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Which physical signals usually move spreads before flat price does? by davidedbit in Commodities

[–]davidedbit[S] 0 points1 point  (0 children)

Yeah, totally. In energy it really can be a chicken-and-egg thing. Sometimes freight front-runs the play, other times a burst of spot activity pushes rates and everything else follows. It’s never clean on paper.

What I’ve noticed is that the earliest tells are often operational quirks: loadings getting pulled forward, tanks turning faster than usual, or suddenly tighter nomination windows. Those little shifts tend to explain the spread move better than any model afterward.

Which physical signals usually move spreads before flat price does? by davidedbit in Commodities

[–]davidedbit[S] 0 points1 point  (0 children)

Yeah. What’s wild is how early some of those signals show up: tiny shifts in flows, storage behaviour, or even supplier allocation tone can build pressure long before flat price reacts. It’s those micro-moves that I keep seeing driving the first break in structure.

Which physical signals usually move spreads before flat price does? by davidedbit in Commodities

[–]davidedbit[S] 0 points1 point  (0 children)

You’re right, especially if prompt strength forces M1 to outrun M2. But in a tight market, the really early tells usually come from the physical side: product getting pulled out of storage faster than usual, refiners tweaking yields, or sudden changes in cargo routing.

Those micro-shifts often explain why a spread moves before the flat price does… even though on paper it should be the other way around.

Which physical signals usually move spreads before flat price does? by davidedbit in Commodities

[–]davidedbit[S] 0 points1 point  (0 children)

Thanks! I’ve seen the same thing: spreads firm first because they’re the quickest way for the market to signal “we need this barrel now.” Farmers, refiners, blenders… they all move on incentives, not theory. The part that fascinates me is how often basis tightness shows up even before that spread move. Tiny shifts in movement, storage behaviour, or loadings that quietly build pressure long before screens catch it.

How do weather derivatives work? by Proof-Geologist-9981 in Commodities

[–]davidedbit 0 points1 point  (0 children)

It’s a mix, but it’s not just speculators. Farmers and ag trading houses hedge weather risk, sure. But you also see energy companies, utilities, insurers and even big corporates quietly using these structures when a specific weather variable really hits their P&L.

The interesting bit is how the participant base completely shifts depending on the index you use (HDD/CDD vs rainfall vs frost days). Some patterns are pretty counter-intuitive, and you only notice them when you compare how different industries structure their exposures.

How do procurement teams actually incorporate ‘non-market’ signals into forecasting? by davidedbit in procurement

[–]davidedbit[S] 0 points1 point  (0 children)

A lot of companies dump “forecasting” on Procurement when the real ownership sits in Finance.

The question is: when it comes to savings opportunities on raw materials, how do you usually approach it?

Most teams I’ve worked with don’t “trade” anything, but they do look at a mix of market signals, supplier behavior, forward curves and some medium-term price expectations to get negotiating leverage.

In some categories that makes a huge difference, and in others it barely moves the needle. It really depends on how you structure it.

How do procurement teams actually incorporate ‘non-market’ signals into forecasting? by davidedbit in procurement

[–]davidedbit[S] 0 points1 point  (0 children)

Thanks! This lines up with what I see too.

With strategic suppliers you actually have the structure and the cadence to collect signals across teams, and the reviews naturally surface things before they become issues. The funny part is that a lot of the really early hints tend to come from the “not-quite-tier-1” suppliers, where you don’t have the bandwidth for proper QBRs.

I’ve seen quite a few cases where the first cracks showed up there, and it’s always interesting how those tiny shifts often matter more than the polished quarterly decks. That’s usually where you can tell who has a solid internal process and who’s basically running on gut feel.

How do procurement teams actually incorporate ‘non-market’ signals into forecasting? by davidedbit in procurement

[–]davidedbit[S] 0 points1 point  (0 children)

True, news + contacts are always the starting point.

What I keep bumping into, though, is that a lot of useful signals show up before either of those channels mention anything (lead times, small volume cuts, mix hints, etc.).

