diff --git a/.gitignore b/.gitignore
index f7503e0..b251fd4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,3 +116,4 @@ profile.pstats
 profile.txt
 bat.cmd
 /backup/
+/data/v4.1/*.csv
diff --git a/README.md b/README.md
index 13386a9..b2725e8 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,9 @@
-# 파이썬를 이용한 딥러닝/강화학습 주식투자 - 파이토치와 케라스를 활용한 인공지능 퀀트 투자 시스템 (개정2판)
+- 추가 개선 사항은 [퀀티랩 프리미엄콘텐츠](https://contents.premium.naver.com/misoncorp/quantylab)에서 공유합니다.
+- [퀀티랩 블로그 관련 포스트](http://blog.quantylab.com/pages/rltrader.html)
+
+---
 
-[퀀티랩 블로그 관련 포스트](http://blog.quantylab.com/pages/rltrader.html)
+# 파이썬를 이용한 딥러닝/강화학습 주식투자 - 파이토치와 케라스를 활용한 인공지능 퀀트 투자 시스템 (개정2판)
 
 강화학습은 스스로 학습하는 머신러닝 기법으로서 주식 데이터 학습에 잘 적용되는 기법입니다. 이 책은 파이썬을 이용한 강화학습 기반의 주식투자 시뮬레이션 프로그램을 개발하는 방법을 설명합니다. 이를 위해 이론과 코드 수준에서 상세한 설명을 덧붙였습니다. 이 책을 통해 딥러닝과 강화학습을 이해하고 이를 주식투자를 비롯한 다양한 도메인에서 활용할 수 있을 것입니다.
 
@@ -112,6 +115,14 @@ v3, v4 학습데이터는 시장 데이터와 종목 데이터를 합하여 사
 - `python -m cProfile -o profile.pstats main.py ...`
 - `python profile.py`
 
+# Tips
+
+- Windows Power Shell에서 로그 Tail 하는 방법
+
+```
+cat D:\dev\rltrader\output\train_000240_a2c_lstm\train_000240_a2c_lstm.log -Wait -Tail 100
+```
+
 # Troubleshooting
 
