Merge pull request #10 from bhanuprasanna527/shyam

.devcontainer/devcontainer.json

@@ -1,33 +0,0 @@
-{
-  "name": "Python 3",
-  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
-  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
-  "customizations": {
-    "codespaces": {
-      "openFiles": [
-        "README.md",
-        "main.py"
-      ]
-    },
-    "vscode": {
-      "settings": {},
-      "extensions": [
-        "ms-python.python",
-        "ms-python.vscode-pylance"
-      ]
-    }
-  },
-  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
-  "postAttachCommand": {
-    "server": "streamlit run main.py --server.enableCORS false --server.enableXsrfProtection false"
-  },
-  "portsAttributes": {
-    "8501": {
-      "label": "Application",
-      "onAutoForward": "openPreview"
-    }
-  },
-  "forwardPorts": [
-    8501
-  ]
-}

.gitignore

@@ -1,5 +1,7 @@
-# Object file
 *.o
-
-# Ada Library Information
 *.ali
+.devcontainer
+.idea
+.ipynb_checkpoints
+__pycache__
+app.py

.idea/.gitignore

@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml

.idea/CapiPort.iml

@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="data_science" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="format" value="GOOGLE" />
-    <option name="myDocStringFormat" value="Google" />
-  </component>
-</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

.idea/misc.xml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="data_science" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="data_science" project-jdk-type="Python SDK" />
-</project>

.idea/modules.xml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/CapiPort.iml" filepath="$PROJECT_DIR$/.idea/CapiPort.iml" />
-    </modules>
-  </component>
-</project>

.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="" vcs="Git" />
-  </component>
-</project>

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -1,95 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "621c09fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bs4 import BeautifulSoup\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Read HTML content from the file\n",
-    "with open(\"index.html\", \"r\", encoding=\"utf-8\") as file:\n",
-    "    html_content = file.read()\n",
-    "\n",
-    "# Parse the HTML content\n",
-    "soup = BeautifulSoup(html_content, \"html.parser\")\n",
-    "\n",
-    "# Find the table\n",
-    "table = soup.find(\"table\")\n",
-    "\n",
-    "# Extract table data\n",
-    "if table:\n",
-    "    rows = table.find_all(\"tr\")\n",
-    "    data = []\n",
-    "    for row in rows:\n",
-    "        columns = row.find_all(\"td\")\n",
-    "        if columns:  # Ensure it's not a header row or empty row\n",
-    "            row_data = [column.text.strip() for column in columns]\n",
-    "            data.append(row_data)\n",
-    "    # Create DataFrame\n",
-    "    df = pd.DataFrame(data)\n",
-    "    \n",
-    "    # Extract first part and last word from the first column\n",
-    "    df['Name'] = df[0].str.split().str[:-1].str.join(\" \")\n",
-    "    df['Ticker'] = df[0].str.split().str[-1]\n",
-    "    df[['Name', 'Ticker']].to_csv(\"Company List.csv\")\n",
-    "else:\n",
-    "    print(\"Table not found in the HTML content.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "38efd2c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'c' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mc\u001b[49m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'c' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "print(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c185203",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

