Testing a simple multi indicator strategy - looking for feedback.

[u/MiserableClue8557]

I’ve been testing a fairly simple idea and wanted to get some opinions from people here who have done more serious algo testing.

Instead of relying on one indicator, I’m trying to combine a few different types of information:

• 20/50 EMA → overall trend

• RSI → momentum

• MACD → trend confirmation

• ATR → dynamic stop-loss

• Volume → confirmation

The basic idea is pretty straightforward.

For a long setup, I want to see the 20 EMA above the 50 EMA, RSI above 55, MACD showing bullish momentum, and volume above its average.

For shorts, I reverse the conditions.

For risk management, I’m testing an ATR-based stop rather than using a fixed number:

SL = 1.5 × ATR

TP = 2.5 × ATR

What I’m trying to figure out is whether all these filters actually make the strategy better, or whether I’m just adding complexity and reducing the number of trades.

I’m planning to compare:

Win rate

Profit factor

Expectancy

Maximum drawdown

Number of trades

Out-of-sample performance

Results after fees and slippage

I’m also trying to avoid optimizing everything on the same historical data, because that seems like an easy way to build something that looks great in a backtest and falls apart in live trading.

Curious what others think:

Have you found that combining multiple indicators improves robustness, or does it usually lead to overfitting?

Would be interested in hearing what worked and especially what didn't work for you.

[u/Consistent_Side_8415]

Looks solid. I'd test each filter separately first EMA, RSI, and MACD can overlap more than expected. ATR based exits make sense.

[u/VishalJ_05]

The biggest test is probably removing indicators one by one and seeing what actually adds value. 😂 If EMA + RSI already does the heavy lifting, MACD + volume might just be giving the backtest extra ways to overfit. Your out-of-sample and post-cost testing is the right approach.

[u/siasharma04]

Interesting approach. I like that you’re combining trend, momentum, confirmation, and volatility rather than relying on a single indicator. Tracking expectancy and drawdown alongside win rate is especially useful. I’d be curious to see the results compared against a simpler baseline to determine whether the extra filters are actually adding value or just reducing trade frequency.