# Efficient Frontier

Use trial database to determine the weight of your portfolio.

## Highlights

• Difficulty：★★★☆☆
• Calculate and visualize efficient frontier.
• Advise：This article will introduces the theory roughly. It will not be discussed in detail. Readers who are interested in theory can search for relevant literature. The mathematical calculations will be relatively complicated, using lot of functional programming to increase efficiency and reduce memory requirements. We use plotly module to visualize the result, and the interactive charts allow us to deeply understand the power of result.

## Preface

Most people often have hard time to determine the weight of portfolio. However, Harry Markowitz, the Nobel Prize winner in economics, gives us a theory based on the volatility and correlation of stocks. Simulated by different weights on portfolio, we can put the restrictions like given the total risk is the same, to find the highest expected rate of return. In this way, we can choose the weight distribution of portfolio according to our own risk tolerance!

## The Editing Environment and Modules Required

Mac OS and Jupyter Notebook

`# basicimport numpy as npimport pandas as pd# plotimport matplotlib.pyplot as pltimport matplotlibimport plotly.express as pximport plotly.graph_objects as go# APIimport tejapitejapi.ApiConfig.api_key = 'Your Key'tejapi.ApiConfig.ignoretz = True`

## Data Retrieval

Step 1. We use TSMC (2330), Evergreen (2603), and President Chain Store (2912) as examples of investment portfolios. The date is selected for 2020, and the option columns selected roi.

`data = tejapi.get('TRAIL/TAPRCD',                  coid=['2330', '2603', '2912'],                  mdate={'gte': '2020-01-01', 'lte': '2020-12-                          31'},                  opts={"sort": "mdate.desc", 'columns': [                        'coid', 'mdate', 'roi']},                  paginate=True)`

Step 2. Reset index value, data pivot, column name with stock code

`data = data.set_index('mdate')returns = data.pivot(columns='coid')returns.columns = [columns for columns in returns.columns]`

Step 3. Calculate average return, covariance matrix

`mean_returns = returns.mean()cov_matrix = returns.cov()`

## Portfolio calculation

We need to randomly generate portfolio weights and use a large number of simulations to find the efficiency frontier. We need to record its return, standard deviation, and weight for each investment group.

`def portfolio_performance(weights, mean_returns, cov_matrix):    returns = np.sum(mean_returns*weights )    std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))     return std, returns`

The number of portfolios we want to simulate.

`num_portfolios = 5000`

Put the function into the function which calculate random investment portfolio to get the results of each investment portfolio.

`def random_portfolios(num_portfolios, mean_returns, cov_matrix):    results = np.zeros((3,num_portfolios))    weights_record = []    for i in range(num_portfolios):        weights = np.random.random(len(coid))        weights /= np.sum(weights)        weights_record.append(weights)        portfolio_std_dev, portfolio_return =         portfolio_performance(weights, mean_returns, cov_matrix)        results[0,i] = portfolio_std_dev        results[1,i] = portfolio_return        results[2,i] = (portfolio_return) / portfolio_std_dev    return results, weights_record`

Start the simulation and save the results in `results` and `weights_record`

`results = np.zeros((3,num_portfolios))weights_record = []for i in range(num_portfolios):    weights = np.random.random(len(coid))    weights /= np.sum(weights)    weights_record.append(weights)    portfolio_std_dev, portfolio_return =     portfolio_performance(weights, mean_returns, cov_matrix)    results[0,i] = portfolio_std_dev    results[1,i] = portfolio_return    results[2,i] = (portfolio_return) / portfolio_std_dev`

Visualization: The first number of the text box represents the risk, the second number represents the reward, and the second row of the array represents the weight of the investment.

`def protfolios_allocation(mean_returns, cov_matrix,                          num_portfolios):    results, weights = random_portfolios(        num_portfolios, mean_returns, cov_matrix)            fig = go.Figure(data=go.Scatter(x=results[0, :],                                     y=results[1, :],                                    mode='markers',                                    text = weights_record,                                   ))        fig.update_layout(title='投資組合表現分佈',                      xaxis_title="投資組合總風險",                      yaxis_title="預期平均報酬率",)fig.update_xaxes(showspikes=True,spikecolor="grey",                     spikethickness=1, spikedash='solid')fig.update_yaxes(showspikes=True,spikecolor="grey",                     spikethickness=1, spikedash='solid')fig.show()`

## Efficient Frontier

There are two conditions：

1. The portfolio with the least risk among all portfolios
2. Portfolio with minimal risk under the same rate of return

This type of condition is equivalent to finding the extreme value, we can use optimize under the scipy module to calculate the minimum value.

`import scipy.optimize as sco`

Here we define four calculation functions

### Volatility

`def portfolio_volatility(weights, mean_returns, cov_matrix): return portfolio_performance(weights,mean_returns, cov_matrix)`

### Minimal Volatility Portfolio (MVP)

We use the optimize calculation under the scipy module to take the extreme value of the Volatility function under the weight limit of 0 to 1, and the algorithm selects SLSQP (Sequential Least Squares Programming) nonlinear programming

fun：objective function

args：parameters that can be set for the objective function

method：Optimal algorithm

bounds：Range of each x

constraints：the input is a tuple composed of a dictionary, the dictionary is mainly composed of’type’ and’fun’, type can be’eq’ and’ineq’, which are equality constraints and inequality constraints, respectively, fun is the corresponding constraint The condition can be a lambda function.

`def min_variance(mean_returns, cov_matrix):    num_assets = len(mean_returns)    args = (mean_returns, cov_matrix)    constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})    bound = (0,1)    bounds = tuple(bound for asset in range(num_assets))    result = sco.minimize(portfolio_volatility, num_assets*             [1/num_assets,], args=args,             method='SLSQP', bounds=bounds,              constraints=constraints)    return result`

### MVP under the same rate of return

Mainly Due to the difference in constraint conditions, the returns should be fixed to a number, and find the minimum of risk.

`def efficient_return(mean_returns, cov_matrix, target):    num_assets = len(mean_returns)    args = (mean_returns, cov_matrix)def portfolio_return(weights):        return portfolio_performance(weights, mean_returns,                                      cov_matrix)constraints = ({'type': 'eq', 'fun': lambda x:                       portfolio_return(x) - target},                   {'type': 'eq', 'fun': lambda x: np.sum(x) - 1})bounds = tuple((0,1) for asset in range(num_assets))    result = sco.minimize(portfolio_volatility, num_assets*                          [1/num_assets,], args=args,                          method='SLSQP', bounds=bounds,                           constraints=constraints)    return result`

### Sample after combined efficient frontier

`def efficient_profolios(mean_returns, cov_matrix, returns_range):    efficients = []    for ret in returns_range:        efficients.append(efficient_return(mean_returns,                           cov_matrix, ret))    return efficients`