Edward Nikulin, Weather Model Expert at Mind Money – Interview Series

Edward Nikulin, weather model expert and head of the trading division at the European broker Mind Money, is a proficient quantitative researcher and data scientist with more than eight years of experience in market modeling, systematic trading, and AI-driven analytics. He is the author of the weather model used for Mind Money’s proprietary trading strategies.

Mind Money is a European investment and brokerage platform based in Limassol, Cyprus, offering access to global stocks, bonds, ETFs, and selected IPO and pre-IPO opportunities. Operating under CySEC regulation and MiFID II compliance, it focuses on transparent pricing, diversified investment options, and professional portfolio management for clients seeking exposure to international financial markets.

Edward Nikulin shares his know‑how and expertise in applying climate and weather modeling to the analysis of commodity markets. His journey from trader to weather‑model specialist has shaped a distinctive perspective on how financial and environmental data intersect. In this piece, he explains how his weather‑commodity model works in practice: what types of climate and weather variables are incorporated — from seasonal temperature shifts to precipitation patterns and extreme events — and how those inputs are converted into actionable trading and risk signals. By combining market intuition with scientific modeling, Edward offers readers a rare insight into how weather intelligence can become a decisive factor in commodity strategies today.

Can you walk us through your journey from being a trader to becoming a climate and weather-model expert, and how that background shapes the way you analyze commodity markets today?

My background is in quantitative trading and data science. For many years, I worked on systematic commodity and derivatives strategies, so I was always thinking in terms of trading signals and risk. Then, I was invited by a startup focused on optimizing marine logistics to lead an AI direction related to weather and climate. That put me in very close daily contact with hydrometeorologists, ecologists, and domain specialists who think very differently from traders.

That experience was critical: I learned how weather experts reason about physical processes, uncertainty, and time delays. And just as importantly, I learned how different their mental models are from those used in markets.

Today, I work at the intersection of those two worlds. I don’t approach weather as a forecasting problem (actually, there is little sense in trying to beat NOAA and ECMWF models), and I don’t approach markets as purely statistical noise. My trading background forces every climate signal to answer very practical questions: when, through which mechanism, and with what probability does this information actually affect price. That combination strongly shapes how I analyze commodity markets now.

How does your weather-commodity model work in practice — what types of climate and weather data do you incorporate, and how are those variables converted into actionable trading or risk signals?

Practically speaking, the model integrates several layers of data rather than relying on a single source.

On the weather side, we take real-time and near-term data from numerical weather prediction models, hyper-local meteorological station data near production zones, and satellite-derived indicators such as vegetation and drought indices. On the climate side, we track longer-horizon signals through reanalysis datasets and large-scale climate indices like ENSO, NAO, PDO, and others.

The real key step is not prediction, but impact translation. Raw weather variables are mapped to production, logistics, or demand-side mechanisms that matter for a specific commodity. Those relationships are formalized through a rules-based and probabilistic framework, often using Monte Carlo simulations to capture uncertainty.

The output is not a weather forecast, but a structured signal: probability-weighted scenarios, risk asymmetry, and time-shifted impact estimates. That’s what allows the signals to be used either for trading decisions or for risk control, depending on the horizon.

Your model has been live for several years with strong performance — how did it behave during major weather disruptions, and what did those periods reveal about market reactions to climate shocks?

One of the most important insights from running the model live is that markets almost never react to weather shocks instantly and very rarely react in a linear way.

A good recent example is Natural Gas. In late October, our model started to flag a high and rising probability of a polar vortex disruption. At that point, spot weather still looked relatively benign, inventories were comfortable, and the market narrative was neutral. Prices barely reacted. 

The important part was that the model didn’t produce a binary signal. It showed a probability curve that kept rising through November and into December, as atmospheric patterns became more unstable. Only later, when colder scenarios started to appear consistently in public forecasts and media narratives, did Natural Gas enter a strong uptrend.

That episode clearly demonstrated something we see again and again:

• first comes physical atmospheric stress,
• then probability accumulation,
• then information diffusion,
• and only at the end, we see price adjustment.

How are commodity traders and investors currently using climate and weather data in their risk-management strategies, and where do you think most participants still fall short?

Most participants still use weather data in a largely discretionary way. They track forecasts, read expert commentary, and adjust positions based on the perceived severity of events. In risk management, climate data is often used retrospectively.

Where they fall short is formalization. Weather signals are often subjective, inconsistently weighted, and poorly integrated with market structure. Another common issue is overreacting to visually dramatic but economically irrelevant anomalies, or underreacting to slow-moving processes that matter much more for supply.

