Phantom Wallet, with 7M monthly users, has integrated the Sui blockchain, marking its first support for a Move-based network and a significant step for Sui’s ecosystem. 

Daily Top Performer — Morpho Protocol (MORPHO)

The S&P 500 fell 0.19% as investors awaited the November jobs report, which may influence the Fed's anticipated rate cut. Coindesk Indices, an index measuring broader cryptocurrency market performance was up by 8.2%, with Bitcoin and Ether rising by 0.17% and 0.40%, respectively in the past 24 hours.

Today's top performer is MORPHO, which surged 54.0% following the launch of DeFi Saver's Automated Leverage Management for Morpho on Base.

Morpho Protocol (MORPHO), founded in 2021, is a decentralized Ethereum-based lending protocol that enhances crypto lending through a P2P order matching system. It allows customizable lending markets and features Morpho Vaults for various risk levels. Initially launched in 2022 with Morpho Optimizers for Aave and Compound, it later introduced Morpho Blue in 2024 for flexible lending markets. Morpho Labs continues to focus on developing Morpho while still supporting the original Morpho Optimizers. 

, enabling Automated Leverage Management. This includes Auto-Repays for unwinding and Auto-Boosts for leveraging up based on set threshold ratios. Users can configure their positions easily. The feature is available for several Morpho markets, such as cbETH/USDC, wstETH/USDC, and more, with additional markets to be added in the future.

Check Out the Latest Prices, Charts, and Data of 

Phantom Wallet, with 7M monthly users, has 

 the Sui blockchain, marking its first support for a Move-based network and a significant step for Sui’s ecosystem. This expands Phantom's multichain support beyond Solana, Bitcoin, and Ethereum, offering Sui users features like staking, swaps, and NFT storage across mobile and browser platforms.

Yesterday, BTC Spot ETF flows recorded a $148.8M outflow from GBTC, partially offset by a $145.0M inflow to non-GBTC funds, resulting in a net outflow of $3.8M.

" hints at a potential $FUEL airdrop happening earlier than expected, despite the Fuel points program running until January 2025. Speculation surrounds whether this refers to the airdrop or another development.

Beginner

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Phantom Wallet Expands Multichain Support with Sui Integration

Our weekly crypto derivatives analytics report delves into macro events, the current state of crypto and trading signals from spot trading volume, and futures, options and perpetual contracts.

BTC has finally surpassed the $100K mark, leading to increased bullish positioning in derivatives markets. This reflects strong sentiment and a desire for upside exposure in perpetual swaps and futures. Short-term implied volatility has risen as traders seek options to capitalize on the rally.

Please check out the report’s highlights.

BTC's successful breach of the $100K barrier has led to a noticeable return of perpetual swap markets to all-time high open interest levels, although the response has been somewhat muted. Earlier in the week, perpetual swap markets shifted their focus to ETH after BTC's repeated failures to break through $100K. While funding rates for both derivatives remained high and positive, open interest in ETH contracts increased at the expense of BTC positions prior to the breakthrough. Additionally, ETH trade volumes have constituted a larger-than-usual market share.

BTC Calls See No Significant Influx Post-$100K

The sudden rise in spot prices has triggered a sharp increase in demand for short-tenor options, as traders seek exposure to the upward movement. BTC's term structure inversion has driven short-tenor implied volatility levels above realized volatility. Although implied volatility for BTC is lower than that of ETH, open interest data shows that the influx of call option open interest in BTC markets wasn’t as significant as for ETH. Nevertheless, BTC's volatility skew remains strongly bullish.

ETH's options market showed the most bullish positioning ahead of the overnight spot move, while the perpetual swap markets for both major cryptocurrencies reflected strong positive sentiment. Open interest is heavily weighted toward OTM calls, signaling a robust preference for upside exposure as realized volatility starts to catch up to implied volatility. This demand for upside exposure is evident for both tokens, particularly with ETH's remarkable increase in call option open interest over the past 24 hours.

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Bybit X Block Scholes Crypto Derivatives Analytics Report (Dec 5, 2024): After BTC Turns $100K, Investors Are Still More Bullish on ETH

The cryptocurrency market is experiencing unprecedented growth, particularly in major assets like XRP, Cardano (ADA) and Stellar (XLM).