Notebooks/MAexp.py

@@ -0,0 +1,183 @@
+import pandas as pd
+import numpy as np
+import yfinance as yf
+import streamlit as st
+import plotly.graph_objects as go
+import time
+from utilities import checker
+import datetime
+
+with open(r"../style/style.css") as css:
+    st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
+
+st.markdown(
+    "<h1 style='text-align: center;'><u>CapiPort</u></h1>", unsafe_allow_html=True
+)
+
+st.markdown(
+    "<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>",
+    unsafe_allow_html=True,
+)
+st.header(
+    "",
+    divider="rainbow",
+)
+
+color = "Quest"
+st.markdown(
+    "<h1 style='text-align: center;'>🔍 Quest for financial excellence begins with meticulous portfolio optimization</u></h1>",
+    unsafe_allow_html=True,
+)
+
+st.header(
+    "",
+    divider="rainbow",
+)
+
+list_df = pd.read_csv("../Data/Company List.csv")
+
+company_name = list_df["Name"].to_list()
+company_symbol = (list_df["Ticker"] + ".NS").to_list()
+
+company_dict = dict()
+company_symbol_dict = dict()
+
+for CSymbol, CName in zip(company_symbol, company_name):
+    company_dict[CName] = CSymbol
+
+for CSymbol, CName in zip(company_symbol, company_name):
+    company_symbol_dict[CSymbol] = CName
+
+st.markdown(
+    """ 
+    <style>
+        .big-font {
+        font-size:20px;
+        }
+    </style>""",
+    unsafe_allow_html=True,
+)
+
+st.markdown('<p class="big-font">Select Multiple Companies</p>', unsafe_allow_html=True)
+
+com_sel_name = st.multiselect("", company_name, default=None)
+com_sel_date = []
+
+for i in com_sel_name:
+    d = st.date_input(
+        f"On which date did you invested in - {i}",
+        value= pd.Timestamp('2021-01-01'),
+        format="YYYY-MM-DD",
+    )
+    d = d - datetime.timedelta(days=3)
+    com_sel_date.append(d)
+
+com_sel = [company_dict[i] for i in com_sel_name]
+
+num_tick = len(com_sel)
+
+
+if num_tick > 1:
+    com_data = pd.DataFrame()
+    for cname, cdate in zip(com_sel, com_sel_date):
+        stock_data_temp = yf.download(cname, start=cdate, end=pd.Timestamp.now().strftime('%Y-%m-%d'))['Low']
+        stock_data_temp.name = cname
+        com_data = pd.merge(com_data, stock_data_temp, how="outer", right_index=True, left_index=True)
+    for i in com_data.columns:
+        com_data.dropna(axis=1, how='all', inplace=True)
+    # com_data.dropna(inplace=True)
+    num_tick = len(com_data.columns)
+
+    # Dataframe of the selected companies
+    st.dataframe(com_data, use_container_width=True)
+
+    # make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company
+    def moving_average(data, window):
+        ma = {}
+        for i in data.columns:
+            ma[i] = data[i].rolling(window=window).mean().values[2]
+        return ma
+
+    moving_avg = moving_average(com_data, 3)
+    MA_df = pd.DataFrame(moving_avg.items(), columns=['Company', 'Purchase Rate (MA)'])
+
+    # calculate percentage return till present date from the moving average price of the stock
+    def percentage_return(data, moving_avg):
+        pr = {}
+        for i in data.columns:
+            pr[i] = f'{round(((data[i].values[-1] - moving_avg[i]) / moving_avg[i]) * 100,2) }%'
+        return pr
+    
+    # make percentage return a dataframe from dictionary
+    percentage_return = pd.DataFrame(percentage_return(com_data, moving_avg).items(), columns=['Company', 'Percentage Return'])
+
+    #merge MA_df and percentage_return on "Company" columns
+    MA_df = pd.merge(MA_df, percentage_return, on='Company')
+
+    st.markdown(
+            "<h5 style='text-align: center;'>Percent Returns & MA price</h5>",
+            unsafe_allow_html=True,
+        )
+
+    st.write("<p style='text-align: center;'>**rate of purchase is moving average(MA) of 3 (t+2) days</p>", unsafe_allow_html=True) 
+    st.dataframe(MA_df,use_container_width=True)
+
+    if num_tick > 1:
+        com_sel_name_temp = []
+        for i in com_data.columns:
+            com_sel_name_temp.append(company_symbol_dict[i])
+        com_sel = com_data.columns.to_list()
+        
+
+        ## Log-Return of Company Dataset
+        log_return = np.log(1 + com_data.pct_change())
+
+        ## Generate Random Weights
+        rand_weig = np.array(np.random.random(num_tick))
+
+        ## Rebalancing Random Weights
+        rebal_weig = rand_weig / np.sum(rand_weig)
+
+        ## Calculate the Expected Returns, Annualize it by * 252.0
+        exp_ret = np.sum((log_return.mean() * rebal_weig) * 252)
+
+        ## Calculate the Expected Volatility, Annualize it by * 252.0
+        exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 252, rebal_weig)))
+
+        ## Calculate the Sharpe Ratio.
+        sharpe_ratio = exp_ret / exp_vol
+
+        # Put the weights into a data frame to see them better.
+        weights_df = pd.DataFrame(
+            data={
+                "company_name": com_sel_name_temp,
+                "random_weights": rand_weig,
+                "rebalance_weights": rebal_weig,
+            }
+        )
+
+        st.divider()
+
+        st.markdown(
+            "<h5 style='text-align: center;'>Random Portfolio Weights</h5>",
+            unsafe_allow_html=True,
+        )
+        st.dataframe(weights_df, use_container_width=True)
+
+        # Do the same with the other metrics.
+        metrics_df = pd.DataFrame(
+            data={
+                "Expected Portfolio Returns": exp_ret,
+                "Expected Portfolio Volatility": exp_vol,
+                "Portfolio Sharpe Ratio": sharpe_ratio,
+            },
+            index=[0],
+        )
+
+        st.markdown(
+            "<h5 style='text-align: center;'>Random Weights Metrics</h5>",
+            unsafe_allow_html=True,
+        )
+        st.dataframe(metrics_df, use_container_width=True)
+
+        st.divider()

