I ran a small experiment with the standard MACD Sample Expert Advisor included with MetaTrader 5.
The purpose was not to optimise the EA or present it as a trading system. I wanted to see how the exact same sequence of closed trades would behave under prop-firm-style objectives: profit targets, daily loss limits, maximum drawdown and repeated challenge fees.
Test setup:
Expert Advisor: MACD Sample
Timeframe: M15
Starting balance: $10,000
Fixed volume: 0.50 lots
Closed trades: 196
The normal MT5 backtest result was negative:
Net profit: −$2,232.70
Balance drawdown: 32.23%
Equity drawdown: 34.40%
So, on its own terms, this test did not show a profitable strategy.
The interesting part appeared when I replayed the same closed-trade history as a $50,000 prop challenge.
With the original risk level, the result was generally poor. The strategy often failed before reaching a profit target. A few simulated payouts could occur, but repeated challenge attempts and fees consumed most or all of the result.
Then I changed only one variable: I doubled the risk per historical trade.
The trading logic stayed unchanged. The order of trades stayed unchanged. The final MT5 backtest was still negative.
However, in several simulated prop-account models, the result changed from negative to positive.
Why?
A prop challenge is path-dependent. It does not care only about the final balance at the end of the full backtest. It cares whether a particular sequence reaches the target before it hits a daily or overall drawdown limit.
At higher risk, favourable winning streaks reached targets faster. This created payout opportunities before the longer-term negative drift of the strategy appeared again.
But this does not mean that increasing risk creates an edge.
In fact, the higher-risk replay also produced far more breaches and more challenge attempts. The apparent profitability depended heavily on a few favourable periods in the historical sequence. A different order of the same trades, slightly worse execution, spread changes or intraday floating drawdown could easily change the conclusion.
For me, the main lesson is this:
A prop-firm payout is not automatically proof that a strategy has positive expectancy.
A strategy may generate a payout because its short-term path happens to fit a challenge’s targets and drawdown rules. That can be useful to analyse, but it is very different from having a robust system.
Before trading any strategy through a prop firm, I think it is necessary to model all of the following together:
profit targets;
daily and maximum drawdown;
payout conditions;
the number and cost of failed attempts;
position sizing;
intraday equity risk;
out-of-sample performance.
This MACD Sample experiment is a useful reminder: risk management can change the shape of outcomes, but it cannot turn a negative expected-value strategy into a durable one.
The purpose was not to optimise the EA or present it as a trading system. I wanted to see how the exact same sequence of closed trades would behave under prop-firm-style objectives: profit targets, daily loss limits, maximum drawdown and repeated challenge fees.
Test setup:
Expert Advisor: MACD Sample
Timeframe: M15
Starting balance: $10,000
Fixed volume: 0.50 lots
Closed trades: 196
The normal MT5 backtest result was negative:
Net profit: −$2,232.70
Balance drawdown: 32.23%
Equity drawdown: 34.40%
So, on its own terms, this test did not show a profitable strategy.
The interesting part appeared when I replayed the same closed-trade history as a $50,000 prop challenge.
With the original risk level, the result was generally poor. The strategy often failed before reaching a profit target. A few simulated payouts could occur, but repeated challenge attempts and fees consumed most or all of the result.
Then I changed only one variable: I doubled the risk per historical trade.
The trading logic stayed unchanged. The order of trades stayed unchanged. The final MT5 backtest was still negative.
However, in several simulated prop-account models, the result changed from negative to positive.
Why?
A prop challenge is path-dependent. It does not care only about the final balance at the end of the full backtest. It cares whether a particular sequence reaches the target before it hits a daily or overall drawdown limit.
At higher risk, favourable winning streaks reached targets faster. This created payout opportunities before the longer-term negative drift of the strategy appeared again.
But this does not mean that increasing risk creates an edge.
In fact, the higher-risk replay also produced far more breaches and more challenge attempts. The apparent profitability depended heavily on a few favourable periods in the historical sequence. A different order of the same trades, slightly worse execution, spread changes or intraday floating drawdown could easily change the conclusion.
For me, the main lesson is this:
A prop-firm payout is not automatically proof that a strategy has positive expectancy.
A strategy may generate a payout because its short-term path happens to fit a challenge’s targets and drawdown rules. That can be useful to analyse, but it is very different from having a robust system.
Before trading any strategy through a prop firm, I think it is necessary to model all of the following together:
profit targets;
daily and maximum drawdown;
payout conditions;
the number and cost of failed attempts;
position sizing;
intraday equity risk;
out-of-sample performance.
This MACD Sample experiment is a useful reminder: risk management can change the shape of outcomes, but it cannot turn a negative expected-value strategy into a durable one.
