moving averages and pct returns

Notebooks/MAexp.py

@@ -0,0 +1,181 @@
+import pandas as pd
+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)
+
+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/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

@@ -86,6 +86,40 @@ if num_tick > 1:
     # 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:
@@ -93,8 +127,6 @@ if num_tick > 1:
 
         com_sel = com_data.columns.to_list()
 
-        st.dataframe(com_data, use_container_width=True)
-
         ## Log-Return of Company Dataset
         log_return = np.log(1 + com_data.pct_change())
 
@@ -122,8 +154,6 @@ if num_tick > 1:
             }
         )
 
-        st.divider()
-
         st.markdown(
             "<h5 style='text-align: center;'>Random Portfolio Weights</h5>",
             unsafe_allow_html=True,
@@ -146,7 +176,6 @@ if num_tick > 1:
         )
         st.dataframe(metrics_df, use_container_width=True)
 
-        st.divider()
 
         ## Let's get started with Monte Carlo Simulations