Profitable breakout trading system with 144-tick range bars for MNQ futures

Before You Read

The system discussed here detects both manual and quantitative elements, leading it back to a hybrid category. The entire process—from development and backtesting to real-time implementation of the system—includes the consideration of commission costs for each trade, and all trades are conducted during Regular Trading Hours (RTH). For a better understanding of this article, we recommend that you read the following posts:

Introduction

In the dynamic and volatile futures trading environment, finding a tool and strategy that fits the needs of traders with different levels of experience and capital can make the difference between success and failure. The MNQ contract, Micro E-mini Nasdaq 100, represents one of the most flexible and accessible options, offering high liquidity and the ability to manage risk in a more controlled manner. In this context, a breakout system, designed to exploit the explosive movements that occur when price breaks key levels, becomes an ideal strategy for maximizing profits. By applying this system on a range chart, we eliminate market noise and focus exclusively on significant price movements, allowing a clearer and more accurate interpretation of market dynamics.

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Hello Colleagues,

We are pleased to present a detailed analysis demonstrating the profitability of a breakout trading system on the MNQ futures contract. This analysis utilizes 144-tick range bars, a strategic choice that sets it apart from traditional time-based bars. The system was developed using data from the period of July 11th to July 15th, which provided a focused window for optimization.

Historically, this system has shown robust profitability with the MES futures contract, particularly when adjustments are made for profit targets, risk management, and volatility considerations. The selection of 144 ticks for the range bars is not arbitrary; it reflects the instrument’s volatility over a specific period and its practical applicability in real trading scenarios.

This is a manual-quantitative system: while the settings, entries, and exits are determined on a discretionary basis, the money management strategy is quantitatively driven. Furthermore, the identification of the “range”—the rectangle that encapsulates a congestion—is also discretionary, adding a personal touch to the process. It is vital for those who want to study and learn this system to have the opportunity to verify it themselves. Though this process might seem stressful, it pays off in the long run.

Range Identification

Upon analyzing the chart, we identified two distinct price swings: an initial downward swing followed by an upward swing. In trading, a “swing” refers to a significant movement in price, either upward or downward, that typically occurs between points of support and resistance. However, a third swing is less obvious due to the presence of a blue bar within the congestion zone. To accurately capture this market structure, we draw a rectangle to highlight the crucial support and resistance levels. It’s important to note that the identification of this range is discretionary—different traders may interpret the boundaries of the range differently based on their experience and the nuances they observe in the market. Given that we are working with a breakout pattern, we decide to place a sell stop order just below the identified support level, rather than using a limit order. This decision is based on our judgment of the price action within the range and our anticipation of a potential breakout.

With that established, let’s delve into some critical metrics.

Key Metrics

The yellow line on the chart represents the Anchored Volume Weighted Average Price (AVWAP), anchored at the beginning of the congestion phase. While exact precision is not the main goal, the AVWAP plays a crucial role in improving system performance and gathering the data we need. Here’s how it works: By tracking the AVWAP, we monitor the Maximum Favorable Excursion (MFE) and Maximum Adverse Excursion (MAE) for all trades until the price returns to this indicator. As we collect data from multiple trades, we reach a point where we have enough information to reliably estimate these metrics. This process is essential for determining appropriate stop loss and take profit levels for the system. In this particular case, after analyzing the collected data for the selected period, we’ve set a take profit at 21 points (84 ticks) and a stop loss at 13.5 points (54 ticks).

Performance Summary

The results show 21 winning trades, 15 losing trades, and 1 trade that was closed at our discretion because it risked going outside regular trading hours. It’s important to understand that there is no perfect number of data points from which to derive our metrics, and the metrics themselves will never be flawless. Our objective is to find an optimal balance that offers a trading edge without excessive optimization, which could result in overfitting—an issue that, while it can be mitigated, can never be completely eliminated.

If you have any questions or suggestions, please don’t hesitate to reach out to us through the contact section. We’re here to assist with any clarifications you may need.

For a overview, please refer to the attached performance graph.

Best regards