Algorithmic trading – the use of computer programs to automate trade orders – has grown from a niche practice into a dominant force across financial markets over the past 15 years. In stock exchanges, foreign exchange and CFD markets, and even cryptocurrency exchanges, non-human trading algorithms now account for a major share of total trading volume. This report examines how the algorithmic trading volume share has evolved globally since 2010 in three key market segments: stock markets, CFD/forex markets, and cryptocurrency markets. We pay special attention to the past 5 years, when advances in artificial intelligence (AI) have begun to influence trading strategies. We also analyze the implications for retail traders – including day traders, swing traders, and long-term investors – outlining the opportunities and challenges that AI-driven algorithmic trading presents in terms of market dynamics, competition, latency, spreads, and accessibility.

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Algorithmic Trading in Stock Markets (2010–2025)

Historical Growth: Algorithmic trading rapidly increased in equity markets during the 2000s and early 2010s. By 2010, an estimated 50% of U.S. stock trading volume was already executed via algorithms (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha), up from only ~15% in 2003 (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). In Europe and Asia, adoption lagged initially (around 20–30% of trades in 2010 were algo-driven in major Asian and European exchanges (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha)), but climbed quickly thereafter. By the late 2000s, some U.S. markets saw algorithmic/high-frequency traders responsible for as much as 60–73% of equity trading volume (Algorithmic trading - Wikipedia), and certain estimates even range up to 80% in peak periods (Algorithmic trading - Wikipedia). A few large high-frequency trading (HFT) firms accounted for the majority of this volume – for example, around 2009, HFT firms (just 2% of firms) were reportedly executing 73% of all U.S. equity trades (Algorithmic trading - Wikipedia). This dramatic rise led to a plateau in recent years: since the mid-2010s, algorithmic trading’s share in developed stock markets has stabilized at roughly 50–70% of volume on average (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com) ( Selling Spirals: Avoiding an AI Flash Crash | Lawfare ). In 2018, about 60–73% of U.S. equity trading was attributed to algorithmic strategies (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com), and today that figure remains in a similar range (often cited around 70%). Notably, roughly half of all U.S. equity volume is now driven specifically by high-frequency trading techniques (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha), while the rest of the algorithmic flow comes from slightly lower-frequency execution algos and institutional strategies.

Global and Current Share: In 2024, algorithmic trades account for roughly two-thirds or more of stock trading volume in major markets (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). U.S. and European exchanges routinely see ~70% of daily volume generated by automated programs, and even many Asian exchanges have caught up to similar levels (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). Emerging markets started lower but have also trended upward – for instance, India’s share of algo trading was around 40% in 2018 (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com) and rising. Overall, globally 60–75% of stock market volume is estimated to be algorithmic in recent years (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). This is a massive leap from 2010, when globally perhaps ~30–40% of equity volume was algorithmic (higher in the US, lower in Asia) (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha). In other words, over 15 years the balance has shifted such that the majority of stock trades worldwide are now executed by machines rather than human traders. Table 1 below summarizes the approximate algorithmic trading share in stocks over time.

Algorithmic Trading in CFD and Forex Markets

Institutional FX vs Retail CFD: The foreign exchange (FX) market was an early adopter of electronic trading, and by the late 2010s it had one of the highest algorithmic participation rates. A 2019 study found that about 92% of overall forex trading volume was handled by algorithms rather than humans (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). In the same period, over 70% of global spot FX turnover was executed through electronic platforms (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). These numbers, however, largely reflect institutional trading (interbank and hedge fund activity in FX). Contract-for-difference (CFD) markets – which are largely retail-focused derivative markets allowing traders to speculate on stocks, indices, or forex with leverage – have historically had lower algorithmic trading shares than institutional markets. In 2010, retail CFD and forex trading was dominated by manual trading; only a single-digit percentage of retail volume might have been driven by automated “expert advisor” scripts or bots at that time (many retail traders had no access to algorithmic tools). Over the past decade, retail brokers began offering more advanced trading platforms (MetaTrader, NinjaTrader, etc.) with algorithmic capabilities, and some tech-savvy retail traders started using expert advisors (EAs) and signal bots. As a result, the share of algorithmic volume in retail CFD/forex trading has risen from perhaps ~10% in 2010 to an estimated 30–40% in 2024 (rough figures). This means that while human traders still initiate a large portion of retail CFD trades, automated strategies now play a significant and growing role.

