def ema_save2db(date, ifcode, period_short, period_long):
if not date or not ifcode or not period_short or not period_long:
return {'error': u'params is fault'}
period_short = int(period_short)
period_long = int(period_long)
return FittingDataCalculator.ema_df(date, ifcode, period_short,
period_long)
def main(ifcode, period_short, period_long):
date_list = ifcode_day_map(ifcode)
for day in date_list:
print '*' * 20
print day
df = FittingDataCalculator.ema_df(day, ifcode, period_short,
period_long)
save_data(df.values.tolist(), day, ifcode, period_short, period_long)
def boll_chart(cls, date, ifcode, period_short=50, period_long=80, pre_point=10):
df = FittingDataCalculator.boll_df(date, ifcode, period_short, period_long, pre_point)
price = df[['time_index', 'price']].values.tolist()
boll_up = df[['time_index', 'boll_up']].values.tolist()
boll_dn = df[['time_index', 'boll_dn']].values.tolist()
boll_mb = df[['time_index', 'boll_mb']].values.tolist()
return price, boll_up, boll_dn, boll_mb
def ema(date, ifcode, period_short, period_long):
if not date or not ifcode or not period_short or not period_long:
return {'error': u'params is fault'}
date = str2day(date, DATE_DISPLAY_FORMAT).strftime(DATE_DISPLAY_FORMAT)
period_short = int(period_short)
period_long = int(period_long)
return FittingDataCalculator.ema_chart(date, ifcode, period_short,
period_long)
def fitting_data():
if not req.args.get('date') or not req.args.get(
'ifcode') or not req.args.get('period'):
return {'error': u'params is fault'}
date = str2day(req.args.get('date')).strftime(DATE_DISPLAY_FORMAT)
ifcode = req.args.get('ifcode')
period = int(req.args.get('period'))
return FittingDataCalculator.fitting_series(period, date, ifcode)
def macd_analysis(cls, date, ifcode, period_short, period_long, \
period_dif, pre_point, pre_time, pre_offset):
#date : 'yyyy-mm-dd'
df = FittingDataCalculator.macd_df(date, ifcode, period_short, period_long, \
period_dif, pre_point, pre_time)
if df.empty:
return []
sig_infos = SellSignal.compare_macd(df, 3, pre_offset)
profit_infos = SellSignal.profit_infos(sig_infos)
return profit_infos
def macd(cls, date, ifcode, period_short=12, period_long=26, period_dif=9, \
pre_point=init_point, pre_time=init_time, offset=init_offset):
#date : 'yyyy-mm-dd'
df = FittingDataCalculator.macd_df(date, ifcode, period_short, period_long, \
period_dif, pre_point, pre_time)
if df.empty:
return []
sig_infos = SellSignal.compare_macd(df, 3, offset)
profit_infos = SellSignal.profit_infos(sig_infos)
flags = SellSignal.out_flags(sig_infos)
return profit_infos
def ema(cls, date, ifcode, period_short, period_long):
_file = '%s/%s_%s_%s_%s' % (ema_file_dir, date, ifcode, period_short, period_long)
if os.path.isfile(_file):
df = read_df(_file)
else:
df = FittingDataCalculator.ema_df(date, ifcode, period_short, period_long)
if df.empty:
return []
sig_infos = SellSignal.compare_ema(df, limit_period=60)
profit_infos = SellSignal.profit_infos(sig_infos)
return profit_infos
def macd_chart(cls, date, ifcode, period_short=12, period_long=26, period_dif=9, \
pre_point=init_point, pre_time=init_time, offset=init_offset):
df = FittingDataCalculator.macd_df(date, ifcode, period_short, period_long, \
period_dif, pre_point, pre_time)
price = df[['time_index', 'price']].values.tolist()
macd_dif = df[['time_index', 'macd_dif']].values.tolist()
macd_dem = df[['time_index', 'macd_dem']].values.tolist()
# flag
sig_infos = SellSignal.compare_macd(df, 3, offset)
flags = SellSignal.out_flags(sig_infos)
return [price, macd_dif, macd_dem, flags]
from simplejson import dumps
from datetime import date
from models.api.backtest.calculator import FittingDataCalculator
save_dir = '/root/ema_backtest_data'
def save_data(df, date, ifcode, period_short, period_long):
f = open('%s/%s_%s_%s_%s' % (save_dir, date, ifcode, period_short, period_long), 'w')
try:
f.write(dumps(df))
f.close()
except BaseException, e:
print e
f.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="save ema dataframe to files")
parser.add_argument("ifcode", type=str, help="ifcode")
parser.add_argument("period_short", type=int, help="short period")
parser.add_argument("period_long", type=int, help="long period")
args = parser.parse_args()
ifcode = args.ifcode
period_short = args.period_short
period_long = args.period_long
thedate = date.today().strftime('%Y-%m-%d')
df = FittingDataCalculator.ema_df(thedate, ifcode, period_short, period_long)
save_data(df.values.tolist(), thedate, ifcode, period_short, period_long)
def boll(cls, date, ifcode, period_short=50, period_long=80, pre_point=10):
df = FittingDataCalculator.boll_df(date, ifcode, period_short, period_long, pre_point)
sig_infos = SellSignal.compare_boll_b_percent(df)
profit_infos = SellSignal.profit_infos(sig_infos)
return profit_infos
def raw_data(date, ifcode):
if not date or not ifcode:
return {'error': u'params is fault'}
date = str2day(date, DATE_DISPLAY_FORMAT).strftime(DATE_DISPLAY_FORMAT)
return FittingDataCalculator.raw_series(date, ifcode)