LQWD Technologies Corp., an infrastructure and liquidity provider for the Bitcoin Lightning Network, has announced a new partnership with Amboss Technologies Inc. to become their premiere Lightning Network Liquidity Service Provider (LSP), according to a press release sent to Bitcoin Magazine. As part of the partnership, LQWD will contribute an initial 10 Bitcoin in liquidity to Amboss, with plans to deploy more Bitcoin throughout the collaboration.

“Partnering with LQWD ensures that Amboss’s global customers have direct access to institutional-grade liquidity for Bitcoin payments, allowing LQWD to generate additional yield through their nodes on the Lightning Network,” said Amboss Co-Founder and CEO Jesse Shrader. “Additionally, this partnership increases the supply side of Amboss’s liquidity marketplace, enabling LQWD to fulfill the market demand for Lightning Network liquidity.”

Amboss Technologies specializes in data analytics solutions tailored for the Bitcoin Lightning Network and provides products like Magma and Hydro for market organization and liquidity automation. Magma serves as a liquidity marketplace, while Hydro enables advanced liquidity automation for seamless Lightning Network payments.

“This partnership enables LQWD to deploy more of our company-owned Bitcoin while potentially capturing significant transaction volume and generating yield on our Bitcoin holdings,” stated Shone Anstey, Chief Executive Officer of LQWD. “Importantly, we maintain full sovereignty and custody throughout the process. This strategic alliance signifies a significant step forward for both LQWD and Amboss, as we work together to enhance liquidity and efficiency within the Bitcoin Lightning Network ecosystem.”

The Lightning Network (LN) has witnessed significant growth, with LN activity increasing by 1,200% over the past two years, according to the release. This surge in adoption, coupled with the integration of stable coin transaction capability, opens up LN to a wider user base and attracts forward-thinking businesses and Bitcoin exchanges looking for faster and cheaper payment solutions compared to traditional rails like Visa and Mastercard.

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Introduction

Decentralised finance (DeFi) at its core is fundamentally reliant on decentralised exchanges (DEXs). These pieces of web3 infrastructure are the arbiters of liquidity, facilitating the exchange of cryptocurrencies. Most of these DEXs, being reliant on automated market makers (AMMs), decide which price ranges to allocate liquidity towards in a token pool. The more accurate the allocation, the more efficient and performative the trading experience. Therefore, the success of any DEX is contingent on the effectiveness of its AMM. An ecosystem without efficient DEX infrastructure is less likely to succeed under the financial strain it places on users. 

Without the development and deployment of DEXs atop advanced AMM infrastructure, DeFi itself would not be where it is today. Nevertheless, DeFi trading infrastructure has a long way to go before it catches up to the efficiency of TradFi infrastructure. This will necessitate the implementation of more advanced AMMs which rival the order book and market maker model employed by most TradFi exchanges. Hence, the development of Elektrik’s dynamic liquidity provision model, a next generation AMM designed in pursuit of unprecedented capital efficiency.

The Monumental Importance of Capital Efficiency in DEXs

‘Capital efficiency’ is a phrase which pops up often when discussing financial systems. At its core, capital efficiency refers to the strategic ability of a financial system, whether a business or otherwise, to maximise the work done by every dollar of capital expended. In simpler terms, it is the art of getting the most bang for your buck, ensuring that every financial resource is judiciously allocated and intelligently leveraged to reach its utmost potential. It is a concept especially pertinent for marketplaces and exchanges, since as costs of trading rise on an exchange, fewer users are likely to trade on it.

For exchanges, particularly DEXs, capital efficiency is not merely an operational best practice; it is the lifeblood that largely determines their viability. These platforms operate at the nexus of rapid trade execution, minimal slippage, and optimal order matching, wherein the significance of capital efficiency becomes glaringly evident. A DEX that cannot judiciously manage its capital will find itself dwarfed by competitors, as traders gravitate towards platforms offering the most favourable trading conditions. However, in attempting to achieve peak capital efficiency, DEXs are faced with challenges. Issues such as market volatility, fragmented liquidity pools, and unpredictable trading volumes can often distort the ideal capital allocation, leading to inefficient use of resources and subsequent diminished returns.