The biggest gap is the absence of a clear mapping between weather, production impact, and price response.

Which specific climate or weather indicators do you believe will be most important for commodity markets through 2026, particularly for agriculture and energy?

For agriculture, persistent moisture balance indicators — drought development, soil moisture, and vegetation health — will matter far more than individual weather events. Temperature extremes during sensitive growth phases will also remain critical.

For energy, the focus will be on demand-side temperature anomalies, especially winter volatility for heating demand and summer heat stress on power systems. On the climate side, large-scale oscillations like ENSO will continue to influence regional supply-demand imbalances.

In what ways does your approach differ from more traditional commodity forecasting models or standard weather-based analysis used in the markets?

The main difference is that we do not attempt to forecast prices or weather directly, and we don’t take vanilla weather forecasts, either. Our team focuses on quantitative modeling of weather and climate impact on prices.

Traditional models often either extrapolate prices statistically or treat weather as an external explanatory variable with weak structure. Standard weather analysis, on the other hand, tends to be descriptive rather than market-aware. Our approach formalizes the causal chain between environmental conditions and market impact, explicitly modeling uncertainty, timing, and relevance.

What are the biggest challenges investors face when trying to integrate climate data into real-world decision-making, and how can they avoid common pitfalls?

The biggest challenge is separating signal from noise. Climate data is high-dimensional, slow-moving, and often emotionally charged by media narratives.

Common pitfalls include overfitting historical correlations, ignoring time lags, and assuming that more data automatically leads to better decisions. Another mistake is treating climate trends as deterministic rather than probabilistic.

In reality, most of these problems come from a lack of understanding. Integrating climate data into trading or risk management is not so easy. It requires a significant amount of time to understand physical mechanisms, regional specifics, data limitations, and, most importantly, how markets actually digest this information. Without that foundation, climate data tends to create false confidence rather than better decisions.

Simply hiring meteorologists may help close the gap in weather expertise, but it does not solve the core problem: translating weather and climate signals into measurable market impact and pricing dynamics. At that point, there are really only two viable paths. Either collaborate with teams that have already built this capability and can provide actionable insights, or invest in building a dedicated weather and climate desk in-house that combines market expertise with environmental science.

How do you expect longer-term climate trends to structurally reshape global commodity markets over the next few years?

We’re already seeing increased volatility rather than smooth trend shifts. Climate change doesn’t just move averages, it increases the frequency and clustering of extremes.

Structurally, this means increased supply uncertainty, higher risk premiums, and a greater emphasis on flexibility in logistics and inventory management. Some regions will become less reliable suppliers, while others may gain strategic importance.

As climate risk becomes more central to institutional frameworks, how do you see climate-informed models influencing portfolio construction, disclosure, or risk reporting?

Climate-informed models will gradually move from qualitative overlays to quantitative risk inputs. In portfolio construction, they can inform position sizing, tail-risk exposure, and diversification assumptions. In disclosure and reporting, they provide a more defensible, scenario-based way to discuss climate exposure without relying on plain narratives.

Over time, I expect climate risk to be treated on commodity markets similarly to macro or volatility risk: not as a separate ESG category, but as a core component of market risk.

Can you share an example where insights from your model helped anticipate a significant commodity price move before it became obvious to the wider market?

As I mentioned previously, natural gas is a clear example of the model insight.

Also, we see even clearer examples applicable to agricultural markets.

For example, in Brazilian orange juice production in 2023, satellite-based moisture and vegetation indices revealed persistent drought stress months before it was reflected in official yield estimates or market commentary. Prices initially stayed flat because the damage was not yet observable. Once production forecasts were revised and headlines followed, prices adjusted rapidly, but by that time, the underlying risk had already been building for weeks.

Another instance is Robusta coffee production in Vietnam in 2023-2024. In that episode, prolonged drought and heat stress gradually reduced production potential, while the market initially treated the situation as temporary. The model captured the cumulative nature of the stress early on. When production losses became undeniable, prices moved sharply.

A similar pattern occurred in cocoa markets in West Africa in November 2023, when unusually persistent Harmattan winds created moisture deficits and pollination issues. Physical stress was evident well before the market reaction, which only accelerated once supply concerns entered mainstream narratives.

Across all these cases, the key insight is the same: the model helps identify slow, accumulating physical stress that shifts risk asymmetry long before the market responds. This is why timing and probabilistic assessment matter far more than reacting to headlines or isolated weather events.

Thank you for the great interview, readers who wish to learn more should visit Mind Money.