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"Weekend Dumps"" and "Settlement Pumps"? Here's a look at the statistics of price changes by hour and day.
I was on my way to work today, and as I glanced at the price I thought, “Man! It seems like every time I’m driving to work I see more price movement than usual!”
I think this is a pretty common thought for a lot of people; that price movements happen more at certain days/times. We hear people around here talking about a “weekend dump”, or a settlement “pump” all of the time. I think I read a comment last week that some guy was going to start “buying right before China wakes up”, because “the price usually goes up”.
So this has had me thinking for a while: Is there really any statistical differences in price movements based on the day of the week or the time of day? I finally took some time today to find out.
Full disclosure: I am no statistician and it’s been a while since my college courses, but I think I got this mostly right. Maybe someone with more expertise can help us out or fix mistakes I might have made, but here it goes:
I chose to look at BITFINEX data over the past year (starting 11/24/14 UTC), because this seemed like a reasonably significant length of time. I also knew I wanted to look at a “long” and a “short” time interval for price changes. I chose to look at the daily price changes as my long interval, mostly because I thought any “Weekend dump” signals would show themselves in this data set. As my “short” interval, I chose to look at the hourly price changes, because it seems like that’s the sort of time period people referring to “Settlement pumps” are on.
So the first thing I did was sort the data by day, and generated an interval plot and a box plot of all of the price changes:
Remember from stats that the interval plot shows the data sets’ central tendency and variability. The dot represents the mean of the data set, and the bars represent the 95% confidence interval of the data set. The box plot also looks at the central tendency and variability, but in a slightly different way. The center line is the median, the box shows the middle 50% (interquartile) of the data, and the whiskers show the loweupper 25% of the data (excluding outliers).
So right off the bat, these data sets are looking pretty darn similar. Wednesday might show some positive skewness, but the overlapping of the confidence intervals (in the interval plot) suggests that the day of the week alone is not responsible for variation in the continuous data set. An analysis of variance (ANOVA) confirms a P value above 0.05:
That is, our continuous data indicate there is likely no statistically significant difference between the data sets (see here for more information on interpreting P-values).
However, I wanted to look at the data a few other ways as well:
So you can see that the average daily price change is the most positive on Wednesday and the most negative on Friday. These simple averages can be thrown off by outliers easily, so what I found much more interesting is the last graph: That the price closed at a gain 67.3% of the time on Wednesday’s, and closed at a loss 66.7% of the time on Fridays (UTC) over the past year.
Huh, maybe there’s something there…. So I changed up my continuous data set from the actual price change, to simply an ”Up” or a ”Down” based on whether it closed at a gain or a loss. This is now a categorical data set, and can be tested for independence using the Chi-Square test.
Here are two resources explaining this statistical test in more detail:So after counting the amount of “Up” and “Down” occurrences, I put together the observed/expected data tables and ran the test:
With a P value below 0.05, we should reject the null hypothesis. That is, we can conclude that there is a relationship between the day of the week and how likely the price is to close at a gain. Although, keep in mind it says nothing about the magnitude of the gain at all.
I’ve been trying to rationalize why we weren’t able to distinguish days from the continuous data, but there is a statistical difference between the categorical sets. It must be that the variation in the continuous data sets is large and similar, and we cannot confidently conclude that the differences aren’t produced by chance alone.
So I’ll save the whole rigmarole, here is the same analysis for the hour of day (UTC):
Interval plot/box plot of continuous data
ANOVA of that data
Other plots of that data
Chi-Square test of the categorical data set
Some other things I’d be curious to look at (both continuous and categorical):
-What does the price change data look like for data only following an interval that closed at a loss? A gain?
-Other time intervals, or intervals on only one day.
-Price changes after chart patterns
Anyhow, I thought this was pretty interesting. I'd like to hear what you guys think.
Data analysis - weight loss (only 14 days)
Data was taken from myfitnesspal and analyzed with minitab.
- scatter plot with weight (Peso) in kg. sorry folks no lbs /stone
- Multiple scatter plot with carbs (carbohidratos), fat (grasa) and protein.
- Multiple scatter plot with % of total calories form breakfast (desayuno), lunch (almuerzo), dinner (cena) and snack (aperitivos)
- Histogram with calories balance =goal - intake
- histogram of carbs, fat and protein
- histogram of breakfast, lunch, dinner and snacks
calories balance is arround +8 calories on average with a sd of 114,1. Not bad at all, but have to decrease the SD.
Carbs are on average on 47,07%, fat on 34,14% and protein 18,79%. Pretty good overall but the were days with spike on fat or protein, that I have to control.
I should lower the amount of calories on dinner since it's on 47,86 +/- 8,42 % of the total calories and increase the lunch.
Hope you enjoy!
I'll update on the 100 days mark.