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Algorithmic

Key Insights: Financial Markets Transformation by 2030For years, this page has been my space to share in-depth market research and personal insights into key financial trends. This post reflects my perspective — a strategic outlook on where I believe the digital finance industry is heading. The financial world is evolving at an unprecedented pace, and it's easy to overlook subtle shifts. But the undeniable fact is that we are now standing at the intersection of three powerful industries — financial markets, blockchain, and artificial intelligence. We are positioned at the cutting edge of technology, where innovation is not a future concept but a present reality. This post serves as a reference point for future trends and a guide to understanding the transformative forces shaping financial markets by 2030. These are not just facts, but my vision of the opportunities and challenges ahead in this rapidly converging digital ecosystem. Staying ahead today means more than following the market — it means recognizing that we are part of a technological shift redefining the core of global finance. 📈 1. Electronic Trading Evolution Full transition from traditional trading floors to AI-driven digital platforms. Integration of blockchain and smart contracts ensures transparency, automation, and risk reduction. Real-time data analytics democratizes market access and enhances strategic decision-making. 🤖 2. Algorithmic Trading Growth Accelerated by AI, machine learning, and big data analytics. High-frequency trading (HFT) boosts efficiency but introduces new volatility factors. Adaptive algorithms dynamically adjust strategies in real time. Strong focus on regulatory compliance and ethical standards. 🔗 3. Tokenization of Real World Assets (RWA) Transforming asset management with projected growth to $18.9 trillion by 2033. (now 18.85B) Enhances liquidity, accessibility, and transparency via blockchain. Institutional adoption is driving mainstream acceptance. Evolving regulations (DLT Act, MiCA) support secure tokenized ecosystems. 🏦 4. Institutional Adoption & Regulatory Frameworks Digitalization of fixed income markets and exponential growth in institutional DeFi participation. Key drivers: compliance, custody solutions, and advanced infrastructure. Global regulatory harmonization and smart contract-based compliance automation are reshaping governance. 💳 5. Embedded Finance & Smart Connectivity Embedded finance market to hit $7.2 trillion by 2030. Seamless integration of financial services into everyday platforms (e-commerce, mobility, etc.). AI, blockchain, and IoT power real-time, personalized financial ecosystems. Smart contracts reduce operational friction and enhance user experience. 🛡 6. Financial Crime Risk Management Market expected to reach $30.28 billion by 2030. AI-driven threat detection and anomaly monitoring strengthen AML compliance. Blockchain ensures data integrity and automates cross-border regulatory adherence. Global collaboration (FATF, EU AML) fortifies defenses against evolving financial crimes. 🌍 7. Consumer Behavior & Financial Inclusion Digital banking bridges the gap for underbanked populations, especially in emerging markets. Mobile solutions like M-Pesa revolutionize access to financial services. Biometrics, microfinance, and AI-powered engagement tools foster inclusive economic participation. 🚀 Conclusion By 2030, financial markets will be defined by technology-driven efficiency, regulatory adaptability, and inclusive growth. Success will favor those who embrace innovation, leverage automation, and engage in cross-sector collaboration. The future belongs to agile stakeholders navigating a landscape shaped by AI, blockchain, tokenization, and smart finance connectivity. Best regards, EXCAVO _____________________ Disclosure: I am part of Trade Nation's Influencer program and receive a monthly fee for using their TradingView charts in my analysis.
TRADENATION:BTCUSDLong
by EXCAVO
9
Gold Buy Possibility Opening Rush Takes out Sellers and creates new all time High Quick Pullback has traders Sceptical of Direction Weak Liquidation attempt adds more early buyers to the pool Next move is a Structural attempt to break most recent consolidation high ( a failed attempt at the FVG or structural high signifies another liquidation attempt) Possible area of final liquidation attempt has a great deal of confluencial factors, signifying a potential opportunity to join the uptrend NB! Theory 1 A break at the most recent consolidation high (above FVG) might cause another liquidation none the less, and has a stronger potential to signify continuation of the uptrend, it would also add buyers, which would possibly be liquidated and turned to sellers, adding more sellers for when the market actually rests and continues with the uptrend, Theory 2 The final Liquidation does come to fruition due to structure attempt not failing, missing an opportunity to buy with the lowest risk Theory 3 if Everything goes as expected, however the final liquidation area is taken out, no trade, Possible change in market direction. Strategy Management if All goes as planned, Entry somewhere at golden Zone, with candlestick confirmation Risk would be around 800 - 1000 points on 2 decimal brokers 8.00 - 10.00 Take profit around 3 - 10 times that total (Exits based on algorithmic confluences) #Let Winners ride and cut losers short #Buy Low, Sell High #SMC
PEPPERSTONE:XAUUSDLong
by codexd
An Algorithmic outlook on BitcoinAs forecasted on Twitter, the rally started exactly from mid may, when BTC bottomed in our support zone of around 26k. In general, I believe the low of 25.8k has been set and only up from here for the coming month or two. As mentioned in my earlier ideas here, and on Twitter too, the main target is 36k - which I suspect will reach sometime in July. That said, in the short term, the price looks somewhat overbought, so if you didn't go long at our entry point, it is risky to do so here. It is recommended to wait a bit for the price to cool off, be it a correction lower, or just sideways. Furthermore, the AlphaNumero algo is very bullish on the 4H, however cautiously bearish on the 30minute. This is good confluence with my TA. As always, trading carries risks and you must be well informed before making a decision. While this bullish scenario is likely, it is not a certainty. Invalidation for the 36k move is at 25.2k. For those interested in the algorithm, it is linked below.
