Optimizing Your Moving Average Strategy

While moving averages may seem simple to use in trading, there are several components that expert traders fine-tune to optimise these tools for their trading style and financial objectives. Properly customising these settings makes a major difference in the performance of any trading strategy built around moving averages.

In this detailed guide, you will understand how to set up a robust moving average strategy that aligns with your timeframe, risk tolerance, and personality as a trader. Let’s get started!

The proper length for a moving average strategy

One of the most crucial decisions is selecting appropriate moving average lengths. The specific period used for the calculation determines how quickly the average responds to the latest price action. For example, a 200-day moving average will shift direction much more gradually than a 20-day average.

Shorter moving averages between 20-50 days are ideal for shorter-term active traders looking to catch smaller price swings and opportunities. The main drawback is they can lead to more whipsaws and false signals during periods of high volatility.

Conversely, traders with a longer-term swing or position trading outlook typically favour longer averages like the 200-day to define the primary trend. The goal is to experiment with different moving average lengths to find the best fit for your trading style and timeframe.

For further information, read this article: What Is A Trading Indicator And How It Helps You As A Trader

Using multiple moving averages

While individual moving averages are helpful, combining shorter and longer-term averages creates an extremely powerful trading strategy. The longer average acts as the trend filter or envelope, while the shorter average is used to generate the actual trading signals and entry points.

But remember to exercise caution and avoid using too many of them. An excessive number of moving averages can be confusing and overwhelming, leading to a state of analysis paralysis where the trader is unable to make sound trading decisions. Therefore, it is recommended to carefully select the appropriate moving averages based on the trading strategy and market conditions.

Benefits of using multiple moving average

A key benefit of using two or more moving averages is it provides structure, improves filterability, and smooths out whipsaws. The longer average defines the broader trend, while the shorter average pinpoints trades within that trend.

Another significant advantage is risk management. Trades taken with the trend carry less risk and generally produce larger yields. On the other hand, trading against momentum is often risky. A properly optimised moving average combination keeps traders in sync with prevailing price action.

Common combinations

Some classic moving average combos traders have used for years:

• 50 and 200-day Simple Moving Averages
• 20 and 50-day Exponential Moving Averages
• 10 and 20-day Simple Moving Averages

Crossovers between the faster and slower averages often signal key turning points. As we discussed earlier, a cross above hints momentum is building in the upward direction. A cross below suggests a potential change toward a bearish bias.

Optimizing parameters

In addition to selecting appropriate moving average lengths, traders can refine entries and exits by customising these additional settings:

Percentage buffers

One simple yet effective technique is to use percentage buffers on crossover signals rather than buying and selling directly at the threshold.

For example, before triggering the trade, rather than immediately entering long when the 20-day EMA crosses over the 50-day EMA, consider waiting until the faster average has moved 1-2% above the slower EMA. This confirms that momentum is genuinely building rather than a false breakout.

Likewise, on the exit side, delay closing out profitable long trades until the 20-day dips 1-2% back below the 50-day. This allowance gives additional room for the upside move to develop before closing positions fully.

The specific buffer percentage depends significantly on the inherent volatility of the security. More active stocks may require wider 3-4% buffers versus more stable trade indexes, which could use <1%. This filter helps reduce premature entries during temporary price spikes while allowing big winners more room to run. Still, note that these buffers depend highly on your strategy. Conduct research before applying these to your trades.

Indicator settings

Fine-tuning the settings of complementary indicators like Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to align with your moving average strategy creates a more synchronized system.

Generally, shorter moving averages pair better with faster indicator readings like the RSI(15) or MACD(12,26) with fewer bars. This keeps the entire system reactive to price fluctuations. Contrarily, longer-term moving averages typically mix better with relaxed indicators like RSI(25) using a smoother lookback window or the MACD(26,52) using longer exponential averages.

Syncing settings keeps the strategy in harmony. For example, buying solely because the MACD(5,15) crossed up would conflict with using a 50-day SMA based on time horizons. Streamlining these components maximises accuracy. By combining optimal moving average lengths with calibrated indicators and logical entry/exit buffers, traders have yet another lever to improve strategy performance.

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Tracking and reviewing results

Optimising a moving average strategy does not simply end after the initial backtesting. To truly evaluate performance, statistics should be tracked across different market environments - both upswings and downswings.

Importance of market cycles

A common mistake of traders is failing to capture data over multiple market cycles. Strategies often perform well when backtested or paper traded during a steady bull run when assets are in a confirmed uptrend. However, this success frequently fails to carry over once sideways or bearish cycles emerge.

Tracking metrics over many years encompassing various regimes, the robustness of a system can be quantified. For example, comparing performance in 2017 (a strong bull market) to 2022 (a bear market) would reveal meaningful data. However, be aware that relying on this data does not guarantee success, as there are multiple factors to consider. Make it a practice to conduct intensive research before making a trading decision.

Quantifying performance

When reviewing results, consistent tracking of these performance metrics is crucial to gauge your strategy’s effectiveness:

1. Percentage of winning vs losing trades: The overall win rate reveals how often the strategy is profitable over many rounds of trades. Aim for the system to win more than 50% of the time before costs. Higher is better when optimised.
2. Average profit/loss per trade: The average gain and loss amounts show expected return and risk. Target average profits at least twice as large as average losses. Tweak stop distances to adjust as needed.
3. Risk-reward ratio per trade: Divide average gain by average loss to quantify the risk-reward profile. Look for ratios over 1:1. More conservative traders may prefer ratios as high as 3:1, depending on the win rate.
4. Win rate consistency across market cycles: Win percentage during bull and bear regimes reveals adaptability. The best strategies win in diverse environments. Drawdowns should be at most 20-30 % over long backtests.

Tracking this reference data covers all bases - absolute returns, relative returns compared to risk taken, and reliability across varying conditions. Reviewing winners and losers objectively, it is possible to pinpoint flaws in the investment strategy and make necessary adjustments to improve performance.

Tweaking settings

Observing actual performance data grants the ability to identify issues and fine-tune:

• If drawdowns consistently exceed 20-30%, reduce position sizing or tighten stop losses. Larger sizes overload the system capacity.
• Additional filters are needed if the win percentage declines below 45-50% during specific cycles like ranges or corrections. Optimise the rules.
• If the average loss size grows more prominent than 1-2 times average gains, tighten stop distances or raise targets. The risk management needs rebalancing to match goals.

Even when initially optimised, tracking statistics and running periodic backtests allow refinement. Markets evolve, and strategies must evolve. By proactively uncovering performance issues, traders can systematically build robust long-term processes.

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In the final analysis,

Mastering any trading strategy involves vigilantly gathering performance data to uncover strengths and flaws. Moving averages have stood the test of time due to versatility – they trend well during sustained moves but also adapt to shifting conditions.

Traders can achieve growth by dedicating focused effort to organising clean backtests and tracking real-time trade metrics across multiple macrocycles. In bear markets and corrections, they can identify areas for improvement, while bull trends provide opportunities for optimising profits.

While meticulous fine-tuning keeps systems competitive, consistent adherence to refined rules and acting decisively is ultimately what determines bottom-line success once live capital commits. Even the most advanced indicators require discipline to deliver over the long run. We encourage traders to take an active role in improving their strategies each day.

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When considering “CFDs” for trading and price predictions, remember that trading CFDs involves a significant degree of risk and could result in capital loss. Past performance is not indicative of any future results. This information is provided for informative purposes only and should not be considered investment advice.”