Introduction: Why Algorithmic Trading Matters in 2026
Algorithmic trading has moved from the specialized desks of large institutions to the laptops and cloud servers of everyday market participants. In 2026, it is no longer a niche technology reserved for hedge funds, proprietary trading firms, and bank trading desks. It is a mainstream approach to engaging with financial markets, powered by faster computing, more accessible data, improved APIs, and a new generation of software platforms that make strategy development and execution simpler than ever before.
This complete guide is designed to serve as a definitive resource on algorithmic trading in 2026. It covers what algorithmic trading is, how it works, the strategies that define the field, the role of artificial intelligence, the legal and regulatory picture, and the realities of performance and risk. It is written to help newer readers build a solid foundation while also giving more experienced readers a clearer, more current view of where the industry is heading.
This guide covers what algorithmic trading is, how it works, the main strategies, the role of AI, how to start, the legal and regulatory picture, common mistakes, and how software platforms like Nurp fit into the broader landscape. Nurp is a SaaS company that licenses algorithmic trading software to customers who want a structured, rules-based way to engage with the markets.
Before going any further, one important note: algorithmic trading involves risk. Past performance is not indicative of future results. Technology can fail or behave unexpectedly, and customers remain responsible for their trades, their configurations, and their overall market participation. No approach, automated or manual, removes market risk.
What is Algorithmic Trading?
Algorithmic trading, often shortened to “algo trading” or referred to as “automated trading,” is the use of computer programs to execute trading decisions based on a predefined set of rules. Instead of a person manually placing an order after watching a chart, an algorithm scans the market, applies logic encoded by its developers, and sends instructions to a broker or exchange when specific conditions are met.
At its core, an algorithm is a recipe. It is a structured sequence of instructions that tells a computer what to do when certain inputs appear. In trading, the inputs are typically market data: price, volume, volatility, order book depth, and sometimes external signals like economic indicators, news, or correlated assets. The outputs are decisions such as buy, sell, adjust position size, set a stop-loss, or take profit.
The defining feature of algorithmic trading is that the decision-making process is codified. Whatever biases, assumptions, or preferences shape the strategy are written into the code itself. The computer then applies that logic consistently, without hesitation or emotional override.
Algorithmic trading is used across many markets, including equities, foreign exchange (forex), futures, options, and cryptocurrencies. It is deployed by some of the largest institutional market participants in the world, and with the rise of accessible trading software and SaaS platforms, it is now also available to individual customers who want a more systematic approach.
How Does Algorithmic Trading Work? A Step-by-Step Breakdown
To understand how algorithmic trading works, it helps to break the process into core stages: data collection, signal generation, risk management, order execution, and ongoing monitoring. Each stage plays a role in whether a strategy runs smoothly or runs into trouble.
Data collection is the foundation. Algorithms depend on market data, and the quality of that data has a direct impact on performance. Market data can include real-time quotes, historical prices, order flow, volatility metrics, and derived indicators such as moving averages or relative strength. More sophisticated systems may also ingest alternative data like news headlines, social sentiment, or macroeconomic releases.
Signal generation is where the rules of the strategy are applied. A simple signal might be: “If the 50-period moving average crosses above the 200-period moving average, generate a buy signal.” A more complex signal might combine dozens of conditions across multiple timeframes and asset classes. Signal generation is the brain of the algorithm and is where most of the strategy design work takes place.
Risk management is where a well-built algorithm separates itself from a fragile one. A strong algorithm does not just decide when to enter a trade; it also defines how large the position should be, where the stop-loss should sit, how much exposure is acceptable, and when to exit. Risk management rules are often the difference between strategies that survive different market regimes and those that do not.
Order execution follows signal generation and risk sizing. The algorithm sends an order to a broker or exchange through an API. Execution can be simple, such as a market order, or it can involve more sophisticated techniques like iceberg orders, VWAP or TWAP execution, or smart order routing designed to reduce market impact.
Monitoring is the final, often-overlooked stage. Markets change, brokers experience outages, internet connections drop, and data feeds glitch. A robust algorithmic trading setup includes monitoring and alerts, so that the customer can intervene when conditions require it. Customers are responsible for their trades and for the overall health of their system.