Current Trends: Retail adoption of algorithmic trading continues to grow, but remains below the near-total automation seen in institutional FX trading. One constraint is accessibility and skill – developing or acquiring a profitable trading algorithm is challenging for the average independent trader. As noted in industry analyses, sophisticated algorithmic trading in forex is “still difficult to acquire and implement” for retail participants, so only a handful of skilled traders deploy such tools, leading to imbalances where those with algorithms can out-compete others (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). Nonetheless, retail brokers report increasing use of automated trading APIs and copy-trading platforms. Many brokers (e.g. TD Ameritrade, Interactive Brokers) now support algorithmic trading tools for retail clients, lowering the barrier to entry. The growth rate is evident: for example, mobile execution of FX algos grew over 50% recently as more traders accessed algorithms on-the-go (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). Overall, algorithmic trading volume in CFD/forex markets has been rising by double digits annually, but as of 2024 it likely still constitutes less than half of total retail trading volume (perhaps ~30%–40% on average, versus >90% on the institutional side). Table 1 provides an approximate historical breakdown.

Algorithmic Trading in Cryptocurrency Markets

Early Years (2010–2015): The cryptocurrency market began in the 2010s as a retail-driven space with relatively low liquidity. During Bitcoin’s first years (2010–2012), trading was mostly manual, peer-to-peer, or on early exchanges, and algorithmic trading was virtually non-existent. As crypto exchanges like Mt. Gox, Bitstamp, and others grew mid-decade, simple trading bots and arbitrage algorithms started to appear – for example, programs to exploit price differences between exchanges or to provide basic market-making. By around 2015, it’s estimated that algorithmic or bot-driven trades still made up a minority (perhaps on the order of 10–20% of crypto volume), with the majority of trading done by individual humans in the nascent market.

Rise to Dominance: The landscape shifted dramatically in the late 2010s. The 2017 crypto boom attracted professional trading firms and high-frequency traders into the market (High-Frequency Trading Comes to Cryptocurrency – The FinReg Blog). Major crypto exchanges introduced features to accommodate algorithmic traders – such as colocation services, low-latency APIs, and fee rebates – which in turn spurred more algorithmic participation (High-Frequency Trading Comes to Cryptocurrency – The FinReg Blog). By 2018–2019, various analyses suggested that most crypto trading volume was already driven by bots or automated programs. One extensive 2019 survey across crypto communities found that, while only 38% of individual users admitted to using trading bots, in terms of volume about 86% of crypto trading was algorithmically executed (i.e. large traders using bots dominated volumes) (Research shows that 86% of crypto trading is done by bots). This aligns with the notion that a few big algorithmic players (market-making firms, arbitrage bots, etc.) were handling the lion’s share of transactions.

Current Share: As of 2024, algorithmic trading is firmly entrenched in crypto markets. Well over half of all crypto trade volume is now estimated to be driven by automated algorithms, and likely a sizable majority. Industry reports indicate that on major exchanges “AI crypto trading bots” account for over 60% of trading volumes (What Are AI Crypto Trading Bots and How Do They Work?), and other estimates (like the 2019 survey above) suggest the share could be in the range of 70–80+%. Practically every large crypto exchange’s order books are dominated by algorithmic market makers and high-frequency strategies operating 24/7. Even decentralized finance has trading bots executing arbitrage and MEV (miner extractable value) strategies. In sum, crypto has gone from a human-driven market to one where algos set the pace, in roughly a decade. This explosion in crypto algo trading corresponds with the market’s maturation – greater liquidity and hundreds of tradable assets created ample opportunities for automated strategies, and even retail crypto traders increasingly leverage bot platforms to trade continuously. The trend is expected to continue as AI-driven crypto trading tools become more advanced.

