embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4166d2ea",
   "metadata": {},
   "source": [
    "## Stock Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5ae7fd10",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%config Completer.use_jedi = False\n",
    "\n",
    "#-- Import all the functions needed\n",
    "from S2V_functions import *\n",
    "from S2V_models import *\n",
    "from visualisation_functions import *\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report\n",
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = 'notebook_connected'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9db5fe",
   "metadata": {},
   "source": [
    "#### Read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ffa0102",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Read in the processed returns data\n",
    "returns_df = pd.read_csv('Data/returns_df_611.csv', index_col=0)\n",
    "returns_df.index = pd.to_datetime(returns_df.index)\n",
    "\n",
    "#-- Read in data containing sectors etc\n",
    "stock_df = pd.read_csv('Data/historical_stocks.csv')\n",
    "\n",
    "#-- Store lists of useful items and create mapping dictionaries\n",
    "tickers, ticker2idx, idx2ticker, sectors, industries, names = get_extras(returns_df, stock_df, misc_include=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c495746",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AB</th>\n",
       "      <th>ABM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ABX</th>\n",
       "      <th>ACXM</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>...</th>\n",
       "      <th>WTS</th>\n",
       "      <th>WWW</th>\n",
       "      <th>WY</th>\n",
       "      <th>XCRA</th>\n",
       "      <th>XLNX</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZIXI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>0.088754</td>\n",
       "      <td>-0.006263</td>\n",
       "      <td>-0.003067</td>\n",
       "      <td>-0.036145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>-0.025093</td>\n",
       "      <td>-0.030242</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>-0.034803</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029661</td>\n",
       "      <td>-0.057143</td>\n",
       "      <td>-0.02698</td>\n",
       "      <td>-0.050279</td>\n",
       "      <td>0.02818</td>\n",
       "      <td>-0.027929</td>\n",
       "      <td>-0.005291</td>\n",
       "      <td>0.066116</td>\n",
       "      <td>-0.062302</td>\n",
       "      <td>-0.061514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 611 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL        AB       ABM       ABT  ABX      ACXM      ADBE  \\\n",
       "2000-01-03  0.088754 -0.006263 -0.003067 -0.036145  0.0  0.041667 -0.025093   \n",
       "\n",
       "                 ADI       ADM       ADP  ...       WTS       WWW       WY  \\\n",
       "2000-01-03 -0.030242 -0.010309 -0.034803  ... -0.029661 -0.057143 -0.02698   \n",
       "\n",
       "                XCRA     XLNX       XOM      XRAY       XRX      ZION  \\\n",
       "2000-01-03 -0.050279  0.02818 -0.027929 -0.005291  0.066116 -0.062302   \n",
       "\n",
       "                ZIXI  \n",
       "2000-01-03 -0.061514  \n",
       "\n",
       "[1 rows x 611 columns]"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AB</th>\n",
       "      <th>ABM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ABX</th>\n",
       "      <th>ACXM</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>...</th>\n",
       "      <th>WTS</th>\n",
       "      <th>WWW</th>\n",
       "      <th>WY</th>\n",
       "      <th>XCRA</th>\n",
       "      <th>XLNX</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "      <th>ZION</th>\n",
       "      <th>ZIXI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-24</th>\n",
       "      <td>0.003109</td>\n",
       "      <td>0.008347</td>\n",
       "      <td>0.004942</td>\n",
       "      <td>0.004101</td>\n",
       "      <td>0.022483</td>\n",
