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python数据分析近年比特币价格涨幅趋势分布

看: 1689次时间：2021-12-20 分类 : 数据分析

曾经有一个真挚的机会，摆在我面前，但是我没有珍惜，等到失去的时候才后悔莫及，尘世间最痛苦的事莫过于此，如果老天可以再给我一个再来一次机会的话，我会买下那个比特币，哪怕付出所有零花钱，如果非要在这个机会加上一个期限的话，我希望是十年前。

看着这份台词是不是很眼熟，我稍稍改了一下，曾经差一点点点就购买比特币了，肠子都悔青了现在，今天对比特币做一个简单的数据分析。

# 安装对应的第三方库
!pip install pandas  
!pip install numpy
!pip install seaborn
!pip install matplotlib
!pip install sklearn
!pip install tensorflow

使用技术点

1. 数据处理 - pandas

2. 科学运算 - numpy

3. 数据可视化 - seaborn matplotlib

使用工具

1. anaconda

2. notebook

3. python3.7版本

导入第三方库

#a|T + enter  notebook运行方式
import pandas as pd # 数据处理
import numpy as np # 科学运算
import seaborn as sns # 数据可视化
import matplotlib.pyplot as plt # 数据可视化
import warnings
import warnings
warnings.filterwarnings('ignore')

如遇到导包报错可以看看是不是自己的第三方库的版本问题

# 设置图表与 线格式
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['lines.linewidth'] = 2
plt.style.use('ggplot')
# 读取数据集
df = pd.read_csv('./DOGE-USD.csv')
df.head() # 查看前5行

	Date	Open	High	Low	Close	Adj Close	Volume
0	2014-09-17	0.000293	0.000299	0.000260	0.000268	0.000268	1463600.0
1	2014-09-18	0.000268	0.000325	0.000267	0.000298	0.000298	2215910.0
2	2014-09-19	0.000298	0.000307	0.000275	0.000277	0.000277	883563.0
3	2014-09-20	0.000276	0.000310	0.000267	0.000292	0.000292	993004.0
4	2014-09-21	0.000293	0.000299	0.000284	0.000288	0.000288	539140.0

df.isnull().sum() # 统计缺失值的总和(sum())
Date         0
Open         5
High         5
Low          5
Close        5
Adj Close    5
Volume       5
dtype: int64
df.duplicated().sum() # 查看重复值
0
# 数据类型 分布基本情况
df.info()

RangeIndex: 2591 entries, 0 to 2590
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       2591 non-null   object 
 1   Open       2586 non-null   float64
 2   High       2586 non-null   float64
 3   Low        2586 non-null   float64
 4   Close      2586 non-null   float64
 5   Adj Close  2586 non-null   float64
 6   Volume     2586 non-null   float64
dtypes: float64(6), object(1)
memory usage: 141.8+ KB
# 转换 Date的类型
df['Date'] = pd.to_datetime(df.Date, dayfirst=True)
# 索引重置 让Date时间格式成为 索引  inplace新建对象
df.set_index('Date', inplace=True)
df

<table>
    <thead>
        <tr>
            <th></th>
            <th>Open</th>
            <th>High</th>
            <th>Low</th>
            <th>Close</th>
            <th>Adj Close</th>
            <th>Volume</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Date</td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
        </tr>
        <tr>
            <td>2014-09-17</td>
            <td>0.000293</td>
            <td>0.000299</td>
            <td>0.000260</td>
            <td>0.000268</td>
            <td>0.000268</td>
            <td>1.463600e+06</td>
        </tr>
        <tr>
            <td>2014-09-18</td>
            <td>0.000268</td>
            <td>0.000325</td>
            <td>0.000267</td>
            <td>0.000298</td>
            <td>0.000298</td>
            <td>2.215910e+06</td>
        </tr>
        <tr>
            <td>2014-09-19</td>
            <td>0.000298</td>
            <td>0.000307</td>
            <td>0.000275</td>
            <td>0.000277</td>
            <td>0.000277</td>
            <td>8.835630e+05</td>
        </tr>
        <tr>
            <td>2014-09-20</td>
            <td>0.000276</td>
            <td>0.000310</td>
            <td>0.000267</td>
            <td>0.000292</td>
            <td>0.000292</td>
            <td>9.930040e+05</td>
        </tr>
        <tr>
            <td>2014-09-21</td>
            <td>0.000293</td>
            <td>0.000299</td>
            <td>0.000284</td>
            <td>0.000288</td>
            <td>0.000288</td>
            <td>5.391400e+05</td>
        </tr>
        <tr>
            <td>...</td>
            <td>...</td>
            <td>...</td>
            <td>...</td>
            <td>...</td>
            <td>...</td>
            <td>...</td>
        </tr>
        <tr>
            <td>2021-10-16</td>
            <td>0.233881</td>
            <td>0.244447</td>
            <td>0.233683</td>
            <td>0.237292</td>
            <td>0.237292</td>
            <td>1.541851e+09</td>
        </tr>
        <tr>
            <td>2021-10-17</td>
            <td>0.237193</td>
            <td>0.241973</td>
            <td>0.226380</td>
            <td>0.237898</td>
            <td>0.237898</td>
            <td>1.397143e+09</td>
        </tr>
        <tr>
            <td>2021-10-18</td>
            <td>0.237806</td>
            <td>0.271394</td>
            <td>0.237488</td>
            <td>0.247281</td>
            <td>0.247281</td>
            <td>5.003366e+09</td>
        </tr>
        <tr>
            <td>2021-10-19</td>
            <td>NaN</td>
            <td>NaN</td>
            <td>NaN</td>
            <td>NaN</td>
            <td>NaN</td>
            <td>NaN</td>
        </tr>
        <tr>
            <td>2021-10-20</td>
            <td>0.245199</td>
            <td>0.246838</td>
            <td>0.242384</td>
            <td>0.246078</td>
            <td>0.246078</td>
            <td>1.187871e+09</td>
        </tr>
    </tbody>
</table>
<p>2591 rows × 6 columns</p>
<div>
<pre>
df = df.asfreq('d') # 按照天数采集数据
df = df.fillna(method='bfill') # 缺失值填充 下一条数据填充
df




