This article will use PYTHON drawing In bitcoin （BTC） For example Drawing analysis （ Xiaobaixiang ）
Pycharm Platform compilation
import requests
from lxml import etree
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
import datetime
Data from abroad coinmarketcap（ You need to go online scientifically ） To extract 2021.2.1-2022.2.3 The data of
coinmarketcap
https://coinmarketcap.com/currencies/bitcoin/historical-data/?start=20210101&end=20220202)
I put the data directly into the network disk , How can you climb behind me and mend it （ But it's no use being an overachiever. You don't have to know anything about it ）
link ：https://pan.baidu.com/s/1C3np-mnd-AAX_4u3XlnZ_A
Extraction code ：7bxn
Crawling is HTML Format , The following code is transformed
The data includes 7 Elements
‘date’： date
‘open’： Opening price
‘high’： The highest price of the day
‘low’： The lowest price of the day
‘close’： Closing price
‘volume’： Total trading volume of the day
‘Market Cap’： Market value of the day 
with open("data.txt", "r") as f: rd = f.read()
selector = etree.HTML(rd)
url_infos = selector.xpath('//tr')
data = []
for url_info in url_infos:
l = []
for i in range(7):
d = url_info.xpath('td[%d+1]/text()' % i)
if i == 0:
l += d
else:
if d[0] == '-':
d[0] = np.nan
l += d
else:
d[0] = d[0].replace(',', '')
d[0] = d[0].strip('$')
d[0] = float(d[0])
l += d
data.append(l)
arr = np.array(data)
df = pd.DataFrame(arr)
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'Market Cap']
df = df.astype({
'open': 'float64', 'high': 'float64', 'low': 'float64', 'close': 'float64', 'volume': 'float64', 'Market Cap': 'float64'})
df = df.reindex(index=df.index[::-1])
df.head() # In reverse order
df.reset_index(drop=True, inplace=True) # After reverse order reset index
df.reset_index(inplace=True) # reset index, The original index Remittance DataFrame in
df['date']=pd.to_datetime(df['date'])
df = df.astype({
'date': 'string'})
df.index = pd.to_datetime(df['date']) # Set up index Value
print(df)
print(df.dtypes)
df['date'] = df['date'].apply(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))# Convert date format
print(df.dtypes)
print(df)
The output is as follows 
First on the renderings , Attached is the code that can be run directly （ The data is from the network disk data.txt）
Code
from lxml import etree
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
import datetime
with open("data.txt", "r") as f: rd = f.read()
selector = etree.HTML(rd)
url_infos = selector.xpath('//tr')
# from data.txt Extract the required data from
data = []
for url_info in url_infos:
l = []
# Obtain single line data and perform preliminary processing
for i in range(7):
d = url_info.xpath('td[%d+1]/text()' % i)
if i == 0:
l += d
else:
if d[0] == '-':
d[0] = np.nan
l += d
else:
d[0] = d[0].replace(',', '')
d[0] = d[0].strip('$')
d[0] = float(d[0])
l += d
data.append(l)
arr = np.array(data)
df = pd.DataFrame(arr) # Convert data to DataFrame data type
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'Market Cap'] # Set column title
# df['date']=df['date'].map(pd.to_datetime)# Convert date format
df = df.astype({
'open': 'float64', 'high': 'float64', 'low': 'float64', 'close': 'float64', 'volume': 'float64', 'Market Cap': 'float64'})
df = df.reindex(index=df.index[::-1])
df.head() # In reverse order
df.reset_index(drop=True, inplace=True) # After reverse order reset index
plt.rcParams['axes.unicode_minus'] = False# Solve the confusion of negative sign of coordinate axis scale
plt.rcParams['font.sans-serif'] = ['Simhei']# Solve the problem of Chinese garbled code
df.reset_index(inplace=True) # reset index, The original index Remittance DataFrame in
df['date']=pd.to_datetime(df['date'])
df = df.astype({
'date': 'string'})
df.index = pd.to_datetime(df['date']) # Set up index Value
print(df)
print(df.dtypes)
df['date'] = df['date'].apply(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))# Convert date format
print(df.dtypes)
print(df)
fig, ax2 = plt.subplots(figsize=(1200 / 72, 480 / 72))
