def main(ifcode, period_short, period_long):
rs = []
total = 0
pos_num = 0
nag_num = 0
date_list = ifcode_day_map(ifcode)
for day in date_list:
df = get_data(ifcode, day)
#df = point_prosess_v2(_df, 30)
df['ema_short'] = Indicator.ewma_metric(period_short, df[['price']],
'price')
df['ema_long'] = Indicator.ewma_metric(period_long, df[['price']],
'price')
sig_infos = SellSignal.compare_ema(df, period_short, offset=5)
profit_infos = SellSignal.profit_infos(sig_infos)
profit_all = 0
trans_num = (len(profit_infos) + 1) / 2
for item in profit_infos:
if item['gain'] != '-':
profit_all += int(item['gain'])
rs.append({day: profit_all, 'trans_num': trans_num})
total += profit_all
if profit_all >= 0:
pos_num += 1
elif profit_all < 0:
nag_num += 1
pprint(rs)
print '%s total: %s' % (ifcode, total)
print '%s profit rate: %.2f' % (ifcode, pos_num * 1.0 / nag_num)
def get_all_trans(ifcode, period_short, period_long, ammout=1):
rs = []
total = 0
pos_num = 0
nag_num = 0
trans_all = 0
date_list = ifcode_day_map(ifcode)
for day in date_list:
_df = get_data(ifcode, day)
df = macd_df(_df, period_short, period_long)
sig_infos = SellSignal.compare_macd(df, 2, 25)
profit_infos = SellSignal.profit_infos(sig_infos)
profit_all = 0
trans_num = (len(profit_infos) - 1) / 2
trans_all += trans_num
for item in profit_infos:
if item['gain'] != '-':
profit_all += int(item['gain']) * ammout
rs.append({day: profit_all, 'trans_num': trans_num})
total += profit_all
if profit_all >= 0:
pos_num += 1
elif profit_all < 0:
nag_num += 1
pprint(rs)
print '%s total: %s' % (ifcode, total)
print '%s profit rate: %.2f' % (ifcode, pos_num * 1.0 / nag_num)
print '%s trans all number: %s' % (ifcode, trans_all)
print '%s fees : %s' % (ifcode, trans_all * 2300)
print '%s real profit : %s' % (ifcode, total - trans_all * 2300)
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]
def get_first_trans(ifcode, period_short, period_long):
rs = []
total = 0
pos_num = 0
nag_num = 0
date_list = ifcode_day_map(ifcode)
for day in date_list:
_df = get_data(ifcode, day)
df = macd_df(_df, period_short, period_long)
sig_infos = Signal.compare_macd(df, 2, 30)
profit_infos = SellSignal.profit_infos(sig_infos)
profit_first = 0
for item in profit_infos:
if item['gain'] != '-':
profit_first = int(item['gain'])
break
rs.append({day: profit_first})
if profit_first >= 0:
pos_num += 1
elif profit_first < 0:
nag_num += 1
print '每天第一笔交易'
pprint(rs)
print '%s profit rate: %.2f' % (ifcode, pos_num * 1.0 / nag_num)
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