Can you really make a living from trading?

Can you really make a living from trading?

A person looks across a lake into a night sky filled with glowing candlesticks and a chart line.
TradingOrientation

Can you really
make a living from trading?

You look up at the sky and imagine what your life could be like. Perhaps with more time for the things that matter to you, or a second source of income. Perhaps even with the opportunity to make a living from trading one day. But is what you want really possible?

Illustration.Image: TradeNeon

What it means to make a living from trading

Yes, it is possible to earn money from trading and make a living from it. Perhaps you want to build an additional income first. Perhaps your long-term goal is to gradually reduce your working hours and eventually live entirely from trading. These are possibilities you can explore for yourself.

For your own path, it makes a difference whether you want to make your first profits, build capital or pay your regular expenses. A profitable trade shows that a single decision has earned money. To cover your living expenses, you need results that match those expenses after costs and, where applicable, taxes, while also allowing for weaker trading periods.

Your skills, the capital available to you and the time you can invest all matter. A small account needs to earn a much higher percentage returnThe gain from a financial product over a given period, expressed as a percentage. than a large one to produce the same amount of money. Assuming a high return does not reliably compensate for a lack of capital in everyday life.

This reality check gives your ambition a direction. You can identify what you already bring to the process and what you still want to develop. Perhaps your path begins with learning alongside your job and growing your own account. That turns a big goal into steps you can actually work on.

Why learning can become a maze

Getting started seems accessible today. You can find videos, podcasts, books and posts about almost every trading approach. One video explains day tradingOpening and closing positions within a single trading day.; the next promises a particularly effective indicator. Soon afterwards, you see someone showing profits made with AI. Everything seems to open up another possibility.

Your learning can then take a path that resembles a maze: you move from one strategy to the next before understanding its requirements. You become familiar with more technical terms, but cannot yet explain why you would enter a particular trade. Having watched a lot does not yet give you a direction for your own decisions.

A screenshot of a winning trade leaves important questions unanswered: How much capital was used? What losses came before it? What costs apply? Can you follow the rules behind the trade? Without this information, you have only a limited basis for assessing how the result relates to your own path.

YouTube can provide good explanations and practical insights. What matters is how you select and work through the content. Are you collecting more ideas, or developing an understanding you can test for yourself? This is where the path out of the maze begins.

How to separate useful information from the noise

You can recognise reliable information by being able to examine its basis and its limitations. Before adopting an approach, these questions can help:

  • Are sources, data and assumptions provided so that you can check them yourself?
  • Can you distinguish a substantiated statement from a personal opinion?
  • Are losses, costs and risks explained alongside possible profits?
  • Are the rules for entries, exits and position sizing clear?
  • Can you see whether someone earns money from a recommendation, for example through advertising or referral payments?

The European regulator ESMA identifies transparency about interests, separating facts from opinions and presenting risks fairly as important requirements for financial communication. As a reader, these points also give you practical criteria for choosing your sources. [1]

To begin with, you can focus on a few sources you can evaluate and one trading discipline. When you do not understand an explanation, write down the question and look for a specific answer. Your learning goal can then determine which information you need next.

Selection and learning develop together. You do not need to be an experienced trader to ask questions or recognise an unsupported claim. As your knowledge grows, you can assess more precisely which statements fit the markets, instruments and rules you want to learn about.

How information becomes knowledge and skill

You build knowledge when you understand connections and can explain them in your own words. In trading, that includes how your market works, the instruments you trade, order types, position sizing and risks. You should be able to explain why a setup fits your trading plan and under which conditions you would choose not to trade it.

Skill develops through practice and evaluation. A structured learning path might look like this:

  • You learn the fundamentals and formulate the rules of your approach so that you can explain them yourself.
  • You practise applying them in a demo account first and document your decisions, including mistakes.
  • You evaluate whether you followed the rules, what results you achieved and which assumptions you still need to examine.

A profit can result from a poorly justified decision. A loss can occur even when you follow your trading rules. It is therefore useful to assess the quality of your decision separately from the financial outcome. Demo trading helps you practise; execution, costs and managing real money can introduce additional demands later.

