Untitled1.ipynb

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     "text": [
      "C:\\github\\NeoFinRL\\.venv\\lib\\site-packages\\setuptools\\distutils_patch.py:25: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.\n",
      "  warnings.warn(\n"
     ]
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     "text": [
      "initial done:\n",
      "observation_list:['Open', 'High', 'Low', 'Close', 'minute', 'hour', 'day', 'macd', 'boll_ub', 'boll_lb', 'rsi_30', 'dx_30', 'close_30_sma', 'close_60_sma']\n",
      " assets:['GBPUSD']\n",
      " time serial: 2017-01-02 01:00:00 -> 2017-01-06 23:55:00 length: 1428\n",
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      "plotting...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\github\\NeoFinRL\\.venv\\lib\\site-packages\\mplfinance\\_arg_validators.py:35: UserWarning: \n",
      "\n",
      " ================================================================= \n",
      "\n",
      "   WARNING: YOU ARE PLOTTING SO MUCH DATA THAT IT MAY NOT BE\n",
      "            POSSIBLE TO SEE DETAILS (Candles, Ohlc-Bars, Etc.)\n",
      "   For more information see:\n",
      "   - https://github.com/matplotlib/mplfinance/wiki/Plotting-Too-Much-Data\n",
      "   \n",
      "   TO SILENCE THIS WARNING, set `type='line'` in `mpf.plot()`\n",
      "   OR set kwarg `warn_too_much_data=N` where N is an integer \n",
      "   LARGER than the number of data points you want to plot.\n",
      "\n",
      " ================================================================ \n",
      "  warnings.warn('\\n\\n ================================================================= '+\n",
      "C:\\github\\NeoFinRL\\.venv\\lib\\site-packages\\mplfinance\\_arg_validators.py:35: UserWarning: \n",
      "\n",
      " ================================================================= \n",
      "\n",
      "   WARNING: YOU ARE PLOTTING SO MUCH DATA THAT IT MAY NOT BE\n",
      "            POSSIBLE TO SEE DETAILS (Candles, Ohlc-Bars, Etc.)\n",
      "   For more information see:\n",
      "   - https://github.com/matplotlib/mplfinance/wiki/Plotting-Too-Much-Data\n",
      "   \n",
      "   TO SILENCE THIS WARNING, set `type='line'` in `mpf.plot()`\n",
      "   OR set kwarg `warn_too_much_data=N` where N is an integer \n",
      "   LARGER than the number of data points you want to plot.\n",
      "\n",
      " ================================================================ \n",
      "  warnings.warn('\\n\\n ================================================================= '+\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- running time: 3.182525873184204---\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Mpf_Figure size 4000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from neo_finrl.env_fx_trading.env_fx import tgym\n",
    "import time\n",
    "import datetime\n",
    "from stable_baselines3 import PPO\n",
    "from stable_baselines3.common.env_util import make_vec_env\n",
    "file =\"./data/split/GBPUSD/weekly/GBPUSD_2017_0.csv\"\n",
    "df = pd.read_csv(file)\n",
    "t = tgym(df)\n",
    "\n",
    "# if model: del model # remove to demonstrate saving and loading\n",
    "model_name='./data/models/GBPUSD-week'\n",
    "model = PPO.load(model_name)\n",
    "\n",
    "start_time = time.time()\n",
    "obs = t.reset()\n",
    "t.current_step=0\n",
    "done = False\n",
    "while not done:\n",
    "    action, _states = model.predict(obs)\n",
    "    obs, rewards, done, info = t.step(action)\n",
    "    print(action, rewards)\n",
    "    t.render(mode='graph')\n",
    "print(f\"--- running time: {(time.time() - start_time)}---\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d03c453b-1cfe-4cce-b246-36f6312d2694",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(env, agent, model_name,parallel, if_vix = True,**kwargs):\n",
    "    learning_rate = kwargs.get('learning_rate', 3e-4)\n",
    "    batch_size = kwargs.get('batch_size', 1024 )\n",
    "    gamma = kwargs.get('gamma', 0.99)\n",
    "    seed = kwargs.get('seed', 1)\n",
    "    total_timesteps = kwargs.get('total_timesteps', 1e5)\n",
    "    net_dimension = kwargs.get('net_dimension', 2**9)\n",
    "    cwd = kwargs.get('cwd','./'+str(agent))\n",
    "\n",
    "    # env_instance = map(env, [pd.read_csv(f) for f in files])\n",
    "    if agent == 'ppo':\n",
    "        from stable_baselines3 import PPO\n",
