Bitget x Reality x Keyring: Decentralizing Stock Basis Strategies with rToken

Bitget and Keyring are collaborating to develop a rules-based equity basis index and to build associated on-chain lending markets using Reality's rToken.

Reality provides tokenized equity exposure, while Keyring is responsible for developing the index methodology and building the associated lending markets. The index aims to combine rToken holdings with hedging equity perpetual futures positions to capture funding rate and basis opportunities. Certain products are planned to be distributed through Bitget Earn, subject to product review, eligibility requirements, and availability.

This collaboration integrates Reality's tokenized equity infrastructure, Keyring's rules-based investment strategies and lending markets, IPOR Fusion's on-chain vault infrastructure, and 1Token's portfolio management and risk monitoring tools. The partners will jointly explore how these components can support the development of on-chain equity basis products while addressing the operational, liquidity, and risk management challenges faced by strategies spanning traditional and decentralized markets.

Bitget and Reality: Equity Basis Strategies

Reality provides on-chain equity exposure through its rToken. Traders can purchase rTokens via the Bitget exchange or Bitget Wallet X, or directly through Reality's request-for-quote (RFQ) service.

Reality's rToken constitutes the on-chain equity position in the basis trade and can also serve as collateral in the associated lending markets. Reality's RFQ service and Bitget Wallet provide support for accessing rToken liquidity and trade execution infrastructure, enabling eligible participants and vaults to obtain tokenized equity exposure, subject to applicable settlement conditions.

Tokenized equities allow equity exposure to be directly combined with decentralized lending and investment infrastructure, making it easier for on-chain strategies to participate in basis trading. Unlike traditional equities, which typically require separate brokerage and financing arrangements, rTokens can serve both as the equity position in a basis trade and as collateral in on-chain lending markets. This enables investors to combine tokenized equity holdings with hedging perpetual futures positions while leveraging decentralized infrastructure to manage financing and execute rules-based strategies.

Equity Basis Trading Using rToken

Risk premium is the additional return above the risk-free rate that traders earn for bearing a certain type of risk. Basis refers to the spread or difference between the spot price and the total return obtained from the corresponding contract or perpetual futures.

Equity basis trading aims to capture the funding fees paid by longs to shorts.

Data source: Keyring Research, January–July 2026

When the funding rate is positive, traders can earn funding fees by holding the corresponding rToken and shorting its perpetual futures. This operation aims to hedge equity price exposure, thereby constructing a delta-neutral basis trade.

Decentralizing Basis Trading

"Equity basis strategies have historically relied on centralized exchanges, managing spot and derivative positions within a unified margin framework. Cross-margin mechanisms allow the gains and collateral value of one position to support the margin requirements of other positions in the portfolio, thereby reducing the need to maintain separate collateral buffers for each trade.

However, concentrating custody, trade execution, and margin management in a single intermediary introduces counterparty and operational risks. The collapse of several large centralized market participants has prompted greater market attention to structures that can separate these functions while still maintaining the capital efficiency required to manage hedged positions.

Decentralized lending and trade execution infrastructure offers an alternative, enabling tokenized equity collateral, financing arrangements, and derivative exposure to be managed across multiple interconnected protocols. While this approach can reduce reliance on a single intermediary, it also introduces risks related to smart contracts, cross-chain bridges, price oracles, and liquidity fragmentation. Therefore, effective risk management requires reliable collateral valuation, adequate margin buffers, and coordinated execution across trading venues."

Lending Markets as a Source of Leverage

Lenders deposit stablecoins into lending pools. Borrowers purchase Reality's rToken and deposit it as collateral. The pool lends stablecoins against the rToken collateral. Borrowers pay interest to the pool, which distributes the interest to lenders after deducting fees. If the loan size becomes too large relative to the collateral value, the pool can liquidate the collateral.

This structure can serve as a source of leverage, enabling borrowers to establish larger positions through the collateral assets listed on the market. This practice is commonly referred to in the DeFi industry as "looping," through which borrowers can obtain leveraged exposure to tokenized equities without opening a margin account on an exchange.

The financing function performed by this collateralized lending structure is similar to repurchase agreements (repos) in traditional markets, enabling investors to obtain financing against their tokenized equity holdings.

