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import streamlit as st
from experiment_runner_for_portfolio import DataLoader, Portfolio
import pickle
from sidebar_portfolio import sidebar
st.set_page_config(
page_title="Portfolio optimization",
page_icon="📊")
st.title("Portfolio Optimization")
sidebar_dict = sidebar()
run = st.sidebar.button('Run portfolio optimization')
dataloader = DataLoader()
portfolio = Portfolio()
if run:
st.header('Price Prediction Results')
dataloader.experiment_data(top_n = sidebar_dict['top_n'], num_scale_steps = sidebar_dict['num_scale_steps'],
scaling_strategy = sidebar_dict['scaling_strategy'], time_step_backward = sidebar_dict['time_step_backward'])
#plot_df, metrics_df, models_dict = experiment(ticker = sidebar_dict['ticker'], num_scale_steps= sidebar_dict['num_scale_steps'],
# scaling_strategy= sidebar_dict['scaling_strategy'], time_step_backward= sidebar_dict['time_step_backward'])
col1_tickers, col2_tickers = st.columns(2)
with col1_tickers:
st.subheader('Valid Tickers:')
st.write(dataloader.valid_tickers)
with col2_tickers:
st.subheader('Invalid Tickers:')
st.write(dataloader.invalid_tickers)
col1_date, col2_date = st.columns(2)
with col1_date:
st.write('Test Min Date:')
st.write(dataloader.global_min_date)
with col2_date:
st.write('Training Max Date:')
st.write(dataloader.global_max_date)
st.subheader('Model Metrics:')
for ticker in dataloader.valid_tickers:
st.write(f'{ticker}:')
st.write('Best model on test data MAPE: ', dataloader.tickers_dict[ticker]['metrics_df'].T.sort_values(by='Test data MAPE', ascending=True).index[0])
st.write(dataloader.tickers_dict[ticker]['metrics_df'])
st.header('Portfolio Optimization Results')
portfolio.optimize_portfolio(cov_matrix=dataloader.cov_matrix, validation_data=dataloader.validation_data, validation_actual=dataloader.validation_actual,
test_data=dataloader.test_data, test_actual=dataloader.test_actual, target_return=sidebar_dict['target_return'], allow_short=sidebar_dict['allow_short'])
col1_weights, col2_weights = st.columns(2)
with col1_weights:
st.subheader('Selected tickers:')
st.write(dataloader.selected_features)
with col2_weights:
st.subheader('Portfolio weights:')
st.write(portfolio.weights)
col1_results, col2_results = st.columns(2)
with col1_results:
st.write(f"Validation Return Accuracy: {portfolio.val_return_accuracy:.4f}")
st.write(f"Validation Volatility Accuracy: {portfolio.val_volatility_accuracy:.4f}")
st.write(f"Validation Sharpe Ratio Deviation: {portfolio.val_sharpe_deviation:.4f}")
st.write(f"Validation Pred Return Sum: {portfolio.val_sum_pred_returns:.4f}")
st.write(f"Validation Actual Return Sum: {portfolio.val_sum_realized_returns:.4f}")
with col2_results:
st.write(f"Test Return Accuracy: {portfolio.test_return_accuracy:.4f}")
st.write(f"Test Volatility Accuracy: {portfolio.test_volatility_accuracy:.4f}")
st.write(f"Test Sharpe Ratio Deviation: {portfolio.test_sharpe_deviation:.4f}")
st.write(f"Test Pred Return Sum: {portfolio.test_sum_pred_returns:.4f}")
st.write(f"Test Actual Return Sum: {portfolio.test_sum_realized_returns:.4f}")
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