Arbitrage¶
Submodules¶
arbitrage module¶
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hardy.arbitrage.arbitrage.apply_tform(raw_df, tform_commands, rgb_col_number=6)[source]¶ Function that applies transformations
Parameters: - raw_df : pd.DataFrame of raw data from list_of_tuples
Un-Transformed data to apply transform to. This will be a call of list_of_tuples[#][1], because as defined elsewhere, each raw data has one tuple in list, contains (Filename, DataFrame, classifier)
- tform_commands : List of Tform Commands
This will be a call of tform_command_dict [tform_command_list[#]], Thus it will contain a list of tform commands: (Index=0, transform, source), (Index=1, transform, source), (Index=2, transform, source), As explained elsewhere
Returns: - tform_df: pd.DataFrame
Each column is placed in “Index”, and gets its name from “source” (New name from SourceColumnName__tform__TformName) Each column’s data is ouput of the tform_1d1d function called and the remainder are passed as zero (with # as col name?)
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hardy.arbitrage.arbitrage.import_tform_config(tform_config_path='./tform_config.yaml', raw_df=None)[source]¶ Function that imports the transformations from configuration
Parameters: - tform_config_path : Str, optional
Path of transform configuration file to apply to the data.
- raw_df: pd.DataFrame
Dataframe of raw data to use for assrting that the configuration file is correctly calling on the data
Returns: - tform_command_list : list of str
Ordered list of transform commands to use. Differs from the dict.keys() because this is ordered!
- tform_command_dict : dict of List-of-Transform-tuples
Each key will return a list of transforms to do on this data loop. Each “List of Transforms” as stated elsewhere contain:
(Index=0, transform, source), (Index=1, transform, source), (Index=2, transform, source),
- where:
“Index” is the output column destination, “transform” is command in transform.list_1d1d, and “source” is the raw data column to be used in the tform
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hardy.arbitrage.arbitrage.tform_tuples(list_of_tuples, tform_commands, rgb_format='RGBrgb')[source]¶ Wrapping function to apply a list of transform commands to each dataframe in the list_of_tuples, and replace it with a same-format list_of_tuples containing transformed data.
Parameters: - list_of_tuples : List of Tuples
Described in depth elsewhere. Standardized list for each raw file, tuple in format (filename_str, DataFrame, label)
- tform_commands : List of List(3)
Described in depth elsewhere
- rgb_format : str, optional
String of how we will parse the output files. Input here to get the output dataframe size. The default is “RGBrgb”.
Returns: - transformed_tuples : List of Tuples
Formatted the same as the input list, but each DataFrame is replaced with the Transformed DF.
transformations module¶
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hardy.arbitrage.transformations.cumsum(raw_array)[source]¶ The function return the cumulative sum of input array
Parameters: - raw_array: Input numpy array
Returns: - cumsum _array: np.ndarray
cumulative sum of values in the input array
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hardy.arbitrage.transformations.cwt_1d(raw_df, xy=0)[source]¶ Transform to execute a “Continuous Wavelet Transform” on a 1d data array pass it a raw XY data and tell it which column to use for the transform. See Documentaion on CWT transform: https://docs.scipy.org/doc/scipy/reference/generated/ scipy.signal.cwt.html#scipy.signal.cwt Note: I need to do testing to understand the in/outputs here… Plan is to simply hard-code a certain type of Wavelet to use… and Output Data may not be able to be square… In that case, we will discuss how to integrate this result with the compression of the data.
Parameters: - raw_df: pandas.DataFrame or 1D array (Mx2 or Mx1)
the raw data which is to be transformed.
- xy: boolean, or string ‘x’, or ‘y’
information on which dataframe column to transform. ignored if an 1D array is passed instead.
- w_method: string or boolean?
input instructions guiding how to choose wavelet sizes. default should be linear, with options for log- or exponential? (Will have to experiment with data to discover best option)
Returns: - cwt_matrix: np.ndarray (MxM)
Square M-by-M matrix of the wavelet transform data (Not yet compressed to plottable 0-1 data)
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hardy.arbitrage.transformations.derivative_1d(raw_array, spacing=0)[source]¶ Function that outputs the gradient of 1-D array using numpy.gradient function
Parameters: - raw_array: numpy array
- spacing: int representing the spacing between each datapoint
Returns: - derivative_array: np.ndarray
array representing gradient at each datapoint
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hardy.arbitrage.transformations.derivative_2d(x, y, meta_data=None)[source]¶ Function that outputs the slope between x and y data
Parameters: - x: numpy.array
array representing values on x-axis
- y: numpy.array
array representing values on y-axis
Returns: - slope_array: numpy.array
array representing the slope between x and y
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hardy.arbitrage.transformations.log10(raw_array)[source]¶ The function that outputs the natural log of input array
Parameters: - raw_array: Input numpy array
Returns: - log_array: np.ndarray
natural log values of each element in the input array
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hardy.arbitrage.transformations.nlog(raw_array)[source]¶ The function that outputs the natural log of input array
Parameters: - raw_array: Input numpy array
Returns: - log_array: np.ndarray
natural log values of each element in the input array
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hardy.arbitrage.transformations.power(x, y='None', meta_data=None)[source]¶ Function that multiplies two arrays x^m & y^n, element by element. If y is None, it return x*x
Parameters: - x: numpy.array
numpy array representing the one array to be multiplied
- y: numpy.array
numpy array representing the second array to be multiplied if None it the module will square the x array
Returns: - multi_array: numpy.array
numpy array representing the one to one multiplication of two arrays