ax2.plot(df['date'], df['close'])
ax2.grid(True)
ax2.set_ylim(0, 70000)
fig.subplots_adjust(bottom=0.2) ## Adjust the bottom distance
ax2.xaxis_date() ## Set up X The axis scale is date time
plt.yticks() ## Set up Y Axis scale mark
plt.xlabel(u" Time ") ## Set up X Axis title
ax2.set_ylabel(' Closing price ')
plt.grid(True, 'major', 'both', ls='--', lw=.5, c='k', alpha=.3) ## Set gridlines
plt.show()
Here it is directly superimposed with the price chart to display 
After zooming in, it looks like this ：
from lxml import etree
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
import datetime
with open("data.txt", "r") as f: rd = f.read()
selector = etree.HTML(rd)
url_infos = selector.xpath('//tr')
# from data.txt Extract the required data from
data = []
for url_info in url_infos:
l = []
# Obtain single line data and perform preliminary processing
for i in range(7):
d = url_info.xpath('td[%d+1]/text()' % i)
if i == 0:
l += d
else:
if d[0] == '-':
d[0] = np.nan
l += d
else:
d[0] = d[0].replace(',', '')
d[0] = d[0].strip('$')
d[0] = float(d[0])
l += d
data.append(l)
arr = np.array(data)
df = pd.DataFrame(arr) # Convert data to DataFrame data type
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'Market Cap'] # Set column title
# df['date']=df['date'].map(pd.to_datetime)# Convert date format
df = df.astype({
'open': 'float64', 'high': 'float64', 'low': 'float64', 'close': 'float64', 'volume': 'float64', 'Market Cap': 'float64'})
df = df.reindex(index=df.index[::-1])
df.head() # In reverse order
df.reset_index(drop=True, inplace=True) # After reverse order reset index
plt.rcParams['axes.unicode_minus'] = False# Solve the confusion of negative sign of coordinate axis scale
plt.rcParams['font.sans-serif'] = ['Simhei']# Solve the problem of Chinese garbled code
df.reset_index(inplace=True) # reset index, The original index Remittance DataFrame in
df['date']=pd.to_datetime(df['date'])
df = df.astype({
'date': 'string'})
df.index = pd.to_datetime(df['date']) # Set up index Value
print(df)
print(df.dtypes)
df['date'] = df['date'].apply(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))# Convert date format
print(df.dtypes)
print(df)
fig, ax1 = plt.subplots(figsize=(1200 / 72, 480 / 72))
da = df[['date', 'open', 'high', 'low', 'close']]
f = da[['date', 'open', 'high', 'low', 'close']].values
ax3 = ax1.twinx()
ax2 = ax1.twinx()
candlestick_ohlc(ax1, f, colordown='g', colorup='r', width=0.3, alpha=0.7)
ax3.bar(df['date'], df['volume'], width=0.6)
ax2.plot(df['date'], df['close'])
ax2.grid(True)
ax3.grid(True)
ax3.set_ylim(0, 500000000000)
ax1.set_ylim(0, 70000)
ax2.set_ylim(0, 70000)
ax1.set_ylabel(' The price of money $')
fig.subplots_adjust(bottom=0.2) ## Adjust the bottom distance
ax1.xaxis_date() ## Set up X The axis scale is date time
ax2.xaxis_date() ## Set up X The axis scale is date time
ax3.xaxis_date() ## Set up X The axis scale is date time
plt.yticks() ## Set up Y Axis scale mark
plt.xlabel(u" Time ") ## Set up X Axis title
ax2.set_ylabel(' Closing price / volume ')
plt.grid(True, 'major', 'both', ls='--', lw=.5, c='k', alpha=.3) ## Set gridlines
plt.show()
Here is a basic indicator ： Simple moving average SMA（ It is used to check the average price ）
For details, please refer to this article ：
https://zhuanlan.zhihu.com/p/422205612
For example ：
Altogether 10 The closing price for consecutive days ：1 2 3 4 5 6 7 8 9 10
I need a word 3SMA Line （ Namely 3 A simple moving average of three cycles ）
Then I will get a group after 7 Day closing price 3SMA
（1+2+3）/3,（2+3+4）/3,…,（8+9+10）/3
Now I draw a 5SMA Graph （ The red line ）
Calculation 5SMA Code for
step=5
dflen=len(df)
sma= {
}
for i in range(step):
sma[i]=0
for i in range(dflen-step):
i+=step
sma[i]=0
for j in range(step):
j+=1
sma[i] += df['close'][i-j]
if j==step: sma[i]=sma[i]/step
sma = pd.DataFrame.from_dict(sma,orient='index',columns=['SMA'])
print(sma)
Output the complete code of the picture
from lxml import etree
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
import datetime
