THURSDAY

Too Long; Didn't Read:

The Answer is Thursday...

or maybe Tuesday...

or Wednesday...

or Friday...

But definitely not Monday.

Pİp Cİrn Trader

Mr GiggleWorth Futures Blog

so you just know, the data that follows is bound to be wrong in one way or another.

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No warranty.

No guarantee.

Yada. Yada.

But none of that really matters.

For, the question of the moment remains:

Still, if you pinned me down and forced me to answer,

that answer would most definitely be sell on Thursday,

or some other weekday out of the bunch,

except for Monday,

which rather than selling,

turns out to be a great day on which to buy.

For these first graphs, the procedure was simple enough:

- For all trade data (corn or soybeans)
- Filter so only trades in the issue year remain (2014 trades for CZ2014)
- Group trades by month
- Select best or worst trade in each month
- Tally which day of the week on which those trades took place
- And then, graph

```
day = [0,0,0,0,0]
```

for year in range(start,2015):

for month in range(1,month):

tF = dF[dF.index.year == year]

tF = tF[tF.index.month == month]

tF = tF.sort("Settle")

if best:

day[tF.tail(1).index.weekday] += 1

else:

day[tF.head(1).index.weekday] += 1

Code, which well may mean nothing to you, so take it for granted that every

**
Best Day in Month
**

**
Worst Day in Month
**

And there you have it. Monday is looking pretty good, so we can all go home now...

Or, you know, since Monday is also looking pretty bad (the best of the bad, I'll have you know, whatever that means), maybe Friday would be a bit better (at least it's good is good and it's bad is bad). Um, whatever.

Thankfully, there's more than one way to slice the data, so no need to worry, we'll get to the bottom of this and prove beyond all reasonable (and unreasonable doubt) that Thursday is the

So with that said, after you've extracted whatever wisdom there is to be had in the graphs above, please continue on to the next section.

In truth, I started my analysis with the tables that appear at the bottom of the page, but tables are boring, so I figured I put all the graphs up here where they'd catch the eye first.

The eight graphs that follow are a straightforward visualization of the raw data used throughout this page and from which we're trying to extract some kind of meaning. We're all familiar with those time series trade graphs showing the settle price of a commodity over time. Well, these are the same thing, only instead of displaying the price of Corn vs Soybeans or Corn vs Oil, the following show the Settle Price over time for Monday, Tuesday, Wednesday, Thursday, and Friday.

The top row is corn, soybeans on the bottom.

Smaller and smaller periods of time are spanned from left to right.

Note: the data for corn spans 1960-2014, whereas soybeans only spans 1970-2014, because that's all I could find (after an exhaustive fifteen minute search).

**
Settle Price of Corn Broken Out by Day of Week
**

**
Settle Price of Soybeans Broken Out by Day of Week
**

So, right there. Thursday, clearly the best day...

Oh, wait. That's the next set of graphs.

- For all data in range
- Split data by day of week
- Sum each day
- Divide by number of items for that weekday
- Subtract out the amount of the overall trade average
- And finally, graph

```
Monday = sum(mondayData)/len(mondayData) - average(allData)
```

And if that's still not clear (and why it wouldn't be is beyond me), this is pretty similar to the first set of graphs, only we're using All Trade Dates in the range (SX2011 excluded) and getting a weighted average of the settle price on each day of the week.

**
Simple Best Day by Sum Average Method
**

And comparing these to the first set gives... almost no correlation whatsoever. But if one values this analysis, Monday clearly is a good day to buy (crappy day to sell on); and Thursday is looking like a pretty solid sell date (green as it is).

So, let's not muck this thing up with any dissenting opinions. Once again, Thursday, clearly the winner!!!