       "      <td>0.003535</td>\n",
       "      <td>0.017549</td>\n",
       "      <td>0.014273</td>\n",
       "      <td>0.003762</td>\n",
       "      <td>0.001674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001804</td>\n",
       "      <td>-0.001557</td>\n",
       "      <td>0.008676</td>\n",
       "      <td>0.005579</td>\n",
       "      <td>0.008611</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>0.009969</td>\n",
       "      <td>-0.001822</td>\n",
       "      <td>-0.002986</td>\n",
       "      <td>0.003663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 611 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                AAPL        AB       ABM       ABT       ABX      ACXM  \\\n",
       "2018-08-24  0.003109  0.008347  0.004942  0.004101  0.022483  0.003535   \n",
       "\n",
       "                ADBE       ADI       ADM       ADP  ...       WTS       WWW  \\\n",
       "2018-08-24  0.017549  0.014273  0.003762  0.001674  ...  0.001804 -0.001557   \n",
       "\n",
       "                  WY      XCRA      XLNX       XOM      XRAY       XRX  \\\n",
       "2018-08-24  0.008676  0.005579  0.008611  0.006829  0.009969 -0.001822   \n",
       "\n",
       "                ZION      ZIXI  \n",
       "2018-08-24 -0.002986  0.003663  \n",
       "\n",
       "[1 rows x 611 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(returns_df.head(1))\n",
    "display(returns_df.tail(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320b66a5",
   "metadata": {},
   "source": [
    "#### Get training and testing split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "762f0d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_returns_df = returns_df.iloc[int(len(returns_df)*0.7):]\n",
    "train_returns_df = returns_df.iloc[:int(len(returns_df)*0.7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba29b494",
   "metadata": {},
   "source": [
    "### Get the context sets \n",
    "    - Either generate new or load from saved"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f3dcf05",
   "metadata": {},
   "source": [
    "#### Generate new context sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3c5cf675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting TGT:CONTEXT Sets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3283/3283 [06:58<00:00,  7.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily pairs added\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 656/656 [01:23<00:00,  7.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weekly pairs added\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 156/156 [00:21<00:00,  7.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monthly pairs added\n",
      "--- DONE ---\n",
      "Number of context sets: 2467418\n",
      "Saved to: TrainingData/IJCNN_TP_daily_weekly_monthly_L0_C10_IQRDailyFalse_Training_0to70.json\n"
     ]
    }
   ],
   "source": [
    "idx_combinations = get_context_sets(train_returns_df, tickers, CONTEXT_SIZE=10, lag=0, \n",
    "                                    save=True, periods=['daily','weekly', 'monthly'],\n",
    "                                    IQR_daily=False, hedging=False)\n",
    "\n",
    "# idx_combinations = get_context_sets(train_returns_df, tickers, CONTEXT_SIZE=3, lag=0, \n",
    "#                                     save=True, periods=['daily'],\n",
    "#                                     IQR_daily=True, hedging=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c212d8ad",
   "metadata": {},
   "source": [
    "#### Or load from saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7324c4cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Sets Loaded ---\n",
      "Number of context sets: 2467418\n"
     ]
    }
   ],
   "source": [
    "#-- Load idx_combinations from saved\n",
    "l = 0\n",
    "C = 10\n",
    "IQR = False\n",
    "p = 'daily_weekly_monthly'\n",
    "# p='daily'\n",
    "hedging=False\n",
    "df = pd.read_json(f'TrainingData/IJCNN_TP_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_Training_0to70{\"_HEDGING\" if hedging else \"\"}.json')\n",
