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output_cppo_llama_risk.log
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nohup: ignoring input
Generating train split: 0 examples [00:00, ? examples/s]Generating train split: 30000 examples [00:00, 216991.28 examples/s]Generating train split: 70000 examples [00:00, 251285.01 examples/s]Generating train split: 110000 examples [00:00, 255847.26 examples/s]Generating train split: 126756 examples [00:00, 251662.29 examples/s]
/root/FinRL_LLM/train_cppo_llm_risk.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
Warning: Log dir /root/spinningup_pytorch/data/cppo/cppo_s0 already exists! Storing info there anyway.
[32;1mLogging data to /root/spinningup_pytorch/data/cppo/cppo_s0/progress.txt[0m
[36;1mSaving config:
[0m
{
"ac_kwargs": {
"hidden_sizes": [
512,
512
]
},
"actor_critic": "MLPActorCritic",
"alpha": 0.85,
"beta": 3000.0,
"clip_ratio": 0.7,
"cvar_clip_ratio": 0.05,
"delay": 1.0,
"env_fn": "<function <lambda> at 0x7e2b9a366830>",
"epochs": 20,
"exp_name": "cppo",
"gamma": 0.995,
"lam": 0.95,
"lam_low_bound": 0.001,
"lam_lr": 0.0005,
"lam_start": 0.01,
"logger": {
"<spinup.utils.logx.EpochLogger object at 0x7e2b9a37dd80>": {
"epoch_dict": {},
"exp_name": "cppo",
"first_row": true,
"log_current_row": {},
"log_headers": [],
"output_dir": "/root/spinningup_pytorch/data/cppo/cppo_s0",
"output_file": {
"<_io.TextIOWrapper name='/root/spinningup_pytorch/data/cppo/cppo_s0/progress.txt' mode='w' encoding='UTF-8'>": {
"mode": "w"
}
}
}
},
"logger_kwargs": {
"exp_name": "cppo",
"output_dir": "/root/spinningup_pytorch/data/cppo/cppo_s0"
},
"max_ep_len": 3000,
"nu_delay": 0.75,
"nu_lr": 0.0005,
"nu_start": 0.1,
"pi_lr": 3e-05,
"save_freq": 10,
"seed": 0,
"steps_per_epoch": 20000,
"target_kl": 0.35,
"train_pi_iters": 100,
"train_v_iters": 100,
"vf_lr": 0.0001
}
/root/FinRL_LLM/train_cppo_llm_risk.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
[32;1m
Number of parameters: pi: 822952, v: 780289
[0m
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 0 |
| AverageEpRet | 195 |
| StdEpRet | 29.5 |
| MaxEpRet | 245 |
| MinEpRet | 150 |
| EpLen | 1.51e+03 |
| AverageVVals | -0.138 |
| StdVVals | 0.202 |
| MaxVVals | 0.663 |
| MinVVals | -0.738 |
| TotalEnvInteracts | 2e+04 |
| LossPi | 0.362 |
| LossV | 1.15e+03 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
| DeltaLossPi | -0.777 |
| DeltaLossV | -514 |
| Entropy | 0.919 |
nu: [nan]
| KL | 0.0703 |
| ClipFrac | 0.615 |
nu: [nan]
lam: 1.5099500000000001
-------------------------------------
| StopIter | 99 |
lam: 1.5099500000000001
| Time | 276 |
-------------------------------------
---------------------------------------
nu: [nan]
-------------------------------------
bad_trajectory_num: 2
update num: 1
lam: 1.5099500000000001
-------------------------------------
nu: [nan]
lam: 1.5099500000000001
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 1 |
| AverageEpRet | 193 |
| StdEpRet | 48.5 |
| MaxEpRet | 274 |
| MinEpRet | 130 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
| EpLen | 1.51e+03 |
| AverageVVals | 15.4 |
nu: [nan]
| StdVVals | 0.867 |
lam: [nan]
| MaxVVals | 16.3 |
nu: [nan]
lam: [nan]
-------------------------------------
-------------------------------------
| MinVVals | 10.9 |
lam: [nan]
-------------------------------------
| TotalEnvInteracts | 4e+04 |
| LossPi | 0.33 |
| LossV | 829 |
| DeltaLossPi | -0.741 |
| DeltaLossV | -234 |
| Entropy | 0.918 |
| KL | 0.105 |
| ClipFrac | 0.656 |
| StopIter | 99 |
| Time | 541 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2742446.66
total_reward: 1742446.66
total_cost: 75013.25
total_trades: 79734
Sharpe: 0.926
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2899313.58
total_reward: 1899313.58
total_cost: 102922.10
total_trades: 82212
Sharpe: 0.900
