def getTYData(self, code_list):
# prices needed
from tspy import ts
prices = pd.DataFrame([])
codes = list(set(zcodeparse(code_list, 'SH600000')))
for group_idx, group in itertools.groupby(enumerate(codes), lambda it: it[0]//10):
print('group: ' + str(group_idx))
_code = list(map(lambda it: it[1], group))
tsql = '''
Setsysparam(pn_cycle(),cy_1s());
Return Select datetimetostr(["date"]) as "time",
['StockID'] as 'ticker', ["price"] as "price",
["buy1"] as "buy1", ["sale1"] as "sale1"
from MarketTable
DateKey strtodate('{0}') to strtodate('{0}')+0.99
Of "{1}"
end;
'''.format(date, ",".join(_code))
_price = ts.calltsl(tsql, None, ['time', 'ticker']).squeeze()
prices = pd.concat([prices, _price])
prices.sort_index(level=['time', 'ticker'], inplace=True)
prices = prices.loc[prices.index.get_level_values('time') < (date + ' 14:57:00')]
for col in prices:
prices.loc[(prices[col] < 1e-6).values, col] = None
prices.fillna(method='ffill', inplace=True)
prices.index.set_levels(zcodeparse(prices.index.levels[1], '600000.SH'),
level='ticker', inplace=True)
# 存储prices
saveData(prices, 'prices')
return prices
def tsbeta(self, index, codes, start_date, end_date, params):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
index = zcodeparse(index, formatlike='SH600000')
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
ns = {'1M': 21, '3M': 63, '6M': 126, '12M': 252}[params['window']]
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
datearray:= MarketTradeDayQk(begt, endt);
total:=array();
SetSysParam(PN_Precision(),6);
for j:=0 to length(datearray)-1 do
begin
SetSysParam(pn_cycle(), cy_day());
t1 := datearray[j];
setsysparam(pn_date(), t1);
t0 := ref(sp_time(), {{ns}});
for i:=0 to length(stockarray)-1 do
begin
setsysparam(Pn_stock(), stockarray[i]);
total union= ``array('ticker': stockarray[i],
'date': datetostr(datearray[j]),
'value': StockBeta('{{index}}', t0, t1));
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date,
index=index,
ns=ns)
return self.calltsl(tsl, None, ['date', 'ticker'])
def tsvolatility2(self, codes, start_date, end_date, params):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
ns = {'1M': 21, '3M': 63, '6M': 126, '12M': 252}[params['window']]
tsql = '''
datearray:= MarketTradeDayQk(strtodate('2005-01-01'),
strtodate('{0}'));
results := array();
for i:=0 to length(datearray)-1 do
begin
results[i] := datetostr(datearray[i]);
end
return results;
'''.format(start_date)
calendar = zsqueeze(ts.calltsl(tsql))
calendar = calendar.apply(lambda t: t.decode('utf-8')).values
calendar = np.sort(calendar)
data_start_date = calendar[calendar < start_date][-ns]
pri = self.price(codes, data_start_date, end_date, {'pn_rate': 1})
pri.loc[pri['amount'] < 1e-6, 'ret'] = None
ret2 = pri['ret']**2
def _std(s):
v = s.dropna().values
if v.shape[0] < 2:
return None
return np.sqrt(v.sum() / (len(v) - 1))
results = ret2.groupby(level='ticker').apply(_std)
return results
def tsfactor(self, codes, start_date, end_date, factor_tsql):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
datearray:= MarketTradeDayQk(begt, endt);
total:=array();
SetSysParam(PN_Precision(),6);
setsysparam(Pn_rate(), 0);
for i:=0 to length(stockarray)-1 do
begin
for j:=0 to length(datearray)-1 do
begin
setsysparam(Pn_stock(), stockarray[i]);
