synthera 0.2.4

Python client for Synthera AI API

Synthera AI SDK

This provides a Python SDK for accessing the Synthera AI API.

Installation

pip install synthera

API Key

You are required to use an API key to access the Synthera AI API.

For ease of use, set as an environment variable: SYNTHERA_API_KEY.

For example:

export SYNTHERA_API_KEY=<api_key>

Or you can pass to the client.

Getting Started

Import the Synthera client:

from synthera import SyntheraClient

Create a client:

client = SyntheraClient()

Check the connection works:

client.healthy()

`True`

For more advanced connection options, pass parameters to the client:

SyntheraClient(api_key: str, host: str, port: int, timeout_secs: int)

Fixed Income

To run Fixed Income Yield Curve simulations.

View Available Models

To view the model labels:

client.fixed_income.model_labels()

Example outputs

['YieldGAN-Augur-15days-v0.1-Q42019',
 'YieldGAN-Augur-15days-v0.1-Q42024']

Name	Description
YieldGAN	Model name
Augur	Dataset: training; inference
15days	Simulation days
v0.1-Q42019	Version (and training end period)

View Model Metadata

To view the metadata for a model label:

client.fixed_income.model_metadata(model_label=<model label>)

Example output

ModelMetadata(model_label='YieldGAN-Augur-10days-v0.1-Q42024', dataset='Augur', universe='g3_par_curves', curve_labels=['USA', 'GBR', 'DEU'], start_date_training='2000-01-01', end_date_training='2025-01-01', simulation_steps=15, conditional_steps=15, tenors=[0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 25.0, 30.0])

Name	Description
model_label	Unique identifier for the model version and universe
universe	Data universe used for training (e.g., g3_par_curves)
curve_labels	List of yield curve identifiers included in the model
start_date_training	Start date of the training data period (YYYY-MM-DD) (inclusive)
end_date_training	End date of the training data period (YYYY-MM-DD) (exclusive)
simulation_steps	Number of forward simulation steps
conditional_steps	Number of conditional simulation steps
tenors	List of tenors (in years) for which yields are simulated

Run Simulations

Prepare input parameters.

Parameter	Type	Description	Values
model_label	string	Version of the model to use	Valid model version (V) and universe (U): "YieldGAN-vV-U"
curve_labels	list[string]	List of yield curves labels to simulate, using their unique identifiers	List of curve names (e.g., ["USA", "UK", "GER"])
no_of_days	integer	Number of days to simulate forward from the reference date	> 0 (e.g., 3, 30, 60, 120)
no_of_samples	integer	Number of simulation paths to generate	> 0 (e.g., 100, 1024, 10000)
reference_date	string	Reference date for the simulation (in the past)	YYYY-MM-DD format

For example:

params = {
    "model_label": "YieldGAN-Augur-10days-v0.1-Q42024",
    "curve_labels": [
        "USA",
        "UK",
    ],
    "no_of_days": 15,
    "no_of_samples": 100,
    "reference_date": "2010-01-01"
}

To run simulations directly:

results = client.fixed_income.simulation_past_date(params=params)

To view yield curves labels:

results.names

To view yield curve dataframe column names:

results.column_names

To view a specific yield curve dataframes, e.g. for USA:

results.dataframes["USA"]

To view all yield curves in a single numpy ndarray (order is same as names):

results.ndarray

To view simulation request metadata:

results.metadata

Alternatively, import the fixed income class:

from synthera import FixedIncome

Pass the client:

fixed_income = FixedIncome(client)

Then to run simulations:

results = fixed_income.simulation_past_date(params=params)