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Barter is an open-source Rust framework for building event-driven live-trading & backtesting systems. Algorithmic trade with the peace of mind that comes from knowing your strategies have been backtested with a near-identical trading Engine. It is:

  • Fast: Barter provides a multi-threaded trading Engine framework built in high-performance Rust (in-rust-we-trust).
  • Easy: Barter provides a modularised data architecture that focuses on simplicity.
  • Customisable: A set of traits define how every Barter component communicates, providing a highly extensible framework for trading.

See Readme.


Barter is an open-source Rust framework for building event-driven live-trading & backtesting systems. It provides a high-performance, easy to customise, trading Engine that enables backtesting strategies on a near-identical system to live trading. The Engine can be controlled by issuing Commands over the Engine’s command_tx. Similarly, the Engine’s Events can be listened to using the event_rx (useful for event-sourcing). At a high level, it provides several de-coupled components that interact via a set of traits:

  • Data: Continuer & MarketGenerator traits govern the generation of a MarketEvents data feed that acts as the system heartbeat. For example, a LiveCandleHandler implementation is provided utilising Barter-Data’s WebSocket functionality to provide a live market Candle data feed to the system.
  • Strategy: The SignalGenerator trait governs potential generation of SignalEvents after analysing incoming MarketEvents. SignalEvents are advisory signals sent to the Portfolio for analysis.
  • Portfolio: MarketUpdater, OrderGenerator, and FillUpdater govern global state Portfolio implementations. A Portfolio may generate OrderEvents after receiving advisory SignalEvents from a Strategy. The Portfolio’s state updates after receiving MarketEvents and FillEvents.
  • Execution: The FillGenerator trait governs the generation of FillEvents after receiving OrderEvents from the Portfolio. For example, a SimulatedExecution handler implementation is provided for simulating any exchange execution behaviour required in dry-trading or backtesting runs.
  • Statistic: Provides metrics such as Sharpe Ratio, Calmar Ratio, and Max Drawdown to analyse trading session performance. One-pass dispersion algorithms analyse each closed Position and efficiently calculates a trading summary.
  • Trader: Capable of trading a single market pair using a customisable selection of it’s own Data, Strategy & Execution instances, as well as shared access to a global Portfolio.
  • Engine: Multi-threaded trading Engine capable of trading with an arbitrary number of Trader market pairs. Each contained Trader instance operates on its own thread.

Getting Started

Data Handler

use barter::data::handler::{Continuation, Continuer, MarketGenerator};
use barter::data::handler::historical::{HistoricalCandleHandler, HistoricalDataLego};
use barter_data::test_util;

let lego = HistoricalDataLego {
    exchange: "binance",
    symbol: "btc_usdt".to_string(),
    candles: vec![test_util::candle(), test_util::candle()].into_iter(),

let mut data = HistoricalCandleHandler::new(lego);

loop {
    let market_event = match data.can_continue() {
        Continuation::Continue => {
            match data.generate_market() {
                Some(market_event) => market_event,
                None => continue,

        Continuation::Stop => {
            // Pass closed Positions to Statistic module for performance analysis


use barter::strategy::SignalGenerator;
use barter::strategy::strategy::{Config as StrategyConfig, RSIStrategy};
use barter::data::MarketEvent;
use barter::test_util;

let config = StrategyConfig {
    rsi_period: 14,

let mut strategy = RSIStrategy::new(config);

let market_event = test_util::market_event();

let signal_event = strategy.generate_signal(&market_event);


use barter::portfolio::{MarketUpdater, OrderGenerator, FillUpdater};
use barter::portfolio::allocator::DefaultAllocator;
use barter::portfolio::risk::DefaultRisk;
use barter::portfolio::repository::redis::RedisRepository;
use barter::event::Event;
use barter::Market;
use barter::portfolio::OrderEvent;
use barter::portfolio::portfolio::{PortfolioLego, MetaPortfolio};
use barter::portfolio::repository::redis::Config as RepositoryConfig;
use barter::portfolio::repository::in_memory::InMemoryRepository;
use barter::statistic::summary::pnl::PnLReturnSummary;
use barter::statistic::summary::trading::{Config as StatisticConfig, TradingSummary};
use barter::test_util;
use std::marker::PhantomData;
use uuid::Uuid;