with open("data.txt", "r") as f: rd = f.read()
selector = etree.HTML(rd)
url_infos = selector.xpath('//tr')
# from data.txt Extract the required data from
data = []
for url_info in url_infos:
l = []
# Obtain single line data and perform preliminary processing
for i in range(7):
d = url_info.xpath('td[%d+1]/text()' % i)
if i == 0:
l += d
else:
if d[0] == '-':
d[0] = np.nan
l += d
else:
d[0] = d[0].replace(',', '')
d[0] = d[0].strip('$')
d[0] = float(d[0])
l += d
data.append(l)
arr = np.array(data)
df = pd.DataFrame(arr) # Convert data to DataFrame data type
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'Market Cap'] # Set column title
# df['date']=df['date'].map(pd.to_datetime)# Convert date format
df = df.astype({
'open': 'float64', 'high': 'float64', 'low': 'float64', 'close': 'float64', 'volume': 'float64', 'Market Cap': 'float64'})
df = df.reindex(index=df.index[::-1])
df.head() # In reverse order
df.reset_index(drop=True, inplace=True) # After reverse order reset index
plt.rcParams['axes.unicode_minus'] = False# Solve the confusion of negative sign of coordinate axis scale
plt.rcParams['font.sans-serif'] = ['Simhei']# Solve the problem of Chinese garbled code
df.reset_index(inplace=True) # reset index, The original index Remittance DataFrame in
df['date']=pd.to_datetime(df['date'])
df = df.astype({
'date': 'string'})
df.index = pd.to_datetime(df['date']) # Set up index Value
print(df)
print(df.dtypes)
df['date'] = df['date'].apply(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))# Convert date format
print(df.dtypes)
print(df)
step=5
dflen=len(df)
sma= {
}
for i in range(step):
sma[i]=0
for i in range(dflen-step):
i+=step
sma[i]=0
for j in range(step):
j+=1
sma[i] += df['close'][i-j]
if j==step: sma[i]=sma[i]/step
sma = pd.DataFrame.from_dict(sma,orient='index',columns=['SMA'])
print(sma)
fig, ax1 = plt.subplots(figsize=(1200 / 72, 480 / 72))
da = df[['date', 'open', 'high', 'low', 'close']]
f = da[['date', 'open', 'high', 'low', 'close']].values
ax3 = ax1.twinx()
ax2 = ax1.twinx()
axsma=ax1.twinx()
candlestick_ohlc(ax1, f, colordown='g', colorup='r', width=0.3, alpha=0.7)
ax3.bar(df['date'], df['volume'], width=0.6)
ax2.plot(df['date'], df['close'])
axsma.plot(df['date'],sma['SMA'],color="r")
ax2.grid(True)
ax3.grid(True)
axsma.grid(True)
ax3.set_ylim(0, 500000000000)
ax1.set_ylim(0, 70000)
ax2.set_ylim(0, 70000)
axsma.set_ylim(0, 70000)
ax1.set_ylabel(' The price of money $')
fig.subplots_adjust(bottom=0.2) ## Adjust the bottom distance
ax1.xaxis_date() ## Set up X The axis scale is date time
ax2.xaxis_date() ## Set up X The axis scale is date time
ax3.xaxis_date() ## Set up X The axis scale is date time
axsma.xaxis_date() ## Set up X The axis scale is date time
plt.yticks() ## Set up Y Axis scale mark
plt.xlabel(u" Time ") ## Set up X Axis title
ax2.set_ylabel(' Closing price / volume /SMA')
plt.grid(True, 'major', 'both', ls='--', lw=.5, c='k', alpha=.3) ## Set gridlines
plt.show()
Next, put 5SMA（ Red ） 、30SMA（ yellow ）、 60SMA（ green ） Reflect on a picture
2 The black arrow at the right shows A golden fork phenomenon often said in the stock market （ But I am a SMA Of ） The short term 5SMA With the long line 60SMA Cross the bottom up , Usually this is a buying signal （ At that time, it was a double offer , The actual combat can only be used as a reference point for judging the trend ）
Finally, analyze this 2 month 3 In the future BTC Possible trends
You can see... In the picture The closing price line is in the middle 、 Below the long-term average , There will be a general trend of moving towards the average price , The buying opportunity should be 60SMA On 5SMA Progressive , Maybe a week or two There will be a cross trend
You should understand the role of indicators here The prediction is actually just to provide some data for you to judge , Indicators vary , Various stocks The monetary software needs the lines of various indicators Like MA EMA BOLL SAR Of Welcome to discuss and analyze , This article is also a little white Introduction , Let you understand The way data is transformed into pictures K Understanding of line diagram
