All rebalancing decisions are based on quantitative rules that are developed from historical price movements. The rules are dynamic and can change as market conditions change. The strategy gets information about price movements and market conditions from a range of quantitative metrics.  Humans run the backtests and use simulation models and data science methods to develop the rules, but the rebalancing decision is made automatically by the computer following the quantitative rules that have been set for it.

The portfolio holds BTC, ETH and USDC as a market-neutral asset in proportions set by the quantitative rules. Those three assets can be held in any combination, so the strategy could hold 80% risky assets with 40% BTC and 40% ETH and 20% cash-like assets in USDC, or it could have nearly 100% risky assets, or be 100% in USDC. The strategy evaluates market conditions in probabilities and makes rebalancing decisions accordingly. The proportions of BTC and ETH depend on which asset is deemed to have the higher upside probability at the time. This means a 50/50 split between BTC and ETH is possible but 60/40 or 80/20 splits might be equally likely. Neither BTC nor ETH is favored over the other but quantitative measurements determine which will become overweight when necessary. 

The portfolio is rebalanced a maximum of once or twice a week. The rebalancing frequency depends on quantitatively measured market conditions and price movements. The portfolio is not expected to trade or rebalance all the time, but only when market conditions dictate. Rebalancing a maximum twice a week means that the strategy is not likely to react quickly to either positive or negative price movements. Backtesting shows that rebalancing more frequently does not have meaningful positive benefit for the strategy. As crypto prices are very volatile, reacting quickly to abrupt price movements is likely to introduce noise and could make the portfolio miss the subsequent price recovery more often than necessary. This is why the portfolio is not rebalanced more often than once or twice a week.

The strategy uses risk management rules to reduce risk exposure if the market conditions are unfavorable. The strategy does not guarantee that losses will be cut, and it is not able to react quickly to extreme news because of its rebalancing frequency. The risk management rules are designed to offer attractive risk-return characteristics and not necessarily to allow market exit when negative news emerges. The strategy is long only, which means that it will gain value only when asset prices go up. In short, the strategy has to take risk to make a profit but it aims to keep profits larger than losses by applying risk management rules.    

The strategy is long only and makes profits only when prices go up. Prices rarely go straight up and as long as price trends emerge, the strategy should be profitable and backtests show it should outperform BTC and ETH. When prices go down, the strategy should once again outperform BTC and ETH as it will reduce market exposure at that time. Unfortunately this does not mean that the strategy will make a profit when BTC and ETH prices go down and it might still lose value, but it is designed to lose significantly less than would be lost by holding spot BTC or ETH in a bear market. The strategy can be used to get into the market when prices start to recover after the bear market. The strategy will have no exposure or significantly reduced market exposure when the market is falling but it will take on market exposure when meaningful positive price movements are detected again. The strategy is expected to lose value when the market zig-zags within a narrow range.

The strategy is designed to offer significantly better risk-return characteristics than holding spot positions in BTC or ETH or in an equal combination of them. The positive effects emerge over a longer time period that includes both positive and negative price movements. The strategy will not capture all of the positive price movements but is designed to capture the majority of them and reduce volatility at the same time. The strategy offers those wanting to hold BTC and ETH for a longer time some potentially attractive risk and return characteristics. It can also offer managed market timing for entering the market and applies risk management rules. 

The backtesting statistics can help to compare strategies and can give insights into the risk and return characteristics of the strategy. Such a comparison is only meaningful when the same time or observation period and the same calculation logic and benchmarks are used. The backtesting results are based on historical data and do not guarantee similar results in the future.

•       APY: is the annualized percentage yield, which shows the average return per year
•       Sharpe ratio: measures the risk adjusted relative performance of the portfolio. A higher Sharpe ratio indicates that the portfolio is able to perform better for each unit of risk taken.
•       Profit-loss ratio: gives insights into the profitable and losing trades the strategy generates. It is calculated by taking the average profit from all the winning trades and dividing it by the average losses on all the losing trades.
•       Worst drawdown: shows the largest price drop as a percentage from peak to trough, and is an indicator of downside risk.
•       Annual std: annual standard deviation is a measure of risk showing the annualized volatility of the portfolio. A higher standard deviation indicates higher volatility or risk.
•       Alpha: indicates the strategy’s ability to beat the benchmark in its risk adjusted returns. Positive alpha indicates it is outperforming the benchmark, while negative alpha indicates underperformance.
•       Cumulative profit: shows the total profit from the strategy from its inception until the end of the backtesting period. To give a better comparison, the cumulative profit should be annualized if the observation period is longer than a year.
•       Win rate: shows the proportion of winning trades in all trades during the observation period.
•       Loss rate: shows the proportion of losing trades in all trades during the observation period.
•       Best month: shows the return of the portfolio during the best month in the observation period.
•       Worst month: shows the return of the portfolio during the worst month in the observation period and can be considered one indicator of downside risk.

Please remember that past performance is not indicative of future results. The backtesting procedure for the ClearGate strategies has followed all the best practices of backtesting to reduce the risks of over-optimising the strategies and takes account of actual trade execution costs and slippage.