 ## TF 1.15에서 다음 에러가 나면 Python 3.6으로 맞춰준다.
diff --git a/conf/data/v4.1/feature_list.txt b/conf/data/v4.1/feature_list.txt
new file mode 100644
index 0000000..df0f1c3
--- /dev/null
+++ b/conf/data/v4.1/feature_list.txt
@@ -0,0 +1,463 @@
+diffratio
+high_close_ratio
+low_close_ratio
+open_lastclose_ratio
+volume_lastvolume_ratio
+trans_price_exp
+trans_price_exp_ma5
+close_ma5_ratio
+close_ma10_ratio
+close_ma20_ratio
+close_ma60_ratio
+close_ma120_ratio
+volume_ma5_ratio
+volume_ma10_ratio
+volume_ma20_ratio
+volume_ma60_ratio
+volume_ma120_ratio
+close_ubb_ratio
+close_lbb_ratio
+macd_signal_ratio
+rsi
+buy_strength_ma5_ratio
+sell_strength_ma5_ratio
+prevvalid_cnt
+per
+pbr
+roe
+roa
+per_krx
+pbr_krx
+roe_krx
+dyr_krx
+marketcap
+ind
+ind_diff
+ind_ma5
+ind_ma10
+ind_ma20
+ind_ma60
+ind_ma120
+inst
+inst_diff
+inst_ma5
+inst_ma10
+inst_ma20
+inst_ma60
+inst_ma120
+foreign
+foreign_diff
+foreign_ma5
+foreign_ma10
+foreign_ma20
+foreign_ma60
+foreign_ma120
+bal_rto
+bal_rto_diff
+bal_rto_ma10
+bal_rto_ma120
+bal_rto_ma20
+bal_rto_ma5
+bal_rto_ma60
+short_ratio
+short_ratio_diff
+short_ratio_ma10
+short_ratio_ma120
+short_ratio_ma20
+short_ratio_ma5
+short_ratio_ma60
+market_kospi_diffratio
+market_kospi_ma5_ratio
+market_kospi_ma20_ratio
+market_kospi_ma60_ratio
+market_kospi_ma120_ratio
+market_kosdaq_diffratio
+market_kosdaq_ma5_ratio
+market_kosdaq_ma20_ratio
+market_kosdaq_ma60_ratio
+market_kosdaq_ma120_ratio
+market_kospi_volume_diffratio
+market_kospi_volume_ma5_ratio
+market_kospi_volume_ma20_ratio
+market_kospi_volume_ma60_ratio
+market_kospi_volume_ma120_ratio
+market_kosdaq_volume_diffratio
+market_kosdaq_volume_ma5_ratio
+market_kosdaq_volume_ma20_ratio
+market_kosdaq_volume_ma60_ratio
+market_kosdaq_volume_ma120_ratio
+fmarket_dji_diffratio
+fmarket_dji_ma5_ratio
+fmarket_dji_ma20_ratio
+fmarket_dji_ma60_ratio
+fmarket_dji_ma120_ratio
+fmarket_ni225_diffratio
+fmarket_ni225_ma5_ratio
+fmarket_ni225_ma20_ratio
+fmarket_ni225_ma60_ratio
+fmarket_ni225_ma120_ratio
+fmarket_hsi_diffratio
+fmarket_hsi_ma5_ratio
+fmarket_hsi_ma20_ratio
+fmarket_hsi_ma60_ratio
+fmarket_hsi_ma120_ratio
+fmarket_dji_volume_diffratio
+fmarket_dji_volume_ma5_ratio
+fmarket_dji_volume_ma20_ratio
+fmarket_dji_volume_ma60_ratio
+fmarket_dji_volume_ma120_ratio
+fmarket_ni225_volume_diffratio
+fmarket_ni225_volume_ma5_ratio
+fmarket_ni225_volume_ma20_ratio
+fmarket_ni225_volume_ma60_ratio
+fmarket_ni225_volume_ma120_ratio
+fmarket_hsi_volume_diffratio
+fmarket_hsi_volume_ma5_ratio
+fmarket_hsi_volume_ma20_ratio
+fmarket_hsi_volume_ma60_ratio
+fmarket_hsi_volume_ma120_ratio
+bond_k3y_diffratio
+bond_k3y_ma5_ratio
+bond_k3y_ma20_ratio
+bond_k3y_ma60_ratio
+bond_k3y_ma120_ratio
+bond_k3y_volume_diffratio
+bond_k3y_volume_ma5_ratio
+bond_k3y_volume_ma20_ratio
+bond_k3y_volume_ma60_ratio
+bond_k3y_volume_ma120_ratio
+interestrates_base_diffratio
+interestrates_base_ma5_ratio
+interestrates_base_ma20_ratio
+interestrates_base_ma60_ratio
+interestrates_base_ma120_ratio
+interestrates_base
+interestrates_us_target_diffratio
+interestrates_us_target_ma5_ratio
+interestrates_us_target_ma20_ratio
+interestrates_us_target_ma60_ratio
+interestrates_us_target_ma120_ratio
+interestrates_us_target
+interestrates_us_10y_diffratio
+interestrates_us_10y_ma5_ratio
+interestrates_us_10y_ma20_ratio
+interestrates_us_10y_ma60_ratio
+interestrates_us_10y_ma120_ratio
+interestrates_us_10y
+interestrates_us_30y_diffratio
+interestrates_us_30y_ma5_ratio
+interestrates_us_30y_ma20_ratio
+interestrates_us_30y_ma60_ratio
+interestrates_us_30y_ma120_ratio
+interestrates_us_30y
+commodity_aluminum_diffratio
+commodity_aluminum_ma5_ratio
+commodity_aluminum_ma20_ratio
+commodity_aluminum_ma60_ratio
+commodity_aluminum_ma120_ratio
+commodity_cocoa_diffratio
+commodity_cocoa_ma5_ratio
+commodity_cocoa_ma20_ratio
+commodity_cocoa_ma60_ratio
+commodity_cocoa_ma120_ratio
+commodity_coffee_diffratio
+commodity_coffee_ma5_ratio
+commodity_coffee_ma20_ratio
+commodity_coffee_ma60_ratio
+commodity_coffee_ma120_ratio
+commodity_copper_diffratio
+commodity_copper_ma5_ratio
+commodity_copper_ma20_ratio
+commodity_copper_ma60_ratio
+commodity_copper_ma120_ratio
+commodity_corn_diffratio
+commodity_corn_ma5_ratio
+commodity_corn_ma20_ratio
+commodity_corn_ma60_ratio
+commodity_corn_ma120_ratio
+commodity_cotton_diffratio
+commodity_cotton_ma5_ratio
+commodity_cotton_ma20_ratio
+commodity_cotton_ma60_ratio
+commodity_cotton_ma120_ratio
+commodity_gold_domestic_diffratio
+commodity_gold_domestic_ma5_ratio
+commodity_gold_domestic_ma20_ratio
+commodity_gold_domestic_ma60_ratio
+commodity_gold_domestic_ma120_ratio
+commodity_gold_world_diffratio
+commodity_gold_world_ma5_ratio
+commodity_gold_world_ma20_ratio
+commodity_gold_world_ma60_ratio
+commodity_gold_world_ma120_ratio
+commodity_heating_oil_diffratio
+commodity_heating_oil_ma5_ratio
+commodity_heating_oil_ma20_ratio
+commodity_heating_oil_ma60_ratio
+commodity_heating_oil_ma120_ratio
+commodity_iron_diffratio
+commodity_iron_ma5_ratio
+commodity_iron_ma20_ratio
+commodity_iron_ma60_ratio
+commodity_iron_ma120_ratio
+commodity_lead_diffratio
+commodity_lead_ma5_ratio
+commodity_lead_ma20_ratio
+commodity_lead_ma60_ratio
+commodity_lead_ma120_ratio
+commodity_lumber_diffratio
+commodity_lumber_ma5_ratio
+commodity_lumber_ma20_ratio
