model_timeseries_environment This function is designed to analyse financial time series data and generate a directional forecast for the final period. It models the entire environment based on observed data by simultaneously analysing all previous data periods. This function shows the AGN’s understanding of the underlying structure of the environment, which is expressed through directional indicators.

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Function Parameters

ParameterTypeRequiredDescription
data_inputobjectTrueThe financial time series data to be analysed. This can be a file path (e.g., ‘folder/ohlc_data.csv’) or a pandas DataFrame. Supported file formats include CSV, TSV, Parquet, Excel, JSON, and HTML.
intervalintTrueThe frequency of the time series data. Use in conjunction with interval_unit.
interval_unitstringTrueThe unit of time for the interval. Accepts one of the following values: ‘seconds’, ‘minutes’ and ‘days’.
reasoning_modestringTrueThe reasoning strategy the architecture uses to make decisions: ‘proactive’ acts early with minimal information while ‘reactive’ waits for sufficient evidence before acting.
output_filestringTrueThe name of the file where the output will be saved. The output is a CSV file. For example, ‘saved_output’ will create saved_output.csv.

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Data Columns

Your input data (file or DataFrame) must contain the following columns:
  • datetime (string): A timestamp for each data point.
  • open (float): The opening price for the interval.
  • high (float): The highest price reached during the interval.
  • low (float): The lowest price reached during the interval.
  • close (float): The closing price for the interval.

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Python Code Example

# See the entire model generated by the AGN for the submitted data

entire_model = client.model_timeseries_environment(
    data_input='folder/ohlc_data.csv', # or can be a pandas dataframe 
    interval=1,
    interval_unit='days',
    reasoning_mode='reactive',
    output_file='saved_output'
)

# Returns a dataframe with columns: [datetime, trend_identified]
# Trend values: 1 (positive change), -1 (negative change), 0 (no clear change)