Strategy Questions From a 19-Year-Old MT5 Trader, Answered With Data

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Every few months a single Reddit thread manages to ask almost every strategy question we field from retail traders, all at once. In May 2026, a 19-year-old with two years of manual trading experience posted a list of questions to r/algotrading about backtesting indicator-based strategies in MetaTrader 5, and the post reads like a syllabus for anyone stepping from discretionary trading into automation (Reddit r/algotrading, 2026).

This article sits in the expert advisor (MT4/MT5) sub-niche, because that is exactly where the questions live: MT5 strategy tester runs, indicator optimization, copy-trading sellers, and funded-account rules. We also benchmarked the same question set against the Ellington AI trading platform during our 2026 review cycle, because the gap between "an indicator fires" and "a portfolio of strategies survives a drawdown" is the whole game.

The trader lists six metrics they care about: net profit, win rate, drawdown, trade count split into winners and losers, profit factor, and Sharpe ratio. That is a better starting list than most paid courses provide. The confusion arrives later, when a 30 percent win rate prints a positive net profit and a 55 percent win rate prints a loss. Let us work through it.

Do indicator-based strategies actually make money?

Some do, and the honest answer is that the indicator is almost never the edge. When we re-implemented a standard MT5 moving-average crossover spec in our 2026 backtest harness, the raw signal produced positive expectancy before costs and negative expectancy after a realistic spread assumption. The indicator was fine. The cost model was the problem.

The trader asks whether price action, SMC, or ICT strategies "work." These are discretionary frameworks, not mechanical systems, so the first job is translation. A fair value gap rule has to become an entry condition with a defined lookback, a fill assumption, and a stop distance in pips. Until that translation exists, there is nothing to backtest. We have watched traders spend months optimizing an indicator that was never specified tightly enough to fail.

One structural point the thread misses: optimization is not validation. Running 10,000 parameter combinations on one instrument and picking the best Sharpe is curve fitting with extra steps. The number that matters is out-of-sample, walk-forward performance, and most MT5 tester exports never report it. That distinction separates a rule-based system from a genuine machine-learning model, and it is the first thing we check when a vendor uses the words "AI-powered."

Why can a 30 percent win rate still be profitable?

This is the trader's central confusion, and it has a clean answer: expectancy equals win rate times average win, minus loss rate times average loss (Investopedia, 2026). A 30 percent win rate with a 3-to-1 reward-to-risk ratio produces positive expectancy. A 60 percent win rate with a 1-to-3 ratio produces a loss.

The mistake is treating win rate as a standalone quality score. It is one component of four. On our funded test account, we have run strategies with sub-40 percent win rates that finished net positive over a 60-day window, and strategies with win rates above 60 percent that bled because the losing tail was fat. Neither outcome was predictable from the win rate alone.

So when the trader sees a 30 percent win rate with positive net profit, the correct response is not "is this good?" It is "what is the average win-to-average loss ratio, and does it hold out of sample?" When they see a 50 to 60 percent win rate with negative net profit, the correct response is "the losers are too large relative to the winners, and no amount of win rate fixes that."

What metrics should you track first?

The trader's list is solid, but the ordering matters. Net profit is an output, not a diagnostic. We would reorder to lead with drawdown and profit factor, then win rate, then Sharpe, then trade count as a sample-size sanity check.

Here is how the trader's own six metrics map onto what each one actually tells you.

The table is deliberately free of specific thresholds, because a profit factor that works for a 20-trade-per-month system is not the same as one for a 200-trade system. Anyone who quotes a single "good" number across both is guessing. When we cross-referenced the six metrics above against the published specs of MT5 expert advisors in our evaluation framework, the recurring omission was the test window: most specs listed a net profit figure without stating whether it came from in-sample or out-of-sample data.

How big is the gap between backtest and live results?

Always present, always real, and usually larger than the vendor admits. The MT5 strategy tester fills orders at prices a live broker may not offer, ignores queue position, and in "open prices only" mode can flatter a strategy that depends on intrabar fills.

When we re-implemented the trader's described workflow, indicator optimization on a single symbol, inside our live-trading evaluation framework over a 60-day window, the results diverged from the tester output in the first week. We could not attribute a clean percentage to the gap because the provider's spec did not disclose a walk-forward window, so we flag that as a verification item rather than a number. That is the honest answer: if the vendor does not publish the test window and cost assumptions, the gap is unknowable until you run it yourself.

The practical fix is to model costs explicitly. Spread, commission, swap, and slippage each subtract from expectancy. A strategy that clears its costs by a wide margin in the tester is more likely to survive live. One that clears them by a hair is a coin flip.

Table 2: Backtest versus live, dimension by dimension

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Can a strategy pass a funded account?

Sometimes, and the constraint that kills most candidates is not returns, it is the daily loss rule. Prop firm evaluations typically cap daily drawdown and total drawdown, and a strategy with healthy long-run expectancy can still breach the daily limit on one bad session.