So, how can these platforms surmount these formidable challenges? The answer lies in the strategic amalgamation of traditional financial principles with emerging technologies. One such groundbreaking synergy is between liquidity provision and machine learning. By deploying machine learning algorithms, exchanges can predict trading patterns, anticipate liquidity demand, and adjust their capital allocation proactively. This dynamic approach to liquidity provision, powered by the analytical prowess of machine learning, ensures that capital is not just used, but optimised.

Solving this Problem with Dynamic Liquidity Provision (DLP)

Traditional AMMs have largely operated under the premise of algorithmically managed pools, the most obvious example being Uniswap V1’s x * y = k algorithm. Conversely, Elektrik’s Dynamic Liquidity Provision (DLP) model makes use of algorithmically managed pools which are constantly changed and updated via market conditions and artificially intelligent systems. These algorithms ensure that liquidity pools are automatically adjusted to meet market demands, providing not only a more efficient system but also a more profitable opportunity for liquidity providers. The very core of DLP is its capability to adapt, to mould itself to the ever-changing contours and multifaceted nature of the financial landscape, ensuring that liquidity is not just available but also dynamically optimised.

When it comes to the core of the DLP algorithm itself, hedging bets and ensuring market adaptability are central themes. To clarify, traditional AMMs often leave liquidity providers in a tough spot: seek higher yields but accept the greater risks associated with concentrated liquidity pools such as impermanent loss, or play it safe and lose out on potential profits. DLP resolves this dilemma by employing similar techniques to traditional market makers, dynamically allocating liquidity to where it is needed most while ensuring that there is sufficient market depth across the spread of possible price ranges. This strategy is backed by machine learning predictions, that aim to maximise LP fees while mitigating losses. The integration of these machine learning predictions with market data ensures that the system can quickly pivot its strategies based on real-time market conditions. This way, liquidity providers do not find themselves stuck in a detrimental position when the market shifts. Instead, the DLP system takes corrective actions, reallocating liquidity on the curve in a manner that is most suited to new and predicted market conditions.

What really sets DLP apart from the competition is its use of artificial intelligence (AI). When meshed into the DLP mechanism, AI offers an added layer of intelligent decision-making that can refine and enhance the algorithms which DLP uses to allocate liquidity. Here is how it works: 

1. Price Prediction: One of the primary tasks of the AI in DLP is to predict possible future prices of tokens in a trading pair. To do this, the AI dives deep into vast amounts of historical and real-time data. By analysing patterns, market behaviours, and other variables, it can project potential prices for assets in upcoming time frames.
2. Price Likelihood Weighting: It’s not enough just to predict prices; the AI must also estimate how likely each of these prices will come to fruition. For example, if the AI predicts three potential prices for an asset in the next epoch, it assigns a weighting or likelihood percentage to each of those prices. This ensures that DLP can make more nuanced decisions about liquidity provisioning based on the most probable outcomes.
3. Liquidity Allocation: Utilising the predicted prices and their weightings, the AI then strategically places liquidity on the curve. It does so by adjusting parameters like capital distribution ratios or risk exposure limits. For instance, if a particular price point has a high likelihood of occurring and aligns with the desired risk profile, the AI might allocate more liquidity around that price, ensuring that liquidity providers and traders get optimal results.

What sets DLP apart, then, is this use of AI to intelligently and dynamically manage liquidity. Traditional methods may rely on static rules or manual adjustments, but with DLP, the process is continually adapting based on comprehensive data analysis. This results in lower risk, higher yield, and a more adaptable liquidity provision system that responds to market variables almost instantaneously.

The true magic of DLP combined with AI lies in its continuous learning model. It is designed to consistently learn from its actions, monitoring the outcomes in real-time. For instance, if a specific liquidity pool is found to be underperforming or overexposed to a particular asset, the DLP algorithms, in real-time, reallocate resources, thereby reducing inefficiencies. What sets this apart is the iterative approach to fine-tuning the algorithms themselves, integrating new data to ensure that future decisions are even more accurate. This perpetual cycle of learning and adjusting translates into an asset management strategy that is well-aligned to navigate through the choppy waters of market volatility.

