Professional Traders vs. Retail: Exploiting Order Flow and Non-Economic Trades

Recent years have seen a surge of retail traders entering markets via zero-commission apps and hype-chasing activity (meme stocks, crypto, etc.).

This influx of inexperienced participants created opportunities for sophisticated Wall Street firms to profit from these less experienced traders through information arbitrage, i.e. using superior information and analysis that retail investors lack.

Professional traders and investors – including hedge funds, proprietary trading firms, and market makers – leverage significant skill, knowledge, and technology advantages to exploit retail traders’ moves and other non-economic flows (trades not driven by fundamental value) across US equities, futures, forex, and crypto markets.

The playing field is far from level, as retail traders unaware of these dynamics are at an inherent disadvantage.

Most retail traders ultimately lose money, with studies showing roughly 70–80% of retail forex/CFD accounts in the red (source: first page of the following SEC document).

Below, we look at how professionals strategically and technically capitalize on retail order flow and predictable market patterns, highlighting the skill gap and structural edges that make it so challenging for retail participants to compete.

Key Takeaways – Professional Traders vs. Retail

• Retail Flow Is Often Bought and Mined – Brokers sell your orders to firms like Citadel, who profit by trading against you before your order hits the market and by analyzing your behavior in real time.
• Speed Is a Weapon You Don’t Have – High-frequency traders use microsecond-level speed to front-run, fade, or exploit retail orders.
• Your Emotions Are Their Strategy – Professionals anticipate fear and greed. They create fake breakouts, trigger your stop-losses, and profit when you panic-buy or panic-sell.
• You’re Trading on Old Information – Hedge funds use AI to predict flows and sentiment before you even enter your trade. They front-run retail trends.
• Your Execution Is Predictable and Punished – Market orders, round-number stops, and entry timing make you easy to read. Pros use algorithms designed to profit off your execution habits without you knowing.
• Individual Traders Can Still Do Well – Retail traders can still do well, especially through disciplined passive investing or, for active traders, by developing a real edge through deep specialization, superior risk control, or unique information others don’t have.

The Skill and Knowledge Gap Between Professionals and Retail

Expertise and Resources

Professional traders typically have deep training in market microstructure, quantitative methods, and risk management that retail traders lack.

They operate in teams with quants and seasoned traders, backed by vast capital and cutting-edge infrastructure (co-located servers, direct data feeds, etc.).

This allows them to process information and execute trades at speeds and sophistication beyond any individual hobbyist.

For example, high-frequency trading (HFT) firms use ultra-fast market data feeds (e.g., Nasdaq TotalView, NYSE OpenBook) and lightning network connections with microsecond latency – the faster the trades, the better the micro edge a firm has. 

In contrast, retail traders often rely on slower broker feeds and consumer internet connections, leaving them reacting on delay.

Information Asymmetry

Professionals continuously ingest and analyze enormous data streams (order books, news feeds, social media sentiment, etc.) with advanced models.

They often know far more about current supply/demand dynamics than retail traders who focus on basic charts or news.

This informational edge is a core reason institutions can identify mispricings and anticipate moves before retail does.

For instance, many retail traders trade on emotions or simplistic signals, whereas a hedge fund might run a machine learning model on years of tick data to predict short-term price patterns.

The result is that pros are often “one step ahead” in forecasting market moves from order flow clues that retail eyes miss.

Structural Advantages

Beyond skill, professionals enjoy structural benefits.

They can trade in dark pools or internal crossing networks (avoiding tipping their hand on public exchanges), access lower trading fees and even rebates (market makers often get paid to provide liquidity), and use leverage or derivatives at institutional scales.

Market makers and prop firms also have direct relationships with exchanges and brokers that provide insights or order flow data unavailable to a typical individual.

In short, the deck is stacked: the combination of better information, technology, and market access enables professionals to consistently extract profits from less-informed retail trading.

Next, we look at the specific methods they use.

Exploiting Retail Order Flow via Payment for Order Flow (PFOF)

One major strategic edge is the monetization and analysis of retail order flow through payment for order flow.

