Lying Numbers: When Trading Statistics Look Good But Systems Aren't Sustainable

Many traders choose trading systems or follow other traders based on impressive statistics such as 80% Win Rate, 3.5 Profit Factor, or 200% cumulative returns in one year. But the frightening truth is that these numbers may not reflect the sustainability of the system at all.

This article will reveal 5 cases where trading statistics look good but hide serious problems, whilst teaching you how to see the truth beneath attractive numbers. This enables you to make informed decisions without being deceived by distorted or incomplete statistics.

Case 1: High Win Rate from Not Using Stop Loss

A trading system with an 85-95% Win Rate sounds very attractive. But often this number results from not setting a Stop Loss or setting it so far away that it's almost never hit. Traders let losing orders float indefinitely until they return to profit.

The problem is that when the market moves violently or a trend is strong, losing orders may never come back. And when hit by a Margin Call just once, the account blows up immediately, even after 20-30 consecutive wins.

How to check: Look at Average Trade Duration. If a system has a very high Win Rate but an unusually long average holding time (e.g., a scalping system but averaging 8-12 hours), it indicates floating losing orders. You should also look at Average Loss and Worst Trade. If the numbers are disproportionately large, it shows the system has hidden risks.

Case 2: High Profit Factor from Too Few Trades

A Profit Factor of 4.0 sounds excellent, but if it comes from only 15-20 trades over 6 months, this number has no statistical significance. It may simply be a lucky period or the market temporarily suiting the system.

A sustainable trading system must be tested with a sufficient number of trades (at least 100-200 times) to ensure the results aren't merely coincidence. Having a high Profit Factor from few trades is like flipping a coin and getting heads 8 out of 10 times, then concluding the coin is biased towards heads.

How to check: Look at Total Trades and the period over which statistics were collected. If there are fewer than 50 trades in a year, be very cautious. You should also look at Trading Frequency to see if it's reasonable for the timeframe being traded, and check how the Profit Factor changes when more data is added.

Case 3: High Returns from Short Abnormal Periods

A system showing 150% returns over 3 months looks very interesting. But if you look deeper into the Equity Curve, you may find that most profits came from just 2-3 weeks when the market had high volatility or very clear trends.

Outside that period, the system may have made very little profit or alternated small losses. When market conditions change, returns will drop rapidly because the system relies too heavily on special market conditions.

How to check: Look at Monthly Return Distribution. If you see profits concentrated heavily in one particular month whilst other months show returns near zero, it indicates the system is inconsistent. You should look at how Profit Per Day is distributed and check whether high-profit periods coincide with special market events.

Case 4: Low Drawdown Because It Hasn't Faced a Crisis

A system with a Maximum Drawdown of only 8% over 6 months looks very safe. But if during that period the market moved steadily without major events, this number tells you nothing about the system's resilience.

When encountering a period of strong market reversal or significant news, Drawdown may surge to 30-40% overnight because the system has never been tested under stress. Looking at low Drawdown from a short period is like assessing a bridge's strength on a day with no traffic.

How to check: Look at the period over which statistics were collected to see if it covers volatile market periods. Check whether there were significant events during that period (such as interest rate announcements, economic crises). You should use Monte Carlo Simulation to simulate worse scenarios to assess true risk, and also look at Maximum Drawdown Duration to see how quickly the system recovers.

Case 5: Good Statistics from Cherry Picking Favourable Periods

Some systems display statistics starting from the date the system began making profits, not from when trading actually began, or cut out periods of heavy losses by claiming the "system was improved". This method is called Cherry Picking, or selecting only good results.

Looking at statistics selected this way doesn't reflect the system's true performance at all, because in real trading you cannot choose to trade only during periods when the system works well. Proper system testing must include all periods, both good and bad.

How to check: Ask from which date statistics begin counting and whether any periods were cut out. Look at the Equity Curve to see if there's an unusual starting point or any gaps. If using Thaifxbook, the system stores data continuously from when the MT5 account is connected, thus preventing Cherry Picking. You should look at at least 12 consecutive months of historical statistics.

Principles for Viewing Trading Statistics Holistically

Proper evaluation of a trading system shouldn't look at just one number but must view the overall picture of multiple metrics together. Here's a checklist that professional traders use:

• Are there sufficient trades? — At least 100 times to have statistical significance
• Does the period cover diverse market conditions? — Should include trending, ranging, and high-volatility periods
• Are the numbers consistent? — For example, high Win Rate should have appropriate Average Win, not unrealistically large
• Is there consistency? — Look at Monthly Return Distribution; shouldn't rely on just short periods
• Is risk balanced with returns? — Look at Risk-Reward Ratio, Sharpe Ratio, or Calmar Ratio

Tools like Thaifxbook help traders see this overall picture more easily because they pull data from real MT5 accounts. Data cannot be edited or selectively chosen, ensuring that the statistics you see are genuine, not doctored numbers.

What to Do When You Find Deceptive Statistics

If you're using a system with the warning signs above, don't panic yet, but you should do the following:

1. Stop adding capital immediately — Don't risk more money until you've finished re-evaluating the system
2. Collect more data — Continue trading with the smallest lot size to gather complete statistics
3. Re-analyse with longer data — See how the numbers change when there's more data
4. Test in various market conditions — Use Backtesting on historical volatile periods to see if the system can cope
5. Adjust expectations — If you find statistics are too good to be true, adjust return targets to be more realistic

Keeping detailed trading records will help you see the true patterns of the system, not just summary numbers that may be distorted.

Conclusion: Good Numbers Don't Equal Good Systems

Trading statistics are important tools for evaluating systems, but you must know how to read and interpret them correctly. Impressive-looking numbers may hide problems that will explode in the future, whether it's not using Stop Loss, too few trades, insufficient time coverage, or data selection.

Smart traders must look beyond single numbers, view the overall picture of multiple metrics together, check data continuity and consistency, and understand the context of market conditions during the period statistics were collected. Using a transparent platform like Thaifxbook reduces the risk of distorted data, but understanding how to read statistics correctly remains an essential skill every trader must have.

Remember that sustainable trading systems don't necessarily have the most beautiful numbers, but must have consistent results, controllable risk, and resilience to market changes over the long term.