Full Replication.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta as rd\n",
    "import time\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stocks data csv read\n",
    "df = pd.read_csv('data.csv')\n",
    "df = df.set_index('Date')\n",
    "\n",
    "# s&p data csv read\n",
    "df_sp = pd.read_csv('sp500.csv')\n",
    "df_sp = df_sp.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_slicer(df, start, duration, rebalancing_period=0):\n",
    "    '''\n",
    "    this function is used to slice out specific section of the data\n",
    "    '''\n",
    "    start = str(datetime.strptime(start, '%Y-%m-%d').date() + rd(months=rebalancing_period))\n",
    "    end = str(datetime.strptime(start, '%Y-%m-%d').date() + rd(months=duration) - rd(days=1))\n",
    "    return df.loc[start:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_process(df):\n",
    "    '''\n",
    "    this function gets the dataframe as input, processes it, and ouputs the cumulative change of the stocks\n",
    "    that is used as input for training the model.\n",
    "    '''\n",
    "    df = df.pct_change()\n",
    "    df = df.tail(-1)\n",
    "    df = df + 1\n",
    "    df = df.cumprod()\n",
    "    df = df - 1\n",
    "    df = df.iloc[-1,:]\n",
    "    df = df.to_numpy()\n",
    "    df = torch.from_numpy(df).type(torch.Tensor)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def daily_change(df):\n",
    "    '''\n",
    "    this function calculate the daily change of stocks included in the dataframe.\n",
    "    '''\n",
    "    df = df.pct_change()\n",
    "    df = df.tail(-1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def daily_return(df):\n",
    "    '''\n",
    "    this function calculate the daily return of stocks included in the dataframe, note that \n",
    "    daily return is equal to daily change + 1\n",
    "    '''\n",
    "    df = df.pct_change()\n",
    "    df = df.tail(-1)\n",
    "    df = df + 1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shallow nnf biuld\n",
    "class shallow_NNF(nn.Module):\n",
    "    '''\n",
    "    this class is used to train the data with Shallow NNF model, consisted of 2 fully connected layers, \n",
    "    a relU activation function in between and a softmax layer output that is translated into stock weights in portfolio.\n",
    "    '''\n",
    "    def __init__(self, input_dim, hidden_size, num_classes):\n",
    "        super(shallow_NNF, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_dim, hidden_size) # fully connected layer\n",
    "        self.fc2 = nn.Linear(hidden_size, num_classes) # fully connected layer\n",
    "        \n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=0)\n",
    "        \n",
    "    def reset_parameters(self):\n",
    "        self.fc1.reset_parameters()\n",
    "        self.fc2.reset_parameters()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.relu(self.fc1(x))\n",
    "        out = self.softmax(self.fc2(out))\n",
    "        weights = out\n",
    "        cumulative_change = sum(out * x)\n",
    "        return cumulative_change, weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep nnf build\n",
    "class deep_NNF(nn.Module):\n",
    "    '''\n",
    "    this class is used to train the data with Deep NNF model, consisted of 6 fully connected layers, \n",
    "    relU activation functions in between and a softmax layer output that is translated into stock weights in portfolio.\n",
    "    dropout is also included in deep NNF model.\n",
    "    '''\n",
    "    def __init__(self, input_dim, hidden_size1, hidden_size2, hidden_size3,\n",
    "                 hidden_size4, hidden_size5, num_classes, dropout_p = 0.2):\n",
    "        super(deep_NNF, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_dim, hidden_size1) # fully connected layer\n",
    "        self.fc2 = nn.Linear(hidden_size1, hidden_size2) # fully connected layer\n",
    "        self.fc3 = nn.Linear(hidden_size2, hidden_size3) # fully connected layer\n",
    "        self.fc4 = nn.Linear(hidden_size3, hidden_size4) # fully connected layer\n",
    "        self.fc5 = nn.Linear(hidden_size4, hidden_size5) # fully connected layer\n",
    "        self.fc6 = nn.Linear(hidden_size5, num_classes) # fully connected layer\n",
    "    \n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(dropout_p)\n",
    "        self.softmax = nn.Softmax(dim=0)\n",
    "        \n",
    "    def reset_parameters(self):\n",
    "        self.fc1.reset_parameters()\n",
    "        self.fc2.reset_parameters()\n",
    "        self.fc3.reset_parameters()\n",
    "        self.fc4.reset_parameters()\n",
    "        self.fc5.reset_parameters()\n",
    "        self.fc6.reset_parameters()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.relu(self.fc1(x))\n",
    "        out = self.dropout(out)\n",
    "        out = self.relu(self.fc2(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.relu(self.fc3(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.relu(self.fc4(out))\n",
    "        out = self.dropout(out)\n",
    "        out = self.relu(self.fc5(out))\n",
    "        out = self.softmax(self.fc6(out))\n",
    "        weights = out\n",
    "        cumulative_change = sum(out * x)\n",
    "        return cumulative_change, weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1/N model build\n",
    "class equal_w_model():\n",
    "    '''\n",
    "    this class is used to construct a portfolio with equal weights.\n",
    "    '''\n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "        self.performance()\n",
    "        \n",
    "    def performance(self):\n",
    "        self.df = np.array(self.df)\n",
    "        weights = np.ones((len(self.df), 1)) * (1/len(self.df))\n",
    "        cumulative_change = sum(np.multiply(weights, self.df.reshape(-1,1)))\n",
    "        return cumulative_change, weights.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rebalancing period = one or three months\n",
    "rbp = 1\n",
    "\n",
    "# epochs\n",
    "num_epochs = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shallow_nnf hyperparameters\n",
    "input_dim = 471\n",
    "hidden_size = 471\n",
    "num_classes = 471\n",
    "lr = 1e-2 # learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shallow nnf tune\n",
    "'''\n",
    "loss function is set to MSE and Adam optimizer is used in this model.\n",
    "'''\n",
