Match-Trade Technologies CaseStudy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>1. Wczytać plik csv.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"dane.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>profit</th>\n",
       "      <th>closed_volume</th>\n",
       "      <th>side</th>\n",
       "      <th>reg_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>223946</td>\n",
       "      <td>165.496791</td>\n",
       "      <td>9917</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1.474020e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>223946</td>\n",
       "      <td>55.195228</td>\n",
       "      <td>9953</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1.474020e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223946</td>\n",
       "      <td>713.153400</td>\n",
       "      <td>10000</td>\n",
       "      <td>SELL</td>\n",
       "      <td>1.474020e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>223961</td>\n",
       "      <td>3.262358</td>\n",
       "      <td>1975</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1.484050e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>223961</td>\n",
       "      <td>-317.982441</td>\n",
       "      <td>3000</td>\n",
       "      <td>BUY</td>\n",
       "      <td>1.484050e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   client_id      profit  closed_volume  side      reg_date\n",
       "0     223946  165.496791           9917  SELL  1.474020e+12\n",
       "1     223946   55.195228           9953  SELL  1.474020e+12\n",
       "2     223946  713.153400          10000  SELL  1.474020e+12\n",
       "3     223961    3.262358           1975   BUY  1.484050e+12\n",
       "4     223961 -317.982441           3000   BUY  1.484050e+12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 2. Znaleźć client_id, closed_volume oraz reg_date dla transakcji, na której był największy profit. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        client_id       profit  closed_volume  side      reg_date\n",
      "402816     224050  1917.008043           1000  SELL  1.533160e+12\n"
     ]
    }
   ],
   "source": [
    "max_profit_row = df[df['profit']== df['profit'].max()]\n",
    "print(max_profit_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>client_id</th>\n",
       "      <th>closed_volume</th>\n",
       "      <th>reg_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402816</th>\n",
       "      <td>224050</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.533160e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        client_id  closed_volume      reg_date\n",
       "402816     224050           1000  1.533160e+12"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_profit_row[['client_id','closed_volume','reg_date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>3. Stworzyć nową tabelę z pogrupowanymi danymi. Kolejne kolumny to:</b>\n",
    "- client_id\n",
    "- suma profitu danego klienta\n",
    "- średni wolumen na transakcji\n",
    "- liczba transakcji klienta\n",
    "- data rejestracji klienta\n",
    "\n",
    "<b>przykładowy wiersz: [client_id, sum(profit), avg(closed_volume), reg_date] = [123156, 1235, 111, 1500000000000]\n",
    "</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.groupby('client_id').agg({'profit': 'sum', 'closed_volume': ['mean','count'], 'reg_date':'min' })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.columns = ['sum(profit)', 'avg(closed_volume)', 'count(transactions)', 'reg_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sum(profit)</th>\n",
       "      <th>avg(closed_volume)</th>\n",
       "      <th>count(transactions)</th>\n",
       "      <th>reg_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143546</th>\n",
       "      <td>-101.903273</td>\n",
       "      <td>3086.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>1.445370e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143565</th>\n",
       "      <td>-2347.015062</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>25</td>\n",
       "      <td>1.452440e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143566</th>\n",
       "      <td>-1753.888235</td>\n",
       "      <td>6421.741497</td>\n",
       "      <td>147</td>\n",
       "      <td>1.481560e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144015</th>\n",
       "      <td>-464.168235</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.496150e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223841</th>\n",
       "      <td>-996.534271</td>\n",
       "      <td>1.053571</td>\n",
       "      <td>56</td>\n",
       "      <td>1.462880e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sum(profit)  avg(closed_volume)  count(transactions)      reg_date\n",
       "client_id                                                                    \n",
       "143546     -101.903273         3086.500000                   10  1.445370e+12\n",
       "143565    -2347.015062            4.160000                   25  1.452440e+12\n",
       "143566    -1753.888235         6421.741497                  147  1.481560e+12\n",
       "144015     -464.168235         1000.000000                    1  1.496150e+12\n",
       "223841     -996.534271            1.053571                   56  1.462880e+12"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>4. Stworzyć zmienną reg_date_bin, która przyjmuje 1 gdy data rejestracji jest większa niż 1515000000000,\n",
    " i 0 w przeciwnym przypadku.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['reg_date_bin']=df['reg_date'].apply(lambda x: 1 if x > 1515000000000 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 5. Stworzyć model regresji liniowej, gdzie zmienną zależną będzie suma profitu, a niezależną średni wolumen,\n",
    "liczba transakcji oraz binarna zmienna reg_date_bin (w modelu uwzględnić stałą). Sprawdzić istotność zmiennych.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y: sum(profit)\n",
    "# x : avg(closed_volume) , count(transactions) , reg_date_bin, constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['reg_date_bin']=df2['reg_date'].apply(lambda x: 1 if x > 1515000000000 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            sum(profit)   R-squared:                       0.994\n",
