import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Line, Kline, Scatter
kline1 = Kline()
kline2 = Kline()
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'))
kline2.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
kline2.extend_axis(yaxis=opts.AxisOpts(type_='value', position='right'))
kline2.extend_axis(yaxis=opts.AxisOpts(type_='value', position='right'))
df = pd.read_csv('F:/Stock/kline.csv')
str1 = str(df.iloc[0, 1])
str2 = str(df.iloc[1, 1])
#-------------------------------------------------------------------------------------------------------------------
df1 = pd.read_csv('F:/Stock/Data_Day/' + df.iloc[0, 1] + ".csv")
df1 = df1.sort_index(ascending=False).reset_index(drop=True)
date1 = df1.xs('Date Time', axis=1).tolist()
data1 = []
vol1 = []
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)
