In this in-depth analysis, we’ll examine how to exploit recurring intraday movements in futures markets for metals. The reference basket includes several underlying assets, but the focus will be on the main ones: Gold, Silver, Copper, and Platinum, markets characterized by high liquidity and wide circulation on the secondary market.
The approach used is based on so-called "bias" strategies, which involve entering and exiting the market at specific times of the session.
Unlike trend-following or mean-reverting logics, bias strategies do not rely on technical indicators or specific price levels. The term bias refers to a behavior that tends to repeat itself, a kind of market habit that emerges during specific time frames. It is precisely during these statistically favorable time windows that the strategy places its orders.
Naturally, not all markets react the same way to this type of approach. To verify the presence of such recurring patterns, we'll use software developed by Unger Academy®, the Bias Finder™, designed to detect repeated behaviors and patterns within historical price series.
System Development and Market Bias Analysis
The first test is conducted on the gold market, considered the benchmark for the entire metals sector. If the results prove encouraging, the analysis will be extended to other closely related instruments.
Figure 1. Intraday bias of Gold using Bias Finder™
Figure 1 shows the average intraday performance of Gold, represented through several curves corresponding to distinct time periods. The blue line shows the overall profile, built from the movements of the past 15 years (2010–2025). In addition to this, there are curves divided into three-year intervals: 2010–2013, 2014–2017, 2018–2021, and finally 2022–2025.
This segmentation into time frames helps clarify whether the bias (i.e., the recurring behavior of the market) has remained stable or has undergone changes over time.
In the case of gold, the various curves almost always show a similar pattern: a phase of weakness during the night hours (from 2:00 a.m. to late morning), followed by a rebound in the afternoon and a resumption of the downward cycle the following night.
Based on this evidence, the strategy is then formalized. The operational rules call for entering a long position at 10:00 a.m. (New York session time) and opening a short position at 2:00 a.m. the following day. The short entry automatically closes the previously opened long position, and vice versa: the opening of a long trade closes the short position (Figure 2).
Figure 2. Example of alternating long and short trades on Gold
Results of the Bias Trading Strategy on Gold
The results obtained are overall positive. As shown in Figure 3, the profit curve from the backtest confirms the validity of the insights provided by the Bias Finder™, reinforcing the idea that this approach holds real operational potential.
Figure 3. Equity line of the bias strategy applied to Gold Futures
Figure 4. Total trade analysis of the bias strategy applied to Gold Futures
Results of the Bias Trading Strategy on Silver, Platinum, and Copper
At this point, as a natural next step, the strategy is tested on the other main metals to assess whether the behavior observed on Gold is also reflected in closely related markets.
From the analysis of the results shown in Figures 5, 6, and 7, it emerges that the bias identified on Gold also tends to replicate on Silver, Platinum, and Copper. These instruments, in fact, display dynamics closely resembling those of Gold, partially mirroring its behavior and thus confirming the hypothesis of a persistent pattern across the entire metals sector.
Figure 5. Equity line of the bias strategy applied to Silver Futures
Figure 6. Equity line of the bias strategy applied to Platinum Futures
Figure 7. Equity line of the bias strategy applied to Copper Futures
Ideas for Further Development of the Bias Trading System on Metals
The results achieved so far show that bias on metals can serve as an interesting foundation for building systematic strategies. However, the next step isn’t simply to stop here, but to explore possible improvements to increase operational quality, particularly in terms of average trade size, which, as often happens in strategies with short market exposure, tends to be the weak point.
Among the potential areas for improvement are the application of operational filters, such as:
- Using specific days of the week, favoring those that have historically aligned more consistently with the bias;
- Selecting recurring market patterns, like sequences of bullish or bearish days;
- Activating the system only during phases of high or low volatility, depending on which historical context offered the best edge.
Another natural development would be the introduction of stop-loss and take-profit mechanisms, essential tools to manage risk and improve the system’s stability in live trading conditions.
These are not definitive rules but rather practical ideas for those looking to go beyond the exploratory phase and test more robust approaches, while always maintaining the bias logic as the foundation.
Final Thoughts on the Bias Trading System on Metals
Introducing filters and additional conditions would likely reduce overall profits. However, this would make the bias approach on metals more appealing for live trading purposes, given that, even with just a few hours of daily market exposure, the average trade could reach sufficiently solid levels.
A key insight that emerged from this analysis is the importance of multi-asset validation. Testing the same logic on multiple related markets, such as gold, silver, copper, and platinum, helps distinguish between a real edge and a result born from chance. The replicability of the idea, therefore, represents a form of robustness that increases confidence in the strategy.
Moreover, this type of validation is not limited to the metals sector: it can be extended to many other areas. Consider, for example, a strategy applied to a wide set of stocks. Since equity instruments tend to move in similar ways, testing an idea across multiple components of an index becomes an effective method to measure its consistency.
In conclusion, intraday biases offer interesting operational cues and represent fertile ground for the development of automated systems. What really makes the difference, however, is the ability to make the strategy tradable by achieving a sufficiently large average trade.
Until next time, happy trading!
Andrea Unger
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