{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bitcoin Returns\n",
    "This project is based on my 4th year thesis project which was looking at timeseries momentum for cryptocurrency returns. In the thesis, I found conclusive evidence that past returns do have some impact on future returns at the monthly and weekly levels and discovered this by basic linear regression. In this project, I aim to go further and look at the impact of past returns combined with past trading volume and see how well this predicts future daily returns. This project will mainly focus on using the machine learning models that I've been exposed to during this class.\n",
    "\n",
    "The dataset is from Yahoo Finance - https://finance.yahoo.com/quote/BTC-USD/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we import the requried libraries and dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression, Lasso\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier, MLPRegressor\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "import sklearn as sk\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BTC-USD.csv')\n",
    "#recognize the date column as a datetime object in pandas\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "#set the date as the index\n",
    "df.set_index('Date', inplace=True)\n",
    "#we'll keep only the open price and volume \n",
    "df.drop(columns=['High', 'Low', 'Close', 'Adj Close'],  inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the daily returns and percentage change in volume. Focusing on returns and percentage change in volume aims to make the data 'stationary' and facilitates more accurate prediction and inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open    Volume  daily_pct_chg  daily_pct_chg_vol\n",
      "Date                                                              \n",
      "2014-09-18  456.859985  34483200      -0.019328           0.637628\n",
      "2014-09-19  424.102997  37919700      -0.071700           0.099657\n",
      "2014-09-20  394.673004  36863600      -0.069394          -0.027851\n",
      "2014-09-21  408.084991  26580100       0.033983          -0.278961\n",
      "2014-09-22  399.100006  24127600      -0.022017          -0.092268\n"
     ]
    }
   ],
   "source": [
    "df['daily_pct_chg'] = df['Open'].pct_change(periods=1)\n",
    "df['daily_pct_chg_vol'] = df['Volume'].pct_change(periods=1)\n",
    "df.dropna(inplace=True)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how the returns look with a few graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2,nrows=2,figsize=(30,30))\n",
    "sns.set(style=\"whitegrid\", font_scale=2.5)\n",
    "\n",
    "#first image\n",
    "image1 = sns.boxplot(data=df['daily_pct_chg'], orient='v',ax=ax[0,0])\n",
    "image1.set(ylabel='Returns (Decimal)', title='Bitcoin Daily Returns Boxplot')\n",
    "\n",
    "#second image\n",
    "image2 = sns.distplot(df['daily_pct_chg'],kde=False,norm_hist=True,bins=100,ax=ax[0,1])\n",
    "image2.set(ylabel='Frequency', title='Bitcoin Daily Returns Distribution',xlabel='Returns')\n",
    "\n",
    "#third image\n",
    "image3 = sns.boxplot(data=df['daily_pct_chg_vol'], orient='v',ax=ax[1,0])\n",
    "image3.set(ylabel='Percentage Change (Decimal)', title='Bitcoin Daily Volume % Change Boxplot')\n",
    "\n",
    "#second image\n",
    "image4 = sns.distplot(df['daily_pct_chg'],kde=False,norm_hist=True,bins=100,ax=ax[1,1])\n",
    "image4.set(ylabel='Frequency', title='Bitcoin Daily Volume % Change Distribution',xlabel='Change in Volume')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the above graphs, the daily returns and volume are centered around 0 which is what we would expect. From the first column of graphs, we also see that the volatility in volume is much greater than the volatility of returns by looking at the vertical spread of the box plot. The volatility in volume traded is part of the motivation to include it as an indicator in the timeseries momentum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets also take a look to see how returns vary by days of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Returns (Decimal)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Day'] = df.index.day_name()\n",
    "fig, ax = plt.subplots(figsize=(7.5,7.5))\n",
    "df.groupby('Day')['daily_pct_chg'].mean().plot(kind='bar',ax=ax)\n",
    "plt.title('Bitcoin Daily Returns')\n",
    "plt.xlabel('Day of Week')\n",
