even when individual investors experience negative returns.

The infrastructure is monetized independently of the investor’s portfolio outcome.

30. The Complete Financial Trading Value Chain

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                         END CUSTOMER
                    Institution / Retail
                              |
                              |
                         Trading Activity
                              |
                              v
                           BROKER
                 Account / Channel / PB
                              |
                              |
                      Algorithm Services
                              |
                 +------------+------------+
                 |                         |
                 v                         v
        ALGORITHM VENDORS         FINANCIAL IT INFRASTRUCTURE
        T+0 / ML / SOR / AI       Trading Core / FPGA / LDP
                 |                         |
                 +------------+------------+
                              |
                              v
                    EXCHANGE / CLEARING
                              |
                              v
                       MARKET ACCESS

The economics of this chain are:

Market Infrastructure
        ↓
Trading Services
        ↓
Algorithmic Capability
        ↓
Investment Outcome

31. Future Trends

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Trend One: From Volume-Based Fees to Performance-Based Fees

#

Transaction Sharing

        ↓

Base Fee + Performance Fee

        ↓

Value-Based Pricing

Trend Two: From Software Products to Platforms

#

Financial IT is moving from:

Single Trading System

to:

Trading Platform
+
Algorithm Platform
+
Data Platform
+
AI Platform

Trend Three: From Buying Tools to Buying Capabilities

#

The customer of the future may not want:

“A T+0 algorithm.”

They may want:

Data
+
Strategy
+
Execution
+
Risk
+
Performance Attribution

as one integrated service.

32. Conclusion: What Does the Financial Trading Industry Really Sell?

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The entire A-share trading ecosystem can ultimately be summarized as:

Exchanges sell the market. Brokers sell access and financial services. Algorithm vendors sell trading intelligence. Infrastructure companies sell the technology that makes trading possible. Investors bear the cost and keep the residual return.

From a business perspective:

Exchange
= Monetize Market Activity

Broker
= Monetize Access + Services

Algorithm Vendor
= Monetize Trading Intelligence

Infrastructure Vendor
= Monetize Technology

Investor
= Bear Risk + Capture Residual Return

This is why financial markets differ fundamentally from ordinary technology platforms.

A conventional Internet business might look like:

User
  ↓
Pays
  ↓
Service Provider

Financial markets look more like:

Customer
   ↓
Transaction
   ↓
Multiple Infrastructure Layers
   ↓
Multiple Revenue Streams
   ↓
Residual Return
   ↓
Customer

The most valuable position in this ecosystem is therefore not necessarily the company with the most advanced algorithm.

It may be the company that owns:

• The customer relationship
• The trading channel
• The infrastructure
• The data
• The technology standard
• Or the most difficult-to-replace capability

Appendix: Financial Trading Value Chain Cheat Sheet

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Author Note

The most useful question in financial technology is not:

“Which algorithm is the best?”

It is:

“Who owns the customer, who owns the channel, who owns the algorithm, who owns the infrastructure, and who has pricing power?”

Once those five questions are answered, the economics of the financial trading ecosystem become much easier to understand.