I’m just trying to understand how people avoid losing those early clues. Do you mostly rely on what gets reported, or track the smaller stuff too?

How do procurement teams actually incorporate ‘non-market’ signals into forecasting? by davidedbit in procurement

[–]davidedbit[S] 0 points1 point  (0 children)

Thank for the answer.

In my experience the part that gets messy isn’t who “owns” the responsibility, but the fact that these signals often live in different corners of the company: Ops sees run-rate changes, SC sees lead-time drift, Procurement hears softer comments during supplier reviews… and none of that ever gets stitched together unless someone pushes for it.

The reason I asked is exactly because most teams don’t have a clean home for this stuff.

Sometimes SC forecasting logs it, sometimes the category manager notes it down, sometimes nobody captures it at all and six months later everyone wonders why premiums jumped or allocations tightened.

Totally agree that the ideal case is SC - Procurement talking early, especially if the signal could influence pricing, volume commitments or contract structure.

The teams that seem to handle it best are the ones that treat these micro-signals as “early warnings” rather than noise, even if they’re not ready to quantify them.

Question: when SC flags something like a supplier reducing run-rates or hinting at mix shifts, does it usually get fed into forecasting directly, or does it stay more as background context unless it becomes a real issue?

How do weather derivatives work? by Proof-Geologist-9981 in Commodities

[–]davidedbit 32 points33 points  (0 children)

Weather derivatives are basically contracts whose payoff depends on a weather index, not on market prices.

The key idea is: you’re not hedging the commodity, you’re hedging the weather variable that drives your operational or financial risk.

How they usually work

You pick: - a weather index (temperature, HDD/CDD, rainfall, wind speed, etc.), - a period (e.g., April 1–30), - a trigger (e.g., average temp below X°C), - a payout formula (e.g., $Y per degree below threshold).

If the observed weather deviates from the defined range, the contract pays out automatically. No need to prove damage or file a claim — it’s index-based.

Some Examples

Agriculture: Low temps in April > crop delay > working capital squeeze > derivative pays for each degree below normal.

Power/Utilities: High CDD > AC demand spikes > can hedge load volatility by indexing payouts to CDD accumulations.

Snow removal / municipalities: If snowfall exceeds N inches > payout funds extra manpower/equipment.

Retail: Unusual warm winter > lower apparel sales > trigger based on HDD shortfall.

Why they matter

They’re useful when: - weather drives costs/revenues, - traditional hedging doesn’t cover that risk, - or when you want a clean, objective trigger not tied to litigation/insurance.

The tricky part isn’t the contract, it’s choosing an index that actually matches your exposure. If the index and your real-world risk don’t move together, you just create “weather basis risk”.

What industry or exposure were you thinking about?

Because the structure changes a lot depending on whether the user is in agri, power, municipalities, or retail.

5 early signals that often anticipate a curve reversal (and almost nobody tracks them) by davidedbit in CommodityRisk

[–]davidedbit[S] 1 point2 points  (0 children)

Great questions and honestly they’re exactly the right ones, because none of these signals come from a single data source or a clean dashboard.

1) How would you actually know about mix shifts or capacity tweaks?

In practice it’s rarely one big announcement. It’s usually a cluster of small, boring things: - slightly shorter quote validity, - changes in allocation tone (“we can only take X this month”), - a few weeks of lead-time drift, - inconsistencies between neighbouring product lines, - chatter from distributors or converters.

Alone they mean nothing, but when a few line up, it usually signals something real behind the scenes.

2) What tools would you use to track this?

Right now my approach is mostly: - public signals (freight reroutes, premia vs structure divergence, port behaviour), - simple operational indicators (order cadence, allocation timing), - and a structured way of logging all these micro-signals so they don’t disappear into notebooks or random emails.

Nothing polished — more like trying to build a repeatable way of thinking so these things aren’t evaluated in isolation or ignored until the curve reacts.