Q149021708
from lxml import etree
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.pylab import date2num
import datetime
with open("data.txt", "r") as f: rd = f.read()
selector = etree.HTML(rd)
url_infos = selector.xpath('//tr')
# from data.txt Extract the required data from
data = []
for url_info in url_infos:
l = []
# Obtain single line data and perform preliminary processing
for i in range(7):
d = url_info.xpath('td[%d+1]/text()' % i)
if i == 0:
l += d
else:
if d[0] == '-':
d[0] = np.nan
l += d
else:
d[0] = d[0].replace(',', '')
d[0] = d[0].strip('$')
d[0] = float(d[0])
l += d
data.append(l)
arr = np.array(data)
df = pd.DataFrame(arr) # Convert data to DataFrame data type
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'Market Cap'] # Set column title
# df['date']=df['date'].map(pd.to_datetime)# Convert date format
df = df.astype({
'open': 'float64', 'high': 'float64', 'low': 'float64', 'close': 'float64', 'volume': 'float64', 'Market Cap': 'float64'})
df = df.reindex(index=df.index[::-1])
df.head() # In reverse order
df.reset_index(drop=True, inplace=True) # After reverse order reset index
plt.rcParams['axes.unicode_minus'] = False# Solve the confusion of negative sign of coordinate axis scale
plt.rcParams['font.sans-serif'] = ['Simhei']# Solve the problem of Chinese garbled code
df.reset_index(inplace=True) # reset index, The original index Remittance DataFrame in
df['date']=pd.to_datetime(df['date'])
df = df.astype({
'date': 'string'})
df.index = pd.to_datetime(df['date']) # Set up index Value
print(df)
print(df.dtypes)
df['date'] = df['date'].apply(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))# Convert date format
print(df.dtypes)
print(df)
step=5
dflen=len(df)
sma= {
}
for i in range(step):
sma[i]=0
for i in range(dflen-step):
i+=step
sma[i]=0
for j in range(step):
j+=1
sma[i] += df['close'][i-j]
if j==step: sma[i]=sma[i]/step
sma = pd.DataFrame.from_dict(sma,orient='index',columns=['5SMA'])
print(sma)
step=30
dflen=len(df)
sma30= {
}
for i in range(step):
sma30[i]=0
for i in range(dflen-step):
i+=step
sma30[i]=0
for j in range(step):
j+=1
sma30[i] += df['close'][i-j]
if j==step: sma30[i]=sma30[i]/step
sma30 = pd.DataFrame.from_dict(sma30,orient='index',columns=['30SMA'])
print(sma30)
step=60
dflen=len(df)
sma60= {
}
for i in range(step):
sma60[i]=0
for i in range(dflen-step):
i+=step
sma60[i]=0
for j in range(step):
j+=1
sma60[i] += df['close'][i-j]
if j==step: sma60[i]=sma60[i]/step
sma60 = pd.DataFrame.from_dict(sma60,orient='index',columns=['60SMA'])
print(sma60)
fig, ax1 = plt.subplots(figsize=(1200 / 72, 480 / 72))
da = df[['date', 'open', 'high', 'low', 'close']]
f = da[['date', 'open', 'high', 'low', 'close']].values
ax3 = ax1.twinx()
ax2 = ax1.twinx()
axsma=ax1.twinx()
axsma30=ax1.twinx()
axsma60=ax1.twinx()
candlestick_ohlc(ax1, f, colordown='g', colorup='r', width=0.3, alpha=0.7)
ax3.bar(df['date'], df['volume'], width=0.6)
ax2.plot(df['date'], df['close'])
axsma.plot(df['date'],sma['5SMA'],color="red")
axsma30.plot(df['date'],sma30['30SMA'],color="blue")
axsma60.plot(df['date'],sma60['60SMA'],color="green")
ax2.grid(True)
ax3.grid(True)
axsma.grid(True)
axsma30.grid(True)
axsma60.grid(True)
ax3.set_ylim(0, 500000000000)
ax1.set_ylim(0, 70000)
ax2.set_ylim(0, 70000)
axsma.set_ylim(0, 70000)
axsma30.set_ylim(0, 70000)
axsma60.set_ylim(0, 70000)
ax1.set_ylabel(' The price of money $')
fig.subplots_adjust(bottom=0.2) ## Adjust the bottom distance
ax1.xaxis_date() ## Set up X The axis scale is date time
ax2.xaxis_date() ## Set up X The axis scale is date time
ax3.xaxis_date() ## Set up X The axis scale is date time
axsma.xaxis_date() ## Set up X The axis scale is date time
plt.yticks() ## Set up Y Axis scale mark
plt.xlabel(u" Time ") ## Set up X Axis title
ax2.set_ylabel(' Closing price / volume /SMA')
plt.grid(True, 'major', 'both', ls='--', lw=.5, c='k', alpha=.3) ## Set gridlines
plt.show()
To be continued …