Table 1: Approximate Percentage of Trading Volume by Algorithmic (Non-Human) Trading

Table 1: Algorithmic trading volume share by market over time. Figures are approximate and represent the proportion of total trading volume executed by algorithmic (non-human) methods. Stock market figures reflect global developed markets (U.S., EU, Asia); CFD (contract-for-difference) figures are estimates for retail forex/CFD trading; Crypto figures reflect exchange trading of major cryptocurrencies. (Sources: compiled from literature and industry reports (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha) (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com) (Research shows that 86% of crypto trading is done by bots) (What Are AI Crypto Trading Bots and How Do They Work?).)

Growth of Algorithmic Trading and AI Adoption (2018–2025)

Algorithmic trading volumes have not only grown in absolute terms, but their growth rate has outpaced overall market volume growth for much of the past 15 years. Even in the already high-tech equity markets, algorithmic trading activity has been growing at double-digit percentage rates in recent years. For example, one analysis projects an 11.2% compound annual growth rate (CAGR) for algorithmic trading volumes in equities from 2021 to 2026 (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). The industry’s expansion is also reflected in market size estimates: the global “algorithmic trading” industry (including software and services) was valued around $17 billion in 2023 and is forecast to reach over $60 billion by 2032 (implying ~16% CAGR) – illustrating strong ongoing investment in this space (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com). Put simply, the influence of algorithmic trading has been steadily increasing, and nowhere is this more evident than in the rapid incorporation of AI (Artificial Intelligence) techniques over the past five years.

AI’s Growing Influence: Since roughly 2018, advances in AI – particularly machine learning and even newer large language models (LLMs) – have begun to transform trading algorithms. Initially, most trading algos followed fixed, pre-programmed rules. Now, AI-driven algorithms can learn from data, adapt to changing market conditions, and even process unstructured information like news or social media in real time. A recent IMF study found a striking indicator of this trend: the share of AI-related content in algorithmic trading patent applications rose from 19% in 2017 to over 50% each year since 2020 (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). This suggests that a wave of innovation is underway, with more than half of new trading algorithm innovations now involving AI components. Market participants echo this – in one survey, 57% of institutional investors said AI/Machine Learning would shape the future of trading in the next 3 years (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha), far outpacing other technologies.

Recent Developments: In practice, AI-driven trading includes techniques like deep learning models for price prediction, natural language processing to interpret news faster than humans, and reinforcement learning algorithms that evolve trading strategies through trial and error. Over the last five years, many hedge funds and proprietary trading firms have integrated these AI techniques. Notably, high-frequency AI-driven trading is expected to become even more prevalent in the most liquid asset classes (stocks, bonds, listed derivatives) in the coming years (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). Already, anecdotal evidence shows AI conferring an edge: for example, when U.S. Federal Reserve meeting minutes are released (long, complex documents), stock price movements in the first 15 seconds after release now more often predict the longer-term move – suggesting that AI models are parsing the text and trading on it faster than any human can (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). In essence, AI can digest market-moving information in seconds or less, a clear advantage over manual traders.

Despite the buzz, it’s important to note that AI adoption in trading is still in its early stages for many investors (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). The most sophisticated firms are deploying AI, but smaller or more traditional players are just beginning to experiment. We are likely at the beginning of an AI-driven transformation of algorithmic trading, meaning the 2020s could bring even higher growth in algo volumes and new dynamics in market behavior. Experts anticipate deeper liquidity and higher volumes as AI optimizes trading (a positive for market efficiency), but also caution about herding effects and volatility – if many algorithms rely on similar AI models, they could react similarly and amplify moves. For instance, some AI-driven funds showed significantly higher turnover during the March 2020 COVID-19 market turmoil, suggesting a propensity for rapid, herd-like selling in times of stress (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). Regulators like the SEC have voiced concerns that unchecked proliferation of AI models might contribute to flash crashes or systemic risks if they all respond to events in tandem ( Selling Spirals: Avoiding an AI Flash Crash | Lawfare ) ( Selling Spirals: Avoiding an AI Flash Crash | Lawfare ).