    "\n",
    "idx_combinations = df.values\n",
    "del df\n",
    "print(\"--- Sets Loaded ---\")\n",
    "print(f'Number of context sets: {len(idx_combinations)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2eebd6",
   "metadata": {},
   "source": [
    "### Define and train the embeddings model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f00a2e6",
   "metadata": {},
   "source": [
    "#### Firstly, get the weights if using the weighted version of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "57b854c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2467418/2467418 [00:12<00:00, 202602.45it/s]\n"
     ]
    }
   ],
   "source": [
    "#-- Get the co-occurrence data necessary to compute weights\n",
    "cooccurrence_df = get_cooccurrence_df(tickers, idx_combinations, idx2ticker)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0f3f6a",
   "metadata": {},
   "source": [
    "#### Define the model and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "89442b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models/model_CBOW_Single_daily_weekly_monthly_L0_C10_IQRDailyTrue_70_Weights.pt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [1:23:16<00:00, 333.09s/it]\n"
     ]
    }
   ],
   "source": [
    "\"\"\" --- Model with weights --- \"\"\"\n",
    "# p='daily'\n",
    "# model = torch.load(f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70_Weights.pt')\n",
    "model = CBOW_StockModeller_Single_Weights(len(tickers),20)\n",
    "\n",
    "print(f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70_Weights{\"_HEDGING\" if hedging else \"\"}.pt')\n",
    "\n",
    "\n",
    "\n",
    "losses = train_model_weights(15, model, cooccurrence_df, idx2ticker, \n",
    "                             loss_function=nn.NLLLoss(), \n",
    "                             optimizer=optim.Adam(model.parameters(),\n",
    "                                                   lr=0.0001, \n",
    "                                                   #weight_decay=0.01\n",
    "                                                  ), \n",
    "                             train_loader=get_train_loader(idx_combinations), early_stop=True)\n",
    "\n",
    "#-- Save the model\n",
    "PATH = f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70_Weights{\"_HEDGING\" if hedging else \"\"}.pt'\n",
    "# Save\n",
    "torch.save(model, PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3ba2979b",
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=1+np.arange(len(losses)),\n",
    "                         y=losses))\n",
    "fig.update_layout(  title=\"Training Loss by Epoch\",\n",
    "                    template='plotly_white',\n",
    "                    yaxis=dict(title='Loss'),\n",
    "                    xaxis=dict(title='Epoch'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d58fe383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models/model_CBOW_Single_daily_L0_C3_IQRDailyTrue_70_HEDGING.pt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [09:28<00:00, 18.96s/it]\n"
     ]
    }
   ],
   "source": [
    "\"\"\" --- Model without weights --- \"\"\"\n",
    "model = CBOW_StockModeller_Single(len(tickers),20)\n",
    "\n",
    "\n",
    "PATH = f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70{\"_HEDGING\" if hedging else \"\"}.pt'\n",
    "print(PATH)\n",
    "\n",
    "losses = train_model(30, model, \n",
    "                     loss_function=nn.NLLLoss(), \n",
    "                     optimizer=optim.Adam(model.parameters(),\n",
    "                                           lr=0.0001, \n",
    "                                           #weight_decay=0.01\n",
    "                                          ), \n",
    "                     train_loader=get_train_loader(idx_combinations), early_stop=True)\n",
    "\n",
    "#-- Save the model\n",
    "\n",
    "\n",
    "# Save\n",
    "torch.save(model, PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164bee66",