by Alien_Algorithms
1
Algorithmic Stablecoins: Will They Ever Find Enough Support?Algorithmic stablecoins, as their name implies, are cryptocurrencies that use algorithms to maintain a stable value instead of being backed up by any sort of reserve asset as collateral. However, in reality, some algorithmic stablecoins have struggled to maintain a stable peg, while others have failed catastrophically. This article examines the major types of algorithmic stablecoins, their design, and shortcomings and then explores how algorithmic stablecoins may develop over time. In conclusion, we believe algorithmic stablecoins will become the most important type of stablecoins globally and serve as the major currencies for the future’s decentralized financial world. Their creation and transaction will happen on a global scale instead of being subject to the regulation of any jurisdiction. Stabilization Mechanisms and Challenges Algorithmic stablecoins have a mechanism like the shadow banking which provides the possibility for offshore money creation. Different from other types of stablecoins, algorithmic stablecoins maintain price stability not by relying on centralized entities but by using algorithms to regulate supply and demand. As a result, algorithmic stablecoins face various challenges, including illiquidity and black swan incidents. Rebasing: This mechanism adjusts the circulating supply in response to price fluctuation. When the price of a stablecoin is higher than the reference, the protocol will mint more tokens. When the price goes the other way, the protocol will burn or repurchase tokens. Ampleforth is an example of stablecoins using this scheme. Seigniorage: This mechanism supports a stablecoin’s value by issuing one or more other cryptocurrencies. When the price of a stablecoin is higher than the reference, the protocol will use seigniorage tokens as collaterals to generate more tokens. Conversely, the protocol will buy back or burn seigniorage tokens. Basis Cash is an example of this type of stablecoins. Besides these two schemes, some new projects are experimenting with other innovative ways to maintain the peg. Take Frax Finance for instance. It introduced a fractional reserve stablecoin, which is partially backed by collateral, i.e. USDC, and partially stabilized algorithmically. Algorithmic stablecoins have faced various challenges in recent years. The major ones are the following. Imbalanced supply and demand: When demand drops, the price of an algorithmic stablecoin tend to be lower than the reference, leading to the burning or repurchasing of a part of the circulating supply to regain balance. However, such a move may further dent market confidence or even trigger a vicious circle of selling. Terra is a bloody lesson. Governance risks: Algorithmic stablecoins are run by smart contracts and decisions are made by the consensus of the majority. Therefore, there may be governance risks such as code defects, hacker attacks, manipulation, or conflict of interests. Legal and regulatory challenges: As algorithmic stablecoins are not backed up by physical assets, they face more legal and regulatory uncertainties. There may be more countries and regions banning or limiting the use of algorithmic stablecoins in the future. Mainstream Models: Semi-decentralized and Overcollateralized There are many subgroups of algorithmic stablecoins based on their design. The collateralized lending model of MakerDAO is representative. The protocol allows users to issue DAI by locking up collateral assets such as ETH and adjusts the supply of the stablecoin according to market demand. Another representative mechanism is the liquidity pool model of Aave, which adjusts the price of a stablecoin in real time based on supply and demand and maintains price stability through arbitrage among multiple stablecoins. Below are three stablecoins representative of the mainstream models. GHO GHO is a multi-collateral stablecoin that pegs its value to the U.S. dollar. Users or borrowers can mint GHO using a diversified set of collateral on Aave. When borrowers borrow GHO, the protocol mint GHO tokens. When the loan is repaid, the previously issued GHO tokens will be destroyed, reducing their circulation. GHO can be used in payment, lending and borrowing, and other use cases. It can also generate yield as the tokens will participate in liquidity mining on Aave automatically. GHO uses the liquidity pool model of Aave V3 where Aave is the only liquidity pool provider and users can only acquire GHO through Aave V3 using the collateral available. Therefore, all the revenue generated from the GHO stablecoin will go to the Aave Treasury and finally be controlled by the Aave DAO. In the future, more liquidity pool providers may be allowed to make the stablecoin more decentralized. In summary, GHO is a decentralized multi-collateral yield-generating stablecoin. Its innovative features, especially interoperability with other services on Aave, give it certain competitive advantages. However, as the stability of GHO relies on the value and liquidity of its collateral, if the market fluctuates widely or meets liquidity crises, the stablecoin may depeg and liquidate. GHO’s risk management and degree of decentralization are areas worthy of attention. If more liquidity pool providers join the system, the allocation of risks and interests will be more complicated. Then, more matured decentralized governance mechanisms will be needed to ensure its long-term stability and sustainability. CrvUSD CrvUSD is an algorithmic stablecoin using a so-called lending-liquidating AMM algorithm or LLAMMA. The algorithm maintains price stability by converting between the collateral (for example, ETH) and the stablecoin (let’s call it USD). If the price of collateral is high — a user has deposits all in ETH, but as it goes lower, it converts to USD. Users may also use liquidity provider positions (LP tokens) as collateral. This is very different from traditional AMM designs where one has USD on top and ETH on the bottom instead. The LLAMMA is designed to provide a soft liquidation mechanism that turns collateral into liquidity provider positions, thus avoiding large asset dumps