AI as a tool and a sparring partner

AI can support your learning. You can ask it to explain terms, work through questions to check your understanding or look for counterarguments to an assumption. You can also ask it to examine a trading rule you have written for ambiguities. Used this way, the tool helps you think more carefully about your own approach.

Trading with AI is not the holy grail, however. A fluent answer can be wrong, incomplete or out of date. The UK's Financial Conduct Authority describes AI as a tool for understanding and research and recommends checking information against its original sources. [2]

You can therefore use AI while you are learning. What matters is that you verify its answers and recognise unresolved questions. If you cannot yet assess the rules and risks of a suggested strategy, you need further foundations before using it to make a trading decision. An AI answer is not evidence that an approach is profitable.

Continue with a structured learning path

If you would like guidance along this path, the pages for the day trading programme and swing trading programme explain their content and structure. Both programmes and these linked pages are currently available in German.

Small groups leave room for your questions and feedback on your own trades.

For us, theory, practice and feedback belong together. That is what creates a sound learning path.

How realistic expectations change your progress

Your goal can be ambitious. To support your progress, it helps to separate your expectations for the learning phase from those for a future income. At the beginning, you can work on understanding the market, applying your rules consistently and evaluating your decisions in a way you can follow.

Imagine expecting to earn a fixed amount for your regular expenses in your very first month. If the profit does not materialise, every next trade could feel like something that has to work. After a loss, it may seem tempting to trade more or take more risk to recover it quickly. That expectation can create additional pressure.

You can assess your learning progress through different questions:

  • Can I explain my approach and its risks clearly?
  • Do I follow my rules even when there is no suitable setup or a trade loses?
  • Do I document my decisions in a way that helps me recognise mistakes and progress?

An evaluation can help you identify whether a losing period is part of the fluctuations of your approach or whether rules, execution or assumptions need reviewing. Good periods deserve the same scrutiny. A short winning streak alone is not a sound basis for your living expenses.

Give your learning phase a financial framework that fits your everyday life. Your job and savings can allow you to build knowledge and experience while your regular expenses remain covered. You can then measure progress through more than the amount earned in a single trading month.

From a second income to a possible livelihood

You can gradually develop the role trading plays in your life. A second source of income alongside your job is a worthwhile goal in its own right. You can start by learning, building your own capital from your income and examining how a trading routine fits into your daily life.

In swing tradingTrading price movements over a medium- to longer-term holding period., positions are held for days to weeks. Preparation and reviewing your rules can therefore be organised differently from trading within a single day. Day trading requires time for the trading session itself, as well as preparation and review. The right path also depends on when you can make that time available.

For an approach alongside your job, you can explore the swing trading programme. If you want to allocate more time to daily trading, the day trading programme offers the other training path. Consider the content and structure of each in relation to your daily life. Both programmes are currently delivered in German.

Keep the savings you need for living expenses separate from the capital you risk in trading. Losses can also substantially reduce your trading capital. In its guidance for US securities day trading, FINRA explicitly cautions against using funds needed for living expenses or important long-term goals as trading capital. [3]

Test your assumptions with the return calculator

How do starting capital, monthly savings and withdrawals interact? What changes when you adjust your assumed return or time horizon? In the return calculator, you can explore your own scenario and compare the swing trading and day trading tabs. The calculator is currently available in German.

The calculator shows results under the assumptions you enter. A desired income that is mathematically possible is not yet an income you have reliably earned. It is therefore particularly important to examine how your plan looks with weaker results and whether your savings and other income sources match your financial needs.

Prop trading or funded trader models can also offer a way to trade. Check whether trading takes place in a real or simulated account, which fees and loss limits apply and when payouts are possible. The advertised account size is not your freely available wealth. These models require a separate review of their contractual terms. [4]

If your actual results after costs match your expenses over a longer period and your financial protection is sound, you can consider changing your working hours. Weaker trading periods, taxes where applicable and the dependence of your income on markets remain part of that decision. Multiple sources of income can give you additional options.

You are allowed to imagine making a living from trading one day. The reality check shows you what you can work on along the way: develop a learning process you can follow, review your decisions and build capital. Your next step today can be to clarify your starting point and explore the training path that fits you.