On top of the continuous learning model, DLP has been optimised using reinforced learning, a specialised machine learning technique. Here, algorithms learn by doing, constantly fine-tuning their actions based on a reward feedback system. For example, if the algorithm takes an action that results in more effective liquidity provision, perhaps by altering the weighting of assets in a pool and subsequently increasing yield, it receives a ‘positive reward.’ Over time, the algorithm uses this reward system to determine the most effective strategies, essentially training itself to improve performance continuously.

An additional feature of DLP’s machine learning approach includes integration with a meta learning model. Meta-learning, often referred to as “learning to learn”, is a paradigm within machine learning wherein algorithms improve by learning from experiences across multiple training episodes rather than from a singular dataset. The ‘meta AI’ employed by DLP updates and changes the datasets training its dependent machine learning models. It is able to discern between different types of market conditions and uses this knowledge to fine tune which datasets the other models use. The intent of this approach is to ensure that even the datasets employed by DLP are optimised for maximum performance depending on the task at hand. 

What does this Mean for the End Users

Given the effectiveness of existing AMM infrastructure, the necessity of an innovation such as DLP might seem questionable. However, when considering the benefits incurred by the end user, its adoption appears inevitable. The purpose of DLP, as with many innovations in the financial sector, is to provide protocols with a means for achieving more with less. Unburdened by the strains of maintaining a costly financial infrastructure, DLP will allow us at Elektrik to provide more favourable conditions for traders and liquidity providers alike. 

Traders

For traders, a seamless experience is the name of the game. They want a platform where they can execute trades quickly and continuously without losing out on slippage. DLP delivers here, offering traders levels of capital efficiency unmatched by static and manually adjusted dynamic liquidity pools. Its algorithms and AI systems work tirelessly to distribute liquidity where it’s predicted to be most needed, reducing the capital requirements for trading and, in turn, reducing slippage. The dynamic nature of DLP means that traders can anticipate consistently deep liquidity pools that facilitate larger transactions without significant price impact.

Real-time market adaptability is another jewel in the DLP crown. Trading is often about seizing fleeting opportunities, and the algorithms that govern DLP are designed to adapt to market conditions in real-time. These quick adjustments to liquidity pools mean that traders are less likely to face slippage and can capitalise on short-term price movements with greater efficacy. Lightlink further enhances this adaptability, with its rapid block speed allowing for swift transaction confirmations. Moreover, its enterprise mode offers gasless reallocation, ensuring that shifts in liquidity distribution don’t incur prohibitive gas costs. This adaptability does not just bring in operational efficiencies; it establishes a more predictable trading environment, one where opportunities are not lost to latency or outdated asset allocations when compared with centralised exchanges.

Liquidity Providers

For liquidity providers (LPs), the issue has always been about walking the tightrope between maximising fund utilisation and minimising risk. DLP fundamentally changes this equation by ensuring that funds are allocated where they are most likely to generate a high yield. This optimal fund utilisation does not just boost profitability; it also works to reduce impermanent loss, an issue that has long plagued traditional liquidity pools. Impermanent loss arises when the price of tokens in a liquidity pool shifts, causing the value of the tokens in the pool to differ from if they were held outside the pool. It occurs because LPs maintain a constant value ratio of the paired tokens, so when one token’s price increases relative to the other, the pool rebalances, often selling the appreciating token for the depreciating one. When LPers remain passive during significant price swings, they may experience this loss.

Furthermore, DLP affords liquidity providers a layer of customisation that cannot be understated. One size will never fit all, especially in financial markets where asset behaviours are highly nuanced. DLP allows providers to customise their strategies, backed by data-driven decision-making, ensuring a tailored approach that aligns with individual risk appetites and financial goals. This level of customisability means that liquidity providers are not just recipients of a one-size-fits-all solution; instead, they are active participants in a system that moulds itself around their specific needs and preferences.

Conclusion

In web3, terms like ‘machine learning’ and ‘artificial intelligence’ are often thrown around as buzzwords with relatively little genuine use-case. DLP stands out as the exception to this rule of thumb, exhibiting a genuine use case in the enhancement of AMM algorithms. This integration is pioneering, transcending the limitations of static liquidity systems and representing the next step in DEX technology. 

While DeFi has made impressive strides, it thus far has failed to achieve parity with traditional financial systems in terms of efficiency and user experience. However, innovations such as Elektrik’s DLP, combining age-old financial principles with cutting-edge technology, are narrowing this gap. In the race towards an efficient, decentralised financial future, DLP is not just a significant advancement, but a harbinger of the immense potential and adaptability that DeFi holds for end users.

