Initial Commit

Portfolio Simulator Using Python/app.py

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+import dash
+import pandas as pd
+import numpy as np
+import dash_daq as daq
+import plotly.graph_objs as go
+import plotly.express as px
+import plotly.figure_factory as ff
+from dash import Dash, html, dcc
+import dash_bootstrap_components as dbc
+from dash.dependencies import Input, Output, State
+from datetime import date
+import layout
+import dataload
+
+
+
+
+app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
+
+server = app.server
+
+
+##################################################################
+# The layout section
+##################################################################
+
+app.layout = layout.Financial_Portfolio_Layout
+
+
+##################################################################
+# The callback section
+##################################################################
+@app.callback(
+ [
+ Output('store-data', 'data'),
+ Output("alert-wip-data", "is_open")
+ ],
+ [
+ Input("id-company-dropdown","value"),
+ Input("id-start-date","date"),
+ Input("id-end-date","date") 
+ ] 
+ 
+)
+def update_storeData(company, start_date, end_date): 
+ """_summary_
+
+ Args:
+ company (List): Loads the data DCC data store with the stock data
+
+ Returns:
+ Dataframe: Returns a dataframe which has price data for selected companies
+ """
+ s_year = int(start_date[0:4])
+ s_month = int(start_date[5:7])
+ s_day = int(start_date[8:10])
+
+ e_year = int(end_date[0:4])
+ e_month = int(end_date[5:7])
+ e_day = int(end_date[8:10])
+
+ s_date = date(s_year,s_month,s_day)
+ e_date = date(e_year,e_month,e_day)
+
+ try:
+ dataset = dataload.load_stock_data(s_date, e_date, company)
+
+ dataset.reset_index(inplace = True)
+ return dataset.to_dict('records'), True
+ except Exception as e:
+ print(f'An exception occurred while updating the data store: {e}')
+
+
+
+@app.callback(
+
+ [
+ Output("id_low_risk", 'value'),
+ # Output("id_lowrisk_returns", 'value'),
+ Output("id_high_risk", 'value'), 
+ # Output("id_highrisk_returns", 'value'), 
+ Output("fig_allocation_low", 'figure'),
+ Output("alert-wip", "is_open"),
+ Output("fig_allocation_high", 'figure'),
+ Output("id-info_low", 'children'),
+ Output("id-info_high", 'children'),
+ 
+ ],
+ [
+ Input('id-btn-create', 'n_clicks'),
+ State('id-company-dropdown', 'value'), 
+ State('store-data', 'data'),
+ State("alert-wip", "is_open")
+ 
+
+ ],
+ prevent_initial_call = True
+)
+def update_allocation(btn_create, company, data, val_is_open): 
+ """_summary_
+
+ Args:
+ company (List): Loads the data DCC data store with the stock data
+ data (Dataframe): The DCC data store which has the stock data
+ val_is_open (Boolean): A boolean value for displaying alert on successful generation of portfolio
+
+ Returns:
+ _type_: _description_
+ """
+ button_id = dash.callback_context.triggered[0]['prop_id'].split('.')[0]
+ global df_risk
+
+ if button_id == 'id-btn-create':
+
+ try:
+ ddf = pd.DataFrame.from_records(data, index=['Date'])
+ ddf = ddf[ddf.columns & company]
+
+ ddf_returns = ddf.pct_change().dropna()
+ dd_returns = ddf_returns.copy()
+
+ ddf_cumulative = ddf_returns.copy()
+ ddf.reset_index(inplace = True)
+ ddf_returns.reset_index(inplace = True)
+
+
