Overview¶
This notebook contains all experiment results exhibited in our paper.
In [1]:
%matplotlib inline
import glob
import numpy as np
import pandas as pd
import json
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
sns.set(style='white')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
from tqdm.auto import tqdm
from joblib import Parallel, delayed
def func(x, N=80):
ret = x.ret.copy()
x = x.rank(pct=True)
x['ret'] = ret
diff = x.score.sub(x.label)
r = x.nlargest(N, columns='score').ret.mean()
r -= x.nsmallest(N, columns='score').ret.mean()
return pd.Series({
'MSE': diff.pow(2).mean(),
'MAE': diff.abs().mean(),
'IC': x.score.corr(x.label),
'R': r
})
ret = pd.read_pickle("data/ret.pkl").clip(-0.1, 0.1)
def backtest(fname, **kwargs):
pred = pd.read_pickle(fname).loc['2018-09-21':'2020-06-30'] # test period
pred['ret'] = ret
dates = pred.index.unique(level=0)
res = Parallel(n_jobs=-1)(delayed(func)(pred.loc[d], **kwargs) for d in dates)
res = {
dates[i]: res[i]
for i in range(len(dates))
}
res = pd.DataFrame(res).T
r = res['R'].copy()
r.index = pd.to_datetime(r.index)
r = r.reindex(pd.date_range(r.index[0], r.index[-1])).fillna(0) # paper use 365 days
return {
'MSE': res['MSE'].mean(),
'MAE': res['MAE'].mean(),
'IC': res['IC'].mean(),
'ICIR': res['IC'].mean()/res['IC'].std(),
'AR': r.mean()*365,
'AV': r.std()*365**0.5,
'SR': r.mean()/r.std()*365**0.5,
'MDD': (r.cumsum().cummax() - r.cumsum()).max()
}, r
def fmt(x, p=3, scale=1, std=False):
_fmt = '{:.%df}'%p
string = _fmt.format((x.mean() if not isinstance(x, (float, np.floating)) else x) * scale)
if std and len(x) > 1:
string += ' ('+_fmt.format(x.std()*scale)+')'
return string
def backtest_multi(files, **kwargs):
res = []
pnl = []
for fname in files:
metric, r = backtest(fname, **kwargs)
res.append(metric)
pnl.append(r)
res = pd.DataFrame(res)
pnl = pd.concat(pnl, axis=1)
return {
'MSE': fmt(res['MSE'], std=True),
'MAE': fmt(res['MAE'], std=True),
'IC': fmt(res['IC']),
'ICIR': fmt(res['ICIR']),
'AR': fmt(res['AR'], scale=100, p=1)+'%',
'VR': fmt(res['AV'], scale=100, p=1)+'%',
'SR': fmt(res['SR']),
'MDD': fmt(res['MDD'], scale=100, p=1)+'%'
}, pnl
Preparation¶
You could prepare the source data as below for the backtest code:
- Linear: see Qlib examples
- LightGBM: see Qlib examples
- MLP: see Qlib examples
- SFM: see Qlib examples
- ALSTM:
qrunconfigs/config_alstm.yaml - Transformer:
qrunconfigs/config_transformer.yaml - ALSTM+TRA:
qrunconfigs/config_alstm_tra_init.yaml &&qrunconfigs/config_alstm_tra.yaml - Tranformer+TRA:
qrunconfigs/config_transformer_tra_init.yaml &&qrunconfigs/config_transformer_tra.yaml
In [2]:
exps = {
'Linear': ['output/Linear/pred.pkl'],
'LightGBM': ['output/GBDT/lr0.05_leaves128/pred.pkl'],
'MLP': glob.glob('output/search/MLP/hs128_bs512_do0.3_lr0.001_seed*/pred.pkl'),
'SFM': glob.glob('output/search/SFM/hs32_bs512_do0.5_lr0.001_seed*/pred.pkl'),
'ALSTM': glob.glob('output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),
'Trans.': glob.glob('output/search/Transformer/head4_hs64_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),
'ALSTM+TS':glob.glob('output/LSTM_Attn_TS/hs256_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),
