def ana_fun(data_df, fut_name, col_name):
raw_return_df = data_df['price_return']
# raw_return_df = data_df['pnl']
signal_df = data_df[col_name]
SignalAnalysis.CDF(signal_df,
raw_return_df,
hold_time=1,
title=f'{fut_name} {col_name} CDF Figure',
lag=1)
def main():
fut_name = 'RB'
data_df = test_fun(fut_name, 400, 1)
# part_data_df = data_df[(data_df['Time'] > '14:51') & (data_df['Time'] <= '21:10')]
col_name = 'Time'
raw_return_df = data_df['pnl']
signal_df = data_df[col_name].shift(2).replace(np.nan, '00:00')
SignalAnalysis.CDF_c(signal_df,
raw_return_df,
hold_time=1,
title=f'{fut_name} {col_name} CDF Figure',
lag=0)
ana_fun(data_df, fut_name, 'Volume_zscore')
ana_fun(data_df, fut_name, 'past_min_pct_change')
ana_fun(data_df, fut_name, 'trend_signal')
return data_df
def single_main():
fut_name, window, limit, cut_num = 'J', 40, 1, 20
data_df = test_fun(fut_name, window, limit, cut_num)
part_data_df = data_df
col_name = 'Time'
raw_return_df = part_data_df['pnl']
signal_df = part_data_df[col_name].shift(2).replace(np.nan, '00:00')
SignalAnalysis.CDF_c(signal_df,
raw_return_df,
hold_time=1,
title=f'{fut_name} {col_name} CDF Figure',
lag=0)
# ana_fun(data_df, fut_name, 'OpenInterest_core')
# ana_fun(data_df, fut_name, 'Volume_zscore')
# ana_fun(data_df, fut_name, 'Volume_boll')
# ana_fun(data_df, fut_name, 'past_min_pct_change')
# ana_fun(data_df, fut_name, 'trend_indicator')
ana_fun(data_df, fut_name, 'boll_score')
ana_fun(data_df, fut_name, 'CCI_score')
ana_fun(data_df, fut_name, 'OpenInterest_core')
ana_fun(data_df, fut_name, 'Volume_zscore')
ana_fun(data_df, fut_name, 'past_min_pct_change')
ana_fun(data_df, fut_name, 'trend_indicator')
ana_fun(data_df, fut_name, 'macd')
ana_fun(data_df, fut_name, 'RSI')
ana_fun(data_df, fut_name, 'obv')
ana_fun(data_df, fut_name, 'atr')
ana_fun(data_df, fut_name, 'adx')
ana_fun(data_df, fut_name, 'trix')
ana_fun(data_df, fut_name, 'willr')
ana_fun(data_df, fut_name, 'Vol_OI')
return data_df
def generation(self, fut_name, window, limit, concat=True, test=False):
result_list = []
pool = Pool(20)
for con_id, part_info_df in self.active_df[[
f'{fut_name}01'
]].groupby(f'{fut_name}01'):
# active_begin = self.next_trade_date(part_info_df.index[0]) + timedelta(hours=17)
# active_end = part_info_df.index[-1] + timedelta(hours=17)
args = [fut_name, con_id, window, limit]
result_list.append(
pool.apply_async(self.part_generation, args=args))
# self.part_generation(*args)
pool.close()
pool.join()
print(123)
pnl_df = pd.concat([res.get()[0] for res in result_list], axis=0)
turnover_df = pd.concat([res.get()[1] for res in result_list], axis=0)
data_df = pd.concat([res.get()[2] for res in result_list], axis=0)
pot = pnl_df.sum() / turnover_df.sum() * 10000
sp = bt.AZ_Sharpe_y(pnl_df)
print(fut_name, sp, pot, window, limit, cut_num)
Close_p = pd.read_csv(f'/mnt/mfs/DAT_FUT/day/{fut_name}/Close',
sep='|',
index_col=0,
parse_dates=True)
Open_p = pd.read_csv(f'/mnt/mfs/DAT_FUT/day/{fut_name}/Open',
sep='|',
index_col=0,
parse_dates=True)
jump_diff = Open_p / Close_p.shift(1) - 1
self.jump_diff = jump_diff.reindex(self.active_df.index)
act_info_sr = self.active_df[f'{fut_name}01']
fut_jump = self.jump_diff.apply(lambda x, y: x * (y == x.name),
