Overview

This strategy uses moving average crossovers between different timeframes to generate trading signals. It allows observing longer timeframe MAs on current chart to detect larger trends. This belongs to inter-timeframe trend following strategies.

Strategy Logic

The strategy uses two moving averages calculated on separate timeframes.

For example on 15min chart it uses 20MA and 50MA:

• 20MA is calculated on current 15min bars
• 50MA is calculated on daily bars

When 15min 20MA crosses above daily 50MA, it goes long. When 15min 20MA crosses below daily 50MA, it goes short.

This achieves the effect of observing longer timeframe trends on current period. Custom MA lengths are also allowed.

Crossover points can be marked for clear trade signals.

Advantages

• Analyze across timeframes, discover larger trends
• Higher TF lines more stable, avoiding false signals
• Lower TF lines more sensitive, catching trend changes fast
• Customizable MA periods combinations
• Clear marked signals on chart

Risks

• Increased complexity with multiple timeframes
• Lower TF false signals still possible
• Overall lagging with MA systems, may miss best entries
• Limited filtering with pure MA system
• Period tuning needed for different products

Risks can be reduced by:

• Keeping longer high TF MAs for robust trend direction
• Adding other indicators for further signal filtering
• Optimizing MA periods for best combinations
• Relaxing entry rules like adding candlestick patterns

Enhancement Directions

The strategy can be improved by:

1. Testing more MA period combinations for optimization

2. Adding secondary confirmation when crossover happens

   e.g. check MACD momentum

3. Optimizing stops to avoid premature exit

   Consider Post123 evidence to decide exits

4. Different filters for short and long TF

   More strict for short TF, more relaxed for long TF

5. Consider different parameter sets for different sessions

   Market conditions vary by sessions

Summary

This strategy observes crossovers between MAs of multiple timeframes to determine trend direction and uncover larger trends. This filters out short-term noises and focuses on larger price moves. However, challenges like timeframe tuning and lagging signals exist. Enhancements can be made via rigorous backtesting and optimization for robust parameters, adding filters for confirmation, live validation for continuous improvements according to market feedback. Persistent learning and optimization is key to adaptivity.