A Brief History of Algorithmic Trading
Algorithmic trading is not new. Its origins trace back to the 1970s, when the New York Stock Exchange introduced its Designated Order Turnaround system, allowing orders to be routed electronically rather than by hand. Through the 1980s and 1990s, institutional desks began using program trading and index arbitrage strategies, often tied to the relationships between stock index futures and their underlying components.
The 2000s brought a major expansion, driven by decimalization in US equities, faster networks, and the rise of electronic communication networks. High-frequency trading emerged as a distinct category, with firms investing heavily in ultra-low-latency infrastructure. By the 2010s, algorithmic strategies accounted for a large share of total volume in many major markets.
The 2020s have been defined by the democratization of algorithmic trading. Cloud computing, API-first brokers, better educational content, and a growing ecosystem of software vendors have made it possible for individuals and small firms to run strategies that once required a dedicated quantitative team. In 2026, algorithmic trading is no longer the exclusive territory of institutions, and the software landscape continues to evolve quickly.
Types of Algorithmic Trading Strategies
There are many ways to classify algorithmic trading strategies, but most fall into a handful of broad categories. Understanding these categories helps customers evaluate software, compare approaches, and set realistic expectations.
Trend-following strategies assume that price moves tend to persist in a direction once they begin. These strategies identify the beginning of a trend and attempt to ride it until evidence suggests the trend is ending. Moving-average crossovers, breakout systems, and momentum filters are common tools used in trend-following approaches. Trend-followers often perform well in strongly directional markets and can struggle in choppy, range-bound conditions.
Mean-reversion strategies take the opposite view. They assume that prices tend to return to an average value after deviating from it. These strategies look for extremes, an asset that has moved too far, too quickly, and aim to capture the move back toward a mean. Common indicators include Bollinger Bands, RSI, and statistical z-scores. Mean-reversion strategies often do well in range-bound markets and can face sharp drawdowns during strong trends.
Arbitrage strategies seek to exploit price differences between related instruments. Classic arbitrage involves buying the same asset on one venue while simultaneously selling it on another where the price is slightly higher. Statistical arbitrage takes this further by identifying historical relationships between instruments and trading deviations from those relationships. Arbitrage strategies tend to require fast execution and strong infrastructure.
Market-making strategies provide liquidity by quoting both a bid and an ask, earning the spread between the two when trades occur on both sides. Market makers need tight risk controls because they take on inventory risk and can be exposed to adverse selection. These strategies are usually run by specialized firms.
Momentum strategies combine elements of trend-following and relative strength. They identify assets that have performed well compared to their peers and attempt to capture continued outperformance over a defined horizon. Momentum has been studied extensively in academic literature and remains a popular approach in systematic equity and futures strategies.
Scalping and high-frequency strategies operate on very short timeframes, often seconds or less. They aim to capture small profits from minor price movements, relying on speed and volume. These strategies generally require institutional-grade infrastructure and are not accessible to most individual customers.
Event-driven strategies react to specific market events such as earnings announcements, economic releases, or corporate actions. The algorithm is designed to anticipate or respond to volatility around these events within strict risk parameters.
Portfolio and allocation strategies sit one level above individual trade logic. Rather than deciding when to buy or sell a single instrument, they decide how to distribute capital across many instruments or sub-strategies. Risk-parity, factor-based allocation, and volatility-targeted portfolios are common examples. These strategies often combine with trade-level logic to form a full system, where one layer decides what to trade and another decides how much weight to give each piece.
Algorithmic Trading vs Manual Trading: What’s the Difference?
One of the most frequent comparisons in the trading space is algorithmic trading versus manual trading. The question is not about which is universally better. Both approaches can work or fail depending on the person, the strategy, and the market environment. The comparison is really about trade-offs.
Manual trading relies on a human to interpret information, make decisions, and execute orders. It offers flexibility and context sensitivity. A skilled manual trader can react to unusual market conditions, use discretion, and adapt on the fly. However, manual trading is subject to fatigue, emotional bias, inconsistent execution, and the physical limits of human attention.
Algorithmic trading offers consistency, speed, and scalability. A well-designed algorithm applies the same logic every time. It can monitor many instruments simultaneously, across multiple timeframes, around the clock. It does not panic during drawdowns or get overconfident during winning streaks. But algorithms only do what they are programmed to do. If market conditions shift in a way the algorithm was not designed for, performance can degrade. Technology can also fail, servers go down, APIs return errors, data feeds glitch.
