金融研究报告撰写

class: center, middle, inverse, title-slide

# 金融研究报告撰写
## 数据绘图与展示
### 吴燕丰
### 江西财大，金融学院
### 2020/12/24, updated on 2022-06-10

---

class: middle, center

## 数据绘图与展示

---
### Matplotlib

Matplotlib 是 Python 的绘图库。

安装(通过Anaconda Prompt命令行窗口)：

```bash
pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple
```

**提示**：如果没安装 NumPy 库，也请安装。

---
### Matplotlib Pyplot

**Pyplot**

Most of the **Matplotlib** utilities lies under the **pyplot** submodule, and are usually imported under the **plt** alias:

```python
import matplotlib.pyplot as plt
```

---
### Draw a Line

Draw a Line from **point (0,0)** to **point (6,250)**.

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([0, 6])
ypoints = np.array([0, 250])

plt.plot(xpoints, ypoints)
plt.show()
```
]

.pull-right[
![](./chapter10_Plot_Documentation_files/figure-html/unnamed-chunk-3-1.png)
]

---
### Adding x, y axis labels and title

添加X、Y轴标签、图标题

.pull-left[

```python
import numpy as np 
from matplotlib import pyplot as plt 
 
x = np.arange(1,11) 
y =  2  * x +  5 
plt.title("Matplotlib demo") 
plt.xlabel("x axis caption") 
plt.ylabel("y axis caption") 
plt.plot(x,y) 
plt.show()
```
]

.pull-right[
![](https://www.runoob.com/wp-content/uploads/2018/10/matplotlib_demo.jpg)
]

---
### 设置中文

.pull-left[

```python
import numpy as np
import matplotlib.pyplot as plt 
import matplotlib.font_manager as fm

# 字体的路径
fname = "C:\Windows\Fonts\SIMLI.TTF"
zhfont1 = fm.FontProperties(fname=fname)
plt.rcParams['figure.dpi'] = 300

x = np.arange(1,11) 
y =  2  * x +  5 
plt.title("YYschools - 测试", fontproperties=zhfont1) 
 
# fontproperties 设置中文显示，fontsize 设置字体大小
plt.xlabel("x 轴", fontproperties=zhfont1)
plt.ylabel("y 轴", fontproperties=zhfont1)
plt.plot(x,y) 
plt.savefig('中文标签.svg')
```
]

.pull-right[
![](./images/中文标签.svg)
]

.footnote[来源：菜鸟教程]

---
### Matplotlib Markers

**Markers**: 
You can use the keyword argument marker to emphasize each point with a specified marker:

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

ypoints = np.array([3, 8, 1, 10])

plt.plot(ypoints, marker = 'o')
plt.show()
```
]

.pull-right[
![](./chapter10_Plot_Documentation_files/figure-html/unnamed-chunk-6-2.png)
]

---
### Marker Reference

|Marker|Description|Marker|Description|
|------|-----------|------|-----------|
|'o'| Circle|'*'| Star|	
|'.'|	Point|','|	Pixel|	
|'x'|	X|'X'|	X (filled)|	
|'+'|	Plus|'P'|	Plus (filled)|	
|'s'|	Square|'D'|	Diamond|	
|'d'|	Diamond (thin)|'p'|	Pentagon|	
|'H'|	Hexagon|'h'|	Hexagon|	
|'v'|	Triangle Down|'^'|	Triangle Up|	
|'<'|	Triangle Left|'>'|	Triangle Right|	
|'1'|	Tri Down|'2'|	Tri Up|	
|'3'|	Tri Left|'4'|	Tri Right|	
|'&VerticalLine;'|	Vline|'_'|	Hline|

---
### Matplotlib Scatter

With **Pyplot**, you can use the **scatter()** function to draw a scatter plot.

