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Merge branch 'master' into update-pulse-software-documentation
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‎legacy_tutorials/aqua/finance/simulation/asian_barrier_spread_pricing.ipynb‎

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‎legacy_tutorials/aqua/finance/simulation/bull_spread_pricing.ipynb‎

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‎legacy_tutorials/aqua/finance/simulation/credit_risk_analysis.ipynb‎

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‎start_here.ipynb‎

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"4. [Grover Optimizer](tutorials/optimization/4_grover_optimizer.ipynb) - Explore each component of the `GroverOptimizer`, which utilizes the techniques described in GAS, by minimizing a Quadratic Unconstrained Binary Optimization (QUBO) problem\n",
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"\n",
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"\n",
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"5. [ADMM Optimizer](tutorials/optimization/5_admm_optimizer.ipynb) - The ADMM Optimizer can solve classes of mixed-binary constrained optimization problems that often appear in logistic, finance, and operation research."
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"5. [ADMM Optimizer](tutorials/optimization/5_admm_optimizer.ipynb) - The ADMM Optimizer can solve classes of mixed-binary constrained optimization problems that often appear in logistic, finance, and operation research.\n",
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"\n",
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"\n",
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"## Finance\n",
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"\n",
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"1. [Portfolio Optimization](tutorials/finance/1_portfolio_optimization.ipynb) - How to build a portfolio optimization problem and solving it using VQE / QAOA.\n",
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"\n",
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"\n",
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"2. [Portfolio Diversification](tutorials/finance/2_portfolio_diversification.ipynb) - How to build a portfolio diversification problem and solving it using VQE / QAOA.\n",
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"\n",
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"\n",
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"3. [European Call Option Pricing](tutorials/finance/3_european_call_option_pricing.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to price a European call option.\n",
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"\n",
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"\n",
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"4. [European Put Option Pricing](tutorials/finance/4_european_put_option_pricing.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to price a European put option.\n",
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"\n",
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"\n",
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"5. [Bull Spread Pricing](tutorials/finance/5_bull_spread_pricing.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to price a Bull Spread option.\n",
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"\n",
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"\n",
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"6. [Basket Option Pricing](tutorials/finance/6_basket_option_pricing.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to price a Basket option.\n",
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"\n",
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"\n",
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"7. [Asian Barrier Spread Pricing](tutorials/finance/7_asian_barrier_spread_pricing.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to price an Asian Barrier Spread option.\n",
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"\n",
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"\n",
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"8. [Fixed Income Pricing](tutorials/finance/8_fixed_income_pricing.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to price a portfolio of fixed income assets.\n",
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"\n",
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"\n",
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"9. [Credit Risk Analysis](tutorials/finance/9_credit_risk_analysis.ipynb) - How to model a quantum circuit that can be used with amplitude estimation to analyse the credit risk of a portfolio of assets that may default.\n",
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"\n",
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"\n",
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"10. [Option Pricing with qGANs](tutorials/finance/10_qgan_option_pricing.ipynb) - How to use a (trained) qGAN to load the uncertainty of a stock price together with amplitude estimation to price a European call option.\n",
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"\n",
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"\n",
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"11. [Loading and Processing Stock-Market Data](tutorials/finance/11_time_series.ipynb) - How to use data providers to generate a portfolio management model, e.g., for portfolio optimization or diversification."
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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"version": "3.7.4"
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},
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"varInspector": {
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‎tutorials/finance/10_qgan_option_pricing.ipynb‎

