Sell Wheat like a Monkey

The Detail

One of the questions I get asked regularly is the marketing strategy for selling grain and beating the market. The reality is that there is no one size fits all strategy for selling grain for many reasons.

• Every year is different, with different factors influencing pricing.
• Aspirations for each farmer are different.
• Production risk remains a huge issue for farmers, especially in Australia.

As we move into 2022, I want to discuss strategy a bit more, and I thought I would start off with a more left-field look at gaining an average price.

A few weeks ago, I talked to a friend of ours over in Argentina, Marcelo Artal, who runs a start-up (HedgIT) attempting to bring algorithmic grain trading to grain producers. Algorithms can’t be that hard, so I thought that I would give it a shot.

So what did we do?

I decided to take some inspiration from the Wall Street Journal and their annual monkey picks of stocks to invest in. The WSJ will randomly pick stocks to invest in and then compare against the broader market later in the year.

So we created a virtual monkey with a dart. Our ‘algo’ randomly selects a series of dates to sell grain on a given year.

The results of one iteration can be seen before. Through a series of tinkering and evaluation, around 20 random days give a reasonably good result in getting the average.

Should you use a monkey with a dart to trade?

Absolutely………..not.

I couldn’t advocate, and no one in their right mind should, for using a monkey with a dartboard to select the dates to trade.

The purpose of this article was to show that it’s relatively easy to get close to an average price by just randomly selecting a number of trading days.

If your sales aim to get an average price, then little and often is one option.

If you are unsure of the direction of the market, especially when there is a lot of volatility, the use of smaller chunks in your trading could remove some of the apprehension. You miss the tops potentially, but also the bottoms.

Taking smaller bites and looking at other indicators/factors such as percentiles, seasonality, basis, production status etc, should make it relatively easy to gain an average.

I have saved an iteration for this year, and we will see how it performed at the end of 2022.

The animation below shows 50 different iterations of the Monkey Trading algorithm from 2011 to 2021. While it is generally quite close to average, it is random and will be based on a little bit of luck. There can also be a lot of variation; this could be reduced with refinement through changing the time frames and taking into account seasonality etc.