Customers who use algorithmic trading software remain responsible for their trades, configuration, and monitoring. Neither algorithmic nor manual trading eliminates market risk, and each approach has its own set of responsibilities for the person deploying it.
The Role of AI and Machine Learning in Algorithmic Trading
One of the most common searches in 2026 is some variation of “does algorithmic trading use AI” or “can AI predict the stock market.” It is a reasonable question, but it also invites a lot of marketing hype, so a careful answer matters.
AI and machine learning play a growing role in algorithmic trading, but not every algorithm uses AI. Many successful strategies are built on classical rules, statistical techniques, and well-understood market-structure concepts. At Nurp, only some algorithms use AI, while others rely on more traditional quantitative approaches. It is important to separate what AI can genuinely contribute from claims that overstate its capabilities.
Where AI does add value in trading, it is usually in pattern recognition, feature engineering, signal generation, and risk modeling. Machine learning models can process large datasets and identify relationships that are difficult for a person to spot. Natural language processing can help summarize news and earnings calls. Reinforcement learning can, in some cases, help tune execution strategies.
However, AI is not magic. AI involves risk and limitations. Models are subject to overfitting, data quality issues, and changing market regimes. AI can produce confident-looking outputs that do not hold up in live markets. Technology can fail or behave unexpectedly, and risk mitigation measures reduce but do not remove risk. No AI model can guarantee future performance, and customers should be skeptical of anyone who suggests otherwise.
Is Algorithmic Trading Legal?
Another common question is whether algorithmic trading is legal. In most major jurisdictions, the answer is yes, with conditions. Algorithmic trading is legal in the United States, the United Kingdom, the European Union, Canada, Australia, and many other markets, provided that market participants follow applicable rules around market manipulation, order handling, registration, and disclosure.
There are specific regulations that apply to certain categories of algorithmic activity. In the United States, rules from the SEC, CFTC, and FINRA cover areas such as market access, anti-manipulation, and algorithmic risk controls. In Europe, MiFID II includes specific requirements for firms engaged in algorithmic trading. Individual customers who license software to trade their own accounts generally have fewer direct regulatory obligations than registered firms, but they are still responsible for following the rules of their broker, exchange, and jurisdiction.
Nurp is a SaaS company that licenses algorithmic trading software. Nurp does not provide investment advice, does not provide capital, does not pool customer assets, and is not offering trading services on behalf of users. Customers use the licensed software to run algorithms on their own accounts and are responsible for their trades and for complying with applicable laws and broker terms.
Is Algorithmic Trading Profitable? An Honest Answer
This is one of the most important questions in the category and also one of the hardest to answer honestly. Algorithmic trading can produce profits, and it can also produce losses. Outcomes depend on the strategy, the market environment, execution quality, costs, risk management, and the discipline of the customer using the software.
There are no guarantees in trading. Words like “guaranteed,” “zero downside,” “safe profits,” “ensure,” or “secure returns” have no place in any honest discussion of algorithmic trading. Any content that suggests performance is certain or that software removes market risk should be treated with skepticism. Past performance is not indicative of future results, and hypothetical or backtested results have important limitations, including the fact that they are calculated with the benefit of hindsight.
What can be said honestly is that a well-designed algorithm, deployed within realistic risk parameters, on a suitable market, with sufficient capital, has the potential to generate returns over time. It can also experience drawdowns, periods of underperformance, and occasional losses. That is the nature of markets. Customers who approach algorithmic trading with realistic expectations, proper risk controls, and a willingness to learn tend to have better experiences than those who expect easy outcomes.
Pros and Cons of Algorithmic Trading
There are real benefits to using algorithmic trading software. Algorithms can monitor multiple markets simultaneously, execute faster than a person, and stay consistent with their rules regardless of emotional pressure. They can be backtested against historical data to evaluate logic before live deployment, although backtested results come with significant limitations. They can be tuned to respect specific risk parameters, such as maximum drawdown limits, daily loss caps, or position-size constraints.
There are also real drawbacks. Algorithms can underperform when market conditions change in ways the designer did not anticipate. Technology outages, broker API problems, and data feed issues can disrupt execution. Over-optimization during development, sometimes called curve fitting, can produce strategies that look excellent on historical data but disappoint in live markets.
