Qiskit Finance is an open-source framework that contains uncertainty components for stock/securities problems, Ising translators for portfolio optimizations and data providers to source real or random data to finance experiments.
We encourage installing Qiskit Finance via the pip tool (a python package manager).
pip install qiskit-financepip will handle all dependencies automatically and you will always install the latest (and well-tested) version.
If you want to work on the very latest work-in-progress versions, either to try features ahead of their official release or if you want to contribute to Finance, then you can install from source. To do this follow the instructions in the documentation.
Now that Qiskit Finance is installed, it's time to begin working with the finance module. Let's try an experiment using Amplitude Estimation algorithm to evaluate a fixed income asset with uncertain interest rates.
import numpy as np
from qiskit import BasicAer
from qiskit.algorithms import AmplitudeEstimation
from qiskit_finance.circuit.library import NormalDistribution
from qiskit_finance.applications import FixedIncomePricing
# Create a suitable multivariate distribution
num_qubits = [2, 2]
bounds = [(0, 0.12), (0, 0.24)]
mvnd = NormalDistribution(
num_qubits, mu=[0.12, 0.24], sigma=0.01 * np.eye(2), bounds=bounds
)
# Create fixed income component
fixed_income = FixedIncomePricing(
num_qubits,
np.eye(2),
np.zeros(2),
cash_flow=[1.0, 2.0],
rescaling_factor=0.125,
bounds=bounds,
uncertainty_model=mvnd,
)
# the FixedIncomeExpectedValue provides us with the necessary rescalings
# create the A operator for amplitude estimation
problem = fixed_income.to_estimation_problem()
# Set number of evaluation qubits (samples)
num_eval_qubits = 5
# Construct and run amplitude estimation
q_i = BasicAer.get_backend("statevector_simulator")
algo = AmplitudeEstimation(num_eval_qubits=num_eval_qubits, quantum_instance=q_i)
result = algo.estimate(problem)
print(f"Estimated value:\t{fixed_income.interpret(result):.4f}")
print(f"Probability: \t{result.max_probability:.4f}")When running the above the estimated value result should be 2.46 and probability 0.8487.
Learning path notebooks may be found in the finance tutorials section of the documentation and are a great place to start.
If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community and for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.
Finance was inspired, authored and brought about by the collective work of a team of researchers. Finance continues to grow with the help and work of many people, who contribute to the project at different levels. If you use Qiskit, please cite as per the provided BibTeX file.
Please note that if you do not like the way your name is cited in the BibTex file then consult the information found in the .mailmap file.
This project uses the Apache License 2.0.