def abc():
ts_code='002713.SZ'
csv_file = '{0}.csv'.format(ts_code)
render_html = '{0}.html'.format(ts_code)
end_date = datetime.now().strftime('%Y%m%d')
df = None
if not os.path.exists(csv_file):
MY_TOKEN = '190040a13eb5b092ca76fa003f58d693c9121e0fc621f6d2ad221468'
ts.set_token(MY_TOKEN)
pro = ts.pro_api()
df1 = pro.daily(ts_code=ts_code, start_date='20140101', end_date=end_date)
df = df1.sort_values(by=['trade_date'])
df.reset_index(level=0, inplace=True)
df.drop(['index'], axis=1, inplace=True)
print(df)
df.to_csv(csv_file)
if not df is None:
df = pd.read_csv(csv_file)
date = df.trade_date.tolist()
data = []
for idx in df.index :
row=[df.iloc[idx]['open'],df.iloc[idx]['close'],df.iloc[idx]['low'],df.iloc[idx]['high']]
data.append(row)
WIDTH = 1100
HEIGHT = 550
chart_init = {
"width": WIDTH,
"height": HEIGHT,
}
kline = Kline()
kline.add_xaxis(date).add_yaxis('日K', data)
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts()],
# title_opts=opts.TitleOpts(title='日K线图:{0}'.format(ts_code)),
)
# kline.add_yaxis(
# "日K",
# date,
# data,
# mark_point=["max"],
# is_datazoom_show=True,
# )
# 生成一个名为 render.html 的文件
kline.render(render_html)
kline.render_notebook()
# kline.render('a.html')
# 在jupyter中使用，只需要使用xxx.render_notebook() 方法即可在Jupyter中显示图
def draw_Kline(self):
"""
画股票的K线图，需要传入四个数据
分别是["open", "close", "high", "low"]
:return: 画出图像
"""
Df_s1 = Read_One_Stock(self.SC).select_col("open", "high", "low",
"close", "vol", "amount")
length = Df_s1.shape[0]
Df_s1.sort_values("trade_date", inplace=True)
Df_s1.index = list(range(length))
price = np.array(Df_s1[["open", "close", "high", "low"]]).tolist()
date = np.array(Df_s1["trade_date"], dtype=np.string_).tolist()
ma_value_5 = calculate_ma_n(list(Df_s1['close']), 5)
ma_value_10 = calculate_ma_n(list(Df_s1['close']), 10)
ma_value = np.array([ma_value_5, ma_value_10]).tolist()
kline = Kline()
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title="K-Line of {}".format(self.SC)),
)
kline.add_xaxis(date)
kline.add_yaxis('K-Line', price)
line = Line()
line.add_xaxis(date)
line.add_yaxis(series_name="ma5",
y_axis=ma_value[0],
label_opts=opts.LabelOpts(is_show=False))
line.add_yaxis(series_name="ma10",
y_axis=ma_value[1],
label_opts=opts.LabelOpts(is_show=False))
line.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[opts.DataZoomOpts()],
title_opts=opts.TitleOpts(title=""))
kline.overlap(line)
kline.render("./Plots/{} Candle Plot.html".format(self.SC))
def _stock_():
stock_code = '000001'
freq = 'day'
# stock_df = download_index_bars(stock_code, freq=freq)
# print(stock_df)
csv = os.path.join(ct.DATA_DIR, 'index_%s_%s.csv' % (stock_code, freq))
stock_df = pd.read_csv(csv)
html = os.path.join(ct.HTML_DIR, 'index_%s_%s.html' % (stock_code, freq))
stock_df.to_csv(csv, index=False)
from pyecharts import options as opts
from pyecharts.charts import Kline
WIDTH = 1100
HEIGHT = 550
chart_init = {
"width": WIDTH,
"height": HEIGHT,
}
kline = Kline()
date = stock_df.datetime.tolist()
data = []
for index, row in stock_df.iterrows():
item = [row.open, row.close, row.low, row.high]
data.append(item)
print(data)
kline.add_xaxis(date).add_yaxis('日K', data)
kline.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
datazoom_opts=[opts.DataZoomOpts()],
# title_opts=opts.TitleOpts(title='日K线图:{0}'.format(ts_code)),
)
# kline.add_yaxis(
# "日K",
# date,
# data,
# mark_point=["max"],
# is_datazoom_show=True,
# )
# 生成一个名为 render.html 的文件
kline.render(html)
def drawLine(self, data, stockcode):
print(data)
# stockcode=self.get_stockcode()
stockname = self.get_stockname(stockcode)
