In this article, we’ll explore one of the most well-known and widely used indicators in the trading world: the Price Channel. We’ll dive into how this technical analysis tool works and how it can be applied to identify opportunities in systematic trading. Specifically, we’ll focus on the energy futures sector, looking at Crude Oil (CL), Heating Oil (HO), Natural Gas (NG), and RBOB Gasoline (RB). These financial instruments are listed on the NYMEX (New York Mercantile Exchange) and are known for their trend-following behavior. In other words, their prices tend to move in long upward or downward trends, movements that traders can potentially profit from by following the prevailing direction.
How the Price Channel Indicator Works in Systematic Trading
This indicator, also known as the Donchian Channel, named after its creator Richard Donchian in the 1950s, is composed of two parallel lines: one upper and one lower. The upper line is calculated by identifying the highest value over the last N bars, while the lower line reflects the lowest value over the same period (Figure 1).
The Price Channel can be used in several ways, but in this article, we’ll focus on the traditional trend-following approach, which suits the characteristics of the markets we’re analyzing.
Figure 1. Graphical representation of the Price Channel.
Building a Trend-Following Strategy with the Price Channel
Let’s start by testing a trend-following strategy, which intuitively aligns well with this type of channel: we’ll buy when the price hits the upper band and sell when it hits the lower band, expecting the price to continue moving in the same direction. Furthermore, once these levels are reached, the position will be reversed, from long to short and vice versa.
This strategy will be applied to a portfolio composed of the previously listed energy futures, using a 60-minute timeframe. For the calculation of the indicator, we’ll use the default setting found on most platforms: 20 bars. To run the backtest and ensure the platform functions correctly, we’ ll set an initial portfolio capital of $1,000,000 for academic purposes only.
Backtest Results: Price Channel Trend-Following Strategy on Energy Futures
Analyzing the backtest results from January 1, 2010, we observe, as shown in Figure 2, an excellent initial performance, particularly considering the consistency of the equity curve. Despite the simplicity of the strategy, it has generated a net profit of just over $1,000,000 over the years, with a relatively low drawdown of only $96,000.
Figure 2. Equity line of the trend-following strategy using the Price Channel applied to energy futures.
Figure 3. Performance report of the trend-following strategy using the Price Channel applied to energy futures.
Figure 4 shows that this strategy produced profits across all four futures included in the test. The results were especially strong on Heating Oil and RBOB Gasoline futures, though the other two also delivered positive returns, confirming the trend-following nature of these instruments.
Figure 4. Individual performance of the Price Channel trend-following strategy on the energy futures in the portfolio.
However, looking at the Total Trade Analysis (Figure 5), and more specifically at the average trade of the strategy applied to the portfolio, we find a value of just $89. This is clearly insufficient to cover the commission costs and slippage associated with trading these instruments.
Figure 5. Total Trade Analysis of the Price Channel trend-following strategy applied to energy futures.
Optimizing Price Channel Settings for Best Performance
We initially used a 20-period setting to calculate the upper and lower bands of the Price Channel, as this is the default value on most trading platforms. Now, let’s evaluate through an optimization process whether 20 is truly the most effective setting, or if other values could yield better results.
As shown in Figure 6, which illustrates the results of optimizations ranging from 10 to 50 periods in steps of 5, we can observe a decline in net profit as the Price Channel period increases across this basket of futures.
Figure 6. Optimization results for the Price Channel calculation period.
This decline is at least partially due to the reduced number of trades executed with longer periods. However, when using values up to 40, the results remain satisfactory overall. Period settings of 10 and 25 seem to offer viable alternatives, but they don’t outperform the 20-period setting used in the earlier backtest, which shows the best net profit of all.
How to Improve Strategy Performance Using the ADX Filter
At this stage of development, we’ll try adding an operational filter to improve the strategy’s average trade value, which, as noted earlier, is currently too low to make the strategy viable for live trading.
The idea is to introduce a condition that allows trades only during phases when breakouts are likely to be more effective, thus reducing the number of false signals. To do this, we’ll use the ADX (Average Directional Index), one of the most well-known indicators for measuring trend strength.
The ADX is a technical analysis indicator that estimates the strength of a market’s directionality, regardless of whether it’s bullish or bearish. It ranges from 0 to 100: lower values indicate a non-directional (sideways) market, while higher values suggest a strong trend.
In this case, we'll use the ADX calculated over 5 daily bars, equivalent to one trading week, and we’ll only open new positions if the ADX is below 30. This helps us target periods where the market hasn’t shown strong directional movement recently. The absence of a clear trend could indicate that a new directional phase is about to begin, which our system will attempt to capture with a breakout-based entry.
It’s important to note that this condition will be applied only at the entry of a trade, not at the exit. That’s because the strategy is designed to use breakout levels as the sole exit criteria, to avoid getting stuck in unprofitable trades due to overly restrictive filters.
Performance of the Trend-Following Strategy on Energy Futures with the Addition of a Filter
After introducing the ADX as an operational filter, we observe a clear improvement in the strategy, as evidenced by the equity line in Figure 7, which appears more stable and linear, particularly in the latter part of the backtest.
Figure 7. Equity line of the trend-following strategy on energy futures with ADX-based filter.
As shown in the report in Figure 8, the total net profit comes in at around $541,000, lower than the version without the operational filter. However, the maximum drawdown is also lower, at approximately $64,500.
Figure 8. Performance report of the trend-following strategy on energy futures with ADX-based filter.
Looking at the average trade in Figure 9, we see a significant improvement: the average trade value rises to around $134, compared to $89 in the previous version. This is a very encouraging figure, as it indicates that using the ADX as a filter helps avoid many false signals and allows the system to focus on conditions more favorable for breakouts.
Figure 9. Total Trade Analysis of the trend-following strategy on energy futures with ADX-based filter.
Is the Price Channel Effective for Energy Futures? Key Takeaways
At this point, there are several ways to further improve the strategy. One option could be to introduce a stop loss to enhance risk management. Although the Price Channel levels are already used as exit points, they can sometimes be quite far from the trade’s entry price, potentially leading to excessive losses. These could be better managed by applying a stop loss. Alternatively, one could optimize the ADX threshold used as a filter.
In any case, the Price Channel proves to be a valuable tool for building automated trading strategies. While the average trade value is still at the edge of what's acceptable for a complete strategy, we’ve seen that with the right adjustments, this indicator can be used effectively, especially in certain markets.
Happy trading,
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