in a short time as in other models. In a nutshell, Curve’s stablecoin mechanism achieves price stability and liquidity by combining the liquidity of different chains and multiple strategies and leveraging composability with other DeFi projects. The stablecoin can also enable investors to generate returns by participating in transactions, borrowing and lending, and liquidity mining, thus motivating more users to participate in its ecosystem. FRAX FRAX is partially backed by collateral assets and partially supported by the native token of Frax Finance, FXS. The ratio of these two in the backing of Frax is called the Collateral Ratio (CR) The collateral, in this case, is USDC. The Frax Protocol adjusts CR in accordance with the market price of Frax. When the market price of FRAX goes under HKEX:1 , the Frax algorithm increases the CR, meaning that each FRAX is required to be backed by a higher percentage of hard collateral (USDC). This action increases market confidence that Frax can maintain its backing, causing the price to rise. In this way, the algorithm maintains the balance and ensures Frax does not break its peg. In Frax V2, a new mechanism called algorithmic market operations controller or AMO was introduced, which reinvests the excess collateral elsewhere to generate additional revenue to support the protocol’s long-term growth. Also, the Frax community has voted to give up on the two-token model and increase the target CR to 100%. This will make Frax more attractive for users looking for a long-term store of value. The target CR will be achieved through AMO instead of selling the FXS token. The AMO module enables programmable monetary policy as long as it does not change the FRAXT price off its peg or lower the collateral ratio. This means that AMO controllers can perform open market operations algorithmically, but they can not arbitrarily mint FRAX out of thin air and break the peg. This keeps FRAX’s base layer stability mechanism pure and untouched while creating maximum flexibility and opportunity, enabling FRAX to become one of the most powerful stablecoin protocols. However, the protocol still needs to rely on external stablecoins as its last defense. If external stablecoins go wrong or are frozen, like what recently happened to USDC in its recent de-pegging, the stability, and security of FRAX and its protocol will be affected. What’s more, the protocol also relies on the FXS tokens for its governance and incentivizing users. If FXS tokens suffer price declines or reduced demand, FRAX and its protocol will be influenced too. In a nutshell, the strengths of GHO and crvUSD are their stable market positions, multiple use cases, and investment value. FRAX is strong in technology, but it has never been through large-scale market turbulences and its use cases and investment value are waiting to be demonstrated. In the future, GHO and crvUSD may continue to deepen their moat by rolling out new products and extending to new use cases. Current Issues The above-mentioned stablecoins face the same risk. With the increased complexity of their protocols, they are subject to more diversified attacks which could jeopardize the whole ecosystem out of the blue. In recent years, we’ve already seen many bankruptcies because of loopholes in stablecoins. In addition, competition in the stablecoin space is getting more and more fierce. A few decentralized stablecoins have built a deep moat in terms of on-chain liquidity and cooperation with other protocols. By contrast, native stablecoins of a single protocol have struggled to get enough liquidity. The cost for them is huge. Currently, there are two major directions in the development of stablecoins: collateral-based and arithmetic. The former can be considered pseudo-arithmetic stablecoins. The two types both have their issues. Collateral-based stablecoins require a large amount of overcollateralized assets, while algorithmic stablecoins are often faced with illiquidity and unfair incentives. In comparison, the previously popular “liquidity mining” model has essentially placed protocol-controlled value over algorithmic stability. But in the last two years, it has been proven that such a design that prioritizes liquidity over collateral also has problems. For example, in times of market contraction, there may be insufficient liquidity, and holders and DAOs may be unfairly rewarded. This may lead to situations where whales manipulate the market, which is detrimental to the long-term stability of the ecosystem. Low acceptance as a store of value These stablecoins have struggled to be accepted by users. The main reason is that they are not as stable as their mainstream peers that are pegged to fiats such as the U.S. dollar. Algorithmic stablecoins are more often used as rewards rather than being regarded as a store of value. DAI, as a pioneer in this space, has accrued market shares. However, with the rise of fiat-pegged stablecoins like USDC, DAI’s position has been shaken. In addition, algorithmic stablecoins often have complicated and incomprehensible mechanisms which require holders’ involvement in maintaining their stability. This means increased costs and risks and somewhat reduced experience for users. Algorithmic stablecoins have yet to be widely adopted, their liquidity and market shares are relatively low. This has restrained their use in payments, lending, and cross-border transfers, which in turn affected their attractiveness as a store of value. In summary, for arthrotomic stablecoins to be more widely accepted, their stability issue will need to be addressed first to enable them to be deemed as better storage of value. Furthermore, more efforts need to be put into understanding users’ needs, such as providing higher yields, to attract more users. Increasing connection to real-world assets is also a promising way to enhance the liquidity and value of algorithmic stablecoins and improve their competitiveness. Reliance on diversified collaterals At present, algorithmic stablecoin protocols still need a diversified set of collaterals such as ETH and CRV to operate, and their scalability also relies on the growth of the value of the collaterals. Meanwhile, they