kldata = data.values[:, [1, 2, 3, 4, 5]] # 分别对应日期，开盘价、收盘价、最低价和最高价
print(kldata)
kdate = kldata[0]
kobj = Kline().add_xaxis(kdate)
kobj.add_yaxis(stockname + "-日K线图", kldata.tolist())
kobj.set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),
xaxis_opts=opts.AxisOpts(is_scale=True),
title_opts=opts.TitleOpts(title=""))
renderfile = stockcode + 'render.html'
kobj.render(renderfile)
br.open(renderfile)
df1.iloc[idx1]['Open'], df1.iloc[idx1]['Close'], df1.iloc[idx1]['Low'],
df1.iloc[idx1]['High']
]
row2 = df1.iloc[idx1]['Volume']
data1.append(row1)
vol1.append(row2)
kline1.add_xaxis(date1)
kline1.add_yaxis(str1, data1)
#-------------------------------------------------------------------------------------------------------------------
df2 = pd.read_csv('F:/Stock/Data_Day/' + df.iloc[1, 1] + ".csv")
df2 = df2.sort_index(ascending=False).reset_index(drop=True)
date2 = df2.xs('Date Time', axis=1).tolist()
data2 = []
vol2 = []
for idx2 in df2.index:
row3 = [
df2.iloc[idx2]['Open'], df2.iloc[idx2]['Close'], df2.iloc[idx2]['Low'],
df2.iloc[idx2]['High']
]
row4 = df2.iloc[idx2]['Volume']
data2.append(row3)
vol2.append(row4)
print(date2)
kline2.add_xaxis(date2)
kline2.add_yaxis(str2, data2)
kline1.overlap(kline2)
kline1.render(path='F:\\Stock/Kline.html')
y_data = hist_data[['open', 'close', 'low', 'high']].values.tolist()
ma5 = hist_data['ma5']
ma10 = hist_data['ma10']
x.reverse()
y_data.reverse()
print(x)
x1 = hist_data.index.tolist()[:20]
y1 = hist_data['open'].iloc[:20]
y2 = hist_data['close'].iloc[:20]
#Line-折线图
line = Line().add_xaxis(x1).add_yaxis('开盘价', y1,
is_smooth=True).add_yaxis('收盘价',
range(20),
is_smooth=True)
line.render('/Users/mac/Desktop/fig.html')
#K-line图
#K_line = Kline().add_xaxis(x).add_yaxis('美的集团-000333',y_data).set_global_opts(datazoom_opts=[opts.DataZoomOpts(type_="inside")])
#K_line.render('/Users/mac/Desktop/fig1.html')
K_line = Kline().add_xaxis(x).add_yaxis(
'美的集团-000333',
y_data,
markline_opts=opts.MarkLineOpts(
[opts.MarkLineItem(type_='max', value_dim='close')]),
).set_global_opts(datazoom_opts=[opts.DataZoomOpts(type_="inside")])
K_line.render('/Users/mac/Desktop/fig1.html')
from pyecharts.charts import Line, Kline, Bar
import pandas as pd
from datetime import datetime, timedelta
fn = 'bar/min1_SR001.csv'
df1 = pd.read_csv(fn)
df1['datetime'] = df1['date'] + ' ' + df1['time']
print(df1.head())
dt_list = list(df1['datetime'])
#print(dt_list)
k_plot_value = df1.apply(
lambda record:
[record['open'], record['close'], record['low'], record['high']],
axis=1).tolist()
#print(k_plot_value)
kline = Kline(init_opts=opts.InitOpts(width='1500px'))
kline.add_xaxis(list(df1['datetime']))
kline.add_yaxis('日K', k_plot_value)
kline.set_global_opts(
title_opts=opts.TitleOpts(title='Kline-基本示例'),
datazoom_opts=[opts.DataZoomOpts()],
)
#xaxis_opts=opts.AxisOpts(type_='time'))
kline.render()
# if __name__ == '__main__':
# pass
from nature import get_dss
fn = get_dss() + 'backtest/fut/al/' + 'day_al01.csv'
df1 = pd.read_csv(fn)
df1['datetime'] = df1['date'] + ' ' + df1['time']
print(df1.head())
dt_list = list(df1['datetime'])
#print(dt_list)
k_plot_value = df1.apply(
lambda record:
[record['open'], record['close'], record['low'], record['high']],
axis=1).tolist()
#print(k_plot_value)
kline = Kline(init_opts=opts.InitOpts(width='1500px', height='700px'))
kline.add_xaxis(list(df1['datetime']))
kline.add_yaxis('日K', k_plot_value)
kline.set_global_opts(
title_opts=opts.TitleOpts(title='Kline-基本示例'),
datazoom_opts=[opts.DataZoomOpts()],
)