- For all trade data (corn or soybeans)
- Filter so only trades in the issue year remain (2014 trades for CZ2014)
- Filter data to include only the top (or bottom) x percent of samples (0.01 = 1%)
- Group by day
- Find averaged for each day
- Subtract the average of all trades remaining after the filters
- And finally, graph.

```
data = data.where(issue == year)
```

data = data.where(data == top_10_percent)

Monday = sum(mondayData)/len(mondayData) - average(data)

And from that, we get the following charts (from most exclusive to least) with the last being the ideological reverse of the first (i.e. limited to the worst of the worst).

Oh, and just by-the-by, one of the ways a person could tell that they're looking at some (complete and utter) degree of randomness (if not out and out chaos) is the way Friday behaves in the last chart on each line. Up and down, up-up-up-up-up, and finally down. Predictive value equals nada.

Thursday on the other hand: golden.

**
Best 0.01
**

**
Best 0.05
**

**
Best 0.10
**

**
Best 0.25
**

**
Worst 0.25
**

**
Worst 0.10
**

**
Worst 0.05
**

**
Worst 0.01
**

It should be noted that since the best trades come from the overall data, these graphs are intrinsically weighted towards times when the price of the underlying commodity is higher (for best) and lower (for worst). The price CZ1960 varied from around $107 to $111 or so; and thus, data from this year dominates the worst figures, while more modern numbers (and their corresponding higher daily averages) dominate the best numbers. This isn't necessarily a bad thing, as it means the

Anyway, from all this, I like Thursday. But then, I don't trade, so seriously, what do I care.

In looking at the question of which day is the

The methodology for this here T-Test for Correlated Pairs is:

- Take a bunch of trade data (corn or soybeans)
- Sort it into week-long blocks
- Run a t-test on the derived values Monday vs Tuesday, Monday vs Wednesday (and so on and so forth) to get a p-value and determine the significance.
- Subtracting the average settle amount for each date to determine a magnitude of effect
- And then, tabulate that sucker up!!!

But then, the p-value is only part of the story, as one can have statistically significant results that no one cares about, so we have a magnitude column. The -0.443939 on the first column represents the Average Settle Price on Monday

Monday | Tuesday | Wednesday | Thursday | Friday | pValue | magnitude |
---|---|---|---|---|---|---|

517.451515 | 517.895455 | 0.212669 | -0.443939 | |||

517.451515 | 517.70303 | 0.376590 | -0.251515 | |||

517.451515 | 517.381818 | 0.818248 | 0.069697 | |||

517.451515 | 516.757576 | 0.022114 | 0.693939 | |||

517.895455 | 517.70303 | 0.081895 | 0.192424 | |||

517.895455 | 517.381818 | 0.031233 | 0.513636 | |||

517.895455 | 516.757576 | 0.013165 | 1.137879 | |||

517.70303 | 517.381818 | 0.696113 | 0.321212 | |||

517.70303 | 516.757576 | 0.022363 | 0.945455 | |||

517.381818 | 516.757576 | 0.020105 | 0.624242 |

If the above makes sense to you, please feel free to skip over the first row walk through that follows.

517.451515 is the average Settle price for this Issue on Monday

(sum of each Monday's settle price / number of trade dates that were on a Monday)

(note how this is the same number for all Monday entries)

517.895455 is the average Settle price for this Issue on Tuesday

(the first row has nothing to do with Wed, Thur, Fri, so these lines are blank)

pValue for Monday vs Tuesday

(pValue for Tuesday vs Monday is the same)

(a pValue of 0.212 isn't very good)

(pValue=0.818 is the least significant p-value: Mon-Thur)

(pValue=0.013 is the most significant p-value: Tue-Fri)

Finally, the average settle price on Monday

In other words, if one sold on Monday, one would have gotten 44c less on average than if they sold on Tuesday, which means, that if one had sold on Tuesday, one would have gotten 44c more (per contract) than if one had sold on Monday.

Simple, really.

Same logic, different data sets for the tables that follow.

Low p-values are the best (most likely NOT due to chance).

