Quarterly Indicator Selection and Evaluation for the FXI ETF

Summary

The article presents a Python-to-MQL5 workflow for selecting technical indicators using FXI, an ETF tracking large-cap Chinese equities. It segments four-hour price history into quarterly windows to compare candidate indicators across changing volatility, momentum, trend, and mean-reversion conditions. The proposed pipeline prepares OHLC data, checks timestamp ordering and gaps, resamples to a regular grid, and applies timezone, session, and holiday filters before evaluating signals.

The article advocates deterministic bullish, bearish, and neutral signal states and scoring based on forward returns, then describes translating selected indicator logic into an MQL5 Expert Advisor. It includes example data counts and preprocessing output, and reports that trend-oriented indicators ranked favorably in its analysis. However, the supplied text does not provide the full scoring details, comparative metrics, or enough evidence to judge robustness. Resampling with forward-filled prices, U.S. market-hour filters on a China equity ETF, and quarter-based evaluation choices can materially affect results. The rankings should therefore be treated as research output requiring validation, not as proof of future performance.

Key ideas

• Quarterly segmentation is intended to expose changes in FXI market regimes.
• The workflow checks OHLC fields, timestamps, gaps, and market-hour filters before scoring indicators.
• Indicator states are expressed as bullish, bearish, or neutral for consistent comparison.
• The proposed scoring uses subsequent returns to assess signal direction.
• Indicator rankings depend on preprocessing and evaluation choices and require further validation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.