+ low_risk_wts, high_risk_wts, low_risk_return, low_risk_volatility, high_risk_return, high_risk_volatility, df_risk = dataload.sim_bootstrap(dd_returns, company)
+
+ fig_allocation_low_risk = px.pie(values = low_risk_wts, names = company, hole= 0.3)
+ fig_allocation_high_risk = px.pie(values = high_risk_wts, names = company, hole= 0.3)
+
+
+
+ return [low_risk_volatility, 
+ # low_risk_return, 
+ high_risk_volatility, 
+ # high_risk_return, 
+ fig_allocation_low_risk, 
+ True, 
+ fig_allocation_high_risk,
+ html.Div(f'At 95% confidence, the maximum loss expected will be {low_risk_volatility} %', id="id-info_low",style={'display': 'block', 'color': 'blue', 'fontSize': 15, 'font-weight': 'bold', "text-align": "center"}),
+ html.Div(f'At 95% confidence, the maximum loss expected will be {high_risk_volatility} %', id="id-info_low",style={'display': 'block', 'color': 'blue', 'fontSize': 15, 'font-weight': 'bold', "text-align": "center"}),
+ ]
+
+
+ except Exception as e:
+ print(f'An exception occurred while executing update_trendsChart(): {e}') 
+
+ else: 
+ return [None, 
+ # None, 
+ None, 
+ # None,
+ layout.blank_fig(), 
+ None,
+ layout.blank_fig(), 
+ None, 
+ None]
+
+
+
+
+
+@app.callback(
+ 
+ Output("fig_trends", 'figure'), 
+ [
+ Input('id-btn-create', 'n_clicks'),
+ Input('id-analysis-options', 'value'), 
+ State('id-company-dropdown', 'value'), 
+ State('store-data', 'data')
+ 
+ 
+ ],
+ prevent_initial_call=True
+ 
+ 
+)
+def update_trends(btn_create, option_selected, company, data):
+
+ """ Stock trends analysis
+
+ Returns:
+ String: The radio button selection value 
+ """
+
+
+ try:
+
+ ddf = pd.DataFrame.from_records(data, index=['Date'])
+ ddf = ddf[ddf.columns & company]
+
+ ddf_returns = ddf.pct_change().dropna()
+ dd_returns = ddf_returns.copy()
+
+ ddf_cumulative = ddf_returns.copy()
+
+ ddf.reset_index(inplace = True)
+ ddf_returns.reset_index(inplace = True)
+
+ # low_risk_wts, high_risk_wts, low_risk_return, low_risk_volatility, high_risk_return, high_risk_volatility, df_risk = dataload.sim_bootstrap(dd_returns, company)
+
+
+ if option_selected == 'STOCK PRICE TRENDS':
+
+ fig_price = px.line(ddf, x="Date", y = ddf.columns, template="simple_white")
+ return fig_price
+
+ elif option_selected == 'STOCK RETURNS TRENDS':
+
+ fig_returns = px.line(ddf_returns, x="Date", y = ddf_returns.columns, template="simple_white")
+ return fig_returns
+
+ elif option_selected == 'CUMULATIVE RETURNS':
+
+ df_cumulative = (ddf_cumulative+1).cumprod()
+ df_cumulative.reset_index(inplace = True)
+
+ fig_cumulative = px.line(df_cumulative, x="Date", y = df_cumulative.columns, template="simple_white")
+ return fig_cumulative
+
+ elif option_selected == 'VOLATILITY IN RETURNS':
+ # df_risk['Risk'] = df_risk['Risk'].abs()
+ df_risk['Risk'] = df_risk['Risk'] * 100
+ fig_risk_returns = px.scatter(df_risk, x = 'Risk', y = 'Returns') 
+ return fig_risk_returns
+
+ else:
+ return layout.blank_fig()
+
+ except Exception as e:
+ print(f'An exception occurred while executing update_trendsChart(): {e}') 
+
+
+
+
+
+
+if __name__ == "__main__": 
+ app.run_server(debug=True, use_reloader=False) 
+
+
+

Portfolio Simulator Using Python/dataload.py

@@ -0,0 +1,123 @@
+import pandas as pd
+import numpy as np
+import datetime
+from datetime import date
+from nsepy import get_history as gh
