Refactor code for improved readability and consistency across multiple Jupyter notebooks

- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.

M1/Portfolio Management/TP-Project.ipynb

@@ -27,9 +27,10 @@
    },
    "outputs": [],
    "source": [
+    "import yfinance as yf\n",
+    "\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
-    "import yfinance as yf"
+    "import pandas as pd"
    ]
   },
   {
@@ -406,7 +407,7 @@
     "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\"\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\",\n",
     ")"
    ]
   },
@@ -452,9 +453,9 @@
     "for i in range(len(std)):\n",
     "    print(f\"The annualized volatilities of the index {Tickers[i]} is {std[i]}\")\n",
     "    print(\n",
-    "        f\"The annualized expected returns of the index {Tickers[i]} is {mean[Tickers[i]]}\"\n",
+    "        f\"The annualized expected returns of the index {Tickers[i]} is {mean[Tickers[i]]}\",\n",
     "    )\n",
-    "    print(\"\")\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}\")"
@@ -494,7 +495,7 @@
     "\n",
     "for i in range(4):\n",
     "    print(\n",
-    "        f\"the sharpe ratio of the index {Tickers[i]} is {(mean[Tickers[i]] - r) / std[i]}\"\n",
+    "        f\"the sharpe ratio of the index {Tickers[i]} is {(mean[Tickers[i]] - r) / std[i]}\",\n",
     "    )"
    ]
   }

M1/Portfolio Management/TP1.ipynb

@@ -13,9 +13,10 @@
    },
    "outputs": [],
    "source": [
+    "import yfinance as yf\n",
+    "\n",
     "import numpy as np\n",
-    "import pandas as pd\n",
-    "import yfinance as yf"
+    "import pandas as pd"
    ]
   },
   {
@@ -530,7 +531,7 @@
     "\n",
     "# Self financing portfolio\n",
     "m_w = np.sqrt(\n",
-    "    (mean - b / a * vec1).T.dot(inv_sigma).dot(mean - b / a * vec1)\n",
+    "    (mean - b / a * vec1).T.dot(inv_sigma).dot(mean - b / a * vec1),\n",
     ")  # Expected return\n",
     "\n",
     "# Tangent portfolio\n",
@@ -580,7 +581,7 @@
     "range_sup = np.max(mean) + 1\n",
     "y = np.linspace(range_inf, range_sup, 50)\n",
     "x_1 = np.array(\n",
-    "    [np.sqrt(((y - m_a) / m_w) ** 2 + sd_a**2)]\n",
+    "    [np.sqrt(((y - m_a) / m_w) ** 2 + sd_a**2)],\n",
     ")  # Sigma values for the frontier\n",
     "x_2 = np.array([(y - r) / (m_T - r) * sd_T])  # Sigma values for the Capital Market Line\n",
     "\n",