Category Archives: R
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.
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.
Lately I have been doing calendar analysis of various markets (future contracts). Not an overly complicated task, but has a few interesting angles and since I haven’t seen anything similar on the Net – here we go.
Markets are very smart in absorbing and reflecting information. If you think otherwise, try making money by trading. If you are new to it, make sure you don’t bet the house.
In other words, markets are efficient. At least most of the time. So then why people trade? The general believe is that there are windows during which prices of certain assets are inefficient. Thus, there are opportunities to make money. Is the presence of autocorrelation one such opportunity? Let’s find out.
Life has been busy and has kept me away from blogging, and from trading, mostly. Still, I can’t stay away from monitoring the markets, and, with the recent rally, I started asking myself – has the situation changed since the 200 day SMA signaled an exit. What do you think – make up your mind before reading further.
In the previous post, I went through a simple exercise which, to me, clearly demonsrtates that 60% out of sample guess rate (on daily basis) for S&P 500 will generate ridiculous returns. From the feedback I got, it seemed that my example was somewhat unconvincing. Let’s dig a bit further then.
One statistic which I find useful to form a first impression of a backtest is the success/winning percentage. Since it can mean different things, let’s be more precise: for a strategy over daily data, the winning percentage is the percentage of the days on which the strategy had positive returns (in other words, the strategy guessed the sign of the return correctly on these days). Now the question – if I see 60% winning percentage for a S&P 500 strategy, does/should my bullshit-alarm go off?