Impact on Retail Traders: Opportunities and Challenges

The rise of AI-driven algorithmic trading presents a mixed landscape for retail traders and anyone still trading manually. On one hand, these innovations bring opportunities in the form of better trading infrastructure, improved market conditions, and new tools accessible to individuals. On the other hand, they introduce challenges by increasing competition, speed, and complexity in the markets. Below, we break down the implications for three categories of retail market participants:

Day Traders (Intraday Traders)

Opportunities: Modern markets are generally more liquid and have tighter bid-ask spreads thanks to algorithmic market-making. Many HFT firms act as market makers, and their presence has narrowed spreads and reduced trading costs for others (Algorithmic trading - Wikipedia). This means a day trader today can enter and exit positions with less “slippage” and pay lower implicit transaction costs than a decade ago. Additionally, retail traders now have access to some algorithmic tools themselves – for example, many day traders utilize broker APIs or customizable bots to automate parts of their strategy (such as scanning for setups or managing orders). AI can even assist day traders by providing fast analysis of news or technical patterns that would be hard to do manually. In short, markets are more efficient and technology is more readily available, which can empower savvy retail traders to trade smarter.

Challenges: The biggest challenge for day traders is competition and latency. Intraday price fluctuations and arbitrage opportunities are now heavily exploited by algorithms that operate on millisecond timescales. A human day trader simply cannot react as fast as an AI-powered trading system that can instantly execute on a signal. This creates an uneven playing field – for instance, HFT firms can detect and capitalize on short-term price anomalies or large incoming orders before a manual trader can even hit the button, often earning profits at the expense of slower traders. Studies have estimated that high-frequency trading activities cost retail investors up to $5 billion per year (through mechanisms like front-running and adverse selection) (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha). Day traders trying to scalp tiny price moves may find it nearly impossible to beat the algos in speed or to “get ahead” of a momentum move that algorithms have already jumped on. Furthermore, AI-driven algorithms can contribute to sudden spikes in volatility (e.g. flash crashes or rapid price spikes), which can stop-out day traders or lead to large quick losses if one is on the wrong side. The 2010 Flash Crash and other algo-induced events (like the 2016 British pound crash overnight ( Selling Spirals: Avoiding an AI Flash Crash | Lawfare )) show how purely algorithmic activity can rapidly move prices. Day traders must be cautious of these events and often need to use risk controls (like circuit breakers or stop-loss orders) to survive in an AI-dominated landscape. Essentially, manual intraday traders are forced to either adapt (by leveraging algorithms/AI themselves) or focus on niche strategies where competition with HFT is less direct (for example, trading less-liquid stocks where not all HFTs operate, or using longer-term intraday patterns rather than ultra-short scalping). The latency arms race in the market means that without enterprise-grade infrastructure, a retail day trader should avoid trying to compete tick-for-tick with the fastest players.

Swing Traders (Multi-day Traders)

Opportunities: Swing traders (who hold positions for days to weeks) are less directly impacted by sub-second algorithmic trading, but they still benefit from some positive changes. Higher overall liquidity and tighter spreads carry over to all timeframes – even if you hold a trade for a week, you generally got a better entry/exit price due to algos tightening the market. Market efficiency can be an advantage: prices usually reflect information more quickly and accurately now, which means swing traders can trust that major news is quickly digested into the price (reducing the chance of trading on stale information unknowingly). Another benefit is that swing traders can make use of technical analysis tools enhanced by AI – for example, AI-based pattern recognition might uncover statistical edges in price data that a human might miss. Since swing traders operate on slower cycles, they can incorporate AI predictions or signals without needing ultra-low latency. Additionally, retail swing traders can use automated screening (algos that run overnight scans for setups across hundreds of stocks/coins) to broaden their opportunity set, something that was harder to do manually. All these factors can improve a swing trader’s decision-making and efficiency.