Sources and context
  1. ESMA: Finfluencers – tips for responsible promotion (German factsheet)
  2. FCA: Using AI for investment research (updated 30 July 2026)
  3. FINRA Rule 2270: Day-Trading Risk Disclosure Statement (US securities day trading)
  4. FSMA: Warning about consumer prop trading models examined (7 March 2024)
Oliver Sparing
Oliver Sparing
Founder & Head of Trading · TradeNeon
Oliver Sparing has been actively trading the financial markets since 2011. His professional roots lie in precious metals trading in Hamburg; in 2017 he learned at a London trading floor. At TradeNeon he leads the day trading and swing trading programmes and is responsible for the trading methodology, with a focus on order flow, market structure and fundamental market data. Level of Interest, Futures Insights and Lab of Trades grew out of this work. Platforms such as ATAS, Bookmap and WH SelfInvest regularly invite him as a guest. All guest appearances

This blog is for educational and informational purposes only. It does not provide investment advice or recommendations to buy or sell. Trading and investing in financial markets involve significant risks, including the loss of all invested capital. You make and remain responsible for your own decisions.

Stock trading systems: rules, backtests and execution

Stock trading systems: rules, backtests and execution

A person in a dark suit examines a stock certificate with a large magnifying glass in blue-green light.
SoftwareTrading systems

Stock trading systems
Rules, backtests and execution

Which stocks do you consider? When do you enter a trade, how large should the position be, and when do you close it? A stock trading strategy gives these decisions a set of rules. Software can prepare the trades for you to review and approve yourself.

Assessing a trading system means examining its rules, test data and execution workflow.Image: TradeNeon · AI-generated illustration.

You do not have to write code or develop a system from scratch to use one. Using the Equity Growth System for US stocks, we explain how an existing approach works, how you approve trades in practice, and what to look for in its backtests.

Which decisions the rules cover

A rules-based strategy specifies the conditions under which trades are placed. These include the stock universe, entry and exit criteria, and position-sizing rules. The stock universe determines which stocks can be considered for a trade.

For an index-based stock strategy, the historical composition of that universe also matters: which stocks actually belonged to the relevant index when a historical trading signal occurred? Accounting for membership at that point in time is part of a properly constructed backtest.

To use a system, you also need to understand the trading routine. How long are positions typically held? How many can be open at once? When do you need to take action? These details help you assess the workload and risks involved.

Develop your own system or use an existing one

When you develop your own system, you define its rules, obtain and check historical data, and study how the rules behave in backtests. Implementing it also means deciding how a planned position becomes an order in your brokerage accountA broker is a securities dealer who executes transactions on the stock exchange on behalf of a client..

With an existing system, that development work has already been done. Before using it, you should understand how it was tested and which tasks remain yours in day-to-day trading. With EGS, the software prepares the trades; you review and approve them yourself.

What to look for in backtests

Assessing a stock strategy includes understanding how its results were produced. Two important criteria are the historical stock selection and the stability of the rules when parameters change.

Historical index membership and survivorship bias

Using today's index constituents for a backtest covering many years can distort the results. Companies that have since left the index are missing, while stocks added later may be included too early in the historical test. Looking only at the companies that survived is known as survivorship bias. This can make an equity curve look better than it would have with the stock universe available at the time. The QuantConnect research guide also explains this issue.

In our Equity Growth System backtests, we include only stocks that actually belonged to the strategy's index at the time of each signal. What matters is their index membership at that point in history.

Few parameters and checks for curve fitting

Curve fitting means adapting rules so closely to historical data that they also capture its random noise. A useful robustness test therefore examines how results change under different parameter settings. The IBKR Campus webinar on backtesting discusses the importance of such checks.

For EGS, we use robust stock strategies with few parameters. Even substantial changes to individual parameters would have left them profitable in the historical tests. This is an important feature of our approach: its results do not depend on a single finely tuned set of parameters.

What the Equity Growth System prepares for you

Titan – the stock trading module in Lab of Trades
Lab of Trades → TitanEquity Growth System

The Equity Growth System is a rules-based approach to swing trading in stocks. Its rules are calculated internally. You can execute the resulting trades through Lab of Trades in your own brokerage account. You will find the Equity Growth System in Titan, its stock trading module.

