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Hem: Om

Powerful core strategies

Build your own trading algos with the same strategies we are using

Prime Core strategy

Best seller

Customizable strategy for you to configure for the index and timeframe of your choosing as-is or with additional filters/indicators/code. Major parts of the code is hidden. 

Diving into its core trading methodology, the algorithm employs Quantum Moving Averages (QMAs) – a term coined by ProRealAlgos. For potential short trades, evaluates the QMAs over adaptive periods, which are influenced by market volatility indexes. When the the QMA dips below a threshold, suggesting a downward trend, and coincides with an inverted price-volume correlation, a short position is entered. The logic is vice versa for long trades.


Central to the algo is price action with advanced code to identify significant candlestick formations. A key strength of this algorithm is its robustness. It combines multiple technical indicators, ensuring that trading decisions aren't based on a singular metric. This multifaceted approach can help mitigate false signals. Additionally, the time-based filters add another layer of protection against unpredictable market behavior.

What are the strengths of this core strategy?

Advanced candlestick analysis

Versatile across markets

Refined coding techniques

Historically robust and adaptive


Carl Eriksson



With a BSc in Computer Science, Carl Eriksson has navigated the vast world of business, leading a thriving web development company and freelancing on numerous coding projects over the years. Diving into the realm of trading algorithms, Carl has innovatively developed over 1,000 systems and indicators, some of which are showcased on this website. His reputation as both an innovative thinker and a meticulous programmer sets him apart in the trading community.

Huge volume discounts

Release date

Oct 2019


Which ever


MA Crossover


Which ever





The same code that many of our most successful Plug&Play algos are using

This very Prime core strategy is the bedrock upon which ProRealAlgos was built over 7 years ago. It stood as the inaugural strategy we crafted, showcasing impressive out-of-sample results across diverse markets and varying timeframes. Over the years, we've refined this strategy, employing even more intricate and sophisticated coding techniques. This not only enhances its robustness but also elevates its adaptability.

Although this code is highly potent in the FX and commodities sectors, it's demonstrated its remarkable adaptability across major indices, including the DAX40, US500, US Tech 100, and Wall Street. This versatility further highlights its strength and potential for future applications.



Consistent across diverse market scenarios, both for long and short positions

A strategy that consistently performs well across various markets and timeframes is a hallmark of reliability. When it demonstrates stability over extended periods and adapts to fluctuating market conditions, it truly stands out. This strategy excels in pinpointing opportune entry points for both long and short positions, regardless of the market's state. Using this framework, you can easily develop a new algorithm by fine-tuning the parameters. Ensure that you only adjust these parameters based on a portion of the historical data on hand. Moreover, it's recommended to test the algorithm in a demo setting for a minimum of 6 months before deploying it live. This precautionary step could save you a significant amount in the long run.

Which are the settings to optimize?

A total of 24 different variables to optimize

The core strategy uses a number of different indicators and filters in order to make those perfect trades. These indicators and filters have to be optimized for your intended timeframe and market. 

Both long and short conditions have 8 variables each to to optimize. In addition to that you can choose to optimize the stoploss level, the target profit level and variables related to the trailing stop code. 

It's also recommended that you think about the trading hours of your system and make sure that the spread you use when developing the algo, aligns with the average spread of the index during the hours your algo is trading. 

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