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 3346796.91
total_reward: 2346796.91
total_cost: 68851.11
total_trades: 80075
Sharpe: 0.912
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2722830.10
total_reward: 1722830.10
total_cost: 94110.32
total_trades: 79544
Sharpe: 0.986
=================================
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 2 |
| AverageEpRet | 179 |
| StdEpRet | 37.8 |
| MaxEpRet | 245 |
| MinEpRet | 134 |
| EpLen | 1.51e+03 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
| AverageVVals | 16.3 |
| StdVVals | 6.05 |
| MaxVVals | 21 |
| MinVVals | -10.2 |
| TotalEnvInteracts | 6e+04 |
| LossPi | 0.318 |
| LossV | 484 |
| DeltaLossPi | -0.781 |
| DeltaLossV | -272 |
| Entropy | 0.917 |
| KL | 0.0987 |
nu: [nan]
bad_trajectory_num: 0
update num: 0
nu: [nan]
nu: [nan]
lam: [nan]
-------------------------------------
| ClipFrac | 0.668 |
| StopIter | 99 |
| Time | 794 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 3 |
| AverageEpRet | 188 |
| StdEpRet | 40.7 |
| MaxEpRet | 262 |
| MinEpRet | 128 |
| EpLen | 1.51e+03 |
| AverageVVals | 18.4 |
| StdVVals | 9.04 |
| MaxVVals | 25.4 |
| MinVVals | -21.6 |
| TotalEnvInteracts | 8e+04 |
| LossPi | 0.314 |
| LossV | 348 |
| DeltaLossPi | -0.762 |
| DeltaLossV | -211 |
| Entropy | 0.916 |
| KL | 0.146 |
| ClipFrac | 0.668 |
| StopIter | 99 |
| Time | 1.04e+03 |
-------------------------------------
---------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
day: 1508, episode: 30
begin_total_asset: 1000000.00
end_total_asset: 2429997.10
total_reward: 1429997.10
total_cost: 82757.21
total_trades: 78887
Sharpe: 0.929
=================================
day: 1508, episode: 30
begin_total_asset: 1000000.00
end_total_asset: 2875192.15
total_reward: 1875192.15
total_cost: 85716.00
total_trades: 79464
Sharpe: 0.803
=================================
day: 1508, episode: 30
begin_total_asset: 1000000.00
end_total_asset: 2691656.81
total_reward: 1691656.81
total_cost: 70841.66
total_trades: 79206
Sharpe: 0.777
=================================
day: 1508, episode: 30
begin_total_asset: 1000000.00
end_total_asset: 2407716.20
total_reward: 1407716.20
total_cost: 70851.55
total_trades: 76623
Sharpe: 0.897
=================================
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 4 |
| AverageEpRet | 180 |
| StdEpRet | 34.3 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
nu: [nan]
| MaxEpRet | 236 |
| MinEpRet | 118 |
lam: [nan]
| EpLen | 1.51e+03 |
| AverageVVals | 16.3 |
| StdVVals | 15.1 |
| MaxVVals | 29.3 |
nu: [nan]
lam: [nan]
-------------------------------------
lam: [nan]
-------------------------------------
-------------------------------------
| MinVVals | -28.6 |
| TotalEnvInteracts | 1e+05 |
| LossPi | 0.286 |
| LossV | 273 |
| DeltaLossPi | -0.762 |
| DeltaLossV | -200 |
| Entropy | 0.916 |
| KL | 0.196 |
| ClipFrac | 0.682 |
| StopIter | 99 |
| Time | 1.3e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 5 |
| AverageEpRet | 181 |
| StdEpRet | 55.3 |
| MaxEpRet | 321 |
| MinEpRet | 109 |
| EpLen | 1.51e+03 |
| AverageVVals | 12.4 |
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
bad_trajectory_num: 0
update num: 0
lam: [nan]
nu: [nan]
lam: [nan]
-------------------------------------
| StdVVals | 17.6 |
| MaxVVals | 31.5 |
| MinVVals | -30.5 |
| TotalEnvInteracts | 1.2e+05 |
| LossPi | 0.262 |
| LossV | 447 |
-------------------------------------
| DeltaLossPi | -0.706 |
| DeltaLossV | -319 |
| Entropy | 0.915 |
| KL | 0.248 |
| ClipFrac | 0.682 |
| StopIter | 99 |
| Time | 1.56e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
---------------------------------------
| Epoch | 6 |
| AverageEpRet | 167 |
| StdEpRet | 22.8 |
| MaxEpRet | 198 |
| MinEpRet | 123 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
bad_trajectory_num: 0
update num: 0
nu: [nan]
nu: [nan]
