Reya-Labs / v1-sbf

Voltz Strategy Backtesting Framework (SBF)

IMPORTANT: this is an alpha version of the SBF. New trading strategies are under active development and will be added by Voltz Labs in due course.

Overview

The Voltz SBF is an event-driven backtesting system, designed to work with generic trading strategies and evaluate their historic performance. Strategies are provided as independent classes which must inheritit from the Strategy abstract base class. The strageies are evaluated on an event-by-event basis, with the Event abstract base class handling the different types of trading events. The corresponding strategy portfolios are evaluated using the Portfolio class.

We define four different events, each inheriting from Event:

1. MarketEvent: Handles the event of receiving new market data e.g. a fresh update from a rates database and/or rate oracle.
2. SignalEvent: Handles the event of sending a Signal from a Strategy object. This is received by a Portfolio object and acted upon.
3. OrderEvent: Handles the event of sending an Order to an execution system. The order contains a token (e.g. "Aave USDC"), IRS Swap notional and a direction. Our in-build ExecutionHandler is resposible for the backtesting execution, mimicing a live trading system.
4. FillEvent: Instantiated events of this type encapsulate the notion of a Filled IRS swap order, as returned by the Voltz protocol after executing the trade. Stores the IRS notional actually traded and at what fixed rate. In addition, stores the fees of the trade collected by liquidity providers.

Both historical and live trading data are handled by a centralised and dedicated DataHandler object. The DataHandler is an abstract base class providing an interface for all subsequent (inherited) data handlers (both live and historic). The goal of a (derived) DataHandler object is to output a generated set of rates for each token requested. This replicates how a live strategy would function as current market data would be sent "down the pipe". Thus a historic and live system will be treated identically by the rest of the backtesting suite.

Individual components

The SBF is modular in that a generic strategy can be added as an independent class, as long as it inherits the baseline functionality from the Strategy interface. This strategy can then be called and incorporated into the backtests.py script -- a clear illustration on how each of these moving parts connect together can be arrived at my following the simplistsic LongRateStrategy class in the code, paying attention to how it is called in backtest.py and the master script, run_strategy_backetster.py, which asutomates the full event loop of the SBF and outputs performance metrics for a given strategy and data-set. The core scripts are as follows:

1. backtest.py: defines the Backtest abstract base class, from which all strategies to be backtests inherit as separate derived strategy backtest classes e.g. LongRateStrategyBacktest.
2. data.py: defines the DataHandler abstract base class, with its derived HistoricCSVDataHandler class for formatting and processing historical rates like an event-level live trading system.
3. dune.py: Dune class for reading the latest on-chain results via Dune Analytics.
4. event_loop.py: the guts of the event-level analysis, defining the actual heartbeat of the framework. Per heartbeat it runs inner and outer loops over events and market rates respectively.
5. event.py: defines the Event base class and all subsequent derived events to be handled.
6. execution.py: defines the ExecutionHandler base class and its derived class for handling historical data.
7. performance.py: defined the PerformanceMetricsCalculator class, which calculate the Sharpe ratio and maximum drawdown for a given trading strategy. For a general introduction to Sharpe ratios, and their associated frequentist statistics, I highly recommend Andrew Lo's excellent paper: https://alo.mit.edu/wp-content/uploads/2017/06/The-Statistics-of-Sharpe-Ratios.pdf .
8. portfolio.py: defines the Portfolio class, which handles the strartegy equity curve asnd PnL calculations, and calls the calculations in the PerformanceMetricsCalculaor.
9. reporter.py: a class for genersting a simple Excel-style results report for the strategy. Called in run_strategy_backtester.py.
10. run_strategy_backtester.py: master script which calls generic srategy backtests and runs the full event-level analysis.
11. strategy.py: defines the Strategy abstracct base class, from which all trading strategies inherit the functionality for computing individual signals and assigning long/short/exit positions per market heartbeat.
12. test_*.py: unit testing scripts for the key SBF components and strategies. Every time a new strategy is created, a corresponding unit test class should also be implemented for good testing and continuous integration practice.

Terms & Conditions

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