The moving average strategy is one of the most common trend-following approaches in technical analysis, easily adapted to binary options. It compares two moving averages with different periods and reacts to the moment a faster line crosses a slower one.
How the strategy works
At its core, the strategy watches a crossover between two moving averages — a fast one (short period, e.g. 9 or 10) and a slow one (longer period, e.g. 21 or 50). When the fast average crosses the slow one from below, it's read as a buy signal: price is gaining upward momentum faster than its historical average. A crossover from above is read as a sell signal.
Choosing moving average periods
Period choice affects the number and quality of signals. Short periods (5–10) generate more signals but with more false triggers in a ranging market. Long periods (50–100) form rarer but statistically heavier signals, suited to medium-term decisions. For binary options with short expirations, a combination of short and medium periods is common — such as 9 and 21 — as a compromise between reaction speed and noise resistance.
Types of moving averages
Besides the period, the type of moving average matters:
- Simple Moving Average (SMA) — weighs all prices in the period equally, reacts more slowly to changes.
- Exponential Moving Average (EMA) — gives more weight to recent prices, reacts faster to a trend change.
- Weighted Moving Average (WMA) — a middle ground between SMA and EMA in reaction speed.
For short timeframes, EMA usually gives more timely signals but also more false triggers compared to SMA.
Filters to reduce false signals
A moving average crossover alone often lags, generating signals after a move has already started, and in a ranging market it produces many false crossovers. To improve signal quality, the strategy is paired with filters:
- Confirmation of the overall trend direction on a higher timeframe.
- A minimum distance between the averages at the crossover — a crossover that's too close more often turns out false.
- An additional oscillator (such as RSI) to filter out signals going against overall momentum.
- Avoiding trading during low-volatility periods, where crossovers happen chaotically.
Applying the strategy across different Pocket Option assets
The moving average strategy works better on assets with a pronounced trending nature and worse on instruments that spend most of their time ranging. Before applying the strategy, it's worth checking the specific asset's historical behavior and tuning periods individually, rather than using one setting across all instruments.
Automating the strategy
Calculating the moving average crossover and applying additional filters is easy to automate. A trading robot for Pocket Option calculates indicator values in real time and generates a signal only when all set conditions are met — details on the features page.
Conclusion
The moving average strategy is a solid basis for trend trading on Pocket Option, provided periods are chosen correctly and filters are used against false signals. Compare it with an alternative trend-following approach in the article on the trend-following strategy, and automatic crossover calculation is available in the robot's plans.