In the US equity and options markets, retail brokers (e.g. Robinhood, Schwab, etc.) often sell their customers’ orders to wholesale market makers (like Citadel Securities or Virtu) in exchange for PFOF fees.

The market maker internalizes those trades, filling the orders internally instead of sending to the open market.

Why do they pay for this flow?

Because retail orders are typically considered “uninformed” and thus highly profitable to trade against.

Academic analysis in this SEC document shows that “uninformed retail order flow…is particularly valuable to wholesalers due to limited adverse selection risk. This leads wholesalers to pay to execute against segmented retail orders,” pocketing the bid-ask spread as profit.

In essence, if you’re a market maker, trading against small trader’s market order is safer (less likely they have insider info) than trading against a hedge fund’s order.

By purchasing retail order flow, market makers gain two advantages: execution edge and data insight.

First, they can execute retail trades “before the trades are [publicly] executed,” seeing the incoming buy/sell interest and pricing the trade on favorable terms.

Retail traders often assume they’re getting the best price, but the internalizer might match or slightly beat the public price while still profiting – essentially capturing the spread with almost no risk. (Indeed, wholesalers explicitly target this flow to avoid the “greater adverse selection” on exchanges from informed traders.)

Second, handling huge volumes of retail orders gives firms a real-time window into retail behavior.

These market makers build data sets showing retail investor behavior, positioning, and flows into popular stocks by seeing all the buy/sell orders coming through.

This information is gold: it enables professionals to gauge sentiment and crowded trades early.

Example – Robinhood/Citadel

During the meme-stock frenzy of early 2021, brokers like Robinhood routed the avalanche of retail orders to wholesalers.

Citadel Securities (a leading PFOF market maker) not only earned spreads on executing the trades, but also saw the order flow of millions of small traders chasing GameStop (GME), AMC, etc.

This gave them foresight that those stocks were becoming wildly overbought by retail.

Market makers and other quant firms saw this flood of retail GME/AMC/etc. activity before others enabling them to take the other side profitably.

In practice, a market maker witnessing an onslaught of retail call-option buying in GME could raise option prices, delta-hedge by shorting the stock into the rally, and be prepared to profit when the retail buying frenzy exhausted itself.

In crypto markets a similar dynamic is emerging – wholesalers reportedly pay even more for crypto retail order flow than for equities, reflecting how lucrative it is to trade against amateur crypto traders.

In all cases, PFOF and internalization segregate retail orders for pros to exploit in a relatively opaque environment. (Regulators have noted concerns that this reduces transparency and may conflict with best execution duties, but it remains common in the US equity market.)

The bottom line is that professionals literally buy the right to take the other side of retail trades, armed with superior pricing models and a knowledge that retail flow is generally uninformed.

Market Microstructure Edges: Order Book Analysis and Spoofing Detection

Another advantage professionals wield is a superior grasp of market microstructure – the detailed mechanics of order books, trade execution, and liquidity.

Where many retail traders barely look past price charts, professional traders look at the Level II order book (depth of market) for actionable signals.

The order book is a trove of microstructure insights showing all current buy/sell orders and revealing supply/demand at each price level.

Skilled traders analyze this in real time: for example, noticing a large bid order stack can indicate support, whereas a sudden wave of order cancellations might signal a coming price move.

Tracking metrics like order imbalances (total buy vs. sell volume) often serves as a precursor to price movement – a heavy imbalance can hint that price will soon move toward the dominant side.

This is often called book skew, as we cover in detail here.

Professionals use fast statistical models to estimate the probability of short-term price moves based on such order book dynamics, allowing them to react or position milliseconds before a price breakout that an untrained retail eye might miss.

Order Flow and Manipulation Signals

With advanced tools, pros also detect market manipulation or false signals that might fool a novice.

A classic example is spoofing – placing large fake orders to nudge prices, then canceling.

Sophisticated firms have algorithms (even AI classifiers) to flag unusual order book patterns that suggest spoofing or layering of orders.

This means they’re less likely to be tricked into trading on a spoofed quote.

In fact, when manipulators attempt tactics like momentum ignition (rapidly pushing the price to trigger others’ reactions), HFT firms often sniff it out and may even counter-exploit it (e.g., fading the artificial move).