setsysparam(pn_date(), datearray[j]);
rdate := NewReportDateOfEndT2(datearray[j]);
total union= ``array('ticker': stockarray[i],
'date': datetostr(datearray[j]),
'value': {{factor_tsql}});
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date,
factor_tsql=factor_tsql)
return self.calltsl(tsl, None, ['date', 'ticker']).squeeze()
def shares(self, codes, start_date, end_date):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
total:=array();
dates:=MarketTradeDayQk(begt,endt);
for i:=0 to length(stockarray)-1 do
begin
for j:=0 to length(dates)-1 do
begin
total union= ``array(
'ticker':stockarray[i],
'date':datetostr(dates[j]),
'totalshares':Spec(StockTotalShares(dates[j]),stockarray[i]),
'floatshares':Spec(StockNegotiableShares(dates[j]),stockarray[i]));
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date)
return self.calltsl(tsl, None, ['date', 'ticker'])
def statement(self, table_name, entries, codes, start_date, end_date):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
table_num = {'balance': '44', 'income': '46', 'cash': '48'}
tsltemp = Template('''
SetSysParam('ReportMode',-1);
Return select ['StockID'] as 'ticker',
['StockName'] as 'name',
['截止日'] as 'report_period',
['公布日'] as 'ann_date'
{%- for tsk, myk in entries.items() -%}
,\n['{{tsk}}'] as '{{myk}}'
{%- endfor %}
from infotable {{tnum}} of array('{{codes}}')
where ['截止日']>=DateToInt(strtodate('{{startdate}}'))
and ['公布日']<=DateToInt(strtodate('{{enddate}}'))
end;
''')
tsl = tsltemp.render(entries=entries,
codes="','".join(codes),
startdate=start_date,
enddate=end_date,
tnum=table_num[table_name])
return self.calltsl(tsl, None, ['ann_date', 'ticker', 'report_period'])
def callfunc(self, func, args):
fail, data, _ = self.ts.RemoteCallFunc(func, args, {})
if not fail:
data["ticker"] = zcodeparse(data["ticker"], "600000.SH")
return pd.DataFrame(data).set_index(["date", "ticker"])
else:
raise Exception("Error when execute callfunc.")
def fsdata_ttm(self, entry, codes, start_date, end_date):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
entries = {
'revenue': 46002,
'net_income': 46033,
'net_income2major': 46078,
'cost': 46005,
'net_value2major': 44140,
'total_asset': 44059,
'total_debt': 44097,
'float_asset': 44028,
'float_debt': 44083,
'inventory': 44019,
'operating_cashflow': 48018
}
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
total:=array();
dates:=MarketTradeDayQk(begt,endt);
for i:=0 to length(stockarray)-1 do
begin
for j:=0 to length(dates)-1 do
begin
setsysparam(pn_stock(),stockarray[i]);
setsysparam(pn_date(), dates[j]);
RDate:=NewReportDateOfEndT2(dates[j]);
v:=Last12MData(RDate,{{entrynum}});
total union= ``array('ticker':stockarray[i],
'date':datetostr(dates[j]),
'{{entry}}':v);
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date,
entry=entry,
entrynum=str(entries[entry]))
return self.calltsl(tsl, None, ['date', 'ticker']).squeeze()
def tsmomentum(self, codes, start_date, end_date, params):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
ns = {'1M': 21, '3M': 63, '6M': 126, '12M': 252}[params['window']]
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
datearray:= MarketTradeDayQk(begt, endt);
total:=array();
setsysparam(Pn_precision(), 6);
t0 := array();
t1 := datearray;
setsysparam(pn_stock(), 'SH000300');
