Track Causal Chains - sumtyme.ai

map_causal_chains maps the trajectory of directional signals as they emerge at the microscale and propagate through a hierarchy of increasing timeframes. By identifying the origin of a change, it constructs causal chains to quantify how micro-scale shifts evolve into structural change across expanding time scales.

Function Parameters

Parameter	Type	Required	Description
data_input	`list[tuple]`	True	A list of tuples containing the source (URL or local path) and the associated timeframe label e.g. (‘path/to/file.csv’, ‘1m’).

Supported Time Resolutions

Unit	Symbol	Seconds ( $s$ )
Planck Time	`pt`	$5.39 \times 10^{-44}$
Yoctosecond	`ys`	$10^{-24}$
Zeptosecond	`zs`	$10^{-21}$
Attosecond	`as`	$10^{-18}$
Femtosecond	`fs`	$10^{-15}$
Picosecond	`ps`	$10^{-12}$
Nanosecond	`ns`	$10^{-9}$
Microsecond	`us`	$10^{-6}$
Millisecond	`ms`	$10^{-3}$
Second	`s`	$1$
Minute	`m`	$60$
Hour	`h`	$3,600$
Day	`d`	$86,400$
Week	`w`	$604,800$

Python Code Example

import sumtyme 

# Initialise the sumtyme client with the provided API key from your dashboard
client = sumtyme.client(apikey='your-api-key-here')

# Ensure API outputs start from the same timestamp
api_outputs = [
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_1s_reactive_outputs.csv", '1s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_5s_reactive_outputs.csv", '5s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_15s_reactive_outputs.csv", '15s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_30s_reactive_outputs.csv", '30s'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_1m_reactive_outputs.csv", '1m'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_2m_reactive_outputs.csv", '2m'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_5m_reactive_outputs.csv", '5m'),
    ("https://raw.githubusercontent.com/sumteam/data_store/main/GLD/api_outputs/GLD_10m_reactive_outputs.csv", '10m'),
]


# Execute Causal Mapping
# initial_chain_starts (dataframe): Specific datetime where a chain first starts based on observed timeframes
# causal_chain_details (dataframe): A detailed breakdown of how the signal moved across different timeframes.
initial_chain_starts, causal_chain_details = client.map_causal_chains(api_outputs=api_outputs)

# Output results for review
print("--- Chain Inception Points ---")
print(initial_chain_starts)

print("\n--- Detailed Causal Path Analysis ---")
print(causal_chain_details)

​Function Parameters

​Supported Time Resolutions

​Python Code Example

Function Parameters

Supported Time Resolutions

Python Code Example