The TradeNeon team has carried out the development work for these systems. The rules produce prepared trades that you can review and execute independently. In the software, you see the model portfolio with its positions and upcoming changes.

Positions are typically held for one to ten trading days. This is swing trading with positions held overnight. Price gapsWhen the closing price of a chart candle and the opening price of the next candle differ, a price gap arises between them. following news outside trading hours are among the risks of this approach.

How approval before the US market open works

To trade the system, you need a brokerage account enabled for US stock trading and Trader Workstation, or TWS. You also need Lab of Trades on your Windows, Mac or Linux computer.

The trades are available before the US market opens. You review them, manually approve each trade and send the orders from Lab of Trades to your TWS. The planned entry then takes place at the market open.

You can complete the approval beforehand, so you do not need to be at your screen at the exact opening time. Semi-automated means that the software prepares the trades while your approval remains necessary for each one.

How to interpret EGS backtests and real-money trading

Our team has traded these systems in their current form in its own real-money accounts for many years. This practical experience complements the historical tests and checks of the trading rules.

The curves shown on the EGS product page are labelled as historical backtests starting in 2000. They apply the rules to historical price data and include standard IB trading fees.

The equity curve shows the simulated performance. Below it is the drawdown: the decline from the previous peak. Together, they show how capital developed during the test and the setbacks along the way. The largest historical drawdown is not a limit on future losses.

In the chart below, you can switch between sizes S, M and L and view different parts of the test period. The accompanying notes explain how the results were calculated.

Past results from backtests and real-money trading are not a reliable indicator of future results.

Lab of Trades / Titan / Equity Growth System

EGS backtests

Loading data from the EGS product page.

Backtests and methodology on the EGS product page

How the Equity Growth System sizes differ

Each larger tier adds further systems and stocks. The sizes therefore differ in their underlying trading rules as well.

SizeIncluded systemsMaximum simultaneous open positions
SOne system for S&P 100 stocks3
MEverything in S, plus another system for S&P 500 stocks8
LEverything in S and M, plus a third system for Nasdaq 100 stocks13

Alongside the backtests, consider the included strategies and the number of possible open positions. A larger package does not automatically mean a smaller drawdown.

Adding to your own strategies

If you already trade your own strategies, you can assess another systematic approach as a complement. The same applies to a long-term portfolio that you want to supplement with an active component. The practical benefit of an existing trading system is that you do not have to develop this additional approach yourself.

The EGS systems remain US stock strategies. How much additional diversificationThe term diversification refers to the spreading, or allocation of assets across different investment types, industries or economic regions. they offer depends on what you already trade and how the approaches behave together. More systems do not automatically mean less overall risk.

The Equity Growth System page provides the sizes, full backtests, requirements and subscription prices. In his German-language video about the Equity Growth System, Oliver explains the workflow and compares the sizes.

To get to know the software, you can use Lab of Trades on the Free Plan, including Smart Entry Equity on Mondays. The Equity Growth System itself is a separate paid subscription.

Oliver Sparing
Oliver Sparing
Founder & Head of Trading · TradeNeon
Oliver Sparing has been actively trading the financial markets since 2011. His professional roots lie in precious metals trading in Hamburg; in 2017 he learned at a London trading floor. At TradeNeon he leads the day trading and swing trading programmes and is responsible for the trading methodology, with a focus on order flow, market structure and fundamental market data. Level of Interest, Futures Insights and Lab of Trades grew out of this work. Platforms such as ATAS, Bookmap and WH SelfInvest regularly invite him as a guest. All guest appearances

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AI in Trading: The Real Edge Stays Human

AI in Trading: The Real Edge Stays Human

An anonymous trader in a hoodie stands beside a blue AI light figure, with three suited figures behind him wearing a bank building, piggy bank and vault door as heads
AI Investing & Trading

The real edge stays human

AI can structure reports, analyze data and produce initial code drafts. What does that offer retail investors and traders — and where are the limits?

As tools become more accessible, judgment stays human.Image: TradeNeon

AI makes some analytical tools more accessible. It does not give retail investors the resources of major market participants or automatically create a trading edge.