Challenges: The primary challenges for swing traders relate to market dynamics and volatility. While not competing on the millisecond level, swing traders still face AI-driven market behavior that can be unpredictable. For instance, algorithms (especially AI models) might induce new patterns – e.g. rapid momentum ignition or reversals – that can whipsaw positions. A swing trader might wake up to a sudden 5% price swing in their stock due to an overnight algorithmic trading surge or an AI reacting to some news in Asia, etc. Managing risk in such an environment can be tricky; stop-loss orders can help, but algos can sometimes hunt stop levels (a practice where algorithms probe common stop-loss levels to trigger liquidations) which may shake out swing traders before a move resumes. Furthermore, as more trading becomes algorithmic, some traditional technical patterns may become less reliable – if AI arbitrages a certain chart pattern whenever it appears, that pattern’s edge for human traders might diminish. Swing traders thus must continually adapt and possibly rely more on understanding the fundamental drivers, as purely technical edges get eroded by automated competition. On the flip side, AI-driven herd behavior could create opportunities for swing traders who go against the crowd. For example, if many algorithms simultaneously sell in a panic, a contrarian swing trader might find attractive entry points to buy oversold assets, betting on mean reversion once the algorithms overshoot. In essence, swing traders have to navigate a market that’s faster and sometimes more erratic, but still one where human judgment and patience (e.g. holding through short-term noise) can pay off in ways that short-term algorithms might not capture.

Long-Term Investors (Buy-and-Hold)

Opportunities: Long-term investors – those holding positions for months or years – arguably benefit the most from the algorithmic revolution, with relatively fewer drawbacks. For these investors, liquidity and low transaction costs are key advantages. Algorithmic trading has made markets more liquid and continuous, meaning large long-term positions can be built or liquidated with far less market impact than in decades past. Institutions managing long-term portfolios often use algorithmic execution (like VWAP or TWAP algorithms) to patiently accumulate stocks over days without moving the price too much. Even an individual long-term investor benefits indirectly: bid-ask spreads on most blue-chip stocks or ETFs are now only a penny wide, which makes buying and selling for your portfolio extremely cost-efficient (Algorithmic trading - Wikipedia). Another opportunity comes from technology access – long-term investors can use robo-advisors and AI-driven portfolio allocation tools to enhance their strategies. For instance, AI can help with portfolio rebalancing or risk management by analyzing vast amounts of financial data, something that was largely the domain of professionals. The overall market accessibility has improved; today, even a small investor can access global markets electronically and benefit from the liquidity provided by algos, whereas 20 years ago markets were less accessible and costlier to trade for the little guy.

Challenges: Long-term investors are least affected by the rapid-fire tactics of HFT, but they are not completely immune to the broader market impacts of AI-driven trading. One concern is the potential for increased systemic volatility – if AI algorithms cause a rapid sell-off (as regulators fear in a worst-case scenario ( Selling Spirals: Avoiding an AI Flash Crash | Lawfare )), a long-term portfolio could see very large short-term swings in value. Although a long-term investor might ride out such swings, it can be psychologically challenging and can even trigger forced selling in some cases (for example, if using margin or if an institutional fund faces redemptions due to a sharp drop). Additionally, markets arguably have become more efficient at pricing in information, which means fewer obvious mispricings or “easy wins” for fundamental investors. In the age of AI, a company’s earnings report or a macroeconomic development is processed by algorithms in seconds, so the window for a human long-term investor to buy an underpriced stock after good news is much smaller – the price may already reflect the news almost immediately. This raises the bar for active long-term investing; it shifts the advantage even more toward those with superior research or unique insights (potentially aided by AI as well). On the other hand, truly long-horizon investors (like those focusing on multi-year fundamental trends, value investing, etc.) may still find plenty of opportunities since many algorithms focus on short-term signals rather than deep value. Another challenge that could emerge is if AI-driven trading strategies cause market correlations to increase (herding behavior); this could reduce the benefits of diversification for long-term portfolios, as many assets might start moving in the same direction during AI-induced rallies or selloffs.

