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Why does maximizing returns perform worse than maximizing (price - price*returns)

I don't understand why maximizing returns yields much worse performance than maximizing (price - price*returns).

I've attached my returns maximization algorithm here.

7 responses

And here's the maximizing (price - price*returns)

You observe that Max(returns) results in worse performance than Max(price - price*returns) = Max(price*(1-returns)).
Honestly i don't know the answer, but i think your returns are 1-day returns, right?
So that seems to me to imply that there is a tendency for higher prices (positive 1 d returns) generally to be followed by lower prices (negative 1d returns), which is consistent with a general tendency toward short-term Mean-Reversion type of behavior. Does that make sense?

It makes sense in theory, but I've extended the time window and am seeing the same thing.

They are one day returns but I get similar results for 20 days (pure returns is only down 20%). For 200 days pure returns has a positive return (about 5%) and (price - price*returns) has returns of ~10% - slightly worse than the shorter time windows.

One question. What is the term price - price*returns or, written another way price*(1-returns), supposed to represent? It's probably obvious but help me out.

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It's mean reversion adjusted price. The price of yesterday

I am very sorry. Still don't get it. Wouldn't yesterday's adjusted price be

price_yesterday = price / (1 + returns)

# Start with the definition of returns then do some algebra...  
returns = (price - price_yesterday) / price_yesterday  
returns = (price/price_yesterday) - (price_yesterday/price_yesterday)  
returns = (price/price_yesterday) - 1  
1 + returns = price/price_yesterday  
price_yesterday = price / (1 + returns)

Yes, by means reversion adjusted price I mean that the returns revert to the mean - so if the returns were X yesterday tomorrow they will be X-1 (obviously this is not exact - but it proves to be a better predictor than returns alone). Sorry if I wasn't clear.