args=(act_info_sr, )).sum(1)
if (abs(sp) > 1.2 and pot > 4) or test:
# if (sp < -1.2) or test:
plt.figure(figsize=[16, 8])
pnl_df.index = pd.to_datetime(pnl_df.index)
plt.plot(pnl_df.cumsum())
plt.grid()
savfig_send(
f'{fut_name} sp:{sp} pot={pot} CCI_window:{window}, CCI_limit:{limit} cut_num={self.cut_num}'
)
part_data_df = data_df
col_name = 'Time'
raw_return_df = part_data_df['pnl']
signal_df = part_data_df[col_name].shift(2).replace(
np.nan, '00:00')
SignalAnalysis.CDF_c(signal_df,
raw_return_df,
hold_time=1,
title=f'{fut_name} {col_name} CDF Figure',
lag=0)
ana_fun(data_df, fut_name, 'test_score')
ana_fun(data_df, fut_name, 'boll_score')
ana_fun(data_df, fut_name, 'CCI_score')
ana_fun(data_df, fut_name, 'trend_indicator')
ana_fun(data_df, fut_name, 'macd')
ana_fun(data_df, fut_name, 'RSI')
ana_fun(data_df, fut_name, 'obv')
ana_fun(data_df, fut_name, 'atr')
ana_fun(data_df, fut_name, 'adx')
ana_fun(data_df, fut_name, 'trix')
ana_fun(data_df, fut_name, 'willr')
ana_fun(data_df, fut_name, 'past_min_pct_change')
ana_fun(data_df, fut_name, 'Vol_OI')
ana_fun(data_df, fut_name, 'OI_boll')
ana_fun(data_df, fut_name, 'adosc')
ana_fun(data_df, fut_name, 'cmo')
ana_fun(data_df, fut_name, 'mfi')
# raw_return_df = data_df.groupby(by=['Date'])['pnl'].apply(lambda x: x.iloc[1:].sum())
# signal_df = fut_jump
# SignalAnalysis.CDF(signal_df, raw_return_df, hold_time=1, title=f'{fut_name} jump CDF Figure', lag=1)
self.pos_to_bkt(data_df, concat)
return data_df, sp, pot
def generation(self, fut_name, window, limit, concat=True):
result_list = []
pool = Pool(20)
for con_id, part_info_df in self.active_df[[
f'{fut_name}01'
]].groupby(f'{fut_name}01'):
# active_begin = self.last_trade_date(part_info_df.index[0]) + timedelta(hours=17)
# active_end = part_info_df.index[-1] + timedelta(hours=17)
args = [fut_name, con_id, window, limit]
result_list.append(
pool.apply_async(self.part_generation, args=args))
# self.part_generation(*args)
pool.close()
pool.join()
pnl_df = pd.concat([res.get()[0] for res in result_list], axis=0)
turnover_df = pd.concat([res.get()[1] for res in result_list], axis=0)
data_df = pd.concat([res.get()[2] for res in result_list], axis=0)
pot = pnl_df.sum() / turnover_df.sum() * 10000
sp = bt.AZ_Sharpe_y(pnl_df)
print(fut_name, sp, pot, window, limit, cut_num)
if abs(sp) > 1.2 and abs(pot) > 6:
plt.figure(figsize=[16, 8])
pnl_df.index = pd.to_datetime(pnl_df.index)
plt.plot(pnl_df.cumsum())
plt.grid()
savfig_send(
f'{fut_name} sp:{sp} pot={pot} CCI_window:{window}, CCI_limit:{limit} cut_num={self.cut_num}'
)
part_data_df = data_df
col_name = 'Time'
raw_return_df = part_data_df['pnl']
signal_df = part_data_df[col_name].shift(2).replace(
np.nan, '00:00')
SignalAnalysis.CDF_c(signal_df,
raw_return_df,
hold_time=1,
title=f'{fut_name} {col_name} CDF Figure',
lag=0)
ana_fun(data_df, fut_name, 'test_score')
ana_fun(data_df, fut_name, 'boll_score')
ana_fun(data_df, fut_name, 'CCI_score')
ana_fun(data_df, fut_name, 'trend_indicator')
ana_fun(data_df, fut_name, 'macd')
ana_fun(data_df, fut_name, 'RSI')
ana_fun(data_df, fut_name, 'obv')
ana_fun(data_df, fut_name, 'atr')
ana_fun(data_df, fut_name, 'adx')