    
        
            
            Open
            High
            Low
            Close
            Adj Close
            Volume
        
    
    
        
            Date
            
            
            
            
            
            
        
        
            2014-09-17
            0.000293
            0.000299
            0.000260
            0.000268
            0.000268
            1.463600e+06
        
        
            2014-09-18
            0.000268
            0.000325
            0.000267
            0.000298
            0.000298
            2.215910e+06
        
        
            2014-09-19
            0.000298
            0.000307
            0.000275
            0.000277
            0.000277
            8.835630e+05
        
        
            2014-09-20
            0.000276
            0.000310
            0.000267
            0.000292
            0.000292
            9.930040e+05
        
        
            2014-09-21
            0.000293
            0.000299
            0.000284
            0.000288
            0.000288
            5.391400e+05
        
        
            ...
            ...
            ...
            ...
            ...
            ...
            ...
        
        
            2021-10-16
            0.233881
            0.244447
            0.233683
            0.237292
            0.237292
            1.541851e+09
        
        
            2021-10-17
            0.237193
            0.241973
            0.226380
            0.237898
            0.237898
            1.397143e+09
        
        
            2021-10-18
            0.237806
            0.271394
            0.237488
            0.247281
            0.247281
            5.003366e+09
        
        
            2021-10-19
            0.245199
            0.246838
            0.242384
            0.246078
            0.246078
            1.187871e+09
        
        
            2021-10-20
            0.245199
            0.246838
            0.242384
            0.246078
            0.246078
            1.187871e+09
        
    

2591 rows × 6 columns
In [14]:

# 开盘价的分布情况
df['Open'].plot(figsize=(12, 8))

<p style="text-align: center;"><img alt="" height="457" src="https://img.94e.cn/media/uploads/full/20211220/2021112310301519.png" width="663"></p>
<p>结论:从上图可以看出 BTB是在2021年份开始爆发式的增长 在2015 到 2021 一直都是没有较大波动</p>
<div>
<pre>
# 成交情况
df['Volume'].plot(figsize=(12, 8))





# 投资价值
df['Total Pos'] = df.sum(axis=1)
df['Total Pos'].plot(figsize=(10, 8))

<p style="text-align: center;"><img alt="" height="497" src="https://img.94e.cn/media/uploads/full/20211220/2021112310301521.png" width="590"></p>
<p>结论:开盘价高 投资价值搞 比较合适做卖出操作 实现一夜暴富(开玩笑的)</p>
<div>
<pre>
# 当前元素与先前元素的相差百分比
df['Daily Reture'] = df['Total Pos'].pct_change(1)
# 日收益率的平均
df['Daily Reture'].mean()
df['Daily Reture'].plot(kind='kde')





SR = df['Daily Reture'].mean() / df['Daily Reture'].std()
all_plot = df/df.iloc[0]
all_plot.plot(figsize=(24, 16))

<p style="text-align: center;"><img alt="" height="918" src="https://img.94e.cn/media/uploads/full/20211220/2021112310301523.png" width="1200"></p>
<div>
<pre>
df.hist(bins=100, figsize=(12, 6))





