conda activate base
(base) drulye@drulye-MS-7885:~/vscode/MCpypto$ conda activate base
 /usr/bin/env /home/drulye/anaconda3/bin/python /home/drulye/.vscode/extensions/ms-python.python-2022.20.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher 36507 -- /home/drulye/vscode/MCpypto/main.py 
(base) drulye@drulye-MS-7885:~/vscode/MCpypto$  /usr/bin/env /home/drulye/anaconda3/bin/python /home/drulye/.vscode/extensions/ms-python.python-2022.20.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher 36507 -- /home/drulye/vscode/MCpypto/main.py 
/home/drulye/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
QObject::moveToThread: Current thread (0x8cbc810) is not the object's thread (0x904ade0).
Cannot move to target thread (0x8cbc810)

qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/home/drulye/anaconda3/lib/python3.9/site-packages/cv2/qt/plugins" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

Available platform plugins are: xcb, eglfs, minimal, minimalegl, offscreen, vnc, webgl.

(base) drulye@drulye-MS-7885:~/vscode/MCpypto$ 

conda activate base
(base) drulye@drulye-MS-7885:~/vscode/MCpypto$ conda activate base
/home/drulye/anaconda3/bin/python /home/drulye/vscode/MCpypto/main.py
(base) drulye@drulye-MS-7885:~/vscode/MCpypto$ /home/drulye/anaconda3/bin/python /home/drulye/vscode/MCpypto/main.py
/home/drulye/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
binance successfully connected
Using cached file ./cache/BTCUSDT_ETHUSDT_ADAUSDT_BNBUSDT_XRPUSDT_SOLUSDT_DOTUSDT_DOGEUSDT_AVAXUSDT_UNIUSDT_binance_2021-09-01_2021-09-02_5m.pickle
tech_indicator_list:  ['macd', 'rsi', 'cci', 'dx']
indicator:  macd
indicator:  rsi
indicator:  cci
indicator:  dx
Succesfully add technical indicators
Successfully transformed into array
| Arguments Remove cwd: ./test_ppo
Traceback (most recent call last):
  File "/home/drulye/vscode/MCpypto/main.py", line 329, in <module>
    train(start_date=TRAIN_START_DATE, 
  File "/home/drulye/vscode/MCpypto/main.py", line 89, in train
    trained_model = agent.train_model(model=model, 
  File "/home/drulye/vscode/FinRL-Meta/agents/elegantrl_models.py", line 79, in train_model
    train_and_evaluate(model)
  File "/home/drulye/vscode/ElegantRL/elegantrl/train/run.py", line 95, in train_and_evaluate
    agent = init_agent(args, gpu_id, env)
  File "/home/drulye/vscode/ElegantRL/elegantrl/train/run.py", line 24, in init_agent
    agent = args.agent_class(args.net_dim, args.state_dim, args.action_dim, gpu_id=gpu_id, args=args)
  File "/home/drulye/vscode/ElegantRL/elegantrl/agents/AgentPPO.py", line 40, in __init__
    AgentBase.__init__(self, net_dim, state_dim, action_dim, gpu_id, args)
  File "/home/drulye/vscode/ElegantRL/elegantrl/agents/AgentBase.py", line 57, in __init__
    self.act = act_class(net_dim, state_dim, action_dim).to(self.device)
  File "/home/drulye/vscode/ElegantRL/elegantrl/agents/net.py", line 397, in __init__
    self.net = build_mlp_net(dims=[state_dim, *dims, action_dim])
TypeError: Value after * must be an iterable, not int
(base) drulye@drulye-MS-7885:~/vscode/MCpypto$ 

import math
import numpy as np

import sys
sys.path.insert(0, "/home/drulye/vscode/FinRL")
sys.path.insert(0, "/home/drulye/vscode/ElegantRL")
sys.path.insert(0, "/home/drulye/vscode/FinRL-Meta")

from agents.elegantrl_models import DRLAgent as DRLAgent_erl
from agents.rllib_models import DRLAgent as DRLAgent_rllib
from agents.stablebaselines3_models import DRLAgent as DRLAgent_sb3

from meta.data_processor import DataProcessor

def train(start_date, end_date, ticker_list, data_source, time_interval, 
          technical_indicator_list, drl_lib, env, model_name, if_vix=True,
          **kwargs):
    