| EpLen | 1.51e+03 |
| AverageVVals | 16.7 |
| StdVVals | 14.4 |
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
| MaxVVals | 34.3 |
| MinVVals | -32.3 |
| TotalEnvInteracts | 1.4e+05 |
| LossPi | 0.302 |
| LossV | 167 |
| DeltaLossPi | -0.762 |
| DeltaLossV | -144 |
| Entropy | 0.914 |
| KL | 0.213 |
| ClipFrac | 0.668 |
| StopIter | 99 |
| Time | 1.82e+03 |
---------------------------------------
lam: [nan]
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 7 |
| AverageEpRet | 166 |
| StdEpRet | 40 |
| MaxEpRet | 246 |
| MinEpRet | 104 |
| EpLen | 1.51e+03 |
| AverageVVals | 17.6 |
| StdVVals | 14.8 |
| MaxVVals | 35.3 |
bad_trajectory_num: 0
update num: 0
| MinVVals | -31.8 |
| TotalEnvInteracts | 1.6e+05 |
| LossPi | 0.308 |
| LossV | 187 |
| DeltaLossPi | -0.765 |
| DeltaLossV | -152 |
nu: [nan]
| Entropy | 0.913 |
| KL | 0.228 |
| ClipFrac | 0.651 |
| StopIter | 99 |
| Time | 2.08e+03 |
nu: [nan]
---------------------------------------
lam: [nan]
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
lam: [nan]
-------------------------------------
day: 1508, episode: 60
begin_total_asset: 1000000.00
end_total_asset: 3175502.12
total_reward: 2175502.12
total_cost: 61115.67
total_trades: 77024
Sharpe: 1.144
=================================
day: 1508, episode: 60
begin_total_asset: 1000000.00
end_total_asset: 3148517.11
total_reward: 2148517.11
total_cost: 63047.76
total_trades: 78903
Sharpe: 1.000
=================================
day: 1508, episode: 60
begin_total_asset: 1000000.00
end_total_asset: 2621330.85
total_reward: 1621330.85
total_cost: 53641.74
total_trades: 76536
Sharpe: 1.057
=================================
day: 1508, episode: 60
begin_total_asset: 1000000.00
end_total_asset: 4014891.46
total_reward: 3014891.46
total_cost: 98696.09
total_trades: 82393
Sharpe: 1.393
=================================
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
---------------------------------------
| Epoch | 8 |
| AverageEpRet | 212 |
| StdEpRet | 53.3 |
| MaxEpRet | 299 |
| MinEpRet | 143 |
| EpLen | 1.51e+03 |
| AverageVVals | 19 |
| StdVVals | 16.4 |
| MaxVVals | 36.7 |
| MinVVals | -34.8 |
| TotalEnvInteracts | 1.8e+05 |
| LossPi | 0.287 |
| LossV | 369 |
| DeltaLossPi | -0.712 |
| DeltaLossV | -247 |
| Entropy | 0.913 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
| KL | 0.273 |
| ClipFrac | 0.663 |
| StopIter | 99 |
| Time | 2.34e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
lam: [nan]
-------------------------------------
lam: [nan]
-------------------------------------
nu: [nan]
bad_trajectory_num: 0
lam: [nan]
-------------------------------------
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 9 |
| AverageEpRet | 164 |
| StdEpRet | 31.4 |
| MaxEpRet | 216 |
| MinEpRet | 114 |
| EpLen | 1.51e+03 |
| AverageVVals | 22.2 |
| StdVVals | 18.2 |
| MaxVVals | 40.6 |
| MinVVals | -39.7 |
| TotalEnvInteracts | 2e+05 |
| LossPi | 0.298 |
| LossV | 276 |
| DeltaLossPi | -0.726 |
| DeltaLossV | -250 |
| Entropy | 0.912 |
| KL | 0.285 |
| ClipFrac | 0.663 |
| StopIter | 99 |
| Time | 2.59e+03 |
---------------------------------------
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
---------------------------------------
| Epoch | 10 |
| AverageEpRet | 217 |
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
| StdEpRet | 48.3 |
| MaxEpRet | 305 |
| MinEpRet | 134 |
| EpLen | 1.51e+03 |
| AverageVVals | 17.9 |
| StdVVals | 19 |
| MaxVVals | 41.6 |
| MinVVals | -39.3 |
| TotalEnvInteracts | 2.2e+05 |
nu: [nan]
| LossPi | 0.254 |
| LossV | 405 |
| DeltaLossPi | -0.699 |
| DeltaLossV | -321 |
| Entropy | 0.911 |
| KL | 0.298 |
| ClipFrac | 0.671 |
| StopIter | 99 |
| Time | 3.02e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
-------------------------------------
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 11 |