+commodity_lumber_ma60_ratio
+commodity_lumber_ma120_ratio
+commodity_natural_gas_diffratio
+commodity_natural_gas_ma5_ratio
+commodity_natural_gas_ma20_ratio
+commodity_natural_gas_ma60_ratio
+commodity_natural_gas_ma120_ratio
+commodity_nickel_diffratio
+commodity_nickel_ma5_ratio
+commodity_nickel_ma20_ratio
+commodity_nickel_ma60_ratio
+commodity_nickel_ma120_ratio
+commodity_oil_diesel_diffratio
+commodity_oil_diesel_ma5_ratio
+commodity_oil_diesel_ma20_ratio
+commodity_oil_diesel_ma60_ratio
+commodity_oil_diesel_ma120_ratio
+commodity_oil_gasoline_diffratio
+commodity_oil_gasoline_ma5_ratio
+commodity_oil_gasoline_ma20_ratio
+commodity_oil_gasoline_ma60_ratio
+commodity_oil_gasoline_ma120_ratio
+commodity_oil_wti_diffratio
+commodity_oil_wti_ma5_ratio
+commodity_oil_wti_ma20_ratio
+commodity_oil_wti_ma60_ratio
+commodity_oil_wti_ma120_ratio
+commodity_orange_juice_diffratio
+commodity_orange_juice_ma5_ratio
+commodity_orange_juice_ma20_ratio
+commodity_orange_juice_ma60_ratio
+commodity_orange_juice_ma120_ratio
+commodity_palladium_diffratio
+commodity_palladium_ma5_ratio
+commodity_palladium_ma20_ratio
+commodity_palladium_ma60_ratio
+commodity_palladium_ma120_ratio
+commodity_platinum_diffratio
+commodity_platinum_ma5_ratio
+commodity_platinum_ma20_ratio
+commodity_platinum_ma60_ratio
+commodity_platinum_ma120_ratio
+commodity_rice_diffratio
+commodity_rice_ma5_ratio
+commodity_rice_ma20_ratio
+commodity_rice_ma60_ratio
+commodity_rice_ma120_ratio
+commodity_silver_diffratio
+commodity_silver_ma5_ratio
+commodity_silver_ma20_ratio
+commodity_silver_ma60_ratio
+commodity_silver_ma120_ratio
+commodity_soybean_diffratio
+commodity_soybean_ma5_ratio
+commodity_soybean_ma20_ratio
+commodity_soybean_ma60_ratio
+commodity_soybean_ma120_ratio
+commodity_soybean_gourd_diffratio
+commodity_soybean_gourd_ma5_ratio
+commodity_soybean_gourd_ma20_ratio
+commodity_soybean_gourd_ma60_ratio
+commodity_soybean_gourd_ma120_ratio
+commodity_soybean_milk_diffratio
+commodity_soybean_milk_ma5_ratio
+commodity_soybean_milk_ma20_ratio
+commodity_soybean_milk_ma60_ratio
+commodity_soybean_milk_ma120_ratio
+commodity_sugar11_diffratio
+commodity_sugar11_ma5_ratio
+commodity_sugar11_ma20_ratio
+commodity_sugar11_ma60_ratio
+commodity_sugar11_ma120_ratio
+commodity_tin_diffratio
+commodity_tin_ma5_ratio
+commodity_tin_ma20_ratio
+commodity_tin_ma60_ratio
+commodity_tin_ma120_ratio
+commodity_wheat_diffratio
+commodity_wheat_ma5_ratio
+commodity_wheat_ma20_ratio
+commodity_wheat_ma60_ratio
+commodity_wheat_ma120_ratio
+commodity_zinc_diffratio
+commodity_zinc_ma5_ratio
+commodity_zinc_ma20_ratio
+commodity_zinc_ma60_ratio
+commodity_zinc_ma120_ratio
+gsci_diffratio
+gsci_ma5_ratio
+gsci_ma20_ratio
+gsci_ma60_ratio
+gsci_ma120_ratio
+fx_usdkrw_diffratio
+fx_usdkrw_ma5_ratio
+fx_usdkrw_ma20_ratio
+fx_usdkrw_ma60_ratio
+fx_usdkrw_ma120_ratio
+fx_eurkrw_diffratio
+fx_eurkrw_ma5_ratio
+fx_eurkrw_ma20_ratio
+fx_eurkrw_ma60_ratio
+fx_eurkrw_ma120_ratio
+fx_jpykrw_diffratio
+fx_jpykrw_ma5_ratio
+fx_jpykrw_ma20_ratio
+fx_jpykrw_ma60_ratio
+fx_jpykrw_ma120_ratio
+fx_cnykrw_diffratio
+fx_cnykrw_ma5_ratio
+fx_cnykrw_ma20_ratio
+fx_cnykrw_ma60_ratio
+fx_cnykrw_ma120_ratio
+fx_hkdkrw_diffratio
+fx_hkdkrw_ma5_ratio
+fx_hkdkrw_ma20_ratio
+fx_hkdkrw_ma60_ratio
+fx_hkdkrw_ma120_ratio
+dx_diffratio
+dx_ma5_ratio
+dx_ma20_ratio
+dx_ma60_ratio
+dx_ma120_ratio
+dx_volume_diffratio
+dx_volume_ma5_ratio
+dx_volume_ma20_ratio
+dx_volume_ma60_ratio
+dx_volume_ma120_ratio
+bdi_diffratio
+bdi_ma5_ratio
+bdi_ma20_ratio
+bdi_ma60_ratio
+bdi_ma120_ratio
+sox_diffratio
+sox_ma5_ratio
+sox_ma20_ratio
+sox_ma60_ratio
+sox_ma120_ratio
+vix_diffratio
+vix_ma5_ratio
+vix_ma20_ratio
+vix_ma60_ratio
+vix_ma120_ratio
+msci_world_diffratio
+msci_world_ma5_ratio
+msci_world_ma20_ratio
+msci_world_ma60_ratio
+msci_world_ma120_ratio
+msci_acwi_diffratio
+msci_acwi_ma5_ratio
+msci_acwi_ma20_ratio
+msci_acwi_ma60_ratio
+msci_acwi_ma120_ratio
+msci_em_diffratio
+msci_em_ma5_ratio
+msci_em_ma20_ratio
+msci_em_ma60_ratio
+msci_em_ma120_ratio
+msci_korea_diffratio
+msci_korea_ma5_ratio
+msci_korea_ma20_ratio
+msci_korea_ma60_ratio
+msci_korea_ma120_ratio
+msci_usa_diffratio
+msci_usa_ma5_ratio
+msci_usa_ma20_ratio
+msci_usa_ma60_ratio
+msci_usa_ma120_ratio
+msci_china_diffratio
+msci_china_ma5_ratio
+msci_china_ma20_ratio
+msci_china_ma60_ratio
+msci_china_ma120_ratio
+msci_japan_diffratio
+msci_japan_ma5_ratio
+msci_japan_ma20_ratio
+msci_japan_ma60_ratio
+msci_japan_ma120_ratio
+msci_hongkong_diffratio
+msci_hongkong_ma5_ratio
+msci_hongkong_ma20_ratio
+msci_hongkong_ma60_ratio
+msci_hongkong_ma120_ratio
+msci_uk_diffratio
+msci_uk_ma5_ratio
+msci_uk_ma20_ratio
+msci_uk_ma60_ratio
+msci_uk_ma120_ratio
+msci_france_diffratio
+msci_france_ma5_ratio
+msci_france_ma20_ratio
+msci_france_ma60_ratio
+msci_france_ma120_ratio
+msci_germany_diffratio
+msci_germany_ma5_ratio
+msci_germany_ma20_ratio
+msci_germany_ma60_ratio
+msci_germany_ma120_ratio
+price_index_cpi
+price_index_ppi
+price_index_cpi_us
+price_index_ppi_us
+current_account
+current_account_goods
+current_account_service
+current_account_primary
+current_account_transfer
+current_account_trade
+stockmarket_investor_ind
+stockmarket_investor_ind_diffratio
+stockmarket_investor_ind_ma5_ratio
+stockmarket_investor_ind_ma20_ratio
+stockmarket_investor_ind_ma60_ratio