The value differs for investors and traders. Checking the data, code and results yourself remains essential.

What "democratization" in the financial markets really means

Let's start by looking back. For decades the financial markets carried a massive imbalance. On one side stood the institutional players — banks, funds, hedge fundsInvestment funds with broader latitude to use leverage, short sales and derivatives; often restricted to professional or wealthy investors. — with expensive data terminals, whole teams of analysts and programmers, and computing power that retail investors could only dream of. Retail traders generally had far fewer resources. Access to current data, specialist analysis and expert support was often more difficult or expensive.

Then: the big players
Data terminals · analyst teams · computing power
vs.
… and you
Less access to data · fewer resources
Today AI can lower barriers to entry: it supports research and analysis. Differences in data, capital and expertise remain.

This gap has never fully disappeared, and we should never claim it has. Lower-cost brokers, accessible real-time data and freely available charting platforms have lowered some barriers to entry. AI can make further analytical tasks easier. How much it helps in a particular case depends on the tool, the data and your checks.

The core of democratization

What makes AI so remarkable is not primarily that it can do something no one could do before — much of it was technically possible already. The decisive point is that it can lower the barrier to entry. For example, you can request an initial code draft without programming experience or have reports structured for you. Recognizing errors in statistics, data and code still requires expertise or specialist support. That is what democratization means here: an easier entry into some analytical tasks does not make anyone a professional.

How AI suddenly opens up analysis

Perhaps the most important breakthrough is that natural language has become the new interface. Custom analyses of a dataset often required programming skills, although spreadsheets and other analytical software existed before generative AI. Today you can ask your question in plain German or English and request a structured answer, a table or a code draft. The output needs checking. That changes three things fundamentally:

  • Speed. An initial analysis draft can be produced faster with AI. The time saved depends on the task, the data and the corrections needed. Faster does not automatically mean better, but AI can help you investigate more ideas.
  • Lower barriers to entry. Programming, statistics and preparing raw data can present substantial barriers to custom analysis. AI can support parts of this work. Asking the right questions, preparing data and interpreting the results with subject knowledge remain your responsibility.
  • Processing large volumes of text. Annual reports, central-bank minutes, earnings calls, news streams — these can be large volumes of text that take time to read in full. AI can summarize and compare texts you supply or that it can access through connected sources, and suggest relevant passages. It may omit or misinterpret important material.

Impressive so far. But this is exactly the point where we need to tap the brakes — because where there is much light, there is also shadow.

Where the dangers and misconceptions lie

At TradeNeon we take seriously our job of pointing out the other side of the coin and giving you a realistic picture of trading and the markets. Precisely because AI is such a powerful tool, the misconceptions around its use are especially critical. We don't want to keep the most important ones from you.

Misconception 1 · AI is a money-printing machine

This is a dangerous misconception. Access to a tool alone does not establish a market edge. If many people use the same tool, that may limit a possible advantage; how quickly and to what extent cannot be stated as a general rule. AI can make access easier. Whether your approach produces a robust edge needs independent testing.

Misconception 2 · Whatever the AI says is true

AI models can sound convincing and still be completely wrong. They occasionally "hallucinate" figures, sources or connections that simply do not exist. In everyday use that is annoying — in an investment decision it can get genuinely expensive. The iron rule: trust is good, checking is mandatory. Ask for sources and verify the key figures yourself against the original sources. Do not base investment decisions solely on AI-generated information. Source: Investor.gov. You need a clear goal in mind and to know where you want your project to go. AI is no substitute for expertise, and for the layperson it can quickly produce plausible-sounding nonsense.

Misconception 3 · More data = better decisions

"Garbage in, garbage out" applies to AI just as it does everywhere else. Ask a sloppy question or feed the model bad data, and you get a bad result — just nicely worded. The quality of your question influences the answer, as do data quality, freshness and the limits of the model. Even a well-phrased question does not prevent errors.

The underestimated trap · Overfitting

When developing and testing trading systems, the temptation is strong to use AI to keep tweaking parameters until the backtestA historical test of a trading strategy. It shows a hypothetical result, not a future return. curve looks gorgeous. The problem: a system can be fitted so closely to past data that it mainly captures chance patterns and fails on new data. We'll come back to this shortly, because it is so central for traders.