| AverageEpRet | 198 |
| StdEpRet | 44.6 |
| MaxEpRet | 312 |
| MinEpRet | 139 |
| EpLen | 1.51e+03 |
| AverageVVals | 19.2 |
| StdVVals | 20.3 |
| MaxVVals | 44.5 |
bad_trajectory_num: 0
update num: 0
nu: [nan]
| MinVVals | -43.8 |
| TotalEnvInteracts | 2.4e+05 |
| LossPi | 0.29 |
| LossV | 262 |
| DeltaLossPi | -0.716 |
| DeltaLossV | -227 |
| Entropy | 0.91 |
| KL | 0.325 |
| ClipFrac | 0.651 |
| StopIter | 99 |
nu: [nan]
| Time | 3.29e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
lam: [nan]
-------------------------------------
nu: [nan]
lam: [nan]
nu: [nan]
-------------------------------------
lam: [nan]
lam: [nan]
-------------------------------------
-------------------------------------
day: 1508, episode: 90
begin_total_asset: 1000000.00
end_total_asset: 2651386.32
total_reward: 1651386.32
total_cost: 54847.01
total_trades: 79627
Sharpe: 1.003
=================================
day: 1508, episode: 90
begin_total_asset: 1000000.00
end_total_asset: 3168206.41
total_reward: 2168206.41
total_cost: 63299.17
total_trades: 80635
Sharpe: 1.157
=================================
day: 1508, episode: 90
begin_total_asset: 1000000.00
end_total_asset: 3090162.78
total_reward: 2090162.78
total_cost: 77606.96
total_trades: 80631
Sharpe: 1.041
=================================
day: 1508, episode: 90
begin_total_asset: 1000000.00
end_total_asset: 2635253.88
total_reward: 1635253.88
total_cost: 32571.16
total_trades: 73837
Sharpe: 0.925
=================================
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
nu: [nan]
| Epoch | 12 |
| AverageEpRet | 201 |
| StdEpRet | 43.7 |
| MaxEpRet | 301 |
| MinEpRet | 135 |
| EpLen | 1.51e+03 |
| AverageVVals | 25.1 |
| StdVVals | 20.7 |
| MaxVVals | 46.2 |
| MinVVals | -45.5 |
nu: [nan]
| TotalEnvInteracts | 2.6e+05 |
| LossPi | 0.284 |
| LossV | 357 |
lam: [nan]
-------------------------------------
| DeltaLossPi | -0.688 |
lam: [nan]
| DeltaLossV | -253 |
| Entropy | 0.91 |
| KL | 0.297 |
| ClipFrac | 0.661 |
| StopIter | 99 |
| Time | 3.54e+03 |
nu: [nan]
---------------------------------------
-------------------------------------
lam: [nan]
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
---------------------------------------
| Epoch | 13 |
| AverageEpRet | 211 |
| StdEpRet | 35 |
| MaxEpRet | 287 |
| MinEpRet | 157 |
| EpLen | 1.51e+03 |
| AverageVVals | 22.1 |
| StdVVals | 22.8 |
| MaxVVals | 48.9 |
| MinVVals | -48.4 |
| TotalEnvInteracts | 2.8e+05 |
| LossPi | 0.26 |
| LossV | 241 |
| DeltaLossPi | -0.718 |
| DeltaLossV | -220 |
| Entropy | 0.909 |
| KL | 0.341 |
| ClipFrac | 0.697 |
| StopIter | 99 |
| Time | 3.79e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [nan]
lam: [nan]
-------------------------------------
day: 1508, episode: 100
begin_total_asset: 1000000.00
end_total_asset: 4310114.52
total_reward: 3310114.52
total_cost: 62717.34
total_trades: 80328
Sharpe: 1.198
=================================
day: 1508, episode: 100
begin_total_asset: 1000000.00
end_total_asset: 2534751.94
total_reward: 1534751.94
total_cost: 42589.93
total_trades: 74111
Sharpe: 0.996
=================================
day: 1508, episode: 100
begin_total_asset: 1000000.00
end_total_asset: 2957348.00
total_reward: 1957348.00
total_cost: 51887.44
total_trades: 79170
Sharpe: 1.042
=================================
day: 1508, episode: 100
begin_total_asset: 1000000.00
end_total_asset: 3392430.59
total_reward: 2392430.59
total_cost: 53543.59
total_trades: 80377
Sharpe: 1.197
=================================
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
Warning: trajectory cut off by epoch at 473 steps.
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 14 |
| AverageEpRet | 221 |
| StdEpRet | 55.8 |
| MaxEpRet | 329 |
| MinEpRet | 148 |
| EpLen | 1.51e+03 |
| AverageVVals | 21.2 |
| StdVVals | 21.9 |
| MaxVVals | 50.1 |