For quite some time now I have been using R’s caret package to choose the model for forecasting time series data. The approach is satisfactory as long as the model is not an evolving model (i.e. is not re-trained), or if it evolves rarely. If the model is re-trained often – the approach has significant computational overhead. Interestingly enough, an alternative, more efficient approach allows also for more flexibility in the area of model selection.
Is trading worth doing for living? Once in a while I am asked this question and, to me, going through this mental exercise makes sense. A lot of sense. So let me share my thoughts.
One approach to trading which has been puzzling me lately, is to sit and wait for opportunities. 🙂 Sounds simplistic, but it is indeed different than, for instance, the asset allocation strategies. In order to be able to even attempt taking advantage of these opportunities, however, we must be able to identify them. Once the opportunities are identified – we can try to explain (forecast) them using historical data.
There is abundance of claims that markets (by definition US markets) are rigged. Rigged in the sense that orders are often executed in a way, which contradicts to the natural intent of the person/machine behind the order. Until now, I have dismissed most of these as (populist) speculation, or, conspiracy theory. There is some truth in it however and my feeling is that it’s detrimental to all participants, yes, even to the “winner” in the examples I am going to show.
It is a common knowledge that Bollinger Bands (price standard deviation added to a moving average of the price) are an indicator for volatility. Expanding bands – higher volatility, squeezing bands – lower volatility. A bit of googling and you get the idea. In my opinion – that’s wrong, unless, one uses a twisted definition of volatility.