let components = PortfolioLego {
    engine_id: Uuid::new_v4(),
    markets: vec![Market {exchange: "binance",symbol: "btc_usdt".to_string()}],
    repository: InMemoryRepository::new(),
    allocator: DefaultAllocator{ default_order_value: 100.0 },
    risk: DefaultRisk{},
    starting_cash: 10000.0,
    statistic_config: StatisticConfig {
        starting_equity: 10000.0 ,
        trading_days_per_year: 365,
        risk_free_return: 0.0
    _statistic_marker: PhantomData::<TradingSummary>::default()

let mut portfolio = MetaPortfolio::init(components).unwrap();

let some_event = Event::OrderNew(test_util::order_event());

match some_event {
    Event::Market(market) => {
    Event::Signal(signal) => {
    Event::SignalForceExit(signal) => {
    Event::Fill(fill) => {
    _ => {}


use barter::execution::simulated::{Config as ExecutionConfig, SimulatedExecution};
use barter::portfolio::OrderEvent;
use barter::execution::Fees;
use barter::execution::ExecutionClient;
use barter::test_util;

let config = ExecutionConfig {
    simulated_fees_pct: Fees {
        exchange: 0.1,
        slippage: 0.05, // Simulated slippage modelled as a Fee
        network: 0.0,

let mut execution = SimulatedExecution::new(config);

let order_event = test_util::order_event();

let fill_event = execution.generate_fill(&order_event);


use barter::statistic::summary::trading::{Config as StatisticConfig, TradingSummary};
use barter::portfolio::position::Position;
use barter::statistic::summary::{Initialiser, PositionSummariser, TableBuilder};
use barter::test_util;

// Do some automated trading with barter components that generates a vector of closed Positions
let positions = vec![test_util::position(), test_util::position()];

let config = StatisticConfig {
    starting_equity: 10000.0,
    trading_days_per_year: 253,
    risk_free_return: 0.5,

let mut trading_summary = TradingSummary::init(config);



Engine & Traders

See Readme Engine Example


Defines a MarketEvent, and provides the Continuer and MarketGenerator traits for handling the generation of them. Contains implementations such as the (tick-by_tick) LiveTradeHandler, and HistoricalCandleHandler that generates a market feed and acts as the system heartbeat.

Multi-threaded trading Engine capable of trading with an arbitrary number market pairs. Contains a Trader for each Market pair that consists of it’s own Data, Strategy & Execution components, as well as shared access to a global Portfolio.

Defines an Event enum that contains variants that are vital to the trading event loop (eg/ MarketEvent). Other variants communicate work done by the system (eg/ FillEvent), as well as changes in system state (eg/ PositionUpdate).

Defines a FillEvent, and provides a useful trait FillGenerator for handling the generation of them. Contains an example SimulatedExecution implementation that simulates live broker execution.

Defines useful data structures such as an OrderEvent and Position. The Portfolio must interact with MarketEvents, SignalEvents, OrderEvents, and FillEvents. The useful traits MarketUpdater, OrderGenerator, & FillUpdater are provided that define the interactions with these events. Contains a MetaPortfolio implementation that persists state in a generic Repository. This also contains example implementations of an OrderAllocator & OrderEvaluator, which help the Portfolio make decisions on whether to generate OrderEvents and of what size.

Defines various iterative statistical methods that can be used to calculate trading performance metrics in one-pass. A trading performance summary implementation has been provided containing several key metrics such as Sharpe Ratio, Calmar Ratio, and Max Drawdown.

Defines a SignalEvent and SignalForceExit, as well as the SignalGenerator trait for handling the generation of them. Contains an example RSIStrategy implementation that analyses a MarketEvent and may generate a new advisory SignalEvent to be analysed by the Portfolio OrderGenerator.


Represents a unique combination of an ExchangeId & a SymbolId. Each [Trader] barters on one Market.


Returns the unique identifier for a given market, where a ‘market’ is a unique exchange-symbol combination (eg/ “market_binance-btc_usdt”).

Type Definitions

Communicates a str is a unique identifier for an Exchange (eg/ “binance”)

Communicates a String represents a unique market identifier (eg/ “market_binance-btc_usdt”).

Communicates a String is a unique identifier for a pair symbol (eg/ “btc_usd”)