visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "import DQNTradingAgent.dqn_agent as dqn_agent\n",
    "from leverage_trading_env import TradingEnv, action2position\n",
    "from custom_hyperparameters import hyperparams\n",
    "import argparse\n",
    "from data_downloader import read_binance_futures_data\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "\n",
    "parser.add_argument('--agent_num', type=int, default=1,\n",
    "                    help='which agent to load')\n",
    "\n",
    "parser.add_argument('--device_num', type=int, default=0,\n",
    "                    help='cuda device num')\n",
    "\n",
    "parser.add_argument('--fee', type=float, default=.001,\n",
    "                    help='fee percentage')\n",
    "\n",
    "parser.add_argument('--save_location', type=str, default=f\"saves/test_visualization\",\n",
    "                    help='where to save?')\n",
    "                    \n",
    "parser.add_argument('--risk_aversion_multiplier', type=float, default=1.,\n",
    "                        help='risk_aversion_level')\n",
    "\n",
    "parser.add_argument('--load_file', type=str, default=\"saves/IQN_1d/IQN_leverage_1200.pth\",\n",
    "                    help='which to load to save?')\n",
    "\n",
    "parser.add_argument('--data_path', type=str, default=\"binance_futures_1d_test.db\",\n",
    "                    help='where to load source data')\n",
    "\n",
    "parser.add_argument('--symbol', type=str, default=\"BTCUSDT\",\n",
    "                    help='what symbol to read')\n",
    "\n",
    "parser.add_argument('--timeframe', type=str, default=\"1h\",\n",
    "                    help='resample period. ex : 10m, 1h, 4h, 1d')\n",
    "\n",
    "args = parser.parse_args([])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "torch.cuda.manual_seed_all(7)\n",
    "device = torch.device(\"cuda:{}\".format(args.device_num))\n",
    "dqn_agent.set_device(device)\n",
    "\n",
    "save_interval  = 100\n",
    "print_interval = 1\n",
    "\n",
    "n_episodes   = 10\n",
    "sample_len   = 250\n",
    "obs_data_len = 192\n",
    "step_len     = 1\n",
    "risk_aversion_multiplier = 0.5 + args.risk_aversion_multiplier / 2\n",
    "n_action_intervals = 5\n",
    "init_budget = 10000\n",
    "\n",
    "# torch.save(hyperparams, os.path.join(args.save_location, \"hyperparams.pth\"))\n",
    "if not os.path.exists(args.save_location):\n",
    "    os.makedirs(args.save_location)\n",
    "\n",
    "df = read_binance_futures_data(args.data_path, args.symbol, args.timeframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "# basic visualization : price trend across the time\n",
    "sns.lineplot(df.index, df.close)\n",
    "locs, labels = plt.xticks()\n",
    "plt.setp(labels, rotation=45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QNetwork(\n",
       "  (embedding): Linear(in_features=5, out_features=512, bias=True)\n",
       "  (pos_encoder): PositionalEncoding(\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder_layer): TransformerEncoderLayer(\n",
       "    (self_attn): MultiheadAttention(\n",
       "      (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True)\n",
       "    )\n",
       "    (linear1): Linear(in_features=512, out_features=2048, bias=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "    (linear2): Linear(in_features=2048, out_features=512, bias=True)\n",
       "    (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "    (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "    (dropout1): Dropout(p=0.1, inplace=False)\n",
       "    (dropout2): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (phi): NoisyLinear(in_features=1, out_features=512, bias=True, initial_sigma=0.5, factorized=True)\n",
       "  (fc): NoisyLinear(in_features=512, out_features=64, bias=True, initial_sigma=0.5, factorized=True)\n",
       "  (fc_q): NoisyLinear(in_features=64, out_features=11, bias=True, initial_sigma=0.5, factorized=True)\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = TradingEnv(custom_args=args, env_id='leverage_trading_env', obs_data_len=obs_data_len, step_len=step_len,\n",
    "                     sample_len=sample_len,\n",
    "                     df=df, fee=0.001, initial_budget=init_budget, n_action_intervals=n_action_intervals,\n",
    "                     deal_col_name='close', sell_at_end=True,\n",
    "                     feature_names=['open', 'high', 'low', 'close', 'volume', ])\n",
    "agent = dqn_agent.Agent(action_size=2 * n_action_intervals + 1, risk_averse_ratio=args.risk_aversion_multiplier, obs_len=obs_data_len,\n",
    "                        num_features=env.observation_space[1], **hyperparams)\n",
    "\n",
    "agent.qnetwork_local.load_state_dict(torch.load(args.load_file, map_location=device))\n",
    "agent.qnetwork_local.to(device)\n",
    "agent.qnetwork_local.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final_score : 0.5184477953809106\n"
     ]
    }
   ],
   "source": [
    "state, info = env.reset()\n",
    "score = 0.\n",
    "actions = []\n",
    "rewards = []\n",
    "price_list = []\n",
    "\n",
    "while True:\n",
    "    action = int(agent.act(state, eps=0.))\n",
    "    actions.append(action)\n",