    "shallow_NNF = shallow_NNF(input_dim=input_dim, hidden_size=hidden_size, num_classes=num_classes)\n",
    "shallow_NNF_loss_fun = torch.nn.MSELoss(reduction='mean')\n",
    "shallow_NNF_optimizer = torch.optim.Adam(shallow_NNF.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep_nnf hyperparameters\n",
    "input_dim = 471\n",
    "hidden_size1 = 471\n",
    "hidden_size2 = 471\n",
    "hidden_size3 = 471\n",
    "hidden_size4 = 471\n",
    "hidden_size5 = 471\n",
    "num_classes = 471\n",
    "lr = 1e-10 # learning rate\n",
    "# probability of a neuron being shutdown that shuffles every epoch minimizing the overfit phenomenon\n",
    "dropout_p = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep nnf tune\n",
    "'''\n",
    "like in shallow NNF, loss function is set to MSE and Adam optimizer is used.\n",
    "'''\n",
    "deep_NNF = deep_NNF(input_dim=input_dim, hidden_size1=hidden_size1, hidden_size2=hidden_size2, \n",
    "                    hidden_size3=hidden_size3, hidden_size4=hidden_size4, hidden_size5=hidden_size5,\n",
    "                    num_classes=num_classes)\n",
    "deep_NNF_loss_fun = torch.nn.MSELoss(reduction='mean')\n",
    "deep_NNF_optimizer = torch.optim.Adam(deep_NNF.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMSE\n",
    "def RMSE(x, y, weights):\n",
    "    '''\n",
    "    this function calculates the root mean squere error of constructed portfollio and benchmark index \n",
    "    that is used for evaluating trained models.\n",
    "    '''\n",
    "    temp = 0\n",
    "    for i in range(len(x)):\n",
    "        temp += (sum(x.iloc[i] * weights) - y.iloc[i]) ** 2\n",
    "    return math.sqrt(temp/len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MEAN\n",
    "def MEAN(x, weights):\n",
    "    '''\n",
    "    this function calculates the mean return of the constructed portfolio during the given period.\n",
    "    '''\n",
    "    temp = []\n",
    "    for i in range(len(x)):\n",
    "        temp.append(sum(x.iloc[i] * weights))\n",
    "    temp = np.array(temp)\n",
    "    return temp.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Volatility\n",
    "def VOL(x, weights):\n",
    "    '''\n",
    "    this function calculates the volatility of the constructed portfolio during the given period.\n",
    "    '''\n",
    "    temp = []\n",
    "    for i in range(len(x)):\n",
    "        temp.append(sum(x.iloc[i] * weights))\n",
    "    temp = np.array(temp)\n",
    "    return temp.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def portfolio_return(df, x_test, model, i, temp):   \n",
    "    '''\n",
    "    this function outputs the cumulative return of the portfolio test dataset of the given dataframe\n",
    "    ''' \n",
    "    x_return = date_slicer(df, '2018-01-01', 1, i)\n",
    "    x_return =  x_return.pct_change()\n",
    "    x_return =  x_return.tail(-1)\n",
    "    x_return =  x_return + 1\n",
    "    x_return =  x_return.cumprod()\n",
    "    \n",
    "    if model == equal_w_model:\n",
    "        weights = model(x_test).performance()[1]\n",
    "    else:\n",
    "        weights = np.array(model(x_test)[1].detach())\n",
    "    \n",
    "    for i in range(len(x_return)):\n",
    "        temp.append(sum(x_return.iloc[i] * weights))\n",
    "    temp = np.array(temp)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def index_return(df_sp, i, temp):\n",
    "    '''\n",
    "    this function outputs the cumulative return of the benchmark index test dataset of the given dataframe\n",
    "    '''\n",
    "    y_return = date_slicer(df_sp, '2018-01-01', 1, i)\n",
    "    y_return = y_return.pct_change()\n",
    "    y_return = y_return.tail(-1)\n",
    "    y_return = y_return + 1\n",
    "    y_return = y_return.cumprod()\n",
    "    \n",
    "    for i in range(len(y_return)):\n",
    "        temp.append(sum(y_return.iloc[i]))\n",
    "    temp = np.array(temp)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def valid_fun(x_valid, i, model):\n",
    "    '''\n",
    "    this function gets validation dataset, model and rebalaning period as input, then outputs the RMSE of given dataset.\n",
    "    '''\n",
    "    x_change = daily_change(date_slicer(df, '2017-07-01', 6, i))\n",
    "    y_change = daily_change(date_slicer(df_sp, '2017-07-01', 6, i))\n",
    "    # x_return = daily_return(date_slicer(df, '2017-07-01', 6, i))\n",
    "    # y_return = daily_return(date_slicer(df_sp, '2017-07-01', 6, i))\n",
    "    \n",
    "    if model == equal_w_model:\n",
    "        weights = model(x_valid).performance()[1]\n",
    "    else:\n",
    "        weights = np.array(model(x_valid)[1].detach())\n",
    "    \n",
    "    valid_rmse = RMSE(x_change, y_change, weights)\n",
    "    # valid_mean = MEAN(x_return, weights)\n",
    "    # valid_vol  = VOL(x_return, weights)\n",
    "    \n",
    "    print(f'Validation RMSE: {valid_rmse}')\n",
    "    # print(f'Validation MEAN: {valid_mean}')\n",
    "    # print(f'Validation VOL: {valid_vol}')\n",
    "    \n",
    "    return valid_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_fun(x_test, i, model):\n",
    "    '''\n",
    "    this function gets test dataset, model and rebalaning period as input, then outputs the RMSE, Mean and volatility \n",
    "    of the given dataset.\n",
    "    '''\n",
    "    x_change = daily_change(date_slicer(df, '2018-01-01', 1, i))\n",
    "    y_change = daily_change(date_slicer(df_sp, '2018-01-01', 1, i))\n",
    "    x_return = daily_return(date_slicer(df, '2018-01-01', 1, i))\n",
    "    y_return = daily_return(date_slicer(df_sp, '2018-01-01', 1, i))\n",
    "    \n",
    "    if model == equal_w_model:\n",
    "        weights = model(x_test).performance()[1]\n",
    "    else:\n",
    "        weights = np.array(model(x_test)[1].detach())\n",
    "    \n",
    "    test_rmse = RMSE(x_change, y_change, weights)\n",
    "    test_mean = MEAN(x_return, weights)\n",
    "    test_vol  = VOL(x_return, weights)\n",
    "    test_dic = {'RMSE': test_rmse, 'MEAN': test_mean, 'VOL': test_vol} # a dictionary for storing the results\n",
    "    \n",
    "    print(f'Test RMSE: {test_rmse}')\n",
    "    print(f'Test MEAN: {test_mean}')\n",
    "    print(f'Test VOL: {test_vol}')\n",
    "    \n",
    "    return test_dic"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Deep NNF Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep nnf training function\n",
