Add requirements.txt for easier setup

notebook/porqua_ui.ipynb

@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "128a655d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Imports and path setup\n",
+    "import sys\n",
+    "import os\n",
+    "sys.path.insert(0, os.path.abspath('../src'))\n",
+    "\n",
+    "import io\n",
+    "import ipywidgets as widgets\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "from optimization import MeanVariance, QEQW, LeastSquares, LAD\n",
+    "from optimization_data import OptimizationData\n",
+    "from constraints import Constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3f148839",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9efc37a43e264d8398f501adef9cbf28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FileUpload(value=(), accept='.csv', description='Upload CSV')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#File upload\n",
+    "uploader = widgets.FileUpload(\n",
+    "    description='Upload CSV',\n",
+    "    accept='.csv',\n",
+    "    multiple=False\n",
+    ")\n",
+    "uploader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8f7540f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded data: 6338 rows, 1 assets\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>NDDLWI</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Index</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>01-01-1999</th>\n",
+       "      <td>-0.000072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>04-01-1999</th>\n",
+       "      <td>0.008686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>05-01-1999</th>\n",
+       "      <td>0.009618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>06-01-1999</th>\n",
+       "      <td>0.020803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>07-01-1999</th>\n",
+       "      <td>-0.003080</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              NDDLWI\n",
+       "Index               \n",
+       "01-01-1999 -0.000072\n",
+       "04-01-1999  0.008686\n",
+       "05-01-1999  0.009618\n",
+       "06-01-1999  0.020803\n",
+       "07-01-1999 -0.003080"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Load and preview data\n",
+    "try:\n",
+    "    uploaded_file = uploader.value[0]\n",
+    "except IndexError:\n",
+    "    print(\"No file uploaded. Please upload a CSV file to proceed.\")\n",
+    "    raise\n",
+    "content = uploaded_file['content']\n",
+    "df = pd.read_csv(io.BytesIO(content), index_col=0, parse_dates=True)\n",
+    "print(f\"Loaded data: {df.shape[0]} rows, {df.shape[1]} assets\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "51cb53e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e6c01f880e84e57a422c630b0af613b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Dropdown(description='Strategy:', options=('Least Squares', 'Weighted Least Squares', 'LAD', 'M…"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Optimisation controls\n",
+    "strategy_dropdown = widgets.Dropdown(\n",
+    "    options=['Least Squares', 'Weighted Least Squares', 'LAD', 'Mean Variance'],\n",
+    "    value='Least Squares',\n",
+    "    description='Strategy:',\n",
+    "    style={'description_width': 'initial'}\n",
+    ")\n",
+    "\n",
+    "n_assets_slider = widgets.IntSlider(\n",
+    "    value=10,\n",
+    "    min=2,\n",
+    "    max=24,\n",
+    "    step=1,\n",
+    "    description='Number of Assets:',\n",
+    "    style={'description_width': 'initial'}\n",
+    ")\n",
+    "\n",
+    "risk_aversion_slider = widgets.FloatSlider(\n",
+    "    value=1.0,\n",
+    "    min=0.1,\n",
+    "    max=5.0,\n",
+    "    step=0.1,\n",
+    "    description='Risk Aversion:',\n",
+    "    style={'description_width': 'initial'}\n",
+    ")\n",
+    "\n",
+    "controls = widgets.VBox([strategy_dropdown, n_assets_slider, risk_aversion_slider])\n",
+    "controls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3521b24e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d356443274274dfea207f5fe92007165",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Button(button_style='success', description='Run Optimisation', icon='check', style=ButtonStyle(…"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Run optimisation and visualise results\n",
+    "\n",
+    "STRATEGY_MAP = {\n",
+    "    'Least Squares': LeastSquares,\n",
+    "    'LAD': LAD,\n",
+    "    'Mean Variance': MeanVariance,\n",
+    "    'Weighted Least Squares': None  # requires extra params, coming soon\n",
+    "}\n",
+    "\n",
+    "def plot_weights(weights_dict):\n",
+    "    \"\"\"Render an interactive Plotly pie chart of portfolio weights.\"\"\"\n",
+    "    filtered = {k: v for k, v in weights_dict.items() if v > 0.001}\n",
+    "    fig = go.Figure(data=[go.Pie(\n",
+    "        labels=list(filtered.keys()),\n",
+    "        values=list(filtered.values()),\n",
+    "        hole=0.3,\n",
+    "        textinfo='label+percent'\n",
+    "    )])\n",
+    "    fig.update_layout(title='Portfolio Allocation', showlegend=True)\n",
+    "    fig.show()\n",
+    "\n",
+    "def on_run_clicked(b):\n",
+    "    with output:\n",
+    "        output.clear_output()\n",
+    "\n",
+    "        strategy_name = strategy_dropdown.value\n",
+    "        StrategyClass = STRATEGY_MAP.get(strategy_name)\n",
+    "\n",
+    "        if StrategyClass is None:\n",
+    "            print(f\"'{strategy_name}' is not yet supported.\")\n",
+    "            return\n",
+    "\n",
+    "        print(f\"Running {strategy_name} optimisation...\")\n",
+    "\n",
+    "        n = n_assets_slider.value\n",
+    "        selection = list(df.columns[:n])\n",
+    "\n",
+    "        opt_data = OptimizationData(\n",
+    "            return_series=df[selection].iloc[1:].astype(float),\n",
+    "            bm_series=df.iloc[1:, 0].astype(float)\n",
+    "        )\n",
+    "\n",
+    "        constraints = Constraints(selection=selection)\n",
+    "        constraints.add_budget()\n",
+    "        constraints.add_box(box_type='LongOnly')\n",
+    "\n",
+    "        if strategy_name == 'Mean Variance':\n",
+    "            model = StrategyClass(risk_aversion=risk_aversion_slider.value, constraints=constraints)\n",
+    "        else:\n",
+    "            model = StrategyClass(constraints=constraints)\n",
+    "\n",
+    "        model.set_objective(opt_data)\n",
+    "        model.solve()\n",
+    "\n",
+    "        weights = model.results['weights']\n",
+    "        print(\"\\nPortfolio Weights:\")\n",
+    "        for asset, w in weights.items():\n",
+    "            print(f\"  {asset}: {w:.4f}\")\n",
+    "\n",
+    "        plot_weights(weights)\n",
+    "\n",
+    "run_button = widgets.Button(\n",
+    "    description='Run Optimisation',\n",
+    "    button_style='success',\n",
+    "    icon='check'\n",
+    ")\n",
+    "output = widgets.Output()\n",
+    "run_button.on_click(on_run_clicked)\n",
+    "widgets.VBox([run_button, output])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6789c89d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.10.5)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+pandas
+scipy
+matplotlib
+qpsolvers