    "plt.ylabel('Mean Returns (Decimal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we can see that there is quite a bit of variability in the mean returns by day. I will therefore add dummy variables indicating the day of the week to try extract more information from the dataset for the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>daily_pct_chg</th>\n",
       "      <th>daily_pct_chg_vol</th>\n",
       "      <th>Day</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-18</th>\n",
       "      <td>456.859985</td>\n",
       "      <td>34483200</td>\n",
       "      <td>-0.019328</td>\n",
       "      <td>0.637628</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-19</th>\n",
       "      <td>424.102997</td>\n",
       "      <td>37919700</td>\n",
       "      <td>-0.071700</td>\n",
       "      <td>0.099657</td>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-20</th>\n",
       "      <td>394.673004</td>\n",
       "      <td>36863600</td>\n",
       "      <td>-0.069394</td>\n",
       "      <td>-0.027851</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-21</th>\n",
       "      <td>408.084991</td>\n",
       "      <td>26580100</td>\n",
       "      <td>0.033983</td>\n",
       "      <td>-0.278961</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-22</th>\n",
       "      <td>399.100006</td>\n",
       "      <td>24127600</td>\n",
       "      <td>-0.022017</td>\n",
       "      <td>-0.092268</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open    Volume  daily_pct_chg  daily_pct_chg_vol       Day  \\\n",
       "Date                                                                           \n",
       "2014-09-18  456.859985  34483200      -0.019328           0.637628  Thursday   \n",
       "2014-09-19  424.102997  37919700      -0.071700           0.099657    Friday   \n",
       "2014-09-20  394.673004  36863600      -0.069394          -0.027851  Saturday   \n",
       "2014-09-21  408.084991  26580100       0.033983          -0.278961    Sunday   \n",
       "2014-09-22  399.100006  24127600      -0.022017          -0.092268    Monday   \n",
       "\n",
       "            Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday  \n",
       "Date                                                                \n",
       "2014-09-18       0         0       0         1        0          0  \n",
       "2014-09-19       0         0       0         0        0          0  \n",
       "2014-09-20       0         1       0         0        0          0  \n",
       "2014-09-21       0         0       1         0        0          0  \n",
       "2014-09-22       1         0       0         0        0          0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating 6 dummies instead of 7 by specifying drop_first=True to avoid the dummy variable trap\n",
    "df = pd.concat([df,pd.get_dummies(df['Day'],drop_first=True)],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create lagged values for daily returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open    Volume  daily_pct_chg  daily_pct_chg_vol       Day  \\\n",
      "Date                                                                           \n",
      "2014-09-18  456.859985  34483200      -0.019328           0.637628  Thursday   \n",
      "2014-09-19  424.102997  37919700      -0.071700           0.099657    Friday   \n",
      "2014-09-20  394.673004  36863600      -0.069394          -0.027851  Saturday   \n",
      "2014-09-21  408.084991  26580100       0.033983          -0.278961    Sunday   \n",
      "2014-09-22  399.100006  24127600      -0.022017          -0.092268    Monday   \n",
      "\n",
      "            Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday  \\\n",
      "Date                                                                 \n",
      "2014-09-18       0         0       0         1        0          0   \n",
      "2014-09-19       0         0       0         0        0          0   \n",
      "2014-09-20       0         1       0         0        0          0   \n",
      "2014-09-21       0         0       1         0        0          0   \n",
      "2014-09-22       1         0       0         0        0          0   \n",
      "\n",
      "            lag_return_1  lag_return_2  lag_return_3  lag_return_4  \n",
      "Date                                                                \n",
      "2014-09-18           NaN           NaN           NaN           NaN  \n",
