By 2025, cryptocurrency trading had entered an unfamiliar phase. Volatility remained elevated by historical standards, yet sustained directional trends were rare. Price action oscillated within broad ranges, repeatedly frustrating traders who relied on momentum, breakout strategies, or long-term conviction bets. In this environment, automation moved from being a niche tool for technically inclined users to a core survival strategy for a growing share of market participants.
A year-end recap from HTX, formerly Huobi, offers a revealing snapshot of this shift. According to the exchange, grid-based trading bots saw explosive growth across its spot markets in 2025, with trading volume tied to grid strategies rising 97% year over year and capital allocation doubling. The data points to a structural change in how traders approached the market—not just a tactical adjustment to short-term conditions.
At the same time, exchanges such as Coinbase were pushing automation in a different direction altogether, expanding the use of AI-powered agents capable of autonomous decision-making, wallet interaction, and onchain execution. Together, these developments suggest that 2025 may be remembered less for price milestones and more for how trading itself evolved.
The Market Context: Volatility Without Direction
To understand the rise of automated strategies, it is necessary to understand the market regime traders were operating in. After the explosive moves of earlier cycles, crypto markets in 2025 displayed a paradoxical mix of traits:
-
Large intraday and intrawweek price swings
-
Prolonged consolidation ranges in major assets
-
Repeated failed breakouts and false momentum signals
For discretionary traders, this environment was punishing. Trend-following strategies suffered from whipsaw, while long-term holders saw limited upside despite enduring drawdowns. Even experienced traders found it difficult to maintain discipline when conviction trades repeatedly reverted to the mean.
In contrast, range-bound markets are almost ideal for certain automated strategies. When prices oscillate within defined bands, small, repeated movements become monetizable—provided execution is fast, consistent, and unemotional. This is precisely the niche that grid trading bots fill.
Grid Trading Moves From the Margins to the Mainstream
Grid trading is not a new concept. It has existed in traditional markets and crypto alike for years. What changed in 2025 was its scale and adoption.
According to HTX, grid trading volume on its spot platform rose 97% year over year, while capital committed to grid strategies doubled. Even more striking was the composition of that growth. Stablecoin pairs saw grid trading volume surge 352% year over year, compared with 122% growth in major cryptocurrencies.
This divergence is telling. Stablecoin pairs typically exhibit lower volatility and tighter ranges, making them well-suited for grid strategies that rely on frequent mean reversion rather than large directional moves. In effect, traders increasingly treated parts of the crypto market less like speculative assets and more like microstructure-driven trading venues.
Grid trading works by dividing a chosen price range into discrete levels and placing buy and sell orders at each interval. As the market moves up and down, the bot systematically buys low and sells high within that range. The strategy does not require forecasting; it requires only that prices continue to fluctuate.
In a year where forecasting repeatedly failed, that simplicity became a feature rather than a limitation.
Automation as Psychological Armor
One underappreciated reason for the rise of trading bots is psychological. Manual trading in choppy markets is exhausting. Traders are forced to make frequent decisions under uncertainty, often reacting emotionally to noise rather than signal.
Automated strategies remove that burden. Once parameters are set, execution is mechanical. There is no fear of missing out, no panic selling, and no second-guessing mid-trade. For many retail traders, bots became a way to remain engaged with the market without subjecting themselves to constant stress.
HTX’s data suggests that traders were not necessarily seeking higher returns, but more consistent ones. Grid bots aim to extract incremental gains repeatedly rather than capture home-run trades. In aggregate, that approach aligns well with markets that refuse to trend.
Stablecoins as a Trading Substrate
The outsized growth of grid trading in stablecoin pairs points to another structural shift: the increasing role of stablecoins as trading substrates rather than mere settlement tools.
By 2025, stablecoins were deeply embedded in crypto market infrastructure, serving as base pairs, collateral, and liquidity hubs. Their relative price stability made them ideal anchors for grid strategies, particularly when paired with volatile altcoins.
The rise in stablecoin-based grid trading suggests that traders were increasingly focused on relative price movements and spreads rather than outright asset appreciation. This is a subtle but important evolution. It implies a maturing trader base that is less fixated on bull-market narratives and more attuned to market mechanics.
HTX and the Exchange-Level Incentives
From an exchange perspective, the growth of automated trading is not incidental. Bots generate consistent volume, tighten spreads, and increase user stickiness. In range-bound markets where discretionary traders may disengage, automation keeps activity alive.
HTX’s position among the world’s 10 largest exchanges by volume, liquidity, and traffic, according to CoinMarketCap, gives it a broad dataset to observe these behavioral shifts. The exchange’s emphasis on grid trading tools can be read as both a response to user demand and a strategic move to stabilize platform activity during uncertain market phases.