    "'''\n",
    "this function is used to train the model using x_train & y_train given to it, printing MSE of trained model in first and last\n",
    " epich and also printing train time of the model\n",
    "'''\n",
    "def train_deep_nnf(x_train, y_train, i):\n",
    "    start_time_deep_nnf = time.time()\n",
    "    print(f'\\nDeep NNF Training & Results for model {(i/rbp)+1}:')\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        y_train_pred = deep_NNF(x_train)[0]\n",
    "        loss_deep_nnf = deep_NNF_loss_fun(y_train_pred, y_train)\n",
    "        if epoch == 0 or epoch == num_epochs-1:\n",
    "            print(f'Epoch {epoch+1} of {num_epochs} | MSE: {loss_deep_nnf.item()}')\n",
    "        deep_NNF_optimizer.zero_grad()\n",
    "        loss_deep_nnf.backward()\n",
    "        deep_NNF_optimizer.step()\n",
    "        \n",
    "    training_time = format(time.time()-start_time_deep_nnf, '0.2f')\n",
    "    print(f'Training time: {training_time}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Deep NNF Training & Results for model 1.0:\n",
      "Epoch 1 of 100 | MSE: 0.04591207206249237\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hamidrezarahimzadeh/opt/anaconda3/lib/python3.9/site-packages/torch/nn/modules/loss.py:536: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  return F.mse_loss(input, target, reduction=self.reduction)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100 of 100 | MSE: 0.04603005573153496\n",
      "Training time: 1.22\n",
      "Validation RMSE: 0.0013773015225169115\n",
      "Test RMSE: 0.0014124789998141121\n",
      "Test MEAN: 1.00195311673848\n",
      "Test VOL: 0.005354001740402835\n",
      "\n",
      "Deep NNF Training & Results for model 2.0:\n",
      "Epoch 1 of 100 | MSE: 0.05343734845519066\n",
      "Epoch 100 of 100 | MSE: 0.053347084671258926\n",
      "Training time: 1.22\n",
      "Validation RMSE: 0.0014116157838104807\n",
      "Test RMSE: 0.0022292380175393998\n",
      "Test MEAN: 0.997938594308412\n",
      "Test VOL: 0.01546957423247805\n",
      "\n",
      "Deep NNF Training & Results for model 3.0:\n",
      "Epoch 1 of 100 | MSE: 0.04575419798493385\n",
      "Epoch 100 of 100 | MSE: 0.04557095468044281\n",
      "Training time: 1.18\n",
      "Validation RMSE: 0.001608785703561048\n",
      "Test RMSE: 0.0021150930197339373\n",
      "Test MEAN: 1.0002466574557312\n",
      "Test VOL: 0.01103983484387354\n",
      "\n",
      "Deep NNF Training & Results for model 4.0:\n",
      "Epoch 1 of 100 | MSE: 0.059435803443193436\n",
      "Epoch 100 of 100 | MSE: 0.05955417454242706\n",
      "Training time: 1.17\n",
      "Validation RMSE: 0.0017888651873876837\n",
      "Test RMSE: 0.0016161226390108095\n",
      "Test MEAN: 1.0013093044447194\n",
      "Test VOL: 0.008811655328159808\n",
      "\n",
      "Deep NNF Training & Results for model 5.0:\n",
      "Epoch 1 of 100 | MSE: 0.047005072236061096\n",
      "Epoch 100 of 100 | MSE: 0.04696932062506676\n",
      "Training time: 1.22\n",
      "Validation RMSE: 0.0017501031080217322\n",
      "Test RMSE: 0.0012539930535172565\n",
      "Test MEAN: 1.0007668594261698\n",
      "Test VOL: 0.006298318345349538\n",
      "\n",
      "Deep NNF Training & Results for model 6.0:\n",
      "Epoch 1 of 100 | MSE: 0.04955502599477768\n",
      "Epoch 100 of 100 | MSE: 0.049569010734558105\n",
      "Training time: 1.16\n",
      "Validation RMSE: 0.001701885284269512\n",
      "Test RMSE: 0.0012062148738656842\n",
      "Test MEAN: 1.000140536984501\n",
      "Test VOL: 0.004748374931335705\n",
      "\n",
      "Deep NNF Training & Results for model 7.0:\n",
      "Epoch 1 of 100 | MSE: 0.038871634751558304\n",
      "Epoch 100 of 100 | MSE: 0.038757164031267166\n",
      "Training time: 1.20\n",
      "Validation RMSE: 0.001680547967668349\n",
      "Test RMSE: 0.0026029941416977824\n",
      "Test MEAN: 1.0015832512626517\n",
      "Test VOL: 0.004982107704185747\n",
      "\n",
      "Deep NNF Training & Results for model 8.0:\n",
      "Epoch 1 of 100 | MSE: 0.03783009946346283\n",
      "Epoch 100 of 100 | MSE: 0.03792211040854454\n",
      "Training time: 1.18\n",
      "Validation RMSE: 0.0019141582175501196\n",
      "Test RMSE: 0.0011958354087457386\n",
      "Test MEAN: 1.001292539865422\n",
      "Test VOL: 0.004710285830174362\n",
      "\n",
      "Deep NNF Training & Results for model 9.0:\n",
      "Epoch 1 of 100 | MSE: 0.027760284021496773\n",
      "Epoch 100 of 100 | MSE: 0.027672234922647476\n",
      "Training time: 1.21\n",
      "Validation RMSE: 0.0017881153337469642\n",
      "Test RMSE: 0.0018837821608511773\n",
      "Test MEAN: 1.000169162764387\n",
      "Test VOL: 0.0035089416265782014\n",
      "\n",
      "Deep NNF Training & Results for model 10.0:\n",
      "Epoch 1 of 100 | MSE: 0.031141748651862144\n",
      "Epoch 100 of 100 | MSE: 0.03116106428205967\n",
      "Training time: 1.14\n",
      "Validation RMSE: 0.0017457163238777578\n",
      "Test RMSE: 0.003105421638951434\n",
      "Test MEAN: 0.9967947065990899\n",
      "Test VOL: 0.01264992987908858\n",
      "\n",
      "Deep NNF Training & Results for model 11.0:\n",
      "Epoch 1 of 100 | MSE: 0.036090556532144547\n",
      "Epoch 100 of 100 | MSE: 0.03608756512403488\n",
      "Training time: 1.15\n",
      "Validation RMSE: 0.0020711993315383364\n",
      "Test RMSE: 0.002477827028251002\n",
      "Test MEAN: 1.0008244315153232\n",
      "Test VOL: 0.009817151604466676\n",
      "\n",
      "Deep NNF Training & Results for model 12.0:\n",
      "Epoch 1 of 100 | MSE: 0.03404118865728378\n",
      "Epoch 100 of 100 | MSE: 0.03412588685750961\n",
      "Training time: 1.16\n",
      "Validation RMSE: 0.00228799841768947\n",
      "Test RMSE: 0.0021159497092578827\n",
      "Test MEAN: 0.9939235090537815\n",
      "Test VOL: 0.017119892063295925\n",
      "\n",
      "Deep NNF Training & Results for model 13.0:\n",
      "Epoch 1 of 100 | MSE: 0.035125020891427994\n",
      "Epoch 100 of 100 | MSE: 0.0351429283618927\n",
      "Training time: 1.15\n",
      "Validation RMSE: 0.0023790221371864043\n",
      "Test RMSE: 0.002590409441772519\n",
      "Test MEAN: 1.0047759750855447\n",
      "Test VOL: 0.010457817224498744\n",
      "\n",
      "Deep NNF Training & Results for model 14.0:\n",
      "Epoch 1 of 100 | MSE: 0.03391863405704498\n",