    #process data using unified data processor
    DP = DataProcessor(data_source, start_date, end_date, time_interval, **kwargs)
    price_array, tech_array, turbulence_array = DP.run(ticker_list,
                                                        technical_indicator_list, 
                                                        if_vix, cache=True)

    data_config = {'price_array': price_array,
                   'tech_array': tech_array,
                   'turbulence_array': turbulence_array}

    #build environment using processed data
    env_instance = env(config=data_config)

    #read parameters and load agents
    current_working_dir = kwargs.get('current_working_dir','./'+str(model_name))

    if drl_lib == 'elegantrl':
        break_step = kwargs.get('break_step', 1e6)
        erl_params = kwargs.get('erl_params')

        agent = DRLAgent_erl(env = env,
                             price_array = price_array,
                             tech_array=tech_array,
                             turbulence_array=turbulence_array)

        model = agent.get_model(model_name, model_kwargs = erl_params)

        trained_model = agent.train_model(model=model, 
                                          cwd=current_working_dir,
                                          total_timesteps=break_step)
        
      
    elif drl_lib == 'rllib':
        total_episodes = kwargs.get('total_episodes', 100)
        rllib_params = kwargs.get('rllib_params')

        agent_rllib = DRLAgent_rllib(env = env,
                       price_array=price_array,
                       tech_array=tech_array,
                       turbulence_array=turbulence_array)

        model,model_config = agent_rllib.get_model(model_name)

        model_config['lr'] = rllib_params['lr']
        model_config['train_batch_size'] = rllib_params['train_batch_size']
        model_config['gamma'] = rllib_params['gamma']

        trained_model = agent_rllib.train_model(model=model, 
                                          model_name=model_name,
                                          model_config=model_config,
                                          total_episodes=total_episodes)
        trained_model.save(current_working_dir)
        
            
    elif drl_lib == 'stable_baselines3':
        total_timesteps = kwargs.get('total_timesteps', 1e6)
        agent_params = kwargs.get('agent_params')

        agent = DRLAgent_sb3(env = env_instance)

        model = agent.get_model(model_name, model_kwargs = agent_params)
        trained_model = agent.train_model(model=model, 
                                tb_log_name=model_name,
                                total_timesteps=total_timesteps)
        print('Training finished!')
        trained_model.save(current_working_dir)
        print('Trained model saved in ' + str(current_working_dir))
    else:
        raise ValueError('DRL library input is NOT supported. Please check.')

def test(start_date, end_date, ticker_list, data_source, time_interval,
            technical_indicator_list, drl_lib, env, model_name, if_vix=True,
            **kwargs):
  
    #process data using unified data processor
    DP = DataProcessor(data_source, start_date, end_date, time_interval, **kwargs)
    price_array, tech_array, turbulence_array = DP.run(ticker_list,
                                                        technical_indicator_list, 
                                                        if_vix, cache=True)
    
    
    np.save('./price_array.npy', price_array)
    data_config = {'price_array':price_array,
                   'tech_array':tech_array,
                   'turbulence_array':turbulence_array}
    #build environment using processed data
    env_instance = env(config=data_config)

    env_config = {
        "price_array": price_array,
        "tech_array": tech_array,
        "turbulence_array": turbulence_array,
        "if_train": False,
    }
    env_instance = env(config=env_config)

    # load elegantrl needs state dim, action dim and net dim
    net_dimension = kwargs.get("net_dimension", 2 ** 7)
    current_working_dir = kwargs.get("current_working_dir", "./" + str(model_name))
    print("price_array: ", len(price_array))

    if drl_lib == "elegantrl":
        episode_total_assets = DRLAgent_erl.DRL_prediction(
            model_name=model_name,
            cwd=current_working_dir,
            net_dimension=net_dimension,
            environment=env_instance,
        )

        return episode_total_assets

    elif drl_lib == "rllib":
        # load agent
        episode_total_assets = DRLAgent_rllib.DRL_prediction(
            model_name=model_name,
            env=env,
            price_array=price_array,
            tech_array=tech_array,
            turbulence_array=turbulence_array,
            agent_path=current_working_dir,
        )

        return episode_total_assets

    elif drl_lib == "stable_baselines3":
        episode_total_assets = DRLAgent_sb3.DRL_prediction_load_from_file(
            model_name=model_name, environment=env_instance, cwd=current_working_dir
        )

        return episode_total_assets
    else:
        raise ValueError("DRL library input is NOT supported. Please check.")

