FinRL/examples/FinRL_PaperTrading_Demo_refactored.py: failed with NASDAQ tickers

[mingqxu7]

Describe the bug
Modify the ticker_list to NAS_100_TICKER[:30] the program fails to load the model.

To Reproduce
Steps to reproduce the behavior:

1. Open examples/FinRL_PaperTrading_Demo_refactored.py
2. Scroll down to 'from finrl.config_tickers import DOW_30_TICKER'
3. Change this and next line to:
   from finrl.config_tickers import DOW_30_TICKER, NAS_100_TICKER

ticker_list = NAS_100_TICKER[:30]

Also change the following occurences of DOW_30_TICKER:

-action_dim = len(DOW_30_TICKER)
+action_dim = len(ticker_list)
state_dim = (
1 + 2 + 3 * action_dim + len(INDICATORS) * action_dim
) # Calculate the DRL state dimension manually for paper trading. amount + (turbulence, turbulence_bool) + (price, shares, cd (holding time)) * stock_dim + tech_dim

paper_trading_erl = PaperTradingAlpaca(

• ticker_list=DOW_30_TICKER,

• ticker_list=ticker_list,

5. Run the program, you see that the test and training steps are running ok, but fails at the
   TRAIN_START_DATE: 2024-02-05
   TRAIN_END_DATE: 2024-02-12
   TEST_START_DATE: 2024-02-13
   TEST_END_DATE: 2024-02-14
   TRAINFULL_START_DATE: 2024-02-05
   TRAINFULL_END_DATE: 2024-02-14
   Alpaca successfully connected

| step: Number of samples, or total training steps, or running times of env.step().
| time: Time spent from the start of training to this moment.
| avgR: Average value of cumulative rewards, which is the sum of rewards in an episode.
| stdR: Standard dev of cumulative rewards, which is the sum of rewards in an episode.
| avgS: Average of steps in an episode.
| objC: Objective of Critic network. Or call it loss function of critic network.
| objA: Objective of Actor network. It is the average Q value of the critic network.
| step time | avgR stdR avgS | objC objA
| 2.00e+04 14 | 4.11 0.00 2339 | 0.21 0.37
| 4.00e+04 26 | 4.15 0.00 2339 | 0.18 0.38
| 6.00e+04 38 | 5.01 0.00 2339 | 0.17 0.36
| 8.00e+04 49 | 3.77 0.00 2339 | 0.14 0.36
| 1.00e+05 61 | 3.06 0.00 2339 | 0.15 0.37
Alpaca successfully connected
price_array: 780
| load actor from: ./papertrading_erl/actor.pth
/Users/mingqxu/opt/anaconda3/envs/finrl/lib/python3.8/site-packages/finrl/meta/paper_trading/common.py:660: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/utils/tensor_new.cpp:233.)
s_tensor = _torch.as_tensor((state,), device=device)
Test Finished!
episode_return 1.0080942088954623
Alpaca successfully connected

| step: Number of samples, or total training steps, or running times of env.step().
| time: Time spent from the start of training to this moment.
| avgR: Average value of cumulative rewards, which is the sum of rewards in an episode.
| stdR: Standard dev of cumulative rewards, which is the sum of rewards in an episode.
| avgS: Average of steps in an episode.
| objC: Objective of Critic network. Or call it loss function of critic network.
| objA: Objective of Actor network. It is the average Q value of the critic network.
| step time | avgR stdR avgS | objC objA
| 2.00e+04 12 | -2.27 0.00 3119 | 0.21 0.37
| 4.00e+04 24 | -0.20 0.00 3119 | 0.16 0.36
| 6.00e+04 36 | 0.02 0.00 3119 | 0.20 0.36
| 8.00e+04 48 | -0.59 0.00 3119 | 0.14 0.38
| 1.00e+05 60 | -0.24 0.00 3119 | 0.17 0.36
| 1.20e+05 72 | 0.31 0.00 3119 | 0.19 0.36
| 1.40e+05 84 | -0.86 0.00 3119 | 0.18 0.38
| 1.60e+05 96 | 0.64 0.00 3119 | 0.21 0.39
| 1.80e+05 108 | 0.86 0.00 3119 | 0.12 0.37
| 2.00e+05 119 | 0.92 0.00 3119 | 0.18 0.37
| load actor from: ./papertrading_erl_retrain/actor.pth
Traceback (most recent call last):
File "/Users/mingqxu/opt/anaconda3/envs/finrl/lib/python3.8/site-packages/finrl/meta/paper_trading/alpaca.py", line 49, in init
actor.load_state_dict(
File "/Users/mingqxu/opt/anaconda3/envs/finrl/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1671, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for ActorPPO:
size mismatch for action_std_log: copying a param with shape torch.Size([1, 26]) from checkpoint, the shape in current model is torch.Size([1, 30]).
size mismatch for net.0.weight: copying a param with shape torch.Size([128, 289]) from checkpoint, the shape in current model is torch.Size([128, 333]).
size mismatch for net.4.weight: copying a param with shape torch.Size([26, 64]) from checkpoint, the shape in current model is torch.Size([30, 64]).
size mismatch for net.4.bias: copying a param with shape torch.Size([26]) from checkpoint, the shape in current model is torch.Size([30]).

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "FinRL_PaperTrading_Demo_refactored.py", line 146, in
paper_trading_erl = PaperTradingAlpaca(
File "/Users/mingqxu/opt/anaconda3/envs/finrl/lib/python3.8/site-packages/finrl/meta/paper_trading/alpaca.py", line 55, in init
raise ValueError("Fail to load agent!")
ValueError: Fail to load agent!

Expected behavior
The code should load the model and continue to trade.

Screenshots
N/A

Desktop (please complete the following information):

• OS: Mac OS
• Version: 14.2.1 (23C71)

Additional context
finrl 0.3.5

Package Version

---

absl-py 1.4.0
aiodns 3.0.0
aiohttp 3.9.3
aiohttp-cors 0.7.0
aiosignal 1.3.1
alpaca-py 0.15.0
alpaca-trade-api 3.2.0
annotated-types 0.6.0
anyio 3.6.2
appdirs 1.4.4
appnope 0.1.3
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
arrow 1.2.3
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async-timeout 4.0.2
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