The cryptocurrency market is experiencing unprecedented growth, particularly in major assets like XRP, Cardano (ADA) and Stellar (XLM), whose tokens have seen substantial price increases (XRP up 444%, ADA 280% and XLM 512%). This surge is being driven largely by aggressive accumulation from whale investors, as well as cautious but optimistic sentiment in the crypto community. 

Meanwhile, interest in meme coins is declining as investors shift focus to more sustainable projects. The integration of AI in blockchain ecosystems and the emergence of hyperliquid token generation events (TGEs) are also reshaping market dynamics, indicating a transformative phase for cryptocurrencies.

Significant Growth in Major Cryptocurrencies

The report highlights impressive price increases for several major cryptocurrencies:

Despite these gains, general sentiment in the crypto community remains cautiously optimistic.

 activity is a key factor in current crypto market dynamics:

Since mid-October, there’s been considerable accumulation of Bitcoin by whale wallets, which have added a total of 91,856 BTC. This accumulation has played a substantial role in driving up BTC’s price.

In contrast, there’s been a decline in stablecoin holdings among these wallets, suggesting a strategic shift toward investing in cryptocurrencies, rather than choosing to hold stable assets.

The following several emerging trends are reshaping the cryptocurrency landscape.

Overall, our report underscores a transformative period in the cryptocurrency market, characterized by significant asset growth, strategic whale behavior and the integration of advanced technologies.

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Whales Accumulate Bitcoin as It Reached $100K; Emergence of AI Agent

Our monthly option volatility report delves into insights from options volatility to inspire your next crypto move.

Option volatility outlines a compelling narrative for Bitcoin as it approaches the $100K milestone. Leading up to the U.S. election on Nov 5, 2024, Bitcoin's implied volatility experienced a dramatic surge, with short-term implied volatility rising approximately 25 percentage points above post-election expirations. This spike indicated strong bullish positioning in the derivatives market as traders sought to capitalize on anticipated price movements driven by electoral outcomes.

In the immediate aftermath of the election, there was a sharp decline in short-term volatility, aligning with a rally in spot prices that, in turn, led to higher realized volatility. However, the decline in implied volatility didn’t reflect a long-term bearish sentiment — instead, it suggested a temporary easing of uncertainty regarding the election results. The term structure of implied volatility demonstrates a complex relationship: while short-term volatility dipped, longer-dated options remained stable above 50%.

The call/put skew indicates strong demand for bullish call options relative to bearish puts since the election, reflecting heightened upside exposure during BTC and ETH rallies. However, when BTC retraced to $91.3K, the skew shifted 6% toward puts, signaling bearish sentiment. This was inconsistent with futures markets, in which prices remained above spot levels, indicating ongoing bullishness. The skew toward OTM puts arose from increased demand for protective hedges, rather than from a lack of call interest. Ultimately, this suggests traders believe any sell-off would be temporary, supporting the idea that volatility is necessary in order for BTC to reach the $100K mark.

In contrast to Bitcoin, Ethereum (ETH) has emerged as a more consistently bullish asset in recent weeks. While both BTC and ETH exhibited similar volatility patterns immediately following the election, Ether has shown notable resilience and increased momentum, particularly in the wake of Gary Gensler's resignation. This political shift has positively influenced ETH’s market dynamics, resulting in a stronger demand for ETH options.

Ether outperformed Bitcoin during critical market events, including both the election and ETF announcements in 2024. Following Gensler's resignation, ETH gained significant traction, with traders increasingly focusing on Ethereum as a more attractive investment. This shift is evident in the significant drops in the ratio of BTC-to-ETH spot price, indicating that traders are reallocating their preferences toward ETH.

The divergence in implied volatility between BTC and ETH options further underscores this trend, as the ratio of implied volatility for ETH options has surpassed that of BTC, reflecting a stronger demand for ETH optionality. Historically, ETH has exhibited higher volatility compared to Bitcoin. This recent uptick in ETH's implied volatility reestablishes the typical pattern of ETH commanding a premium in the options market.