For eligible Bitget users, the proposed lending structure is expected to provide returns generated from overcollateralized lending backed by rToken collateral, with actual return levels depending on factors such as borrowing demand, applicable fees, collateral valuation, and liquidation risk. Keyring brings institutional-grade risk frameworks (i.e., ISDA standardized initial margin model parameters) into its decentralized lending framework, providing a structured approach to managing collateral and margin requirements.

Stablecoin lending and equity basis index exposure are two distinct products. Stablecoin lenders earn the interest paid by borrowers, net of applicable fees. Index investors, by contrast, earn the net performance of the rToken and perpetual futures combination strategy, including funding fee and basis gains and losses, net of financing and trade execution costs. The index performance shown below does not represent the returns earned by stablecoin lenders.

One issue that warrants particular attention is that the value of rToken positions in the lending market needs to be balanced against the short positions on Hyperliquid, and the lending market must be able to correctly value the rToken price. This is precisely why cross-margin mechanisms are critical in any market.

Reality's rToken price oracle uses official price sources during regular U.S. stock market trading hours and Hyperliquid quotes outside those hours. These price data are used to support collateral valuation; cross-venue margin hedging depends on the specific applicable market and account arrangements. The basis strategy also requires coordinated management of lending positions and perpetual futures margin.

For example, if the U.S. stock market is closed while the corresponding perpetual futures price falls sharply, the rToken collateral valuation may also decline because its oracle uses Hyperliquid quotes outside regular trading hours. Even if the underlying stock has not traded during this period, this could reduce the borrowing capacity of the lending position and potentially trigger liquidation. At the same time, gains from the short perpetual futures position do not automatically offset the decline in collateral value across protocols. This highlights the importance of coordinated margin management, reliable price data sources, and adequate liquidity for position management during periods of market volatility.

The liquidation process uses Keyring's [un]wind product, connected to Reality RFQ, to coordinate collateral redemption and debt repayment. Settlement may need to be completed over multiple trading days during regular U.S. stock market trading hours. The mechanism is designed to support the orderly liquidation of large rToken positions, with actual outcomes depending on redemption capacity, settlement timing, and ultimately recovered amounts.

On-Chain Index Strategies

Reality's rToken provides the on-chain equity holdings for these index strategies.

Quantitative Investment Strategies (QIS) are a popular category of products traditionally created by investment banks and issued to clients seeking transparent ways to capture known market inefficiencies (i.e., risk premia).

Smart contracts can automate the parts of index calculation, trade execution, and risk control that traditionally rely on centralized financial intermediaries. They provide a programmable framework for implementing rules-based strategies, enabling automated execution, transparent risk parameter settings, and integration with decentralized finance infrastructure. IPOR's Fusion vault infrastructure executes pre-defined investment mandates through smart contracts, enforcing the permitted asset universe, authorized markets, risk limits, and trade execution rules. This makes the strategy fully programmable while ensuring that its implementation strictly follows the established rulebook.

In terms of strategy construction, a common challenge for QIS is ensuring that the strategy can correctly capture the underlying risk premia in a scalable manner. To this end, a simple yet robust rulebook is essential.

A Decentralized and Robust Strategy Rulebook

The index follows a rules-based methodology built around three core steps:

1. Determine the eligible universe of tokenized equities and corresponding perpetual futures markets
2. Rank eligible basis opportunities over the past three weeks
3. Allocate capital subject to pre-defined portfolio construction, liquidity, and risk management constraints

The complexity lies in how to handle contingencies in a decentralized, trustless manner. For example, setting margin buffer rules for cross-chain bridge failure scenarios is one such case; how to account for trading costs and properly handle slippage and trade execution issues is another.

The following analysis evaluates the historical performance of this rules-based equity basis index based on simulated net funding fee returns from December 15, 2025, to October 19, 2026. The dataset is divided into an in-sample period used to determine strategy parameters and an out-of-sample period used to evaluate performance beyond the initial calibration window. The simulation also accounts for trading constraints, trade execution costs, financing assumptions, and portfolio concentration limits.

The above results are all based on historical simulations, are sensitive to the assumptions used, and may not reflect actual trade execution or future market movements.

The index is also compared against a theoretical maximum-return portfolio constructed using future market information. This hypothetical benchmark measures the highest cumulative return achievable under the trading constraints set by the model (including daily volume limits, slippage, concentration limits, and margin requirements). Because it relies on information unavailable to investors at the time of trading, it serves only as a retrospective reference and does not represent an actually investable strategy.