face risks of low demand. Some protocols have already run out of their insurance funds due to risk incidents. We are doubtful about the legitimacy of having multiple collaterals. From a short-term view, support for a diversified set of collaterals will improve the network effects and bring more liquidity to a stablecoin, especially in a bull market. However, from a long-term view, it’s an irresponsible speculative move that endangers the stablecoin’s stability and safety. Obtaining liquidity from centralized exchanges to improve the feasibility of these protocols may be a possible solution. Take Frax for instance. Although it is algorithmic in its stability mechanism, when faced with strong redemption pressure, its degree of decentralization reduces, leaving holders with more risks. Algorithmic stablecoins should be undercollateralized in nature and they are naturally riskier. However, such a nature makes it difficult for them to be compliant, which in turn is a prerequisite for them to be competitive against centralized stablecoins such as USDC. Therefore, finding a better solution to their reliance on diversified collaterals and expanding to more use cases will be the key to their future potential. Exploring Decentralized Algorithmic Stablecoins BTC/ETH Collateralized Crypto-native Stablecoins LUSD: Liquity’s Crypto Native Stablecoin LUSD is a stablecoin issued by the decentralized lending protocol Liquity which allows users to pledge ETH to obtain loans with 0% interest. The algorithm of LUSD requires borrowers to maintain over-collateralization, or else their borrowings will be liquidated. LUSD can be redeemed at any time for HKEX:1 of ETH. LUSD also benefits from its strong soft peg mechanism which adjusts the supply and demand of LUSD in line with market expectations to maintain its value within $1.00-$1.10. LUSD provides interest-free borrowing on Ethereum that allows users to obtain an ETH-backed loan without any recurring costs, making borrowing highly capital efficient. Additionally, it has innovative features such as collateral pools, stabilizations pools, and a liquidation mechanism to ensure its safety and stability. Nevertheless, it faces competition from protocols providing similar services, such as MakerDAO and Compound. In the meantime, regulatory pressures from different countries and regions, such as the U.S. and the European Union, are also a concern. DLLR: Sovryn’s Sovereign Stablecoin The Sovryn Dollar (DLLR) is a BTC-backed stablecoin aggregating multiple exclusively BTC-backed “constituent” stablecoins. It aims to maintain a 1:1 peg with the value of USD and provide a great form of payment and a reliable store of value. By aggregating more BTC-backed stablecoins such as ZUSD and DOC which rely on a combination of algorithmic and incentive-based mechanisms to stabilize, DLLR is designed to be more stable and more resilient to market volatility and collateral risks than any of the individual stablecoins backing it. The supply of DLLR is determined by market demand. When the price of DLLR is higher or lower than HKEX:1 , there will be arbitrage opportunities to restore the balance. Sovryn is a decentralized finance (DeFi) protocol deployed on a Bitcoin sidechain called Rootstock. It supports leveraged trading, perpetual futures, lending, and other DeFi activities and adopts zero-knowledge-proof technologies to protect user privacy. It also has the security assurance of the Bitcoin blockchain. All services on Sovryn are priced in BTC and secured by the Bitcoin network. Sovryn runs on EVM-compatible smart contracts on Rootstock and is interoperable with the Bitcoin network, lightning network, Ethereum, and the Binance Smart Chain. Most of the features of Rootstock or RSK, are like Ethereum. The uniqueness of the RSK blockchain is that it has 2-way interoperability with the Bitcoin blockchain, and it has a merged mining mechanism that allows it to be mined simultaneously with the Bitcoin blockchain. All Sovryn’s ownable contracts are currently controlled by the Exchequer multisig, an anonymous group of key holders, except Staking and FeeSharingProxy which can be updated according to the votes of SOV stakers. Changes to all contracts and the project’s codebase can be voted on in Bitocracy DAO, with the right to vote given to stakers of SOV tokens. The potential of DLLR lies in its being a fully transparent, decentralized, and censorship-resistant stablecoin that is exclusively backed by BTC, free from the intervention and risks of any centralized third party. It increases the value and utility of BTC and facilitates the circulation and usage of BTC. DLLR is also a powerful lending tool for the Sovryn platform, enabling users to borrow DLLR against BTC collateral with 0% interest and generate high yields. Multiassets-backed Stablecoins sUSD: leveraging the tailwind of synthetic assets sUSD is a stablecoin issued by Synthetix. It tracks the price of the U.S. dollar and relies on a decentralized oracle network to obtain price feeds. sUSD is baked by crypto-native collateral, i.e., the SNX token issued by Synthetix. sUSD has multiple use cases in the Synthetix ecosystem including trading, lending, saving, and buying other synthetic assets or Synths such as synthetic stocks, commodities, and cryptocurrencies. The pegging mechanism of sUSD relies mainly on arbitrage and the balance of demand and supply. When the price of sUSD goes below HKEX:1 , arbitragers can buy sUSD at external exchanges using the U.S. dollar or other stablecoins and then use sUSD to buy Synths on the platform or stake sUSD to borrow SNX or ETH. When the price of sUSD goes above HKEX:1 , arbitragers can stake SNX or ETH to borrow sUSD on the Synthetix platform and sell sUSD into the U.S. dollar or other stablecoins on external exchanges. Such arbitrage operations will increase the demand or supply of sUSD accordingly, helping it to restore the peg to $1. Benefiting from Synthetix’s multichain strategy, the use cases of sUSD have been greatly expanded with the introduction of Atomic Swaps, Curve, and Perps v2. Furthermore, the capital efficiency issue of sUSD will likely be addressed in Synthetix v3 which will support multiple