时间：2021-12-20 数据分析 查看: 3175
大家好，我是辣条。
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 tensorflow1. 数据处理 - pandas
2. 科学运算 - numpy
3. 数据可视化 - seaborn matplotlib
1. anaconda
2. notebook
3. python3.7版本
#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)
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