class CryptoEnv:  # custom env
    def __init__(self, config, lookback=1, initial_capital=1e6, 
                 buy_cost_pct=1e-3, sell_cost_pct=1e-3, gamma=0.99):
        self.lookback = lookback
        self.initial_total_asset = initial_capital
        self.initial_cash = initial_capital
        self.buy_cost_pct = buy_cost_pct
        self.sell_cost_pct = sell_cost_pct
        self.max_stock = 1
        self.gamma = gamma
        self.price_array = config['price_array']
        self.tech_array = config['tech_array']
        self._generate_action_normalizer()
        self.crypto_num = self.price_array.shape[1]
        self.max_step = self.price_array.shape[0] - lookback - 1
        
        # reset
        self.time = lookback-1
        self.cash = self.initial_cash
        self.current_price = self.price_array[self.time]
        self.current_tech = self.tech_array[self.time]
        self.stocks = np.zeros(self.crypto_num, dtype=np.float32)

        self.total_asset = self.cash + (self.stocks * self.price_array[self.time]).sum()
        self.episode_return = 0.0  
        self.gamma_return = 0.0
        

        '''env information'''
        self.env_name = 'MulticryptoEnv'
        self.state_dim = 1 + (self.price_array.shape[1] + self.tech_array.shape[1])*lookback
        self.action_dim = self.price_array.shape[1]
        self.if_discrete = False
        self.target_return = 10


    def reset(self) -> np.ndarray:
        self.time = self.lookback-1
        self.current_price = self.price_array[self.time]
        self.current_tech = self.tech_array[self.time]
        self.cash = self.initial_cash  # reset()
        self.stocks = np.zeros(self.crypto_num, dtype=np.float32)
        self.total_asset = self.cash + (self.stocks * self.price_array[self.time]).sum()
        
        state = self.get_state()
        return state

    def step(self, actions) -> (np.ndarray, float, bool, None):
        self.time += 1
        
        price = self.price_array[self.time]
        for i in range(self.action_dim):
            norm_vector_i = self.action_norm_vector[i]
            actions[i] = actions[i] * norm_vector_i
            
        for index in np.where(actions < 0)[0]:  # sell_index:
            if price[index] > 0:  # Sell only if current asset is > 0
                sell_num_shares = min(self.stocks[index], -actions[index])
                self.stocks[index] -= sell_num_shares
                self.cash += price[index] * sell_num_shares * (1 - self.sell_cost_pct)
                
        for index in np.where(actions > 0)[0]:  # buy_index:
            if price[index] > 0:  # Buy only if the price is > 0 (no missing data in this particular date)
                buy_num_shares = min(self.cash // price[index], actions[index])
                self.stocks[index] += buy_num_shares
                self.cash -= price[index] * buy_num_shares * (1 + self.buy_cost_pct)

        """update time"""
        done = self.time == self.max_step
        state = self.get_state()
        next_total_asset = self.cash + (self.stocks * self.price_array[self.time]).sum()
        reward = (next_total_asset - self.total_asset) * 2 ** -16  
        self.total_asset = next_total_asset
        self.gamma_return = self.gamma_return * self.gamma + reward 
        self.cumu_return = self.total_asset / self.initial_cash
        if done:
            reward = self.gamma_return
            self.episode_return = self.total_asset / self.initial_cash
        return state, reward, done, None

    def get_state(self):
        state =  np.hstack((self.cash * 2 ** -18, self.stocks * 2 ** -3))
        for i in range(self.lookback):
            tech_i = self.tech_array[self.time-i]
            normalized_tech_i = tech_i * 2 ** -15
            state = np.hstack((state, normalized_tech_i)).astype(np.float32)
        return state
    
    def close(self):
        pass

    def _generate_action_normalizer(self):
        action_norm_vector = []
        price_0 = self.price_array[0]
        for price in price_0:
            x = math.floor(math.log(price, 10)) #the order of magnitude 
            action_norm_vector.append(1/((10)**x)) 
            
        action_norm_vector = np.asarray(action_norm_vector) * 10000
        self.action_norm_vector = np.asarray(action_norm_vector)

env = CryptoEnv

TICKER_LIST = ['BTCUSDT','ETHUSDT','ADAUSDT','BNBUSDT','XRPUSDT',
                'SOLUSDT','DOTUSDT', 'DOGEUSDT','AVAXUSDT','UNIUSDT']