+stockmarket_investor_ind_ma120_ratio
+stockmarket_investor_inst
+stockmarket_investor_inst_diffratio
+stockmarket_investor_inst_ma5_ratio
+stockmarket_investor_inst_ma20_ratio
+stockmarket_investor_inst_ma60_ratio
+stockmarket_investor_inst_ma120_ratio
+stockmarket_investor_foreign
+stockmarket_investor_foreign_diffratio
+stockmarket_investor_foreign_ma5_ratio
+stockmarket_investor_foreign_ma20_ratio
+stockmarket_investor_foreign_ma60_ratio
+stockmarket_investor_foreign_ma120_ratio
+price_index_cpi
+price_index_ppi
+price_index_cpi_us
+price_index_ppi_us
+current_account
+current_account_goods
+current_account_service
+current_account_primary
+current_account_transfer
+current_account_trade
+stockmarket_investor_ind
+stockmarket_investor_ind_diffratio
+stockmarket_investor_ind_ma5_ratio
+stockmarket_investor_ind_ma20_ratio
+stockmarket_investor_ind_ma60_ratio
+stockmarket_investor_ind_ma120_ratio
+stockmarket_investor_inst
+stockmarket_investor_inst_diffratio
+stockmarket_investor_inst_ma5_ratio
+stockmarket_investor_inst_ma20_ratio
+stockmarket_investor_inst_ma60_ratio
+stockmarket_investor_inst_ma120_ratio
+stockmarket_investor_foreign
+stockmarket_investor_foreign_diffratio
+stockmarket_investor_foreign_ma5_ratio
+stockmarket_investor_foreign_ma20_ratio
+stockmarket_investor_foreign_ma60_ratio
+stockmarket_investor_foreign_ma120_ratio
diff --git a/data/v4.2/taylor_us_20150101_20221211.csv b/data/v4.2/taylor_us_20150101_20221211.csv
new file mode 100644
index 0000000..42af032
--- /dev/null
+++ b/data/v4.2/taylor_us_20150101_20221211.csv
@@ -0,0 +1,32 @@
+"date","taylor"
+"20150101",1.7078307768550722
+"20150401",1.724433088893079
+"20150701",1.4462448762985962
+"20151001",1.0291966940521597
+"20160101",1.0295877340709223
+"20160401",1.2087607650353087
+"20160701",1.2579751231315932
+"20161001",2.101412866252205
+"20170101",3.009920860232667
+"20170401",2.4454630298296114
+"20170701",2.9764999782673605
+"20171001",3.495406372123368
+"20180101",3.7586487885746065
+"20180401",4.707793582205765
+"20180701",4.5762150504747146
+"20181001",4.073498291583118
+"20190101",3.7614520106414377
+"20190401",3.4030793343201604
+"20190701",3.5929197705263243
+"20191001",3.4456170494063585
+"20200101",2.6555462405685484
+"20200401",-3.127008823599276
+"20200701",1.0108701989034807
+"20201001",1.672590217099207
+"20210101",3.4834689757738686
+"20210401",7.065090260355881
+"20210701",8.176953131163982
+"20211001",10.406591234422619
+"20220101",11.200545412762523
+"20220401",11.916978625573226
+"20220701",11.170479869600562
diff --git a/data/v4.2/tips_20150101_20221211.csv b/data/v4.2/tips_20150101_20221211.csv
new file mode 100644
index 0000000..a579a65
--- /dev/null
+++ b/data/v4.2/tips_20150101_20221211.csv
@@ -0,0 +1,2051 @@
+"date","value"
+"20150101",""
+"20150102",0.41
+"20150105",0.4
+"20150106",0.41
+"20150107",0.39
+"20150108",0.41
+"20150109",0.36
+"20150112",0.35
+"20150113",0.37
+"20150114",0.29
+"20150115",0.19
+"20150116",0.23
+"20150119",""
+"20150120",0.23
+"20150121",0.26
+"20150122",0.29
+"20150123",0.21
+"20150126",0.22
+"20150127",0.17
+"20150128",0.12
+"20150129",0.15
+"20150130",0.03
+"20150202",0.03
+"20150203",0.09
+"20150204",0.1
+"20150205",0.13
+"20150206",0.24
+"20150209",0.25
+"20150210",0.31
+"20150211",0.32
+"20150212",0.33
+"20150213",0.34
+"20150216",""
+"20150217",0.43
+"20150218",0.36
+"20150219",0.37
+"20150220",0.38
+"20150223",0.34
+"20150224",0.27
+"20150225",0.24
+"20150226",0.24
+"20150227",0.17
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diff --git a/main.py b/main.py
index fe09fea..9c30319 100644
--- a/main.py
+++ b/main.py
@@ -12,15 +12,15 @@
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--mode', choices=['train', 'test', 'update', 'predict'], default='train')
-    parser.add_argument('--ver', choices=['v1', 'v2', 'v3', 'v4'], default='v2')
+    parser.add_argument('--ver', choices=['v1', 'v2', 'v3', 'v4', 'v4.1', 'v4.2'], default='v4.1')
     parser.add_argument('--name', default=utils.get_time_str())
     parser.add_argument('--stock_code', nargs='+')
-    parser.add_argument('--rl_method', choices=['dqn', 'pg', 'ac', 'a2c', 'a3c', 'monkey'])
+    parser.add_argument('--rl_method', choices=['dqn', 'pg', 'ac', 'a2c', 'a3c', 'monkey'], default='a2c')
     parser.add_argument('--net', choices=['dnn', 'lstm', 'cnn', 'monkey'], default='dnn')
     parser.add_argument('--backend', choices=['pytorch', 'tensorflow', 'plaidml'], default='pytorch')
     parser.add_argument('--start_date', default='20200101')
     parser.add_argument('--end_date', default='20201231')
-    parser.add_argument('--lr', type=float, default=0.0001)
+    parser.add_argument('--lr', type=float, default=0.001)
     parser.add_argument('--discount_factor', type=float, default=0.7)
     parser.add_argument('--balance', type=int, default=100000000)
     args = parser.parse_args()
@@ -32,7 +32,7 @@
     value_network_name = f'{args.name}_{args.rl_method}_{args.net}_value.mdl'
     policy_network_name = f'{args.name}_{args.rl_method}_{args.net}_policy.mdl'
     start_epsilon = 1 if args.mode in ['train', 'update'] else 0
-    num_epoches = 1000 if args.mode in ['train', 'update'] else 1
+    num_epoches = 100 if args.mode in ['train', 'update'] else 1
     num_steps = 5 if args.net in ['lstm', 'cnn'] else 1
 