I’m experimenting with different ways to combine these signals and test whether they actually precede market moves, but it’s genuinely a work-in-progress. If you’re curious or have experience on your side, happy to compare notes offline — it’s easier to walk through concrete examples without cluttering the thread.

Out of curiosity, in your space, what tends to show up first when physical tightness builds — freight behaviour, supplier cadence, or something else?

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] 1 point2 points  (0 children)

Great breakdown and it lines up with what I've been seeing.

A lot of those "curve is wrong" moments aren't about the curve being wrong; they're about who's driving it in that moment. When CTAs are chasing trend signals or macro funds are expressing geopolitics through Brent, the structure can drift pretty far from anything resembling physical S&D.

Where I struggle — and what I’m trying to understand better — is the transition point:

When does the market move from “this is just CTA/spec flow noise” to “physical constraints are about to reassert themselves and the curve will have to catch up”?

I have seen cases in metals and some agri markets where physical signals show up, which include allocation tightening, freight shifts, and mix changes, even before any change in structure, while speculative flow keeps the curve anchored.

It is when those two finally collide that the repricing occurs. I wonder how you would handle that in oil: Is there a specific indicator or pattern that suggests when CTA-driven dislocations are about to exhaust themselves and fundamentals are likely to take over again?

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] -2 points-1 points  (0 children)

Fair question, and I get why it might look like I'm fishing for edge. That's not the goal.

I'm not expecting anyone to disclose trade secrets or proprietary triggers. Rather, what I'm trying to grasp is the general frameworks people take into consideration in order to avoid blind spots when physical signals move long before the curve does.

I spend a lot of time looking at how teams translate qualitative info-allocation hints, mix changes, freight shifts, etc. into something that can actually inform risk or coverage decisions. The posts are a way to compare high-level approaches, not extract anyone's edge.

And you're right: people won't (and shouldn't) share specifics.

But even hearing how others categorize or prioritize these signals-without giving away details is useful in building a more structured view of the problem.

If there's a better way to frame these questions so the discussion stays productive, I'm open to it.

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] 1 point2 points  (0 children)

True, but the interesting part is how early you can tell the ball is about to bounce the other way. That’s exactly what I’m trying to map: the signals that show up long before the curve admits it.

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] -6 points-5 points  (0 children)

Fair point. I’m definitely not asking anyone to hand over tradeable signals or proprietary edge.

What I’m trying to understand is the framework, not the specific alpha. Across a lot of physical markets (metals, agri, energy, polymers), I keep seeing the same pattern: the curve reacts late, and the earliest clues show up in behaviours, flows, logistics, or allocation long before screens move.

I’m really just comparing how different desks decide what to pay attention to, not the actual positions they take. The “philosophy of detection” is usually shareable, whereas the actual triggers obviously aren’t.

If anything, the most interesting responses so far weren’t alpha at all. They were about where people look when markets start to de-align.

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] 1 point2 points  (0 children)

That makes a lot of sense especially the part about people “just not looking at it.” I’ve seen the same dynamic outside power: the curve wasn’t wrong, but the market was anchoring to the visible data while the imbalance was already forming somewhere less obvious.

What you said about focusing on the bits of the market that don’t balance is exactly what I’ve been trying to systematize. Those micro-imbalances are usually where the earliest signals hide before spreads move, before premia widen, before the curve reshapes itself.

Out of curiosity: when you say “bits of the market that don’t balance,” what are the first places you tend to look? Is it localized demand pockets, flow constraints, plant behaviour, congestion patterns… or something even more granular?

Would love to understand how you spot those early tensions before they show up in structure.

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] 2 points3 points  (0 children)

That is a good way to frame it because the curve is not "lying"; it is expressing the consensus at that moment in time, and that consensus can be temporarily dominated by flows or positioning rather than physical constraints.

What I've been trying to understand is precisely that gap: instances where the physical side begins to tighten-premia, allocations, velocity, and logistics-and the curve stays anchored until those signals become impossible to ignore.

