Grademark release notes

v0.2.1

BREAKING CHANGES

Rebuilt the optimization function:
- You now have the option of two algorithms: grid and hill-climb.
- The grid search algorithm is exhaustive but slow and gets slower the more parameters you are trying to optimize.
- The hill-climb algorithm is non-exhausive but much faster, especially as the number of parameters increases. Use the option numStartingPoints to choose the number of random starting points that are used to seed the algorithm.
- Restructured the output of optimization for memory efficiency and easier visualization.
- You can now set the seed used for random number generation.
Reduced the dependency of Grademark on Data-Forge:
- Most results from and inputs to Grademark no longer use DataFrame. They are just plain old JavaScript arrays.
- The inputSeries parameter still does use a DataFrame.
- To convert your old Grademark code:
  - Instead of inputing a DataFrame call toArray on it instead to convert it to an array.
  - If you want output as a DataFrame just wrap the returned array in a new one, e.g. const trades = new DataFrame(backtest(...));

v0.0.1

BREAKING CHANGES

Arguments to functions for strategy rules have changed. Instead of having individual arguments to each function, arguments are now bundled in objects for future expandability and better auto-competion.