def print_progress(self, _date):
_year = StockDataSource.datetime(_date).year
if self.year != _year:
# _total_value = max(1000*10000, self.sums)
_total_value = self.sums
self.year_values.append( [self.year, '%.02f'%(self.account.total_value*0.0001), \
'%.02f'%(self.account.total_value*100/_total_value-100)] )
self.year = _year
self.sums = self.account.total_value
def fixed_invest(self, code, data_list, pe_vec):
self._start_test(0, 10000)
all_days, curr_month = len(data_list[0]), -1
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
for _idx in range(0, all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
stock_data = {
'ts_code': code,
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
_month = StockDataSource.datetime(int_date).month
self.account.ProfitDaily(int_date)
if curr_month != _month:
curr_month = _month
self.account.Rechange(10000)
kp = opens[_idx]
if pe_vec[_idx] > 20.83: # sell
_volume = self.account.credit / kp
elif pe_vec[_idx] > 13.89:
_volume = self.account.cash / kp
else:
_volume = self.account.cash / kp * 1.7
self._order(int_date, stock_data, _volume, kp, 0)
self.curr_closes[code] = closes[_idx]
self.account.UpdateValue(self.curr_closes, int_date)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
print(
pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price', 'volume',
'total_value', 'credit', 'lever', 'ts_code'
]))
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
self.account.status_info()
def start_test(self, long_per, init_cash=1000 * 10000):
funcName = sys._getframe().f_back.f_code.co_name #获取调用函数名
lineNumber = sys._getframe().f_back.f_lineno #获取行号
coname = sys._getframe().f_code.co_name #获取当前函数名
print(coname, funcName, lineNumber)
self.account = StockAccount(init_cash, 0)
self.year, self.start = StockDataSource.datetime(
self.dates[0]).year, time.time()
self.order_count, self.open_value, self.sums = 0, 0, self.account.cash
self.records, self.year_values = [], []
self.curr_orders, self.hist_orders = [], []
self.market_values, self.position_ratios = [], []
for i in range(0, long_per):
# self.account.ProfitDaily()
_total_value = max(1000 * 10000, self.account.total_value)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
_total_value)
def fixed_invest(self, code, data_list, index_long):
self._start_test(0, 10 * 10000)
all_days, curr_month = len(data_list[0]), -1
opens, highs, lows, closes = data_list[1], data_list[2], data_list[
3], data_list[4]
for _idx in range(0, all_days):
float_date = data_list[0][_idx]
int_date = StockDataSource.int_date(float_date)
self.print_progress(int_date)
stock_data = {
'ts_code': code,
'open': opens[_idx],
'high': highs[_idx],
'low': lows[_idx],
'close': closes[_idx]
}
_month = StockDataSource.datetime(int_date).month
self.account.ProfitDaily(int_date)
if curr_month != _month:
curr_month = _month
self.account.Rechange(10000)
# _open_unit = self.account.cash / opens[_idx]
# _open_unit = (self.account.cash + 120000 - self.account.credit) / opens[_idx]
# self._order(int_date, stock_data, _open_unit, opens[_idx], 0)
# print( int_date, self.account.cash, self.account.credit )
kp = index_long['short'][_idx] * 1.05
kp = index_long['long'][_idx] * 0.95
_cash = self.account.cash + 120000 - self.account.credit
if lows[_idx] < kp and _cash > kp * 100:
if kp > opens[_idx]:
kp = opens[_idx]
# if highs[_idx] > kp and _cash > kp*100:
# if kp < opens[_idx]:
# kp = opens[_idx]
_open_unit = _cash / kp
self._order(int_date, stock_data, _open_unit, kp, 0)
self.curr_closes[code] = closes[_idx]
self.account.UpdateValue(self.curr_closes, int_date)
self.market_values.append(self.account.total_value)
self.position_ratios.append(self.account.position_value /
self.account.total_value)
print(
pandas.DataFrame(self.hist_orders,
columns=[
'date', 'trade_price', 'stop_price', 'volume',
'total_value', 'credit', 'lever', 'ts_code'
]))
print(
pandas.DataFrame(self.year_values,
columns=['year', 'cash', 'ratio']))
self.account.status_info()