for j:=0 to length(t1)-1 do
begin
setsysparam(pn_date(), t1[j]);
t0 union= array(nday({{ns}}+1, 't0',
strtodate(datetimetostr(sp_time())))[0]);
end
t0 := sselect ['t0'] from t0 end;
for i:=0 to length(stockarray)-1 do
begin
for j:=0 to length(t1)-1 do
begin
setsysparam(Pn_stock(), stockarray[i]);
setsysparam(Pn_rate(), 1);
total union= ``array('ticker': stockarray[i],
'date': datetostr(t1[j]),
'momentum': stockzf(t0[j],t1[j]));
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date,
ns=ns)
return self.calltsl(tsl, None, ['date', 'ticker'])
def tsvolatility(self, codes, start_date, end_date, params):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
ns = {'1M': 21, '3M': 63, '6M': 126, '12M': 252}[params['window']]
tsql = '''
datearray:= MarketTradeDayQk(strtodate('2005-01-01'),
strtodate('{0}'));
results := array();
for i:=0 to length(datearray)-1 do
begin
results[i] := datetostr(datearray[i]);
end
return results;
'''.format(start_date)
calendar = zsqueeze(ts.calltsl(tsql))
calendar = calendar.apply(lambda t: t.decode('utf-8')).values
calendar = np.sort(calendar)
data_start_date = calendar[calendar < start_date][-ns]
pri = self.price(codes, data_start_date, end_date, {'pn_rate': 1})
pri.loc[pri['amount'] < 1e-6, 'ret'] = None
ret = pri['ret'].sort_index(level=['ticker', 'date'])
def _std(s):
v = s[-ns:].dropna().values
if v.shape[0] < 2:
return None
return np.std(v, ddof=1)
results = ret.groupby(level='ticker').apply(_std)
'''
results = pri.groupby(level='ticker', as_index=False)['ret']\
.rolling(ns, min_periods=2)\
.apply(_std, raw=False)
results.index = results.index.droplevel(level=0)
'''
return results
def price(self, codes, start_date, end_date, params={}):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
pn_rate = params.setdefault('pn_rate', 0)
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
datearray:= MarketTradeDayQk(begt, endt);
total:=array();
for i:=0 to length(stockarray)-1 do
begin
for j:=0 to length(datearray)-1 do
begin
setsysparam(Pn_stock(), stockarray[i]);
setsysparam(Pn_rate(), {{pn_rate}});
setsysparam(pn_date(), datearray[j]);
total union= ``array('ticker': stockarray[i],
'date': datetostr(datearray[j]),
'open': open(),
'high': high(),
'low': low(),
'prevclose': StockPrevClose3(),
'close': close(),
'ret': stockzf3(),
'vol': vol(),
'amount': amount());
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date,
pn_rate=pn_rate)
return self.calltsl(tsl, None, ['date', 'ticker'])
def analyze(self, date, acct, params={'assetclass': '股票', 'action': '买卖'}):
# data to be analyzed
ords = self.allorders[((self.allorders['资金帐号'] == acct.zfill(12))
& (self.allorders['日期'] == date)).values]
knos = self.allknocks[((self.allknocks['资金帐号'] == acct.zfill(12))
& (self.allknocks['日期'] == date)).values]
if params['assetclass'] == '股票':
ords = ords[ords['证券类别'] == '股票']
knos = knos[knos['证券类别'] == '股票']
if params['action'] == '买卖':
ords = ords[ords['交易类型'] == '正常买卖']
knos = knos[knos['交易类型'] == '正常买卖']
ords.index = range(ords.shape[0])
knos.index = range(knos.shape[0])
ords.loc[:, '证券代码'] = zcodeparse(ords['证券代码'], '600000.SH')
knos.loc[:, '证券代码'] = zcodeparse(knos['证券代码'], '600000.SH')
ords['消息类别'] = 'Order'
knos['消息类别'] = 'Knock'
# 天软获取证券市场价格
# prices = self.getTYData(ords['证券代码'])
# 读取已存储的市场价格数据
store = pd.HDFStore('tradeanalyst_data.h5')
