Corn Edition: just how good is our platform at predicting prices?
One of the most common questions we get from prospective customers and reporters is, “Sure, you predicted the increase in cocoa prices - but how well does the Helios platform do against other soft commodities?” This is the first (of many) blog posts where we seek to answer that question head-on, namely: How effective is our platform at predicting the price of soft commodities? If you had been using our signals over the past 10 years, how accurate and profitable would they have been?
In this blog post, we tested the predictiveness of Helios’ climate risk signals against Corn prices, to identify the optimal long only trading strategy. Key points for the backtesting:
Helios risk signals are leading indicators for price - the higher the risk, the more likely it is that prices will rise (i.e., a higher climate risk signals a higher likelihood of crop disruptions)
Every trade was for a single contract - we did not increase or decrease the size of our bets based on the strength of the risk signal
We initially looked at a “long only” strategy, where we would enter a trade after the weighted risk signal surpassed a certain level (y-axis, or rows) and then exited when it passed back below another level (x-axis, or columns)
As you can see in Figure 1, our risk signal becomes predictive above the 25% weighted risk threshold, and becomes even more accurate the higher it goes. This means that for almost all trades above this threshold, the risk signal is a leading indicator of price increases and a trader would make money.
Figure 1: Accuracy of trading signal (i.e., how often did this trade make money?)
Figure 2 quantifies the absolute dollar return of each of these trading strategies in a ten year period. Again, the stronger the risk signal the more money each strategy is able to make. We calculated this as the total dollar return of buying and selling a single contract. For example, every time the weighted risk went over 45, a single contract was bought, and then exited when the signal dropped below 10.
The sum of all of these trades over a ten year period was $10,350, which was calculated by adding the profit/loss of each individual trade over this period. For example, if the weighted risk signal went over 45 we would have purchased a contract at its full price. Let’s assume it was $412. When the risk signal dropped below 10, we would have exited this position. Let’s assume the price then was $422. The “profit” from this trade would have been our exit price ($422) minus our entry price ($412), so $10. We then added all of the profit/loss outcomes for this particular trade strategy over the last ten years, to come up with the $10,350 figure. Note this is absent any implied leverage (i.e., we assume in this strategy that we paid the full amount for a single contract).
Figure 2: How much money would have been made in a ten year period?
Intuitively, you would expect our risk signals to predict the price of soft commodities. We have built the most comprehensive system of climate risk signals ever (14M+ locations; individual ML models for each commodity), which are often the primary driver of commodity prices. The importance, and strength, of climate risk signals will only continue to increase as the impacts of climate change worsen. Already, we are seeing greater price and climate risk volatility than ever before.
Similar to the rise of quantitative trading algorithms, climate risk and artificial intelligence are becoming table stakes for institutional commodity traders. So if you’re a commodity trader that isn’t using AI-based predictive climate risk signals (not basic weather data), get moving! And, you know, reach out :)