      "2014-09-19     -0.019328           NaN           NaN           NaN  \n",
      "2014-09-20     -0.071700     -0.019328           NaN           NaN  \n",
      "2014-09-21     -0.069394     -0.071700     -0.019328           NaN  \n",
      "2014-09-22      0.033983     -0.069394     -0.071700     -0.019328  \n"
     ]
    }
   ],
   "source": [
    "for i in range(1,5):\n",
    "    column_name = 'lag_return_' + str(i)\n",
    "    df[column_name] = df['daily_pct_chg'].shift(i)\n",
    "del i\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat for the lagged volume indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open    Volume  daily_pct_chg  daily_pct_chg_vol        Day  \\\n",
      "Date                                                                            \n",
      "2014-09-22  399.100006  24127600      -0.022017          -0.092268     Monday   \n",
      "2014-09-23  402.092010  45099500       0.007497           0.869208    Tuesday   \n",
      "2014-09-24  435.751007  30627700       0.083710          -0.320886  Wednesday   \n",
      "2014-09-25  423.156006  26814400      -0.028904          -0.124505   Thursday   \n",
      "2014-09-26  411.428986  21460800      -0.027713          -0.199654     Friday   \n",
      "\n",
      "            Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday  \\\n",
      "Date                                                                 \n",
      "2014-09-22       1         0       0         0        0          0   \n",
      "2014-09-23       0         0       0         0        1          0   \n",
      "2014-09-24       0         0       0         0        0          1   \n",
      "2014-09-25       0         0       0         1        0          0   \n",
      "2014-09-26       0         0       0         0        0          0   \n",
      "\n",
      "            lag_return_1  lag_return_2  lag_return_3  lag_return_4  \\\n",
      "Date                                                                 \n",
      "2014-09-22      0.033983     -0.069394     -0.071700     -0.019328   \n",
      "2014-09-23     -0.022017      0.033983     -0.069394     -0.071700   \n",
      "2014-09-24      0.007497     -0.022017      0.033983     -0.069394   \n",
      "2014-09-25      0.083710      0.007497     -0.022017      0.033983   \n",
      "2014-09-26     -0.028904      0.083710      0.007497     -0.022017   \n",
      "\n",
      "            lag_return_vol_1  lag_return_vol_2  lag_return_vol_3  \\\n",
      "Date                                                               \n",
      "2014-09-22         -0.278961         -0.027851          0.099657   \n",
      "2014-09-23         -0.092268         -0.278961         -0.027851   \n",
      "2014-09-24          0.869208         -0.092268         -0.278961   \n",
      "2014-09-25         -0.320886          0.869208         -0.092268   \n",
      "2014-09-26         -0.124505         -0.320886          0.869208   \n",
      "\n",
      "            lag_return_vol_4  \n",
      "Date                          \n",
      "2014-09-22          0.637628  \n",
      "2014-09-23          0.099657  \n",
      "2014-09-24         -0.027851  \n",
      "2014-09-25         -0.278961  \n",
      "2014-09-26         -0.092268  \n"
     ]
    }
   ],
   "source": [
    "for i in range(1,5):\n",
    "    column_name = 'lag_return_vol_' + str(i)\n",
    "    df[column_name] = df['daily_pct_chg_vol'].shift(i)\n",
    "df.dropna(inplace=True)\n",
    "print(df.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions\n",
    "\n",
    "The next part of this project will investigate the predictive accuracy of several models including logstic and neural networks. To make it easier to compare between models, I've decided to frame the problem as a classification problem where I will be predicting whether the daily returns move up or down, a binary outcome, instead of how large the returns are. I've printed out a few error scores for the models below after each cell, but I also compare them in a graph later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Open', 'Volume', 'daily_pct_chg', 'daily_pct_chg_vol', 'Day', 'Monday',\n",
      "       'Saturday', 'Sunday', 'Thursday', 'Tuesday', 'Wednesday',\n",
      "       'lag_return_1', 'lag_return_2', 'lag_return_3', 'lag_return_4',\n",
      "       'lag_return_vol_1', 'lag_return_vol_2', 'lag_return_vol_3',\n",