Moreover, the skew in options pricing has shifted in favor of calls for both BTC and ETH, but ETH's skew has remained more robust. This suggests a stronger bullish sentiment for ETH, as traders are positioning more aggressively for its price appreciation compared to that of Bitcoin. As the market continues to evolve, Ethereum's increasing dominance and the bullish signals from its options market position it as a leading contender in the cryptocurrency landscape, making it likely to outperform Bitcoin in the near future.

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Bybit X Block Scholes Option Volatility Report: November Option Volatility Report: Volatility on the Road to $100K

Trading

Explainers

Want to know if your crypto trading strategy works? Backtesting is a key component to assess the effectiveness. Learn how it works here.

 trading can be a daunting task. Crypto backtest is a powerful tool that allows traders to optimize their trading strategies and navigate this unpredictable landscape with confidence. Are you ready to unlock the power of crypto backtesting and elevate your trading game like a pro? Let’s dive in!

Backtesting serves as a critical process in which historical data is used to test a trading strategy, in order to evaluate its potential and fine-tune it for future application. It helps traders uncover the potential of their strategies and make necessary adjustments before investing real capital.

With the right approach, backtesting can be an indispensable tool in a trader’s arsenal.

The Role of Backtesting in Crypto Trading

 performance heavily depends on backtesting historical data. This process holds significant importance in the world of crypto trading, as it enables traders to recognize the strengths, weaknesses, and potential hazards of their strategies before investing real capital. By analyzing historical price data, traders can gain valuable insights into how their strategy would have performed in different market conditions and adjust their approach accordingly.

The backtesting process assists traders in recognizing potential strengths and weaknesses, refining their systems, and ultimately building confidence in their decision-making processes. However, backtesting relies on certain factors and is not without its limitations and challenges, such as the need for a significant amount of data to be accurate and the potential influence of overfitting and bias in the results. Using diverse data sets and out-of-sample data for validation can help mitigate these issues.

Although backtesting offers various benefits, it does have certain limitations, such as the unpredictability of future market conditions. While backtesting provides valuable insights, it does not guarantee future performance due to changing market dynamics and additional factors in live trading such as slippage, 

, and execution delays. Scenario analysis can be used to test the strategy under different market conditions and improve its robustness.

Another challenge is the potential for overfitting or bias in the backtesting results. Overfitting occurs when a trading strategy is excessively optimized to match historical data, yet fails to demonstrate effective results in actual trading scenarios. Bias, on the other hand, refers to the inclination to modify the model until an ‘ideal’ result is obtained, which in turn leads to unrealistic expectations and unsatisfactory performance in actual trading. To avoid overfitting and bias, traders should incorporate diverse data sets, utilize out-of-sample testing, and refrain from excessive parameter optimization.

Manual and automated are the two main approaches for crypto backtesting. They offer distinct advantages for testing a cryptocurrency trading strategy. Each method comes with its advantages and drawbacks, and the choice between them will depend on a trader’s individual requirements and circumstances.

Analyzing past price data is a requisite for manual backtesting. This helps evaluate the trading strategy to gauge how it would have fared in the past. This process is similar to paper trading, where traders simulate their trades without risking real capital. Manual backtesting can be time-consuming and prone to errors, but it also offers the advantage of a deeper understanding of the strategy and its underlying mechanics.

To facilitate manual backtesting, traders can use spreadsheets to compute various statistics such as:

Manual backtesting can help traders evaluate the performance of a backtesting strategy using historical data, recognize any flaws or weaknesses, and instill confidence prior to implementing backtesting trading strategies in real-time trading.

Contrarily, automated backtesting leverages technology and coding languages to assess trading strategies with increased effectiveness and precision. This method is particularly useful for backtesting complex strategies that require a large amount of data and computational power. Some popular platforms for automated crypto backtesting include MetaTrader 4/5, FXOpen’s TickTrader, and Cryptohopper.

The primary difficulty associated with automated backtesting is having the technical aptitude to code the strategy or having the resources to employ someone who can. However, automated backtesting is capable of rapidly and precisely processing a large amount of data, thereby reducing the possibility of human error. Python, C++, and Java are commonly employed in automated crypto backtesting.

