def generate(self, cdate, sh_df, sz_df, sh_rzrq_df, sz_rzrq_df, av_df,
limit_info, stock_info, industry_info, index_info,
all_stock_info, bull_ration_df):
image_dir = os.path.join(self.sdir, "%s-StockReView" % cdate)
file_name = "%s.md" % image_dir
if os.path.exists(file_name): return True
os.makedirs(image_dir, exist_ok=True)
md = MarkdownWriter()
md.addTitle(cdate)
md.addHeader("股票复盘", 1)
#指数行情
index_info = index_info[[
'name', 'open', 'high', 'close', 'low', 'volume', 'amount',
'pchange'
]]
md.addHeader("指数行情", 2)
t_index = MarkdownTable(
headers=["名称", "价格", "涨幅(百分比)", "成交量", "成交额(亿)"])
for index in range(len(index_info)):
data_list = index_info.loc[index].tolist()
data_list = [
data_list[0],
round(data_list[3], 2),
round(data_list[7], 2),
int(data_list[5] / 100),
round(data_list[6] / 100000000, 2)
]
data_list = [str(i) for i in data_list]
t_index.addRow(data_list)
md.addTable(t_index)
# 资金面分析
md.addHeader("资金面分析:", 2)
x_dict = dict()
x_dict['日期'] = sh_df.date.tolist()
#上海和深圳的成交额分析
md.addHeader("成交额分析:", 3)
self.market_plot(sh_df, sz_df, x_dict, 'amount', dir_name=image_dir)
md.addImage("market_amount.png", imageTitle="成交额")
#上海和深圳的流通市值分析
md.addHeader("流通市值分析:", 3)
self.market_plot(sh_df,
sz_df,
x_dict,
'negotiable_value',
dir_name=image_dir)
md.addImage("market_negotiable_value.png", imageTitle="流通市值")
#上海和深圳的换手率分析
md.addHeader("市场换手率分析:", 3)
self.market_plot(sh_df, sz_df, x_dict, 'turnover', dir_name=image_dir)
md.addImage("market_turnover.png", imageTitle="换手率")
#上海和深圳的融资融券分析
md.addHeader("融资融券分析:", 3)
y_dict = dict()
y_dict['日期'] = sh_rzrq_df.date.tolist()
self.market_plot(sh_rzrq_df,
sz_rzrq_df,
y_dict,
'rzrqye',
dir_name=image_dir)
md.addImage("market_rzrqye.png", imageTitle="融资融券")
#平均股价走势
md.addHeader("平均股价分析:", 3)
self.plot_ohlc(av_df, '平均股价', '平均股价走势图', image_dir, 'average_price')
md.addImage("average_price.png", imageTitle="平均股价")
#活点地图
md.addHeader("活点地图分析:", 3)
mmap_clinet = MarauderMap()
mmap_clinet.plot(cdate, image_dir, 'marauder_map')
md.addImage("marauder_map.png", imageTitle="活点地图")
#大盘是否超跌
md.addHeader("大盘是否超跌:", 3)
moj = MarketOversoldJudger()
md.addText("大盘是否超跌:%s" % moj.judge(stock_info))
#牛熊股比
md.addHeader("牛熊股比:", 3)
self.plot_bullration(bull_ration_df, image_dir, 'bull_ration')
md.addImage("bull_ration.png", imageTitle="牛熊股比")
#涨停分析
md.addHeader("涨停跌停分析:", 3)
self.static_plot(stock_info,
limit_info,
dir_name=image_dir,
file_name='pchange_static')
md.addImage("pchange_static.png", imageTitle="活点地图")
#行业分析
md.addHeader("行业分析:", 2)
##总成交额分析
total_amount = industry_info['amount'].sum()
df = industry_info.sort_values(by='amount', ascending=False)
df = df[['name', 'code', 'amount']]
df = df.head(min(9, len(df)))
df.at[len(df)] = ['其他', '999999', total_amount - df['amount'].sum()]
df['amount'] = df['amount'] / 1e8
xtuple = tuple(
(df['name'] + ':' + df['amount'].astype('str') + '亿').tolist())
md.addHeader("总成交额分析:", 3)
self.plot_pie(df,
'amount',
'每日成交额行业分布',
xtuple,
image_dir,
'industry_amount_distribution',
ctype='func')
md.addImage("industry_amount_distribution.png", imageTitle="总成交额分析")
##总涨幅分析
df = industry_info[industry_info['pchange'] > 0]
if not df.empty:
df = df[['name', 'code', 'pchange']]
df = df.sort_values(by='pchange', ascending=False)
df = df.head(min(10, len(df)))
xtuple = tuple((df['name'] + ':' + df['pchange'].astype('str') +
'%').tolist())
md.addHeader("总涨幅分析:", 3)
self.plot_pie(df, 'pchange', '每日涨幅行业分布', xtuple, image_dir,
'industry_price_increase_distribution')
md.addImage("industry_price_increase_distribution.png",
imageTitle="总涨幅分析")
##金额增加额的行业分布
df = industry_info[industry_info['money_change'] > 0]
if not df.empty:
df = df[['name', 'code', 'money_change']]
df = df.sort_values(by='money_change', ascending=False)
df = df.head(min(10, len(df)))
xtuple = tuple((df['name'] + ':' +
df['money_change'].astype('str') + '亿').tolist())
md.addHeader("金额增加额的行业分布:", 3)
self.plot_pie(df, 'money_change', '每日成交增加额行业分布', xtuple, image_dir,
'industry_money_increase_distribution')
md.addImage("industry_money_increase_distribution.png",
imageTitle="金额增加额的行业分布")
##金额增加百分比的行业分布
df = industry_info[industry_info['mchange'] > 0]
if not df.empty:
df = df[['name', 'code', 'mchange']]
