How is the Hectare Trading Price Score calculated?

The Hectare Trading price score is found on the Daily Prices tool and attempts to give a quick, summarised context to the current price. It is currently displayed as:

The price is summarised by three factors, displayed in the full bullet points:

How the price compares to recent prices (in the last 52 weeks).

We use this to general feel of the price, E.g. are we currently in a seasonal high? This is the level of information our sellers most commonly use. Think Noggers' "this is the price at the moment" level of context.

How the price compares to seasonal performance (in the same month in previous years).

This provides a deeper context surrounding how the market normally performs at the same point as the price was offered at this point in the year. E.g. Yes, I'm in a seasonal high, but is the price better what I can normally expect this time of year

How the market is moving and it's momentum.

While our little arrow can tell us the difference in price from the previous day, it doesn't give us a great idea if our price is part of a rally or a decline. Or if the price might be on the point of turning, this is what this point attempts to display.

Let's dig into how these are calculated:

Recent Price Comparison

The recent price comparison compares the most recent bid price to all bids made in the last 52 weeks. For this example let's say we were generating this bullet point for price of Feed Wheat on the 28th November 2024. This bullet point would consider all bids made between 29th November 2023 - 28th November 2024.

The last price for the day is £178.5 is this is the price we will compare to the rest of the prices in the 52 week range.

We then split the prices over the last 52 weeks into percentiles. To get our bid percentiles we split the prices in 100 equal sized groups by plotting them in order of price. We can then we can see which percentile our price falls into.

For our example this looks would look like:

Don't panic! While it looks very different, all we have done is plot the bids from above in order of their price rather than in time series. We can now group the bids into 100 bins which make up our percentiles. For example, on the chart we can see the 5th, 25th, 50th, 75th and 95th percentile. In reality, all this means is that if bid a for example: £165 or lower, it is in the bottom 5% of bids. Where any bid of £200 or more would be in the top 65% of bids. We can then put any price and get it's position compared to other bids in a 52 week range in this percentile format.

Let's plot these same bids back to our time-series chart with our percentile groups.

We can see that our latest bid price of £178.5 sits in the bottom % of bids. To try this for other values, move the price range to create and bid and see where your bid price falls.

Seasonal Price Comparison

The seasonal comparison applies the exact same comparison via percentiles as the above section. But only for bids made in the same month as the price that is being compared. For example, for our bid made on the 28th November 2024, we would compare price to all bids made for the same commodity in November 2024, November 2023, November 2022 and so on.

We can then sort and calculate quantiles like we did before:

We can see that this price is in the bottom 2% of prices offered for Spot Feed Wheat in November! 😱

Market Direction

Finally, the price score offers a quick summary on the direction the price is travelling and how strong the price is. This is calculated with two indicators used frequently in trading. The Moving Average Convergence Divergence (MACD) gives us an indication of market movement, and the Relative Strength Index (RSI) approximates how strong the price is.

In the actual page, this is calculated using the TechnicalIndicators package (alternatively I've been told Tulip Indicators is also good).

Calculating the MACD

The MACD is calculated as the difference between the 12-period and 26-period exponential moving averages (EMAs) of the price series:

Now that sounds scary, there's even an equation! Don't worry, it's quite simple in practice, let's break it down:

• First of all, the '12/26-period' part of the equation just refers to how many values there are in the average. We'll be using our daily prices to create our EMA, so the period is just the day. So you can think of this as the 12 day EMA and the 26 day EMA.

• But what is EMA? The equation to calculate is very intimidating, but all you need to is that the EMA is just a type of average that weights more recent values more than older values.

Again, don't worry if all that in the scary grey box above seems like an alien language. Basically what the EMA is giving us is a average price value which is very sensitive to recent changes in price and the difference between them gives us the MACD. Let's plot the MACD for our spot price data in 2024.

As we can see above, when the price rises rapidly like from April to May, the MACD score rises as well. But you can also see that as the price declines in June, the MACD is still high, which wouldn't give our users a good summary of market conditions. To summarise the momentum of the market we need to include a signal line. The signal line is calculated by applying calculating the 6 day EMA of the MACD. I know, real inception stuff. Click the box below to add the signal line.

The difference between the MACD and the signal is the key to getting an idea of how rapidly the price is rising or falling, when these lines meet each other and converge, the price is at the turning point and may be the key point before a rapid increase of decrease. This difference is known as the MACD Histogram. Let's plot this below.

We can see on the chart above that as the typical spot price goes through a rapid period of growth until mid June, the MACD Histogram is very high (the signal and MACD are diverging). But as the growth rate slows towards the beginning of June the histogram value begins to fall to zero (a convergence!). We now have a numeric expression of the direction of the market which we can summarise and show to users. We do this with the following rules:

• MACD Histogram < -2: "is falling extremely quickly"
• MACD Histogram < -1: "is falling quickly"
• MACD Histogram < -0.2: "is falling"
• MACD Histogram -0.2 - +0.2: "may be turning"
• MACD Histogram > 0.2: "is growing"
• MACD Histogram > 1: "is growing quickly"
• MACD Histogram > 2: "is growing extremely quickly"

Calculating the RSI

The Relative Strength Index (RSI) is a very commonly used financial indicator used to measure how strong a price is compared to recent values. It does this by calculating the average gains and loses made when purchasing the commodity over a period time. We calculate the average percentage gained when the price closed higher than the previous day, and divide it by the average lost when the price closed lower than the previous day.

Again, don't freak out! The equation is more simple than you think. The period (or t) is just the number of days you look back across when calculating the RSI. The standard is 14 which is what we use.

For example, imagine the market closed higher seven out of the past 14 days with an initial average gain of 1%. The remaining seven days all closed lower with an initial average loss of −0.8%.

Look at that, a score of 55.55. But what does that actually mean? The RSI is a relative score that ranges between 1-100, in the financial world a RSI of 70 is considered overbought or overvalues, and under 30 as oversold. Which is generally seen as a basic signal of when to possibly buy and sell stock/options. It isn't the most scientific measure, which is why you'll see it worded as 'may be overvalued' etc. That being said, let's plot the RSI for our 2024 spot prices.

While the typical spot price was began at a potentially undervalued state a the beginning of the year, the RSI has been balanced state since then. We now have a numeric metric of the general strength of the price dependant on recent market conditions, we then set some threshold values to summarise this to the user on third bullet point. See below:

• RSI <30: "may be undervalued"
• RSI 30-40: "may be slightly undervalued"
• RSI 40-60: "is a balanced price"
• RSI 60-70: "may be slightly overvalued"
• RSI >70 "may be overvalued"

Combining these metrics

Using these both in combination can give a general signal of when to buy and sell, especially in extreme cases. For example, at the end of price rallies as the price hits a plateau, the RSI will most likely be high, and the MACD/Signal close to 0 in which the score would read "The price may overvalued and the the market may be turning". We could make this a clearer signal that now would probably be a good time to sell.

During periods of continuous growth the RSI will balance to values closer to 50, while the MACD/Signal will continue to show growth, indicating it might be a good time to hold. I've attached below an example of how the MACD/Signal and RSI would have changed over time in comparison with the Nov Movement as well as the text string I'm using currently. We could also split them into two points for price strength and market direction.