collaterals thus bringing down sUSD’s pledge ratio. In this multichain era, Synthetix has the potential to grow into a super application and sUSD may leverage the tailwind of synthetic assets to find more support from real-world assets. TiUSD: multi-asset reserves stablecoin TiUSD is an algorithmic stablecoin issued by the TiTi Protocol that pegs to the value of 1 U.S. dollar. It is decentralized, backed by multi-assets reserves, and has a use-to-earn mechanism to ensure its stability and scalability. As an elastic supply stablecoin, its supply will be automatically adjusted according to market demand and supply and price fluctuation. Its reserve pool consists of multiple crypto assets, such as ETH, BTC, and DAI, which enhances its reserve diversification and risk resilience. However, TiUSD faces competition and challenges from other algorithmic stablecoins, especially those with more complex or advanced algorithmic designs or governance models, such as MakerDAO, Ampleforth, etc. Additionally, TiUSD needs to ensure its reserve diversification and risk resilience to avoid the risk of reserve depletion or attacks. Omnichain Stablecoins USD0: Tapioca-based stablecoin LayerZero is an innovative cross-chain messaging infrastructure that allows for the secure transfer of tokens between different chains without the need for asset wrapping, intermediate chains, or liquidity pools. Big Bang, an omnichain money market based on Layerzero, allows users to mint an omnichain stablecoin called usd0. It has no borrowing ceiling but a debt ceiling. The collateral that the program accepts for minting usd0 is the native Gas token (or its staked derivative). These include ETH, MATIC, AVAX, wstETH, rETH, stMATIC, and sAVAX. The pegging mechanism of USD0 is based on an algorithm called Tapioca, which employs a dynamic debt ceiling and a variable borrowing fee to maintain a 1:1 ratio between USD0 and the U.S. dollar. Tapioca adjusts the debt ceiling and borrowing fee in response to market conditions and changes in the value of the collateral. When the price of USD0 is above HKEX:1 , Tapioca increases the debt ceiling and lowers the borrowing fee to encourage users to mint more USD0. When the price of USD0 is below HKEX:1 , Tapioca lowers the debt ceiling and raises the borrowing fee to encourage users to repay or buy more USD0. In theory, USD0 can be used on any chain. It does not require asset wrapping or intermediate chains, thereby reducing costs. It can also leverage LayerZero for seamless token transfer and trading and can be integrated with other LayerZero-based applications, such as Stargate. However, its risk comes from the security and compatibility of the LayerZero protocol itself and its underlying chain. IST: enabling cross-chain asset transfer Inter Protocol’s IST is a fully collateralized, cryptocurrency-backed stable token for use across the Cosmos ecosystem. It’s designed to maintain parity with the US dollar (USD) for broad accessibility and have minimum price fluctuations. IST can be minted through three methods: the Parity Stability Module, Vaults, and BLD Boost. Using the Parity Stability Module, you can mint IST by using specified stablecoins as collateral, such as DAI, USDT, USDC and etc. Vaults allow users to mint IST by locking crypto assets at different pledge ratios set by a DAO. And BLD Boost enables users to mint IST with BLD as collateral against future staking rewards. IST has stability mechanisms similar to DAI, which consist of liquidations, pledge ratios, debt ceilings, the Reserve Pool for emergent debt reduction, and BLD issuance for debt repayment. These mechanisms are controlled with fine-grained restrictions to create a dynamic stablecoin model that has never existed before. Inter Protocol is built on Agoric, which is a blockchain within the Cosmos ecosystem where smart contracts can be developed using JavaScript. The native token of the Algoric blockchain is $BLD. IST is not only a stablecoin but also the native fee token of the Agoric platform, which adds utility to the stablecoin and enhances the stability of its token economy. Known as the “Internet of Blockchains”, Cosmos achieves blockchain interconnectivity through the IBC protocol, allowing for asset transfer between different blockchains and improving the interoperability and scalability of blockchains. Within its ecosystem, many projects are eyeing stablecoins. Being a representative, Inter Protocol’s IST has the potential to provide a more stable and reliable medium of value exchange for the whole system. As interoperability increases within the Cosmos ecosystem, it will have a positive spillover effect on the Inter Protocol. With more and more protocols being built on the Cosmos SDK achieving interoperability through IBC, there will be more protocols that the Inter Protocol can interact with, resulting in increases in the overall liquidity and potential user base. It can be foreseen that applications on Cosmos will be used more actively and so will the stablecoin of Inter Protocol. Finding Support Protocols like Curve are leading a new paradigm shift in DeFi. Specifically, DeFi protocols are increasingly realizing the need to control the issuance, circulation, and borrowing of stablecoins. With Frax and Aave following suit, more and more protocols are joining the quest to find solutions to the stablecoin trilemma. Differentiation on the product level alone will not be enough. Compared with MakerDAO, Curve, and Aave have stronger brand awareness and team capability. Therefore, their stablecoins have a relatively brighter prospect. Currently, demand for algorithmic stablecoins mainly falls into three categories: as a store of value; as a medium of exchange in transactions, and as an alternative to fiat-backed stablecoins. Meanwhile, there exist a lot of issues and challenges when introducing real-world assets into algorithmic stablecoins, for instance, issues related to the scalability and risks of real-world assets. Also, many stablecoin projects pay too much attention to the stabilization mechanism and decentralization to overlook market fitness. This is exactly why many of them struggle. Through a comprehensive overview of the