diff --git a/M1/Portfolio Management/TP-Project.ipynb b/M1/Portfolio Management/TP-Project.ipynb
new file mode 100644
index 0000000..843e152
--- /dev/null
+++ b/M1/Portfolio Management/TP-Project.ipynb	
@@ -0,0 +1,518 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "# Project - Portfolio Management\n",
+    "\n",
+    "## Group: Danjou Arthur & Forest Thais\n",
+    "\n",
+    "### Time period studied from 2017-01-01 to 2018-01-01\n",
+    "\n",
+    "### Risk-free rate: 2%"
+   ],
+   "id": "81049114d821d00e"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:46.298758Z",
+     "start_time": "2024-11-25T13:43:46.293696Z"
+    }
+   },
+   "source": [
+    "import yfinance as yf\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ],
+   "outputs": [],
+   "execution_count": 51
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:47.318911Z",
+     "start_time": "2024-11-25T13:43:47.198820Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Data Extraction\n",
+    "Tickers = [\"^RUT\", \"^IXIC\", \"^GSPC\", \"XWD.TO\"]\n",
+    "start_input = \"2017-01-01\"\n",
+    "end_input = \"2018-01-01\"\n",
+    "S = pd.DataFrame()\n",
+    "for t in Tickers:\n",
+    "    S[t] = yf.Tickers(t).history(start=start_input, end=end_input)[\"Close\"]\n",
+    "\n",
+    "S = S.interpolate(method=\"pad\")\n",
+    "\n",
+    "# Show the first five and last five values extracted\n",
+    "display(S.head())\n",
+    "display(S.tail())\n",
+    "print(S.shape)"
+   ],
+   "id": "9f9fc36832c97e0",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed\n",
+      "[*********************100%***********************]  1 of 1 completed\n",
+      "[*********************100%***********************]  1 of 1 completed\n",
+      "[*********************100%***********************]  1 of 1 completed\n",
+      "/var/folders/tp/_ld5_pzs6nx6mv1pbjhq1l740000gn/T/ipykernel_92506/348989065.py:9: FutureWarning: DataFrame.interpolate with method=pad is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  S = S.interpolate(method=\"pad\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "                                  ^RUT        ^IXIC        ^GSPC     XWD.TO\n",
+       "Date                                                                       \n",
+       "2017-01-03 00:00:00+00:00  1365.489990  5429.080078  2257.830078  38.499630\n",
+       "2017-01-04 00:00:00+00:00  1387.949951  5477.000000  2270.750000  38.553375\n",
+       "2017-01-05 00:00:00+00:00  1371.939941  5487.939941  2269.000000  38.481716\n",
+       "2017-01-06 00:00:00+00:00  1367.280029  5521.060059  2276.979980  38.517544\n",
+       "2017-01-09 00:00:00+00:00  1357.489990  5531.819824  2268.899902  38.383186"
+      ],
+      "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>^RUT</th>\n",
+       "      <th>^IXIC</th>\n",
+       "      <th>^GSPC</th>\n",
+       "      <th>XWD.TO</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-01-03 00:00:00+00:00</th>\n",
+       "      <td>1365.489990</td>\n",
+       "      <td>5429.080078</td>\n",
+       "      <td>2257.830078</td>\n",
+       "      <td>38.499630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-04 00:00:00+00:00</th>\n",
+       "      <td>1387.949951</td>\n",
+       "      <td>5477.000000</td>\n",
+       "      <td>2270.750000</td>\n",
+       "      <td>38.553375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-05 00:00:00+00:00</th>\n",
+       "      <td>1371.939941</td>\n",
+       "      <td>5487.939941</td>\n",
+       "      <td>2269.000000</td>\n",
+       "      <td>38.481716</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-06 00:00:00+00:00</th>\n",
+       "      <td>1367.280029</td>\n",
+       "      <td>5521.060059</td>\n",
+       "      <td>2276.979980</td>\n",
+       "      <td>38.517544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-09 00:00:00+00:00</th>\n",
+       "      <td>1357.489990</td>\n",
+       "      <td>5531.819824</td>\n",
+       "      <td>2268.899902</td>\n",
+       "      <td>38.383186</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "                                  ^RUT        ^IXIC        ^GSPC     XWD.TO\n",
+       "Date                                                                       \n",