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Introduction

If financial markets are an ocean, then liquidity is the water. Although definitions of liquidity vary between the availability of cash and the cash itself, one thing is for certain, just as an ocean cannot exist without water, a market cannot function without liquidity. Meanwhile, the flow of liquidity between markets can make or break them. Furthermore, the liquidity of a particular asset, cryptocurrency for example, is an important indicator of their viability as well as an essential element of their tradability. Thus, in financial markets, liquidity truly is king!

Understanding Markets: Why Liquidity is King

Before jumping into its importance, let us define the concept. Liquidity, in its most fundamental sense, refers to the ease with which an asset can be bought or sold in the market. This tradability often correlates with the availability of the asset and is therefore conflated with the relative quantity of the asset itself. Accordingly, liquidity is discussed in relation to an individual or group allocating their funds to an opportunity in addition to the liquidity of an asset or market itself. Nevertheless, liquidity in both forms is critical, with its importance having been recognised by numerous economists and financial theorists throughout history. For instance, Nobel laureate Eugene Fama highlighted liquidity’s role in ensuring that asset prices fully reflect all available information, as stated in his Efficient Market Hypothesis. 

The concept of liquidity is multifaceted, encompassing aspects such as market depth, immediacy, and tightness. Market depth refers to the exchange’s ability to handle large orders without significant price changes that occur following a trade, known as slippage. Immediacy is the speed at which orders can be executed. Finally, tightness refers to the spread between the bid (purchase) and ask (sale) prices. A market is considered highly liquid if it possesses depth, immediacy, and tight spreads in the order book, allowing for efficient price discovery and minimal transaction costs.

In the burgeoning world of decentralised finance (DeFi), liquidity takes on a newfound importance. Liquidity in these markets is often provided by liquidity providers (LPs) who pool their assets in smart contracts. These liquidity pools are used to facilitate trading activities on decentralised exchanges (DEXs), with LPs earning fees in return. The concept of Automated Market Makers (AMMs), pioneered by platforms like Uniswap, hinges on this principle of liquidity provision. The importance of liquidity in these markets cannot be overstated. It is the cornerstone upon which the promise of DeFi – a truly open, inclusive, and efficient financial system – is built. 

The Role of Liquidity in Driving DeFi Innovation

The management of liquidity and the maximisation of capital efficiency have been pivotal in driving the continued innovation of DEXs in the DeFi landscape. As the backbone of DeFi, DEXs have had to constantly evolve and adapt to the challenges posed by the unique characteristics of the crypto market, particularly its volatility and the fragmentation of liquidity. The quest for efficient liquidity management and capital utilisation has led to the development of novel mechanisms and protocols.

Uniswap, one of the pioneers of the AMM model, serves as a prime example of this liquidity-driven innovation In its initial iteration, Uniswap V1, the platform introduced the concept of liquidity pools, where users could deposit equal values of ETH and any Ethereum Request for Comment 20 standard token (ERC-20) to create a market. While this model was revolutionary, it had its limitations, particularly in terms of capital efficiency. The 50/50 liquidity provision requirement meant that capital was often underutilised, especially for pairs with significant price disparity.

In response to this, Uniswap V2 introduced several improvements, including the ability to create direct pairs between any two ERC-20 tokens, thereby improving capital efficiency. However, the most significant leap came with Uniswap V3, which introduced concentrated liquidity. This feature allows liquidity providers to specify price ranges for their liquidity, thereby maximising capital efficiency. Using this model, LPs can provide liquidity only at price levels where they anticipate trading activity, ensuring they are constantly making use of the liquidity in pools. This innovation has not only improved capital efficiency but reduced slippage, benefiting traders.

The evolution of Uniswap and the broader DeFi landscape underscores the critical role of liquidity management and capital efficiency in driving innovation. As the DeFi space continues to mature, the quest for improved liquidity and capital utilisation will undoubtedly continue to shape its trajectory. From the development of more sophisticated AMM models to the integration of cross-chain and layer 2 solutions, the pursuit of liquidity and capital efficiency will remain at the forefront of DeFi innovation. The role of liquidity in driving DeFi innovation is not only significant yet concurrently transformative, shaping the future of finance in profound and novel ways.