industry landscape, we believe the following are promising directions of development for algorithmic stablecoins. Crypto-native stablecoin protocols. BTC and ETH are generating great network effects and they form the cornerstones for trust in cryptocurrencies. Therefore, these stablecoin projects will have a more solid backing in terms of assets. But user experience, size of lock-in assets, and reliable consolidation mechanisms will be key differentiators. Stablecoins issued by super applications. In essence, these protocols bypass the need for a trust intermediary and issue stablecoins directly to their users. In this scenario, protocols like Curve, Aave, and Synthetix will become super pawnshops enabling their users to enjoy customized financial services that are much faster and more friction-free than the real world. Considering that their user base and innovativeness will determine how far they can go, we are more bullish on Synthetix. Omnichain deployed stablecoins. They have the potential to realize true decentralization, cross-chain interoperability, and transferability. By issuing, transferring, and trading stablecoins freely on any chain, they will be able to ensure sufficient liquidity. More importantly, an omnichain insurance mechanism will help mitigate liquidity crises when a run happens. Delta-neutral stablecoins. Delta-neutral stablecoins may become an important trend in the future, but they will need to be supported by futures protocols and a large futures market. Market fitness and risk control are also worth paying attention to. Is there a possibility that an algorithmic stablecoin protocol could encompass all the features? Unfortunately, we have yet to come across such a project. Algorithmic stablecoins need to have an efficient and reliable algorithmic design that can maintain price stability in various market conditions and prevent collapses in extreme scenarios. They also need a large and loyal user base to support their economic model and provide efficient demand and liquidity. What’s more, a robust and innovative ecosystem will also be needed to bring more use cases and added value to the stablecoin through integration with on-and-off-chain services. When a stablecoin meets all these requirements, then more importance should be placed on the healthiness of its value network and assets used instead of the diversification of collateral. The rise of algorithmic stablecoins has its reasons and background, but that doesn’t mean they will eventually replace centralized stablecoins, especially in large-scale applications. Therefore, finding a more efficient and scalable solution under the premise of safety should be the focus. Also, stablecoins that are backed by the U.S. dollar such as USDC are still dominating the market because their issuers provide users with more reliable safeguards with their financial strength and compliance capability. For users seeking to avoid centralized risks and legal and regulatory risks, algorithmic stablecoins are a valuable alternative. While admitting their constraints, we hope more innovative solutions can be explored to drive the development of the whole DeFi industry.
CRYPTO:FRAXUSD
by BingVentures
Education: Why your trading strategy win rate doesn't matter!What makes a profitable automated strategy? Probably the biggest misconception for trading perpetuated in the mainstream is that you need to have a greater than 50% win rate to be profitable. This is followed by a close second, of constantly assuming you need to have a risk-reward ratio of greater than 1:1 (e.g. 1:2, 1:3 etc). This one is perpetuated mostly by forex and stock market gurus. By the end of this article, I hope to dispel these myths and aim to shed some truths on how to assess a profitable strategy. Why your win rate doesn't matter: Let's simplify this down using an example. Consider the following two strategies. Which one would you rather trade? Strategy A: 50% win rate - When you win you make 2 dollars, but when you lose, you lose 1 dollar Strategy B: 50% win rate - When you win you make 5 dollars, but when you lose, you lose 1 dollar This one was a very obvious case of choosing Strategy B. In this case, both strategies have the same win rate, but Strategy B nets you 5 dollars per win, whereas Strategy A only makes you 2. Let's take another example. A little less obvious this time. Which one would you rather trade here? Strategy A: 90% win rate - When you win you make 1 dollar, but when you lose you lose 50 dollars Strategy B: 10% win rate - When you win you make 200 dollars, but when you lose, you lose 1 dollar Now the 90% win rate strategy may look attractive on the surface, but when you dig into it, you realise that you could get a massive 50 dollar loss in the 10% of times you do lose! For those of you who chose strategy B, this is the correct answer. One way we can assess the above strategies is using Expectancy . The formula for Expectancy is as follows: (Win % x Average Win Size) – (Loss % x Average Loss Size) We can calculate the expectancies of the strategy below: Strategy A: (0.9 * 1) - (0.1 * 50) = -4.1 Meaning you are expected to lose an average of $4.10 per trade using strategy A. Not a good sign. Strategy B: (0.1 * 200) - (0.9*1) = 19.1 Meaning you are expected to win an average of $19.10 per trade using strategy B. This is a major winner here! As you've probably realised. It is possible to have a profitable strategy using a low win rate. Many trend trading/breakout strategies tend to have lower win rates, but with larger rewards to risk, whilst mean-reversion strategies tend to have higher win rates with less frequent but larger drawdowns. The backtest shown in this post shows an example of a low win rate, and high win amount strategy using the Smoothed Heikin Ashi Trend on Chart indicator which I have developed, with an overall positive expectancy, backtest (note, no strategy is perfect, should not just blindly trust backtest data). Why you may still choose to define a risk/reward Better consistency of your strategy Psychological factor of knowing that you can be expected to lose only x amount (assuming no slippage etc) As an aside, note that defining a fixed risk-reward