     # Backend 설정
diff --git a/requirements_common.txt b/requirements_common.txt
index 2fe9b51..22ee241 100644
--- a/requirements_common.txt
+++ b/requirements_common.txt
@@ -3,3 +3,4 @@ pandas
 matplotlib
 mplfinance
 tqdm
+sklearn
diff --git a/requirements_tf1_cpu.txt b/requirements_tf1_cpu.txt
index f7db0bf..42f13bf 100644
--- a/requirements_tf1_cpu.txt
+++ b/requirements_tf1_cpu.txt
@@ -1,2 +1,2 @@
-keras==2.2.4
+keras==2.13.1
 tensorflow==1.15.2
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..76b73e1
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,3 @@
+[metadata]
+description-file=README.md
+license_files=LICENSE
diff --git a/setup.py b/setup.py
index 96cb841..23bb485 100644
--- a/setup.py
+++ b/setup.py
@@ -2,10 +2,21 @@
 
 setup(
     name='rltrader',
-    version='3.0',
+    version='3.2.1',
     description='Quantylab Reinforcement Learning for Stock Trading',
     author='Quantylab',
     author_email='quantylab@gmail.com',
     url='https://github.com/quantylab/rltrader',
-    packages=find_namespace_packages(include=['quantylab.*']),
+    packages=find_namespace_packages(where='src', include=['quantylab.*']),
+    package_dir={'': 'src'},
+    install_requires=[
+        'numpy',
+        'pandas',
+        'matplotlib',
+        'mplfinance',
+        'tqdm',
+        'sklearn',
+        'tensorflow==2.7.0',
+        'torch==1.10.1',
+    ]
 )
diff --git a/quantylab/rltrader/__init__.py b/src/quantylab/rltrader/__init__.py
similarity index 100%
rename from quantylab/rltrader/__init__.py
rename to src/quantylab/rltrader/__init__.py
diff --git a/quantylab/rltrader/agent.py b/src/quantylab/rltrader/agent.py
similarity index 97%
rename from quantylab/rltrader/agent.py
rename to src/quantylab/rltrader/agent.py
index 4b26ff2..5aa9c25 100644
--- a/quantylab/rltrader/agent.py
+++ b/src/quantylab/rltrader/agent.py
@@ -4,14 +4,14 @@
 