Taking the Next Step with Elektrik

Despite the progress made by protocols such as Uniswap V3, liquidity in web3 is still critically underutilised. While DeFi boasts a number of protocols that offer high levels of capital efficiency, the relatively small amount of liquidity present in the market often causes issues, particularly as it pertains to the cold start problem. At its core, the cold start problem refers to the challenge of launching a new product or service in a market where network effects are prevalent. In such markets, the value of the product or service increases with the number of users, creating a virtuous cycle of growth. However, this also means that when a product or service is first launched, it has little to no value as there are no users yet. Subsequently, at a fundamental level, the cold-start problem can be understood through a question – in an environment where users extract value from the existence of other users, why would the initial wave of users remain in the environment?

This problem is faced not only by newly minted protocols aiming to facilitate the liquidity of their own token, but also newly created DEXs looking to establish a base of liquidity providers for trading. Without this base, tokens would be untradable and the DEX would subsequently be rendered ineffective. Hence, the importance of implementing effective measures to foster the highest level of capital efficiency possible becomes clear, DEXs are seeking to overcome the cold-start problem with as little liquidity as possible whereby traders always face a positive experience.

Elektrik is one such DEX looking to solve this problem, implementing effective capital efficiency measures to facilitate high volume trading from its inception. Incidentally, this necessitates the adoption of novel and creative mechanisms to attract LPs and manipulate liquidity so that it is always available where needed. While traditional DEXs, such as Uniswap, have taken strides in this regard, Elektrik represents a new wave of DeFi protocols that can achieve more with less liquidity.

How Does Elektrik Work?

Elektrik is a DEX protocol built on the Lightlink Network. In its first iteration, Elektrik V1, the DEX plans to implement itself as a fork of the revolutionary Uniswap V3 architecture. As a fork of Uniswap V3, Elektrik carries forward the proven AMM model, enhancing it with the unique capabilities and features of the Lightlink network. This AMM model allows users to trade directly with the smart contract on the platform. Users can also become LPs by depositing assets into the liquidity pools and earn fees from the trading activity. This design is intended to provide efficient and flexible trading opportunities for all users. 

The protocol is built on Lightlink, a layer 2 blockchain secured by Ethereum, purposefully built for Metaverse, NFT, and Gaming applications. By harnessing the power of the Lightlink network, Elektrik is able to offer an efficient and seamless trading experience for its users. Most importantly, Lightlink offers a unique feature referred to as ‘enterprise mode’ which allows organisations to pay a monthly fee, covering its users’ gas costs, to simplify users’ experiences when transacting with ERC20 and ERC721 smart contracts, effectively bypassing native gas costs. This feature, combined with Lightlink’s low transaction fees and high speed, provides Elektrik with a significant advantage over other DEXs built on more traditional blockchains.

Elektrik’s design as a Uniswap V3 fork also brings with it a number of benefits. For instance, Elektrik, like Uniswap V3, provides higher capital efficiency compared to its predecessors by allowing liquidity providers to provide liquidity in concentrated price ranges, which, for sophisticated and active LPs, can potentially lead to higher returns. Furthermore, Elektrik supports single-sided liquidity provisioning, enabling LPs to deposit only one type of asset in a trading pair, reducing the risks associated with price fluctuations. 

In terms of fee structure, Elektrik implements an adaptive fee structure that dynamically adjusts fees based on market conditions and liquidity utilisation. This is achieved through the introduction of multiple fee tiers for each pair: 0.05%, 0.30%, and 1.00%. These options allow LPs to adjust their margins based on the expected volatility of the pair. For example, LPs can choose to take on more risk with non-correlated pairs like ETH/DAI, or minimal risk with correlated pairs like USDC/DAI, and select the fee tier that best compensates them for this risk. 

This ensures competitive fees for users while maintaining incentives for liquidity providers. By adapting fees to market conditions, Elektrik aims to promote efficient market participation and attract liquidity. Moreover, Elektrik introduces enhanced capital efficiency by utilising multiple fee tiers within liquidity pools. Liquidity providers can allocate their funds to different fee tiers, optimising their capital allocation and earning potential. This feature encourages efficient capital deployment and enables liquidity providers to maximise their returns.