may hurt your win rate (which could impact your expectancy) so it's important to backtest to see if you get better results with defined risk-reward parameters. This is beyond the scope of the current article, but an important consideration. Why do traders gravitate toward a higher win rate? The simple answer here is that everyone wants to be a winner! It's human nature to want to be right, whether this be about a market direction or when to open or close a trade. It's often easier to brag about how much you win whether that be on social media or just feeling good about yourself. For algorithmic traders, having a higher win rate may also provide psychological benefits, as losing 20 times in a row can sometimes be very daunting for traders and can throw doubt into the efficacy of your system. I hope that through this article, I have managed to convey that it may be prudent consider strategies with low win rates also, as these can be very profitable in their own right. Digging further: This article is only just scratching the surface of how to create and validate if a strategy is something that you should consider trading. There are many aspects of backtesting including Monte Carlo simulation, understanding standard deviation of returns and risk, Sharpe ratio, Sortino ratio, walk-forward analysis, and out-of-sample analysis to name a few that you should conduct before you evaluate a strategy as suitable for live trading. If you've made it this far, thanks for reading. If you like the content, feel free to like and share, as well as check out some of the free scripts, strategies and indicators that I have published under the scripts tab. Thank you! Disclaimer: Not to be taken as financial advice, anything published by me is purely for education and entertainment purposes
INDEX:BTCUSDEducation
by TraderHalai
3
Changed my mind with Bitcoin. My bias is bearish now. I was expecting bitcoin to respect certain price area which it did not. Now i am looking to target long time resided liquidity on the down side. I have already marked my points of interest in d1 and h1 but i will bw only publishing h1 for now and will update as it goes through it. Note that i do not take entry if my designed structure is nowhere to be found in points of interest. If you have any questions or suggestions i am always open for that. Thanks and have good trading.
COINBASE:BTCUSDShort
by hamzatariq76512
The Reasons We Follow An Algorithmic-Systematic Approach To All We have been trading and investing in markets for decades since the early 1980s. Experienced and successful traders and market participants tend to remember their losses and mistakes instead of victories. Profits feed the ego; losses are teachers for those who realize that valuable lessons come from adversity instead of triumph. Everyone has an opinion- The only objective measure is the current price The trend is your only friend- News, experts, and all other information are subjective Trading and investing can be stressful A plan and discipline are the building blocks for success You have to be in to win- Drawdowns are a part of any trading or investing system A batting average of .300 is good enough to get a professional baseball player into the Hall of Fame in Cooperstown, New York. Each time a future hall of Famer steps up to the plate, a success rate of below 30% is good enough for infamy. Trading and investing are similar. No one is correct in their market calls all of the time. When approaching any market, there are always three potential outcomes, a profit, a loss, or a breakeven. The success rate of calling a market correctly takes a back seat to other factors. We have seen market participants who have had the foresight to call the market correctly 75% of the time and still wind up losing money. Conversely, a seasoned trader can be right 20% of the time and still make an overall profit. I usually write about specific markets on Trading View, but it is essential to look at the methodology, mindset, and path to growing capital over time this week. We follow an algorithmic-systematic approach to trading and investing. Our models come from decades of experience and the knowledge gained from mistakes that led to losses. We all have the same goal; to make money and grow our capital. The route to achieving the goal is what separates the winners from the losers. Everyone has an opinion- The only objective measure is the current price I am sure we have all heard an “expert” or pundit tell us that the current price of an asset is wrong. They may provide many compelling and convincing reasons, but they are 100% wrong when challenging a price level. An asset price at any moment in time is always the correct price for one objective reason. It is the level where buyers and sellers meet in a transparent environment, the market. The “experts” and pundits take a subjective leap of faith when using the terms expensive or cheap. Too many variables establish a price. The only accurate measure of value is the current price itself. The trend is your only friend- News, experts, and all other information are subjective Prices are snapshots. Trends are the living and breathing extension of price action. Many market participants become junkies, watching each news event, “expert” forecast, and other exogenous events that could push asset prices higher or lower. They make investment or trading decisions based on what they hear and see. The approach is flawed for three significant reasons: Trading off what one sees and hears is stale before it reaches our ears and eyes. Others have seen the news or forecast before us, and some had seen it before it appeared on a medium for all to see. The translation of an event, forecast, or news item is purely subjective as it assumes, we will make a correct analysis. The expression “buy the rumor and sell the news” or the converse runs counter to even the most complete analytical decision-making approach. Finally, reacting to any stimulus involves a primary human response, emotion. Emotions are a trader or investor’s worst enemy. They trigger responses and decisions based on fear and greed, a deadly duo that increases the chances of mistakes, miscalculations, and