Event-Driven Architecture

Overview

NanoARB uses an event-driven architecture for both backtesting and live trading. All market data updates, order lifecycle events, and timer callbacks flow through a priority queue ordered by timestamp, ensuring deterministic replay and accurate latency simulation.

Event Types

Defined in nano-backtest/src/events.rs:11:

pub enum EventType {
    /// Market data update received
    MarketData { instrument_id: u32 },
    
    /// Order submitted to exchange
    OrderSubmit { order: Order },
    
    /// Order acknowledged by exchange
    OrderAck { order_id: OrderId },
    
    /// Order rejected by exchange
    OrderReject { order_id: OrderId, reason: String },
    
    /// Order filled (partial or complete)
    OrderFill { fill: Fill },
    
    /// Order cancelled
    OrderCancel { order_id: OrderId },
    
    /// Cancel rejected
    CancelReject { order_id: OrderId, reason: String },
    
    /// Timer event for scheduled actions
    Timer { timer_id: u64, data: Option<String> },
    
    /// Signal from strategy
    Signal { name: String, value: f64 },
    
    /// End of data
    EndOfData,
}

Event Structure

From nano-backtest/src/events.rs:79:

pub struct Event {
    /// Event timestamp (when it occurs)
    pub timestamp: Timestamp,
    
    /// Sequence number for ordering events at same timestamp
    pub sequence: u64,
    
    /// Event type and data
    pub event_type: EventType,
}

The sequence field ensures deterministic ordering when multiple events occur at the exact same nanosecond:

impl Ord for Event {
    fn cmp(&self, other: &Self) -> Ordering {
        // Reverse ordering for min-heap behavior
        match other.timestamp.as_nanos().cmp(&self.timestamp.as_nanos()) {
            Ordering::Equal => other.sequence.cmp(&self.sequence),
            ord => ord,
        }
    }
}

Event Queue

Implemented as a binary min-heap in nano-backtest/src/events.rs:187:

pub struct EventQueue {
    /// Internal heap storage
    heap: BinaryHeap<Event>,
    /// Sequence counter for tie-breaking
    sequence_counter: u64,
}

impl EventQueue {
    /// Push an event with auto-generated sequence number
    pub fn push(&mut self, timestamp: Timestamp, event_type: EventType) {
        self.sequence_counter += 1;
        self.heap.push(Event::new(
            timestamp,
            self.sequence_counter,
            event_type,
        ));
    }
    
    /// Pop the next event (earliest timestamp)
    pub fn pop(&mut self) -> Option<Event> {
        self.heap.pop()
    }
    
    /// Peek at next event without removing
    pub fn peek(&self) -> Option<&Event> {
        self.heap.peek()
    }
}

Queue Complexity

Push: O(log n)
Pop: O(log n)
Peek: O(1)
Memory: O(n) where n = pending events

Event Flow

Scheduling Events

The event queue provides helper methods:

let mut queue = EventQueue::new();

// Schedule market data
queue.schedule_market_data(
    Timestamp::from_nanos(1_000_000),
    instrument_id,
);

// Schedule order submission
queue.schedule_order_submit(
    Timestamp::from_nanos(1_001_000),
    order,
);

// Schedule fill
queue.schedule_fill(
    Timestamp::from_nanos(1_002_000),
    fill,
);

// Schedule timer callback
queue.schedule_timer(
    Timestamp::from_nanos(2_000_000),
    timer_id,
    Some("rebalance".to_string()),
);

Latency Simulation

Implemented in nano-backtest/src/latency.rs:54:

pub struct LatencySimulator {
    /// Base order latency in nanoseconds
    order_latency_ns: i64,
    /// Base market data latency in nanoseconds  
    market_data_latency_ns: i64,
    /// Base acknowledgment latency in nanoseconds
    ack_latency_ns: i64,
    /// Jitter model
    jitter_model: JitterModel,
    /// Random number generator
    rng: StdRng,
}

Jitter Models

From nano-backtest/src/latency.rs:26:

pub enum JitterModel {
    /// No jitter (deterministic)
    None,
    
    /// Uniform jitter around base latency
    Uniform { max_jitter_ns: i64 },
    
    /// Normal distribution jitter
    Normal { std_dev_ns: f64 },
    
    /// Log-normal distribution (realistic for network latency)
    LogNormal { mu: f64, sigma: f64 },
    
    /// Empirical distribution from historical data
    Empirical { percentiles: [i64; 5] },
}