Customers also need to understand the markets they are trading, the brokers they are using, and the underlying logic of the software they license. Algorithmic trading is not a shortcut around learning how markets work. Phrases like “low-involvement” or “install-and-ignore” misrepresent the reality: customers remain responsible for their trades and for monitoring their systems appropriately.
Markets Where Algorithmic Trading Is Used
Algorithmic trading is used across virtually every major market. In equities, algorithms are used for portfolio construction, execution, and both short-term and long-term strategies. In foreign exchange, the 24-hour nature of the market makes algorithmic approaches particularly useful for continuous monitoring and execution across sessions. In futures and options, algorithms are used for hedging, spread trading, and systematic strategies.
Cryptocurrency markets, which trade continuously, have become a popular environment for algorithmic strategies due to their volatility, API accessibility, and growing liquidity. Fixed-income and commodity markets also make extensive use of algorithms, particularly for execution and relative-value strategies. The common thread across all of these markets is the ability to connect programmatically and apply consistent, rules-based logic at scale.
How to Start Algorithmic Trading: A Beginner’s Roadmap
If you are just beginning to explore algorithmic trading, a few steps can help build a solid foundation. Think of this as a roadmap rather than a checklist, each step deserves real time and attention.
Start by learning the basics of financial markets. Understand order types, market microstructure, bid-ask spreads, slippage, and the difference between market and limit orders. Learn how different asset classes behave and why volatility varies between them. Without this foundation, any strategy you build or license will be harder to evaluate.
Next, decide on your approach. Some customers want to develop their own algorithms from scratch using programming languages like Python. Others prefer to license existing algorithmic trading software that has already been built and tested. Nurp falls into the second category, offering a SaaS platform where customers license access to algorithms rather than building them from zero. Both approaches are valid, and each comes with different trade-offs in flexibility, learning curve, and time commitment.
Choose a broker that supports algorithmic trading. Look for API access, reasonable fees, reliable execution, and regulated operations. Read the broker’s documentation carefully because every broker handles orders differently, and small execution details can have a significant impact on performance over time.
Start small. Whether you are using custom code or licensed software, begin with conservative settings and position sizes. Observe how the system behaves in live conditions. Keep detailed records. Adjust gradually and only based on evidence, not emotion. Customers are responsible for their trades, their configurations, and their overall risk management.
Programming Languages and Tools for Algorithmic Trading
For customers who want to build their own algorithms from scratch, Python has become the dominant language in retail and much of institutional quantitative work. Libraries like pandas, NumPy, scikit-learn, PyTorch, and vectorbt make data handling and strategy development accessible. C++ remains standard where latency matters, particularly in high-frequency strategies. R, Julia, and specialized languages like kdb+ are used in specific niches.
Tools for backtesting, live deployment, and risk management vary widely. Some customers use open-source libraries. Others use commercial platforms that provide integrated environments, and a growing number of customers license pre-built algorithmic trading software from SaaS vendors. Each option has its place depending on the goals, skills, and time availability of the user. The right choice depends on whether a customer wants to be a developer of strategies, a licensee of them, or a combination of both.
Backtesting and Performance Verification
Backtesting is the process of running a trading algorithm against historical data to evaluate how it would have performed. It is an important part of strategy development, but it has well-known limitations. Backtests are calculated with the benefit of hindsight. They can be affected by survivorship bias, look-ahead bias, and over-fitting. They may not reflect realistic execution, slippage, spreads, or costs.
Because of these limitations, serious algorithmic trading usually involves additional verification steps. Forward testing on a small live account, walk-forward analysis, and out-of-sample testing are all common practices. Many customers also look for third-party verification of live trading results on real broker accounts.
Nurp uses MyFXBook to verify its algorithms’ trading performance. MyFXBook connects directly to broker accounts and tracks activity in a way that makes it harder to present cherry-picked or misleading numbers. It is important to keep in mind that past performance is not indicative of future results, and that hypothetical or backtested numbers have inherent limitations. Customers should evaluate any performance data in that context and understand that no approach removes market risk. Verified performance is useful information, not a promise.
Common Mistakes to Avoid in Algorithmic Trading
There are several mistakes that both new and experienced customers make when engaging with algorithmic trading. Awareness of these pitfalls is often the difference between a durable approach and a short-lived experiment.
Overleveraging is a frequent problem, using position sizes that are too large relative to account equity. Even a strategy with strong long-term potential can be undone by a handful of outsized losses early on. Ignoring slippage and transaction costs is another common issue, particularly for strategies that trade frequently. These “invisible” costs can quietly erode otherwise promising results.