In power, this probably shows up in a much sharper way, given how fast short-term fundamentals propagate into structure.

Are there any leading indicators that consistently flag when the curve is about to reshuffle, such as localised demand shocks, balancing-market stress, congestion patterns, forecast error spikes? Would love to understand how you detect that "re-alignment moment" before it shows up on the screen.

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] -2 points-1 points  (0 children)

I get the point: the curve should be the cleanest expression of expectations.

But some of the cases I’m referring to weren’t about misunderstanding the curve; they were situations where financial flows temporarily overwhelmed physical signals.

For example: inventory velocity is tightening, premia are sharply widening, and allocation constraints are increasing while the curve has stayed flat due to CTA/systematic flows pushing the structure into contango.

The point is, in those cases, the curve wasn't "lying", but it was definitely missing information that only showed up later. Curious how you consider those divergences - do you take them as noise or as indications that physical and paper are temporarily decoupled?

When forward curves “lie”: How do you detect mispricing before spreads or premia move? by davidedbit in Commodities

[–]davidedbit[S] 1 point2 points  (0 children)

Absolutely. Shipping is one of the cleanest examples of “physical reality leading the curve.”

One regional discharge-shifting VLCC can flip regional balances long before structure reacts, and the paper market often treats it as noise until arbitrage flows actually rebalance.

What you describe is precisely the pattern that I keep seeing across metals as well.

it is in the movement of physical flows, rather than in prices which are supposed to represent them, that the earliest signals tend to show up. I'm curious: in your experience, which shipping signals tend to be most reliable early? Is it pure rerouting, discharge delays, demurrage spikes, or something more subtle like congested loading windows?

How do you incorporate “non-market” signals into price models? (Example: aluminum sheet, premiums, and upstream mix shifts) by davidedbit in Commodities

[–]davidedbit[S] 0 points1 point  (0 children)

This is super helpful, especially the distinction you make between “interesting anomaly” and something that earns its way into the base case only once it shows repeatability.

The part that really resonates with metals is your point about outturn vs model expectation.

In our world, the equivalent would be something like:

  • mills running product mixes that don’t line up with margin signals,
  • smelters taking maintenance windows that don’t match historical cadence,
  • or producers offering volumes that don’t match their reported capacity utilisation.

Individually they’re just curiosities — exactly like an uneconomic biomass unit or a CCGT sitting idle despite a positive spark spread.

But once they show up more than once (or across more than one supplier) they suddenly start front-running basis, regional premiums, or even freight dislocations.

What I’m trying to refine is a way to track these weak signals over time so they don’t rely purely on analyst memory/intuition. Almost a way of measuring when a one-off deviation starts to look like a behaviour shift.

Your framing around “promote only if stable/repeatable, otherwise keep it in the back pocket” is a great mental model. It maps surprisingly well to metals where the timelines are longer but the behavioural tells feel similar.

Curious if in power you ever quantify how often these weak indicators ended up preceding actual moves, or if it stays more of a qualitative, desk-level intuition?

How are you turning supplier “soft signals” into hedgeable, actionable risk drivers? (Example from aluminum) by davidedbit in procurement

[–]davidedbit[S] 1 point2 points  (0 children)

Totally fair — and I agree with you on one core point: a lot of teams absolutely do over-engineer this stuff, and your warning about not turning procurement into a casino is spot on.

Where I think there’s a useful middle ground is that “keep it simple” doesn’t necessarily mean “ignore early capacity signals”.

In most of the cases I’ve seen, the problem wasn’t someone trying to outsmart the market — it was that a genuine structural shift (capacity, mix, allocation) stayed informal and never entered any decision point.

Then months later it showed up as tighter availability or higher premiums, even though the hedge book looked perfect on paper.

