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Here is a KeyPoint analysis of the data in result-cds-data-test.csv:

The data contains time series financial data with technical indicators generated by the python code in JGTIDS.py.

Some of the main technical indicators included are:

  • ac (jaw) - Accelerator Oscillator
  • teeth - Relative Strength Index
  • lips - Williams %R oscillators
  • ao - Absolute Momentum
  • fb, fs - Fast Stochastic
  • aof - Average Directional Movement

The dataset also includes trend, volatility and momentum indicators calculated over different time periods like fb3, fs3 etc.

It calculates oscillator thresholds like fdbblow, fdbbhigh to detect cycles. Color codes oscillator values to show buy/sell signals.

The attached documents provide more context on leveraging fractal geometry and pattern recognition across timeframes. The goal appears to be creating a rules-based trading strategy incorporating these technical indicators.

To summarize the key discussion points and develop an outline:

  1. Introduction a. Overview of technical analysis goals b. Using indicators to identify patterns
  2. Indicators included a. Description of each indicator i. What it measures ii. How it generates signals b. Timeframes calculated
  3. Approach a. Rules to combine indicators b. Entry/exit criteria c. Backtesting methodology
  4. Results a. Performance metrics b. Learning from backtests
  5. Conclusion

Let me know if analyzing any specific part of the data or code in more detail would be helpful. I can also start developing the reference section from the information provided.