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

plt.scatter(x, y)
plt.show()
```
]

.pull-right[
![](./chapter10_Plot_Documentation_files/figure-html/unnamed-chunk-7-3.png)
]

---
### Compare Plots

Draw **two plots** on the same figure:

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

#day one, the age and speed of 13 cars:
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
plt.scatter(x, y)

#day two, the age and speed of 15 cars:
x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y)

plt.show()
```
]

.pull-right[
![](./chapter10_Plot_Documentation_files/figure-html/unnamed-chunk-8-4.png)
]

---
### Matplotlib Bars

With **Pyplot**, you can use the **bar()** function to draw bar graphs:

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

x = np.array(["南昌", "九江", "上饶", "赣州"])
y = np.array([3, 8, 1, 10])
plt.xticks(fontproperties=zhfont1)
plt.bar(x,y)
plt.show()
```
]

.pull-right[
![](./images/直方图.svg)
]

---
### Matplotlib Histograms

A **histogram** is a graph showing **frequency distributions**.

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

x = np.random.normal(170, 10, 250)

plt.hist(x)
plt.show() 
```
]

.pull-right[
![](./chapter10_Plot_Documentation_files/figure-html/unnamed-chunk-10-6.png)
]

---
### Matplotlib Pie Charts

With **Pyplot**, you can use the **pie()** function to draw pie charts:

.pull-left[

```python
import matplotlib.pyplot as plt
import numpy as np

y = np.array([35, 25, 25, 15])
mylabels = ["Apples", "Bananas", "Cherries", "Dates"]

plt.pie(y, labels = mylabels)
plt.show()
```
]

.pull-right[
![](./chapter10_Plot_Documentation_files/figure-html/unnamed-chunk-11-7.png)
]

---

### 参考资料

- https://www.runoob.com/numpy/numpy-matplotlib.html

- https://matplotlib.org/tutorials/introductory/pyplot.html

---
class: middle, center

## 撰写报告

---

### 编写报告--Jupyter Notebook

打开 Jupyter Notebook

![](./images/Anaconda_jupyter_notebook.png)

**切换文件夹|目录**：
`Windows`下利用`cmd`指令`cd`（**c**hange **d**irectory: cd）进入指定文件夹（也叫目录）：

- 若要进入非C盘，比如D盘，则用`d:`，E盘，则用`e:`；
- 如果，文件夹名称有空格，则用英文双引号引起来，比如"`Program Files`"；
- 若要进入上一级目录，则用`cd ..`；
- 若要采用绝对路径文件夹名，则用`cd c:\folder1\folder11\folder111`类似命令。

下一页续

---

### 从指定文件夹打开Jupyter Notebook

如果想从指定的文件夹，比如`Documents`打开Jupyter Notebook，则先用`cd`命令，进入该文件夹，然后再打开Jupyter Notebook:

![](./images/cd_2.png)

若要从非C盘某文件夹，比如`E:\Program Files`，开启Jupyter Notebook，则先用`e:`进入E盘，然后操作类似上面步骤：

![](./images/cd_4.png)

---

### 界面

![](./images/jupyter_notebook_interface.png)

---

### 示例

.center[
<img width=650 src='./images/jupyter_notebook_sample.png'>
]

---
### demo

- 插入代码

- 插入Markdown元素

下载

- <a download href='documentation.ipynb'>documentation.ipynb</a>

- <a download href='pyplot.ipynb'>pyplot.ipynb</a>

---
### Code

![](./images/jupyter_notebook_sample_code.png)

---
### Markdown

![](./images/jupyter_notebook_sample_markdown.png)

---

### Jupyter 不能正确连接

![](images/email_jupyter_connecting.png)

详细介绍与解决方案：
[jupyter_connecting_problem](../../application/jupyter_connecting_problem/jupyter_connecting_problem.html)

---
class: middle, center

### 制作幻灯片

### RStudio

---
### R + RStudio + xaringan package

1. [Download R](https://mirrors.tuna.tsinghua.edu.cn/CRAN/)

2. [Download RStudio Desktop](https://www.rstudio.com/products/rstudio/download/)

3. 安装 `xaringan` package: RStudio菜单Tools -> Install Packages
  
  .center[
  <img width=400 src='./images/install_packages.png'>
  ]

4. 也安装 `xaringanthemer` package

---
### Rmarkdown

.center[
<img width=500 src='./images/newRmarkdown.png'>
]

---
### xaringan 幻灯片

.center[
<img width=500 src='./images/newPresentation.png'>
]

---
### 撰写+预览

在控制台（Console）输入 '`xaringan::inf_mr()`'指令，可实现撰写的同时预览。

.center[
<img width=800 src='./images/preview.png'>
]