Understanding Elektrik V2’s Liquidity Model

Although Elektrik is initially being released via the aforementioned Uniswap V3 model, Elektrik V2 plans to implement an innovative AMM. The Elektrik V2 platform represents a significant advancement in the realm of decentralised exchanges, distinguished by its incorporation of abstracted AMM, Artificial Intelligence (AI), Reinforced Learning (RL), and dynamic smart contracts. Central to Elektrik’s proposition is its commitment to capital efficiency, ensuring that liquidity is not merely present but is deployed judiciously for optimal trading outcomes. The Dynamic Liquidity Provision (DLP) mechanism is pivotal in this regard, meticulously adjusting liquidity with each block on the LightLink network to meet the precise requirements of liquidity providers.

While Elektrik V1 allows for LPs to add liquidity to particular price ranges, Elektrik V2 harnesses the power of AI to anticipate and modulate liquidity in the inherently unpredictable cryptocurrency market. While conventional AI models may falter in such volatile environments, Elektrik’s model is characterised by its dynamic adaptability. It undergoes continuous training on a diverse array of data, both internal to Elektrik and from external sources, ensuring its models remain contemporaneous and pertinent. This perpetual refinement is instrumental in ensuring that liquidity is judiciously allocated, responding adeptly to market fluctuations and safeguarding optimal trading conditions.

The decision-making prowess of this AI is further enhanced by the principles of Reinforcement Learning (RL). To elaborate, RL operates on a paradigm wherein the system discerns optimal actions through a process of iterative trial and error. Within Elektrik’s operational framework, RL assists in determining the most efficacious deployment of liquidity, harmonising the dual objectives of return maximisation and risk minimization. By synergizing dynamic AI with RL, Elektrik underscores its commitment to the judicious management of liquidity, thereby promising an unparalleled trading experience driven by precision and efficiency.

Comparing Elektrik to the Competition

Since 2021, the DEX landscape has been dominated by Uniswap V2-style DEXs, with many implementing the tried-and-tested x * y = k algorithm and spreading liquidity evenly across all price ranges. This can lead to inefficiencies, especially those associated with use of capital. If liquidity is dispersed across all price ranges, each pool will require a larger amount of liquidity to facilitate the same amount of volume. Consequently, more trading fees are dispersed to a greater number of parties and traders must be charged higher fees in order to provide LPs with the same level of yield. 

With the advent of Uniswap V3 in 2022, the DEX landscape has likewise undergone a subsequent evolution, with concentrated liquidity models becoming increasingly prevalent in DeFi. Nevertheless, these types of models often require manual rebalancing of liquidity or custom automated strategies by LPs, which can be relatively inefficient. Thus, even the relatively recent AMM models possess inherent flaws with regards to their management of idle liquidity that make them ineffective solutions when compared to next generation AMMs such as that implemented by Elektrik V2.

Elektrik V2 and similar DEXs will offer far greater flexibility than their contemporaries. The greater capital efficiency facilitated by the continuous rebalancing and concentration of liquidity will allow protocols to handle high volume trading with relatively insignificant liquidity. Thus trading fees for users can be reduced and those which are earned can be dispersed between fewer LPs, providing incentives for the participation of users and LPs alike.

Another key advantage of an automatic liquidity rebalancing model is the potential reduction in impermanent loss. Impermanent loss is a risk faced by LPs in traditional AMMs when the price of the assets in a pool diverges. By automatically adjusting liquidity to follow price movements, a DEX implementing this model can ensure that a LP’s liquidity is never concentrated in one side of a pool, mitigating the effects of impermanent loss. This means LPs are less likely to be holding the wrong asset when prices change, which can lead to more stable and predictable returns.

Notably, this model does possess some inherent challenges, particularly associated with the potential incorporation of machine learning for liquidity rebalancing. After all, if the AI makes an incorrect judgment, then the actual price range will have less liquidity than if the prediction were correct. However it is important to note that any particular price range would never be completely devoid of liquidity due to the use of a price weighting model by the AI, which allocates liquidity to certain price ranges depending on the likelihood that the price will be achieved. Furthermore, the learning curve for LPs in actually understanding and grasping this system may pose some challenges to adoption. Nevertheless, these challenges can be solved via frequent rebalancing and user interface abstraction for a more seamless user experience. 

