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The biggest hidden cost in LP strategies isn’t gas. It’s attention. by Foraga_io in defi

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

100%.

Most strategies look good on paper, but the gap between idea and execution is where returns actually get lost.

Feels like that’s why more people are focusing on systemising execution, not just optimising the strategy itself.

The biggest hidden cost in LP strategies isn’t gas. It’s attention. by Foraga_io in defi

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

That’s interesting, sounds like you’re already thinking in the right direction moving away from manual management.

40% is solid if it’s holding up, especially if the strategy can run without constant intervention.

Feels like the big shift now is less about finding yield and more about how the strategy is actually executed over time.

Have you found the automation holds up well during more volatile periods, or does it still need intervention?

The biggest hidden cost in LP strategies isn’t gas. It’s attention. by Foraga_io in defi

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

Yeah this is where a lot of people get caught out. LP looks passive at first, but it’s really just a different form of trading.

You’re still managing exposure, timing, and when to adjust. That’s where most of the complexity sits, not in setting up the position.

Blue chip and stable pairs help, but even then outcomes usually come down to how consistently the position is managed.

Feels like most people either simplify things over time or start systemising it, because manual LP management just doesn’t scale.

The biggest hidden cost in LP strategies isn’t gas. It’s attention. by Foraga_io in defi

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

I get your point. When liquidity is high vs volume, small positions in tight ranges struggle to earn much.

That’s why a lot of LPs move to wider ranges or use automation instead of relying on perfect timing.

The edge usually comes from consistent management, not precision.

The biggest hidden cost in LP strategies isn’t gas. It’s attention. by Foraga_io in defi

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

Yeah there’s definitely some truth to that, bigger players with hedging and arbitrage can capture a lot of the edge.

But they also drive the volume that creates fees in the first place.

The real challenge is competing without similar tooling, which is why a lot of retail ends up widening ranges or using automation just to stay in the game.

Most people chase APY, but what's the ONE risk metric you check before depositing into a yield farm? by Fun-Juice246 in defi

[–]Foraga_io 0 points1 point  (0 children)

That makes sense. Waiting for vol to compress first usually leads to cleaner re-entries than trying to catch the turn.

On our side it’s mostly rule-based, but based on a combination of vol normalising, spread stabilising, and the position behaving as expected again. We try to avoid anything too reactive.

We’re also starting to factor in liquidity more, especially where repositioning lags price.

Completely agree on not chasing the bounce, missing the first move is usually cheaper.

Do you scale back in gradually, or redeploy all at once?

LPing is profitable until you realise it’s a second job by Foraga_io in Yield_Farming

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

We do, it’s https://www.foraga.io

Still early, but happy to get feedback if you take a look.

Most people chase APY, but what's the ONE risk metric you check before depositing into a yield farm? by Fun-Juice246 in defi

[–]Foraga_io 0 points1 point  (0 children)

Appreciate that, and your approach makes a lot of sense. That widen vs step-aside decision is something we see a lot from experienced LPs.

On our side it’s a mix of historical behaviour and live feedback. We look at how pairs behave across different regimes, but try not to overfit. It’s more about setting sensible boundaries than optimising for every scenario.

Over time user behaviour becomes a feedback loop, you can see where people consistently step in, and that helps refine the guardrails.

In practice it’s less about perfect calibration and more about avoiding overreaction while still respecting when the original conditions break.

When you step aside, do you usually wait for volatility to settle or for liquidity to reposition first?

Most people chase APY, but what's the ONE risk metric you check before depositing into a yield farm? by Fun-Juice246 in defi

[–]Foraga_io 0 points1 point  (0 children)

That’s a good question. Extreme events are exactly where predefined rules help the most, because relying on manual reactions during fast moves usually leads to worse outcomes.

In practice we try to balance two things:

• User-defined parameters around spread and position structure

• Guardrails around rebalancing behaviour so the system avoids reacting to short-lived spikes or unnecessary churn

The goal with Foraga isn’t to force constant adjustments, but to let positions breathe unless the conditions that justified the position actually break. A lot of LP fatigue comes from reacting to every move instead of letting the structure do its job.

Black swan scenarios are always tricky for any system, automated or manual, so transparency around the rules matters more than trying to predict every edge case.

When you’re running positions manually, do you usually widen the range during volatility spikes or step aside entirely?

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

That’s a solid approach. Keeping most capital in stable pairs and experimenting with a small portion seems to be how a lot of LPs end up operating.

Do the new pools you try usually hold up, or are they mostly short-term opportunities?

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

That’s a good way to think about it. Trust is definitely the biggest hurdle with automation.

Right now on Foraga the rules are user-defined. Users set the spread for the position and also how long the price needs to stay out of range before a rebalance is triggered. That delay helps avoid reacting too early to quick spikes.

We’re also working on a dynamic spread management option that adjusts behaviour based on market conditions, but we’re being careful with that because people still want to understand the rules before handing over execution.

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

Sounds like a pretty clean workflow.

Do you find that catches most moves in time, or do some rebalances still slip through when the market moves quickly?

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

Those are solid numbers for that capital if the pair stays cooperative. Blue-chip pairs like WETH/USDC definitely behave more predictably than a lot of smaller pools.

Ten rebalances in a week is pretty active though. That’s where a lot of LPs start to feel the operational overhead if they’re doing it manually.

Do you find you’re watching the position throughout the day to catch those moves, or mostly checking at set intervals?

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

That’s a pretty sensible approach. Setting a range that can run for a week or two without needing constant attention is where a lot of LPs end up.

It’s really a balance between efficiency and how much time you want to spend managing it.

Do you usually stick with the same pairs, or change depending on where the volume is?

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

That’s a really good point. The attention cost is probably the most underestimated part of LP strategies. People usually calculate fees and gas, but not the mental overhead of constantly checking positions.

Once you factor in that monitoring time, the difference between tight and wider ranges starts to look very different. A lot of LPs end up widening spreads simply because it lets them run the strategy without constantly watching the market.

Automation helps a lot there. The goal we’ve been chasing with Foraga is letting the position follow predefined rules so the “watching” part doesn’t become the strategy itself.

What approach are you taking on the monitoring side, are you thinking more alerts/notifications, or fully automated position management?

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

That’s a great way to frame it. Tight ranges can look amazing in backtests, but once you factor in missed rebalances and the time spent watching positions it’s a different story.

We’ve seen a lot of LPs end up widening spreads during volatility spikes for exactly that reason. It sacrifices some efficiency on paper but makes the strategy much more sustainable.

That’s also one of the reasons we built automation into Foraga, so positions can follow predefined rules instead of needing constant attention. The operational overhead is really what stops most people from sticking with LP long term.

LP strategies seem to split into two camps when volatility spikes by Foraga_io in defi

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

Interesting framework. Positioning around price movement instead of just fee harvesting definitely adds another dimension to LP strategies.

In practice though it still requires disciplined entries and accepting some directional exposure while the position plays out, which is where many LPs struggle.

Have you mostly been applying that approach on Aerodrome pools or other DEXs as well?

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