   "metadata": {},
   "source": [
    "## Evaluation Bits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e642a4a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models/model_CBOW_Single_daily_weekly_monthly_L0_C3_IQRDailyTrue_70.pt\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#-- Load in the desired model\n",
    "l = 0\n",
    "C = 3\n",
    "IQR = True\n",
    "p='daily_weekly_monthly'\n",
    "hedging=False\n",
    "weighted = False\n",
    "print(f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70{\"_HEDGING\" if hedging else \"\"}.pt')\n",
    "model = torch.load(f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70{\"_HEDGING\" if hedging else \"\"}{\"_Weights\" if weighted else \"\"}.pt')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8a7c05",
   "metadata": {},
   "source": [
    "### PCA Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f20cb66",
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "#-- Visualise embeddings in 3D space\n",
    "pca_plot_from_embeddings(model.embeddings.weight.detach().numpy(),\n",
    "                         sectors, tickers, industries, names,  dimensions=3,\n",
    "                         method='PCA')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "723090aa",
   "metadata": {},
   "source": [
    "### High Similarity Mismatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "736094e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pairs of stocks with over 0.924 similarity are in the same sector 98.0% of the time\n"
     ]
    }
   ],
   "source": [
    "threshold=0.924\n",
    "\n",
    "#-- Get similarity matrix\n",
    "similarity_matrix = cosine_similarity(model.embeddings.weight.detach().numpy())\n",
    "#-- Set diagonal entries to 0 similarity so they dont get an edge\n",
    "np.fill_diagonal(similarity_matrix, 0)\n",
    "#-- Get a list of pairs which have a similarity over the threshold\n",
    "edge_pairs = np.transpose((similarity_matrix>threshold).nonzero())\n",
    "\n",
    "count=0\n",
    "mismatch_list = []\n",
    "for i,j in edge_pairs[:int(len(edge_pairs)/2)]:\n",
    "    if sectors[i]==sectors[j]:\n",
    "        count+=1\n",
    "    else:\n",
    "        mismatch_list.append((i,j))\n",
    "        \n",
    "print(f'Pairs of stocks with over {threshold} similarity are in the same sector {round(count/(len(edge_pairs)/2),2)*100}% of the time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6cca95ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- - - - - - - - - - - - - - - - - - - - \n",
      "- - - - - - - - - - - - - - - - - - - - \n",
      "Similarity 0.95\n",
      "KB HOME  --  CAPITAL GOODS  --  HOMEBUILDING\n",
      "LENNAR CORPORATION  --  BASIC INDUSTRIES  --  HOMEBUILDING\n",
      "====================\n"
     ]
    }
   ],
   "source": [
    "#-- Mismatch examples\n",
    "print(\"- \"*20)\n",
    "print(\"- \"*20)\n",
    "for i,j in mismatch_list:\n",
    "    print(f'Similarity {similarity_matrix[i,j]:.2f}')\n",
    "    print(names[i], \" -- \" ,sectors[i], \" -- \",industries[i])\n",
    "    print(names[j], \" -- \" ,sectors[j], \" -- \",industries[j])\n",
    "    print(\"=\"*20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e16c04",
   "metadata": {},
   "source": [
    "### Sector Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0771ffc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded : Models/model_CBOW_Single_daily_weekly_monthly_L0_C3_IQRDailyTrue_70.pt\n",
      "Precision Score: 0.6\n",
      "Recall Score: 0.58\n",
      "F1 Score: 0.58\n",
      "Accuracy Score: 0.58\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE, ADASYN, RandomOverSampler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score#, top_k_accuracy_score\n",
    "\n",
    "def get_sector_score(model):\n",
    "    embedding_matrix = model.embeddings.weight.detach().numpy()\n",
    "\n",
    "\n",
    "    # X = embedding_matrix\n",
    "    # y = sectors\n",
    "\n",
    "    # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)\n",