      "Model:                            OLS   Adj. R-squared:                  0.993\n",
      "Method:                 Least Squares   F-statistic:                     3283.\n",
      "Date:                Wed, 30 Jan 2019   Prob (F-statistic):           4.02e-69\n",
      "Time:                        23:17:15   Log-Likelihood:                -787.89\n",
      "No. Observations:                  67   AIC:                             1584.\n",
      "Df Residuals:                      63   BIC:                             1593.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "const                3078.4170   5799.813      0.531      0.597   -8511.585    1.47e+04\n",
      "avg(closed_volume)     -0.1701      0.061     -2.769      0.007      -0.293      -0.047\n",
      "count(transactions)   -10.6745      0.110    -96.896      0.000     -10.895     -10.454\n",
      "reg_date_bin         7879.0078   8191.686      0.962      0.340   -8490.770    2.42e+04\n",
      "==============================================================================\n",
      "Omnibus:                       18.669   Durbin-Watson:                   1.747\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               63.258\n",
      "Skew:                          -0.620   Prob(JB):                     1.84e-14\n",
      "Kurtosis:                       7.596   Cond. No.                     1.73e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.73e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X = df2[['avg(closed_volume)','count(transactions)', 'reg_date_bin']] \n",
    "Y = df2['sum(profit)']\n",
    "X = sm.add_constant(X) # add a constant\n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: There is little to no statistical relationship between dependent and independent variable.\n",
      "If P value less than the confidence level, assume 0.05, there is a statistically significant relationship between the term and the response.\n",
      "\n",
      "Variables significance test results: \n",
      "\n",
      "As we can see from the above summary, assuming 0.05 confidence level,\n",
      "We can reject H0 for avg(closed_volume) and count(transactions).\n",
      "On the other hand, we fail to reject H0 for const and reg_date_bin .\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"H0: There is little to no statistical relationship between dependent and independent variable.\n",
    "If P value less than the confidence level, assume 0.05, there is a statistically significant relationship between the term and the response.\\n\"\"\")\n",
    "print (\"Variables significance test results: \\n\")\n",
    "print (\"As we can see from the above summary, assuming 0.05 confidence level,\")\n",
    "print (\"We can reject H0 for avg(closed_volume) and count(transactions).\")\n",
    "print (\"On the other hand, we fail to reject H0 for const and reg_date_bin .\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>6. Sprawdzić dopasowanie modelu do danych za pomocą jednej (dowolnie wybranej) miary.</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model fit test: \n",
      "\n",
      "The Prob (F-statistic) in the model: 0.000000000000000000000000000000000000000000000000000000000000000000004 is much lower than significance level 0.005.\n",
      "\n",
      "This implies that the regression model fits the data way better than model with no independent variables\n"
     ]
    }
   ],
   "source": [
    "ProbF= 4.02e-69 \n",
    "print (\"Model fit test: \\n\") \n",
    "print (\"The Prob (F-statistic) in the model:\", \"{:.69f}\".format(ProbF) ,\"is much lower than significance level 0.005.\") \n",
    "print(\"\\nThis implies that the regression model fits the data way better than model with no independent variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>7. Dopasować rozkład normalny do zmiennej profit w pierwotnym pliku dane (polecamy utworzyć obiekt \n",
    " norm z pakietu scipy.stats, dla rozjaśnienia sytuacji polecamy również wykonać histogram profitu z transakcji).\n",
    " Na tej podstawie policzyć prawdopodobieństwo, że profit z transakcji będzie większy niż 1000. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_dist = list(df['profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "# Fit a normal distribution to the data:\n",
    "mu, std = norm.fit(profit_dist)\n",
    "\n",
    "# Plot the histogram.\n",
    "plt.hist(profit_dist, bins=25, density=True, alpha=0.6, color='g')\n",
    "\n",
    "# Plot the PDF.\n",
    "xmin, xmax = plt.xlim()\n",
    "x = np.linspace(xmin, xmax, 100)\n",
    "p = norm.pdf(x, mu, std)\n",
    "plt.plot(x, p, 'k', linewidth=2)\n",
    "title = \"Fit results: mu = %.2f,  std = %.2f\" % (mu, std)\n",
    "plt.title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of profit being more than 1000 estimated at 0.0085 %\n"
     ]
    }
   ],
   "source": [
    "#Probability of profit more than 1000 from scipy:    \n",
    "less_than_value = norm.cdf(1000, loc = -10.55, scale = 423.41)\n",
    "# 1- Pr(less_than_value) for Pr(more_than_value)\n",
    "more_than_value = 1-less_than_value\n",
    "print (\"Probability of profit being more than 1000 estimated at\" , \"{:.4f}\".format((more_than_value)),\"%\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Bonus: </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphial representation of variables regression showing high correlation of transaction count and profits\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "fig = sm.graphics.plot_partregress_grid(model, fig=fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c1fc34550>,\n",
       " <matplotlib.lines.Line2D at 0x1c1fc34668>,\n",
       " <matplotlib.lines.Line2D at 0x1c1fc34630>,\n",
       " <matplotlib.lines.Line2D at 0x1c1fc347b8>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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   ],
   "source": [
    "# Plotted data below and R^2 at 0.994 confirms a good fit of the model\n",
    "plt.plot(X, Y, 'o', label=\"data\")\n",
    "plt.plot(X, model.fittedvalues, 'x', label=\"OLS\")"
   ]
  }
 ],
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