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"collapsed": true
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},
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"source": [
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"# _*Qiskit Finance: Portfolio Optimization*_ \n",
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"\n",
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"The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorials.\n",
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"\n",
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"***\n",
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"## Contributors\n",
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"Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
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"### Affiliation\n",
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"- <sup>[1]</sup>IBMQ"
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"# _*Portfolio Optimization*_ \n"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-13T20:35:15.224327Z",
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-13T20:35:15.231767Z",
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},
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"outputs": [],
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"source": [
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"q = 0.5 # set risk factor\n",
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"budget = int(num_assets / 2) # set budget\n",
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"penalty = num_assets # set parameter to scale the budget penalty term\n",
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"q = 0.5 # set risk factor\n",
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"budget = num_assets // 2 # set budget\n",
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"penalty = num_assets # set parameter to scale the budget penalty term\n",
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"\n",
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"qubitOp, offset = portfolio.get_operator(mu, sigma, q, budget, penalty)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-13T20:35:15.243604Z",
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-13T20:35:15.264319Z",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Optimal: selection [1 1 0 0], value -0.0125\n",
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"Optimal: selection [1 1 0 0], value 0.0019\n",
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"\n",
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"----------------- Full result ---------------------\n",
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"selection\tvalue\t\tprobability\n",
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"---------------------------------------------------\n",
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" [1 1 0 0]\t-0.0125\t\t1.0000\n",
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" [1 1 1 1]\t15.9925\t\t0.0000\n",
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" [0 1 1 1]\t4.0009\t\t0.0000\n",
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" [1 0 1 1]\t3.9968\t\t0.0000\n",
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" [0 0 1 1]\t0.0052\t\t0.0000\n",
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" [1 1 0 1]\t3.9903\t\t0.0000\n",
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" [0 1 0 1]\t-0.0014\t\t0.0000\n",
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" [1 0 0 1]\t-0.0054\t\t0.0000\n",
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" [0 0 0 1]\t4.0029\t\t0.0000\n",
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" [1 1 1 0]\t3.9899\t\t0.0000\n",
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" [0 1 1 0]\t-0.0020\t\t0.0000\n",
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" [1 0 1 0]\t-0.0057\t\t0.0000\n",
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" [0 0 1 0]\t4.0024\t\t0.0000\n",
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" [0 1 0 0]\t3.9955\t\t0.0000\n",
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" [1 0 0 0]\t3.9920\t\t0.0000\n",
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" [1 1 0 0]\t0.0019\t\t1.0000\n",
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" [1 1 1 1]\t16.0201\t\t0.0000\n",
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" [0 1 1 1]\t4.0196\t\t0.0000\n",
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" [1 0 1 1]\t4.0186\t\t0.0000\n",
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" [0 0 1 1]\t0.0182\t\t0.0000\n",
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" [1 1 0 1]\t4.0183\t\t0.0000\n",
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" [0 1 0 1]\t0.0178\t\t0.0000\n",
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" [1 0 0 1]\t0.0168\t\t0.0000\n",
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" [0 0 0 1]\t4.0164\t\t0.0000\n",
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" [1 1 1 0]\t4.0038\t\t0.0000\n",
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" [0 1 1 0]\t0.0033\t\t0.0000\n",
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" [1 0 1 0]\t0.0024\t\t0.0000\n",
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" [0 0 1 0]\t4.0019\t\t0.0000\n",
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" [0 1 0 0]\t4.0014\t\t0.0000\n",
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" [1 0 0 0]\t4.0004\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n"
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]
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}
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 6,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-13T20:35:26.536878Z",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Optimal: selection [0. 1. 0. 1.], value -0.0014\n",
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"Optimal: selection [0. 1. 1. 0.], value 0.0033\n",
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"\n",
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"----------------- Full result ---------------------\n",
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"selection\tvalue\t\tprobability\n",
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"---------------------------------------------------\n",
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" [0 1 0 1]\t-0.0014\t\t0.8753\n",
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" [1 0 0 1]\t-0.0054\t\t0.1214\n",
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" [1 0 1 0]\t-0.0057\t\t0.0019\n",
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" [0 1 1 0]\t-0.0020\t\t0.0008\n",
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" [0 0 1 1]\t0.0052\t\t0.0002\n",
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" [1 1 0 0]\t-0.0125\t\t0.0002\n",
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" [1 0 0 0]\t3.9920\t\t0.0001\n",
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" [1 1 1 0]\t3.9899\t\t0.0000\n",