prices = store['prices']
store.close()
# analyze
records = nested_dict()
for _, order in ords.iterrows():
# 合并orders
ticker, bs = order['证券代码'], order['委托方向']
if bs in {'买入', '卖出'}:
records[ticker, bs][order['下单时间']] = order.squeeze()
elif bs == '撤单':
bs = ords.loc[((ords['委托方向'].isin({'买入', '卖出'}))
& (ords['合同序号'] == order['合同序号'])).values,
'委托方向'].squeeze()
records[ticker, bs][order['下单时间']] = order.squeeze()
else:
raise Exception("unknown ord['委托方向']")
for _, kno in knos.iterrows():
# 合并knocks
ticker, bs = kno['证券代码'], kno['买卖方向']
records[ticker, bs][kno['成交时间']] = kno.squeeze()
trade_intents = nested_dict()
for ticker, bs in records:
# 将委托及其成交、撤单信息合并
bs_astk = OrderedDict(sorted(records[ticker, bs].items(), key=lambda s: s[0]))
for time in bs_astk:
if bs_astk[time]['消息类别'] == 'Order' and bs_astk[time]['撤单标志'] != '撤单':#撤单时委托方向也可能是买入或卖出，为实现同合同号数据合并，以撤单标志判断该笔记录
trade_intents[ticker, bs][bs_astk[time]['合同序号']] = {'ticker': bs_astk[time]['证券代码'],
'bs': bs_astk[time]['委托方向'],
'date_time': time,
'pit_price': prices.loc[(time, ticker), 'price'],
'pit_b1': prices.loc[(time, ticker), 'buy1'],
'pit_s1': prices.loc[(time, ticker), 'sale1'],
'order_price': bs_astk[time]['委托价格'],
'order_qty': bs_astk[time]['委托数量'],
'status': bs_astk[time]['委托状态'],
'finished_qty': 0,#成交量
'finished_amt': 0#成交金额
}
elif bs_astk[time]['消息类别'] == 'Knock':
# 此合同号下完成成交量
trade_intents[ticker, bs][bs_astk[time]['合同序号']]['finished_qty'] = bs_astk[time]['成交数量']
# 此合同号下完成成交金额
trade_intents[ticker, bs][bs_astk[time]['合同序号']]['finished_amt'] = bs_astk[time]['成交金额']
# 合并报单数据
trade_result = defaultdict(dict)
for ticker, bs in trade_intents:
# 将相关委托合并
_qty = 0 # 剩余量
_bs = '' # 委托方向
_ckey = '' # 合同号
for key,value in trade_intents[ticker, bs].items():
if value['order_qty'] > _qty + 100 or value['bs'] != _bs:
# 此次需求结束，开始下一次交易
_qty = value['order_qty'] - value['finished_qty']
_bs = value['bs']
_ckey = key
trade_result[ticker, bs, _ckey] = {'ticker': value['ticker'],
'bs': value['bs'],
'con_list':[key], #合同号序列
'pit_price_list': [value['pit_price']], #最新价序列
'pit_b1_list': [value['pit_b1']], #买一价序列
'pit_s1_list': [value['pit_s1']], #卖一价序列
'order_price_list': [value['order_price']], #委托价序列
'order_qty_list': [value['order_qty']], #委托数量序列
'status_list': [value['status']], #委托状态序列
'finished_qty_list': [value['finished_qty']], #成交量序列
'finished_amt_list': [value['finished_amt']], #成交金额序列
're_qty': _qty,#未成交量
'time': [value['date_time']], # 订单发出时间
'num_order': 0, #撤补次数
'time_fee': 0, #花费时间
'ave_price': 0 #成交均价
}
else:
# 继续完成此次委托
trade_result[ticker, bs, _ckey]['con_list'].append(key)
trade_result[ticker, bs, _ckey]['pit_price_list'].append(value['pit_price'])
trade_result[ticker, bs, _ckey]['pit_b1_list'].append(value['pit_b1'])
trade_result[ticker, bs, _ckey]['pit_s1_list'].append(value['pit_s1'])
trade_result[ticker, bs, _ckey]['order_price_list'].append(value['order_price'])
trade_result[ticker, bs, _ckey]['order_qty_list'].append(value['order_qty'])
trade_result[ticker, bs, _ckey]['status_list'].append(value['status'])
trade_result[ticker, bs, _ckey]['finished_qty_list'].append(value['finished_qty'])
trade_result[ticker, bs, _ckey]['finished_amt_list'].append(value['finished_amt'])
trade_result[ticker, bs, _ckey]['time'].append(value['date_time'])