Latency Calculation

impl LatencySimulator {
    /// Calculate order arrival time at exchange
    pub fn order_arrival_time(&mut self, submit_time: Timestamp) -> Timestamp {
        let latency = (self.order_latency_ns + self.get_jitter()).max(0);
        submit_time.add_nanos(latency)
    }
    
    /// Calculate market data reception time
    pub fn market_data_reception_time(
        &mut self,
        exchange_time: Timestamp,
    ) -> Timestamp {
        let latency = (self.market_data_latency_ns + self.get_jitter()).max(0);
        exchange_time.add_nanos(latency)
    }
    
    /// Calculate order acknowledgment reception time
    pub fn ack_reception_time(&mut self, exchange_ack_time: Timestamp) -> Timestamp {
        let latency = (self.ack_latency_ns + self.get_jitter()).max(0);
        exchange_ack_time.add_nanos(latency)
    }
}

Colo Latency Models

Pre-configured for common colo facilities:

use nano_backtest::latency::ColoLatencyModel;

// CME Aurora (primary datacenter)
let aurora = ColoLatencyModel::aurora();
assert_eq!(aurora.order_latency(), 5_000); // 5 microseconds

// NY5 (generic NY colo)
let ny5 = ColoLatencyModel::ny5();
assert_eq!(ny5.order_latency(), 50_000); // 50 microseconds

// Custom colo
let custom = ColoLatencyModel::new(
    10_000,  // 10us to exchange
    3_000,   // 3us exchange processing
);

let rtt = custom.round_trip_estimate();
assert_eq!(rtt, 23_000); // 23 microseconds RTT

Event Processing Loop

From nano-backtest/src/engine.rs:118:

pub fn process_event<S: Strategy>(&mut self, event: Event, strategy: &mut S) {
    self.current_time = event.timestamp;
    self.events_processed += 1;
    
    match &event.event_type {
        EventType::MarketData { instrument_id } => {
            self.on_market_data(*instrument_id, strategy);
        }
        EventType::OrderSubmit { order } => {
            self.on_order_submit(*order);
        }
        EventType::OrderAck { order_id } => {
            strategy.on_order_ack(*order_id);
        }
        EventType::OrderFill { fill } => {
            self.on_fill(*fill, strategy);
        }
        EventType::OrderCancel { order_id } => {
            strategy.on_order_cancel(*order_id);
        }
        EventType::OrderReject { order_id, reason } => {
            strategy.on_order_reject(*order_id, reason);
        }
        EventType::Timer { timer_id, data } => {
            self.on_timer(*timer_id, data.as_deref());
        }
        EventType::Signal { name, value } => {
            self.on_signal(name, *value);
        }
        EventType::EndOfData => {
            self.state = EngineState::Completed;
        }
    }
}

Timing Considerations

Market Data Timestamps

Market data has multiple timestamps:

// Exchange timestamp (when event occurred on exchange)
let exchange_time = book_update.transact_time;

// Gateway timestamp (when we received it)
let gateway_time = exchange_time + market_data_latency;

// Strategy timestamp (when strategy processes it)
let strategy_time = gateway_time;

Order Timestamps

// Strategy decision time
let decision_time = Timestamp::now();

// Order submission time (includes serialization)
let submit_time = decision_time + serialization_latency;

// Exchange arrival time
let arrival_time = submit_time + network_latency;

// Acknowledgment time
let ack_time = arrival_time + exchange_processing;

// Ack reception time
let recv_time = ack_time + network_latency;

// Total latency
let total_latency = recv_time - decision_time;

Fill Timestamps

pub struct Fill {
    pub order_id: OrderId,
    pub price: Price,
    pub quantity: Quantity,
    pub side: Side,
    pub is_maker: bool,
    pub timestamp: Timestamp,  // Exchange fill time
    pub fee: f64,
}

// Strategy receives fill notification later:
let notification_time = fill.timestamp + ack_latency;

Event Ordering Example

let mut queue = EventQueue::new();

// Events arrive out of order
queue.push(Timestamp::from_nanos(100), EventType::EndOfData);
queue.push(Timestamp::from_nanos(50), EventType::MarketData { instrument_id: 1 });
queue.push(Timestamp::from_nanos(75), EventType::MarketData { instrument_id: 1 });

// But are processed in timestamp order
assert_eq!(queue.pop().unwrap().timestamp.as_nanos(), 50);
assert_eq!(queue.pop().unwrap().timestamp.as_nanos(), 75);
assert_eq!(queue.pop().unwrap().timestamp.as_nanos(), 100);