Notebooks/Untitled.ipynb

@@ -86,8 +86,92 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "                                0             1           2            3  \\\ncount                        2062          2062        2062         2062   \nunique                       2062          2044        2060         2056   \ntop     20 Microns Ltd. 20MICRONS  ₹1.14 -4.16%  ₹8.8/₹3.79  ₹128.05 Crs   \nfreq                            1             4           2            2   \n\n                      4                 Name     Ticker  \ncount              2062                 2062       2062  \nunique              125                 2061       2062  \ntop     Pharmaceuticals  Gallantt Ispat Ltd.  20MICRONS  \nfreq                105                    2          1  ",
-      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>Name</th>\n      <th>Ticker</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2062</td>\n      <td>2062</td>\n      <td>2062</td>\n      <td>2062</td>\n      <td>2062</td>\n      <td>2062</td>\n      <td>2062</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>2062</td>\n      <td>2044</td>\n      <td>2060</td>\n      <td>2056</td>\n      <td>125</td>\n      <td>2061</td>\n      <td>2062</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>20 Microns Ltd. 20MICRONS</td>\n      <td>₹1.14 -4.16%</td>\n      <td>₹8.8/₹3.79</td>\n      <td>₹128.05 Crs</td>\n      <td>Pharmaceuticals</td>\n      <td>Gallantt Ispat Ltd.</td>\n      <td>20MICRONS</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>1</td>\n      <td>4</td>\n      <td>2</td>\n      <td>2</td>\n      <td>105</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Ticker</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2062</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>2062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>2062</td>\n",
+       "      <td>2044</td>\n",
+       "      <td>2060</td>\n",
+       "      <td>2056</td>\n",
+       "      <td>125</td>\n",
+       "      <td>2061</td>\n",
+       "      <td>2062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>20 Microns Ltd. 20MICRONS</td>\n",
+       "      <td>₹1.14 -4.16%</td>\n",
+       "      <td>₹8.8/₹3.79</td>\n",
+       "      <td>₹128.05 Crs</td>\n",
+       "      <td>Pharmaceuticals</td>\n",
+       "      <td>Gallantt Ispat Ltd.</td>\n",
+       "      <td>20MICRONS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>105</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                0             1           2            3  \\\n",
+       "count                        2062          2062        2062         2062   \n",
+       "unique                       2062          2044        2060         2056   \n",
+       "top     20 Microns Ltd. 20MICRONS  ₹1.14 -4.16%  ₹8.8/₹3.79  ₹128.05 Crs   \n",
+       "freq                            1             4           2            2   \n",
+       "\n",
+       "                      4                 Name     Ticker  \n",
+       "count              2062                 2062       2062  \n",
+       "unique              125                 2061       2062  \n",
+       "top     Pharmaceuticals  Gallantt Ispat Ltd.  20MICRONS  \n",
+       "freq                105                    2          1  "
+      ]
      },
      "execution_count": 4,
      "metadata": {},
@@ -111,7 +195,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "2062"
+      "text/plain": [
+       "2062"
+      ]
      },
      "execution_count": 5,
      "metadata": {},
@@ -150,8 +236,68 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "                                                     0               1  \\\n610  Gallantt Ispat Ltd.\\n                         ...   ₹64.15 -0.77%   \n611  Gallantt Ispat Ltd.\\n                         ...  ₹216.94 +1.33%   \n\n                 2             3             4                 Name     Ticker  \n610     ₹76/₹44.64  ₹1807.11 Crs  Iron & Steel  Gallantt Ispat Ltd.  GALLISPAT  \n611  ₹236.4/₹49.54   ₹5235.8 Crs  Iron & Steel  Gallantt Ispat Ltd.   GALLANTT  ",
-      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>Name</th>\n      <th>Ticker</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>610</th>\n      <td>Gallantt Ispat Ltd.\\n                         ...</td>\n      <td>₹64.15 -0.77%</td>\n      <td>₹76/₹44.64</td>\n      <td>₹1807.11 Crs</td>\n      <td>Iron &amp; Steel</td>\n      <td>Gallantt Ispat Ltd.</td>\n      <td>GALLISPAT</td>\n    </tr>\n    <tr>\n      <th>611</th>\n      <td>Gallantt Ispat Ltd.\\n                         ...</td>\n      <td>₹216.94 +1.33%</td>\n      <td>₹236.4/₹49.54</td>\n      <td>₹5235.8 Crs</td>\n      <td>Iron &amp; Steel</td>\n      <td>Gallantt Ispat Ltd.</td>\n      <td>GALLANTT</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Ticker</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>610</th>\n",
+       "      <td>Gallantt Ispat Ltd.\\n                         ...</td>\n",
+       "      <td>₹64.15 -0.77%</td>\n",
+       "      <td>₹76/₹44.64</td>\n",
+       "      <td>₹1807.11 Crs</td>\n",
+       "      <td>Iron &amp; Steel</td>\n",
+       "      <td>Gallantt Ispat Ltd.</td>\n",
+       "      <td>GALLISPAT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>611</th>\n",
+       "      <td>Gallantt Ispat Ltd.\\n                         ...</td>\n",
+       "      <td>₹216.94 +1.33%</td>\n",
+       "      <td>₹236.4/₹49.54</td>\n",
+       "      <td>₹5235.8 Crs</td>\n",
+       "      <td>Iron &amp; Steel</td>\n",
+       "      <td>Gallantt Ispat Ltd.</td>\n",
+       "      <td>GALLANTT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     0               1  \\\n",
+       "610  Gallantt Ispat Ltd.\\n                         ...   ₹64.15 -0.77%   \n",
+       "611  Gallantt Ispat Ltd.\\n                         ...  ₹216.94 +1.33%   \n",
+       "\n",
+       "                 2             3             4                 Name     Ticker  \n",
+       "610     ₹76/₹44.64  ₹1807.11 Crs  Iron & Steel  Gallantt Ispat Ltd.  GALLISPAT  \n",
+       "611  ₹236.4/₹49.54   ₹5235.8 Crs  Iron & Steel  Gallantt Ispat Ltd.   GALLANTT  "
+      ]
      },
      "execution_count": 7,
      "metadata": {},
@@ -179,12 +325,13 @@
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [],
+   "execution_count": null,
+   "id": "e9274e3c3011e6fc",
    "metadata": {
     "collapsed": false
    },
-   "id": "e9274e3c3011e6fc"
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -203,7 +350,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

