Before my family and I moved to a remote swath of the Green Mountain National Forest in Vermont, we lived in NYC’s West Village.
It was quite fashionable and respectable, but without my permission, my kids continued to grow and need more space.
Thus, our burgeoning family was in a dilemma. Do we continue down the path of financing a trading software company, or do we put our savings into a larger apartment?
After many wonderfully crafted debates, I, like many Neanderthals before me, ever so lovingly dragged my wife and kids to The Woods.
We purchased a reasonably priced house and I hunkered down to finish my algo R&D.
The net affect of this move was that massive change was brought down upon every aspect of our daily lifestyle.
One aspect of our ex-urban lifestyle that strikes a dramatic parallel with algorithmic data is dinner.
If you have lived in Manhattan you know that there are two primary methods of getting fed; Ordering in or going out.
Kitchens in the city are for hanging plants and keeping vodka cold.
One of the big lifestyle changes that is thrust upon you when you move to The Woods is caused by the incessant inability to quickly get food. The closest restaurant to our homestead is 25 minutes to the Northeast and the closest supermarket is 5 minutes beyond the restaurant.
If you head south looking for grub, the minimal trek is 35 minutes.
So, we (my wife), cook a lot at home.
In order to regularly feed a family of 4 means that you have to have provisions, you have to have a meal plan, you have to have time allocated to prep the food and cook the food. You also have to clean up afterwards. It is a process.
And as a process that is so critical to running a household, it is better for all parties if it is done efficiently.
This morning, an e-mail from a colleague asking some architectural questions about …sceeto got me thinking about the manual effort that we expend on dinner.
Why do all of the chopping and cooking and cleaning when one could either just pick up the phone, or, go out and eat?
For us, our geographical remoteness, though it offers a great deal of benefits with regards to raising children such as fresh air, no crime, and avoidance of the more blatant detriments of city living, forces us to either cook or to go hungry.
If we had the option of having sushi delivered, we would exercise that option and let others do the more of the grunt work.
I look at algorthmic output the same way.
Why should a trader develop advanced algos, manage the torrent of data needed to run these algos, manage the hardware, and all of the other associated technical chores, just to get output that is already being generated elsewhere?
Probably, because this data, like dinner in The Woods, can’t be delivered upon request.
…sceeto changes most of this dilemma. …sceeto, like a good restauranteur, does all of grunt work for you – the data acquisition, prepping, chopping, cooking, serving, and cleaning.
You get the delicious meal, which is the algorithmic output, without all of the effort associated with preparing it.
Traders don’t want to be software and hardware engineers. They want to trade.
Our philosophy at …sceeto, is ‘give ‘em the data they need and don’t force ‘em to get their hands dirty, and especially don’t ask them to help with the dishes‘.
Hope this helps.
If you are interested in being a beta tester for …sceeto, and you use either NinjaTrader or TradeStation, please drop us a note here.