Hedging decisions based on a structured approach
In the face of volatile agricultural markets, producers must make hedging decisions that take into account their income, costs and margin objectives.
Agrintel’s proprietary algorithm is a decision-support tool that helps producers make informed hedging decisions in futures markets and better manage risks related to their revenues and production costs.
Developed in Quebec, Agrintel’s proprietary algorithm is a decision-making tool that supports positioning on futures markets using a structured method. It does not seek to predict prices and does not automatically execute trades.
The coverage recommendations are part of a personalized support that takes into account the reality and financial capacity of each farm.
Market conditions change over time. Economists and agronomists participate in the regular evaluation of Agrintel’s algorithm.
- Emerging economic indicators and market trends
- Supply chain disruptions and climate events
- Policy changes affecting agricultural markets
- Seasonal patterns and historical volatility data
Combining algorithmic precision with expert oversight is essential to the system’s effectiveness. Hedging recommendations are considered alongside each farm’s circumstances, production cycles, objectives and financial capacity.
This dual-layer approach – automated analysis plus professional judgment – helps connect structured analysis with the producer’s circumstances.
This technology provides producers with structured decision support for agricultural market risk management.
Independent producers gain:
- Professional-grade market analysis capabilities
- Reduced exposure to price volatility
- Support for more informed margin and liquidity planning
- Enhanced operational autonomy in managing market risk
- Strategic alignment with Quebec and Canadian agricultural competitiveness goals.
The algorithm serves as a decision-support tool that helps producers to make informed, timely hedging decisions while maintaining full control over their risk management.
Combined with the Aggregate Pork approach, it helps analyze the interrelated exposures related to pigs, piglets, corn, soybean meal and the Canadian-American exchange rate.
The Pork Aggregate brings together five interdependent components:
- Hog sales revenue.
- Piglet purchase costs.
- Corn costs.
- Soybean meal costs.
- The Canadian–U.S. exchange rate.
This approach makes it possible to analyze their combined effects.
FAQ
No. The Agrintel algorithm is not intended to predict agricultural market prices.
It is a proprietary decision-making tool, developed in Quebec, that supports positioning on the futures markets using a structured method.
Its role is to help prepare hedging decisions and limit the influence of emotional reactions, in addition to personalized advice.
A decision support tool analyzes information and supports the preparation of decisions. An automated trading system executes trades according to defined rules.
The Agrintel algorithm belongs to the first category: it contributes to hedging recommendations, but does not automatically execute trades.
Decisions must also take into account the producer’s situation, including volumes, timelines, objectives and financial capacity
The Pork Aggregate brings together five interdependent components:
- Income from the sale of pigs.
- The cost of purchasing piglets.
- The cost of corn.
- The cost of soybean meal.
The Canadian-U.S. exchange rate.
This approach allows for the analysis of their combined effects on the feed cost margin rather than looking at each price in isolation.
Hedging decisions do not depend solely on market developments.
They must also take into account the characteristics of the farm, the volumes involved, the deadlines, the financial objectives and the producer’s ability to support its positions.
At Agrintel, the algorithm supports the work of advisors. Economists and agronomists also participate in its regular evaluation.
Technology supports decision-making; it is not a substitute for professional judgment or knowledge of the terrain.