**D****escription of the trading strategy**** **

**D**

**escription of the trading strategy**

Trading strategy代写 In order to reduce the impact of market volatility, reduce withdrawal and improve the stability of returns

In order to reduce the impact of market volatility, reduce withdrawal and improve the stability of returns, the strategy uses hedging market index to obtain alpha part of the returns.

Considering the above requirements, we use the long energy industry index, short the market index, and obtain the income independent of market volatility.

We use monthly data for frequency, so as to achieve monthly warehouse adjustment.

**Advantages of the strategy Trading strategy代写**

**Advantages of the strategy Trading strategy代写**

#### As a result of hedging the overall volatility of the securities market,

our earnings curve will not rise and fall sharply with the market, which will make the earnings curve more stable. Although we won’t be able to make long profits in extremely good market conditions, our measurement strategy can also perform relatively smoothly in extremely poor market conditions.

Because we are monthly warehouse transfer, only one handling fee a month, so the impact of handling fees and commissions is relatively small, so we can only focus on the revenue of the strategy itself.

**Alpha ret **

**Alpha ret**

Our strategy can produce relatively stable alpha. We counted the performance of the strategy from 1963 to 2016. The average monthly return of the strategy is 0.00485, the fluctuation of the return is 0.052, and the Sharp ratio of the return is 0.0935.

Further, we obtained the p value on the right side of 0 is 0.0089, the ratio is 99%. At a significant level, the sequence of revenue can be considered to be greater than 0. It can be seen that this strategy has smaller volatility, relatively stable returns, and can generate positive alpha. As for the Commission and handling fee of the strategy, since the strategy is monthly warehouse transfer, the transaction frequency is not high, so the impact of transaction cost is relatively small, which we will discuss in the last part.

**The base strategy Trading strategy代写**

**The base strategy Trading strategy代写**

#### Firstly,

the beta of energy market index and market index is inconsistent, that is, the fluctuation of energy market index and market index is inconsistent. So we need to consider how many units of energy market index we should short when we do more than one unit of energy market index.

Here we use market index as independent variable and energy market index as dependent variable to carry out regression with constant term. The data used are monthly data from 1963 to 2016.The formula for calculating beta is as follows.

##### The following is a detailed report on regression.

Through the above table, we can find that the value of R-square is 0.238 , and the p value of constant term is 0.02 , and the p value of independent variable is 0 , which shows that the effect of regression is acceptable.

So we can get the coefficient of the independent variable, i. e. beta is 0.6612, which means that when we do more than 10 billion energy market indices, we need to short 6.6 billion market indices. The cost is 3 billion 400 million.

##### After getting beta of the formula , Trading strategy代写

next we need to calculate the revenue of the alpha part of the strategy. alpha gains can be obtained by subtracting beta from energy market gains and then multiplying market gains. The profit of alpha is the return of strategy. The formula for calculating alpha is as follows.

We can get the historical return of Alpha ETF by matrix operation of data. In order to further describe the data characteristics of Alpha ETF’s historical return, we use mean, standard deviation and Sharp ratio of revenue to evaluate the return curve.

Mean Ret | Std Ret | Sharpe radio |

0.00485 | 0.0518 | 0.0934 |

As can be seen from the above table, the average return of this strategy is greater than 0, that is to say,

every transaction of energy market index and market index can produce positive returns in the long run.

From the standard deviation index of earnings of 0.0518, we can see that the overall volatility of good strategies is relatively small,

there is no huge withdrawal, and changes with the market are small. It can be seen from Sharpe radio 0.0934 that the strategy performs well from two aspects of revenue and volatility.

#### Next, Trading strategy代写

we draw the trend of market index, energy market index and Alpha ETF income curve in a chart, which will help us to further observe the relationship between Alpha ETF and market index, energy market index.The following is the trend of the three yield curves.

The figures in the picture are from 1961 to 2016.

The yellow line shows the income trend of the energy market index, the green line shows the income trend of the market index, and the blue line shows the income trend of the Alpha ETF.

#### Further, **Trading strategy代写**

**Trading strategy代写**

we calculate the return mean, return variance and Sharp ratio of the energy market index, as well as the return mean, return variance and Sharp ratio of the market index, to compare the relationship between the Alpha ETF and the energy market index and the market index.

