S◯I証券風のチャートをmatplotlibで作ってみた 〜Python〜
S◯I証券風のチャートをmatplotlibで作ってみました。
サイバネット（4312）の約半年間分のチャートをプロットしています。
sma＝移動平均線で、５日、２５日、６０日、１００日、３００日を表示しています。
出来高のグラフも合わせて、作りました。
調整後終値と一緒にプロットしているので比べやすいと思います。
無料で使えて、かつ自分が表示したい機能をすべて持っているチャートはなかなか無いので、自分で作ってみました。
簡単ではありますが、コードの解説もしているのでもしよければご覧ください。
以下がソースコードです。ごちゃごちゃしているので、時間があれば整理をします。
import pandas
import datetime
import matplotlib.pyplot as plt
def scraping_yahoo(code, start, end, term):
base = "http://info.finance.yahoo.co.jp/history/?code={0}.T&{1}&{2}&tm={3}&p={4}"
start = str(start)
start = start.split("-")
start = "sy={0}&sm={1}&sd={2}".format(start[0], start[1], start[2])
end = str(end)
end = end.split("-")
end = "ey={0}&em={1}&ed={2}".format(end[0], end[1], end[2])
page = 1
result = []
while True:
url = base.format(code, start, end, term, page)
df = pandas.read_html(url, header=0)
if len(df[1]) == 0:
break
result.append(df[1])
page += 1
result = pandas.concat(result)
result.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close']
return result
def generate(data):
data_frame = data[["Date","Open","Close","High","Low",'Adj Close',"Volume",]]
hiduke = pandas.DataFrame(data_frame["Date"])
hiduke.columns = ["Hiduke"]
dates = []
for day in data_frame["Date"]:
day = day.replace("年", "-")
day = day.replace("月", "-")
day = day.replace("日", "")
time = datetime.datetime.strptime(day, '%Y-%m-%d')
dates.append(time)
data_frame["Date"] = dates
tmp = data_frame["Date"].values.astype("datetime64[D]")
d = tmp
data_frame["Date"] = tmp.astype(float)
index = len(data_frame["Date"])
index_data = []
for x in range(index):
index_data.append(x)
data_frame.index = index_data
hiduke.index = index_data
data_frame = pandas.concat([data_frame, hiduke], axis=1)
return data_frame
class Score:
def __init__(self, data, code):
self.data = data
self.code = code
def sma_addition(self):
sma5 = pandas.Series.rolling(self.data['Adj Close'], window=5,center=False).mean()
sma5 = pandas.DataFrame(sma5)
sma5.columns = ["sma5"]
sma5 = sma5.dropna(subset=["sma5"])
kago = [go for go in range(len(sma5))]
sma5.index = kago
new_data = pandas.concat([self.data, sma5], axis=1)
sma25 = pandas.Series.rolling(self.data['Adj Close'], window=25,center=False).mean()
sma25 = pandas.DataFrame(sma25)
sma25.columns = ["sma25"]
sma25 = sma25.dropna(subset=["sma25"])
kago = [go for go in range(len(sma25))]
sma25.index = kago
new_data = pandas.concat([new_data, sma25], axis=1)
sma60 = pandas.Series.rolling(self.data['Adj Close'], window=60,center=False).mean()
sma60 = pandas.DataFrame(sma60)
sma60.columns = ["sma60"]
sma60 = sma60.dropna(subset=["sma60"])
kago = [go for go in range(len(sma60))]
sma60.index = kago
new_data = pandas.concat([new_data, sma60], axis=1)
sma100 = pandas.Series.rolling(self.data['Adj Close'], window=100,center=False).mean()
sma100 = pandas.DataFrame(sma100)
sma100.columns = ["sma100"]
sma100 = sma100.dropna(subset=["sma100"])
kago = [go for go in range(len(sma100))]
sma100.index = kago
new_data = pandas.concat([new_data, sma100], axis=1)
sma300 = pandas.Series.rolling(self.data['Adj Close'], window=300,center=False).mean()
sma300 = pandas.DataFrame(sma300)
sma300.columns = ["sma300"]
sma300 = sma300.dropna(subset=["sma300"])
kago = [go for go in range(len(sma300))]
sma300.index = kago
new_data = pandas.concat([new_data, sma300], axis=1)
choice_data = new_data.ix[:, ["Date","Open","Close","High","Low",'Adj Close',"Volume","Hiduke","sma5","sma25","sma60","sma100","sma300"]]
self.choice_data = choice_data
return self.choice_data
def ochl(self):
from matplotlib.finance import candlestick_ochl
ax1 = plt.subplot()
try:
candlestick_ochl(ax1, self.choice_data.values, width=0.7, colorup='#ff1e1e', colordown='#1eff1e')
except TypeError:
pass
plt.plot(self.choice_data["Date"], self.choice_data["sma5"], color="#1e8eff", label = "sma5")
plt.plot(self.choice_data["Date"], self.choice_data["sma25"], color="#ff8e1e", label = "sma25")
plt.plot(self.choice_data["Date"], self.choice_data["sma60"], color="#ff1e8e", label = "sma60")
plt.plot(self.choice_data["Date"], self.choice_data["sma100"], color="#1effff", label = "sma100")
plt.plot(self.choice_data["Date"], self.choice_data["sma300"], color="#ff7fbf", label = "sma300")
plt.legend(loc='best')
plt.xticks(self.choice_data["Date"][::40], self.choice_data["Hiduke"][::40])
plt.grid(color='#f5f5f5')
ax1.patch.set_facecolor('#333333')
try:
x_left = self.choice_data["Date"][120]
except KeyError:
left = len(self.choice_data["Date"])
x_left = left - 1
x_right = self.choice_data["Date"][0]
plt.xlim(x_left - 5, x_right + 5)
filename_candle_day = self.code + "_candle_day.svg"
plt.savefig(filename_candle_day)
return plt.clf()
def values(self):
ax1 = plt.subplot()
ax1.bar(self.data["Date"], self.data["Volume"], color='#1e8eff', edgecolor="#333333")
ax2 = ax1.twinx()
ax1.patch.set_facecolor('#333333')
plt.grid(color='#f5f5f5')
try:
x_left = self.choice_data["Date"][120]
except KeyError:
left = len(self.choice_data["Date"])
x_left = left - 1
x_right = self.choice_data["Date"][0]
plt.xlim(x_left - 5, x_right + 5)
ax2.plot(self.data["Date"], self.data["Adj Close"], color="#ff1e8e")
filename_values_day = self.code + "_values_day.svg"
plt.savefig(filename_values_day)
return plt.clf()
if __name__ == "__main__":
company = 4312
EndDate = datetime.date.today()
StartDate = EndDate - datetime.timedelta(days=60)#640
data = scraping_yahoo(company, StartDate, EndDate, "d")
generate_data = generate(data)
big_company = Score(generate_data, str(company))
big_company.sma_addition()
big_company.ochl()
big_company.values()