TRAIN_START_DATE = '2021-09-01'
TRAIN_END_DATE = '2021-09-02'

TEST_START_DATE = '2021-09-21'
TEST_END_DATE = '2021-09-30'

INDICATORS = ['macd', 'rsi', 'cci', 'dx'] #self-defined technical indicator list is NOT supported yet

ERL_PARAMS = {"learning_rate": 2**-15,"batch_size": 2**11,
                "gamma": 0.99, "seed":312,"net_dimension": 2**9, 
                "target_step": 5000, "eval_gap": 30, "eval_times": 1}

train(start_date=TRAIN_START_DATE, 
      end_date=TRAIN_END_DATE,
      ticker_list=TICKER_LIST, 
      data_source='binance',
      time_interval='5m', 
      technical_indicator_list=INDICATORS,
      drl_lib='elegantrl', 
      env=env, 
      model_name='ppo', 
      current_working_dir='./test_ppo',
      erl_params=ERL_PARAMS,
      break_step=5e4,
      if_vix=False
      )

account_value_erl = test(start_date = TEST_START_DATE, 
                        end_date = TEST_END_DATE,
                        ticker_list = TICKER_LIST, 
                        data_source = 'binance',
                        time_interval= '5m', 
                        technical_indicator_list= INDICATORS,
                        drl_lib='elegantrl', 
                        env=env, 
                        model_name='ppo', 
                        current_working_dir='./test_ppo', 
                        net_dimension = 2**9, 
                        if_vix=False
                        )

TypeError                                 Traceback (most recent call last)
Cell In[17], line 16
      3 env_kwargs = {
      4     "hmax": 100, 
      5     "initial_amount": 1000000, 
   (...)
     13     
     14 }
     15 #Instantiate the training gym compatible environment
---> 16 e_train_gym = StockTradingEnv(df = train, **env_kwargs)

TypeError: __init__() missing 1 required positional argument: 'num_stock_shares'

----> 1 data_risk_indicator = processed_full[(processed_full.date<TRAIN_END_DATE) & (processed_full.date>=TRAIN_START_DATE)] 
      2 insample_risk_indicator = data_risk_indicator.drop_duplicates(subset=['date']) 
NameError: name 'processed_full' is not defined

----> 1 trained_a2c = A2C.load("agent_a2c") if if_using_a2c else None 
      2 trained_ddpg = DDPG.load("agent_ddpg") if if_using_ddpg else None
FileNotFoundError: [Errno 2] No such file or directory: 'agent_a2c.zip'

Building wheels for collected packages: elegantrl, box2d-py 
Building wheel for elegantrl (setup.py) ... done 
Created wheel for elegantrl: filename=elegantrl-0.3.6-py3-none-any.whl size=195057 sha256=3ae3abaacb514d4ddfcd61705ed71e0563a61e993b566db6aaedfc234d0d8ad4 
Stored in directory: /tmp/pip-ephem-wheel-cache-ic4gkgzy/wheels/d6/fb/88/7d5e5490b35f78191267fee312ce81baac55c6b9d89151e72a 
error: subprocess-exited-with-error 

× python setup.py bdist_wheel did not run successfully. 
│ exit code: 1 
╰─> See above for output. 

note: This error originates from a subprocess, and is likely not a problem with pip. 
Building wheel for box2d-py (setup.py) ... error 
ERROR: Failed building wheel for box2d-py 
Running setup.py clean for box2d-py 
Successfully built elegantrl 
Failed to build box2d-py 