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" [0 1 0 0]\t3.9955\t\t0.0000\n",
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" [0 1 1 0]\t0.0033\t\t0.9727\n",
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" [1 0 1 0]\t0.0024\t\t0.0216\n",
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" [0 1 0 1]\t0.0178\t\t0.0028\n",
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" [1 1 0 0]\t0.0019\t\t0.0016\n",
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" [0 0 1 1]\t0.0182\t\t0.0007\n",
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" [1 1 0 1]\t4.0183\t\t0.0005\n",
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" [1 0 0 1]\t0.0168\t\t0.0001\n",
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" [1 0 1 1]\t4.0186\t\t0.0000\n",
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" [1 1 1 1]\t16.0201\t\t0.0000\n",
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" [0 1 0 0]\t4.0014\t\t0.0000\n",
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" [1 0 0 0]\t4.0004\t\t0.0000\n",
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" [0 0 0 1]\t4.0164\t\t0.0000\n",
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" [0 0 1 0]\t4.0019\t\t0.0000\n",
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" [1 1 1 0]\t4.0038\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n",
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" [1 0 1 1]\t3.9968\t\t0.0000\n",
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" [0 1 1 1]\t4.0009\t\t0.0000\n",
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" [0 0 1 0]\t4.0024\t\t0.0000\n",
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" [1 1 1 1]\t15.9925\t\t0.0000\n",
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" [1 1 0 1]\t3.9903\t\t0.0000\n",
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" [0 0 0 1]\t4.0029\t\t0.0000\n"
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" [0 1 1 1]\t4.0196\t\t0.0000\n"
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]
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-13T20:35:28.570970Z",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Optimal: selection [0. 0. 1. 1.], value 0.0052\n",
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"Optimal: selection [1. 1. 0. 0.], value 0.0019\n",
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"\n",
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"----------------- Full result ---------------------\n",
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"selection\tvalue\t\tprobability\n",
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"---------------------------------------------------\n",
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" [0 0 1 1]\t0.0052\t\t0.1681\n",
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" [0 1 0 1]\t-0.0014\t\t0.1670\n",
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" [0 1 1 0]\t-0.0020\t\t0.1669\n",
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" [1 0 0 1]\t-0.0054\t\t0.1664\n",
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" [1 0 1 0]\t-0.0057\t\t0.1663\n",
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" [1 1 0 0]\t-0.0125\t\t0.1653\n",
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" [1 1 1 0]\t3.9899\t\t0.0000\n",
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" [0 0 0 1]\t4.0029\t\t0.0000\n",
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" [1 1 0 1]\t3.9903\t\t0.0000\n",
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" [0 0 1 0]\t4.0024\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n",
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" [1 1 1 1]\t15.9925\t\t0.0000\n",
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" [0 1 0 0]\t3.9955\t\t0.0000\n",
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" [1 0 1 1]\t3.9968\t\t0.0000\n",
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" [1 0 0 0]\t3.9920\t\t0.0000\n",
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" [0 1 1 1]\t4.0009\t\t0.0000\n"
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" [1 1 0 0]\t0.0019\t\t0.1674\n",
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" [1 0 1 0]\t0.0024\t\t0.1674\n",
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" [0 1 1 0]\t0.0033\t\t0.1673\n",
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" [1 0 0 1]\t0.0168\t\t0.1660\n",
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" [0 1 0 1]\t0.0178\t\t0.1659\n",
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" [0 0 1 1]\t0.0182\t\t0.1658\n",
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" [1 0 0 0]\t4.0004\t\t0.0000\n",
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" [0 1 1 1]\t4.0196\t\t0.0000\n",
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" [1 0 1 1]\t4.0186\t\t0.0000\n",
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" [0 1 0 0]\t4.0014\t\t0.0000\n",
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" [1 1 0 1]\t4.0183\t\t0.0000\n",
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" [0 0 1 0]\t4.0019\t\t0.0000\n",
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" [1 1 1 0]\t4.0038\t\t0.0000\n",
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" [0 0 0 1]\t4.0164\t\t0.0000\n",
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" [1 1 1 1]\t16.0201\t\t0.0000\n",
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" [0 0 0 0]\t16.0000\t\t0.0000\n"
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]
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}
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"metadata": {
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"end_time": "2020-07-13T20:35:29.079589Z",
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{
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"data": {
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"text/html": [
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"<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.19.6</td></tr><tr><td>Terra</td><td>0.14.2</td></tr><tr><td>Aer</td><td>0.5.2</td></tr><tr><td>Ignis</td><td>0.3.3</td></tr><tr><td>Aqua</td><td>0.7.3</td></tr><tr><td>IBM Q Provider</td><td>0.7.2</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.7 (default, May 6 2020, 04:59:01) \n",
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"[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Mon Jul 13 16:35:29 2020 EDT</td></tr></table>"
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"<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.19.1</td></tr><tr><td>Terra</td><td>0.14.1</td></tr><tr><td>Aer</td><td>0.5.1</td></tr><tr><td>Ignis</td><td>0.3.0</td></tr><tr><td>Aqua</td><td>0.7.0</td></tr><tr><td>IBM Q Provider</td><td>0.7.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
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"[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>6</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Jul 15 19:59:08 2020 CEST</td></tr></table>"
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