ana_fun(data_df, fut_name, 'trix')
ana_fun(data_df, fut_name, 'willr')
ana_fun(data_df, fut_name, 'Vol_OI')
self.pos_to_bkt(data_df, concat)
return data_df, sp, pot
def send_cdf_fun(window, bar_num):
for fut_name in list(fut_name_dict.keys()):
print(fut_name)
# print(fut_data.act_info_df[fut_name.upper() + '01'])
if fut_name.upper() + '01' not in fut_data.act_info_df.columns:
continue
active_df = fut_data.act_info_df[fut_name.upper() + '01'] \
.truncate(datetime(2019, 6, 1), datetime(2019, 6, 30))
sub_dict = fut_name_dict[fut_name]
signal_list = []
return_list = []
for target_date, target_id in list(active_df.dropna().items()):
con_intra_data = fut_data.load_intra_data(target_id,
usecols_list=['Close'])
con_time_range = con_intra_data.index
# target_date = datetime(2019, 6, 27)
target_id_low = target_id.split('.')[0].lower()
# print(target_id)
# print(sub_dict)
# print(sub_dict[target_id_low])
daily_path = f"{root_path}/{target_id_low}_{target_date.strftime('%Y%m%d')}.csv"
data = pd.read_csv(daily_path, encoding='gbk')
data['way'] = data['方向'].apply(lambda x: 1 if x == 'B' else
(-1 if x == 'S' else 0))
data.index = pd.to_datetime(data['时间'])
today_begin = pd.to_datetime(
f"{data.index[0].strftime('%Y%m%d')} 21:00")
today_end = pd.to_datetime(
f"{data.index[-1].strftime('%Y%m%d')} 16:00")
today_min_range = con_time_range[(con_time_range > today_begin)
& (con_time_range < today_end)]
# print(target_id_low, target_date)
# tick_data = data[['时间', '最新', '成交量', '持仓', '增仓', '买一价', '买一量', '卖一价', '卖一量', '方向']]
# tick_data.index = pd.to_datetime(tick_data['时间'])
# print(big_vol)
# for i in range(int(len(today_min_range) / bar_num)):
# # print(today_min_range[bar_num * i:bar_num * (i + 1)])
# tmp_begin = today_min_range[bar_num * i] - timedelta(minutes=1)
# tmp_end = today_min_range[bar_num * (i + 1) - 1]
# bs_rt = get_big_vol_and_rt(data, tmp_begin, tmp_end)
# print(tmp_begin, tmp_end, bs_rt)
# bs_rt_list_1 = [get_big_vol_and_rt(data, today_min_range[bar_num * i] - timedelta(minutes=1),
# today_min_range[bar_num * (i + 1) - 1])
# for i in range(int(len(today_min_range) / bar_num))]
bs_rt_list = [
get_big_vol_and_rt(
data, i_time + timedelta(minutes=-(window - bar_num) - 1),
i_time + timedelta(minutes=bar_num - 1))
for i_time in today_min_range[::bar_num]
]
# bs_rt_list = np.array(bs_rt_list)
trade_time_list = today_min_range[::bar_num]
# return_df = con_intra_data['']
price_diff = con_intra_data['Close'].shift(-bar_num).loc[trade_time_list] - \
con_intra_data['Close'].loc[trade_time_list]
price_diff_list = list(price_diff.values)
signal_list += bs_rt_list[:-1]
return_list += price_diff_list[1:]
SignalAnalysis.CDF(
pd.Series(signal_list),
pd.Series(return_list),
hold_time=1,
title=f'{fut_name} {window} {bar_num}{target_date} CDF Figure',
lag=1)
def test_fun(fut_name, CCI_window, CCI_limit, hold_time, cut_num):
result_list = []
pool = Pool(20)
for con_id, part_info_df in active_df[[f'{fut_name}01'
]].groupby(f'{fut_name}01'):
active_begin = fut_data.last_trade_date(
part_info_df.index[0]) + timedelta(hours=17)
active_end = part_info_df.index[-1] + timedelta(hours=17)
args = [
con_id, active_begin, active_end, CCI_window, CCI_limit, hold_time,
cut_num