 class Agent:
     # 에이전트 상태가 구성하는 값 개수
-    # 주식 보유 비율, 현재 손익, 평균 매수 단가 대비 등락률
+    # 주식 보유 비율, 손익률, 주당 매수 단가 대비 주가 등락률
     STATE_DIM = 3
 
     # 매매 수수료 및 세금
     TRADING_CHARGE = 0.00015  # 거래 수수료 0.015%
     # TRADING_CHARGE = 0.00011  # 거래 수수료 0.011%
     # TRADING_CHARGE = 0  # 거래 수수료 미적용
-    TRADING_TAX = 0.0025  # 거래세 0.25%
+    TRADING_TAX = 0.002  # 거래세 0.2%
     # TRADING_TAX = 0  # 거래세 미적용
 
     # 행동
@@ -42,7 +42,7 @@ def __init__(self, environment, initial_balance, min_trading_price, max_trading_
 
         # Agent 클래스의 상태
         self.ratio_hold = 0  # 주식 보유 비율
-        self.profitloss = 0  # 현재 손익
+        self.profitloss = 0  # 손익률
         self.avg_buy_price = 0  # 주당 매수 단가
 
     def reset(self):
diff --git a/quantylab/rltrader/data_manager.py b/src/quantylab/rltrader/data_manager.py
similarity index 79%
rename from quantylab/rltrader/data_manager.py
rename to src/quantylab/rltrader/data_manager.py
index fef28be..1aff4f9 100644
--- a/quantylab/rltrader/data_manager.py
+++ b/src/quantylab/rltrader/data_manager.py
@@ -124,6 +124,12 @@
     'msci_germany_diffratio', 'msci_germany_ma5_ratio', 'msci_germany_ma20_ratio', 'msci_germany_ma60_ratio', 'msci_germany_ma120_ratio',
 ]
 
+COLUMNS_TRAINING_DATA_V4_1 = []
+with open(os.path.join(settings.BASE_DIR, 'conf', 'data', 'v4.1', 'feature_list.txt')) as f:
+    for line in f:
+        COLUMNS_TRAINING_DATA_V4_1.append(line.strip())
+
+
 def preprocess(data, ver='v1'):
     windows = [5, 10, 20, 60, 120]
     for window in windows:
@@ -174,9 +180,44 @@ def preprocess(data, ver='v1'):
 
 
 def load_data(code, date_from, date_to, ver='v2'):
-    if ver in ['v3', 'v4']:
+    if ver in ['v1', 'v1.1', 'v2']:
+        return load_data_v1_v2(code, date_from, date_to, ver)
+    elif ver in ['v3', 'v4']:
         return load_data_v3_v4(code, date_from, date_to, ver)
+    elif ver in ['v4.1', 'v4.2']:
+        stock_filename = ''
+        market_filename = ''
+        data_dir = os.path.join(settings.BASE_DIR, 'data', 'v4.1')
+        for filename in os.listdir(data_dir):
+            if code in filename:
+                stock_filename = filename
+            elif 'market' in filename:
+                market_filename = filename
+        
+        chart_data, training_data = load_data_v4_1(
+            os.path.join(data_dir, stock_filename),
+            os.path.join(data_dir, market_filename),
+            date_from, date_to
+        )
+        if ver == 'v4.1':
+            return chart_data, training_data
+        
+        tips_filename = ''
+        taylor_us_filename = ''
+        data_dir = os.path.join(settings.BASE_DIR, 'data', 'v4.2')
+        for filename in os.listdir(data_dir):
+            if filename.startswith('tips'):
+                tips_filename = filename
+            if filename.startswith('taylor_us'):
+                taylor_us_filename = filename
+        return load_data_v4_2(
+            pd.concat([chart_data, training_data], axis=1),
+            os.path.join(data_dir, tips_filename),
+            os.path.join(data_dir, taylor_us_filename)
+        )
+
 
+def load_data_v1_v2(code, date_from, date_to, ver):
     header = None if ver == 'v1' else 0
     df = pd.read_csv(
         os.path.join(settings.BASE_DIR, 'data', ver, f'{code}.csv'),
@@ -212,7 +253,7 @@ def load_data(code, date_from, date_to, ver='v2'):
     else:
         raise Exception('Invalid version.')
     
-    return chart_data, training_data
+    return chart_data, training_data.values
 
 
 def load_data_v3_v4(code, date_from, date_to, ver):
@@ -243,18 +284,85 @@ def load_data_v3_v4(code, date_from, date_to, ver):
     # 날짜 오름차순 정렬
     df = df.sort_values(by='date').reset_index(drop=True)
 
+    # NaN 처리
+    df = df.fillna(method='ffill').fillna(method='bfill').reset_index(drop=True)
+    df = df.fillna(0)
+
     # 기간 필터링
     df['date'] = df['date'].str.replace('-', '')
     df = df[(df['date'] >= date_from) & (df['date'] <= date_to)]
-    df = df.fillna(method='ffill').reset_index(drop=True)
+    df = df.reset_index(drop=True)
 
     # 데이터 조정
-    df.loc[:, ['per', 'pbr', 'roe']] = df[['per', 'pbr', 'roe']].apply(lambda x: x / 100)
+    if ver == 'v3':
+        df.loc[:, ['per', 'pbr', 'roe']] = df[['per', 'pbr', 'roe']].apply(lambda x: x / 100)
 
