Long time ago I stumbled across interesting volatility measurement at quantifiableedges.blogspot.com. The idea is following: take 3-day historical volatility of S&P 500 index and divide that by 10-day historical volatility. Then mark all points which are less that 0.25 and measure the volatility of 3 following days. On average, the volatility of following 3 days
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3 weak days in a row
Recently, Trading the odds posted one of many flavors of mean reverting strategies and I decided to get my hands dirty by writing R code and testing it. You can find full description of the strategy by following latter link above. Long story short – if SPY shows lower open, high and close 3 days in
Millionaire’s advice
Viktor Uspaskich is euro-parliamentarian delegated by Lithuania. His was born in Russia, Arkhangelsk Oblast and later on he moved to Lithuania where he made his fortune and first millions. Recently, I saw an interview with him and I found interesting to test his claim, that gold and oil are negatively correlated. Meaning that, when the price
Mean reverting strategies and volatility
Mean reverting strategies are beating on mean reversion of the prices. There are various flavors of mean reverting strategies, but as a proxy I chose RSI(2). You can find many entries on blogosphere about this strategy, but nowadays its popularity dried up. What made me wondering is that there was an idea about correlation between return
Intraday volatility of OMX Baltic stocks
Usually, intraday volatility exhibits a “smile” – it is high at open and close and it is lower during the trading day. DJI index, 5 min. intervals, CET time: MOS stock, 5 s. intervals, CET time: Because many readers of this blog are trading Nasdaq OMX Baltic stocks, it is worth to share my findings about volatility in
R package Blotter
How many times have you been disappointed by nice trading system, because neither trading cost or slippage or bid/ask spread were included into back-test results? Did you find difficult to back-test a portfolio in R or many portfolios with different stocks? Blotter package is supposed to solve these problems. In really – it is complicated. I
Predicting April month return
Bespoke blogged about average monthly returns of the DJI and emphasized April. Before jumping on that information, let’s check some weak points. In that post, only average returns are presented. We need at least extreme points (min;max) and confidence ranges. Second problem – the normal market have upward trend and we need to get rid of
Returns on Easter week and one week after
Inspired by CXO group report, I did a rerun of the same strategy on my data. Easter’s dates can be find at wikipedia. Overall, my results are similar to CXO group’s results. In the graph below, I plotted daily returns on Easter week (Monday to Thursday) from 1982 to 2009. I prefer this way of showing
Strategy: what if SPY & VIX are up?
Recently, I was busy testing the following strategy: If SPY and VIX daily returns are positive, then short SPY at close and keep it for one day. The strategy is dump simple and it has very good feature – short side. There are not so many successful short side strategies. For testing purpose I used daily Yahoo
End of the month investment
It is know, that the first day of the month provides bullish edge. According to Quantifiable edges not all the months are equal. So, I made a test on S&P500 index, from January, 1980 until February, 2010. It is true, March isn’t the best month to run this strategy. Only 3 months have significant results
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