Monday | Tuesday | Wednesday | Thursday | Friday | pValue | magnitude |
---|---|---|---|---|---|---|

508.372605 | 508.688378 | 0.350577 | -0.315773 | |||

508.372605 | 509.38825 | 0.173412 | -1.015645 | |||

508.372605 | 509.046616 | 0.014682 | -0.674010 | |||

508.372605 | 508.311622 | 0.014805 | 0.060983 | |||

508.688378 | 509.38825 | 0.025619 | -0.699872 | |||

508.688378 | 509.046616 | 0.018526 | -0.358238 | |||

508.688378 | 508.311622 | 0.023642 | 0.376756 | |||

509.38825 | 509.046616 | 0.182308 | 0.341635 | |||

509.38825 | 508.311622 | 0.374430 | 1.076628 | |||

509.046616 | 508.311622 | 0.023810 | 0.734994 |

Monday | Tuesday | Wednesday | Thursday | Friday | pValue | magnitude |
---|---|---|---|---|---|---|

1198.194286 | 1198.075714 | 0.940657 | 0.118571 | |||

1198.194286 | 1198.73 | 0.017068 | -0.535714 | |||

1198.194286 | 1198.661429 | 0.014698 | -0.467143 | |||

1198.194286 | 1197.327143 | 0.013417 | 0.867143 | |||

1198.075714 | 1198.73 | 0.018595 | -0.654286 | |||

1198.075714 | 1198.661429 | 0.017434 | -0.585714 | |||

1198.075714 | 1197.327143 | 0.016414 | 0.748571 | |||

1198.73 | 1198.661429 | 0.115698 | 0.068571 | |||

1198.73 | 1197.327143 | 0.046024 | 1.402857 | |||

1198.661429 | 1197.327143 | 0.029681 | 1.334286 |

Monday | Tuesday | Wednesday | Thursday | Friday | pValue | magnitude |
---|---|---|---|---|---|---|

1160.338774 | 1160.598726 | 0.099886 | -0.259952 | |||

1160.338774 | 1161.324841 | 0.026171 | -0.986067 | |||

1160.338774 | 1162.039013 | 0.015582 | -1.700239 | |||

1160.338774 | 1161.269904 | 0.012297 | -0.931131 | |||

1160.598726 | 1161.324841 | 0.020731 | -0.726115 | |||

1160.598726 | 1162.039013 | 0.014790 | -1.440287 | |||

1160.598726 | 1161.269904 | 0.013819 | -0.671178 | |||

1161.324841 | 1162.039013 | 0.052408 | -0.714172 | |||

1161.324841 | 1161.269904 | 0.016025 | 0.054936 | |||

1162.039013 | 1161.269904 | 0.016307 | 0.769108 |

Do we need an analysis?

OK. Look at the second to last chart, SX2014, Wed vs Fri, there's a $1.40 difference in contracts per day. In the financial world, that's literally a fortune waiting to be made. Why, if you'd put x dollars, on y contracts, you'd wind up with something like z dollars and never have to work another day for the rest of your life. Let me say that again (if not slowly, at least in capitalized bold):

!!!ANOTHER DAY FOR THE REST OF YOUR LIFE!!!

!!!

Unfortunately, what was true in 2014 was not true for the range 2010,2012-14 during which the difference per contract drops to $0.05, which means if the spread was up by $1,40 in 2014, it was down by something like -$0.50, in each of those other years.

So, what does this mean?

Glad you asked. It means: Thursday!!!

So, here's another methodology, which as far as I'm concerned, is an equally valid way of looking at the data.

