I created a portfolio rebalancing strategy, that I am currently paper trading with. It is, primarily, based on mean-reversion principle with a few rules in place, and geared towards cryptocurrencies, in general.

I double the commission to account for slippage as well as when rebalancing I first sell all assets I am holding and then, buy them even if same. This adds a few pips of random slippage as well, at the least.

Here are a few plots I obtained from this strategy in backtesting. I used 1h as well as 1d ticks from Bittrex.

1d timeframe backtesting result with a 0.5% (0.25% bittrex + 0.25% slippage) commission fee:

1d timeframe backtest results - 0.5% commission

1hr timeframe with 0 fee:

1hr timeframe - 0 fee

with 0.2% fee (binance 0.1% plus 0.1% slippage): 1hr timeframe - 0.2fee

with 0.5% fee (bittrex 0.25% plus 0.25% slippage): 1hr timeframe - 0.5fee

Now, since the chosen domain does not provide extensive history, I am unable to test this strategy on a longer timeframe.

And, therefore, I would like to know how I can ascertain that the strategy is indeed useful? I, particularly, would like the 1hr timeframe equity curve, but adding commission there ruins everything.

  • $\begingroup$ It looks good. The only problem with this kind of rebalancing or "volatilty pumping" strategy (other than transaction costs which you have already addressed) is the risk that one or both assets will go to zero. $\endgroup$ – Alex C Apr 17 '18 at 2:06

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