By contrast, retail traders are far more prone to react emotionally to these fake-outs – buying because they see a sudden surge, not realizing it’s a head-fake.

Professionals thrive by being on the other side of such scenarios.

Stop-Loss Hunting

An especially pervasive microstructure tactic is stop-loss hunting.

Professionals are well aware that many retail traders place stop-loss orders at obvious support/resistance levels or round prices.

Knowing this, a larger player can deliberately push the price to those levels to trigger the stops – causing a cascade of sell orders – and then buy back at the artificially low prices.

Stop-hunting is a strategy to force others out of their positions by triggering stop-loss orders, which in turn creates a burst of volatility that the instigators can exploit.

For instance, if a stock has support at $50 and many retail stops just below, a hedge fund might sell aggressively to drive the price to $49.90, tripping a flood of stop-loss sells and thus driving the price down sharply.

The fund can then cover its shorts or accumulate shares at the distressed price before the market recovers.

This practice, though viewed as predatory, is not illegal per se (unless done via manipulative spoofing) and is common in forex and crypto markets especially.

Retail traders often complain of being “stop hunted” when they see prices dip just enough to hit their stop then promptly rebound – a pattern attributable to savvy players exploiting retail’s predictable clustering of stop orders.

High-Frequency Trading: Speed, Latency Arbitrage, and Queue Priority

HFT Speed Arms Race

High-frequency trading firms represent the extreme end of professional trading, exploiting every technological edge for speed.

Their guiding principle: trade faster than the competition to seize short-lived profits.

This leads to strategies like latency arbitrage – profiting from tiny time lags in price dissemination across markets.

For example, if the price of a stock ticks up on Exchange A, an HFT firm can race to buy that stock on Exchange B before Exchange B updates its price.

This requires sub-millisecond reaction times. Research shows that in today’s fragmented US equity markets, “96% of all trades are subject to latency arbitrage” opportunities by faster players under current market design.

In other words, nearly every time a trader sends orders to two exchanges, a fast trader can potentially intercept one leg of the trade on the slower venue and profit from the price difference.

This is essentially electronic front-running: not using insider info, but using speed to beat others to the punch.

HFT firms spend millions on co-locating their servers next to exchange matching engines, microwave communication networks between Chicago and New York, and optimized code – all to shave microseconds off transmission time.

Retail traders, using normal retail brokers, have virtually zero chance to compete in this arena; by the time a retail order reacts to a price move, the HFTs have already re-priced the market.

Queue Position Management

Speed also confers an edge in the limit order book queue.

When placing limit orders, being first in line at a given price is crucial – you get filled before others.

Professional market makers use dynamic quote updating strategies to maintain priority.

For instance, if they are second in queue and see the front order filled, they instantly adjust their quote or cancel/replace orders to move to the front.

HFT market makers routinely cancel and replace thousands of orders per second to ensure their quotes are always at the best price and with priority.

In fact, it’s estimated that only a tiny fraction of HFT orders ever execute; the rest are canceled and re-posted, often in under 50 milliseconds, as algorithms jockey for queue position.

This rapid-fire adjustment is impossible for a human retail trader.

The result: when a retail trader submits a limit order, they’re often far back in the queue behind these always-on market-making algos, meaning their order may not execute unless the price moves through it.

Professionals, by contrast, capture most of the volume at key price levels due to their relentless queue management.

Other HFT Tactics

High-frequency firms use a menagerie of tactics that exploit microstructure minutiae.

Sniffing/Pinging refers to sending tiny orders to gauge hidden liquidity or to detect large iceberg orders (so they can trade against a big player trying to hide).

Sniping involves jumping in or out right before a price change – for example, algorithms monitor for any predictable patterns (like the end of a dark pool’s batch auction or the last seconds of a volume-weighted average price trade) and then execute aggressively to seize a better price.

Some strategies even anticipate incoming orders: for example, certain algorithms infer that a large institutional order is being executed in slices and will start buying ahead of it (driving the price up and forcing the institution to pay more – a profit for the HFT).