# 按照年份进行采样
df.resample(rule='A').mean()

<table>
    <thead>
        <tr>
            <th></th>
            <th>Open</th>
            <th>High</th>
            <th>Low</th>
            <th>Close</th>
            <th>Adj Close</th>
            <th>Volume</th>
            <th>Total Pos</th>
            <th>Daily Reture</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>Date</td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
            <td></td>
        </tr>
        <tr>
            <td>2014-12-31</td>
            <td>0.000249</td>
            <td>0.000259</td>
            <td>0.000240</td>
            <td>0.000248</td>
            <td>0.000248</td>
            <td>8.059213e+05</td>
            <td>8.059213e+05</td>
            <td>1.028630</td>
        </tr>
        <tr>
            <td>2015-12-31</td>
            <td>0.000143</td>
            <td>0.000147</td>
            <td>0.000139</td>
            <td>0.000143</td>
            <td>0.000143</td>
            <td>1.685476e+05</td>
            <td>1.685476e+05</td>
            <td>0.139461</td>
        </tr>
        <tr>
            <td>2016-12-31</td>
            <td>0.000235</td>
            <td>0.000242</td>
            <td>0.000229</td>
            <td>0.000235</td>
            <td>0.000235</td>
            <td>2.564834e+05</td>
            <td>2.564834e+05</td>
            <td>0.259038</td>
        </tr>
        <tr>
            <td>2017-12-31</td>
            <td>0.001576</td>
            <td>0.001708</td>
            <td>0.001468</td>
            <td>0.001601</td>
            <td>0.001601</td>
            <td>1.118996e+07</td>
            <td>1.118996e+07</td>
            <td>0.225833</td>
        </tr>
        <tr>
            <td>2018-12-31</td>
            <td>0.004368</td>
            <td>0.004577</td>
            <td>0.004125</td>
            <td>0.004350</td>
            <td>0.004350</td>
            <td>2.172325e+07</td>
            <td>2.172325e+07</td>
            <td>0.109586</td>
        </tr>
        <tr>
            <td>2019-12-31</td>
            <td>0.002564</td>
            <td>0.002631</td>
            <td>0.002499</td>
            <td>0.002563</td>
            <td>0.002563</td>
            <td>4.463969e+07</td>
            <td>4.463969e+07</td>
            <td>0.027981</td>
        </tr>
        <tr>
            <td>2020-12-31</td>
            <td>0.002736</td>
            <td>0.002822</td>
            <td>0.002660</td>
            <td>0.002744</td>
            <td>0.002744</td>
            <td>1.290465e+08</td>
            <td>1.290465e+08</td>
            <td>0.052314</td>
        </tr>
        <tr>
            <td>2021-12-31</td>
            <td>0.200410</td>
            <td>0.215775</td>
            <td>0.185770</td>
            <td>0.201272</td>
            <td>0.201272</td>
            <td>4.620961e+09</td>
            <td>4.620961e+09</td>
            <td>0.260782</td>
        </tr>
    </tbody>
</table>
<div>
<pre>
# 年平均收盘价
df['Open'].resample('A').mean().plot.bar(title='Yearly Mean Closing Price', color=['#b41f7d'])





# 月度
df['Open'].resample('M').mean().plot.bar(figsize=(18, 12), color='red')

<p style="text-align: center;"><img alt="" height="796" src="https://img.94e.cn/media/uploads/full/20211220/2021112310301526.png" width="1041"></p>
<div>
<pre>
# 分别获取对应时间窗口  6 12 2 均值
df['6-month-SMA'] = df['Open'].rolling(window=6).mean()
df['12-month-SMA'] = df['Open'].rolling(window=12).mean()
df['2-month-SMA'] = df['Open'].rolling(window=2).mean()
df.head(10)




    
        
            
            Open
            High
            Low
            Close
            Adj Close
            Volume
            Total Pos
            Daily Reture
            6-month-SMA
            12-month-SMA
            2-month-SMA
        
    
    
        
            Date
            
            
            
            
            
            
            
            
            
            
            
        
        
            2014-09-17
            0.000293
            0.000299
            0.000260
            0.000268
            0.000268
            1463600.0
            1.463600e+06
            NaN
            NaN
            NaN
            NaN
        
        
            2014-09-18
            0.000268
            0.000325
            0.000267
            0.000298
            0.000298
            2215910.0
            2.215910e+06
            0.514013
            NaN
            NaN
            0.000281
        
        
            2014-09-19
            0.000298
            0.000307
            0.000275
            0.000277
            0.000277
            883563.0
            8.835630e+05
            -0.601264
            NaN
            NaN
            0.000283
        
        
            2014-09-20
            0.000276
            0.000310
            0.000267
            0.000292
            0.000292
            993004.0
            9.930040e+05
            0.123863
            NaN
            NaN
            0.000287
        
        
            2014-09-21
            0.000293
            0.000299
            0.000284
            0.000288
            0.000288
            539140.0
            5.391400e+05
            -0.457062
            NaN
            NaN
            0.000285
        
