Our core finance users are commodity hedge-fund desks (systematic and discretionary), macro/commodity portfolio managers at asset management companies, and alternative-data/quantitative research teams that pipe the nightly API/CSV into factor models or event-driven strategies.
These teams rely on Helios’ climate-risk spikes as leading indicators for entries and exits—an approach supported by our multi-year backtests showing 65–71% directional accuracy and $283k cumulative profit over the same time period using simple, long-only rules on single contracts.
For portfolio managers who don’t want to build from scratch, the dashboard and weekly PDFs surface WA%R, price context, and climate developments by crop and region—giving a decision-ready view that complements internal research and risk workflows. In short, finance users span from quantitative traders ingesting raw signals to discretionary managers consuming narrative insights—all working from the same underlying climate-risk engine tailored to trading and portfolio decisions.
About Our Signals
Helios’ climate-risk signals have been shown to be predictive and tradable, which is why both systematic and discretionary commodity desks use them to generate alpha and manage exposure.
Our ten-year backtest proved to be very profitable, and in particular, the last four years, where climate risk impact has increased. In this four-year deep-dive, simple, long-only threshold strategies built on Helios risk indices produced 65%–71% directional accuracy across corn, soy, spring wheat (Minneapolis), and cocoa, and a combined $283K in cumulative profit trading a single contract per signal—no leverage, no sizing by signal strength.
The approach displayed on our platform follows concrete rules (e.g., “enter when risk crosses a level; exit when it falls X% from peak” or “enter after a 200–500% risk increase; exit after a small retrace”) and shows they consistently made money across crops despite different seasonality and trading windows (e.g., soy traded ~9 months/year, corn ~7, wheat ~3, cocoa year-round). The takeaway for asset managers is straightforward: Helios’ climate-driven risk spikes lead subsequent price moves with enough reliability to support event-driven entries and exits, and they do so with rules simple enough to audit and replicate.
For quantitative teams, these signals slot naturally into feature sets for models because they capture supply-side stress before it is fully priced; for discretionary portfolio managers, the same risk indices function as a timing overlay on fundamental views (e.g., adding on when climate risk accelerates; scaling out when it decelerates).
The backtest lays out per-commodity results that help with allocation sizing and capacity planning: soy (500% rise / -3% from peak exit) ~65% accuracy over 20 trades; corn (300% rise / -11% exit) ~68% over 22 trades; spring wheat (200% rise / -5% exit) ~71% over 14 trades; and cocoa (cross 20% / recross) ~71% over 28 trades, with cocoa alone contributing about $250K over the same period.
Because each strategy is defined ex-ante, accuracy is measured simply as “% of trades that made money,” and profit and loss math is spelled out trade-by-trade, these results are transparent, auditable, and easy to port into an internal backtester or live pilot.
In practice, funds use Helios both to capture alpha and to improve risk control—e.g., reducing drawdowns by cutting exposure when climate risk rolls over—and several desks also treat the risk indices as an ESG-aligned factor for attribution across multi-asset books.