Notebooks/movingaveragesexp.ipynb

@@ -0,0 +1,607 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "11ae7d38-5af8-4b51-91d4-a3fcde0eb00b",
+   "metadata": {},
+   "source": [
+    "# Trial 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9e628f09-b78e-4737-8b97-227901cf61c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-03-11 19:43:50.457 `label` got an empty value. This is discouraged for accessibility reasons and may be disallowed in the future by raising an exception. Please provide a non-empty label and hide it with label_visibility if needed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import yfinance as yf\n",
+    "import streamlit as st\n",
+    "import plotly.graph_objects as go\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "with open(r\"../style/style.css\") as css:\n",
+    "    st.markdown(f\"<style>{css.read()}</style>\", unsafe_allow_html=True)\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"<h1 style='text-align: center;'><u>CapiPort</u></h1>\", unsafe_allow_html=True\n",
+    ")\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "st.header(\n",
+    "    \"\",\n",
+    "    divider=\"rainbow\",\n",
+    ")\n",
+    "\n",
+    "color = \"Quest\"\n",
+    "st.markdown(\n",
+    "    \"<h1 style='text-align: center;'>🔍 Quest for financial excellence begins with meticulous portfolio optimization</u></h1>\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "\n",
+    "st.header(\n",
+    "    \"\",\n",
+    "    divider=\"rainbow\",\n",
+    ")\n",
+    "\n",
+    "list_df = pd.read_csv(\"../Data/Company List.csv\")\n",
+    "\n",
+    "company_name = list_df[\"Name\"].to_list()\n",
+    "company_symbol = (list_df[\"Ticker\"] + \".NS\").to_list()\n",
+    "\n",
+    "company_dict = dict()\n",
+    "company_symbol_dict = dict()\n",
+    "\n",
+    "for CSymbol, CName in zip(company_symbol, company_name):\n",
+    "    company_dict[CName] = CSymbol\n",
+    "\n",
+    "for CSymbol, CName in zip(company_symbol, company_name):\n",
+    "    company_symbol_dict[CSymbol] = CName\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"\"\" \n",
+    "    <style>\n",
+    "        .big-font {\n",
+    "        font-size:20px;\n",
+    "        }\n",
+    "    </style>\"\"\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "\n",
+    "st.markdown('<p class=\"big-font\">Select Multiple Companies</p>', unsafe_allow_html=True)\n",
+    "\n",
+    "com_sel_name = st.multiselect(\"\", company_name, default=None)\n",
+    "com_sel_date = []\n",
+    "\n",
+    "for i in com_sel_name:\n",
+    "    d = st.date_input(\n",
+    "        f\"On which date did you invested in - {i}\",\n",
+    "        value= pd.Timestamp('2021-01-01'),\n",
+    "        format=\"YYYY-MM-DD\",\n",
+    "    )\n",
+    "    d = d - datetime.timedelta(days=3)\n",
+    "    com_sel_date.append(d)\n",
+    "\n",
+    "com_sel = [company_dict[i] for i in com_sel_name]\n",
+    "\n",
+    "num_tick = len(com_sel)\n",
+    "\n",
+    "if num_tick > 1:\n",
+    "    com_data = pd.DataFrame()\n",
+    "    for cname, cdate in zip(com_sel, com_sel_date):\n",
+    "        stock_data_temp = yf.download(cname, start=cdate, end=pd.Timestamp.now().strftime('%Y-%m-%d'))['Low']\n",
+    "        stock_data_temp.name = cname\n",
+    "        com_data = pd.merge(com_data, stock_data_temp, how=\"outer\", right_index=True, left_index=True)\n",
+    "    for i in com_data.columns:\n",
+    "        com_data.dropna(axis=1, how='all', inplace=True)\n",
+    "    # com_data.dropna(inplace=True)\n",
+    "    num_tick = len(com_data.columns)\n",
+    "\n",
+    "    # make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company\n",
+    "    def moving_average(data, window):\n",
+    "        ma = {}\n",
+    "        for i in data.columns:\n",
+    "            ma[i] = data[i].rolling(window=window).mean().values[2]\n",
+    "        return ma\n",
+    "\n",
+    "    st.write('your average rate of purchase for stock with a moving average of 3 days is:')    \n",
+    "    moving_avg = moving_average(com_data, 3)\n",
+    "    st.write(moving_avg)\n",
+    "\n",
+    "\n",
+    "\n",
+    "    if num_tick > 1:\n",
+    "        com_sel_name_temp = []\n",
+    "        for i in com_data.columns:\n",
+    "            com_sel_name_temp.append(company_symbol_dict[i])\n",
+    "        print(com_sel_name_temp)\n",
+    "        print(com_data)\n",
+    "        \n",
+    "        com_sel = com_data.columns.to_list()\n",
+    "        print(com_sel)\n",
+    "        \n",
+    "        st.dataframe(com_data, use_container_width=True)\n",
+    "\n",
+    "        ## Log-Return of Company Dataset\n",
+    "        log_return = np.log(1 + com_data.pct_change())\n",
+    "\n",
+    "        ## Generate Random Weights\n",
+    "        rand_weig = np.array(np.random.random(num_tick))\n",
+    "\n",
+    "        ## Rebalancing Random Weights\n",
+    "        rebal_weig = rand_weig / np.sum(rand_weig)\n",
+    "\n",
+    "        ## Calculate the Expected Returns, Annualize it by * 252.0\n",
+    "        exp_ret = np.sum((log_return.mean() * rebal_weig) * 252)\n",
+    "\n",
+    "        ## Calculate the Expected Volatility, Annualize it by * 252.0\n",
+    "        exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 252, rebal_weig)))\n",
+    "\n",
+    "        ## Calculate the Sharpe Ratio.\n",
+    "        sharpe_ratio = exp_ret / exp_vol\n",
+    "\n",
+    "        # Put the weights into a data frame to see them better.\n",
+    "        weights_df = pd.DataFrame(\n",
+    "            data={\n",
+    "                \"company_name\": com_sel_name_temp,\n",
+    "                \"random_weights\": rand_weig,\n",
+    "                \"rebalance_weights\": rebal_weig,\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "        st.divider()\n",
+    "\n",
+    "        st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Random Portfolio Weights</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "        st.dataframe(weights_df, use_container_width=True)\n",
+    "\n",
+    "        # Do the same with the other metrics.\n",
+    "        metrics_df = pd.DataFrame(\n",
+    "            data={\n",
+    "                \"Expected Portfolio Returns\": exp_ret,\n",
+    "                \"Expected Portfolio Volatility\": exp_vol,\n",
+    "                \"Portfolio Sharpe Ratio\": sharpe_ratio,\n",
+    "            },\n",
+    "            index=[0],\n",
+    "        )\n",
+    "\n",
+    "        st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Random Weights Metrics</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "        st.dataframe(metrics_df, use_container_width=True)\n",
+    "\n",
+    "        st.divider()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eed54a79-e2b6-4bba-b4fc-9c16f9c225d2",
+   "metadata": {},
+   "source": [
+    "# Trial 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b936aa3-324e-4695-9059-a7d25efe2754",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import yfinance as yf\n",
+    "import streamlit as st\n",