'Trans.+TS':glob.glob('output/Transformer_TS/head4_hs64_bs1024_do0.1_lr0.0002_seed*/pred.pkl'),
'ALSTM+TRA(Ours)': glob.glob('output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),
'Trans.+TRA(Ours)': glob.glob('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb1.0_head4_hs64_bs512_do0.1_lr0.0005_seed*/pred.pkl')
}
In [3]:
res = {
name: backtest_multi(exps[name])
for name in tqdm(exps)
}
report = pd.DataFrame({
k: v[0]
for k, v in res.items()
}).T
0%| | 0/10 [00:00<?, ?it/s]
In [4]:
report
# print(report.to_latex())
Out[4]:
| MSE | MAE | IC | ICIR | AR | VR | SR | MDD | |
|---|---|---|---|---|---|---|---|---|
| Linear | 0.163 | 0.327 | 0.020 | 0.132 | -3.2% | 16.8% | -0.191 | 32.1% |
| LightGBM | 0.160 | 0.323 | 0.041 | 0.292 | 7.8% | 15.5% | 0.503 | 25.7% |
| MLP | 0.160 (0.002) | 0.323 (0.003) | 0.037 | 0.273 | 3.7% | 15.3% | 0.264 | 26.2% |
| SFM | 0.159 (0.001) | 0.321 (0.001) | 0.047 | 0.381 | 7.1% | 14.3% | 0.497 | 22.9% |
| ALSTM | 0.158 (0.001) | 0.320 (0.001) | 0.053 | 0.419 | 12.3% | 13.7% | 0.897 | 20.2% |
| Trans. | 0.158 (0.001) | 0.322 (0.001) | 0.051 | 0.400 | 14.5% | 14.2% | 1.028 | 22.5% |
| ALSTM+TS | 0.160 (0.002) | 0.321 (0.002) | 0.039 | 0.291 | 6.7% | 14.6% | 0.480 | 22.3% |
| Trans.+TS | 0.160 (0.004) | 0.324 (0.005) | 0.037 | 0.278 | 10.4% | 14.7% | 0.722 | 23.7% |
| ALSTM+TRA(Ours) | 0.157 (0.000) | 0.318 (0.000) | 0.059 | 0.460 | 12.4% | 14.0% | 0.885 | 20.4% |
| Trans.+TRA(Ours) | 0.157 (0.000) | 0.320 (0.000) | 0.056 | 0.442 | 16.1% | 14.2% | 1.133 | 23.1% |
RQ1¶
Case study
In [5]:
df = pd.read_pickle('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed1000/pred.pkl')
code = 'SH600157'
date = '2018-09-28'
lookbackperiod = 50
prob = df.iloc[:, -3:].loc(axis=0)[:, code].reset_index(level=1, drop=True).loc[date:].iloc[:lookbackperiod]
pred = df.loc[:,["score_0","score_1","score_2","label"]].loc(axis=0)[:, code].reset_index(level=1, drop=True).loc[date:].iloc[:lookbackperiod]
e_all = pred.iloc[:,:-1].sub(pred.iloc[:,-1], axis=0).pow(2)
e_all = e_all.sub(e_all.min(axis=1), axis=0)
e_all.columns = [r'$\theta_%d$'%d for d in range(1, 4)]
prob = pd.Series(np.argmax(prob.values, axis=1), index=prob.index).rolling(7).mean().round()
fig, axes = plt.subplots(1, 2, figsize=(7, 3))
e_all.plot(ax=axes[0], xlabel='', rot=30)
prob.plot(ax=axes[1], xlabel='', rot=30, color='red', linestyle='None', marker='^', markersize=5)
plt.yticks(np.array([0, 1, 2]), e_all.columns.values)
axes[0].set_ylabel('Predictor Loss')
axes[1].set_ylabel('Router Selection')
plt.tight_layout()
# plt.savefig('select.pdf', bbox_inches='tight')
plt.show()
RQ2¶
You could prepared the source data for this test as below:
- Random: Setting
src_info= "NONE" - LR: Setting
src_info= "LR" - TPE: Setting
src_info= "TPE" - LR+TPE: Setting
src_info= "LR_TPE"
In [6]:
exps = {
'Random': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcNONE_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),
'LR': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcLR_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),
'TPE': glob.glob('output/search/LSTM_Attn_tra/K10_traHs16_traSrcTPE_traLamb1.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl'),