]
# part_test(*args)
# print(part_info_df.index[0], part_info_df.index[-1])
result_list.append(pool.apply_async(part_test, args=args))
pool.close()
pool.join()
pnl_df = pd.concat([res.get()[0] for res in result_list], axis=0)
turnover_df = pd.concat([res.get()[1] for res in result_list], axis=0)
data_df = pd.concat([res.get()[2] for res in result_list], axis=0)
pot = pnl_df.sum() / turnover_df.sum() * 10000
sp = bt.AZ_Sharpe_y(pnl_df)
# for x, part_data_df in data_df.groupby(['weekday']):
# part_pnl = part_data_df['pnl'].fillna(0).values
# print(x, part_pnl.sum())
# plt.plot(part_pnl.cumsum())
# savfig_send(subject=f'{x} {bt.AZ_Sharpe_y(part_pnl)}')
#
# for x, part_data_df in data_df.groupby(['month']):
# part_pnl = part_data_df['pnl'].fillna(0).values
# print(x, part_pnl.sum())
# plt.plot(part_pnl.cumsum())
# savfig_send(subject=f'{x} {bt.AZ_Sharpe_y(part_pnl)}')
print(fut_name, sp, pot, CCI_window, CCI_limit, hold_time, cut_num)
if abs(sp) > 1. and abs(pot) > 10:
plt.figure(figsize=[16, 8])
pnl_df.index = pd.to_datetime(pnl_df.index)
plt.plot(pnl_df.cumsum())
plt.grid()
savfig_send(
f'{fut_name} sp:{sp} pot={pot} CCI_window:{CCI_window}, CCI_limit:{CCI_limit}, cut_num={cut_num}'
)
part_data_df = data_df
col_name = 'Time'
raw_return_df = part_data_df['pnl']
signal_df = part_data_df[col_name].shift(2).replace(np.nan, '00:00')
SignalAnalysis.CDF_c(signal_df,
raw_return_df,
hold_time=1,
title=f'{fut_name} {col_name} CDF Figure',
lag=0)
ana_fun(data_df, fut_name, 'test_score')
ana_fun(data_df, fut_name, 'boll_score')
ana_fun(data_df, fut_name, 'CCI_score')
ana_fun(data_df, fut_name, 'OpenInterest_core')
ana_fun(data_df, fut_name, 'Volume_zscore')
ana_fun(data_df, fut_name, 'past_min_pct_change')
ana_fun(data_df, fut_name, 'trend_indicator')
ana_fun(data_df, fut_name, 'macd')
ana_fun(data_df, fut_name, 'RSI')
ana_fun(data_df, fut_name, 'obv')
ana_fun(data_df, fut_name, 'atr')
ana_fun(data_df, fut_name, 'adx')
ana_fun(data_df, fut_name, 'trix')
ana_fun(data_df, fut_name, 'willr')
ana_fun(data_df, fut_name, 'Vol_OI')
return data_df
'adj_r',
act_info_df,
active_num='01').fillna(0)
# Close_01 = fut_data.load_act_fut_data_r(fut_name, 'Close', act_info_df, active_num='01')
# sig_rtn = adj_r_01 # - bt.AZ_Rolling_mean(adj_r_01, 5)
sig_rtn = bt.AZ_Rolling_mean(adj_r_01, 5)
# sig_rtn = Close_01/bt.AZ_Rolling_mean(Close_01, 5) - 1
cond2 = sig_rtn.apply(lambda x: 0 if abs(x) == 0 else (1 if x > 0 else 0))
signal = cond1 * cond2
pnl_df = signal.shift(1) * adj_r_01
asset_df = pnl_df.cumsum()
SignalAnalysis.CDF(sig_rtn * cond1,
adj_r_01,
hold_time=1,
title=f'{fut_name} CDF Figure',
lag=1,
zero_drop=True)
plt.figure(figsize=[16, 10])
plt.plot(asset_df.index, asset_df.values)
plt.legend()
plt.grid()
savfig_send(fut_name)
# Close = fut_data.load_fut_data(fut_name, 'Close')
# OpenInterest = fut_data.load_fut_data(fut_name, 'OpenInterest')
# def part_test_fun(instrument):
# adj_close_df = FutData().load_act_adj_fut_data(instrument, 'Close', active_df, aadj_factor_df)
# adj_high_df = FutData().load_act_adj_fut_data(instrument, 'High', active_df, aadj_factor_df)
# adj_low_df = FutData().load_act_adj_fut_data(instrument, 'Low', active_df, aadj_factor_df)