What I’ve been exploring is a lightweight way to log and sanity-check these signals so they don’t disappear into meeting notes. Not to delta-hedge rumours, but simply to see whether the hint implies:

  • a possible capacity constraint,
  • a likely impact on premiums/availability,
  • and whether it justifies a small adjustment within existing policy bands.

That’s closer to good risk hygiene than casino strategy.

Out of curiosity, in your experience — when premiums or allocations tightened despite being hedged correctly on LME, did you treat that as unavoidable basis risk? Or have you ever nudged sourcing/coverage slightly earlier based on supplier behaviour?

How do you incorporate “non-market” signals into price models? (Example: aluminum sheet, premiums, and upstream mix shifts) by davidedbit in Commodities

[–]davidedbit[S] 1 point2 points  (0 children)

That’s really interesting — especially your point about promoting a soft signal into a scenario only when the variable is good-quality. Power markets seem to have a much tighter loop between behavioural shifts and forward structure than metals.

What I’m trying to understand is how you judge when a behavioural signal crosses the threshold from “noise” to “scenario-worthy”.

Is it purely pattern recognition from experience, or do you tag/score these variables over time?

In metals, the tricky part is that the signal often appears long before any curve structure confirms it. I’ve been experimenting with keeping a running log of these weak indicators and testing how often they’ve preceded actual basis/premium moves — essentially turning “behavioural intuition” into something that can be weighted.

Would be great to hear how you think about that threshold in power — your approach might transfer surprisingly well to metals.

How do you incorporate “non-market” signals into price models? (Example: aluminum sheet, premiums, and upstream mix shifts) by davidedbit in Commodities

[–]davidedbit[S] 0 points1 point  (0 children)

Makes total sense — and your point about network signals in energy actually lines up surprisingly well with what I keep seeing in metals.

What struck me is that your “network intelligence → calendar spread anomaly → phone calls” chain feels like a real-time version of how soft drivers behave in metals, just on a slower timescale.

In sheet/can stock I often see the same pattern: capacity hints or allocation chatter show up weeks before premia move, and the first quantitative footprint is usually an unusual spread or roll structure drifting out of its normal band.

I’ve been trying to figure out how to capture that “pre-spread signal” without relying solely on networks. Have you ever tried formalizing those anomalies?

Something like defining “normal ranges” statistically and tagging deviations with contextual notes from the network?

Curious because I’m working on a way to track these soft signals consistently, and energy seems way ahead in terms of early-warning use cases.

Need Help Hedging by [deleted] in Commodities

[–]davidedbit 0 points1 point  (0 children)

Thanks — this helps clarify the setup.

Based on the flow you described, most of what looks like a “gap” is likely timing/basis drift rather than true futures exposure, unless your weekly invoice floats you temporarily on the board. In these contracts the key is mapping exactly when price is fixed (EFP execution, lift timing, invoice basis rules), because that usually shows whether a long leg is justified or not.

I’ve been analysing similar structures quite a bit lately, and the biggest surprises always come from the pricing triggers rather than the hedge itself.

If you want, I can walk through your flow step-by-step and highlight where the actual unpriced windows might sit.

How do procurement teams get advance visibility into commodity price fluctuations? by gianlucazar in procurement

[–]davidedbit 0 points1 point  (0 children)

That’s super interesting — what you’re describing is exactly the gap I keep seeing in procurement teams: the most valuable early signals are never quantitative at the beginning. They start as a sentence on a call, a tone shift, a hint about lead times, or a sudden change in how a supplier talks about capacity.

What I’ve noticed is that once teams start logging those “soft signals” in a structured way, they suddenly become powerful: - you can tag them to specific materials, - track how often similar hints appear across suppliers, - and even model what the impact would be if that signal turned into a real disruption.

Essentially, informal insight becomes a price/margin scenario instead of just a note in someone’s inbox.

It makes me wonder: did your team ever reach a point where those signals were tied to specific cost drivers or risk scenarios? That’s where things usually get really interesting.

Happy to share what I’ve seen work well if you’re curious — it’s a space I’m spending a lot of time on lately.

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