      "Epoch 100 of 100 | MSE: 0.03408525884151459\n",
      "Training time: 1.18\n",
      "Validation RMSE: 0.002330636914054238\n",
      "Test RMSE: 0.001396947239289976\n",
      "Test MEAN: 1.0020294887254046\n",
      "Test VOL: 0.004558173150714552\n",
      "\n",
      "Deep NNF Training & Results for model 15.0:\n",
      "Epoch 1 of 100 | MSE: 0.03601036220788956\n",
      "Epoch 100 of 100 | MSE: 0.036342620849609375\n",
      "Training time: 1.17\n",
      "Validation RMSE: 0.002398906911558007\n",
      "Test RMSE: 0.0014314849827398238\n",
      "Test MEAN: 1.000269715089041\n",
      "Test VOL: 0.007638716895903196\n",
      "\n",
      "Deep NNF Training & Results for model 16.0:\n",
      "Epoch 1 of 100 | MSE: 0.03937210142612457\n",
      "Epoch 100 of 100 | MSE: 0.03927314653992653\n",
      "Training time: 1.18\n",
      "Validation RMSE: 0.002306814516125057\n",
      "Test RMSE: 0.0019738173846858983\n",
      "Test MEAN: 1.0012019621739248\n",
      "Test VOL: 0.004406011544570898\n",
      "\n",
      "Deep NNF Training & Results for model 17.0:\n",
      "Epoch 1 of 100 | MSE: 0.02291029319167137\n",
      "Epoch 100 of 100 | MSE: 0.022843066602945328\n",
      "Training time: 1.21\n",
      "Validation RMSE: 0.002026268237764373\n",
      "Test RMSE: 0.0014760005339705712\n",
      "Test MEAN: 0.9972194567158693\n",
      "Test VOL: 0.00875510303221644\n",
      "\n",
      "Deep NNF Training & Results for model 18.0:\n",
      "Epoch 1 of 100 | MSE: 0.02305326797068119\n",
      "Epoch 100 of 100 | MSE: 0.023009099066257477\n",
      "Training time: 1.18\n",
      "Validation RMSE: 0.0018725517117411392\n",
      "Test RMSE: 0.0017196905141830228\n",
      "Test MEAN: 1.003640770453338\n",
      "Test VOL: 0.006678461778941843\n",
      "\n",
      "Deep NNF Training & Results for model 19.0:\n",
      "Epoch 1 of 100 | MSE: 0.020379506051540375\n",
      "Epoch 100 of 100 | MSE: 0.02036859840154648\n",
      "Training time: 1.16\n",
      "Validation RMSE: 0.001969704990618942\n",
      "Test RMSE: 0.0018673943247066549\n",
      "Test MEAN: 1.0002626160432988\n",
      "Test VOL: 0.005181787870334201\n",
      "\n",
      "Deep NNF Training & Results for model 20.0:\n",
      "Epoch 1 of 100 | MSE: 0.04237934201955795\n",
      "Epoch 100 of 100 | MSE: 0.04239603132009506\n",
      "Training time: 1.17\n",
      "Validation RMSE: 0.001823913588857829\n",
      "Test RMSE: 0.0013681709371625242\n",
      "Test MEAN: 0.9993199127326445\n",
      "Test VOL: 0.014308307264089486\n",
      "\n",
      "Deep NNF Training & Results for model 21.0:\n",
      "Epoch 1 of 100 | MSE: 0.04218906909227371\n",
      "Epoch 100 of 100 | MSE: 0.04200343042612076\n",
      "Training time: 1.28\n",
      "Validation RMSE: 0.0018296617496469498\n",
      "Test RMSE: 0.00220867818972881\n",
      "Test MEAN: 1.0021161939044227\n",
      "Test VOL: 0.005398017883856264\n",
      "\n",
      "Deep NNF Training & Results for model 22.0:\n",
      "Epoch 1 of 100 | MSE: 0.028263771906495094\n",
      "Epoch 100 of 100 | MSE: 0.02832220308482647\n",
      "Training time: 1.30\n",
      "Validation RMSE: 0.0019518348590802787\n",
      "Test RMSE: 0.0019738651297767223\n",
      "Test MEAN: 1.0013487106195016\n",
      "Test VOL: 0.007932622973470058\n",
      "\n",
      "Deep NNF Training & Results for model 23.0:\n",
      "Epoch 1 of 100 | MSE: 0.026714636012911797\n",
      "Epoch 100 of 100 | MSE: 0.02667141892015934\n",
      "Training time: 1.53\n",
      "Validation RMSE: 0.0019483047322523931\n",
      "Test RMSE: 0.0009321951634130287\n",
      "Test MEAN: 1.0011339947760123\n",
      "Test VOL: 0.0034536518866357473\n",
      "\n",
      "Deep NNF Training & Results for model 24.0:\n",
      "Epoch 1 of 100 | MSE: 0.017222054302692413\n",
      "Epoch 100 of 100 | MSE: 0.017259081825613976\n",
      "Training time: 2.58\n",
      "Validation RMSE: 0.0017508228946146451\n",
      "Test RMSE: 0.001315219852618145\n",
      "Test MEAN: 1.0017064594947553\n",
      "Test VOL: 0.004492480710913461\n",
      "\n",
      "Min Valid RMSE is: 0.0013773015225169115 for model i = 1\n",
      "Selected Model Test Results are:\n",
      "RMSE = 0.0014124789998141121\n",
      "MEAN = 1.00195311673848\n",
      "VOL = 0.005354001740402835\n"
     ]
    }
   ],
   "source": [
    "# deep nnf\n",
    "'''\n",
    "in this cell,firstly, train, validation and test datasets are sliced in each loop. then deep NNf model gets trained and\n",
    "then best model will be chosen. Also RMSE, Mean and volatility of all models and then the best model is printed.\n",
    "'''\n",
    "deep_nnf_valid_rmse_list = []\n",
    "deep_nnf_test_results = []\n",
    "deep_nnf_test_plot = [] # storing the deep model test data return for plotting later on\n",
    "index_test_plot = [] # storing the index test data return for plotting later on\n",
    "\n",
    "for i in range(int(24/rbp)):\n",
    "    x_train = data_process(date_slicer(df, '2014-07-01', 36, i*rbp))\n",
    "    y_train = data_process(date_slicer(df_sp, '2014-07-01', 36, i*rbp))\n",
    "    x_valid = data_process(date_slicer(df, '2017-07-01', 6, i*rbp))\n",
    "    y_valid = data_process(date_slicer(df_sp, '2017-07-01', 6, i*rbp))\n",
    "    x_test = data_process(date_slicer(df, '2018-01-01', 1, i*rbp))\n",
    "    y_test = data_process(date_slicer(df_sp, '2018-01-01', 1, i*rbp))\n",
    "    train_deep_nnf(x_train, y_train, i*rbp)\n",
    "    deep_nnf_valid_rmse_list.append(valid_fun(x_valid, i*rbp, deep_NNF))\n",
    "    deep_nnf_test_results.append(test_fun(x_test, i*rbp, deep_NNF))\n",
    "    portfolio_return(df, x_test, deep_NNF, i, deep_nnf_test_plot)\n",
    "    index_return(df_sp, i, index_test_plot)\n",
    "    deep_NNF.reset_parameters()\n",
    "\n",
    "print(f'\\nMin Valid RMSE is: {min(deep_nnf_valid_rmse_list)} for model i = {deep_nnf_valid_rmse_list.index(min(deep_nnf_valid_rmse_list))+1}')\n",
    "print('Selected Model Test Results are:')\n",
    "print('RMSE =', deep_nnf_test_results[deep_nnf_valid_rmse_list.index(min(deep_nnf_valid_rmse_list))]['RMSE'])\n",
    "print('MEAN =', deep_nnf_test_results[deep_nnf_valid_rmse_list.index(min(deep_nnf_valid_rmse_list))]['MEAN'])\n",
    "print('VOL =', deep_nnf_test_results[deep_nnf_valid_rmse_list.index(min(deep_nnf_valid_rmse_list))]['VOL'])\n",
    "\n",
    "deep_best_result_index = deep_nnf_valid_rmse_list.index(min(deep_nnf_valid_rmse_list))\n",