+    "import plotly.graph_objects as go\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "with open(r\"../style/style.css\") as css:\n",
+    "    st.markdown(f\"<style>{css.read()}</style>\", unsafe_allow_html=True)\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"<h1 style='text-align: center;'><u>CapiPort</u></h1>\", unsafe_allow_html=True\n",
+    ")\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "st.header(\n",
+    "    \"\",\n",
+    "    divider=\"rainbow\",\n",
+    ")\n",
+    "\n",
+    "color = \"Quest\"\n",
+    "st.markdown(\n",
+    "    \"<h1 style='text-align: center;'>🔍 Quest for financial excellence begins with meticulous portfolio optimization</u></h1>\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "\n",
+    "st.header(\n",
+    "    \"\",\n",
+    "    divider=\"rainbow\",\n",
+    ")\n",
+    "\n",
+    "list_df = pd.read_csv(\"../Data/Company List.csv\")\n",
+    "\n",
+    "company_name = list_df[\"Name\"].to_list()\n",
+    "company_symbol = (list_df[\"Ticker\"] + \".NS\").to_list()\n",
+    "\n",
+    "company_dict = dict()\n",
+    "company_symbol_dict = dict()\n",
+    "\n",
+    "for CSymbol, CName in zip(company_symbol, company_name):\n",
+    "    company_dict[CName] = CSymbol\n",
+    "\n",
+    "for CSymbol, CName in zip(company_symbol, company_name):\n",
+    "    company_symbol_dict[CSymbol] = CName\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"\"\" \n",
+    "    <style>\n",
+    "        .big-font {\n",
+    "        font-size:20px;\n",
+    "        }\n",
+    "    </style>\"\"\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "\n",
+    "st.markdown('<p class=\"big-font\">Select Multiple Companies</p>', unsafe_allow_html=True)\n",
+    "\n",
+    "com_sel_name = st.multiselect(\"\", company_name, default=None)\n",
+    "com_sel_date = []\n",
+    "\n",
+    "for i in com_sel_name:\n",
+    "    d = st.date_input(\n",
+    "        f\"On which date did you invested in - {i}\",\n",
+    "        value= pd.Timestamp('2021-01-01'),\n",
+    "        format=\"YYYY-MM-DD\",\n",
+    "    )\n",
+    "    d = d - datetime.timedelta(days=3)\n",
+    "    com_sel_date.append(d)\n",
+    "\n",
+    "com_sel = [company_dict[i] for i in com_sel_name]\n",
+    "\n",
+    "num_tick = len(com_sel)\n",
+    "\n",
+    "if num_tick > 1:\n",
+    "    com_data = pd.DataFrame()\n",
+    "    for cname, cdate in zip(com_sel, com_sel_date):\n",
+    "        stock_data_temp = yf.download(cname, start=cdate, end=pd.Timestamp.now().strftime('%Y-%m-%d'))['Low']\n",
+    "        stock_data_temp.name = cname\n",
+    "        com_data = pd.merge(com_data, stock_data_temp, how=\"outer\", right_index=True, left_index=True)\n",
+    "    for i in com_data.columns:\n",
+    "        com_data.dropna(axis=1, how='all', inplace=True)\n",
+    "    # com_data.dropna(inplace=True)\n",
+    "    num_tick = len(com_data.columns)\n",
+    "\n",
+    "    # Dataframe of the selected companies\n",
+    "    st.dataframe(com_data, use_container_width=True)\n",
+    "\n",
+    "    # make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company\n",
+    "    def moving_average(data, window):\n",
+    "        ma = {}\n",
+    "        for i in data.columns:\n",
+    "            ma[i] = data[i].rolling(window=window).mean().values[2]\n",
+    "        return ma\n",
+    "\n",
+    "    st.write('your average rate of purchase for stock with a moving average of 3 (t+2) days is:')    \n",
+    "    moving_avg = moving_average(com_data, 3)\n",
+    "    st.write(moving_avg)\n",
+    "\n",
+    "    # calculate percentage return till present date from the moving average price of the stock\n",
+    "    def percentage_return(data, moving_avg):\n",
+    "        pr = {}\n",
+    "        for i in data.columns:\n",
+    "            pr[i] = f'{round(((data[i].values[-1] - moving_avg[i]) / moving_avg[i]) * 100,2) }%'\n",
+    "        return pr\n",
+    "    \n",
+    "    # make percentage return a dataframe from dictionary\n",
+    "    percentage_return = pd.DataFrame(percentage_return(com_data, moving_avg).items(), columns=['Company', 'Percentage Return'])\n",
+    "    st.write('your percentage return till present date from the moving average price of the stock is:')\n",
+    "    st.write(percentage_return)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    if num_tick > 1:\n",
+    "        com_sel_name_temp = []\n",
+    "        for i in com_data.columns:\n",
+    "            com_sel_name_temp.append(company_symbol_dict[i])\n",
+    "        com_sel = com_data.columns.to_list()\n",
+    "        \n",
+    "\n",
+    "        ## Log-Return of Company Dataset\n",
+    "        log_return = np.log(1 + com_data.pct_change())\n",
+    "\n",
+    "        ## Generate Random Weights\n",
+    "        rand_weig = np.array(np.random.random(num_tick))\n",
+    "\n",
+    "        ## Rebalancing Random Weights\n",
+    "        rebal_weig = rand_weig / np.sum(rand_weig)\n",
+    "\n",
+    "        ## Calculate the Expected Returns, Annualize it by * 252.0\n",
+    "        exp_ret = np.sum((log_return.mean() * rebal_weig) * 252)\n",
+    "\n",
+    "        ## Calculate the Expected Volatility, Annualize it by * 252.0\n",
+    "        exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 252, rebal_weig)))\n",
+    "\n",
+    "        ## Calculate the Sharpe Ratio.\n",
+    "        sharpe_ratio = exp_ret / exp_vol\n",
+    "\n",
+    "        # Put the weights into a data frame to see them better.\n",
+    "        weights_df = pd.DataFrame(\n",
+    "            data={\n",
+    "                \"company_name\": com_sel_name_temp,\n",
+    "                \"random_weights\": rand_weig,\n",
+    "                \"rebalance_weights\": rebal_weig,\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "        st.divider()\n",
+    "\n",
+    "        st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Random Portfolio Weights</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "        st.dataframe(weights_df, use_container_width=True)\n",
+    "\n",
+    "        # Do the same with the other metrics.\n",
+    "        metrics_df = pd.DataFrame(\n",
+    "            data={\n",
+    "                \"Expected Portfolio Returns\": exp_ret,\n",
+    "                \"Expected Portfolio Volatility\": exp_vol,\n",
+    "                \"Portfolio Sharpe Ratio\": sharpe_ratio,\n",
+    "            },\n",
+    "            index=[0],\n",
+    "        )\n",
+    "\n",
+    "        st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Random Weights Metrics</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "        st.dataframe(metrics_df, use_container_width=True)\n",
+    "\n",
+    "        st.divider()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1599354f-fd00-4312-be42-0ae156540f9b",
+   "metadata": {},
+   "source": [
+    "# Trial 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4777b2e7-da34-4a68-83cd-984850734708",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import yfinance as yf\n",
+    "import streamlit as st\n",
+    "import plotly.graph_objects as go\n",
+    "import time\n",
+    "import datetime\n",
+    "\n",
