Bollinger fuel algorithm
Takeaway: The Bollinger fuel algorithm is one strategy that
answer the question of refueling a car (a truck, or any
vehicle that has a known capacity for the fuel and
some user-provided information about how much the
vehicle consumes). The Bollinger comes from a well-known financial technical
analysis instrument called Bollinger’s bands. In this article we will be investigating
the use of Bollinger’s bands in refueling strategies.
“Given uncertain future prices of a commodity, when
should one replenish a fuel tank of a car?”
Limited by: one has to use the car, ie. the tank cannot be a negative number, and
in practise it cannot be even close to zero (too risky).
Bollinger’s bands to the rescue!
Bollinger’s bands are a method to estimate the likelihood
of a current minimum or maximum, based on the variance of
a commodity (a price) in the last D samples.
In this application we try to find out whether using
these limits are useful in deciding when and how much to
take fuel into a car’s fuel tank.
=> local minima exploited => local maxima avoided - does fuel as commodity have the same rules as a stock price (or index)? - economic shocks - how much does the car's fuel capacity affect decisions? And in what way? Algorithm [prototype draft] function BREP(F,stn,C) - Bollinger replenishment algorithm - given Fuel (=amount) - stn list is a list of [x,y,price] stating the known prices in an area - C is a consumption function of some kind -> sets limit to the practical search cutoff value (can't search forever if there's a real consumption)