2023年1月30日 星期一

使用Yahoo Finance套件利用Python來分析亞馬遜股票

 原文：Amazon Stock Analysis in Python with Yahoo Finance

範例1：列出前五天的的交易情形

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import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px
today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=365)
d1 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('AMZN',
                   start=start_date,
                   end=end_date,
                   progress=False)

data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())

執行結果：

範例2: 畫出一年趨勢圖

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import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px
today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=365)
d1 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('AMZN',
                   start=start_date,
                   end=end_date,
                   progress=False)

data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())
figure = go.Figure(data=[go.Candlestick(x=data["Date"],
                                        open=data["Open"], high=data["High"],
                                        low=data["Low"], close=data["Close"])])
figure.update_layout(title = "Amazon Stock Price Analysis",
                     xaxis_rangeslider_visible = False)
figure.show()

執行結果：

範例3:一年長條圖
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import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px
today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=365)
d1 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('AMZN',
                   start=start_date,
                   end=end_date,
                   progress=False)

data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())
figure = px.bar(data, x="Date", y="Close")
figure.show()

執行結果：

範例4: 使用時間區塊來瞭解某一時段的交易情形

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import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px
today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=365)
d1 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('AMZN',
                   start=start_date,
                   end=end_date,
                   progress=False)

data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())
figure = px.line(data, x='Date', y='Close',
                 title='Stock Market Analysis with Rangeslider')
figure.update_xaxes(rangeslider_visible=True)
figure.show()

執行結果：


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