df = df.sort_values(by='mchange', ascending=False)
df = df.head(min(10, len(df)))
xtuple = tuple((df['name'] + ':' + df['mchange'].astype('str') +
'%').tolist())
md.addHeader("金额增加百分比的行业分布:", 3)
self.plot_pie(df, 'mchange', '每日成交增加比例行业分布', xtuple, image_dir,
'industry_money_increase_percent_distribution')
md.addImage("industry_money_increase_percent_distribution.png",
imageTitle="金额增加百分比的行业分布")
##总跌幅分析
df = industry_info[industry_info['pchange'] < 0]
if not df.empty:
df = df[['name', 'code', 'pchange']]
df = df.sort_values(by='pchange', ascending=True)
df = df.head(min(10, len(df)))
df['pchange'] = df['pchange'] * -1
xtuple = tuple((df['name'] + '跌幅:' + df['pchange'].astype('str') +
'%').tolist())
md.addHeader("总跌幅分析:", 3)
self.plot_pie(df, 'pchange', '每日涨幅行业分布', xtuple, image_dir,
'industry_price_decrease_distribution')
md.addImage("industry_price_decrease_distribution.png",
imageTitle="总跌幅分析")
##金额减少额的行业分布
df = industry_info[industry_info['money_change'] < 0]
if not df.empty:
df = df[['name', 'code', 'money_change']]
df = df.sort_values(by='money_change', ascending=True)
df = df.head(min(10, len(df)))
df['money_change'] = df['money_change'] * -1
xtuple = tuple((df['name'] + ':减少' +
df['money_change'].astype('str') + '亿').tolist())
md.addHeader("金额减少额的行业分布:", 3)
self.plot_pie(df, 'money_change', '每日成交减少额行业分布', xtuple, image_dir,
'industry_money_decrease_distribution')
md.addImage("industry_money_decrease_distribution.png",
imageTitle="金额减少额的行业分布")
##金额减少百分比的行业分布
df = industry_info[industry_info['mchange'] < 0]
if not df.empty:
df = df[['name', 'code', 'mchange']]
df = df.sort_values(by='mchange', ascending=False)
df = df.head(min(10, len(df)))
df['mchange'] = df['mchange'] * -1
xtuple = tuple((df['name'] + ':减少' + df['mchange'].astype('str') +
'%').tolist())
md.addHeader("金额减少百分比的行业分布:", 3)
self.plot_pie(df, 'mchange', '每日成交减少百分比行业分布', xtuple, image_dir,
'industry_money_decrease_percent_distribution')
md.addImage("industry_money_decrease_percent_distribution.png",
imageTitle="金额减少百分比的行业分布")
#选股指标
md.addHeader("选股器选股", 2)
t_selector = MarkdownTable(headers=["方法", "股票列表"])
stm = StrongerThanMarketSelecter()
stm_code_list = stm.choose(all_stock_info, av_df)
t_selector.addRow(['强于平均股价10%', json.dumps(stm_code_list)])
amus = AntiMarketUpSelecter()
amus_code_list = amus.choose(stock_info)
t_selector.addRow(['逆势上涨', json.dumps(amus_code_list)])
lvhps = LowVolumeHighProfitSelecter()
lvhps_code_list = lvhps.choose(stock_info)
t_selector.addRow(['高盈利低换手', json.dumps(lvhps_code_list)])
gkblvs = GameKLineBigraiseLargeVolumeSelecter()
gkblvs_code_list = gkblvs.choose(stock_info)
t_selector.addRow(['博弈K线带量长阳', json.dumps(gkblvs_code_list)])
gkbsvs = GameKLineBigraiseSmallVolumeSelecter()
gkbsvs_code_list = gkbsvs.choose(stock_info)
t_selector.addRow(['博弈K线无量长阳', json.dumps(gkbsvs_code_list)])
ncis = NeiChipIntensiveSelecter()
ncis_code_list = ncis.choose(stock_info)
t_selector.addRow(['低位筹码密集', json.dumps(ncis_code_list)])
bmbl = BullMoreBearLessSelecter()
bmbl_code_list = bmbl.choose(all_stock_info)
t_selector.addRow(['牛长熊短', json.dumps(bmbl_code_list)])
ncns = NoChipNetSpaceSelecter()
ncns_code_list = ncns.choose(stock_info)
t_selector.addRow(['无筹码净空间', json.dumps(ncns_code_list)])
md.addTable(t_selector)
with open(file_name, "w+") as f:
f.write(md.getStream())
#industry analysis
industry_info = get_industry_data(cdate)
#all stock info
all_stock_info = RIndexStock().get_k_data_in_range(start_date, end_date)
stm = StrongerThanMarketSelecter()
stm_code_list = stm.choose(all_stock_info, av_df)
amus = AntiMarketUpSelecter()
amus_code_list = amus.choose(stock_info)
lvhps = LowVolumeHighProfitSelecter()
lvhps_code_list = lvhps.choose(stock_info)
gkblvs = GameKLineBigraiseLargeVolumeSelecter()
gkblvs_code_list = gkblvs.choose(stock_info)
gkbsvs = GameKLineBigraiseSmallVolumeSelecter()
gkbsvs_code_list = gkbsvs.choose(stock_info)
ncis = NeiChipIntensiveSelecter()
ncis_code_list = ncis.choose(stock_info)
bmbl = BullMoreBearLessSelecter()
bmbl_code_list = bmbl.choose(all_stock_info)
ncns = NoChipNetSpaceSelecter()
ncns_code_list = ncns.choose(stock_info)
moj = MarketOversoldJudger()
is_over_sold = moj.judge(stock_info)