One study found HFTs “identify patterns in past trades and orders that allow them to anticipate and trade ahead of other investors’ order flow”, effectively predicting when a big buy or sell wave is in progress.

By doing so, “HFTs’ aggressive purchases and sales lead those of other investors” and increase trading costs for the slower players.

All these techniques underscore how professionals use speed and algorithms to exploit any delay or pattern in others’ trading, something completely out of reach for typical retail traders.

Statistical Arbitrage and Order Flow Prediction

Stat Arb

Beyond instantaneous trading, professional firms also engage in statistical arbitrage – systematic strategies that exploit pricing inefficiencies and predict future price moves from statistical patterns.

These strategies often involve holding positions for minutes to days (not just microseconds), but still leverage superior data and models.

For example, quant hedge funds run mean-reversion or pairs trading strategies: if Stock A is usually correlated with Stock B and diverges significantly, they might short the overperformer and buy the underperformer, betting they’ll converge.

Such strategies require analyzing vast historical price series and correlations, which retail traders rarely have the tools or data for.

Large funds might monitor hundreds of assets and detect subtle anomalies (e.g. a certain futures contract is trading slightly cheap relative to an ETF).

They exploit these with bulk trades, often before the gap closes. By the time a retail trader notices an arbitrage (if ever), the professionals have already arbitraged it away.

Order Flow Prediction

A particularly important edge is order flow prediction.

Professionals use both simple stats and AI to forecast where aggregate orders will push the market.

For instance, a market maker might model the probability that an order book imbalance leads to a price uptick in the next second – and adjust quotes accordingly.

More aggressively, prop trading firms identify “large institutional orders and then trade ahead of those orders in anticipation that they will move the market.” 

If a hedge fund’s algorithm detects footprints of a big buyer (say, a pattern of small buys every second), it might start buying as well, expecting the price to rise as the large order continues – then sell at the higher price.

This anticipatory trading is a legal form of front-running through inference.

It puts the institutional or retail trader executing slowly at a disadvantage, as their own buying pushes prices straight into the waiting hands of the anticipators.

Cross-Market Arbitrage

Another domain is cross-market arbitrage.

Sophisticated traders predict order flow across related markets – for example, between futures and the underlying stocks, or between forex/bonds and interest rate markets.

If retail traders are aggressively buying S&P 500 index futures in the morning (perhaps out of bullish sentiment), an algo might predict that this futures pressure will lift the prices of the index’s constituent stocks momentarily.

The algo can buy a basket of stocks milliseconds before they actually move.

Likewise in forex, if a flood of retail orders hits one trading platform causing EUR/USD to tick up, a fast trader will buy on other platforms before they adjust.

These statistical and cross-asset strategies are capital-intensive and knowledge-intensive – one needs significant capital to short one asset and buy another simultaneously, and complex models to manage the risk.

Professionals have both, whereas retail traders operating in isolation can rarely arbitrage mispricings (and often aren’t even aware of them).

Alt Data and ML

Modern quant firms also incorporate alternative data and machine learning to predict flows.

For instance, they analyze sentiment data (Twitter, Reddit, news headlines) to predict surges of orders from retail or other players.

If an AI model detects extremely bullish chatter on social media about a small cap stock, a hedge fund might predict a wave of buy orders incoming and position long before the price pops.

Banks and hedge funds mine alternative data from platforms like Reddit, X (Twitter), and TikTok to monitor retail investor chatter. Using NLP algorithms, they detect shifts in sentiment and crowded positions—enabling them to anticipate and front-run retail trading behavior.

In short, statistical arbitrageurs don’t care about a stock’s story – they exploit patterns in trading behavior.

And retail trading behavior – often herding into the same stocks or reacting similarly to news – provides plentiful patterns to exploit for those with the data and expertise to model it.

Behavioral Exploitation of Retail Biases (Fear, Greed, and Herding)

Retail traders are notorious for emotional trading – chasing rallies (greed) and panic-selling selloffs (fear).

Professionals are keenly aware of these tendencies and design strategies to capitalize on them.

“Smart money” often literally sets traps for retail.

