tutorial/Tutorial3_SARL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# SARL for Portfolio Management on DJ30\n",
    "This tutorial is to demonstrate an example of using SARL to do portfolio management on DJ30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Step1: Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-04 18:50:30,910\tINFO services.py:1476 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8267\u001b[39m\u001b[22m\n",
      "2023-03-04 18:50:32,917\tINFO worker.py:973 -- Calling ray.init() again after it has already been called.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import sys\n",
    "from pathlib import Path\n",
    "import os\n",
    "import torch\n",
    "\n",
    "ROOT = os.path.dirname(os.path.abspath(\".\"))\n",
    "sys.path.append(ROOT)\n",
    "from trademaster.utils import plot\n",
    "import argparse\n",
    "import os.path as osp\n",
    "from mmcv import Config\n",
    "from trademaster.utils import replace_cfg_vals\n",
    "from trademaster.datasets.builder import build_dataset\n",
    "from trademaster.trainers.builder import build_trainer\n",
    "from trademaster.utils import set_seed\n",
    "set_seed(2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Step2: Import Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "parser = argparse.ArgumentParser(description='Download Alpaca Datasets')\n",
    "parser.add_argument(\"--config\", default=osp.join(ROOT, \"configs\", \"portfolio_management\", \"portfolio_management_dj30_sarl_sarl_adam_mse.py\"),\n",
    "                    help=\"download datasets config file path\")\n",
    "parser.add_argument(\"--task_name\", type=str, default=\"train\")\n",
    "args, _ = parser.parse_known_args()\n",
    "\n",
    "cfg = Config.fromfile(args.config)\n",
    "task_name = args.task_name\n",
    "cfg = replace_cfg_vals(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Config (path: /data/sunshuo/qml/TradeMaster/configs/portfolio_management/portfolio_management_dj30_sarl_sarl_adam_mse.py): {'data': {'type': 'PortfolioManagementDataset', 'data_path': 'data/portfolio_management/dj30', 'train_path': 'data/portfolio_management/dj30/train.csv', 'valid_path': 'data/portfolio_management/dj30/valid.csv', 'test_path': 'data/portfolio_management/dj30/test.csv', 'tech_indicator_list': ['high', 'low', 'open', 'close', 'adjcp', 'zopen', 'zhigh', 'zlow', 'zadjcp', 'zclose', 'zd_5', 'zd_10', 'zd_15', 'zd_20', 'zd_25', 'zd_30'], 'length_day': 5, 'initial_amount': 10000, 'transaction_cost_pct': 0.001, 'test_dynamic_path': 'data/portfolio_management/dj30/test_with_label.csv'}, 'environment': {'type': 'PortfolioManagementSARLEnvironment'}, 'trainer': {'type': 'PortfolioManagementSARLTrainer', 'agent_name': 'ddpg', 'if_remove': False, 'configs': {'framework': 'tf2', 'num_workers': 0}, 'work_dir': 'work_dir/portfolio_management_dj30_sarl_sarl_adam_mse', 'epochs': 2}, 'loss': {'type': 'MSELoss'}, 'optimizer': {'type': 'Adam', 'lr': 0.001}, 'task_name': 'portfolio_management', 'dataset_name': 'dj30', 'net_name': 'sarl', 'agent_name': 'sarl', 'optimizer_name': 'adam', 'loss_name': 'mse', 'work_dir': 'work_dir/portfolio_management_dj30_sarl_sarl_adam_mse'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Step3: Build Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = build_dataset(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Step4: Build Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Arguments Keep work_dir: /data/sunshuo/qml/TradeMaster/work_dir/portfolio_management_dj30_sarl_sarl_adam_mse\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "work_dir = os.path.join(ROOT, cfg.trainer.work_dir)\n",
    "\n",
    "if not os.path.exists(work_dir):\n",
    "    os.makedirs(work_dir)\n",
    "cfg.dump(osp.join(work_dir, osp.basename(args.config)))\n",
    "\n",
    "trainer = build_trainer(cfg, default_args=dict(dataset=dataset, device = device))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Step5: Train, Valid and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-04 18:50:35,319\tINFO trainer.py:2322 -- Executing eagerly (framework='tf2'), with eager_tracing=False. For production workloads, make sure to set eager_tracing=True  in order to match the speed of tf-static-graph (framework='tf'). For debugging purposes, `eager_tracing=False` is the best choice.\n",
      "2023-03-04 18:50:35,321\tWARNING deprecation.py:47 -- DeprecationWarning: `config['prioritized_replay']` has been deprecated. Replay prioritization specified at new location config['replay_buffer_config']['prioritized_replay'] will be overwritten. This will raise an error in the future!\n",
      "2023-03-04 18:50:35,322\tWARNING deprecation.py:47 -- DeprecationWarning: `config[prioritized_replay_alpha]` has been deprecated. config['replay_buffer_config'][prioritized_replay_alpha] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide prioritized_replay_alpha. This will raise an error in the future!\n",
      "2023-03-04 18:50:35,322\tWARNING deprecation.py:47 -- DeprecationWarning: `config[prioritized_replay_beta]` has been deprecated. config['replay_buffer_config'][prioritized_replay_beta] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide prioritized_replay_beta. This will raise an error in the future!\n",
      "2023-03-04 18:50:35,323\tWARNING deprecation.py:47 -- DeprecationWarning: `config[prioritized_replay_eps]` has been deprecated. config['replay_buffer_config'][prioritized_replay_eps] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide prioritized_replay_eps. This will raise an error in the future!\n",
      "2023-03-04 18:50:35,324\tWARNING deprecation.py:47 -- DeprecationWarning: `config[learning_starts]` has been deprecated. config['replay_buffer_config'][learning_starts] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide learning_starts. This will raise an error in the future!\n",
      "2023-03-04 18:50:35,325\tINFO trainer.py:906 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags.\n",