      "       'lag_return_vol_4', 'target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#create a binary column for daily returns where 0 represents the negative returns and 1 is the positive returns\n",
    "def fn_binary(value):\n",
    "    if value < 0:\n",
    "        return 0 \n",
    "    else:\n",
    "        return 1\n",
    "df['target'] = df['daily_pct_chg'].apply(fn_binary)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and test data\n",
    "First we'll make the training and test data for our models. We'll use a random 90% of the data for training and the remaining 10% of the data to see how our models generalize to data they haven't seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1758, 15)\n",
      "(196, 15)\n"
     ]
    }
   ],
   "source": [
    "#Split and sort into train, test \n",
    "dftrain, dftest = train_test_split(df, test_size=0.1, random_state=101)\n",
    "\n",
    "#training data, drop the columns we don't need\n",
    "trainy = pd.to_numeric(dftrain['target'])\n",
    "trainx = (dftrain.drop(['Open','Volume','daily_pct_chg','Day','target'], axis=1))\n",
    "#Test data\n",
    "testy = pd.to_numeric(dftest['target'])\n",
    "testx = dftest.drop(['Open','Volume','daily_pct_chg','Day', 'target'], axis=1)\n",
    "\n",
    "print(trainx.shape)\n",
    "print(testx.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first train a logistic regression and analyze the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Train Accuracy is: 0.5523321956769056\n",
      "Logistic Test Accuracy is: 0.5255102040816326\n",
      "Logistic LogLoss Train is: 15.462214756886304\n",
      "Logistic LogLoss Test is: 16.388668204555312\n"
     ]
    }
   ],
   "source": [
    "model_logistic = LogisticRegression()\n",
    "model_logistic.fit(trainx, trainy)\n",
    "\n",
    "logistic_train_accuracy = model_logistic.score(trainx,trainy)\n",
    "logistic_test_accuracy = model_logistic.score(testx,testy)\n",
    "print(f'Logistic Train Accuracy is: {logistic_train_accuracy}')\n",
    "print(f'Logistic Test Accuracy is: {logistic_test_accuracy}')\n",
    "\n",
    "log_loss_logistic_train = sk.metrics.log_loss(trainy, model_logistic.predict(trainx))\n",
    "log_loss_logistic_test = sk.metrics.log_loss(testy, model_logistic.predict(testx))\n",
    "print(f'Logistic LogLoss Train is: {log_loss_logistic_train}')\n",
    "print(f'Logistic LogLoss Test is: {log_loss_logistic_test}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a neural network classifier. We should scale the data to make it easier for the model to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Train Accuracy is: 0.5443686006825939\n",
      "Neural Network Test Accuracy is: 0.47959183673469385\n",
      "Neural Network LogLoss Train is: 15.737233471176113\n",
      "Neural Network LogLoss Test is: 17.974575312668442\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "#fit and transform on the input training data\n",
    "trainx = scaler.fit_transform(trainx)\n",
    "#use the same scaler to transform the input test data\n",
    "testx = scaler.transform(testx)\n",
    "\n",
    "#early stopping to try and avoid overfitting on the training data\n",
    "#this should lead to better generalization to test data\n",
    "model_nn = MLPClassifier(early_stopping=True, hidden_layer_sizes=(20,20,20))\n",
    "model_nn.fit(trainx, trainy)\n",
    "\n",
    "nn_train_accuracy = model_nn.score(trainx,trainy)\n",
    "nn_test_accuracy = model_nn.score(testx,testy)\n",
    "print(f'Neural Network Train Accuracy is: {nn_train_accuracy}')\n",
    "print(f'Neural Network Test Accuracy is: {nn_test_accuracy}')\n",
    "\n",
    "log_loss_nn_train = sk.metrics.log_loss(trainy, model_nn.predict(trainx))\n",
    "log_loss_nn_test = sk.metrics.log_loss(testy, model_nn.predict(testx))\n",
    "print(f'Neural Network LogLoss Train is: {log_loss_nn_train}')\n",
    "print(f'Neural Network LogLoss Test is: {log_loss_nn_test}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll try a Gradient Boosting Classifier, with an implementation of a grid search cross validation to try and tune some of the parameters of the model. When trying to use this model to make predictions for trading with real money, often the uncertainity around the prediction influences how much money is invested, therefore I've also implemented a log loss error for the GridSearchCV. The logloss penalizes the model when it predicts a high probability and is wrong and therefore emphasizes accountability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree Train Accuracy is: 0.5443686006825939\n",