Price, volume, and order book data are crucial for crypto backtesting. These types of data provide valuable insights into market trends and liquidity, helping traders make informed decisions about their strategies.

Price data sources include historical price charts and spreadsheets, which can be obtained from exchanges or third-party providers. The quality of price data plays a significant role in the accuracy of backtesting results, and it is crucial to obtain data from reliable sources and verify its accuracy.

Some sources that can provide accurate and reliable price data for crypto backtesting include Tradewell and Cryptodatadownload. In addition to obtaining data from exchanges, traders can also use APIs provided by platforms such as CryptoCompare or CoinGecko, which offer historical and real-time data at various time intervals.

Volume data is crucial for understanding market depth and liquidity. It refers to the total number of cryptocurrency units bought and sold within a specific time period on a particular exchange or across multiple exchanges. Monitoring volume data can help traders assess market trends, identify potential buying or selling opportunities, and make informed trading decisions.

High trading volumes can suggest increased interest and participation in the market, while low trading volumes may indicate a lack of market activity or limited liquidity. By analyzing volume data, traders can ascertain the level of liquidity in the market and recognize areas of support and resistance.

Order book data offers a more accurate representation of market conditions, allowing traders to simulate bid-ask spreads, slippage, and liquidity when testing their strategies. An order book is a compilation of snapshots of the market, including the price, volume, and depth of buy and sell orders.

Obtaining order book data for backtesting can be challenging, as the data may be difficult to procure due to storage costs for exchanges. A developer must collect and store data from the exchange itself, or they can access the order book snapshots through a third-party service. As a result, they have to ensure they have their data source available.

Utilizing order book data in backtesting provides a more precise demonstration of market conditions, permitting traders to make well-informed decisions regarding their strategies.

Selecting the Right Tools for Crypto Backtesting

The selection of appropriate tools for crypto backtesting hinges on various factors, including personal preferences, technical abilities, and the intricacy of the trading strategy. In this section, we’ll explore popular backtesting platforms and criteria for selecting the most suitable tool for your needs.

Popular crypto backtesting platforms include Altrady, Holderlab, Gekko, and Zenbot, each offering unique features and capabilities. Altrady is a professional trading platform specifically designed for cryptocurrency trading, offering smart trading, real-time market data and alerts, connectivity to multiple exchanges, and scaled ladders for an optimized trading experience.

Holderlab’s backtesting platform offers features such as portfolio optimization, correlation analysis, a trading bot for rebalancing, and an intuitive and advanced interface. Gekko is an open-source trading bot that enables users to implement and evaluate their trading strategies automatically, while Zenbot is a command-line cryptocurrency trading bot compatible with multiple digital assets.

By exploring the features and capabilities of these platforms, traders can determine the most suitable backtesting tool for their needs.

When selecting a backtesting tool, factors such as ease of use, customization options, data quality, and community support should be considered. A user-friendly backtesting tool should offer:

Data quality is essential for obtaining precise backtesting results, and traders should ensure that their backtesting tool offers reliable data from reputable sources. Community support can also play a significant role in the efficacy of a backtesting tool, providing an avenue for:

By considering these factors, traders can select the most suitable backtesting tool for their needs.

Analyzing and Interpreting Backtesting Results

Following the completion of the backtesting process, the subsequent step involves analyzing and interpreting the results through forward performance testing. Evaluating key performance metrics can help traders identify strengths and weaknesses in their trading strategy, allowing them to make necessary adjustments for improvement.

Key performance metrics, such as the success (win) ratio, Sharpe ratio, maximum drawdown, and equity curve, can help traders assess the effectiveness of their strategies. The Sharpe ratio is a metric that measures the performance of an investment strategy relative to a risk-free rate. It’s calculated by taking the excess return of a strategy and dividing it by its standard deviation. A higher 

 indicates that the strategy is more effective at compensating for the risk taken on the investment.