'LR+TPE': glob.glob('output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/pred.pkl')
}
In [7]:
res = {
name: backtest_multi(exps[name])
for name in tqdm(exps)
}
report = pd.DataFrame({
k: v[0]
for k, v in res.items()
}).T
0%| | 0/4 [00:00<?, ?it/s]
In [8]:
report
# print(report.to_latex())
Out[8]:
| MSE | MAE | IC | ICIR | AR | VR | SR | MDD | |
|---|---|---|---|---|---|---|---|---|
| Random | 0.159 (0.001) | 0.321 (0.002) | 0.048 | 0.362 | 11.4% | 14.1% | 0.810 | 21.1% |
| LR | 0.158 (0.001) | 0.320 (0.001) | 0.053 | 0.409 | 10.3% | 13.4% | 0.772 | 20.8% |
| TPE | 0.158 (0.001) | 0.321 (0.001) | 0.049 | 0.381 | 10.3% | 14.0% | 0.741 | 21.2% |
| LR+TPE | 0.157 (0.000) | 0.318 (0.000) | 0.059 | 0.460 | 12.4% | 14.0% | 0.885 | 20.4% |
RQ3¶
Set lamb = 0 to obtain results without Optimal Transport(OT)
In [9]:
a = pd.read_pickle('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb0.0_head4_hs64_bs512_do0.1_lr0.0005_seed3000/pred.pkl')
b = pd.read_pickle('output/search/finetune/Transformer_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_head4_hs64_bs512_do0.1_lr0.0005_seed3000/pred.pkl')
a = a.iloc[:, -3:]
b = b.iloc[:, -3:]
b = np.eye(3)[b.values.argmax(axis=1)]
a = np.eye(3)[a.values.argmax(axis=1)]
res = pd.DataFrame({
'with OT': b.sum(axis=0) / b.sum(),
'without OT': a.sum(axis=0)/ a.sum()
},index=[r'$\theta_1$',r'$\theta_2$',r'$\theta_3$'])
res.plot.bar(rot=30, figsize=(5, 4), color=['b', 'g'])
del a, b
RQ4¶
You could prepared the source data for this test as below:
- K=1: which is exactly the alstm model
- K=3: Setting
num_states= 3 - K=5: Setting
num_states= 5 - K=10: Setting
num_states= 10 - K=20: Setting
num_states= 20
In [10]:
exps = {
'K=1': glob.glob('output/search/LSTM_Attn/hs256_bs1024_do0.1_lr0.0002_seed*/info.json'),
'K=3': glob.glob('output/search/finetune/LSTM_Attn_tra/K3_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),
'K=5': glob.glob('output/search/finetune/LSTM_Attn_tra/K5_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),
'K=10': glob.glob('output/search/finetune/LSTM_Attn_tra/K10_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json'),
'K=20': glob.glob('output/search/finetune/LSTM_Attn_tra/K20_traHs16_traSrcLR_TPE_traLamb2.0_hs256_bs1024_do0.1_lr0.0001_seed*/info.json')
}
In [11]:
report = dict()
for k, v in exps.items():
tmp = dict()
for fname in v:
with open(fname) as f:
info = json.load(f)
tmp[fname] = (
{
"IC":info["metric"]["IC"],
"MSE":info["metric"]["MSE"]
})
tmp = pd.DataFrame(tmp).T
report[k] = tmp.mean()
report = pd.DataFrame(report).T
In [12]:
fig, axes = plt.subplots(1, 2, figsize=(6,3)); axes = axes.flatten()
report['IC'].plot.bar(rot=30, ax=axes[0])
axes[0].set_ylim(0.045, 0.062)
axes[0].set_title('IC performance')
report['MSE'].astype(float).plot.bar(rot=30, ax=axes[1], color='green')
axes[1].set_ylim(0.155, 0.1585)
axes[1].set_title('MSE performance')
plt.tight_layout()
# plt.savefig('sensitivity.pdf')
In [13]:
report
Out[13]:
| IC | MSE | |
|---|---|---|
| K=1 | 0.053247 | 0.157792 |
| K=3 | 0.055535 | 0.157410 |
| K=5 | 0.059224 | 0.156796 |
| K=10 | 0.059403 | 0.156766 |
| K=20 | 0.059193 | 0.156801 |
In [ ]: