Rishat Tuktamyshov / 个人资料

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.

# Why I Built a Stock Portfolio EA Instead of Another Forex Scalper

Most Expert Advisors in the retail trading space are built around Forex scalping.

Some of them try to catch very small movements. Some use aggressive grids. Some depend heavily on spreads, execution speed and broker conditions. This approach can work in some cases, but I wanted to build something different.

My idea was to create an EA focused on large-cap stocks and stock CFDs.

Stocks are a different type of market. Large companies often move in waves. Even strong stocks can experience temporary pullbacks, short-term weakness and recovery phases. This creates an environment where a portfolio-based rebound strategy can be tested in a structured way.

That is why I developed Equity Rebound Portfolio EA.

The EA is designed mainly for:

Large-cap stocks and stock CFDs
Daily timeframe trading
Multi-symbol portfolio scanning
Long-only rebound setups
Exposure-based money management
Hedging accounts

The EA does not try to trade every tick. It analyzes completed candles and looks for internal rebound setups across a basket of symbols.

One important part of the system is portfolio exposure control. Instead of simply limiting the number of positions, the EA uses MaxLeverage to control total gross exposure across all open trades.

For example, users can test different risk profiles:

Conservative: 25% per trade, MaxLeverage 1.0
Balanced: 25% per trade, MaxLeverage 1.5
Aggressive: 50% per trade, MaxLeverage 2.0

The EA also supports live MQL5 signal monitoring, so users can observe real trading behavior, not only backtest results.

Product page:

https://www.mql5.com/en/market/product/177342

Live signal:

https://www.mql5.com/en/signals/2369449

Of course, this is not a guarantee of future results. Trading stocks, stock CFDs, Forex, metals and other leveraged instruments involves risk. Backtests and live monitoring are only tools for evaluation.

My goal with this EA is not to present a “magic robot”. The goal is to offer a structured stock-focused portfolio approach for MetaTrader 5 users who understand risk and want to test algorithmic trading beyond traditional Forex scalping.

This signal applies robust medium-term strategies to large-cap U.S. stocks. It is built around simple, time-tested market behavior, not over-optimized models. The system uses only a small number of parameters and was designed with long-term stability in mind. Compared with noisy intraday Forex trading, medium-term stock strategies are generally more resilient, more transparent, and less dependent on execution speed. The focus here is on disciplined, repeatable trading in liquid American

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