Installing collected packages: pyglet, py-spy, ply, opencensus-context, nvidia-ml-py, msgpack, gputil, distlib, colorful, websockets, websocket-client, virtualenv, thriftpy2, tensorboardX, soupsieve, setuptools, pymysql, pyluach, psutil, prometheus-client, lz4, jedi, importlib-metadata, html5lib, gym, frozendict, deprecation, charset-normalizer, blessed, requests, pycares, gpustat, cryptography, beautifulsoup4, aiohttp, yfinance, stockstats, stable-baselines3, ray, jqdatasdk, exchange_calendars, alpaca_trade_api, aiohttp-cors, aiodns, opencensus, empyrical, ccxt, pyfolio, finrl Attempting uninstall: msgpack 
...
 Attempting uninstall: aiohttp 
Found existing installation: aiohttp 3.8.4 
Uninstalling aiohttp-3.8.4: 
Successfully uninstalled aiohttp-3.8.4 

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed.
This behaviour is the source of the following dependency conflicts. cvxpy 1.2.3 requires setuptools<=64.0.2, but you have setuptools 67.4.0 which is incompatible. 
...

ImportError Traceback (most recent call last) " style="color: var(--colab-anchor-color);">[<ipython-input-3-c2f4f1f1ce9a>](https://localhost:8080/#) in <module>
 ----> 1 from agents.elegantrl_models import DRLAgent as DRLAgent_erl
 2 from agents.rllib_models import DRLAgent as DRLAgent_rllib 
 3 from agents.stablebaselines3_models import DRLAgent as DRLAgent_sb3
 4 from meta.data_processor import DataProcessor 5 [/FinRL-Meta/agents/elegantrl_models.py](https://localhost:8080/#) in <module>
 5 from elegantrl.agents import AgentSAC
 6 from elegantrl.agents import AgentTD3
 ----> 7 from elegantrl.train.config import Arguments
 8 from elegantrl.train.run import init_agent
 9 from elegantrl.train.run import train_and_evaluate
 ImportError: cannot import name
 'Arguments' from 'elegantrl.train.config' (/usr/local/lib/python3.8/dist-packages/elegantrl/train/config.py)

    这里通过tushare获取A股数据时没有复权，后续的处理里也没有看到复权的操作。是否应该在tushare获取数据时加入复权的参数？

class Net(BaseFeaturesExtractor):
    def __init__(self, observation_space: gym.spaces.Box, stock_dim: int = 28):
        super(Net, self).__init__(observation_space, stock_dim)
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc3 = nn.Linear(672,stock_dim)
`

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = nn.Flatten()(x)
       # x = x.view(x.size(0), -1)
        x = F.relu(self.fc3(x))
        return x
policy_kwargs ={
    'features_extractor_class':Net,
}
model = PPO("MlpPolicy", env= env_train, policy_kwargs=policy_kwargs)
model.learn(total_timesteps=100000)

df = YahooDownloader(start_date = TRAIN_START_DATE,
                     end_date = TRADE_END_DATE,
                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()

[*********************100%***********************]  1 of 1 completed

1 Failed download:
- AXP: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- AMGN: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- AAPL: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- BA: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- CAT: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- CSCO: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- CVX: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- GS: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- HD: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- HON: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- IBM: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- INTC: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- JNJ: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- KO: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- JPM: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- MCD: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- MMM: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- MRK: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- MSFT: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- NKE: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- PG: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- TRV: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- UNH: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- CRM: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- VZ: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- V: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- WBA: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- WMT: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- DIS: No data found for this date range, symbol may be delisted
[*********************100%***********************]  1 of 1 completed

1 Failed download:
- DOW: No data found for this date range, symbol may be delisted
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-50-b49b1e107d2c>](https://localhost:8080/#) in <cell line: 1>()
----> 1 df = YahooDownloader(start_date = TRAIN_START_DATE,
      2                      end_date = TRADE_END_DATE,
      3                      ticker_list = config_tickers.DOW_30_TICKER).fetch_data()

[/usr/local/lib/python3.9/site-packages/finrl/meta/preprocessor/yahoodownloader.py](https://localhost:8080/#) in fetch_data(self, proxy)
     58                 num_failures += 1
     59         if num_failures == len(self.ticker_list):
---> 60             raise ValueError("no data is fetched.")
     61         # reset the index, we want to use numbers as index instead of dates
     62         data_df = data_df.reset_index()

ValueError: no data is fetched.

df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,
                                                 PPO_model_kwargs,
                                                 DDPG_model_kwargs,
                                                 timesteps_dict)