Market Accessibility: It’s worth noting a double-edged aspect of market accessibility due to AI and algorithms. On one side, retail traders have more tools and cheaper execution than ever, effectively leveling some of the playing field – for example, commission-free trading, algorithmic strategy platforms (some open-source), and AI-based analytics are available to individuals. This democratization means a retail trader today can deploy a basic algorithm on the cloud and participate in markets in a way that only institutional traders could in the past. On the other side, the most powerful AI trading resources (e.g. ultra-low-latency data feeds, cutting-edge proprietary AI models, alternative data sources) are still in the hands of large funds and firms, potentially widening the performance gap between those actors and the average person. In short, everyone can “access” the market, but not everyone can compete equally. Retail traders should consider strategic adaptation: some choose to join the trend by using semi-automated trading and AI-based research to augment their decision-making, while others focus on investment horizons or niches where human insight can still outperform.

Conclusion

The global trading landscape in 2025 is one where algorithmic trading is the dominant force, accounting for the majority of volume in stocks, a significant chunk in retail derivatives like CFDs, and virtually the bulk of volume in cryptocurrencies. This marks a profound shift from 15 years ago, fundamentally changing how markets operate. In the last five years, the infusion of AI into these trading algorithms has accelerated, promising even faster and more adaptive markets ahead. For retail and manual traders, these developments present a paradox: markets are more accessible, liquid, and efficient than ever (a boon to all investors), yet the competition is fiercer and more technologically advanced (posing challenges to those who don’t adapt). Day traders face the brunt of high-speed AI competition, swing traders must navigate AI-driven volatility, and long-term investors enjoy cheaper trading but must contend with an AI-influenced market environment.

Looking forward, the trend of rising algorithmic and AI-driven trading volumes is likely to continue. Algorithmic trading volumes have been growing at ~10–15% per year in many segments (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com), and with AI adoption, this could even increase. We may see AI algorithms managing large swathes of liquidity and making markets more transparent and tight in normal times, yet also requiring new safeguards to mitigate any flash crashes or correlated AI sell-offs in crises (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). For retail traders, staying informed and flexible is key – embracing new tools (possibly even using AI for one’s own trading systems) and focusing on strategies where human creativity or longer-term perspective adds value. The markets of the future will likely be a hybrid of human and machine intelligence, but as it stands today, the scales have tipped heavily toward the machines in terms of trading volume. By understanding these trends and their implications, traders and investors can better position themselves to exploit the opportunities and manage the risks in an algorithm-driven market era (Algorithmic trading - Wikipedia) ( Selling Spirals: Avoiding an AI Flash Crash | Lawfare ).

Sources: Major statistical insights were drawn from financial market research and institutional reports, including SEC and IOSCO studies on algorithmic trading, industry analyses (e.g. Coalition Greenwich, SIFMA), and surveys compiled in sources like Quantified Strategies (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com) (What Percentage of Trading Is Algorithmic? (Algo Trading Market Statistics) - QuantifiedStrategies.com) and Analyzing Alpha (79+ Amazing Algorithmic Trading Statistics (2025) - Analyzing Alpha). Data on crypto markets and AI adoption were referenced from crypto-specific research (Research shows that 86% of crypto trading is done by bots), exchange reports, and the IMF’s 2024 Global Financial Stability Report on AI in markets (Artificial Intelligence Can Make Markets More Efficient—and More Volatile) (Artificial Intelligence Can Make Markets More Efficient—and More Volatile). These sources collectively confirm the high and growing share of algorithmic trading in all major markets and illuminate the fast-growing role of AI in shaping trading dynamics.