Over-optimizing a strategy to look perfect on historical data often leads to disappointment in live markets. If a strategy only works because it has been tuned to every bump in a historical dataset, it is unlikely to handle future data the same way. Another mistake is treating algorithmic trading as a shortcut that removes the need for understanding markets. Algorithms encode assumptions, and if a customer does not understand those assumptions, it becomes very hard to evaluate whether the software is working as intended.
Finally, expecting certain or predictable results is a setup for disappointment. Markets change, strategies go through drawdowns, and no approach is immune. A realistic mindset is one of the most valuable tools a customer can bring to algorithmic trading.
Algorithmic Trading and Different Asset Classes
Customers often ask how algorithmic trading applies to specific asset classes, so it helps to give a quick overview of a few of the most common.
In equities, algorithms are used at every level of the market. Long-term quantitative strategies select stocks based on factors such as value, momentum, and quality. Short-term strategies trade intraday patterns, earnings reactions, or statistical relationships between related stocks. Execution algorithms also help route orders efficiently across multiple venues.
In forex, the 24-hour market structure and deep liquidity in major currency pairs make algorithmic approaches particularly attractive. Trend-following, mean-reversion, and carry-based strategies are all common in forex algorithmic trading. Because forex markets operate continuously, software that can run without human attention around the clock has a natural fit.
In futures, algorithms are used for directional strategies, spread trading, and hedging. Futures markets offer leverage and clean data, which make them popular for systematic strategies. In cryptocurrencies, algorithmic trading has grown quickly due to the continuous nature of the market, deep API coverage, and high volatility. As always, volatility cuts both ways, and risk management is critical regardless of the asset class.
The Future of Algorithmic Trading in 2026 and Beyond
Looking ahead, several trends are likely to shape the next few years of algorithmic trading. First, software accessibility will continue to improve. SaaS platforms, including Nurp, are making it easier for customers to license algorithmic trading software without having to build everything themselves. Instead of writing code from scratch, customers can focus on strategy selection, risk parameters, and ongoing monitoring.
Second, AI will continue to be integrated into trading workflows, particularly for research, signal generation, and risk modeling. At the same time, only some algorithms use AI, and the hype will continue to outpace the reality in many cases. Customers should look for clarity about what AI is actually doing in any given product rather than accepting broad claims that everything is AI-augmented.
Third, regulation will continue to evolve. Regulators around the world are paying closer attention to algorithmic activity, retail participation, and the role of AI in financial markets. Fourth, education and transparency are likely to become bigger differentiators. Customers increasingly want to understand how algorithms work, what their risks are, and what performance verification looks like before committing.
Finally, the boundary between institutional and individual algorithmic trading will keep narrowing. Better tools, better data, and better software will continue to expand what is possible for serious individual market participants, while the fundamentals of risk, responsibility, and disciplined execution remain unchanged.
How Nurp Fits Into the Algorithmic Trading Landscape
Nurp is a SaaS company that licenses algorithmic trading software. Nurp’s customers license software and run it on their own broker accounts. Nurp does not provide investment advice, does not provide capital, does not pool customer assets, and is not offering trading services on behalf of users.
Only some Nurp algorithms use AI, while others rely on more traditional rule-based or quantitative techniques. The specific mix depends on the algorithm in question. Nurp uses MyFXBook to verify its algorithms’ trading performance, which helps provide a more transparent picture of results over time. Verified performance data is intended to inform, not to promise specific outcomes.
Customers remain responsible for their trades, for configuring the software appropriately, and for understanding the risks involved. Algorithmic trading involves risk. Past performance is not indicative of future results. Hypothetical or backtested performance has limitations. Technology can fail or behave unexpectedly, and risk mitigation measures reduce but do not remove risk. Licensing fees represent the cost of accessing the software and should not be framed as investment contributions or assured outcomes.
Frequently Asked Questions About Algorithmic Trading
Is algorithmic trading the same as automated trading? The terms are often used interchangeably, but there are small differences. Algorithmic trading refers to decision-making based on rules encoded in software. Automated trading typically refers to the execution part, orders being sent without manual intervention. Most modern algorithmic trading systems include automation, but automation alone does not always imply a sophisticated algorithm behind it.