The point that stands above all

The responsibility stays with you. The generative AI described here is an assistant and does not replace qualified investment advice. It takes work off your hands, but never the decision — and certainly never the risk. Every trading decision is yours to make and to answer for. Not even the most impressive model changes that.

So much for the necessary caution. Now let's look at where AI — used properly — actually makes a real difference. Here we need to draw a clean line, because the value looks quite different for investors than it does for speculators.

AI for investors: fundamentals with your own checks

The investor thinks long term. It is not about the next tick, but about a question: is this company, this sector, this market worth letting my capital work there for years? This is where AI can support research and analysis. The information available depends on the data supplied and the connected sources.

  • Understanding annual reports and earnings calls. A single annual report easily runs to hundreds of pages. AI can pull out the key statements, name the risks buried in the fine print, and compare management's language in the earnings call with the previous quarter — if the tone shifts from "optimistic" to "cautious", that can be a reason to examine the original statements and their context more closely.
  • Comparing companies across an entire sector. Instead of laboriously digging through a dozen balance sheets, you can have metrics like margins, debt or growth laid out side by side in a structured way, and gain an initial overview of a sector. Check that reporting periods, currencies and metric definitions are comparable.
  • Decoding macro statements. Wording and context matter in statements from the Fed and the ECB. AI can help you compare these statements with earlier meetings and identify possible changes. Check the interpretation against the original text.
  • Condensing the news and sentiment picture. From a confusing stream of headlines you can distil a condensed sentiment picture for a stock or a sector — as a starting point for your own, deeper research.

The decisive point: none of this replaces your own judgment, but it can help the retail investor analyze available information more purposefully. The homework stays the same — check the numbers yourself before you commit capital.

AI for traders and speculators: statistics, backtests, systems and indicators

For the active trader it looks different. This is not about the fundamental valuation of a company over years, but about probabilities, recurring patterns and one question: does what I'm doing actually carry a statistical edge? In this world of data, rules and tests, AI is an exceptionally useful ally.

  • Pulling statistics from your own data. What is your average hit rate at a particular time of day, really? What does the distribution of your wins and losses actually look like? Where do you make money, and where do you burn it? AI can analyze your own trading data and present the results clearly. Calculate metrics using transparent formulas or checked code, and verify them against a sample of your data.
  • Preparing backtests with AI. Turning a trading idea into a testable backtest can be a substantial hurdle when programming skills are missing. AI can produce an initial code draft from an idea described in words, allowing you to test it against historical data. You need to check, or have a qualified person check, whether the code implements your rules correctly and models data, costs and execution realistically. The result remains hypothetical. Source on checking code: NIST.
  • Developing your own trading systems. From the first vague idea through clearly defined entry and exit rules to well-thought-out risk and position management — AI can accompany you through the entire process as a sparring partner, helping you structure your thoughts and expose gaps in your logic.
  • Building your own indicators. You always had an idea for an indicator but didn't know how to put it into Pine Script or Python? AI can lower that hurdle, but the code and data still need checking. From a simple momentumThe observation that prices may continue in an established direction for a time. filter to a more complex statistical indicator, it can help with design and implementation. The work needed for tests and corrections depends on the project.
  • Supporting recurring routines with AI. Data pulls, daily evaluations, preparing market data — AI can help prepare scripts for these routines. Check data sources, permissions and results, and retain control over the process. This can free up time for trading and analysis.
Caution · In-sample vs. out-of-sample

Here lies the overfittingFitting a strategy so closely to past data that it captures chance patterns rather than a robust rule. trap mentioned earlier. A backtest provides a hypothetical result under specific assumptions, not evidence of future profits. Distinguish between in-sample data used for development and separate out-of-sample test data. Do not keep adjusting the rules to those test data, or they too lose their value as a check. Be suspicious of systems with too many optimized parameters and ask whether the result could be achieved in real trading: have fees, slippage, data quality and different market conditions been considered? Even a good out-of-sample test does not guarantee a future return. Source on overfitting: Bailey et al.. The gap between a beautiful backtest curve and lived reality in the markets is, in our experience, wider than most people think.