Same Timestamp Ordering

When events occur at the same nanosecond, sequence numbers determine order:

let mut queue = EventQueue::new();
let t = Timestamp::from_nanos(100);

// All at same timestamp
queue.push(t, EventType::MarketData { instrument_id: 1 });
queue.push(t, EventType::OrderSubmit { order });
queue.push(t, EventType::Timer { timer_id: 1, data: None });

// Processed in submission order (sequence 1, 2, 3)
let e1 = queue.pop().unwrap();
assert!(e1.is_market_data());

let e2 = queue.pop().unwrap();
assert!(matches!(e2.event_type, EventType::OrderSubmit { .. }));

let e3 = queue.pop().unwrap();
assert!(matches!(e3.event_type, EventType::Timer { .. }));

Performance Characteristics

Event Processing Rate

Typical: 1-5 million events/second
With complex strategies: 500K-1M events/second
Bottleneck: Strategy logic, not event queue

Memory Usage

Each Event: ~64 bytes
1M pending events: ~64 MB
Queue pre-allocates capacity to avoid reallocations

Latency Distribution

Typical HFT latencies:

let config = LatencyConfig {
    order_latency_ns: 10_000,        // 10 microseconds
    market_data_latency_ns: 5_000,   // 5 microseconds
    ack_latency_ns: 15_000,          // 15 microseconds
    jitter_ns: 2_000,                // +/- 2 microseconds
    use_random_jitter: true,
};

// Results in:
// P50: 10us, P95: 14us, P99: 16us order latency

Best Practices

Always use event queue - Don’t process events directly
Include latency simulation - Realistic backtests require realistic latency
Pre-allocate capacity - Avoid reallocations during backtest
Use sequence numbers - Ensure deterministic ordering
Timestamp everything - Critical for analysis and debugging

Debugging Events

impl Event {
    /// Check event type
    pub fn is_market_data(&self) -> bool {
        matches!(self.event_type, EventType::MarketData { .. })
    }
    
    pub fn is_order_event(&self) -> bool {
        matches!(
            self.event_type,
            EventType::OrderSubmit { .. }
                | EventType::OrderAck { .. }
                | EventType::OrderFill { .. }
                | EventType::OrderCancel { .. }
                | EventType::OrderReject { .. }
        )
    }
}

// Log events for debugging
for event in events {
    tracing::debug!(
        "Event @ {}ns (seq={}): {:?}",
        event.timestamp.as_nanos(),
        event.sequence,
        event.event_type,
    );
}

Order Books - Market data events update order books
Strategies - Strategies respond to events
Price Types - Timestamp type for event ordering

Get Started

Architecture

Core Concepts

Building Strategies

Backtesting

Data & Features

Deployment

Event-Driven Architecture

Overview

Event Types

Event Structure

Event Queue

Queue Complexity

Event Flow

Scheduling Events

Latency Simulation

Jitter Models

Latency Calculation

Colo Latency Models

Event Processing Loop

Timing Considerations

Market Data Timestamps

Order Timestamps

Fill Timestamps

Event Ordering Example

Same Timestamp Ordering

Performance Characteristics

Event Processing Rate

Memory Usage

Latency Distribution

Best Practices

Debugging Events

Get Started

Architecture

Core Concepts

Building Strategies

Backtesting

Data & Features

Deployment

​Overview

​Event Types

​Event Structure

​Event Queue

​Queue Complexity

​Event Flow

​Scheduling Events

​Latency Simulation

​Jitter Models

​Latency Calculation

​Colo Latency Models

​Event Processing Loop

​Timing Considerations

​Market Data Timestamps

​Order Timestamps

​Fill Timestamps

​Event Ordering Example

​Same Timestamp Ordering

​Performance Characteristics

​Event Processing Rate

​Memory Usage

​Latency Distribution

​Best Practices

​Debugging Events

​Related Topics

Overview

Event Types

Event Structure

Event Queue

Queue Complexity

Event Flow

Scheduling Events

Latency Simulation

Jitter Models

Latency Calculation

Colo Latency Models

Event Processing Loop

Timing Considerations

Market Data Timestamps

Order Timestamps

Fill Timestamps

Event Ordering Example

Same Timestamp Ordering

Performance Characteristics

Event Processing Rate

Memory Usage

Latency Distribution

Best Practices

Debugging Events

Related Topics