        
            2014-09-22
            0.000288
            0.000301
            0.000285
            0.000298
            0.000298
            620222.0
            6.202220e+05
            0.150391
            0.000286
            NaN
            0.000291
        
        
            2014-09-23
            0.000298
            0.000318
            0.000295
            0.000313
            0.000313
            739197.0
            7.391970e+05
            0.191826
            0.000287
            NaN
            0.000293
        
        
            2014-09-24
            0.000314
            0.000353
            0.000310
            0.000348
            0.000348
            1277840.0
            1.277840e+06
            0.728687
            0.000295
            NaN
            0.000306
        
        
            2014-09-25
            0.000347
            0.000383
            0.000332
            0.000375
            0.000375
            2393610.0
            2.393610e+06
            0.873169
            0.000303
            NaN
            0.000331
        
        
            2014-09-26
            0.000374
            0.000467
            0.000373
            0.000451
            0.000451
            4722610.0
            4.722610e+06
            0.973007
            0.000319
            NaN
            0.000361
        
    

进行可视化 查看对应分布情况


df[['Open', '6-month-SMA', '12-month-SMA', '2-month-SMA']].plot(figsize=(24, 10))






df[["Open","6-month-SMA"]].plot(figsize=(18,10))






df[['Open','6-month-SMA']].iloc[:100].plot(figsize=(12,6)).autoscale(axis='x',tight=True)






df['EWMA12'] = df['Open'].ewm(span=14,adjust=True).mean()
df[['Open','EWMA12']].plot(figsize=(24,12))






df[['Open','EWMA12']].iloc[:50].plot(figsize=(12,6)).autoscale(axis='x',tight=True)




以上就是python数据分析近年比特币价格涨幅趋势分布的详细内容，更多关于python数据分析比特币价格涨幅的资料请关注python博客其它相关文章！

	Open	High	Low	Close	Adj Close	Volume
Date
2014-09-17	0.000293	0.000299	0.000260	0.000268	0.000268	1.463600e+06
2014-09-18	0.000268	0.000325	0.000267	0.000298	0.000298	2.215910e+06
2014-09-19	0.000298	0.000307	0.000275	0.000277	0.000277	8.835630e+05
2014-09-20	0.000276	0.000310	0.000267	0.000292	0.000292	9.930040e+05
2014-09-21	0.000293	0.000299	0.000284	0.000288	0.000288	5.391400e+05
...	...	...	...	...	...	...
2021-10-16	0.233881	0.244447	0.233683	0.237292	0.237292	1.541851e+09
2021-10-17	0.237193	0.241973	0.226380	0.237898	0.237898	1.397143e+09
2021-10-18	0.237806	0.271394	0.237488	0.247281	0.247281	5.003366e+09
2021-10-19	0.245199	0.246838	0.242384	0.246078	0.246078	1.187871e+09
2021-10-20	0.245199	0.246838	0.242384	0.246078	0.246078	1.187871e+09

	Open	High	Low	Close	Adj Close	Volume	Total Pos	Daily Reture	6-month-SMA	12-month-SMA	2-month-SMA
Date
2014-09-17	0.000293	0.000299	0.000260	0.000268	0.000268	1463600.0	1.463600e+06	NaN	NaN	NaN	NaN
2014-09-18	0.000268	0.000325	0.000267	0.000298	0.000298	2215910.0	2.215910e+06	0.514013	NaN	NaN	0.000281
2014-09-19	0.000298	0.000307	0.000275	0.000277	0.000277	883563.0	8.835630e+05	-0.601264	NaN	NaN	0.000283
2014-09-20	0.000276	0.000310	0.000267	0.000292	0.000292	993004.0	9.930040e+05	0.123863	NaN	NaN	0.000287
2014-09-21	0.000293	0.000299	0.000284	0.000288	0.000288	539140.0	5.391400e+05	-0.457062	NaN	NaN	0.000285
2014-09-22	0.000288	0.000301	0.000285	0.000298	0.000298	620222.0	6.202220e+05	0.150391	0.000286	NaN	0.000291
2014-09-23	0.000298	0.000318	0.000295	0.000313	0.000313	739197.0	7.391970e+05	0.191826	0.000287	NaN	0.000293
2014-09-24	0.000314	0.000353	0.000310	0.000348	0.000348	1277840.0	1.277840e+06	0.728687	0.000295	NaN	0.000306
2014-09-25	0.000347	0.000383	0.000332	0.000375	0.000375	2393610.0	2.393610e+06	0.873169	0.000303	NaN	0.000331
2014-09-26	0.000374	0.000467	0.000373	0.000451	0.000451	4722610.0	4.722610e+06	0.973007	0.000319	NaN	0.000361