    "\n",
    "    # sm = SMOTE(random_state=42)\n",
    "    # X_res, y_res = sm.fit_resample(X_train, y_train)\n",
    "\n",
    "    # classifier = SVC(kernel='rbf', probability=True)\n",
    "\n",
    "    # classifier.fit(X_res,y_res)\n",
    "\n",
    "    # print(classification_report(y_test, classifier.predict(X_test)))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    #-- Cross validation approach\n",
    "\n",
    "    accuracy_list = []\n",
    "    accuracy_list_top_k = []\n",
    "    k=3\n",
    "    f1_list = []\n",
    "    recall_list = []\n",
    "    precision_list = []\n",
    "\n",
    "    X = embedding_matrix\n",
    "    y = np.expand_dims(np.array(sectors),axis=1)\n",
    "\n",
    "\n",
    "    kf = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "\n",
    "    for fold, (train_index, test_index) in enumerate(kf.split(X, y.flatten())):\n",
    "        X_train = X[train_index]\n",
    "        y_train = y[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
    "        X_test = X[test_index]\n",
    "        y_test = y[test_index]  # See comment on ravel and  y_train\n",
    "        sm = SMOTE()\n",
    "        X_train_oversampled, y_train_oversampled = sm.fit_resample(X_train, y_train)\n",
    "        classifier = SVC(kernel='rbf', probability=True)\n",
    "        classifier.fit(X_train_oversampled, y_train_oversampled )  \n",
    "        y_pred = classifier.predict(X_test)\n",
    "        # print(f'For fold {fold}:')\n",
    "        # print(f'Accuracy: {classifier.score(X_test, y_test)}')\n",
    "        # print(f'f-score: {f1_score(y_test, y_pred, average=\"weighted\")}')\n",
    "\n",
    "        accuracy_list.append(classifier.score(X_test, y_test))\n",
    "        # accuracy_list_top_k.append(top_k_accuracy_score(y_test, classifier.predict_proba(X_test),k=k))\n",
    "        f1_list.append(f1_score(y_test, y_pred, average=\"weighted\"))\n",
    "        recall_list.append(recall_score(y_test, y_pred, average=\"weighted\"))\n",
    "        precision_list.append(precision_score(y_test, y_pred, average=\"weighted\"))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    print(f'Precision Score: {np.round(np.mean(precision_list),2)}')\n",
    "    print(f'Recall Score: {np.round(np.mean(recall_list),2)}')\n",
    "    print(f'F1 Score: {np.round(np.mean(f1_list),2)}')\n",
    "    print(f'Accuracy Score: {np.round(np.mean(accuracy_list),2)}')\n",
    "    # print(f'Accuracy Score Top-{k}: {np.round(np.mean(accuracy_list_top_k),2)}')\n",
    "\n",
    "\n",
    "l = 0\n",
    "C = 3\n",
    "IQR = True\n",
    "p='daily_weekly_monthly'\n",
    "weighted = False\n",
    "model = torch.load(f'Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70{\"_Weights\" if weighted else \"\"}.pt')\n",
    "print(f'Loaded : Models/model_CBOW_Single_{p}_L{l}_C{C}_IQRDaily{str(IQR)}_70{\"_Weights\" if weighted else \"\"}.pt')\n",
    "\n",
    "get_sector_score(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0ba21fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_dict = {'Embedding': torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{3}_IQRDaily{str(False)}_70.pt'),\n",
    "                'embedding_no_IQR_weights': torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{10}_IQRDaily{str(False)}_70_weights.pt'),\n",
    "                'Embedding + IQR': torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{3}_IQRDaily{str(True)}_70.pt'),\n",
    "                'embedding_IQR_weights': torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{10}_IQRDaily{str(True)}_70_Weights.pt'),\n",
    "                'embedding_IQR_no_weights_Hedging': torch.load(f'Models/model_CBOW_Single_{\"daily\"}_L{0}_C{3}_IQRDaily{str(True)}_70_HEDGING.pt'),\n",
    "                'embedding_IQR_weights_Hedging': torch.load(f'Models/model_CBOW_Single_{\"daily\"}_L{0}_C{3}_IQRDaily{str(True)}_70_Weights_HEDGING.pt')\n",
    "                }\n",
    "\n",
    "for key,m in models_dict.items():\n",
    "    print(\"=\"*50)\n",