You can see it: while AI mainly helps the investor to understand, it mainly helps the trader to build and test — to develop, test and refine rules whose possible edge still needs to be checked.

AI for investors

Time horizonYears — long term

The questionIs the company worth it?

AI helps withUnderstanding

In practiceReports, macro, sectors, sentiment

Biggest trapTaking hallucinated numbers at face value

AI for traders

Time horizonShort to medium term — depending on the strategy

The questionDoes this carry a statistical edge?

AI helps withBuilding & testing

In practiceStatistics, backtests, systems, indicators

Biggest trapOverfitting (in- vs. out-of-sample)

The real edge stays human

Amid all the enthusiasm, one thought matters most to us, and it is the one we want to close on: AI can make analytical tools more accessible. Access to reliable data still depends on the sources actually available to you. This does not make success universally available. As similar tools become easier to access, the following question becomes more important.

"It is no longer 'who has the better tools?' but 'who asks the better questions, has the better strategy, and the discipline to execute it?'"

Oliver Sparing — TradeNeon

This is exactly where what we most like to talk about at TradeNeon comes back in: market understanding, risk awareness, structure and personal responsibility. AI makes the toolkit accessible — whether you build something solid from it still depends on you.

AI can make analytical and programming tools more accessible to retail investors. For investors, it can help structure and compare annual reports, sector metrics and central-bank statements. For traders and speculators, it can help produce statistics, prepare backtests, develop systems and build indicators. An initial draft is possible without years of programming training; expert checking, reliable data and risk management remain necessary. Access to tools is not a demonstrated trading edge.

"AI is an amplifier, not a replacement. It doesn't hand you an edge — at best it helps you find your own edge faster and execute it more cleanly."

Oliver Sparing — TradeNeon

Those who take these limits into account can use AI to support research and the testing of ideas. For those who don't, there is a real risk of being lulled into false security by nicely worded answers and perfect backtest curves. Use the new possibilities — but stay the critical, self-responsible decision-maker you have to be in the markets anyway.

Frequently asked questions about AI in trading

What does democratization in trading through AI mean?

AI can make it easier to get started with research, analysis and programming. You can ask questions in natural language and receive an initial draft. Access to reliable data, expertise and checking the results remain necessary. Easier access to tools does not establish a trading edge.

Can I rely on AI answers for trading decisions?

Do not rely on them alone. AI can provide incorrect figures, outdated information or invented sources. Check key claims against the original sources and verify which data the answer is based on. The decision and the financial risk remain yours.

Does a good AI backtest prove that a strategy works?

No. A backtest shows a hypothetical result under specific assumptions. Code, data, fees and execution need checking. Separate test data help with validation, but repeatedly adjusting a strategy to those data can overfit the test as well. A good test does not guarantee future profits.

Do I need expertise if AI writes the code?

Yes. AI can produce an initial code draft even if you lack programming experience. You still need to assess, or have a qualified person assess, whether it implements your trading rules correctly and produces meaningful results. For an introduction to market fundamentals, explore our free trading crash course, currently available in German.

Prefer to start with the basics?
Start exploring the markets with our free trading crash course, step by step, with no prior knowledge required. The course is currently available in German.
Explore the free crash course (German)
Oliver Sparing
Oliver Sparing
Founder & Head of Trading · TradeNeon
Oliver Sparing has been actively trading the financial markets since 2011. His professional roots lie in precious metals trading in Hamburg; in 2017 he learned at a London trading floor. At TradeNeon he leads the day trading and swing trading programmes and is responsible for the trading methodology, with a focus on order flow, market structure and fundamental market data. Level of Interest, Futures Insights and Lab of Trades grew out of this work. Platforms such as ATAS, Bookmap and WH SelfInvest regularly invite him as a guest. All guest appearances

Glad you are here.

Sources and review date

Content reviewed on 8 October 2026. Possibilities and limits of AI-assisted research: FCA, updated 30 July 2026. Risks of public AI tools: ESMA, 2025 and Investor.gov, 2024. Checking generated outputs and code: NIST AI 600-1, 2024. Backtest overfitting: Bailey et al., The Probability of Backtest Overfitting.