This symbiosis between exchanges and automated traders raises important questions about market quality. On one hand, bots improve liquidity and execution. On the other, they can amplify certain patterns, reinforcing ranges and dampening breakout dynamics. In 2025, that feedback loop may have contributed to the very conditions that made grid trading attractive in the first place.
From Rule-Based Bots to AI Agents
While grid bots represent a relatively simple form of automation, 2025 also saw growing experimentation with a more ambitious model: AI-managed trading agents.
Coinbase emerged as one of the most active proponents of this approach. As early as August 2024, CEO Brian Armstrong described internal tests involving AI agents capable of transacting with one another using crypto tokens—what he famously summarized as “tokens buying tokens.”
By October 2024, Coinbase had launched “Based Agent,” a tool allowing users to create AI agents linked to crypto wallets for automated onchain activities such as trading, swaps, and staking. The pace of development accelerated further in October 2025 with the introduction of Payments MCP, a system designed to let AI agents interact directly with onchain financial services without API keys.
Payments MCP leverages the Model Context Protocol to allow large language models to access wallets, onramps, and stablecoin payments through natural language prompts. In practical terms, this means an AI system can interpret user intent and execute financial actions autonomously.
Why AI Agents Represent a Different Risk Profile
The distinction between grid bots and AI agents is more than technical. Grid bots operate under fixed, transparent rules. Their behavior is predictable, and their risk can be bounded by parameters such as price range and capital allocation.
AI agents, by contrast, introduce adaptive decision-making. They can respond to new information, interact with external systems, and modify behavior over time. This flexibility is powerful—but it also expands the attack surface.
An April survey by CoinGecko found that approximately 36% of respondents would allow AI agents to manage most of their crypto holdings. That figure suggests rising trust in automation, but it also underscores how quickly responsibility can be delegated away from users.
Aaron Ratcliff, attributions lead at Merkle Science, has warned that giving AI agents direct access to wallets introduces a new trust layer into systems originally designed to minimize trust. In effect, security responsibility shifts back to users—not in managing private keys, but in managing the behavior and integrity of the AI itself.
Trustless Systems, Trusted Agents
One of crypto’s foundational promises has always been trust minimization. Smart contracts execute deterministically. Consensus rules are transparent. Human discretion is reduced where possible.
AI agents complicate that narrative. While they may operate onchain, their decision-making logic is often opaque, especially when powered by large language models. Users are asked to trust not only the code, but the training data, inference process, and alignment of systems they may not fully understand.
This does not mean AI-managed trading is inherently flawed. It does mean that the risk model is different. Failures may not come from market moves, but from misinterpretation, prompt injection, or unforeseen interactions between systems.
Automation and the Changing Skill Set of Traders
The rise of bots and AI agents also reshapes what it means to be a “skilled” trader. In earlier eras, success depended on chart reading, macro analysis, or narrative positioning. In 2025, it increasingly depended on configuration, monitoring, and risk management.
Traders who succeeded were often those who understood how to:
-
Define effective parameter ranges
-
Allocate capital across multiple automated strategies
-
Monitor performance without over-intervening
-
Adjust systems as market regimes shifted
In this sense, trading began to resemble portfolio management of strategies rather than assets. Automation did not eliminate skill; it changed its expression.
A Structural Shift, Not a Passing Trend
The data from HTX and the initiatives from Coinbase point to a deeper conclusion: automation is no longer optional in crypto trading. In markets characterized by noise, speed, and fragmented liquidity, human-only strategies struggle to compete.
Grid bots flourished because they matched the market regime. AI agents gained traction because they promised scalability and abstraction. Together, they signal a market increasingly mediated by software, not sentiment.
Whether this leads to more efficient markets or simply different forms of risk remains an open question. What is clear is that 2025 marked a turning point. Crypto trading became less about predicting direction and more about engineering process.
As markets evolve, the traders—and platforms—that understand this distinction are likely to be the ones that endure.
Michael Lebowitz is a financial markets analyst and digital finance writer specializing in cryptocurrencies, blockchain ecosystems, prediction markets, and emerging fintech platforms. He began his career as a forex and equities trader, developing a deep understanding of market dynamics, risk cycles, and capital flows across traditional financial markets.
In 2013, Michael transitioned his focus to cryptocurrencies, recognizing early the structural similarities—and critical differences—between legacy markets and blockchain-based financial systems. Since then, his work has concentrated on crypto-native market behavior, including memecoin cycles, on-chain activity, liquidity mechanics, and the role of prediction markets in pricing political, economic, and technological outcomes.
Alongside digital assets, Michael continues to follow developments in online trading and financial technology, particularly where traditional market infrastructure intersects with decentralized systems. His analysis emphasizes incentive design, trader psychology, and market structure rather than short-term price action, helping readers better understand how speculative narratives form, evolve, and unwind in fast-moving crypto markets.