Algorithmic trading and backtesting framework in pure julia
Author louisponet
25 Stars
Updated Last
1 Year Ago
Started In
May 2022


Build Status codecov Package Downloads

This is an algorithmic trading and backtesting package written in Julia. It provides a framework for defining and executing trading strategies based on technical indicators, as well as backtesting these strategies on historical data. Behind the scenes it relies on an ECS paradigm as implemented by Overseer.jl, making it extremely easy to extend.

Simple Example

To define a trading strategy, users need to implement a Julia struct with an update function that defines the trading logic. The update function is called periodically by the framework, and has access to tick data for the specified assets, as well as any technical indicators requested by the strategy.

struct StratSys <: System end

Overseer.requested_components(::StratSys) = (Open, Close, SMA{20, Close}, SMA{200, Close})

function Overseer.update(s::StratSys, trader, asset_ledgers)
   for ledger in asset_ledgers
        for e in new_entities(ledger, s)
            #Trading logic goes here

The package includes several built-in technical indicators, such as simple moving averages, relative strength index, and exponential moving averages, but users can also define their own custom indicators.

To execute a trading strategy in real-time, users can create a Trader object with the desired strategies and assets, and connect it to a real-time data source through the different broker APIs.

broker = AlpacaBroker("<key_id>", "<secret>")

strategy = Strategy(:strat, [StratSys()], assets=[Stock("AAPL")])

trader = Trader(broker, strategies=[strategy])


If one wants to backtest a trading strategy on historical data, users can instead use BackTester instead of Trader with the desired data range, interval, and strategies. The backtester will simulate the behavior of a realtime trader on the specified data. Afterwards a TimeArray can be created with the data from the trader, and used for performance analysis.

trader = BackTester(HistoricalBroker(broker), 
                    start = <start date>, 
                    stop  = <stop date>, 
                    dt    = <data timeframe>)


ta = TimeArray(trader)

using Plots

The package is designed to be flexible and customizable, and can be extended with new technical indicators, trading strategies, and data sources.

See Documentation for more details.

Future Roadmap

  • Improved performance analysis, statistics
  • Implement standard plotting functionality
  • Trader loading and saving
  • Implement further signals and Indicators
  • Backtest comparisons
  • Support for different Brokers

Used By Packages

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