    "    print(key)\n",
    "    get_sector_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41f9e5cd",
   "metadata": {},
   "source": [
    "### Analogies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "87110ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================\n",
      "MSFT is to TECHNOLOGY as MRK is to Tech\n",
      "MSFT -- MICROSOFT CORPORATION -- 0.79 -- TECHNOLOGY -- COMPUTER SOFTWARE: PREPACKAGED SOFTWARE\n",
      "MRK -- MERCK & COMPANY, INC. -- 0.82 -- HEALTH CARE -- MAJOR PHARMACEUTICALS\n"
     ]
    }
   ],
   "source": [
    "\n",
    "embedding_matrix = model.embeddings.weight.detach().numpy()\n",
    "\n",
    "\n",
    "#-- Get the average vector for various sectors\n",
    "\n",
    "#-- Create a dictionary with the index position of all tickers of that sector\n",
    "sector_idx_dict = {}\n",
    "#-- Create a dictionary to store the sector embeddings\n",
    "sector_embed_dict = {}\n",
    "for sector in sectors:\n",
    "    #-- Add indexes\n",
    "    sector_idx_dict[sector]=[i for i, x in enumerate(tickers) if sectors[i]==sector]\n",
    "    #-- Compute and add the average embedding for each sector\n",
    "    sector_embed_dict[sector] = embedding_matrix[sector_idx_dict[sector]].mean(axis=0)\n",
    "    \n",
    "tick = \"MSFT\"\n",
    "temp_sector1 = \"TECHNOLOGY\"\n",
    "temp_sector2 = \"HEALTH CARE\"\n",
    "\n",
    "\n",
    "embed = (embedding_matrix[ticker2idx[tick]] - sector_embed_dict[temp_sector1]) + sector_embed_dict[temp_sector2]\n",
    "\n",
    "\n",
    "#-- Get cosine similarity of query embedding with each row of embedding matrix\n",
    "temp_sims = cosine_similarity(np.expand_dims(embed, 0), embedding_matrix)\n",
    "\n",
    "for idx in np.argsort(-temp_sims).flatten()[:1]:\n",
    "\n",
    "    print(\"=\"*20)\n",
    "    print(f'{tick} is to {temp_sector1} as {idx2ticker[idx]} is to Tech')\n",
    "    print(tick, \"--\", names[ticker2idx[tick]], \"--\", round(temp_sims.flatten()[ticker2idx[tick]],2), \"--\", stock_df[stock_df.ticker==idx2ticker[ticker2idx[tick]]].sector.iloc[0], \"--\", stock_df[stock_df.ticker==idx2ticker[ticker2idx[tick]]].industry.iloc[0])\n",
    "    print(idx2ticker[idx], \"--\", names[idx], \"--\", round(temp_sims.flatten()[idx],2), \"--\", stock_df[stock_df.ticker==idx2ticker[idx]].sector.iloc[0], \"--\", stock_df[stock_df.ticker==idx2ticker[idx]].industry.iloc[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a7f17ef",
   "metadata": {},
   "source": [
    "### Portfolio Hedging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8f04a494",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vol(tick1, tick2, plot=False):\n",
    "    \"\"\" -- Simulate a portfolio with two stocks and return realised volatility --\"\"\"\n",
    "    #-- Equal weight\n",
    "    combined_returns = (test_returns_df[tick1].values+test_returns_df[tick2].values)/2\n",
    "    #-- Simulate accumulated value of portfolio\n",
    "    portfolio_evolution = np.cumprod(1+combined_returns)\n",
    "    \n",
    "    if plot:\n",
    "        #-- Plot the portfolio evolution\n",
    "        fig = go.Figure()\n",
    "        fig.add_trace(go.Scatter(x=test_returns_df.index, \n",
    "                                 y=portfolio_evolution,\n",
    "                                 name='Portfolio'))\n",
    "        fig.add_trace(go.Scatter(x=test_returns_df.index, \n",
    "                                 y=np.cumprod(1+test_returns_df[tick1].values),\n",
    "                                 name=tick1,\n",
    "                                 opacity=0.6))\n",
    "        fig.add_trace(go.Scatter(x=test_returns_df.index, \n",
    "                                 y=np.cumprod(1+test_returns_df[tick2].values),\n",
    "                                 name=tick2,\n",
    "                                 opacity=0.6))\n",
    "        fig.update_layout(template='plotly_white',\n",
    "                          yaxis=dict(title='Cumulative Returns'),\n",