    "deep_nnf_test_plot = np.array(deep_nnf_test_plot).reshape(-1,1)\n",
    "index_test_plot = np.array(index_test_plot).reshape(-1,1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Shallow NNF Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shallow nnf training function\n",
    "'''\n",
    "this function is used to train the model using x_train & y_train given to it, printing MSE of trained model in first and last\n",
    "epoch and also printing train time of the model\n",
    "'''\n",
    "def train_shallow_nnf(x_train, y_train, i):\n",
    "    start_time_shallow_nnf = time.time()\n",
    "    print(f'\\nShallow NNF Training & Results for model {(i/rbp)+1}:')\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        y_train_pred = shallow_NNF(x_train)[0]\n",
    "        loss_shallow_nnf = shallow_NNF_loss_fun(y_train_pred, y_train)\n",
    "        if epoch == 0 or epoch == num_epochs-1:\n",
    "            print(f'Epoch {epoch+1} of {num_epochs} | MSE: {loss_shallow_nnf.item()}')\n",
    "        shallow_NNF_optimizer.zero_grad()\n",
    "        loss_shallow_nnf.backward()\n",
    "        shallow_NNF_optimizer.step()\n",
    "        \n",
    "    training_time = format(time.time()-start_time_shallow_nnf, '0.2f')\n",
    "    print(f'Training time: {training_time}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Shallow NNF Training & Results for model 1.0:\n",
      "Epoch 1 of 100 | MSE: 0.04515352472662926\n",
      "Epoch 100 of 100 | MSE: 2.395228875684552e-05\n",
      "Training time: 1.06\n",
      "Validation RMSE: 0.00491036790348907\n",
      "Test RMSE: 0.003911203078241354\n",
      "Test MEAN: 1.0010024477980122\n",
      "Test VOL: 0.004704220264448412\n",
      "\n",
      "Shallow NNF Training & Results for model 2.0:\n",
      "Epoch 1 of 100 | MSE: 0.051357485353946686\n",
      "Epoch 100 of 100 | MSE: 6.790323823224753e-07\n",
      "Training time: 1.49\n",
      "Validation RMSE: 0.00805324842634709\n",
      "Test RMSE: 0.003316657018021812\n",
      "Test MEAN: 0.9974118026919273\n",
      "Test VOL: 0.014810658035143486\n",
      "\n",
      "Shallow NNF Training & Results for model 3.0:\n",
      "Epoch 1 of 100 | MSE: 0.045442190021276474\n",
      "Epoch 100 of 100 | MSE: 0.0011111318599432707\n",
      "Training time: 1.09\n",
      "Validation RMSE: 0.009907707259388155\n",
      "Test RMSE: 0.0030436030251882523\n",
      "Test MEAN: 1.0001808368873146\n",
      "Test VOL: 0.011150956745009048\n",
      "\n",
      "Shallow NNF Training & Results for model 4.0:\n",
      "Epoch 1 of 100 | MSE: 0.05762476846575737\n",
      "Epoch 100 of 100 | MSE: 0.03032963164150715\n",
      "Training time: 0.88\n",
      "Validation RMSE: 0.009231528271078681\n",
      "Test RMSE: 0.007276818824132668\n",
      "Test MEAN: 1.000917197392642\n",
      "Test VOL: 0.008376762882884336\n",
      "\n",
      "Shallow NNF Training & Results for model 5.0:\n",
      "Epoch 1 of 100 | MSE: 0.04508083313703537\n",
      "Epoch 100 of 100 | MSE: 0.00014589073543902487\n",
      "Training time: 1.12\n",
      "Validation RMSE: 0.011238500639979903\n",
      "Test RMSE: 0.007059554484813161\n",
      "Test MEAN: 1.0011107553194796\n",
      "Test VOL: 0.008645330669999349\n",
      "\n",
      "Shallow NNF Training & Results for model 6.0:\n",
      "Epoch 1 of 100 | MSE: 0.05625779926776886\n",
      "Epoch 100 of 100 | MSE: 0.005442459601908922\n",
      "Training time: 0.91\n",
      "Validation RMSE: 0.013856563813694912\n",
      "Test RMSE: 0.0026749832135706027\n",
      "Test MEAN: 1.000772853475007\n",
      "Test VOL: 0.004774407931317925\n",
      "\n",
      "Shallow NNF Training & Results for model 7.0:\n",
      "Epoch 1 of 100 | MSE: 0.03859590366482735\n",
      "Epoch 100 of 100 | MSE: 0.007067754399031401\n",
      "Training time: 0.96\n",
      "Validation RMSE: 0.011784441821155617\n",
      "Test RMSE: 0.013244908220194172\n",
      "Test MEAN: 1.0010087343951297\n",
      "Test VOL: 0.011211548242584739\n",
      "\n",
      "Shallow NNF Training & Results for model 8.0:\n",
      "Epoch 1 of 100 | MSE: 0.03929710388183594\n",
      "Epoch 100 of 100 | MSE: 7.000672485446557e-05\n",
      "Training time: 0.83\n",
      "Validation RMSE: 0.007405994177273435\n",
      "Test RMSE: 0.00893769041368567\n",
      "Test MEAN: 1.000164967533482\n",
      "Test VOL: 0.00845069986191062\n",
      "\n",
      "Shallow NNF Training & Results for model 9.0:\n",
      "Epoch 1 of 100 | MSE: 0.026033470407128334\n",
      "Epoch 100 of 100 | MSE: 0.0027148621156811714\n",
      "Training time: 0.72\n",
      "Validation RMSE: 0.012109463488614264\n",
      "Test RMSE: 0.0028823804216097505\n",
      "Test MEAN: 1.0003589825193777\n",
      "Test VOL: 0.004006756803230203\n",
      "\n",
      "Shallow NNF Training & Results for model 10.0:\n",
      "Epoch 1 of 100 | MSE: 0.02989465370774269\n",
      "Epoch 100 of 100 | MSE: 0.000605866895057261\n",
      "Training time: 0.69\n",
      "Validation RMSE: 0.01962619934352468\n",
      "Test RMSE: 0.008450097413593288\n",
      "Test MEAN: 0.9945185081143703\n",
      "Test VOL: 0.015869473697312828\n",
      "\n",
      "Shallow NNF Training & Results for model 11.0:\n",
      "Epoch 1 of 100 | MSE: 0.03954996541142464\n",
      "Epoch 100 of 100 | MSE: 0.000824745453428477\n",
      "Training time: 0.70\n",
      "Validation RMSE: 0.006089658705754462\n",
      "Test RMSE: 0.008790730497347339\n",
      "Test MEAN: 1.0023611333694618\n",
      "Test VOL: 0.009738736309897713\n",
      "\n",
      "Shallow NNF Training & Results for model 12.0:\n",
      "Epoch 1 of 100 | MSE: 0.04132217913866043\n",
      "Epoch 100 of 100 | MSE: 0.010678594931960106\n",
      "Training time: 0.69\n",
      "Validation RMSE: 0.0088440452401986\n",
      "Test RMSE: 0.007167633192858705\n",
      "Test MEAN: 0.9937936188558649\n",
      "Test VOL: 0.018217431391978747\n",
      "\n",
      "Shallow NNF Training & Results for model 13.0:\n",
      "Epoch 1 of 100 | MSE: 0.04314024746417999\n",
      "Epoch 100 of 100 | MSE: 0.0022930616978555918\n",
      "Training time: 0.68\n",
      "Validation RMSE: 0.008596965544713496\n",
      "Test RMSE: 0.006782072284853121\n",
      "Test MEAN: 1.001425591922026\n",
      "Test VOL: 0.010464823420581642\n",
      "\n",
      "Shallow NNF Training & Results for model 14.0:\n",