     # 차트 데이터 분리
     chart_data = df[COLUMNS_CHART_DATA]
 
     # 학습 데이터 분리
-    training_data = df[columns]
+    training_data = df[columns].values
+
+    # 스케일링
+    if ver == 'v4':
+        from sklearn.preprocessing import RobustScaler
+        from joblib import dump, load
+        scaler_path = os.path.join(settings.BASE_DIR, 'scalers', f'scaler_{ver}.joblib')
+        scaler = None
+        if not os.path.exists(scaler_path):
+            scaler = RobustScaler()
+            scaler.fit(training_data)
+            dump(scaler, scaler_path)
+        else:
+            scaler = load(scaler_path)
+        training_data = scaler.transform(training_data)
 
     return chart_data, training_data
+
+
+def load_data_v4_1(stock_data_path, market_data_path, date_from, date_to):
+    df_stock = None
+    if stock_data_path.endswith('.csv'):
+        df_stock = pd.read_csv(stock_data_path, dtype={'date': str})
+    elif stock_data_path.endswith('.json'):
+        import json
+        with open(stock_data_path) as f:
+            df_stock = pd.DataFrame(**json.load(f))
+    df_market = None
+    if market_data_path.endswith('.csv'):
+        df_market = pd.read_csv(market_data_path, dtype={'date': str})
+    elif market_data_path.endswith('.json'):
+        import json
+        with open(market_data_path) as f:
+            df_market = pd.DataFrame(**json.load(f))
+    df = pd.merge(df_stock, df_market, on='date', how='left')
+    df = df[(df['date'] >= date_from) & (df['date'] <= date_to)]
+    df = df[COLUMNS_CHART_DATA + COLUMNS_TRAINING_DATA_V4_1]
+    df = df.fillna(method='ffill').fillna(method='bfill').reset_index(drop=True)
+    df = df.fillna(0)
+    return df[COLUMNS_CHART_DATA], df[COLUMNS_TRAINING_DATA_V4_1].values
+
+
+def load_data_v4_2(df_v4_1, stock_data_path, market_data_path):
+    df_tips = None
+    if stock_data_path.endswith('.csv'):
+        df_tips = pd.read_csv(stock_data_path, dtype={'date': str})
+    elif stock_data_path.endswith('.json'):
+        import json
+        with open(stock_data_path) as f:
+            df_tips = pd.DataFrame(**json.load(f))
+    df_taylor_us = None
+    if market_data_path.endswith('.csv'):
+        df_taylor_us = pd.read_csv(market_data_path, dtype={'date': str})
+    elif market_data_path.endswith('.json'):
+        import json
+        with open(market_data_path) as f:
+            df_taylor_us = pd.DataFrame(**json.load(f))
+    df = pd.merge(df_v4_1, df_tips.rename(columns={'value': 'tips'}), on='date', how='left')
+    df = pd.merge(df, df_taylor_us.rename(columns={'taylor': 'taylor_us'}), on='date', how='left')
+    df[['tips', 'taylor_us']] = df[['tips', 'taylor_us']] / 100
+    COLUMNS_TRAINING_DATA = COLUMNS_TRAINING_DATA_V4_1 + ['tips', 'taylor_us']
+    df = df[COLUMNS_CHART_DATA + COLUMNS_TRAINING_DATA]
+    df = df.fillna(method='ffill').fillna(method='bfill').reset_index(drop=True)
+    df = df.fillna(0)
+    return df[COLUMNS_CHART_DATA], df[COLUMNS_TRAINING_DATA].values
diff --git a/quantylab/rltrader/environment.py b/src/quantylab/rltrader/environment.py
similarity index 100%
rename from quantylab/rltrader/environment.py
rename to src/quantylab/rltrader/environment.py
diff --git a/quantylab/rltrader/learners.py b/src/quantylab/rltrader/learners.py
similarity index 95%
rename from quantylab/rltrader/learners.py
rename to src/quantylab/rltrader/learners.py
index 16addd8..d0534a4 100644
--- a/quantylab/rltrader/learners.py
+++ b/src/quantylab/rltrader/learners.py
@@ -29,7 +29,7 @@ def __init__(self, rl_method='rl', stock_code=None,
                 discount_factor=0.9, num_epoches=1000,
                 balance=100000000, start_epsilon=1,
                 value_network=None, policy_network=None,
-                output_path='', reuse_models=True):
+                output_path='', reuse_models=True, gen_output=True):
         # 인자 확인
         assert min_trading_price > 0
         assert max_trading_price > 0
@@ -80,6 +80,7 @@ def __init__(self, rl_method='rl', stock_code=None,
         self.batch_size = 0
         # 로그 등 출력 경로
         self.output_path = output_path
+        self.gen_output = gen_output
 
     def init_value_network(self, shared_network=None, activation='linear', loss='mse'):
         if self.net == 'dnn':
@@ -158,7 +159,7 @@ def build_sample(self):
         self.environment.observe()
         if len(self.training_data) > self.training_data_idx + 1:
             self.training_data_idx += 1
-            self.sample = self.training_data.iloc[self.training_data_idx].tolist()
+            self.sample = self.training_data[self.training_data_idx].tolist()
             self.sample.extend(self.agent.get_states())
             return self.sample
         return None
@@ -221,12 +222,13 @@ def run(self, learning=True):
         self.visualizer.prepare(self.environment.chart_data, info)
 
