Rates, credit conditions, dollar funding, and risk appetite shape available depth, spreads, order flow, and inventory risk in an order book. Our latest article looks at how those conditions reach live execution.

Network economics emerges through repetition: — people use the network — fees are paid — infrastructure is maintained — incentives are distributed — the market prices participation @bitmakerfi focuses on the trading layer: liquidity, execution quality, and market monitoring as those feedback loops play out.

Trading adds another feedback loop. A liquid market gives users, operators, and capital providers an executable price for entering, exiting, hedging, and valuing their exposure. That price feeds into decisions about holding, staking, allocating capital, and building on the network.

@solana’s latest changelog shows the other side of the equation. Slot times are moving from 250ms to 200ms, alongside recent work on lower rent and cheaper program deployment. Lower costs can support more activity. The network still needs sufficient incentives to maintain the capacity that activity uses.

The token connects these flows. It may be used for fees, staking, governance, collateral, or incentives. Each role needs to be concrete and compatible with the others, so that network usage, capital commitment, and participation reinforce one another over time.

Fees also need to support the people and systems that keep the network running. Validators, sequencers, operators, and service providers have capital and operating costs. Rewards determine whether that capacity remains available as demand and market conditions change.

Fees price scarcity: blockspace, computation, data availability, or an application service. @ethereum’s upcoming Glamsterdam testnet includes gas-pricing changes intended to reflect execution and state-growth costs more closely. A fee market works when the price follows the resource being consumed.

Network Economics Starts With One Transaction A user pays a fee. That action begins a chain: it prices a network resource, compensates the infrastructure that provides it, and adds real activity to the token economy. Here is how the loop works ↓

@origamitech_ Points are here! A great opportunity for traders to earn rewards, with a special boost for @HyperliquidX activity in the first epoch.

Thanks to @ProofOfLiq for bringing together so many great teams. Looking forward to the next edition!

Exactly. The rules need to say what source settles the market and what happens if the result is unclear or later revised. Otherwise, that uncertainty gets priced in too.

Prediction markets put every layer of a trading venue under pressure. Our new article looks at how liquidity, execution, market surveillance, and settlement behave when new information can reprice a contract within seconds.

It was a pleasure to join Proof of Liquidity Singapore during TOKEN2049. Good conversations, familiar faces, and new connections. Thanks to @ProofOfLiq for organizing the event and bringing everyone together. Looking forward to the next one!

A trading firm can hold a money market fund, earn yield on that position, and still need credit to trade. Bringing that asset into a collateral workflow has traditionally meant moving through separate custody, funding, and settlement processes. On September 28, @FTDA_US and @Bybit_Official launched a programme that lets eligible institutional clients use tokenised money market fund shares as off-exchange collateral for trading credit. The fund shares remain in custody while the client can access USDT or USDC credit on the venue. The collateral covers the exposure created by trading. The cash leg still needs to settle alongside it. In September, major UK banks completed interbank transactions using tokenised deposits, showing how commercial bank money can move through a digital settlement workflow. Together, these developments bring more of the trade lifecycle into the same operating environment: trade agreed → collateral allocated → eligibility and valuation checked → exposure recalculated → payment and asset transfer settled As prices and positions change, the collateral requirement changes as well. The process therefore depends on clear rules for asset eligibility, valuation sources, haircuts, custody, substitution, credit limits, and the transfer of assets between participants. Tokenisation allows collateral and cash to move through a shared digital workflow. The value appears when the trading desk, custodian, and venue can work from a current view of the same balances, positions, and settlement obligations. For execution infrastructure, this raises the standard for market data, reconciliation, reporting, and operational control. @bitmakerfi works on that market-facing layer by managing live execution, tracking balances and positions, and keeping venue state aligned with the trading system.

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In Singapore for TOKEN2049? Meet @bitmakerfi at @ProofOfLiq

Agreed. If transfers depend on an eligibility register, it becomes part of the settlement path. A Saturday trade then depends on who administers it, how eligibility is checked, and when an update can be completed. Issuers should make those timings clear before trading begins.

RWA market design continues after issuance through every decision to buy, sell, redeem, or hold. Pricing, custody, valuation, redemption, and secondary liquidity each shape that experience. @bitmakerfi works on the trading layer through order-book liquidity, execution quality, and market monitoring after an asset enters public trading.

Execution brings another layer. A holder selling before maturity needs to understand who has custody, whether the token can transfer to the buyer, which redemption rights travel with it, and how much size the order book can absorb before price impact becomes material.

For a tokenised bond, a Saturday sell order raises a practical question about price. The last cash-bond trade may be from Friday, while rates, credit spreads, foreign exchange, or issuer news have moved since. The market needs a disclosed approach to fair value, valuation timing, and the data behind every quote.