      "Tree Test Accuracy is: 0.47959183673469385\n",
      "Tree LogLoss Train is: 12.063293102727505\n",
      "Tree LogLoss Test is: 16.036207233257343\n"
     ]
    }
   ],
   "source": [
    "opt_max_depth=2\n",
    "opt_n_estimators=500\n",
    "opt_learning_rate=0.1\n",
    "\n",
    "#Optimize some parameters\n",
    "param_test1 = {'n_estimators':[100,200,600],'learning_rate':[0.001,0.01,0.1],'max_depth':[1,3,4]}\n",
    "gsearch1 = GridSearchCV(estimator = GradientBoostingClassifier(),\n",
    "                                                              param_grid = param_test1,scoring='accuracy',\n",
    "                                                              cv=5, verbose=0)\n",
    "gsearch1.fit(trainx, trainy)\n",
    "opt_n_estimators=gsearch1.best_params_['n_estimators']\n",
    "opt_learning_rate=gsearch1.best_params_['learning_rate']\n",
    "opt_max_depth=gsearch1.best_params_['max_depth']\n",
    "\n",
    "model_tree = GradientBoostingClassifier(n_estimators=opt_n_estimators,\n",
    "                                max_depth=opt_max_depth,learning_rate=opt_learning_rate)\n",
    "model_tree.fit(trainx,trainy\n",
    "              )\n",
    "tree_train_accuracy = model_nn.score(trainx,trainy)\n",
    "tree_test_accuracy = model_nn.score(testx,testy)\n",
    "print(f'Tree Train Accuracy is: {tree_train_accuracy}')\n",
    "print(f'Tree Test Accuracy is: {tree_test_accuracy}')\n",
    "\n",
    "log_loss_tree_train = sk.metrics.log_loss(trainy, model_tree.predict(trainx))\n",
    "log_loss_tree_test = sk.metrics.log_loss(testy, model_tree.predict(testx))\n",
    "print(f'Tree LogLoss Train is: {log_loss_tree_train}')\n",
    "print(f'Tree LogLoss Test is: {log_loss_tree_test}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Model Comparisons\n",
    "Now we'll compare the error scores for the three models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accuracy = (logistic_train_accuracy, nn_train_accuracy, tree_train_accuracy)\n",
    "test_accuracy = (logistic_test_accuracy, nn_test_accuracy, tree_test_accuracy)\n",
    "train_logloss = (log_loss_logistic_train, log_loss_nn_train, log_loss_tree_train)\n",
    "test_logloss = (log_loss_logistic_test, log_loss_nn_test, log_loss_tree_test)\n",
    "\n",
    "ind = np.arange(len(train_accuracy))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,20),nrows=2)\n",
    "rects1 = ax[0].bar(ind - width/2, train_accuracy, width,\n",
    "                label='train')\n",
    "rects2 = ax[0].bar(ind + width/2, test_accuracy, width,\n",
    "                label='test')\n",
    "rects3 = ax[1].bar(ind - width/2, train_logloss, width,\n",
    "                label='train')\n",
    "rects4 = ax[1].bar(ind + width/2, test_logloss, width,\n",
    "                label='test')\n",
    "\n",
    "\n",
    "        \n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "\n",
    "ax[0].set_ylabel('Error')\n",
    "ax[0].set_title('Accuracy by Model')\n",
    "ax[0].set_xticks(ind)\n",
    "ax[0].set_xticklabels(('Logistic', 'NN', 'Tree'))\n",
    "ax[0].legend(loc='lower right')\n",
    "\n",
    "ax[1].set_ylabel('Error')\n",
    "ax[1].set_title('LogLoss by Model')\n",
    "ax[1].set_xticks(ind)\n",
    "ax[1].set_xticklabels(('Logistic', 'NN', 'Tree'))\n",
    "ax[1].legend(loc='lower right')\n",
    "\n",
    "x=0\n",
    "for i in train_accuracy:\n",
    "    #ax[0].text(ind[0]-(width/2),logistic_train_accuracy,str(logistic_train_accuracy)[0:4])\n",
    "    ax[0].text(ind[x]-(width),i,str(i)[0:5])\n",
    "    x = x + 1\n",
    "\n",
    "x=0\n",
    "for i in test_accuracy:\n",
    "    ax[0].text(ind[x]+(width/2),i,str(i)[0:5])\n",
    "    x = x + 1\n",
    "    \n",
    "x=0\n",
    "for i in train_logloss:\n",
    "    ax[1].text(ind[x]-(width),i,str(i)[0:5])\n",
    "    x = x + 1\n",
    "\n",
    "x=0\n",
    "for i in test_logloss:\n",
    "    ax[1].text(ind[x]+(width/2),i,str(i)[0:5])\n",
    "    x = x + 1\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Results\n",
    "From the above graphs, we see that the logistic regression performs better in terms of accuracy on both the train and test sets. As it is a classification problem, the accuracy is just the amount of predictions that it get's correct. Therefore the accuracy value of 52.5 on the test set means that it correctly predicts 52.5% of the time. Although this may not seem like a good score, predicting a financial market is difficult and predicting over 50% accuracy could lead to a profitable trading strategy, however, that is beyond the scope of this project.\n",