+
+
+
+##################################################################
+# Data Loading
+##################################################################
+def load_stock_data(start_date, end_date, ticker):
+ """_summary_
+
+ Args:
+ start_date (Date): Start data for stock selection
+ end_date (Date): End date for stock selection
+ ticker (List): List of stocks
+
+ Returns:
+ Dataframe: price data for selected stock for a selected period
+ """
+ try:
+ df = pd.DataFrame()
+
+ for i in range(len(ticker)):
+ data = gh(symbol=ticker[i],start= start_date, end=end_date)[['Symbol','Close']]
+ data.rename(columns={'Close':data['Symbol'][0]},inplace=True)
+ data.drop(['Symbol'], axis=1,inplace=True)
+ if i == 0:
+ df = data
+
+ if i != 0:
+ df = df.join(data)
+
+
+ return df
+ except Exception as e:
+ print(f'An exception occurred while executing data load: {e}')
+
+
+
+
+##################################################################
+# Data Loading
+##################################################################
+
+def sim_portfolio(weights, dd_returns):
+ """_summary_
+
+ Args:
+ weights (List): Weights for each of the stock
+
+ Returns:
+ Float: Returns the risk at 95% percentile
+ """
+ try:
+ tmp_pp = (weights * dd_returns.values).sum(axis=1)
+ var_sim_port = np.percentile(tmp_pp, 5, interpolation = 'lower')
+ return var_sim_port
+ except Exception as e:
+ print(f'An exception occurred while executing sim_portfolio: {e}')
+
+
+def sim_bootstrap(dd_returns, company):
+ """_summary_
+
+ Args:
+ dd_returns (Dataframe): the dataframe which has stock returns.
+ company (List): List of stocks
+
+ Returns:
+ int: returns various metrics
+ """
+
+ try:
+
+ port_returns = []
+ port_volatility = []
+ port_weights = []
+
+ num_assets = len(company)
+ num_portfolios = 100
+ np.random.seed(1357)
+ for port in range(num_portfolios):
+ weights = np.random.random(num_assets)
+ weights = weights/sum(weights)
+ port_weights.append(weights)
+ df_wts_returns = dd_returns.mean().dot(weights)
+ port_returns.append(df_wts_returns*100)
+
+ var_port_95 = sim_portfolio(weights, dd_returns)
+ port_volatility.append(var_port_95)
+
+
+ port_weights = [wt for wt in port_weights]
+ dff = {'Returns': port_returns, 'Risk': port_volatility, 'Weights': port_weights}
+ df_risk = pd.DataFrame(dff)
+
+ min_risk = df_risk.iloc[df_risk['Risk'].idxmax()]
+
+ # low_risk_return = f'{round(abs(min_risk[0]),4)*100:.2f}'
+ # low_risk_volatility = f'{round(abs(min_risk[1]),4)*100:.2f}'
+ low_risk_return = f'{round((min_risk[0]),4)*100:.2f}'
+ low_risk_volatility = f'{round((min_risk[1]),4)*100:.2f}'
+ low_risk_wts = min_risk[2]
+
+ print(f'{low_risk_volatility} and {low_risk_return}')
+
+
+ max_risk = df_risk.iloc[df_risk['Risk'].idxmin()]
+ # high_risk_return = f'{round(abs(max_risk[0]),4)*100:.2f}'
+ # high_risk_volatility = f'{round(abs(max_risk[1]),4)*100:.2f}'
+ high_risk_return = f'{round((max_risk[0]),4)*100:.2f}'
+ high_risk_volatility = f'{round((max_risk[1]),4)*100:.2f}'
+ high_risk_wts = max_risk[2]
+
+ print(f'{high_risk_volatility} and {high_risk_return}')
+
+ return low_risk_wts, high_risk_wts, low_risk_return,low_risk_volatility, high_risk_return, high_risk_volatility, df_risk
+
+ except Exception as e:
+ print(f'An exception occurred while executing sim_bootstrap: {e}')