identify_timeseries_directional_change Recommended number of data periods: 5001 This function determines if a directional change is forecasted for the final period.

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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 algorithms use to make decisions: ‘proactive’ acts early with minimal information while ‘reactive’ waits for sufficient evidence before acting.

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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

from sumtyme import EIPClient

# Replace 'your-subdomain' with your assigned EIP subdomain.
client = EIPClient(subdomain='your-subdomain',apikey_path='config.txt')

# Identify if a directional change exists at the last data period
latest_change = client.identify_timeseries_directional_change(
    data_input='folder/ohlc_data.csv', # or can be a pandas dataframe 
    interval=1,
    interval_unit='days',
    reasoning_mode='proactive'
)

# Returns a list: [datetime_string, trend_value]
# Trend values: 1 (positive change), -1 (negative change), 0 (no clear change)