Improved forecasters logic #126
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…ence (before 1yr was wrt day before, now wrt current day); current one makes more sense; tested with examples.strategies and accepted
…ll tests & cleanup required
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enzbus
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Feb 21, 2024
This minor release contains some new features, as expected under semantic versioning. The two major pull requests merged are #126 and #127. We significantly enhanced the built-in forecaster classes, like HistoricalMeanReturn and HistoricalCovariance, which are used by Cvxportfolio objects (ReturnsForecast and FullCovariance, for example) as default forecasters. These now take new “rolling” and “half_life” parameters, used to specify the length of the historical period used for estimation (by default, all history available at each point in time) and the half-life of exponential smoothing applied (by default, no smoothing), respectively. These apply to all forecasters. Internally, these more complex estimations are done with minimal extra computation (by updating, at each point in time, the estimation from the previous step). These parameters can be specified by instantiating the forecasters explicitely before passing them to each Cvxportfolio object (as is explained in the documentation). Thanks to the improved forecasters, we enhanced the trading cost classes TransactionCost and HoldingCost, clearing up some minor discrepancies with their original (2016) implementation. For TransactionCost, most notably, it is now possible to provide all forecasts without relying on internal forecasters. This will make it easier to translate the code of the original examples to the stable API. The changes are backward compatible with the documented interfaces of the previous versions. One interesting change we introduced on the cost objects is that now the same code paths are used to compute the cost values in simulation and optimization, further improving safety and auditability of the library. Finally, we re-wrote the data cleaning and data quality check code applied by the YahooFinance default data interface to US and international stock market data. This has been partially factored out in a new base class for open-low-high-close-volume data, which we expect to use for other (future) data interfaces. The cleaning is done by first removing impossible observations for stock market data (non-positive prices, …), then removing unlikely data (prices that imply 100x returns, …) with threshold based-testing (where all thresholds can be modified by the user), and finally by filling missing values being careful to avoid look-ahead biases (open prices that are missing get filled with close prices from the day before, …). A new example, “data_cleaning.py”, can be used to see what exactly is being done on each given name. Logs are also highly informative. All this is thoroughly tested both by the unit tests and by the example strategies that are run on every trading day a few minutes after the open time of both the US and now also international stock markets: we added a daily strategy that is run on the FTSE100 universe of the London stock market. Other minor edits and bug fixes are also present.
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Improve forecast.py
Target 1.1.0 or perhaps later release.