Market Structure Strategy - Level 1

This strategy identifies peaks and valleys (local tops and bottoms) in price to construct a dynamic market structure, labeling Higher Highs (HH), Lower Lows (LL), Higher Lows (HL), and Lower Highs (LH). From this evolving structure, the script determines the dominant market regime, which can be:

• Bull (green) – when price forms new highs and maintains a bullish structure, the strategy favors long entries.
  
• Bear (red) – when price forms new lows and confirms a bearish structure, the strategy favors short entries.
  
• Range (brown) – when price fluctuates between recent highs and lows, suggesting sideways conditions where no trades are initiated.
  

The transition between these regimes depends mainly on four key parameters.
The first parameter controls the lookback period into the past to find the top or bottom.
The second controls the period of the looback to the right of the top or bottom.
The “Entry Margin” parameter determines how much ranging behavior the model will detect before switching regimes.
The 4 parameter select the source to construct the top and bottom, the close the wick, etc.

The user can configure the strategy to run long-only, short-only, or both directions, depending on the market or preference. In addition to the core regime logic, the strategy includes several risk and trade management controls that are featured in all my strategies.

Four oscillators are also integrated into the logic to detect short-term overbought and oversold conditions. These help the strategy avoid entering or exiting a trade when price has already extended too far in one direction, improving timing and potentially reducing false entries and exits. When overbought or oversold are detected, a red or green dot appears on the chart.

The script is designed to be flexible across different assets and timeframes. However, to achieve consistent results, it is important to optimize parameters carefully. A recommended workflow is as follows:

Disable the walk-forward option during the optimization phase.
Optimize the first main parameter while keeping others fixed.
Once a satisfactory value is found, move to the second parameter.
Continue the process for subsequent parameters.
Optionally, repeat the full sequence once more to refine the results.
Finally, activate walk-forward analysis and check the out-of-sample results.

This strategy is published as invite-only with hidden source code. Access may be granted upon request for research or evaluation purposes. It is part of a broader collection of technical analysis strategies I have developed, which focus on regime detection and adaptive trading systems.

There are five levels of strategy complexity and performance in my collection. This script represents a Level 1 strategy, designed as a solid foundation and introduction to the framework. More advanced levels progressively add greater complexity, adaptability, and robustness.

Finally, when multiple strategies are combined under this same framework, the results become more robust and stable. In particular, combining my suite of technical analysis strategies with my macro strategies and alternative data strategies, such as onchain for cryptocurrencies. It creates a multi-layered system that adapts across regimes, timeframes, and market conditions.