Successful Backtesting Algorithmic Trading Strategies
Algorithmic backtesting requires knowledge of many areas, including psychology, mathematics, statistics, software development and market/exchange microstructure. I couldn’t hope to cover all of those topics in one chapter, so I’m going to split them into two or three smaller pieces. What will we discuss in this section? I’ll begin by defining backtesting and then I will describe the basics of how it is carried out. Then I will elucidate upon the biases we touched upon in previous chapters.
In subsequent chapters we will look at the details of strategy implementations that are often barely mentioned or ignored elsewhere. We will also consider how to make the backtesting process more realistic by including the idiosyncrasies of a trading exchange. Then we will discuss transaction costs and how to correctly model them in a backtest setting. We will end with a discussion on the performance of our backtests and finally provide detailed examples of common quant strategies.
Let’s begin by discussing what backtesting is and why we should carry it out in our algorithmic trading.
Why Backtest Algo Trading Strategies?
Algorithmic trading stands apart from other types of investment classes because we can more reliably provide expectations about future performance from past performance, as a consequence of abundant data availability. The process by which this is carried out is known as backtesting.
In simple terms, backtesting is carried out by exposing your particular strategy algorithm to a stream of historical financial data, which leads to a set of trading signals. Each trade (which we will mean here to be a ’round-trip’ of two signals) will have an associated profit or loss. The accumulation of this profit/loss over the duration of your strategy backtest will lead to the total profit and loss (also known as the ’P & L’ or ’PnL’). That is the essence of the idea, although of course the “devil is always in the details”!
What are key reasons for backtesting an algorithmic strategy?
- Filtration – If you recall from the previous chapter on Strategy Identification, our goal at the initial research stage was to set up a strategy pipeline and then filter out any strategy that did not meet certain criteria. Backtesting provides us with another filtration mechanism, as we can eliminate strategies that do not meet our performance needs.
- Modelling – Backtesting allows us to (safely!) test new models of certain market phenomena, such as transaction costs, order routing, latency, liquidity or other market microstructure
- Optimisation – Although strategy optimisation is fraught with biases, backtesting allows us to increase the performance of a strategy by modifying the quantity or values of the parameters associated with that strategy and recalculating its performance.
- Verification – Our strategies are often sourced externally, via our strategy pipeline. Backtesting a strategy ensures that it has not been incorrectly implemented. Although we will rarely have access to the signals generated by external strategies, we will often have access to the performance metrics such as the Sharpe Ratio and Drawdown characteristics. Thus we can compare them with our own implementation.
Steps of Backtesting in Algorithmic Trading Strategy with suitable Example;
1.) Set Up the Environment
- MetaTrader 5 (MT5) Terminal
- MetaTrader 5 Python API (MetaTrader5 library)
- Historical data for the asset (EURUSD, USDJPY)
- A backtesting framework (Python script or MT5 strategy tester)
2.) Import Necessary Libraries
3.) Connect to MT5 & Fetch Historical Data
4.) Define the Trading Strategy
For your strategy:
- SELL on bullish candles
- BUY on bearish candles
- 2 pips TP/SL (0.0002 for EURUSD, 0.02 for USDJPY)
- 1 lot per trade
- Stop if total loss reaches 20 pips
5.) Analyze the Results
- Total Profit/Loss
- Winning vs Losing Trades
- Equity Curve Visualization
- Max Drawdown
6.) Optimize & Adjust the Strategy
- Modify SL/TP values
- Add additional indicators for confirmation
- Experiment with different lot sizes or risk management rules
Backtesting provides a host of advantages for algorithmic trading. However, it is not always possible to straightforwardly backtest a strategy. In general, as the frequency of the strategy increases, it becomes harder to correctly model the microstructure effects of the market and exchanges. This leads to less reliable backtests and thus a trickier evaluation of a chosen strategy. This is a particular problem where the execution system is the key to the strategy performance, as with ultra-high frequency algorithms.
Read Also; Best Programming Language For Algorithmic Trading
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