+    "with open(r\"../style/style.css\") as css:\n",
+    "    st.markdown(f\"<style>{css.read()}</style>\", unsafe_allow_html=True)\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"<h1 style='text-align: center;'><u>CapiPort</u></h1>\", unsafe_allow_html=True\n",
+    ")\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"<h5 style='text-align: center; color: gray;'>Your Portfolio Optimisation Tool</h5>\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "st.header(\n",
+    "    \"\",\n",
+    "    divider=\"rainbow\",\n",
+    ")\n",
+    "\n",
+    "color = \"Quest\"\n",
+    "st.markdown(\n",
+    "    \"<h1 style='text-align: center;'>🔍 Quest for financial excellence begins with meticulous portfolio optimization</u></h1>\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "\n",
+    "st.header(\n",
+    "    \"\",\n",
+    "    divider=\"rainbow\",\n",
+    ")\n",
+    "\n",
+    "list_df = pd.read_csv(\"../Data/Company List.csv\")\n",
+    "\n",
+    "company_name = list_df[\"Name\"].to_list()\n",
+    "company_symbol = (list_df[\"Ticker\"] + \".NS\").to_list()\n",
+    "\n",
+    "company_dict = dict()\n",
+    "company_symbol_dict = dict()\n",
+    "\n",
+    "for CSymbol, CName in zip(company_symbol, company_name):\n",
+    "    company_dict[CName] = CSymbol\n",
+    "\n",
+    "for CSymbol, CName in zip(company_symbol, company_name):\n",
+    "    company_symbol_dict[CSymbol] = CName\n",
+    "\n",
+    "st.markdown(\n",
+    "    \"\"\" \n",
+    "    <style>\n",
+    "        .big-font {\n",
+    "        font-size:20px;\n",
+    "        }\n",
+    "    </style>\"\"\",\n",
+    "    unsafe_allow_html=True,\n",
+    ")\n",
+    "\n",
+    "st.markdown('<p class=\"big-font\">Select Multiple Companies</p>', unsafe_allow_html=True)\n",
+    "\n",
+    "com_sel_name = st.multiselect(\"\", company_name, default=None)\n",
+    "com_sel_date = []\n",
+    "\n",
+    "for i in com_sel_name:\n",
+    "    d = st.date_input(\n",
+    "        f\"On which date did you invested in - {i}\",\n",
+    "        value= pd.Timestamp('2021-01-01'),\n",
+    "        format=\"YYYY-MM-DD\",\n",
+    "    )\n",
+    "    d = d - datetime.timedelta(days=3)\n",
+    "    com_sel_date.append(d)\n",
+    "\n",
+    "com_sel = [company_dict[i] for i in com_sel_name]\n",
+    "\n",
+    "num_tick = len(com_sel)\n",
+    "\n",
+    "if num_tick > 1:\n",
+    "    com_data = pd.DataFrame()\n",
+    "    for cname, cdate in zip(com_sel, com_sel_date):\n",
+    "        stock_data_temp = yf.download(cname, start=cdate, end=pd.Timestamp.now().strftime('%Y-%m-%d'))['Low']\n",
+    "        stock_data_temp.name = cname\n",
+    "        com_data = pd.merge(com_data, stock_data_temp, how=\"outer\", right_index=True, left_index=True)\n",
+    "    for i in com_data.columns:\n",
+    "        com_data.dropna(axis=1, how='all', inplace=True)\n",
+    "    # com_data.dropna(inplace=True)\n",
+    "    num_tick = len(com_data.columns)\n",
+    "\n",
+    "    # Dataframe of the selected companies\n",
+    "    st.dataframe(com_data, use_container_width=True)\n",
+    "\n",
+    "    # make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company\n",
+    "    def moving_average(data, window):\n",
+    "        ma = {}\n",
+    "        for i in data.columns:\n",
+    "            ma[i] = data[i].rolling(window=window).mean().values[2]\n",
+    "        return ma\n",
+    "\n",
+    "    moving_avg = moving_average(com_data, 3)\n",
+    "    MA_df = pd.DataFrame(moving_avg.items(), columns=['Company', 'Purchase Rate (MA)'])\n",
+    "\n",
+    "    # calculate percentage return till present date from the moving average price of the stock\n",
+    "    def percentage_return(data, moving_avg):\n",
+    "        pr = {}\n",
+    "        for i in data.columns:\n",
+    "            pr[i] = f'{round(((data[i].values[-1] - moving_avg[i]) / moving_avg[i]) * 100,2) }%'\n",
+    "        return pr\n",
+    "    \n",
+    "    # make percentage return a dataframe from dictionary\n",
+    "    percentage_return = pd.DataFrame(percentage_return(com_data, moving_avg).items(), columns=['Company', 'Percentage Return'])\n",
+    "\n",
+    "    #merge MA_df and percentage_return on \"Company\" columns\n",
+    "    MA_df = pd.merge(MA_df, percentage_return, on='Company')\n",
+    "\n",
+    "    st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Percent Returns & MA price</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "\n",
+    "    st.write(\"<p style='text-align: center;'>**rate of purchase is moving average(MA) of 3 (t+2) days</p>\", unsafe_allow_html=True) \n",
+    "    st.write(MA_df)\n",
+    "\n",
+    "    if num_tick > 1:\n",
+    "        com_sel_name_temp = []\n",
+    "        for i in com_data.columns:\n",
+    "            com_sel_name_temp.append(company_symbol_dict[i])\n",
+    "        com_sel = com_data.columns.to_list()\n",
+    "        \n",
+    "\n",
+    "        ## Log-Return of Company Dataset\n",
+    "        log_return = np.log(1 + com_data.pct_change())\n",
+    "\n",
+    "        ## Generate Random Weights\n",
+    "        rand_weig = np.array(np.random.random(num_tick))\n",
+    "\n",
+    "        ## Rebalancing Random Weights\n",
+    "        rebal_weig = rand_weig / np.sum(rand_weig)\n",
+    "\n",
+    "        ## Calculate the Expected Returns, Annualize it by * 252.0\n",
+    "        exp_ret = np.sum((log_return.mean() * rebal_weig) * 252)\n",
+    "\n",
+    "        ## Calculate the Expected Volatility, Annualize it by * 252.0\n",
+    "        exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 252, rebal_weig)))\n",
+    "\n",
+    "        ## Calculate the Sharpe Ratio.\n",
+    "        sharpe_ratio = exp_ret / exp_vol\n",
+    "\n",
+    "        # Put the weights into a data frame to see them better.\n",
+    "        weights_df = pd.DataFrame(\n",
+    "            data={\n",
+    "                \"company_name\": com_sel_name_temp,\n",
+    "                \"random_weights\": rand_weig,\n",
+    "                \"rebalance_weights\": rebal_weig,\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "        st.divider()\n",
+    "\n",
+    "        st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Random Portfolio Weights</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "        st.dataframe(weights_df, use_container_width=True)\n",
+    "\n",
+    "        # Do the same with the other metrics.\n",
+    "        metrics_df = pd.DataFrame(\n",
+    "            data={\n",
+    "                \"Expected Portfolio Returns\": exp_ret,\n",
+    "                \"Expected Portfolio Volatility\": exp_vol,\n",
+    "                \"Portfolio Sharpe Ratio\": sharpe_ratio,\n",
+    "            },\n",
+    "            index=[0],\n",
+    "        )\n",
+    "\n",
+    "        st.markdown(\n",
+    "            \"<h5 style='text-align: center;'>Random Weights Metrics</h5>\",\n",
+    "            unsafe_allow_html=True,\n",
+    "        )\n",
+    "        st.dataframe(metrics_df, use_container_width=True)\n",
+    "\n",
+    "        st.divider()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