+       "2017-12-22 00:00:00+00:00  1542.930054  6959.959961  2683.340088  44.323349\n",
+       "2017-12-26 00:00:00+00:00  1544.229980  6936.250000  2680.500000  44.323349\n",
+       "2017-12-27 00:00:00+00:00  1543.939941  6939.339844  2682.620117  44.052303\n",
+       "2017-12-28 00:00:00+00:00  1548.930054  6950.160156  2687.540039  43.857414\n",
+       "2017-12-29 00:00:00+00:00  1535.510010  6903.390137  2673.610107  43.784576"
+      ],
+      "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>^RUT</th>\n",
+       "      <th>^IXIC</th>\n",
+       "      <th>^GSPC</th>\n",
+       "      <th>XWD.TO</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-12-22 00:00:00+00:00</th>\n",
+       "      <td>1542.930054</td>\n",
+       "      <td>6959.959961</td>\n",
+       "      <td>2683.340088</td>\n",
+       "      <td>44.323349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-26 00:00:00+00:00</th>\n",
+       "      <td>1544.229980</td>\n",
+       "      <td>6936.250000</td>\n",
+       "      <td>2680.500000</td>\n",
+       "      <td>44.323349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-27 00:00:00+00:00</th>\n",
+       "      <td>1543.939941</td>\n",
+       "      <td>6939.339844</td>\n",
+       "      <td>2682.620117</td>\n",
+       "      <td>44.052303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-28 00:00:00+00:00</th>\n",
+       "      <td>1548.930054</td>\n",
+       "      <td>6950.160156</td>\n",
+       "      <td>2687.540039</td>\n",
+       "      <td>43.857414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-29 00:00:00+00:00</th>\n",
+       "      <td>1535.510010</td>\n",
+       "      <td>6903.390137</td>\n",
+       "      <td>2673.610107</td>\n",
+       "      <td>43.784576</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(251, 4)\n"
+     ]
+    }
+   ],
+   "execution_count": 52
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:50.080380Z",
+     "start_time": "2024-11-25T13:43:50.073119Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "R = S / S.shift() - 1\n",
+    "R = R[1:]\n",
+    "mean_d = R.mean()\n",
+    "covar_d = R.cov()\n",
+    "corr = R.corr()"
+   ],
+   "id": "53483cf3a925a4db",
+   "outputs": [],
+   "execution_count": 53
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:50.965092Z",
+     "start_time": "2024-11-25T13:43:50.961969Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "mean = mean_d * 252\n",
+    "covar = covar_d * 252\n",
+    "std = np.sqrt(np.diag(covar))"
+   ],
+   "id": "c327ed5967b1f442",
+   "outputs": [],
+   "execution_count": 54
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:51.701725Z",
+     "start_time": "2024-11-25T13:43:51.695020Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(\"Mean:\\n\")\n",
+    "print(mean)\n",
+    "print(\"\\nCovariance:\\n\")\n",
+    "print(covar)\n",
+    "print(\"\\nStandard Deviation:\\n\")\n",
+    "print(std)\n",
+    "print(\"\\nCorrelation:\\n\")\n",
+    "print(corr)"
+   ],
+   "id": "6bc6a850bf06cc9d",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean:\n",
+      "\n",
+      "^RUT      0.125501\n",
+      "^IXIC     0.246863\n",
+      "^GSPC     0.172641\n",
+      "XWD.TO    0.133175\n",
+      "dtype: float64\n",
+      "\n",
+      "Covariance:\n",
+      "\n",
+      "            ^RUT     ^IXIC     ^GSPC    XWD.TO\n",
+      "^RUT    0.014417  0.008400  0.006485  0.004797\n",
+      "^IXIC   0.008400  0.009182  0.005583  0.004337\n",
+      "^GSPC   0.006485  0.005583  0.004426  0.003309\n",
+      "XWD.TO  0.004797  0.004337  0.003309  0.006996\n",
+      "\n",
+      "Standard Deviation:\n",
+      "\n",
+      "[0.12007222 0.09582499 0.06653127 0.08364295]\n",
+      "\n",
+      "Correlation:\n",
+      "\n",
+      "            ^RUT     ^IXIC     ^GSPC    XWD.TO\n",
+      "^RUT    1.000000  0.730047  0.811734  0.477668\n",
+      "^IXIC   0.730047  1.000000  0.875687  0.541087\n",
+      "^GSPC   0.811734  0.875687  1.000000  0.594658\n",
+      "XWD.TO  0.477668  0.541087  0.594658  1.000000\n"
+     ]
+    }
+   ],
+   "execution_count": 55
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Question 1",
+   "id": "fc4bec874f710f7c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:53.113423Z",
+     "start_time": "2024-11-25T13:43:53.109514Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "r = 0.02\n",