      "2023-03-04 18:50:39,289\tWARNING env.py:136 -- Your env doesn't have a .spec.max_episode_steps attribute. This is fine if you have set 'horizon' in your config dictionary, or `soft_horizon`. However, if you haven't, 'horizon' will default to infinity, and your environment will not be reset.\n",
      "2023-03-04 18:50:46,304\tINFO trainable.py:163 -- Trainable.setup took 10.986 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.\n",
      "2023-03-04 18:50:46,307\tWARNING util.py:65 -- Install gputil for GPU system monitoring.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Episode: [1/2]\n",
      "WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x7feb1782b710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function _BaseOptimizer._update_step_xla at 0x7feb1782b710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x7feb1782b710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:6 out of the last 6 calls to <function _BaseOptimizer._update_step_xla at 0x7feb1782b710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valid Episode: [1/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "|  8.418111%   |   0.410622  | 2.239796%  |  34.994217%  |\n",
      "+--------------+-------------+------------+--------------+\n",
      "Train Episode: [2/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "| -23.813643%  |  -0.205806  | 0.799392%  |  62.305931%  |\n",
      "+--------------+-------------+------------+--------------+\n",
      "Valid Episode: [2/2]\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "|  10.970900%  |   0.475267  | 2.276330%  |  35.440930%  |\n",
      "+--------------+-------------+------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "trainer.train_and_valid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-04 19:03:01,742\tINFO services.py:1476 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8267\u001b[39m\u001b[22m\n",
      "2023-03-04 19:03:04,049\tWARNING deprecation.py:47 -- DeprecationWarning: `config['prioritized_replay']` has been deprecated. Replay prioritization specified at new location config['replay_buffer_config']['prioritized_replay'] will be overwritten. This will raise an error in the future!\n",
      "2023-03-04 19:03:04,050\tWARNING deprecation.py:47 -- DeprecationWarning: `config[prioritized_replay_alpha]` has been deprecated. config['replay_buffer_config'][prioritized_replay_alpha] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide prioritized_replay_alpha. This will raise an error in the future!\n",
      "2023-03-04 19:03:04,051\tWARNING deprecation.py:47 -- DeprecationWarning: `config[prioritized_replay_beta]` has been deprecated. config['replay_buffer_config'][prioritized_replay_beta] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide prioritized_replay_beta. This will raise an error in the future!\n",
      "2023-03-04 19:03:04,051\tWARNING deprecation.py:47 -- DeprecationWarning: `config[prioritized_replay_eps]` has been deprecated. config['replay_buffer_config'][prioritized_replay_eps] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide prioritized_replay_eps. This will raise an error in the future!\n",
      "2023-03-04 19:03:04,052\tWARNING deprecation.py:47 -- DeprecationWarning: `config[learning_starts]` has been deprecated. config['replay_buffer_config'][learning_starts] should be used for Q-Learning algorithms. Ignore this warning if you are not using a Q-Learning algorithm and still provide learning_starts. This will raise an error in the future!\n",
      "2023-03-04 19:03:04,422\tWARNING env.py:136 -- Your env doesn't have a .spec.max_episode_steps attribute. This is fine if you have set 'horizon' in your config dictionary, or `soft_horizon`. However, if you haven't, 'horizon' will default to infinity, and your environment will not be reset.\n",
      "2023-03-04 19:03:04,948\tWARNING util.py:65 -- Install gputil for GPU system monitoring.\n",
      "2023-03-04 19:03:04,963\tWARNING eager_tf_policy.py:725 -- Cannot restore an optimizer's state for tf eager! Keras is not able to save the v1.x optimizers (from tf.compat.v1.train) since they aren't compatible with checkpoints.\n",
      "2023-03-04 19:03:05,032\tINFO trainable.py:589 -- Restored on 172.21.100.16 from checkpoint: /home/sunshuo/ray_results/DDPGTrainer_portfolio_management_sarl_2023-03-04_19-03-0483sh7wyz/tmp5y7bl526restore_from_object/checkpoint-2\n",
      "2023-03-04 19:03:05,033\tINFO trainable.py:597 -- Current state after restoring: {'_iteration': 2, '_timesteps_total': 512, '_time_total': 689.0092370510101, '_episodes_total': 1}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Best Episode\n",
      "+--------------+-------------+------------+--------------+\n",
      "| Total Return | Sharp Ratio | Volatility | Max Drawdown |\n",
      "+--------------+-------------+------------+--------------+\n",
      "|  13.393640%  |   1.111919  | 0.772255%  |  7.716845%   |\n",
      "+--------------+-------------+------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "import ray\n",
    "from ray.tune.registry import register_env\n",
    "from trademaster.environments.portfolio_management.sarl_environment import PortfolioManagementSARLEnvironment\n",
    "def env_creator(env_name):\n",
    "    if env_name == 'portfolio_management_sarl':\n",
    "        env = PortfolioManagementSARLEnvironment\n",
    "    else:\n",
    "        raise NotImplementedError\n",
    "    return env\n",
    "ray.init(ignore_reinit_error=True)\n",
    "register_env(\"portfolio_management_sarl\", lambda config: env_creator(\"portfolio_management_sarl\")(config))\n",
    "trainer.test();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(trainer.test_environment.save_asset_memory(),alg=\"SARL\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TradeMaster",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "a1f97403911abd3f02553c8f2b0c54537fddc7efadd9f5d3e31784db6e40c347"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}