    "                          title=f'Hedged Portfolio Evolution | {tick1} - {tick2}')\n",
    "        fig.show()\n",
    "        \n",
    "    #-- Compute volatiltiy as standard deviation of log returns\n",
    "    portfolio_log_returns = [np.log(1 + (portfolio_evolution[i+1]-portfolio_evolution[i])/portfolio_evolution[i] ) for i in range(len(portfolio_evolution)-1)]\n",
    "    portfolio_vol = np.std(portfolio_log_returns)*np.sqrt(252)\n",
    "    return round(portfolio_vol,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9f267a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_vol_array(similarities):\n",
    "    #-- Get vol_list for a given similarity matrix\n",
    "\n",
    "    n=50\n",
    "\n",
    "    vol_list = []\n",
    "\n",
    "    #-- Ensure diagonal is nan so a stock will never hedge with itself\n",
    "    np.fill_diagonal(similarities,np.nan)\n",
    "\n",
    "    #-- Get the LOWEST similarity stock for each metric\n",
    "    hedge_idxs = np.argsort(similarities, axis=0)\n",
    "    #-- Choose hedge stock randomly from top-n least similar stocks\n",
    "    #- Prevents the same stocks being chosen again and again\n",
    "    hedge_idxs = [hedge_idxs[np.random.randint(0,n),i] for i in range(len(hedge_idxs))]\n",
    "\n",
    "    for i in range(len(tickers)):\n",
    "        tick1 = tickers[i]\n",
    "        tick2 = tickers[hedge_idxs[i]]\n",
    "\n",
    "\n",
    "        temp_vol = get_vol(tick1, tick2, plot=False)\n",
    "\n",
    "        vol_list.append(temp_vol)\n",
    "        \n",
    "        \n",
    "    return np.array(vol_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f5ea7df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [00:14<00:00,  1.56s/it]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "p = 'daily_weekly_monthly'\n",
    "\n",
    "metrics_dict = {'Embedding': cosine_similarity(torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{3}_IQRDaily{str(False)}_70.pt').embeddings.weight.detach().numpy()),\n",
    "                'embedding_no_IQR_weights': cosine_similarity(torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{10}_IQRDaily{str(False)}_70_weights.pt').embeddings.weight.detach().numpy()),\n",
    "                'Embedding + IQR': cosine_similarity(torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{3}_IQRDaily{str(True)}_70.pt').embeddings.weight.detach().numpy()),\n",
    "                'embedding_IQR_weights': cosine_similarity(torch.load(f'Models/model_CBOW_Single_{p}_L{0}_C{10}_IQRDaily{str(True)}_70_Weights.pt').embeddings.weight.detach().numpy()),\n",
    "                'embedding_IQR_no_weights_Hedging': cosine_similarity(torch.load(f'Models/model_CBOW_Single_{\"daily\"}_L{0}_C{3}_IQRDaily{str(True)}_70_HEDGING.pt').embeddings.weight.detach().numpy()),\n",
    "                'embedding_IQR_weights_Hedging': cosine_similarity(torch.load(f'Models/model_CBOW_Single_{\"daily\"}_L{0}_C{3}_IQRDaily{str(True)}_70_Weights_HEDGING.pt').embeddings.weight.detach().numpy()),\n",
    "                'Pearson': train_returns_df.corr().values,\n",
    "                'geometric': np.genfromtxt('geometric_similarities.csv', delimiter=\",\"),\n",
    "                'spearman': spearmanr(train_returns_df.values)[0],\n",
    "                #'random': np.random.rand(611,611)\n",
    "                }\n",
    "\n",
    "\n",
    "\n",
    "vol_dict = {}\n",
    "for name, similarities in tqdm(metrics_dict.items()):\n",
    "    vol_dict[name]=get_vol_array(similarities)\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "629bc555",
   "metadata": {},
   "outputs": [
    {
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Volatiltiy\"}},\"yaxis\":{\"title\":{\"text\":\"Probability Density\"}},\"legend\":{\"orientation\":\"h\",\"yanchor\":\"bottom\",\"y\":1.02,\"xanchor\":\"right\",\"x\":0.75},\"height\":450,\"width\":550},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "linestyles = ['dash','dot', 'dashdot', 'dash']\n",
    "count = 0\n",
    "for name, vols in vol_dict.items():\n",