      "Epoch 1 of 100 | MSE: 0.0366533063352108\n",
      "Epoch 100 of 100 | MSE: 0.011402367614209652\n",
      "Training time: 0.71\n",
      "Validation RMSE: 0.006096665037712275\n",
      "Test RMSE: 0.0065344936752232815\n",
      "Test MEAN: 0.9990423218297377\n",
      "Test VOL: 0.008389839709500403\n",
      "\n",
      "Shallow NNF Training & Results for model 15.0:\n",
      "Epoch 1 of 100 | MSE: 0.028630314394831657\n",
      "Epoch 100 of 100 | MSE: 0.016561225056648254\n",
      "Training time: 0.70\n",
      "Validation RMSE: 0.003927206273122629\n",
      "Test RMSE: 0.009777907257869544\n",
      "Test MEAN: 1.001608426285744\n",
      "Test VOL: 0.00824400587177854\n",
      "\n",
      "Shallow NNF Training & Results for model 16.0:\n",
      "Epoch 1 of 100 | MSE: 0.03763806074857712\n",
      "Epoch 100 of 100 | MSE: 0.0016599162481725216\n",
      "Training time: 0.78\n",
      "Validation RMSE: 0.008441692341780823\n",
      "Test RMSE: 0.007960597217704271\n",
      "Test MEAN: 0.9996731726748834\n",
      "Test VOL: 0.007175858038872038\n",
      "\n",
      "Shallow NNF Training & Results for model 17.0:\n",
      "Epoch 1 of 100 | MSE: 0.02486419677734375\n",
      "Epoch 100 of 100 | MSE: 0.0017286045476794243\n",
      "Training time: 0.68\n",
      "Validation RMSE: 0.008748484018203467\n",
      "Test RMSE: 0.005458068793459462\n",
      "Test MEAN: 0.9945187470862006\n",
      "Test VOL: 0.011638017035134236\n",
      "\n",
      "Shallow NNF Training & Results for model 18.0:\n",
      "Epoch 1 of 100 | MSE: 0.02443239465355873\n",
      "Epoch 100 of 100 | MSE: 6.459338328568265e-05\n",
      "Training time: 0.69\n",
      "Validation RMSE: 0.010421057016091202\n",
      "Test RMSE: 0.012068683258557497\n",
      "Test MEAN: 1.0073059402895668\n",
      "Test VOL: 0.01602526719246879\n",
      "\n",
      "Shallow NNF Training & Results for model 19.0:\n",
      "Epoch 1 of 100 | MSE: 0.02161198854446411\n",
      "Epoch 100 of 100 | MSE: 2.603711664050934e-06\n",
      "Training time: 0.68\n",
      "Validation RMSE: 0.012934859124025064\n",
      "Test RMSE: 0.002438680932981166\n",
      "Test MEAN: 1.000381277520665\n",
      "Test VOL: 0.005522035707090603\n",
      "\n",
      "Shallow NNF Training & Results for model 20.0:\n",
      "Epoch 1 of 100 | MSE: 0.040128618478775024\n",
      "Epoch 100 of 100 | MSE: 1.7213622413692065e-05\n",
      "Training time: 0.68\n",
      "Validation RMSE: 0.01046689397013759\n",
      "Test RMSE: 0.002233775104735203\n",
      "Test MEAN: 1.000059898357629\n",
      "Test VOL: 0.013300868560237999\n",
      "\n",
      "Shallow NNF Training & Results for model 21.0:\n",
      "Epoch 1 of 100 | MSE: 0.04393913224339485\n",
      "Epoch 100 of 100 | MSE: 3.0303335734060965e-05\n",
      "Training time: 0.69\n",
      "Validation RMSE: 0.010241483122573105\n",
      "Test RMSE: 0.012089738139536507\n",
      "Test MEAN: 1.0055557728388644\n",
      "Test VOL: 0.013696943784622844\n",
      "\n",
      "Shallow NNF Training & Results for model 22.0:\n",
      "Epoch 1 of 100 | MSE: 0.030702168121933937\n",
      "Epoch 100 of 100 | MSE: 0.0006195346359163523\n",
      "Training time: 0.71\n",
      "Validation RMSE: 0.01027980278469984\n",
      "Test RMSE: 0.004607299428920739\n",
      "Test MEAN: 1.0014177855286854\n",
      "Test VOL: 0.00828973750932579\n",
      "\n",
      "Shallow NNF Training & Results for model 23.0:\n",
      "Epoch 1 of 100 | MSE: 0.024821262806653976\n",
      "Epoch 100 of 100 | MSE: 0.006491666194051504\n",
      "Training time: 0.72\n",
      "Validation RMSE: 0.008964756524652789\n",
      "Test RMSE: 0.005252170339821972\n",
      "Test MEAN: 1.0022811937650424\n",
      "Test VOL: 0.00690218940880332\n",
      "\n",
      "Shallow NNF Training & Results for model 24.0:\n",
      "Epoch 1 of 100 | MSE: 0.016793739050626755\n",
      "Epoch 100 of 100 | MSE: 0.00038085298729129136\n",
      "Training time: 0.70\n",
      "Validation RMSE: 0.009747527522968083\n",
      "Test RMSE: 0.0017845884560604265\n",
      "Test MEAN: 1.001493663788119\n",
      "Test VOL: 0.004684007247036233\n",
      "Selected Model Test Results for model i = 1 are: \n",
      "RMSE = 0.003911203078241354\n",
      "MEAN = 1.0010024477980122\n",
      "VOL = 0.004704220264448412\n"
     ]
    }
   ],
   "source": [
    "#shallow nnf\n",
    "'''\n",
    "in this cell,firstly, train, validation and test datasets are sliced in each loop. then shallow NNf model gets trained and\n",
    "then best model will be chosen. Also RMSE, Mean and volatility of all models and then the best model is printed.\n",
    "'''\n",
    "shallow_nnf_valid_rmse_list = []\n",
    "shallow_nnf_test_results = [] \n",
    "shallow_nnf_test_plot = [] # storing the shallow model test data return for plotting later on\n",
    "\n",
    "for i in range(int(24/rbp)):\n",
    "    x_train = data_process(date_slicer(df, '2014-07-01', 36, i*rbp))\n",
    "    y_train = data_process(date_slicer(df_sp, '2014-07-01', 36, i*rbp))\n",
    "    x_valid = data_process(date_slicer(df, '2017-07-01', 6, i*rbp))\n",
    "    y_valid = data_process(date_slicer(df_sp, '2017-07-01', 6, i*rbp))\n",
    "    x_test = data_process(date_slicer(df, '2018-01-01', 1, i*rbp))\n",
    "    y_test = data_process(date_slicer(df_sp, '2018-01-01', 1, i*rbp))\n",
    "    \n",
    "    train_shallow_nnf(x_train, y_train, i*rbp)\n",
    "    shallow_nnf_valid_rmse_list.append(valid_fun(x_valid, i*rbp, shallow_NNF))\n",
    "    shallow_nnf_test_results.append(test_fun(x_test, i*rbp, shallow_NNF))\n",
    "    portfolio_return(df, x_test, shallow_NNF, i, shallow_nnf_test_plot)\n",
    "    shallow_NNF.reset_parameters()\n",
    "    \n",
    "\n",
    "# print(f'\\nMin Valid RMSE is: {min(valid_rmse_list)} for model i = {(deep_best_result_index)+1}')\n",
    "print('Selected Model Test Results for model i =', (deep_best_result_index)+1, 'are: ')\n",
    "print('RMSE =', shallow_nnf_test_results[(deep_best_result_index)]['RMSE'])\n",
    "print('MEAN =', shallow_nnf_test_results[(deep_best_result_index)]['MEAN'])\n",
    "print('VOL =', shallow_nnf_test_results[(deep_best_result_index)]['VOL'])\n",
    "\n",
    "shallow_nnf_test_plot = np.array(shallow_nnf_test_plot).reshape(-1,1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1/N Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Equal Weights Model Results for model 1:\n",