# 剩余未成交数量
_qty = _qty - value['finished_qty']
trade_result[ticker, bs, _ckey]['re_qty'] = _qty
# 成本核算：时间、交易、撤补量
for key,value in trade_result.items():
if value['re_qty'] < 100:
# 撤补次数
trade_result[key]['num_order'] = len(value['con_list']) - 1
# 花费时间
trade_result[key]['time_fee'] = 0 if len(value['con_list']) == 1 \
else (datetime.datetime.strptime(value['time'][-1], "%Y-%m-%d %H:%M:%S") - \
datetime.datetime.strptime(value['time'][0], "%Y-%m-%d %H:%M:%S")).seconds / 60
# 成交均价
trade_result[key]['ave_price'] = np.matmul(value['finished_qty_list'], value['order_price_list']) / np.sum(value['finished_qty_list'])
else:
trade_result[key]['num_order'] = len(value['con_list']) - 1
trade_result[key]['time_fee'] = '未完成'
trade_result[key]['ave_price'] = '未成交' if np.sum(value['finished_qty_list']) == 0 else np.matmul(value['finished_qty_list'], value['order_price_list']) / np.sum(value['finished_qty_list'])
return trade_result
def fsdata_raw(self, entry, codes, start_date, end_date):
codes = zlist(zcodeparse(codes, formatlike='SH600000'))
start_date = ztimeparse(start_date, '%Y-%m-%d')
end_date = ztimeparse(end_date, '%Y-%m-%d')
entries = {
'net_value2major': 44140,
'total_asset': 44059,
'inventory': 44019,
'receivable': 44009,
'revenue': 46002,
'operating_profit': 46015,
'cost': 46005,
'net_income2major': 46078
}
tsltemp = Template('''
stockarray:=array('{{codes}}');
begt:=strtodate('{{startdate}}');
endt:=strtodate('{{enddate}}');
datearray:= MarketTradeDayQk(begt, endt);
total:=array();
for i:=0 to length(stockarray)-1 do
begin
for j:=0 to length(datearray)-1 do
begin
setsysparam(pn_stock(),stockarray[i]);
setsysparam(pn_date(), datearray[j]);
RDate:=NewReportDateOfEndT2(datearray[j]);
RtDate:=PreviousReportDate(RDate);
Rt2Date:=PreviousReportDate(RtDate);
RyDate:=strtoint(FormatDateTime('yyyy',inttodate(RtDate))+'1231');
Ry2Date:=strtoint(FormatDateTime('yyyy',inttodate(Rt2Date))+'1231');
RpDate:=PreviousReportDateOfQuarter(RDate,1);
RptDate:=PreviousReportDate(RpDate);
RpyDate:=strtoint(FormatDateTime('yyyy',inttodate(RptDate))+'1231');
v1:=ReportOfAll({{entrynum}},RDate);
v2:=ReportOfAll({{entrynum}},RyDate);
v3:=ReportOfAll({{entrynum}},RtDate);
v4:=ReportOfAll({{entrynum}},RpDate);
v5:=ReportOfAll({{entrynum}},RpyDate);
v6:=ReportOfAll({{entrynum}},RptDate);
v7:=ReportOfAll({{entrynum}},Rt2Date);
v8:=ReportOfAll({{entrynum}},Ry2Date);
total union= ``array('ticker':stockarray[i],
'date':datetostr(datearray[j]),
'latest_report':datetostr(inttodate(RDate)),
'latest_data':v1,
'latest_annual_report':datetostr(inttodate(RyDate)),
'latest_annual_data': v2,
'latest_lastyear_report':datetostr(inttodate(RtDate)),
'latest_lastyear_data': v3,
'latest_annual2_report': Ry2Date,
'latest_annual2_data': v8,
'latest_last2year_report': Rt2Date,
'latest_last2year_data': v7,
'latest_prev_report':datetostr(inttodate(RpDate)),
'latest_prev_data': v4,
'latest_prev_annual_report':datetostr(inttodate(RpyDate)),
'latest_prev_annual_data': v5,
'latest_prev_lastyear_report':datetostr(inttodate(RptDate)),
'latest_prev_lastyear_data': v6 );
end
end
return total;
''')
tsl = tsltemp.render(codes="','".join(codes),
startdate=start_date,
enddate=end_date,
entrynum=entries[entry])
return self.calltsl(tsl, None, ['date', 'ticker'])