It should be particularly noted that the index always maximizes capital allocation, so the signal does not include any tactical risk overlay mechanism. This means that when funding rates turn significantly negative (as in April), even the optimal portfolio would incur losses, as there was no premium to capture at that time. It is also worth noting that in some cases (such as March and July), the optimal portfolio underperformed our index. Due to daily trading limits, establishing and closing positions takes time, so choices that boost overall returns over the entire period may actually reduce returns in a particular week. Under the simulation assumptions, the index recorded a Sharpe ratio of approximately 6 in the in-sample period and over 9 in the out-of-sample period. The simulated maximum drawdown was approximately 60 basis points, occurring in the in-sample period. The above results reflect outcomes under the historical dataset and modeling assumptions and should not be interpreted as indicative of future performance.

The simulated index delivered an annualized net return of approximately 7% in the in-sample period and approximately 4% in the out-of-sample period, while the theoretical maximum-return portfolio delivered annualized returns of approximately 11% and 5% in the respective periods. The above results all account for modeled trade execution and financing costs and do not represent actual returns earned by investors. The analysis shows that the gross funding fee premium was approximately 11%–14% before deducting applicable strategy costs. The simulation assumes a trade execution cost of 20 basis points per rebalance, approximately 40 basis points for a complete round trip, and an annualized financing cost of 6% for leveraged positions. Under the model assumptions, every 10 basis point reduction in trade execution costs could improve the strategy's annualized return by approximately 1 percentage point; every 1 percentage point reduction in financing costs could bring a further improvement of approximately 1 percentage point.

There is also a dilution effect. Specifically, as slippage increases, the strategy cannot deploy 100% of capital, and some capital remains idle. For this reason, as well as the need for margin buffers, the strategy holds approximately 25% of capital in cash at any given time, which could potentially add approximately +1% to overall returns by earning the risk-free rate.

The simulation assumes a loan-to-value (LTV) ratio of 50% for borrowing against rToken collateral, with tokenized equity positions eligible for up to 2x leverage. The corresponding Hyperliquid perpetual futures positions are modeled at 8x to 20x leverage, depending on the margin buffer requirements determined by stressed market scenarios. Under these assumptions, the average notional rToken exposure is approximately 1.5x the capital allocated to the basis strategy. For example, a $100 million capital allocation corresponds to an average rToken exposure of approximately $150 million. These leverage assumptions increase the strategy's sensitivity to margin requirements, liquidity constraints, and adverse market fluctuations.

Composable Indexes to Build Your Own Strategy

Keyring has created multiple "modular basis pair" index components based on Reality's rToken. Each component can be used independently, enabling managers to build systematic strategies with differentiated risk, return, and allocation objectives.

Trading the index. A common approach is for managers to apply a "risk overlay" mechanism to adjust their exposure to the equity basis index as market conditions change. For example, momentum indicators such as Bollinger Bands can be used to identify changes in funding rate trends and inform allocation decisions. These methods are for illustrative purposes only and do not guarantee improved performance.

Trading basis pairs. Managers can also build strategies around individual rToken basis pairs and seek to outperform the index by trading these pairs. Statistical arbitrage techniques can employ principal component analysis (PCA), predicated on the assumption that basis pair movements exhibit some typical "structure," i.e., a covariance relationship. If the covariance matrix contains several key directional drivers, we can calculate these principal components (mathematically known as eigenvectors) and trade around the noise fluctuations surrounding these principal directions. This is a common strategy type known as statistical arbitrage, often run by hedge funds, but beware of the trading costs involved!

Such trading is suitable for professional investors, and executing it on-chain requires a clear understanding of rToken holdings, perpetual futures hedges, and collateral positions across protocols. Tools such as 1Token help consolidate these positions into a unified portfolio view, enabling managers to monitor P&L and margin status throughout the trading process. The index and associated lending markets are planned to be offered through Bitget Earn.

About Bitget

Bitget is the world's largest Universal Exchange (UEX), serving over 125 million users, with more than 2 million crypto tokens listed and support for trading over 500 tokenized stocks, ETFs, commodities, forex, and precious metals (such as gold). Its ecosystem is committed to empowering smarter trading through AI agent-assisted execution. Bitget partners with strategic organizations such as MotoGP™ to drive crypto adoption. Under its global impact strategy, Bitget has joined forces with UNICEF to provide blockchain education to 1.1 million people by 2027. Bitget leads the tokenized TradFi market, offering the industry's lowest fees and highest liquidity across 150 regions worldwide.

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