      "Validation RMSE: 0.0013752466607634818\n",
      "Test RMSE: 0.00141308068973907\n",
      "Test MEAN: 1.00194947794337\n",
      "Test VOL: 0.005348201810361211\n",
      "\n",
      "Equal Weights Model Results for model 2:\n",
      "Validation RMSE: 0.0014103582761839522\n",
      "Test RMSE: 0.002225473810475016\n",
      "Test MEAN: 0.9979382024584036\n",
      "Test VOL: 0.015471723202182033\n",
      "\n",
      "Equal Weights Model Results for model 3:\n",
      "Validation RMSE: 0.00161101492926301\n",
      "Test RMSE: 0.0021228799964131116\n",
      "Test MEAN: 1.0002504247997188\n",
      "Test VOL: 0.011031703269168955\n",
      "\n",
      "Equal Weights Model Results for model 4:\n",
      "Validation RMSE: 0.00178815568268382\n",
      "Test RMSE: 0.0016167972928903618\n",
      "Test MEAN: 1.001305977361471\n",
      "Test VOL: 0.008808539875808678\n",
      "\n",
      "Equal Weights Model Results for model 5:\n",
      "Validation RMSE: 0.0017533218637125797\n",
      "Test RMSE: 0.0012496906685729259\n",
      "Test MEAN: 1.0007751300633236\n",
      "Test VOL: 0.0062942423298088844\n",
      "\n",
      "Equal Weights Model Results for model 6:\n",
      "Validation RMSE: 0.0017002638599400465\n",
      "Test RMSE: 0.0012049049143032751\n",
      "Test MEAN: 1.000132556099627\n",
      "Test VOL: 0.004753505074253049\n",
      "\n",
      "Equal Weights Model Results for model 7:\n",
      "Validation RMSE: 0.0016838896697022906\n",
      "Test RMSE: 0.002608250111522712\n",
      "Test MEAN: 1.0015818555353888\n",
      "Test VOL: 0.004983170010212011\n",
      "\n",
      "Equal Weights Model Results for model 8:\n",
      "Validation RMSE: 0.0019078407523448037\n",
      "Test RMSE: 0.0011962789884695558\n",
      "Test MEAN: 1.0012933034193634\n",
      "Test VOL: 0.004707598960828587\n",
      "\n",
      "Equal Weights Model Results for model 9:\n",
      "Validation RMSE: 0.0017918743187162148\n",
      "Test RMSE: 0.001888238767926475\n",
      "Test MEAN: 1.000176838763966\n",
      "Test VOL: 0.0035131312053199725\n",
      "\n",
      "Equal Weights Model Results for model 10:\n",
      "Validation RMSE: 0.0017472761704575702\n",
      "Test RMSE: 0.003108435537956791\n",
      "Test MEAN: 0.9967994846709011\n",
      "Test VOL: 0.012654039350899505\n",
      "\n",
      "Equal Weights Model Results for model 11:\n",
      "Validation RMSE: 0.002080505442953261\n",
      "Test RMSE: 0.0024845370945334663\n",
      "Test MEAN: 1.000826368012285\n",
      "Test VOL: 0.009815043138294987\n",
      "\n",
      "Equal Weights Model Results for model 12:\n",
      "Validation RMSE: 0.00228250529001297\n",
      "Test RMSE: 0.002110325599208719\n",
      "Test MEAN: 0.99392170780843\n",
      "Test VOL: 0.017125701805097735\n",
      "\n",
      "Equal Weights Model Results for model 13:\n",
      "Validation RMSE: 0.002378854806423318\n",
      "Test RMSE: 0.0025991203186737635\n",
      "Test MEAN: 1.0047820361360924\n",
      "Test VOL: 0.010457672576285474\n",
      "\n",
      "Equal Weights Model Results for model 14:\n",
      "Validation RMSE: 0.0023376364047417086\n",
      "Test RMSE: 0.001402228062171977\n",
      "Test MEAN: 1.002025461923509\n",
      "Test VOL: 0.004559034383661699\n",
      "\n",
      "Equal Weights Model Results for model 15:\n",
      "Validation RMSE: 0.002388129123328023\n",
      "Test RMSE: 0.0014276920122888483\n",
      "Test MEAN: 1.000264732576357\n",
      "Test VOL: 0.007639654783347461\n",
      "\n",
      "Equal Weights Model Results for model 16:\n",
      "Validation RMSE: 0.0023085543362458107\n",
      "Test RMSE: 0.0019700691339755265\n",
      "Test MEAN: 1.0012020349198691\n",
      "Test VOL: 0.004404697243899707\n",
      "\n",
      "Equal Weights Model Results for model 17:\n",
      "Validation RMSE: 0.002032437718144319\n",
      "Test RMSE: 0.001475205067385024\n",
      "Test MEAN: 0.9972273915102033\n",
      "Test VOL: 0.008748691989180815\n",
      "\n",
      "Equal Weights Model Results for model 18:\n",
      "Validation RMSE: 0.0018717109499381298\n",
      "Test RMSE: 0.001720766888220823\n",
      "Test MEAN: 1.0036391934325273\n",
      "Test VOL: 0.006673775895724877\n",
      "\n",
      "Equal Weights Model Results for model 19:\n",
      "Validation RMSE: 0.0019686564797122682\n",
      "Test RMSE: 0.0018643344043743812\n",
      "Test MEAN: 1.0002658041495434\n",
      "Test VOL: 0.005183267607845902\n",
      "\n",
      "Equal Weights Model Results for model 20:\n",
      "Validation RMSE: 0.0018238046048378409\n",
      "Test RMSE: 0.001359914467857588\n",
      "Test MEAN: 0.9993162870787218\n",
      "Test VOL: 0.014303584166600736\n",
      "\n",
      "Equal Weights Model Results for model 21:\n",
      "Validation RMSE: 0.0018329033576296974\n",
      "Test RMSE: 0.002201019621821213\n",
      "Test MEAN: 1.0021142585317577\n",
      "Test VOL: 0.00539380418680695\n",
      "\n",
      "Equal Weights Model Results for model 22:\n",
      "Validation RMSE: 0.0019462210070632896\n",
      "Test RMSE: 0.0019706191075476273\n",
      "Test MEAN: 1.0013487467662525\n",
      "Test VOL: 0.007930844134341473\n",
      "\n",
      "Equal Weights Model Results for model 23:\n",
      "Validation RMSE: 0.0019529162145969603\n",
      "Test RMSE: 0.0009264363066448112\n",
      "Test MEAN: 1.0011367709735588\n",
      "Test VOL: 0.0034522863961233786\n",
      "\n",
      "Equal Weights Model Results for model 24:\n",
      "Validation RMSE: 0.0017551968016899662\n",
      "Test RMSE: 0.0013201770489190275\n",
      "Test MEAN: 1.0017054738565843\n",
      "Test VOL: 0.004493494843845112\n",
      "Selected Model Test Results for model i = 1 are: \n",
      "RMSE = 0.00141308068973907\n",
      "MEAN = 1.00194947794337\n",
      "VOL = 0.005348201810361211\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "here we run the 1/N model, for the number of stocks, each stock gets the weight of 1/N meaning that\n",
    "every stock is equally important, this model play the role of a benchmark to see how effective our model are\n",
    "'''\n",
    "equal_w_model_valid_rmse_list = []\n",
    "equal_w_model_test_results = []\n",
    "equal_w_model_test_plot = [] # storing the 1/n model test data return for plotting later on\n",
    "\n",
    "for i in range(int(24/rbp)):\n",