    "    if name not in ['Embedding', 'Pearson','Embedding + IQR', 'embedding_no_IQR_weights']:\n",
    "        continue\n",
    "    \n",
    "#     fig.add_trace(go.Histogram(x=get_vol_array(similarities), name=name))\n",
    "    xvals = np.linspace(0.15,0.5,200)\n",
    "    kde = gaussian_kde(vols)\n",
    "    fig.add_trace(go.Scatter(x=xvals, y=kde(xvals), name=name, #marker=dict(color=color),\n",
    "                             opacity=1,\n",
    "                             #fill='tozeroy',\n",
    "                             line=dict(dash=linestyles[count])\n",
    "                            ))\n",
    "    count+=1\n",
    "    \n",
    "fig.update_layout(template='plotly_white')\n",
    "fig.update_layout(xaxis=dict(title=\"Portfolio Volatiltiy\"),\n",
    "                  yaxis = dict(title=\"Probability Density\"))\n",
    "\n",
    "fig.update_layout(legend=dict(\n",
    "                    orientation=\"h\",\n",
    "                    yanchor=\"bottom\",\n",
    "                    y=1.02,\n",
    "                    xanchor=\"right\",\n",
    "                    x=0.75\n",
    "                ))\n",
    "fig.update_layout(height=450, width=550)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "aaea8dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "========================================\n",
      "Embedding: 0.229\n",
      "Pearson: 0.238\n"
     ]
    },
    {
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       "      group1 group2  meandiff  p-adj   lower   upper  reject\n",
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      "========================================\n",
      "========================================\n",
      "embedding_no_IQR_weights: 0.227\n",
      "Pearson: 0.238\n"
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       "                     group1 group2  meandiff  p-adj   lower  upper  reject\n",
       "0  embedding_no_IQR_weights  pears    0.0109  0.001  0.0097  0.012    True"
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     "text": [
      "========================================\n",
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      "Embedding + IQR: 0.213\n",
      "Pearson: 0.238\n"
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      "Pearson: 0.238\n"
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       "                             group1 group2  meandiff  p-adj   lower   upper  \\\n",
       "0  embedding_IQR_no_weights_Hedging  pears    0.0428  0.001  0.0417  0.0438   \n",
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      "========================================\n",
      "========================================\n",
      "embedding_IQR_weights_Hedging: 0.215\n",
      "Pearson: 0.238\n"
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   "source": [
    "#-- Run experiment multiple times for HSD test\n",
    "sims_dict =  {}\n",
    "for name, vols in vol_dict.items():\n",
    "    sims_dict[name]=[]\n",
    "\n",
    "for i in tqdm(range(50)):\n",
    "    vol_dict = {}\n",
    "    for name, similarities in metrics_dict.items():\n",
    "        vol_dict[name]=get_vol_array(similarities)\n",
    "\n",
    "    for name,vols in vol_dict.items():\n",
    "        sims_dict[name].append(np.mean(vols))\n",
    "\n",
    "\n",
    "import statsmodels.stats.multicomp as multi\n",
    "for name, mean_vols in sims_dict.items():\n",
    "    print(\"=\"*40)\n",
    "    print(\"=\"*40)\n",
    "    print(f'{name}: {np.mean(mean_vols):.3f}')\n",
    "    print(f'{\"Pearson\"}: {np.mean(sims_dict[\"Pearson\"]):.3f}')\n",
    "    mc = multi.MultiComparison(np.concatenate([sims_dict[name],sims_dict['Pearson']]),\n",
    "                                    [name]*len(sims_dict[name])+['pears']*len(sims_dict['Pearson']))\n",
    "    Results = mc.tukeyhsd(alpha=0.01)\n",
    "    display(pd.DataFrame(data=Results._results_table.data[1:], columns=Results._results_table.data[0]))   \n"
   ]
  }
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