+    "d = len(Tickers)\n",
+    "vec1 = np.linspace(1, 1, d)\n",
+    "sigma = covar\n",
+    "inv_sigma = np.linalg.inv(sigma)\n",
+    "\n",
+    "a = vec1.T.dot(inv_sigma).dot(vec1)\n",
+    "b = mean.T.dot(inv_sigma).dot(vec1)"
+   ],
+   "id": "780c9cca6e0ed2d3",
+   "outputs": [],
+   "execution_count": 56
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:54.545400Z",
+     "start_time": "2024-11-25T13:43:54.541579Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Tangent portfolio\n",
+    "pi_T = inv_sigma.dot(mean - r * vec1) / (b - r * a)\n",
+    "sd_T = np.sqrt(pi_T.T.dot(sigma).dot(pi_T))  # Variance\n",
+    "m_T = pi_T.T.dot(mean)  # expected return\n",
+    "\n",
+    "print(f\"Expected return m_T: {m_T}\")\n",
+    "print(f\"Standard deviation sd_T: {sd_T}\")\n",
+    "print(f\"Allocation pi_T: {pi_T}\")\n",
+    "print(\n",
+    "    f\"We can verify that the allocation is possible as the sum of the allocations for the different indices is {sum(pi_T)}, that is very close to 1\")"
+   ],
+   "id": "81c956f147c68070",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected return m_T: 0.2364033641931515\n",
+      "Standard deviation sd_T: 0.07276528490265963\n",
+      "Allocation pi_T: [-0.60853811  0.45748917  1.17944152 -0.02839259]\n",
+      "We can verify that the allocation is possible as the sum of the allocations for the different indices is 0.9999999999999993, that is very close to 1\n"
+     ]
+    }
+   ],
+   "execution_count": 57
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Question 2",
+   "id": "2e121c2dfb946f3c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:43:59.797115Z",
+     "start_time": "2024-11-25T13:43:59.792462Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "for i in range(len(std)):\n",
+    "    print(f\"The annualized volatilities of the index {Tickers[i]} is {std[i]}\")\n",
+    "    print(f\"The annualized expected returns of the index {Tickers[i]} is {mean[Tickers[i]]}\")\n",
+    "    print(\"\")\n",
+    "\n",
+    "print(f\"The annualized volatility of the Tangent Portfolio is {sd_T * np.sqrt(252)}\")\n",
+    "print(f\"The annualized expected return of the Tangent Portfolio is {m_T * 252}\")"
+   ],
+   "id": "c169808384ca1112",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The annualized volatilities of the index ^RUT is 0.12007221535411407\n",
+      "The annualized expected returns of the index ^RUT is 0.12550141384538263\n",
+      "\n",
+      "The annualized volatilities of the index ^IXIC is 0.09582499431305072\n",
+      "The annualized expected returns of the index ^IXIC is 0.24686267015709437\n",
+      "\n",
+      "The annualized volatilities of the index ^GSPC is 0.06653126757186174\n",
+      "The annualized expected returns of the index ^GSPC is 0.17264098207081371\n",
+      "\n",
+      "The annualized volatilities of the index XWD.TO is 0.08364295296865466\n",
+      "The annualized expected returns of the index XWD.TO is 0.1331750489518068\n",
+      "\n",
+      "The annualized volatility of the Tangent Portfolio is 1.155113087587201\n",
+      "The annualized expected return of the Tangent Portfolio is 59.57364777667418\n"
+     ]
+    }
+   ],
+   "execution_count": 58
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "# Question 3",
+   "id": "af8d29ecdbf2ae1"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-25T13:44:01.393591Z",
+     "start_time": "2024-11-25T13:44:01.388830Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "print(\"sharpe ratio of the Tangent portfolio :\", (m_T - r) / sd_T)\n",
+    "\n",
+    "for i in range(4):\n",
+    "    print(f\"the sharpe ratio of the index {Tickers[i]} is {(mean[Tickers[i]] - r) / std[i]}\")"
+   ],
+   "id": "2e0215ab7904906a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sharpe ratio of the Tangent portfolio : 2.9739918490340687\n",
+      "the sharpe ratio of the index ^RUT is 0.8786496820620858\n",
+      "the sharpe ratio of the index ^IXIC is 2.3674686524473625\n",
+      "the sharpe ratio of the index ^GSPC is 2.294274371158541\n",
+      "the sharpe ratio of the index XWD.TO is 1.353073330567601\n"
+     ]
+    }
+   ],
+   "execution_count": 59
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}