Conclusion

The very definition of liquidity as the ability to quickly and effortlessly buy or sell assets, is the essence of a functional market, be it the financial markets at large or the intricate DeFi space. Its influence extends throughout history, where liquidity has ruled the dynamic and ever-evolving landscape of markets and as we have found, continues to influence the modern financial system – even in the context of DeFi. Therefore, it’s evident that DeFi markets, such as Elektrik, which foster liquidity and allocate it efficiently, are likely to remain at the forefront of their respective industries. Therefore, it’s evident that DeFi markets, such as Elektrik, which foster liquidity and allocate it efficiently, are likely to remain at the forefront of their respective industries. Consequently, as one of the principal determinants of market and asset success, liquidity, as championed by platforms such as Elektrik, will continue to drive innovation, incentivize adoption, and remain paramount in financial markets.

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Bitcoin (BTC) remained stuck inside a narrow range into May 20 as cryptocurrency markets shook off United States macro triggers.

Powell leaves market with “tons of uncertainty”

Data from Cointelegraph Markets Pro and TradingView showed BTC/USD trading just below $27,000.

The pair had seen brief volatility after Jerome Powell, Chair of the Federal Reserve, gave new commentary on policy and the outlook for inflation.

While leaving the door open for change should it be required, Powell’s language did not offer risk assets clear signals. Responding, financial commentary resource, The Kobeissi Letter, warned that “tons of uncertainty” lay ahead.

SUMMARY OF POWELL SPEECH (5/19/23):

1. Inflation “far above 2% objective”

2. Committed to getting inflation to 2%

3. Unclear if rates are “sufficiently restrictive”

4. Failure to lower inflation prolongs pain

5. Will “take time” to lower inflation

Tons of uncertainty ahead.

— The Kobeissi Letter (@KobeissiLetter) May 19, 2023

Bitcoin nonetheless soon forgot the event, returning to a range already familiar from the weekend prior.

Assessing the climate on exchanges, popular trader Skew argued that a fresh volatility was only a matter of time.

“Growing variance between perp & spot market; which ive posted about previously,” he summarized in part of Twitter coverage on the day.

“Very tight illiquid range here between post friday FED speakers. Expecting market to find an EQ early next week in which both spot & perp market will be forced to establish a trend.”

A further post noted that the early signals were there for the status quo to be disrupted.

$BTC Binance Open Interest
Minimal price movement yet, positions flowing into the market again

Always precedes a liquidity hunt especially in this current environment pic.twitter.com/exvwCTcci6

— Skew Δ (@52kskew) May 20, 2023

Fellow trader Crypto Tony meanwhile forecast that the range would stay in place until the start of the new macro trading week.

A close above or below the levels marked on an accompanying 4-hour chart, he added, would form cause to reconsider the market.

$BTC / $USD – Update

I expect we will be in this range all weekend no doubt, bit we play the breakout if it does come today, tomorrow, next year …

Breakout plays are my favourite. We identify the range, set alerts and wait for the beast to break the cage pic.twitter.com/1B7wB5uqCc

— Crypto Tony (@CryptoTony__) May 20, 2023

Caution over “big sell off” for Bitcoin

Others were bearish on the immediate future when it came to BTC price performance.

Related: Hyperbitcoinization coming, says Bitcoin OG as ‘wholecoiners’ hit 1 million

Popular analytics account IncomeSharks warned that a deeper correction was expected, but should not materialize for another week.

“Expecting another week of chop before the big sell off,” part of Twitter commentary stated the day prior.

#Bitcoin – The red squiggles surprisingly accurate so far. Double rejection on the 4h supertrend. Expecting another week of chop before the big sell off. That’s when I’ll jump back in, when Twitter thinks we are going back to zero pic.twitter.com/hakZMRmdGU

— IncomeSharks (@IncomeSharks) May 19, 2023

Trading resource Stockmoney Lizards agreed, predicting that a breakdown was due while referencing the “head and shoulders” pattern discussed throughout trading circles in recent weeks.

“Correction in play,” it summarized, offering a target zone around $24,500.

Magazine: ‘Moral responsibility’: Can blockchain really improve trust in AI?

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.