Can beginners do algorithmic trading? Yes, beginners can engage with algorithmic trading, especially by licensing existing software rather than building from scratch. However, beginners should invest time in understanding the basics of markets, risk management, and the specific software they use. Customers are responsible for their trades.
How much money do you need to start algorithmic trading? There is no single answer. Some brokers allow small accounts, while some strategies require more capital to function properly due to position sizing, margin requirements, or minimum trade sizes. Starting with capital that a customer can reasonably afford to put at risk is generally considered sensible.
Is algorithmic trading better than manual trading? Neither is universally better. Algorithmic trading offers consistency, speed, and scalability. Manual trading offers flexibility and human judgment. Many experienced market participants use a combination of both approaches.
Do all algorithms use AI? No. Only some algorithms use AI, including some of Nurp’s algorithms. Many effective algorithmic strategies rely on classical statistics, technical analysis, or market-microstructure concepts without any AI component.
Can I lose money with algorithmic trading? Yes. Algorithmic trading does not remove market risk. Losses are possible, and past performance is not indicative of future results. Any marketing that suggests otherwise should be treated with caution.
What is the difference between algorithmic trading and high-frequency trading? High-frequency trading is a subset of algorithmic trading that operates on very short timeframes and requires specialized infrastructure. Most algorithmic trading runs on slower timeframes and is accessible to a wider range of customers.
How is Nurp different from a broker? Nurp is a software company, not a broker. Nurp licenses algorithmic trading software. Customers connect the software to their own broker accounts. Nurp does not hold customer funds and does not pool customer assets.
Does Nurp guarantee profits? No. There are no guarantees in trading. Past performance is not indicative of future results, and hypothetical or backtested performance has limitations. Customers should never make licensing decisions based on the expectation of certain outcomes.
What kind of customer is algorithmic trading suitable for? Algorithmic trading software tends to suit customers who are comfortable with technology, willing to learn how markets work, and realistic about risk. Customers who expect steady, predictable, or effort-free outcomes are generally a poor fit, because markets do not behave that way. A willingness to read documentation, monitor results, and make informed decisions about configuration is more important than any single technical skill.
Conclusion: Bringing It All Together
Algorithmic trading in 2026 is more accessible, more mature, and more scrutinized than ever before. For customers who approach it with realistic expectations, solid fundamentals, and a clear understanding of risk, it can be a valuable way to engage with markets in a structured, rules-based manner. For those looking for shortcuts, certain returns, or outcomes that require no active involvement, the reality is very different. There is no such thing as certain profits in trading. Markets involve risk, technology can fail, and customers remain responsible for their trades.
Nurp’s role in this landscape is to license algorithmic trading software to customers who want a systematic approach. Only some Nurp algorithms use AI. Nurp uses MyFXBook to verify its algorithms’ trading performance. Past performance is not indicative of future results, and hypothetical or backtested performance has limitations.
If you are considering algorithmic trading, take the time to learn the fundamentals, understand the software you use, set realistic risk parameters, and evaluate results with honest eyes. The better you understand what you are doing and why, the more likely it is that algorithmic trading becomes a durable part of how you engage with the markets, rather than a short-lived experiment built on unrealistic expectations.
The themes worth repeating are simple: understand the markets you participate in, understand the software you license, respect risk, stay skeptical of claims that sound too clean, and treat verified performance as useful information rather than a promise. Customers who internalize these ideas tend to have a more stable relationship with algorithmic trading over time.
Important Disclosures
Nurp is a SaaS company that licenses algorithmic trading software. Nurp does not provide investment advice, does not provide capital, does not pool customer assets, and is not offering trading services on behalf of users. Customers are responsible for their trades, their configurations, and their compliance with applicable laws and broker terms.
Algorithmic trading involves risk, including the risk of loss. Technology can fail or behave unexpectedly, and risk mitigation measures reduce but do not remove risk. Past performance is not indicative of future results. Hypothetical and backtested performance has inherent limitations, including the fact that it is calculated with the benefit of hindsight. Only some Nurp algorithms use AI; AI involves risk and limitations and should not be understood as guaranteeing performance.
Nurp uses MyFXBook to verify its algorithms’ trading performance. Verified performance data is intended to inform customers and should not be read as a promise of future results. Any performance examples, scenarios, or illustrations are for informational purposes only and are not a guarantee of outcomes.
