{ "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\"\", unsafe_allow_html=True)\n", "\n", "st.markdown(\n", " \"

CapiPort

\", unsafe_allow_html=True\n", ")\n", "\n", "st.markdown(\n", " \"
Your Portfolio Optimisation Tool
\",\n", " unsafe_allow_html=True,\n", ")\n", "st.header(\n", " \"\",\n", " divider=\"rainbow\",\n", ")\n", "\n", "color = \"Quest\"\n", "st.markdown(\n", " \"

🔍 Quest for financial excellence begins with meticulous portfolio optimization

\",\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", " \"\"\",\n", " unsafe_allow_html=True,\n", ")\n", "\n", "st.markdown('

Select Multiple Companies

', 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", " \"
Random Portfolio Weights
\",\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", " \"
Random Weights Metrics
\",\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\"\", unsafe_allow_html=True)\n", "\n", "st.markdown(\n", " \"

CapiPort

\", unsafe_allow_html=True\n", ")\n", "\n", "st.markdown(\n", " \"
Your Portfolio Optimisation Tool
\",\n", " unsafe_allow_html=True,\n", ")\n", "st.header(\n", " \"\",\n", " divider=\"rainbow\",\n", ")\n", "\n", "color = \"Quest\"\n", "st.markdown(\n", " \"

🔍 Quest for financial excellence begins with meticulous portfolio optimization

\",\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", " \"\"\",\n", " unsafe_allow_html=True,\n", ")\n", "\n", "st.markdown('

Select Multiple Companies

', 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", " \"
Random Portfolio Weights
\",\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", " \"
Random Weights Metrics
\",\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\"\", unsafe_allow_html=True)\n", "\n", "st.markdown(\n", " \"

CapiPort

\", unsafe_allow_html=True\n", ")\n", "\n", "st.markdown(\n", " \"
Your Portfolio Optimisation Tool
\",\n", " unsafe_allow_html=True,\n", ")\n", "st.header(\n", " \"\",\n", " divider=\"rainbow\",\n", ")\n", "\n", "color = \"Quest\"\n", "st.markdown(\n", " \"

🔍 Quest for financial excellence begins with meticulous portfolio optimization

\",\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", " \"\"\",\n", " unsafe_allow_html=True,\n", ")\n", "\n", "st.markdown('

Select Multiple Companies

', 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", " \"
Percent Returns & MA price
\",\n", " unsafe_allow_html=True,\n", " )\n", "\n", " st.write(\"

**rate of purchase is moving average(MA) of 3 (t+2) days

\", 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", " \"
Random Portfolio Weights
\",\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", " \"
Random Weights Metrics
\",\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 }