- Separate the data into weekly groups
- From each group select the
*Best*and*Worst*day - Tabulate how many times each day of the week is in the
*Best*or*Worst*Group - And then, subtract the weekly average close from the
*Best*and*Worst*to get a relative magnitude of difference

Monday | Tuesday | Wednesday | Thursday | Friday | |
---|---|---|---|---|---|

Number Trades | 182 | 202 | 199 | 198 | 197 |

Trade Average | 517 | 516 | 519 | 518 | 518 |

Best Day | 47 | 29 | 35 | 43 | 50 |

Over Average | 295 | 177 | 192 | 212 | 245 |

Worst Day | 52 | 31 | 30 | 36 | 55 |

Under Average | -321 | -172 | -145 | -233 | -383 |

Net Difference | -26 | 5 | 47 | -21 | -138 |

This should be pretty self explanatory:

(the simple stuff usually is)

The data is grouped by day of week into columns across the top and then down the side in rows we have:

Total Number of Trades Recorded:

(This is greater than 50, because issues trade for longer than a year)

Trade Average:

(sum of the daily settle divided by total number of trades)

Best Day:

(number of times this day was the weekly best)

Over Average:

(cumulative gain to be had buying one contract on this day and selling at the weekly average, i.e. best day settle less the weekly average settle)

Worst Day:

(sort of like the Best Day, only not, it's the worst)

Under Average:

(cumulative loss)

Net Difference:

(Over Average plus Under Average: the most interesting thing being how often this is a negative number, whereas I would have expected this number to tend towards zero)

Monday | Tuesday | Wednesday | Thursday | Friday | |
---|---|---|---|---|---|

Number Trades | 875 | 950 | 954 | 939 | 931 |

Trade Average | 507 | 507 | 508 | 508 | 508 |

Best Day | 216 | 128 | 167 | 187 | 274 |

Over Average | 1871 | 1036 | 1065 | 1230 | 2165 |

Worst Day | 263 | 174 | 129 | 140 | 266 |

Under Average | -2144 | -1318 | -780 | -1058 | -2505 |

Net Difference | -273 | -282 | 285 | 172 | -340 |

Monday | Tuesday | Wednesday | Thursday | Friday | |
---|---|---|---|---|---|

Number Trades | 190 | 208 | 206 | 204 | 205 |

Trade Average | 1199 | 1193 | 1200 | 1201 | 1198 |

Best Day | 47 | 37 | 36 | 33 | 57 |

Over Average | 605 | 349 | 315 | 335 | 640 |

Worst Day | 47 | 37 | 31 | 31 | 64 |

Under Average | -568 | -420 | -268 | -308 | -863 |

Net Difference | 37 | -71 | 47 | 27 | -223 |

Monday | Tuesday | Wednesday | Thursday | Friday | |
---|---|---|---|---|---|

Number Trades | 685 | 748 | 749 | 734 | 729 |

Trade Average | 1159 | 1158 | 1160 | 1162 | 1161 |

Best Day | 154 | 133 | 120 | 126 | 222 |

Over Average | 2404 | 1586 | 1237 | 1648 | 3263 |

Worst Day | 201 | 134 | 110 | 92 | 218 |

Under Average | -2995 | -1712 | -1092 | -1217 | -3533 |

Net Difference | -591 | -126 | 145 | 431 | -270 |

Anyway, after staring at all that for a while, I'm will to state:

- The difference between best day and worst is only a dollar or two.
- And the day of the week that is the best day, varies from year to year.

Or what the heck am I saying.

So, um, who knows?

Maybe I've got the wrong test.

Or maybe my logic is off.

What I do know is that I am partial to the

And anyone who knows me, knows I am quite happy to express strong opinion on things of which I know next to nothing based on superficial data and limited analysis. Add to that the fact that I don't trade, and I think it's all random gibberish (noise in the words of my statistician friends), I'd have to say, nay, declare most enthusiastically:

to

Dear Old Uncle Brett

unless, of course, that advice is to give me

10% of every trade that you make

Corn Index

Brett Stuff - Home

İ 2014 copyright Brett Paufler

Brett@Paufler.net

Sometimes you get less.

Just remember, this site was free.

Unless, of course, you're paying me that 10% honorarium

In which case, let me be the first to say:

You've been had!

Or, I mean, to say:

Thank you!

Thank you, very much, indeed!

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