For example, a hedge fund might engineer a bull trap: push a stock’s price up with aggressive buying (sometimes aided by upbeat news or social media hype), inducing retail traders to pile in, only for the pros to sell into that strength and let the price collapse – leaving retail holding losses.

Conversely, a bear trap involves triggering a sell-off (perhaps via a well-timed negative rumor or a big sudden short sale) to scare retail into selling, then the smart money quickly buys the shares on the cheap, causing a rebound.

These tactics tie into market psychology.

Professionals often take a contrarian stance to retail at extremes: when retail euphoria has driven a price far above intrinsic value, pros will be lining up to short or sell.

When retail panic pushes an asset far below fair value, pros start buying.

In doing so, they effectively use the retail crowd as liquidity for their entries and exits – buying when retail is desperate to sell, and selling when retail is clamoring to buy.

Herd behavior by retail (such as everyone rushing into tech stocks or meme coins at once) creates exploitable inefficiencies.

A dramatic example was the GameStop saga: a retail mob drove GME up 1000% in January 2021, catching some hedge funds off guard.

But after the initial squeeze, many professional traders profited from the subsequent crash, shorting at inflated levels or using options complexity to their advantage.

As a HedgeWeek analysis noted post-mortem, once the dust settled, hedge funds were largely able to “ramp up risk positions” again while “retail traders licked their wounds,” implying the professionals ultimately reasserted dominance.

Beyond trend extremes, pros also exploit intra-day behavioral patterns.

Many retail traders follow similar routines – for instance, placing market orders right at the market open (leading to bursts of volatility), or using popular technical indicators that generate common entry/exit points.

Sophisticated algorithms can detect these patterns.

For example, if an execution algo knows that at 9:30am a flood of retail buy orders typically lifts the price by 0.5%, it can pre-position liquidity or fade the move accordingly.

Similarly, retail traders often set obvious chart-based triggers (like breakouts above recent highs) which algos anticipate – sometimes even manufacturing a quick breakout (the “head-fake” move) only to reverse it.

In crypto markets, where many levered retail traders set liquidation levels, large players (whales) will deliberately target those levels.

Whales distort market structure by using spoofing and stop-hunting to generate deceptive price signals. These tactics, in turn, often trigger liquidation cascades, which increase volatility and create profit opportunities for them.

In practice, a whale might see that lots of traders have leveraged long positions that will be liquidated (force-sold) if Bitcoin falls to $80,000.

The whale can aggressively sell to push the price to $79,900, triggering a chain of auto-liquidations that crash the price further, then buy back at the bottom – profiting handsomely while retail longs get wiped out.

These maneuvers feed on the typical retail behaviors: overleveraging, poor risk management, and herd-like stop placements.

So, whether by orchestrating traps or simply taking advantage of the fear/greed cycle (buying when others are fearful, selling when others are greedy), the pros systematically exploit behavioral biases that plague individual traders.

AI and Machine Learning: Modeling Order Flow and Sentiment

The cutting edge of professional trading involves AI and machine learning models that digest enormous amounts of historical and real-time data to find patterns invisible to humans.

In recent years, hedge funds and proprietary firms have invested heavily in ML to maintain their edge, especially as markets electronify and data proliferates.

Historical Order Flow Data

One major application is analyzing historical order flow data – essentially training models on how prices reacted to certain order book conditions or trading signals in the past.

For example, a machine learning model might learn that a particular combination of order book imbalance, volatility, and recent momentum gives an X% probability of a 0.1% price uptick in the next 5 seconds.

The trading firm can then program algorithms to exploit this by trading in advance of the predicted move.

These models can capture extremely complex nonlinear patterns that a human couldn’t notice (e.g., subtle sequences of trade executions and quote changes that precede a price move).

Using such predictive algorithms, professionals squeeze out profits from very short-term forecasting – effectively predicting near-term order flow and price impact.

Retail traders, with far less data and analysis, simply react to moves after they happen, often wondering “what just happened?” as a stock suddenly moves with no obvious news (when in fact it might be an algorithmic prediction playing out).

