A Spring Boot application that implements the core components of algorithmic trading systems with a focus on high-frequency trading (HFT) concepts. This project provides a framework and API for algorithmic trading strategies, signal generation, and risk management.
Algorithmic Trading is the use of computer programs to automate trading decisions and execution based on predefined rules.
High-Frequency Trading (HFT) is a specialized subset of algorithmic trading characterized by:
- Extremely high speed: Executing orders in microseconds or milliseconds
- High volume: Generating thousands or millions of orders per day
- Short holding periods: Often holding positions for seconds or less
- Tiny profit margins: Making very small profits per trade but at high volume
Algorithms analyze market data to identify patterns and execute trades faster than humanly possible, typically seeking to profit from small price discrepancies that may exist for only fractions of a second.
- Statistical Arbitrage: Exploiting price differences between related assets
- Mean Reversion: Trading on the assumption that prices will return to their average
- Momentum: Following short-term price trends
This project implements these strategies in a simplified form to demonstrate the core concepts of algorithmic trading systems.
This project is an educational implementation of algorithmic trading concepts with a focus on HFT principles. It's important to note that this is not a production-ready HFT system, which would require:
- Ultra-low latency network connections and specialized hardware
- Co-location with exchange servers
- Hardware acceleration (FPGAs, specialized NICs)
- Extremely optimized code (often in C++ or even assembly)
- Microsecond-level timing precision
Instead, this project demonstrates the fundamental architecture and components of an algorithmic trading system through a Spring Boot application with a REST API interface. It can be used for learning, prototyping strategies, and as a starting point for more sophisticated implementations.
graph TD
X[Exchange]
A[Market Data Processor]
B[Signal Generator]
C[Order Manager]
D[Risk Manager]
E[(Market Data)]
G[(Trading Positions)]
X -->|Price Updates| A
A -->|Updates| E
B -->|Reads| E
B -->|Trading Decision| C
C -->|Submits Orders| X
C -->|Updates| G
D -->|Monitors| G
D -->|Enforces Limits| C
subgraph "Trading Signals"
S1[Statistical Arbitrage]
S2[Mean Reversion]
S3[Momentum]
S4[Combined Signal]
S1 --> S4
S2 --> S4
S3 --> S4
S4 -->|If Strong Enough| B
end
classDef exchange fill:#f96,stroke:#333,stroke-width:2px,color:#000000
classDef processor fill:#bbf,stroke:#333,stroke-width:1px,color:#000000
classDef storage fill:#dfd,stroke:#333,stroke-width:1px,color:#000000
classDef signal fill:#ffd,stroke:#333,stroke-width:1px,color:#000000
class X exchange
class A,B,C,D processor
class E,G storage
class S1,S2,S3,S4 signal
sequenceDiagram
participant Exchange
participant MDP as Market Data Processor
participant SG as Signal Generator
participant OM as Order Manager
participant RM as Risk Manager
Exchange->>MDP: Market Tick Data
activate MDP
MDP->>MDP: Update Order Book
MDP->>MDP: Store Historical Data
deactivate MDP
SG->>MDP: Request Market Data
activate MDP
MDP-->>SG: Return Order Book & History
deactivate MDP
activate SG
SG->>SG: Calculate Volatility
SG->>SG: Calculate Signals
SG->>SG: Combine Signals
alt Signal > Threshold
SG->>SG: Calculate Order Size
SG->>OM: Submit Order
deactivate SG
activate OM
OM->>Exchange: Submit Order
OM->>OM: Store in Active Orders
OM->>OM: Update Position
deactivate OM
Exchange-->>OM: Order Confirmation
activate RM
RM->>OM: Check Position Limits
alt Position > MAX_POSITION_SIZE
RM->>OM: Reduce Position
OM->>Exchange: Submit Reduction Order
end
RM->>RM: Check Daily Loss Limit
alt PnL < -MAX_DAILY_LOSS
RM->>OM: Emergency Shutdown
OM->>Exchange: Cancel All Orders
OM->>Exchange: Close All Positions
end
deactivate RM
end
- Multi-strategy signal generation: Combines statistical arbitrage, mean reversion, and momentum strategies
- Real-time market data processing: Processes and stores order book updates in real-time
- Comprehensive risk management: Enforces position limits and implements loss-limiting safeguards
- REST API: Control and monitor the algorithm through HTTP endpoints
- Spring Boot implementation: Built on the robust Spring framework
| Method | Endpoint | Description |
|---|---|---|
| POST | /api/algorithm/start |
Start the trading algorithm |
| POST | /api/algorithm/stop |
Stop the trading algorithm |
| GET | /api/algorithm/status |
Check if the algorithm is running |
| GET | /api/algorithm/positions |
Get current positions and P&L |
| GET | /api/algorithm/orderbooks |
Get current order book data |
| GET | /api/algorithm/performance |
Get performance metrics |
| POST | /api/algorithm/updatemarket |
Update market data (testing) |
| POST | /api/algorithm/addsymbol |
Add a new symbol to track |
-
Clone the Repository
git clone https://github.com/yourusername/hft-application.git cd hft-application -
Build the Application
./mvnw clean package
-
Run the Application
java -jar target/hft-application-1.0.0.jar
-
Start the Algorithm
curl -X POST http://localhost:8080/api/algorithm/start
-
Check Algorithm Status
curl http://localhost:8080/api/algorithm/status
The application comes preconfigured with the following symbols:
- BTC-USD (Bitcoin)
- ETH-USD (Ethereum)
- SOL-USD (Solana)
Additional symbols can be added through the API.
Contributions are welcome! Please feel free to submit a Pull Request.