irrational behavior. A market’s trend is purely objective as it reflects the path of least resistance of a price based on market consensus and sentiment. Prices tend to move to levels on the upside and downside that can defy logic, run counter to reason and are not rational. Trend following blocks out logic, reason, and rational thought and favors one of the leading theories of physics. Newton’s first law states that a body at rest will remain at rest unless an outside force acts on it, and a body in motion at a constant velocity will remain in motion in a straight line unless acted upon by an external force. Trend following embodies Newton’s first law of physics. Asset prices reflect the market’s sentiment, which is the inertia that drives those prices. If Sir Isaac Newton were a modern-day trader or investor, his mantra would be the trend is your only friend as it is compatible with his first law. The physical sciences are objective. Trading and investing can be stressful We have found that decision-making creates stress. When we buy or sell an asset based on anything but the market’s trend, we make a subjective judgment. The attempt to buy at the bottom or sell at the top is a value judgment that runs counter to logic as it implies the sentiment and current prices are incorrect, a fatal flaw. Sometimes some market participants get lucky, but that only reinforces a strategy that leads to future mistakes. Picking tops or bottoms in a market is a strategy that rewards the ego as it gratifies that one called the market correctly. However, ego and vanity lead us down a dangerous path. In the 1997 film, The Devil’s Advocate, Al Pacino, the actor who played Satan, said, “Vanity-definitely my favorite sin.” Reducing stress comes from following the path of least resistance. We use an algorithmic, systematic approach to trading based on models that remain long during a bullish trend and short during a bearish one. We never miss a significant trend as we are constantly long or short the assets in our portfolio. We do not adjust our risk positions on an intra-day basis. We only reverse risk positions based on closing prices at the end of a session and execute the position at the start of the next session. Our proprietary models come from decades of trading and investing experience in a wide range of markets across all asset classes. We never look to sell tops or buy bottoms. We are long at the top and short at the bottom. However, we tend to capture significant trends, taking the filet mignon out of price trends. We have found that our mechanical approach, with a better than even-money win rate, reduces stress as it takes any decision-making out of the equation. The only job is to follow the rules, always remaining in the markets on the long or short side and reversing positions based on the model’s instructions. A plan and discipline are the building blocks for success Emotions lead to impulsive behavior. Acting on impulse leaves little or no time for planning and throws discipline out of the window. Albert Einstein said that the definition of insanity is doing the same thing repeatedly and expecting a different result. Impulsive decision-making is the root of Einstein’s insanity definition. Any risk position in any market must have a plan, which is simply balancing the financial risk versus the potential reward. Before pressing the buy or sell button, we must establish risk parameters for trades or investments when not following an algorithmic approach. The discipline is following the plan. Many market participants run into problems when a risk position goes against them, and they have no plan for risk and reward, or they modify it to allow them to stick with a wrong decision. Turning a short-term trade into a long-term investment is a common mistake. The mistake comes from a subjective call that the market price is incorrect. A way to prevent this is to remind yourself that the market price is always the correct price. We are often wrong; the market is never wrong. You have to be in to win- Drawdowns are a part of any trading or investing system We are constantly long or short the highly liquid assets in our investment portfolio because we never know when a significant trend will begin. Being in a risk position that follows trends is the only way to catch the bulk of a bullish or bearish trend. Drawdowns or losses are a part of life and any trading or investment approach. A choppy market near the high or low end of a trend will result in short-term losses. However, that is the price for capturing the long-term trend. There is no free lunch in life, and the same goes for trading and investing. The goal is always the same for every market participant, to make money over time and build wealth and our nest eggs. The strategy is what separates winners from losers. We take a long-term systematic approach and do not veer from the path. We know that drawdowns are a part of any investment or trading approach. We are in it to win it on a long-term basis. Join us for the Monday Night Call each week - all you have to do is use the link below. There's also a link to sign up for early access to these articles as well. Trading advice given in this communication, if any, is based on information taken from trades and statistical services and other sources that we believe are reliable. The author does not guarantee that such information is accurate or complete and it should not be relied upon as such. Trading advice reflects the author’s good faith judgment at a specific time and is subject to change without notice. There is no guarantee that the advice the author provides will result in profitable trades. There is risk of loss in all futures and options trading. Any investment involves substantial risks, including, but not limited to, pricing volatility, inadequate liquidity, and the potential complete loss of principal. This article does not in any way constitute an offer or solicitation of an offer to buy or sell any investment, security, or commodity discussed herein, or any security in any jurisdiction in which such an offer would be unlawful under the securities laws of such jurisdiction.
CME_MINI:ES1!Education
by Andy_Hecht
47