It’s said that you plan to fail if you fail to plan. Tackling the combat zone that is the volatile, unpredictable crypto arena requires battle-tested strategies. It’s impossible to revisit years of history to learn about the best trading systems. Backtesting exists to bridge the gap, ensuring that strategies can excel well into the future using past data.
What Is Backtesting in Crypto?
Backtesting doesn’t only apply to those who trade crypto. It’s a concept applicable to all financial markets revolving around simulating a strategy using historical data. The idea is that history repeats itself. In studying repeatable patterns from the past, crypto traders can confidently use a particular trading strategy, knowing it has been tested using this information.
Another backtesting element is helping traders uncover system flaws without committing real money using a demo or virtual account. This is where they can fine-tune or make the required adjustments to ensure the strategies perform as desired.
The following essential part of backtesting in crypto is the depth of data analyzed. Generally, the more years of data one can observe, the more effective their strategies can be.
However, analyzing years of past price action doesn’t provide an accurate picture of future movements due to the unpredictable nature of crypto. Thus, traders should back-test and forward-test their strategies in real time for the best results.
The Different Types of Backtesting in Crypto
Back-testing when we trade crypto generally falls into two categories: manual and automated.
Manual back-testing involves reviewing past data and recording it on paper and spreadsheets. As expected, it’s the most time-consuming but allows traders to visualize their strategies better. It’s most suitable for discretionary traders or traders who don’t utilize ‘bots’ to speculate in the markets.
Automated back-testing involves using dedicated trading software capable of processing vast data. Unsurprisingly, this way of back-testing is much quicker than the manual approach. It also reduces the potential for human error. Automated back-testing is often used by algo traders or those who rely on bots when they trade crypto.
In either case, a trader will have formulated a strategy with well-defined, objective rules before they back-test. After this process, it’s about analyzing the results and what they mean for measuring the strategy’s performance.
What Is the Most Important Data Analyzed in Crypto Backtesting?
Understandably, backtesting produces a wide range of data. But which is the most important? These are some crucial performance metrics to consider when testing any crypto strategy.
- Drawdown: This statistic refers to how much a trading account is down percentage-wise from its peak to its lowest value.
- Annualized Return: This represents how much an investment has increased, on average, yearly.
- Annualized Volatility: This is a measure of risk based on the standard deviation of the investment returns for the trading strategy.
- Risk of Ruin: This refers to the chances of losing the entire account balance based on the win rate and risk used per trade.
- Win Rate: Also referred to as the success or hit rate, this describes the percentage of winning positions for a strategy out of all positions executed.
- Profit Factor: the gross profit divided by the gross loss (including commissions).
Pros and Cons of Crypto Backtesting
No trader would test a trading strategy with real money. This is the first advantage of back-testing. Traders can explore an unlimited number of trading systems without risking their well-being or their funds.
The second benefit is backtesting allows traders to continuously adjust and improve any weaknesses in their trading strategy (or strategies) until it meets the accepted standards. For instance, a recommended drawdown percentage is 10%. So, a strategy with a higher figure presents more risks and means that specific parameters (like how much is risked per trade) should be reduced.
In looking at the cons, the most significant disadvantage of backtesting is overfitting or curve fitting. This concept defines the tendency for traders to optimize their backtesting results excessively. Traders can easily overlook the weaknesses of their strategies when back-testing, adding minor tweaks to every backtest, which prove impossible under actual conditions.
Bias also relates to overfitting. The natural inclination is to only focus on the best part of a trading strategy when tested while ignoring other red flags.
Finally, while a clichéd statement, past performance doesn’t guarantee future outcomes. Forward-testing is the solution here, accounting for the ever-changing dynamics of crypto. While back-testing provides the foundation, it can be rough around the edges.
Forward-testing irons out any issues under real-time conditions. Back-testing shouldn’t be the be-all and end-all. The most successful trading strategies have gone through a highly iterative process considering past and current conditions.
Back-Test and Forward-Test for Strategy Success
It’s human nature to base predictions on previous events. Through back-testing, we can reconstruct future trading outcomes with a decent degree of accuracy in preparation for the real markets. Yet, humans are complex beings who can act in ever-changing ways. Forward-testing is the final piece of the puzzle. As with back-testing, you can use paper trading (or a demo account) for forward testing. Ideally, traders should spend equal time back-testing and forward-testing instead of only back-testing or spending most of their time doing that.