    "\n",
    "## Log Loss Results\n",
    "In terms of log loss, which may be a more appropriate measure to use as a trading strategy, the Gradient Boosting Classifier (Tree model) performed the best on the test set with a log loss error score of 16.03. It should be noted that a Grid Search was only implemented for the Gradient Boosting Classifier and not for the Neural Network due to computation power of the Jupyter notebook and scikitlearn packages.\n",
    "\n",
    "# Next Step - Predicting the magnitude of the change\n",
    "Now I will change from a classification problem back to a regression problem that will allow me predict the magnitude of the future daily return. I will compare the results between a linear regression model and a neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear MSE Train is: 1.437706353931729\n",
      "Linear MSE Test is: 1.502959519956661\n",
      "Neural Network MSE Train is: 1.708511977501341\n",
      "Neural Network MSE Test is: 1.704793189477996\n"
     ]
    }
   ],
   "source": [
    "#Split and sort into train, test \n",
    "dftrain, dftest = train_test_split(df, test_size=0.1, random_state=101)\n",
    "\n",
    "#training data, drop the columns we don't need\n",
    "trainy = pd.to_numeric(dftrain['daily_pct_chg'])\n",
    "trainx = (dftrain.drop(['Open','Volume','target','Day','target','daily_pct_chg'], axis=1))\n",
    "#Test data\n",
    "testy = pd.to_numeric(dftest['daily_pct_chg'])\n",
    "testx = dftest.drop(['Open','Volume','target','Day', 'target', 'daily_pct_chg'], axis=1)\n",
    "\n",
    "linear_model= LinearRegression()\n",
    "linear_model.fit(trainx, trainy)\n",
    "\n",
    "mse_linear_train = sk.metrics.mean_squared_error(trainy, linear_model.predict(trainx))\n",
    "mse_linear_test = sk.metrics.mean_squared_error(testy, linear_model.predict(testx))\n",
    "print(f'Linear MSE Train is: {mse_linear_train*1000}')\n",
    "print(f'Linear MSE Test is: {mse_linear_test*1000}')\n",
    "\n",
    "\n",
    "nn_model= MLPRegressor(hidden_layer_sizes=(20,20,20))\n",
    "nn_model.fit(trainx, trainy)\n",
    "\n",
    "mse_nn_train = sk.metrics.mean_squared_error(trainy, nn_model.predict(trainx))\n",
    "mse_nn_test = sk.metrics.mean_squared_error(testy, nn_model.predict(testx))\n",
    "print(f'Neural Network MSE Train is: {mse_nn_train*1000}')\n",
    "print(f'Neural Network MSE Test is: {mse_nn_test*1000}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Linear model performs better in terms of mean squared error on both the training and test set.\n",
    "Let's now try to visualize how the models compare in their predictions with the real values of the returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_results = pd.DataFrame(data={'Linear':linear_model.predict(testx),\n",
    "                          'Neural Network':nn_model.predict(testx),\n",
    "                               'Actual':testy})\n",
    "\n",
    "f, ax = plt.subplots(figsize=(20,20))\n",
    "ax.scatter(testy, linear_model.predict(testx),color='#2ca02c', label='Linear Model')#green\n",
    "ax.scatter(testy, nn_model.predict(testx),  color='#9467bd', label='Neural Network Model')#purple\n",
    "ax.legend()\n",
    "ax.set_ylabel('Predicted Return')\n",
    "ax.set_title('Predicted vs Actual Returns (Test Data)')\n",
    "\n",
    "ax.set_xlabel('Actual Return')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph we see that the linear model(green) predicts values much closer to 0 while the Neural Network model has a much wider range to it's predictions. Although the linear model does have lower error scores, it's unclear whether either of the models would be sufficiently accurate to implement in a trading system.\n",
    "\n",
    "Therefore to conclude, this project was mostly just to gain experience working with a dataset and using some of Python's prediction tools as an extension to my thesis. It's clear that a simple logistic model performs well in a classification problem and a linear regression performs well on non-binary data, however, it is important to note that the hyperparameter tuning of the Neural Network and Gradient Boosting models is an art of it's own and therefore we cannot conclude which model is definitively the best."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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}