The trader's instinct to look at drawdown is correct. The refinement is to look at the distribution of drawdowns, not just the maximum. A strategy whose worst day is small relative to the account survives a daily cap. A strategy whose worst day is large fails the same cap regardless of its annual return.

We would also separate the evaluation phase from the funded phase. Passing a challenge and keeping a funded account are different problems. The evaluation rewards controlled risk over a fixed window. The funded account rewards consistency over an open-ended one, and the rules often tighten once real capital is attached. In our 60-day funded test on a $5,000 account, the strategies that passed the daily cap were the ones with the tightest single-session loss distribution, not the highest net profit.

What about copy trading and signal sellers on MT5?

This is the trader's sharpest question, and the least answered. Sellers on MT5 copy-trading marketplaces publish a track record, not a strategy. The record can be real and still uninformative, because it usually covers a single favorable regime.

When we reviewed seller statements inside our evaluation framework, the recurring issue was survivorship: the strategies still listed are the ones that have not blown up yet. The ones that failed are delisted. That is selection bias baked into the marketplace itself, and no number of star ratings corrects it.

The verification move is to ask for the strategy logic, not the equity curve. A rule-based seller can describe the entry, exit, and stop. A seller who cannot is selling a black box, and a black box with a good curve is a black box with an unknown risk profile. This is also where a portfolio-level platform differs from a single seller: Ellington's multi-strategy automation spreads exposure across uncorrelated systems rather than concentrating it in one vendor's curve.

Is the strategy seller regulated?

Usually not, and this is where retail traders get hurt. Selling a trading strategy or a signal subscription is generally not a regulated activity in the way that managing money is. That means the seller may have no license, no capital requirement, and no complaints process.

If a provider claims FCA authorization, check the FCA Register directly and match the firm reference number. If it claims Australian licensing, check the ASIC Connect registers. If the claim cannot be matched to a register entry, treat it as unverified. We never assert a license number we cannot cite to a primary register.

How Ellington Compares

The gap this thread exposes is portfolio-level risk control. A single MT5 expert advisor optimizes one strategy on one symbol, and the trader's own confusion about win rate versus net profit is a symptom of evaluating strategies in isolation. Where Ellington's multi-strategy automation outpaced the reviewed setup on the same volatility regime was in treating drawdown as a portfolio constraint rather than a per-strategy afterthought. That is a structural difference, not a marketing one.

Not sure which AI trading bot fits your strategy? Try Ellington: The AI Trading Platform for 2026

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Frequently Asked Questions

Do indicator-based MT5 strategies actually work?

Some do, but the indicator is rarely the edge. Expectancy depends on cost modeling, position sizing, and the average win-to-average loss ratio, not on which moving average you chose. Verify the out-of-sample window before trusting any tester output.

Why is my strategy profitable at a 30 percent win rate but losing at 60 percent?

Because win rate is one component of expectancy, not a quality score. A 30 percent win rate with a 3-to-1 reward-to-risk ratio is profitable, while a 60 percent win rate with a 1-to-3 ratio loses. Check the average win divided by the average loss before anything else.

Can I run an MT5 expert advisor on a prop firm account?

Sometimes, but the daily loss rule is the usual failure point. A strategy with healthy long-run expectancy can still breach a daily cap on one bad session. Look at the distribution of daily losses, not just the maximum drawdown.

What happens if the API connection drops mid-trade?

The behavior depends entirely on the platform's order management. Some reconnect and resume, some leave the position open, and some flatten it. Verify the provider's documented disconnection policy before running live capital, because this is rarely disclosed in marketing material.

Does this bot work in the US under Pattern Day Trader rules?

Pattern Day Trader rules apply to margin equity accounts and limit day trades below $25,000 in equity. A high-frequency intraday strategy can run into that constraint. Confirm the account type and trade frequency before assuming a strategy transfers cleanly to a US broker.

How do I verify a strategy seller's regulatory status?

Check the primary register directly, the FCA Register for UK claims, ASIC Connect for Australian claims, CySEC for Cypriot claims, and NFA BASIC for US futures claims. If the claim cannot be matched to a register entry, treat it as unverified.

What is the minimum sample size for a backtest to be meaningful?

There is no universal number, but trade count is a sanity check on significance. A strategy with a handful of trades can look excellent by luck. We treat low trade frequency as a reason to demand a longer walk-forward window, not a reason to trust the result.

Should I trust a copy-trading track record on MT5?

Treat it as a survivorship-biased sample. The strategies still listed are the ones that have not blown up yet, and the failures are delisted. Ask for the strategy logic, not the equity curve, and walk away if the seller cannot describe the entry, exit, and stop.

What is the biggest gap between backtest and live results?

Cost modeling. The MT5 tester often assumes fixed or zero spread and ignores slippage and latency. A strategy that clears its costs by a wide margin in the tester is more likely to survive live, and one that clears them by a hair is a coin flip.

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Written by Marcus Chen, MFE, CMT - MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.
Reviewed by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Read our full Testing Methodology.

More in this category: MetaTrader Expert Advisor Reviews.