main.py

@@ -3,6 +3,8 @@ import numpy as np
 import yfinance as yf
 import streamlit as st
 import plotly.graph_objects as go
+import time
+import datetime
 
 with open(r"style/style.css") as css:
     st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
@@ -31,7 +33,6 @@ st.header(
     divider="rainbow",
 )
 
-
 list_df = pd.read_csv("Data/Company List.csv")
 
 company_name = list_df["Name"].to_list()
@@ -59,43 +60,100 @@ st.markdown(
 st.markdown('<p class="big-font">Select Multiple Companies</p>', unsafe_allow_html=True)
 
 com_sel_name = st.multiselect("", company_name, default=None)
+com_sel_date = []
+
+for i in com_sel_name:
+    d = st.date_input(
+        f"On which date did you invested in - {i}",
+        value=pd.Timestamp("2021-01-01"),
+        format="YYYY-MM-DD",
+    )
+    d = d - datetime.timedelta(days=3)
+    com_sel_date.append(d)
 
 com_sel = [company_dict[i] for i in com_sel_name]
 
 num_tick = len(com_sel)
 
 if num_tick > 1:
-
-    com_data = yf.download(com_sel, start="1900-01-01", end="2024-03-08")["Adj Close"]
+    com_data = pd.DataFrame()
+    for cname, cdate in zip(com_sel, com_sel_date):
+        stock_data_temp = yf.download(
+            cname, start=cdate, end=pd.Timestamp.now().strftime("%Y-%m-%d")
+        )["Low"]
+        stock_data_temp.name = cname
+        com_data = pd.merge(
+            com_data, stock_data_temp, how="outer", right_index=True, left_index=True
+        )
     for i in com_data.columns:
-        com_data.dropna(axis=1, how='all', inplace=True)
-    com_data.dropna(inplace=True)
+        com_data.dropna(axis=1, how="all", inplace=True)
+    # com_data.dropna(inplace=True)
     num_tick = len(com_data.columns)
 
+    # Dataframe of the selected companies
+    st.dataframe(com_data, use_container_width=True)
+
+    # make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company
+    def moving_average(data, window):
+        ma = {}
+        for i in data.columns:
+            ma[i] = data[i].rolling(window=window).mean().values[2]
+        return ma
+
+    moving_avg = moving_average(com_data, 3)
+    MA_df = pd.DataFrame(moving_avg.items(), columns=["Company", "Purchase Rate (MA)"])
+
+    # calculate percentage return till present date from the moving average price of the stock
+    def percentage_return(data, moving_avg):
+        pr = {}
+        for i in data.columns:
+            pr[i] = (
+                f"{round(((data[i].values[-1] - moving_avg[i]) / moving_avg[i]) * 100,2) }%"
+            )
+        return pr
+
+    # make percentage return a dataframe from dictionary
+    percentage_return = pd.DataFrame(
+        percentage_return(com_data, moving_avg).items(),
+        columns=["Company", "Percentage Return"],
+    )
+
+    # merge MA_df and percentage_return on "Company" columns
+    MA_df = pd.merge(MA_df, percentage_return, on="Company")
+
+    st.markdown(
+        "<h5 style='text-align: center;'>Percent Returns & MA price</h5>",
+        unsafe_allow_html=True,
+    )
+
+    st.write(
+        "<p style='text-align: center;'>**rate of purchase is moving average(MA) of 3 (t+2) days</p>",
+        unsafe_allow_html=True,
+    )
+    st.dataframe(MA_df, use_container_width=True)
+
     if num_tick > 1:
         com_sel_name_temp = []
         for i in com_data.columns:
             com_sel_name_temp.append(company_symbol_dict[i])
 
         com_sel = com_data.columns.to_list()
-        com_sel_name.sort()
-
-        st.dataframe(com_data, use_container_width=True)
 
         ## Log-Return of Company Dataset
         log_return = np.log(1 + com_data.pct_change())
 
         ## Generate Random Weights
-        rand_weig = np.array(np.random.random(num_tick))
-
+        rand_weig = np.array([100 / len(com_sel)] * len(com_sel))
         ## Rebalancing Random Weights
         rebal_weig = rand_weig / np.sum(rand_weig)
 
-        ## Calculate the Expected Returns, Annualize it by * 247.0
-        exp_ret = np.sum((log_return.mean() * rebal_weig) * 247)
+        ## Calculate the Expected Returns, Annualize it by * 252.0
+        exp_ret = np.sum((log_return.mean() * rebal_weig) * 252)
 
-        ## Calculate the Expected Volatility, Annualize it by * 247.0
-        exp_vol = np.sqrt(np.dot(rebal_weig.T, np.dot(log_return.cov() * 247, rebal_weig)))
+        ## Calculate the Expected Volatility, Annualize it by * 252.0
+        exp_vol = np.sqrt(
+            np.dot(rebal_weig.T, np.dot(log_return.cov() * 252, rebal_weig))
+        )
 