Type | Mean Ret | Std Ret | Sharpe radio |

Alpha ETF | 0.00485 | 0.0518 | 0.0934 |

energy market index | 0.00905 | 0.0439 | 0.2058 |

market index | 0.0108 | 0.0594 | 0.18215 |

#### From the charts and tables, **Trading strategy代写**

**Trading strategy代写**

we can see that the correlation between the return of Alpha ETF and the return of the market index and the return of the energy market index is not great. From the charts, we can see that when the return of the market index is rising, the Alpha ETF will not necessarily follow the rise. When the market index is falling, the Alpha ETF will not necessarily follow the fall. The same happens in the energy market index.

It can also be found from the average income indicators in the table that the income of Alpha ETF is twice as small as that of the energy market index and the market index, and the correlation is not great. From the standard deviation of return in the table, we can find that the standard deviation of return of strategy is relatively small, which can ensure the stability of return.

#### Although our strategy is not as good as the market index and the energy market index,

it is because we hedge the market risk, and there is no market trend part of the revenue.

The revenue of the strategy comes from the energy market index part after hedging the market index income. Although this part of earnings is not as good as the market part, it is more stable, because the market has been in the rising stage for a long time, so the advantages are not highlighted.

When the market is in decline, the earnings of this part will not fall with the decline of the market. At this time, the advantages of the strategy will be more prominent.

**Beta analysis Trading strategy代写**

**Beta analysis Trading strategy代写**

#### An industry index is not necessarily stable to the beta of the market index,

whether it exists or not may be that the industry’s beta is changing, so here we re-estimate.

Now we can try to do an operation, because the original data is monthly, and estimating beta needs a certain amount of data, such as if we use three. The monthly data estimate beta, that is, three data estimate beta is not reasonable. You can try to make a new regression every one or three years, reassess Beta, and adjust your warehouse next.

##### Calculate a beta every year.

Apply the beta to the calculation of alpha in that year. By rolling the calculation of alpha, the final profit curve of Alpha ETF, pofolio_ret_Y, is obtained.

Similarly, a beta is calculated every three years, and the beta is applied to the calculation of alpha in these three years. By rolling the calculation of alpha, the final profit curve of Alpha ETF, pofolio_ret_3Y, is obtained. The pofolio_ret_Y and pofolio_ret_3Y are plotted and compared in a graph.

The following figure is the beta_3Y calculated by beta_Y in the one year period corresponding to the time point and calculated in the three year period.

From the above two graphs, we can find that the change of beta calculated in one year period is more drastic than that calculated in three years period.

Similarly, the change of income curve calculated by beta_Y is more dramatic than that calculated by beta_3Y. From this, we can know that beta with shorter time period can react to the market faster, so as to allocate resources more reasonably and achieve hedging effect.

On the other hand, we can also find that two betas are calculated by different time periods, and then two profit curves are obtained by two betas.

In the long run, although there are some differences between the two profit curves, the trend of the two profit curves is roughly the same, so the time period used to calculate beta is inconsistent, resulting in the difference of limited income.

**Out Of Sample Test Trading strategy代写**

**Out Of Sample Test Trading strategy代写**

In this section, we will test the calculation of beta values in the sample,

and apply the calculated beta values in the sample to the outside of the sample, and compare the differences between the two.

Below we calculate beta_Y in one year and three years respectively, and apply it to data outside the sample,The following figure is the income from the samples obtained from two beta calculations.

It can be found that when the test time is longer, the performance in the sample is not much different from that outside the sample.

**The Cost Of The Pofolio Trading strategy代写**

**The Cost Of The Pofolio Trading strategy代写**

First we suppose the cost of a transaction is 0.0003,That is to say, the actual return of a transaction is 0.0003 minus the return of no transaction cost.

On this basis, the situation of Alpha ETF return under transaction cost is further discussed. The following is a picture of the yield curve obtained from the one year beta_Y and the three year beta_3Y after deducting transaction costs.

#### It can be seen that the transaction cost has little impact on the overall revenue in the case of low transaction frequency.

if the beta change from 1.56 to 1.54,and suppose long 100 indus index， short 156 market index,then because the change of beta,

if without cost,we should change short 156 market index to short 154 market index to protect alpha,but if we caculate the alpha without change beta is not more than cost ,in this way ,the cost of the action can not fill return ,we should not take action.

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