    "    print(f'\\nEqual Weights Model Results for model {i+1}:')\n",
    "    x_train = data_process(date_slicer(df, '2014-07-01', 36, i*rbp))\n",
    "    y_train = data_process(date_slicer(df_sp, '2014-07-01', 36, i*rbp))\n",
    "    x_valid = data_process(date_slicer(df, '2017-07-01', 6, i*rbp))\n",
    "    y_valid = data_process(date_slicer(df_sp, '2017-07-01', 6, i*rbp))\n",
    "    x_test = data_process(date_slicer(df, '2018-01-01', 1, i*rbp))\n",
    "    y_test = data_process(date_slicer(df_sp, '2018-01-01', 1, i*rbp))\n",
    "    \n",
    "    equal_w_model_valid_rmse_list.append(valid_fun(x_valid, i*rbp, equal_w_model))\n",
    "    equal_w_model_test_results.append(test_fun(x_test, i*rbp, equal_w_model))\n",
    "    portfolio_return(df, x_test, equal_w_model, i, equal_w_model_test_plot)\n",
    "    \n",
    "print('Selected Model Test Results for model i =', (deep_best_result_index)+1, 'are: ')\n",
    "print('RMSE =', equal_w_model_test_results[(deep_best_result_index)]['RMSE'])\n",
    "print('MEAN =', equal_w_model_test_results[(deep_best_result_index)]['MEAN'])\n",
    "print('VOL =', equal_w_model_test_results[(deep_best_result_index)]['VOL'])\n",
    "\n",
    "equal_w_model_test_plot = np.array(equal_w_model_test_plot).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models test results with rebalancing period of 1 month(s) are: \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>Deep NNF</th>\n",
       "      <th>Shallow NNF</th>\n",
       "      <th>1/N Model</th>\n",
       "      <th>S&amp;P 500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RMSE</th>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.003911</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEAN</th>\n",
       "      <td>1.001953</td>\n",
       "      <td>1.001002</td>\n",
       "      <td>1.001949</td>\n",
       "      <td>1.002337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL</th>\n",
       "      <td>0.005354</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.005348</td>\n",
       "      <td>0.005601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      Deep NNF  Shallow NNF  1/N Model   S&P 500\n",
       "RMSE  0.001412     0.003911   0.001413         -\n",
       "MEAN  1.001953     1.001002   1.001949  1.002337\n",
       "VOL   0.005354     0.004704   0.005348  0.005601"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print test results\n",
    "'''\n",
    "here we compare the results in a dataframe featuring RMSE, MEAN and, volatility of each model in the test dataset\n",
    "that has the best results for deep nnf model. this dataframe can cope with the understanding of why we bother \n",
    "implementing a complex neural network\n",
    "'''\n",
    "print(f'Models test results with rebalancing period of {rbp} month(s) are: ')\n",
    "deep_temp = pd.DataFrame(deep_nnf_test_results)\n",
    "deep_temp = deep_temp.iloc[deep_best_result_index]\n",
    "shallow_temp = pd.DataFrame(shallow_nnf_test_results)\n",
    "shallow_temp = shallow_temp.iloc[deep_best_result_index]\n",
    "equal_w_temp = pd.DataFrame(equal_w_model_test_results)\n",
    "equal_w_temp = equal_w_temp.iloc[deep_best_result_index]\n",
    "\n",
    "# extract the mean and volatility of the s&p index on the test dataset\n",
    "sp_temp_rmse = '-'\n",
    "sp_temp_mean = daily_return(date_slicer(df_sp, '2018-01-01', 1, deep_best_result_index)).mean()[0]\n",
    "sp_temp_std = daily_return(date_slicer(df_sp, '2018-01-01', 1, deep_best_result_index)).std()[0]\n",
    "sp_temp = pd.DataFrame([sp_temp_rmse, sp_temp_mean, sp_temp_std], index=deep_temp.index)\n",
    "\n",
    "# concatinating the result in a unified dataframe\n",
    "final_result = pd.concat([deep_temp, shallow_temp, equal_w_temp, sp_temp], axis=1, join='inner')\n",
    "final_result.columns = ['Deep NNF', 'Shallow NNF', '1/N Model', 'S&P 500']\n",
    "final_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of test RMSE for each model: \n",
      "Deep NNF: 0.0018112010160534961\n",
      "Shallow NNF: 0.006406013945540833\n",
      "Equal weight model: 0.001811103162995504\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "to further showcase the results, here we compute the average RMSE of each model in test dataset\n",
    "'''\n",
    "print(f'Average of test RMSE for each model: ')\n",
    "\n",
    "deep_nnf_test_rmse_mean = 0 # temp variable for storing each tmse for deep nnf model\n",
    "for i in range(int(24/rbp)):\n",
    "    deep_nnf_test_rmse_mean += deep_nnf_test_results[i]['RMSE']\n",
    "print(f'Deep NNF: {deep_nnf_test_rmse_mean/int(24/rbp)}')\n",
    "\n",
    "shallow_nnf_test_rmse_mean = 0 # temp variable for storing each tmse for shallow nnf model\n",
    "for i in range(int(24/rbp)):\n",
    "    shallow_nnf_test_rmse_mean += shallow_nnf_test_results[i]['RMSE']\n",
    "print(f'Shallow NNF: {shallow_nnf_test_rmse_mean/int(24/rbp)}')\n",
    "\n",
    "equal_w_model_test_rmse_mean = 0 # temp variable for storing each tmse for 1/n model model\n",
    "for i in range(int(24/rbp)):\n",
    "    equal_w_model_test_rmse_mean += equal_w_model_test_results[i]['RMSE']\n",
    "print(f'Equal weight model: {equal_w_model_test_rmse_mean/int(24/rbp)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatinating the test dataset return results of each model + index return for plot\n",
    "plot_test = pd.concat([pd.DataFrame(equal_w_model_test_plot), pd.DataFrame(shallow_nnf_test_plot),\n",
    "                       pd.DataFrame(deep_nnf_test_plot), pd.DataFrame(index_test_plot)], axis=1, join='inner')\n",
    "\n",
    "plot_test.columns = ['1/N Model', 'Shallow NNF', 'Deep NNF', 'S&P 500']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
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Return\"},\"zerolinecolor\":\"#E1E5ED\"}},                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}                    ).then(function(){\n",
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   ],
   "source": [
    "# importing a module for better and more interactive plot\n",
    "import cufflinks as cf\n",
    "cf.set_config_file(offline = True)\n",
    "\n",
    "'''\n",
    "plotting deep nnf, shallow nnf and, 1/n model performance on the test dataset, compare them with\n",
    "index (s&p) for better understanding\n",
    "'''\n",
    "plot_test.iplot(xTitle='Days', yTitle='Comulative Return')"
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