#xaxis_opts=opts.AxisOpts(type_='time'))
fn = get_dss() + 'backtest/render/bar1.html'
kline.render(fn)
# if __name__ == '__main__':
# pass
def run_strategy(smaPeriod):
df_new = pd.DataFrame(columns=('shares_ID', 'total number of trades',
'Profit times number of trades',
'Profit Percentage',
'Final portfolio value'))
for (root, dirs, files) in os.walk("E:/Stock/Data_Day"):
z = 0
for x in range(len(files)):
z = z + 1
#K线图
df = pd.read_csv("E:/Stock/Data_Day/" + files[x])
df = df.sort_index(ascending=False).reset_index(drop=True)
print("进度：", z / len(files) * 100, "%")
date = df.xs('Date Time', axis=1).tolist()
data = []
vol = []
for idx in df.index:
row1 = [
df.iloc[idx]['Open'], df.iloc[idx]['Close'],
df.iloc[idx]['Low'], df.iloc[idx]['High']
]
row2 = df.iloc[idx]['Volume']
data.append(row1)
vol.append(row2)
kline1 = Kline()
line1 = Line()
line2 = Line()
line3 = Line()
scatter1 = Scatter()
scatter2 = Scatter()
kline1.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
kline1.extend_axis(
yaxis=opts.AxisOpts(type_='value', position='right'))
kline1.extend_axis(
yaxis=opts.AxisOpts(type_='value', position='right'))
# 价格均线
line1.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
# 成交量
line2.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
#资金量
line3.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
#买入点
scatter1.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
#卖出点
scatter2.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
kline1.add_xaxis(date)
kline1.add_yaxis(files[x], data)
line2.add_xaxis(date)
line2.add_yaxis("vol",
vol,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False))
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("fd", "E:/Stock/Data_Day/" + files[x])
global myStrategy
myStrategy = MyStrategy(feed, "fd", smaPeriod)
trade_situation = trades.Trades()
myStrategy.attachAnalyzer(trade_situation)
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot("test").addDataSeries(
"SMA", myStrategy.getSMA())
# Plot the simple returns on each bar.
# plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
myStrategy.run()
scatter1.add_xaxis(myStrategy.Buyday)
scatter1.add_yaxis("Buy", myStrategy.Buyprice)
scatter2.add_xaxis(myStrategy.Sellday)
scatter2.add_yaxis("Sell", myStrategy.Sellprice)
line1.add_xaxis(myStrategy.Barday)
line1.add_yaxis("Price_SMA",
myStrategy.Sma_price,
label_opts=opts.LabelOpts(is_show=False))
line3.add_xaxis(date)
line3.add_yaxis("money",
myStrategy.money,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False))
plt.savePlot("E:/Stock/html_png_Total_day/" + files[x] + ".png")
print(files[x] + " Final portfolio value:$%.2f" %
myStrategy.getBroker().getEquity())
print("total number of trades", trade_situation.getCount())
print("Profit times number of trades ",
trade_situation.getProfitableCount())
if trade_situation.getCount() > 0:
Percentage = trade_situation.getProfitableCount(
) / trade_situation.getCount()
print("百分比", Percentage)
else:
Percentage = 0
print("百分比", 0)
df1 = pd.DataFrame({
"shares_ID": [files[x]],
'total number of trades': [trade_situation.getCount()],
'Profit times number of trades':
trade_situation.getProfitableCount(),
'Profit Percentage':
Percentage,
'Final portfolio value': [myStrategy.getBroker().getEquity()]
})
kline1.overlap(scatter1)
kline1.overlap(scatter2)
kline1.overlap(line1)
kline1.overlap(line2)
kline1.overlap(line3)
kline1.render(path='E:\\Stock/html_png_Total_day/' + files[x] +
'.html')
df_new = pd.concat([df1, df_new], ignore_index=True)
df_new.to_csv("E:/Stock/html_png_Total_day/Total_Min.csv", index=False)