    "        from stable_baselines3.common.vec_env import SubprocVecEnv, DummyVecEnv\n",
    "        ppo_kwargs = {\"learning_rate\":learning_rate,\n",
    "                        \"n_steps\":2048, \n",
    "                        \"batch_size\":batch_size, \n",
    "                        \"ent_coef\":0.0, \n",
    "                        \"gamma\":gamma, \n",
    "                        \"seed\":seed, \n",
    "                        \"verbose\":0, \n",
    "                        \"epsilon\":0.5,\n",
    "                        \"tensorboard_log\":\"./data/tensorboard/\"}\n",
    "\n",
    "        # env_train = [x.get_sb_env for x in env_instance ]\n",
    "        vector_env = [lambda:env for _ in range(4)]\n",
    "        env_train = DummyVecEnv(vector_env)\n",
    "        start_time = time.time()\n",
    "        s = datetime.datetime.now()\n",
    "        print(f'Training start: {s}')\n",
    "        if os.path.exists(model_name):\n",
    "            model = PPO.load(model_name, env=env_train, **ppo_kwargs)\n",
    "            print(f'retaining model: {model_name}')\n",
    "            model.learn(total_timesteps=total_timesteps)\n",
    "            model.save(model_name)\n",
    "        else:\n",
    "            model = PPO(\"MlpPolicy\", env=env_train, **ppo_kwargs)\n",
    "            model.learn(total_timesteps=total_timesteps)\n",
    "            model.save(model_name)\n",
    "\n",
    "        print('Training finished!')\n",
    "        model_name_session = \"./data/models/GBPUSD-week-\" + s.strftime('%Y%m%d%H%M%S')\n",
    "        model.save(model_name_session)\n",
    "        print(f'Trained model saved in {model_name_session}')\n",
    "        print(f\"trainning time: {(time.time() - start_time)}\")\n",
    "\n",
    "    else:\n",
    "        raise ValueError('DRL library input is NOT supported. Please check.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80d6936b-4245-4cee-a4f5-7ce1d599703b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7880/4026668012.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'./data/models/gbpusd-week1.zip'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtgym\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0magent\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"ppo\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparallel\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7880/3355572296.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(env, agent, model_name, parallel, if_vix, **kwargs)\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[1;31m# env_train = [x.get_sb_env for x in env_instance ]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0mvector_env\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0menv\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0menv_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvector_env\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[0mstart_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\github\\NeoFinRL\\.venv\\lib\\site-packages\\stable_baselines3\\common\\vec_env\\dummy_vec_env.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, env_fns)\u001b[0m\n\u001b[0;32m     27\u001b[0m         \u001b[0mVecEnv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv_fns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[0mobs_space\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobs_space_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs_space\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuf_obs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\github\\NeoFinRL\\.venv\\lib\\site-packages\\stable_baselines3\\common\\vec_env\\util.py\u001b[0m in \u001b[0;36mobs_space_info\u001b[1;34m(obs_space)\u001b[0m\n\u001b[0;32m     72\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msubspaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m         \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m         \u001b[0mshapes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m         \u001b[0mdtypes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshapes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "m = './data/models/gbpusd-week1.zip'\n",
    "train(env=tgym,agent=\"ppo\",model_name = m, parallel =4)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b90726c-5a2a-40cb-9053-3f73cd8acee8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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