Sentiment Analysis and Alt Data

Another realm is sentiment analysis and alternative data, where AI parses data that reflect trader sentiment or intentions.

Institutions use alternative data sources tracking retail discussions on Reddit, X, TikTok, using natural language processing to gauge sentiment shifts.

If millions of retail traders are collectively turning bullish on a certain crypto token on social media, AI can detect the surge in positive sentiment (keywords, volume of mentions, etc.) faster than any human analyst reading forums.

This provides an early warning that a wave of buy orders may be coming.

Hedge funds have built models that correlate social media sentiment with subsequent price moves, allowing them to front-run crowds.

During the meme stock craze, for instance, some funds reportedly created dashboards of Reddit/WallStreetBets mentions and sentiment; when retail chat volume spiked, they could take positions accordingly.

AI can also scour Google search trends, news headlines, even satellite imagery (for macro trading) – all contributing to a mosaic of where money might flow next.

Retail vs. Institutional Flow Patterns

Moreover, AI/ML helps in distinguishing retail vs. institutional flow patterns.

Deep learning models can be trained on trade data labeled by source (in markets where that’s available, or inferred) to identify the telltale signatures of retail trades versus “smart money” trades.

For example, an AI might learn that retail traders tend to place more market orders in the first and last 30 minutes of the trading day, or that institutional algorithmic orders have a certain “steadiness” (e.g. executing 100 shares every minute).

By recognizing these patterns in real time, a professional trader can adjust – perhaps fading moves that appear retail-driven (assuming they will revert) or following along moves that appear institution-driven (assuming there’s real information behind them).

Optimizing

Finally, AI is used to generate optimal trading strategies and execution algorithms (discussed more below) which can dynamically adapt to market conditions.

Some funds now let reinforcement learning algorithms experiment with trading strategies in simulations and then use the best ones live.

AI is already powering advanced information arbitrage strategies.

And as the technology evolves, its use will only become more widespread, deepening the disadvantage for those without access.

Sophisticated Execution Algorithms vs. Known Retail Patterns

Not only do professionals decide what to trade more strategically, they also execute trades with far more finesse than retail traders.

Execution Algos

Execution algorithms are designed to optimize how orders are placed, seeking to minimize market impact and exploit predictable patterns in the market’s liquidity – often at the expense of noisier traders.

A retail trader might simply hit buy or sell as a market order and be done; an institution will typically use an algorithm to drip a large order into the market or route it in pieces to various venues for best pricing.

Common execution algos include Volume-Weighted Average Price (VWAP), Time-Weighted Average Price (TWAP), Percent-of-Volume, and implementation shortfall algorithms.

These break large trades into smaller child orders over time or across exchanges, to avoid tipping off others and to reduce how much the price moves against the order.

The edge here is that professionals can acquire or dispose of large positions without alerting the market, whereas a retail trader’s sizable order (by their standards) might move the price unfavorably because it’s not hidden.

In fact, if an uninformed trader repeatedly places big market orders (say, buying 10,000 shares in one go), HFTs and market makers will notice this as an impactful order and adjust prices upward as a response, meaning the trader pays more.

Pros never let that happen to themselves if they can help it.

Exploit Retail Patterns

Even more, execution algos can exploit retail patterns.

For example, if an algo knows that retail traders tend to place many market orders right after a major news announcement, it might wait to execute its own orders until that flurry pushes price slightly in one direction, then trade on the mean reversion.

Or conversely, some algos will provide liquidity (post limit orders) when they anticipate a short-term surge of uninformed market orders.

They essentially become the counterparties to panicky retail trades, earning the bid-ask spread or better.

A concrete case: retail traders often use market orders at the open, when volatility and spreads are high.

Smart algorithms might post buy orders just below the market and sell orders just above at 9:30am, capturing the spread from eager retail traders who cross the bid/ask.

These algos can adjust in milliseconds if needed or pull quotes if a bigger informed order appears – flexibility a manual retail trader lacks.

Using limit orders instead of market orders can help prevent high-frequency traders from front-running your trades.

This alludes to how HFT algorithms can see a large market order and sometimes anticipate it (via speed or pinging), then jump ahead to sell higher or buy lower, leaving the slower order with a worse fill.