    "    next_state, reward, done, info  = env.step(action2position[action])\n",
    "    price_list.append(info.cur_price)\n",
    "    rewards.append(reward)\n",
    "    score += reward\n",
    "    # print(state[-1][3], f\"r={reward:4f}, a={action}, asset={info.budget:.2f}, pos={info.position:.2f}, p.m={info.price_mean:.2f}\")\n",
    "    if reward < 0:\n",
    "        reward *= risk_aversion_multiplier\n",
    "    if done:\n",
    "        action = 2 * n_action_intervals\n",
    "    # agent.step(state, action, reward, next_state, done)\n",
    "    state = next_state\n",
    "    if done:\n",
    "        break\n",
    "\n",
    "print(f\"final_score : {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "# basic visualization : price trend across the time\n",
    "sns.lineplot(env.df_sample.index, env.df_sample.close)\n",
    "locs, labels = plt.xticks()\n",
    "plt.setp(labels, rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-04-17 00:00:00')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.df_sample.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>action</th>\n",
       "      <th>position</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-26</th>\n",
       "      <td>63078.78</td>\n",
       "      <td>63293.48</td>\n",
       "      <td>59817.55</td>\n",
       "      <td>60328.81</td>\n",
       "      <td>40217.50083</td>\n",
       "      <td>5</td>\n",
       "      <td>clear</td>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-27</th>\n",
       "      <td>60328.81</td>\n",
       "      <td>61496.00</td>\n",
       "      <td>58000.00</td>\n",
       "      <td>58413.44</td>\n",
       "      <td>62124.49016</td>\n",
       "      <td>5</td>\n",
       "      <td>clear</td>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-28</th>\n",
       "      <td>58413.44</td>\n",
       "      <td>62499.00</td>\n",
       "      <td>57820.00</td>\n",
       "      <td>60575.89</td>\n",
       "      <td>61056.35301</td>\n",
       "      <td>5</td>\n",
       "      <td>clear</td>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-29</th>\n",
       "      <td>60575.90</td>\n",
       "      <td>62980.00</td>\n",
       "      <td>60174.81</td>\n",
       "      <td>62253.71</td>\n",
       "      <td>43973.90414</td>\n",
       "      <td>1</td>\n",
       "      <td>long</td>\n",
       "      <td>2021-10-29</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-30</th>\n",
       "      <td>62253.70</td>\n",
       "      <td>62359.25</td>\n",
       "      <td>60673.00</td>\n",
       "      <td>61859.19</td>\n",
       "      <td>31478.12566</td>\n",
       "      <td>5</td>\n",
       "      <td>clear</td>\n",
       "      <td>2021-10-30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high       low     close       volume  action  \\\n",
       "timestamp                                                                 \n",
       "2021-10-26  63078.78  63293.48  59817.55  60328.81  40217.50083       5   \n",
       "2021-10-27  60328.81  61496.00  58000.00  58413.44  62124.49016       5   \n",
       "2021-10-28  58413.44  62499.00  57820.00  60575.89  61056.35301       5   \n",
       "2021-10-29  60575.90  62980.00  60174.81  62253.71  43973.90414       1   \n",
       "2021-10-30  62253.70  62359.25  60673.00  61859.19  31478.12566       5   \n",
       "\n",
       "           position  timestamp  size  \n",
       "timestamp                             \n",
       "2021-10-26    clear 2021-10-26   0.0  \n",
       "2021-10-27    clear 2021-10-27   0.0  \n",
       "2021-10-28    clear 2021-10-28   0.0  \n",
       "2021-10-29     long 2021-10-29   0.4  \n",
       "2021-10-30    clear 2021-10-30   0.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_position(action):\n",
    "    if 0<=action<=4:\n",
    "        return \"long\"\n",
    "    elif action==5:\n",
    "        return \"clear\"\n",
    "    elif 5<action:\n",
    "        return \"short\"\n",
    "\n",
    "from leverage_trading_env import action2position\n",
    "\n",
    "df_vis = env.df_sample[-len(actions):]\n",
    "df_vis[\"action\"] = np.array(actions)\n",
    "df_vis[\"position\"] = df_vis[\"action\"].apply(get_position)\n",
    "df_vis[\"timestamp\"] = df_vis.index\n",
    "df_vis[\"size\"] = df_vis[\"action\"].apply(lambda x: abs(action2position[x]))\n",
    "df_vis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeffrey/anaconda3/envs/gpu/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.set_style('whitegrid')\n",
    "sns.lineplot(df_vis.index, df_vis.close)\n",
    "locs, labels = plt.xticks()\n",
    "plt.setp(labels, rotation=45)\n",
    "\n",
    "\n",
    "# sns.diverging_palette(145, 300, s=60, as_cmap=True)\n",
    "sns.scatterplot(data=df_vis, x=\"timestamp\", y=\"close\", hue=\"position\", size=\"size\", markers=\"^\")\n",
    "\n",
    "fig.savefig(\"trade_visualization.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "fa19752063a99a8f5db2dacef7b22e2ec238927163830f1a9e70fe2782c164ec"
  },
  "kernelspec": {
   "display_name": "Python 3.7.12 64-bit ('gpu': conda)",
   "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": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}