         ## Calculate the Sharpe Ratio.
         sharpe_ratio = exp_ret / exp_vol
@@ -104,13 +162,10 @@ if num_tick > 1:
         weights_df = pd.DataFrame(
             data={
                 "company_name": com_sel_name_temp,
-                "random_weights": rand_weig,
                 "rebalance_weights": rebal_weig,
             }
         )
 
-        st.divider()
-
         st.markdown(
             "<h5 style='text-align: center;'>Random Portfolio Weights</h5>",
             unsafe_allow_html=True,
@@ -133,8 +188,6 @@ if num_tick > 1:
         )
         st.dataframe(metrics_df, use_container_width=True)
 
-        st.divider()
-
         ## Let's get started with Monte Carlo Simulations
 
         ## How many times should we run Monte Carlo
@@ -152,9 +205,15 @@ if num_tick > 1:
         ## Create an Array to store the Sharpe Ratios as they are generated
         sharpe_arr = np.zeros(num_of_port)
 
-        ## Let's start the Monte Carlo Simulation
+        ## Track Progress with a Bar
+        progress_text = "Simulations in progress. Please wait."
+        my_bar = st.progress(0, text=progress_text)
 
-        for ind in range(num_of_port):
+        ## Let's start the Monte Carlo Simulation
+        for ind in range(
+            num_of_port
+        ):  # Corrected the range to iterate from 0 to num_of_port
+            time.sleep(0.001)
             ## Let's first Calculate the Weights
             weig = np.array(np.random.random(num_tick))
             weig = weig / np.sum(weig)
@@ -170,6 +229,10 @@ if num_tick > 1:
 
             ## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array
             sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]
+            if ind % 100 == 0:
+                my_bar.progress((ind + 1) / num_of_port, text=progress_text)
+        # clear progress bar
+        my_bar.empty()
 
         ## Let's create a Data Frame with Weights, Returns, Volatitlity, and the Sharpe Ratio
         sim_data = [ret_arr, vol_arr, sharpe_arr, all_weights]
@@ -213,8 +276,8 @@ if num_tick > 1:
 
         min_volatility_weights_df = pd.DataFrame(
             data={
-                "company_name": com_sel_name_temp,
-                "random_weights": min_volatility["Portfolio Weights"],
+                "company name": com_sel_name_temp,
+                "optimized weights": min_volatility["Portfolio Weights"],
             }
         )
 
@@ -227,47 +290,51 @@ if num_tick > 1:
 
         st.divider()
 
-        st.markdown("<h1 style='text-align: center;'>Plotting</h1>", unsafe_allow_html=True)
+        st.markdown(
+            "<h1 style='text-align: center;'>Plotting</h1>", unsafe_allow_html=True
+        )
 
+        # plot a pie chart using plotly for max sharpe ratio
         fig = go.Figure(
-            data=go.Scatter(
-                x=sim_df["Volatility"],
-                y=sim_df["Returns"],
-                mode="markers",
-                marker=dict(color=sim_df["Sharpe Ratio"], colorscale="RdYlBu", size=10),
+            data=go.Pie(
+                labels=com_sel_name_temp,
+                values=max_sharpe_ratio["Portfolio Weights"],
+                hole=0.3,
+                textinfo="percent+label",  # Information to display on the pie slices
+                hoverinfo="label+percent",  # Information to display on hover
+                marker=dict(line=dict(color="white", width=2)),
+            )
+        )
+
+        # update colors
+        fig.update_traces(
+            marker=dict(
+                colors=[
+                    "lightseagreen",
+                    "lightcoral",
+                    "lightskyblue",
+                    "lightgreen",
+                    "lightpink",
+                    "lightyellow",
+                    "lightblue",
+                    "lightgrey",
+                    "lightgoldenrodyellow",
+                    "lightcyan",
+                ]
             )
         )
 
+        # update layout of the pie chart
+
         # Add color bar
         fig.update_layout(coloraxis_colorbar=dict(title="Sharpe Ratio"))
 
         # Add title and axis labels
         fig.update_layout(
-            title="Portfolio Returns Vs. Risk",
-            xaxis=dict(title="Standard Deviation / Volatility"),
-            yaxis=dict(title="Returns"),
-        )
-
-        # Plot the Max Sharpe Ratio, using a `Red Star`.
-        fig.add_trace(
-            go.Scatter(
-                x=[max_sharpe_ratio[1]],
-                y=[max_sharpe_ratio[0]],
-                mode="markers",
-                marker=dict(color="red", symbol="star", size=20),
-                name="Max Sharpe Ratio",
-            )
+            title="Portfolio Composition",
+            showlegend=False,
+            height=500,
+            width=700,
+            margin=dict(l=50, r=50, t=50, b=50),
         )
-
-        # Plot the Min Volatility, using a `Blue Star`.
-        fig.add_trace(
-            go.Scatter(
-                x=[min_volatility[1]],
-                y=[min_volatility[0]],
-                mode="markers",
-                marker=dict(color="blue", symbol="star", size=20),
-                name="Min Volatility",
-            )
-        )
-
         st.plotly_chart(fig, use_container_width=True)

utilities/checker.py

@@ -0,0 +1,26 @@
+import yfinance as yf
+
+# 1. Check if the company is listed on Yahoo Finance
+def check_company(company_dict):
+    com_sel = []
+    for i in company_dict.keys():
+        if yf.Ticker(company_dict[i]).info:
+                com_sel.append(i)
+
+    return com_sel
+
+#2. make a function to calculate moving averages from the dataframe com_data, store those moving averages in dictionary for respective company
+def moving_average(data, window):
+    ma = {}
+    for i in data.columns:
+        ma[i] = data[i].rolling(window=window).mean().values[2]
+    return ma
+
+
+# calculate percentage return till present date from the moving average price of the stock
+def percentage_return(data, moving_avg):
+    pr = {}
+    for i in data.columns:
+        pr[i] = f'{round(((data[i].values[-1] - moving_avg[i]) / moving_avg[i]) * 100,2) }%'
+    return pr
+