Retail market orders are essentially prey to those sniper algorithms that optimize execution against them.

Smart Order Routing

Additionally, pros leverage smart order routing (SOR) technology.

Markets like US equities are fragmented among 10+ exchanges and many off-exchange venues.

An institutional trader uses SOR algorithms to route parts of their order to where liquidity is best or to dark pools if they want hidden execution.

Retail orders, in contrast, often all go to one venue (often the wholesaler via PFOF, or a single exchange) without such nuanced routing.

The difference can be subtle but significant: smart routing might avoid places where a retail order would have leaked info or moved the price.

Iceberg/Discretionary Pegged Orders

Institutions can also use iceberg orders (only part of size displayed) or discretionary pegged orders that jump to the best price at execution – all sophisticated order types that most retail trading apps don’t offer.

These tools help professionals hide their hand and avoid being exploited themselves.

Meanwhile, retail traders broadcasting a 5,000-share market order on Level II are basically shouting their intentions, and fast traders will react accordingly (widening spreads, etc.).

In short, professional execution algorithms not only shield the pros from slippage and exploitation, but also actively capitalize on the naive execution of retail.

Retail traders who place obvious orders at predictable times or prices become easy targets – their trades are absorbed by the pros’ liquidity or picked off by algos, resulting in retail getting worse prices on average.

The combination of smart strategy and smart execution means pros squeeze extra basis points of profit on every trade, which compounds to a huge performance gap over time.

Challenges for Retail Traders

The above points illustrate just how many headwinds retail traders face when going up against professional market participants.

Professionals exploit retail and other non-economic flows through:

• superior information (order flow data, sentiment analysis and other customized analysis)
• speed and technology (HFT and low-latency infrastructure)
• sophisticated strategies (arbitrage, prediction models), and
• refined execution (algos and order book savvy)

The skill and knowledge gap is immense – it’s not that every pro trade wins and every retail trade loses, but the odds are heavily tilted in favor of the professionals who effectively act as the house in the casino.

Little surprise then that the vast majority of active retail traders underperform or lose money over the long run.

By contrast, well-capitalized trading firms like market makers can even enjoy nearly every day being profitable because they’re systematically earning from providing liquidity to (or opportunistically trading against) less informed flow.

Uninformed retail trades are the underlying source of value driving competition among wholesalers for PFOF – in essence, retail trading is the resource to be harvested.

From a regulatory and ethical standpoint, some of these practices draw scrutiny.

Payment for Order Flow is controversial (banned in some countries) because of potential conflicts of interest and its role in segmenting the market.

Market manipulation tactics like spoofing and pump-and-dumps are outright illegal, though identifying and proving them remains a cat-and-mouse game.

Regulators have fined some firms for “momentum ignition” and manipulation schemes, and there is ongoing debate about reforms to make markets fairer for individuals (such as auction mechanisms for retail orders, or round lot changes to give retail more visibility).

However, even with stricter rules, the fundamental advantages of professionals – better training, faster systems, and economies of scale – will persist.

Conclusion

Retail traders are up against highly skilled adversaries who exploit every edge: they buy insight into retail orders, analyze the order book tick-by-tick, leverage superior speed for arbitrage, predict flows with advanced models, and cleverly game execution.

They capitalize on retail’s emotional mistakes and systematic patterns.

The result is that trading for retail often feels like fighting an uphill battle against unseen but formidable opponents.

This doesn’t mean retail traders can never win – occasionally a niche or a burst of collective retail action (as in rare short squeezes) can surprise the pros.

But it’s analogous to beating the casino by playing the slots.

Consistently beating the market on short-term trades is extraordinarily difficult when competing against these professional tactics.

The skill and knowledge gap between Wall Street and Main Street manifests in every aspect of trading, and it underscores why so many retail traders eventually quit or shift to passive investing.

The market isn’t overtly “rigged,” but it is a highly evolved system where any participant without a significant edge of their own is likely providing edge to someone else.

And more often than not, that “someone else” is a professional trader on the other side of the screen, exploiting the flow that retail provides.