# 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)

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