The Sortino ratio, another important metric, measures the risk-adjusted performance of an investment or trading strategy, focusing on downside volatility and taking into account only the standard deviation of negative returns. A higher Sortino ratio suggests that the strategy is more effective in avoiding significant losses and achieving the desired return.

By evaluating these key performance metrics, traders can optimize their trading strategies, minimize potential risks, and manage trading costs effectively.

Identifying strengths and weaknesses in a forex trading strategy allows traders to optimize their approach and minimize potential risks. By analyzing backtesting results, traders can pinpoint areas where their strategy excels and areas where it falls short. This information can be used to make necessary adjustments to the strategy, ultimately improving its performance and increasing the likelihood of success in real-world trading scenarios.

Continuous improvement and optimization involve refining the crypto trading strategy based on backtesting results and staying up-to-date with market trends and conditions. By regularly iterating and refining their strategies, traders can ensure that their approach remains relevant and effective in the ever-changing crypto market.

For successful crypto backtesting, it’s important for traders to evade overfitting and bias, ensure data quality, and continuously improve and optimize their trading strategy. By following these tips, traders can ensure that their backtesting efforts yield valuable insights and help them make more informed decisions in the crypto market.

Overfitting and bias are common pitfalls in backtesting that can lead to inaccurate results and unreliable trading strategies. Overfitting occurs when a strategy is excessively optimized to match historical data, yet fails to demonstrate effective results in actual trading scenarios. To avoid overfitting, traders should use diverse data sets and out-of-sample testing, which can help validate the strategy’s effectiveness in a broader range of market conditions.

Bias, on the other hand, refers to the inclination to modify the model until an ‘ideal’ result is obtained, which in turn leads to unrealistic expectations and unsatisfactory performance in actual trading. To avoid bias, traders should be cautious of excessive parameter optimization and ensure that their trading strategy is based on sound principles rather than fitting the data too precisely.

Ensuring data quality is crucial for accurate backtesting results. Poor-quality data can lead to erroneous and misleading analysis, ultimately resulting in ineffective trading strategies. To ensure data quality, traders should obtain data from reliable sources, such as reputable exchanges or third-party providers, and verify its accuracy.

In addition to obtaining accurate price data, traders should also consider volume and order book data, which can provide valuable insights into market depth and liquidity. By using high-quality data and verifying its accuracy, traders can ensure that their backtesting results are reliable and meaningful.

Continuous improvement and optimization involve refining the trading strategy based on backtesting results and staying up-to-date with market trends and conditions. By analyzing and interpreting backtesting results, traders can identify the strengths and weaknesses of their trading system and make necessary adjustments for improvement.

Furthermore, remaining aware of current market trends and conditions helps traders adjust their strategies to better suit the dynamic crypto market. By consistently adapting and refining their trading strategies based on new data and market conditions, traders can enhance their backtesting performance and ultimately, their success in the crypto market.

Crypto backtesting is an essential tool for optimizing trading strategies and navigating the volatile world of cryptocurrency trading. By understanding the backtesting process, selecting the right tools and data sources, and analyzing and interpreting results, traders can fine-tune their strategies and minimize potential risks. With continuous improvement and optimization, traders can confidently tackle the ever-changing crypto market and maximize their trading potential.

 is a great option for backtesting cryptocurrency as it allows users to perform backtests without writing code and supports backtesting against 5, 20 and 50 years of historical data.

 which contains an extensive collection of crypto futures historical data enabling users to backtest and optimize trading strategies.

What is the most profitable strategy in crypto?

 is the most profitable strategy in crypto. It involves exploiting the price differences between various crypto exchanges and platforms to make money. Furthermore, the lack of regulation on the crypto market allows traders to take advantage of triangular arbitrage and other forms of arbitrage trading.

What is the main purpose of crypto backtesting?

The main purpose of crypto backtesting is to evaluate a chosen trading strategy using historical data in order to optimize it for future use and minimize potential risks.

What are the two primary methods for crypto backtesting?

The two primary methods for crypto backtesting are manual and automated backtesting. Both provide valuable insight into how a given trading system may perform under different market conditions.

Unlock the Power of Crypto Backtest: Master Your Trading Strategy like a Pro

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