         # 가시화 결과 저장할 폴더 준비
-        self.epoch_summary_dir = os.path.join(self.output_path, f'epoch_summary_{self.stock_code}')
-        if not os.path.isdir(self.epoch_summary_dir):
-            os.makedirs(self.epoch_summary_dir)
-        else:
-            for f in os.listdir(self.epoch_summary_dir):
-                os.remove(os.path.join(self.epoch_summary_dir, f))
+        if self.gen_output:
+            self.epoch_summary_dir = os.path.join(self.output_path, f'epoch_summary_{self.stock_code}')
+            if not os.path.isdir(self.epoch_summary_dir):
+                os.makedirs(self.epoch_summary_dir)
+            else:
+                for f in os.listdir(self.epoch_summary_dir):
+                    os.remove(os.path.join(self.epoch_summary_dir, f))
 
         # 학습에 대한 정보 초기화
         max_portfolio_value = 0
@@ -308,8 +310,9 @@ def run(self, learning=True):
                 f'Loss:{self.loss:.6f} ET:{elapsed_time_epoch:.4f}')
 
             # 에포크 관련 정보 가시화
-            if self.num_epoches == 1 or (epoch + 1) % int(self.num_epoches / 10) == 0:
-                self.visualize(epoch_str, self.num_epoches, epsilon)
+            if self.gen_output:
+                if self.num_epoches == 1 or (epoch + 1) % max(int(self.num_epoches / 10), 1) == 0:
+                    self.visualize(epoch_str, self.num_epoches, epsilon)
 
             # 학습 관련 정보 갱신
             max_portfolio_value = max(
@@ -355,16 +358,17 @@ def predict(self):
             pred_value = None
             pred_policy = None
             if self.value_network is not None:
-                pred_value = self.value_network.predict(list(q_sample))
+                pred_value = self.value_network.predict(list(q_sample)).tolist()
             if self.policy_network is not None:
-                pred_policy = self.policy_network.predict(list(q_sample))
+                pred_policy = self.policy_network.predict(list(q_sample)).tolist()
             
             # 신경망에 의한 행동 결정
-            action, confidence, _ = self.agent.decide_action(pred_value, pred_policy, 0)
-            result.append((self.environment.observation[0], int(action), float(confidence)))
+            result.append((self.environment.observation[0], pred_value, pred_policy))
+
+        if self.gen_output:
+            with open(os.path.join(self.output_path, f'pred_{self.stock_code}.json'), 'w') as f:
+                print(json.dumps(result), file=f)
 
-        with open(os.path.join(self.output_path, f'pred_{self.stock_code}.json'), 'w') as f:
-            print(json.dumps(result), file=f)
         return result
 
 
diff --git a/quantylab/rltrader/networks/__init__.py b/src/quantylab/rltrader/networks/__init__.py
similarity index 82%
rename from quantylab/rltrader/networks/__init__.py
rename to src/quantylab/rltrader/networks/__init__.py
index 90b57b1..39fbd56 100644
--- a/quantylab/rltrader/networks/__init__.py
+++ b/src/quantylab/rltrader/networks/__init__.py
@@ -1,8 +1,10 @@
 import os
 
 if os.environ.get('RLTRADER_BACKEND', 'pytorch') == 'pytorch':
+    print('Enabling PyTorch...')
     from quantylab.rltrader.networks.networks_pytorch import Network, DNN, LSTMNetwork, CNN
 else:
+    print('Enabling TensorFlow...')
     from quantylab.rltrader.networks.networks_keras import Network, DNN, LSTMNetwork, CNN
 
 __all__ = [
diff --git a/quantylab/rltrader/networks/networks_keras.py b/src/quantylab/rltrader/networks/networks_keras.py
similarity index 100%
rename from quantylab/rltrader/networks/networks_keras.py
rename to src/quantylab/rltrader/networks/networks_keras.py
diff --git a/quantylab/rltrader/networks/networks_pytorch.py b/src/quantylab/rltrader/networks/networks_pytorch.py
similarity index 100%
rename from quantylab/rltrader/networks/networks_pytorch.py
rename to src/quantylab/rltrader/networks/networks_pytorch.py
diff --git a/quantylab/rltrader/settings.py b/src/quantylab/rltrader/settings.py
similarity index 90%
rename from quantylab/rltrader/settings.py
rename to src/quantylab/rltrader/settings.py
index 2008e18..682b688 100644
--- a/quantylab/rltrader/settings.py
+++ b/src/quantylab/rltrader/settings.py
@@ -9,7 +9,7 @@
 
 # 경로 설정
 BASE_DIR = os.environ.get('RLTRADER_BASE', 
-    os.path.abspath(os.path.join(__file__, os.path.pardir, os.path.pardir, os.path.pardir)))
+    os.path.abspath(os.path.join(__file__, os.path.pardir, os.path.pardir, os.path.pardir, os.path.pardir)))
 
 
 # 로케일 설정
diff --git a/quantylab/rltrader/utils.py b/src/quantylab/rltrader/utils.py
similarity index 100%
rename from quantylab/rltrader/utils.py
rename to src/quantylab/rltrader/utils.py
diff --git a/quantylab/rltrader/visualizer.py b/src/quantylab/rltrader/visualizer.py
similarity index 100%
rename from quantylab/rltrader/visualizer.py
rename to src/quantylab/rltrader/visualizer.py