Portfolio Optimizer


1. Use Yahoo! Finance symbol for the portfolio optimizer to work.
2. You can input various assets including cryptocurrency.
3. We use adjusted closing prices for calculations.
4. Minimum of two assets are needed for the correlation map to work.

Portfolio Optimizer by Moneybestpal.com

What have we got here?

Adjusted Closing Price

The adjusted closing price is used by our portfolio optimizer to compute several metrics. The adjusted closing price takes into account the effects of corporate actions like dividends, interest, stock splits, and other changes on asset prices, in contrast to the closing price, which is just the value of the last transaction price before the market closing.

Adjustments enable investors to obtain an accurate record of the stock's performance. Investors should understand how corporate actions are accounted for in a stock's adjusted closing price. Because it gives analysts a precise picture of the firm's equity worth, it is particularly helpful when analyzing historical returns.

Performance Summary

Cumulative Return:

A cumulative return is the total amount, expressed as a percentage, that the investment has increased or decreased over time regardless of the length of time involved. Consider the case where a $10,000 investment in the stock of ABC Technology Company yields $50,000 after ten years. That represents a cumulative return of 400 percent without taxes and without reinvested dividends.

This graph's y-axis is adjusted to a logarithmic value to allow for comparison of growth across extensive periods.

Daily Return:

Daily return is the price of stocks at today's close compared to the price of the same stock at yesterday's close and is used to measure the day-to-day performance of stocks. A positive daily return indicates that the stock price has increased daily.

Portfolio Allocation

The ratio of assets to all the assets in a portfolio is known as portfolio weight. The weight of each asset in a portfolio that provides the best return in relation to its cost is determined using the ROI (Return on Investment) approach.

Efficient Frontier

An efficient frontier is the collection of ideal portfolios known and provides either the lowest risk or the highest projected return for a specified level of risk. Because they do not offer a sufficient return for the level of risk, portfolios that are below the efficient frontier are not ideal. Because they have a higher degree of risk for the specified rate of return, portfolios that cluster to the right of the efficient frontier are not ideal.

A portfolio that has been perfectly balanced between risk and return is said to be optimal. The best portfolio balances investments with tolerable risk and the highest potential returns, or investments with the lowest risk for a given return.

Value-at-Risk (VaR)

Value at risk (VaR) is a statistic that measures how much money could be lost by a portfolio, position, or company over a certain period. Investment and commercial banks most frequently use this indicator to assess the size and likelihood of prospective losses in their institutional portfolios.

VaR is a tool used by risk managers to gauge and manage the degree of risk exposure. VaR calculations can be used to assess individual holdings, entire portfolios, or the overall risk exposure of an organization.

For instance, a financial company might find that an asset has a 5% one-month VaR of 3%, which indicates a 5% possibility that the item will lose value by 3% in that time frame. The probability of a 3% loss is one day every month when the occurrence rate of 5% is converted to a daily ratio.


Drawdown reflects the amount that investment has fallen from its peak before recovering to its peak. A drawdown occurs each time the cumulative return falls below the maximum cumulative return. A trading account had a 10% drawdown if it had $10,000 in funds and those funds dropped to $9,000 before rising back to $10,000.

A drawdown continues as long as the price is below the peak. In the preceding example, we don't know the drawdown is only 10% until the account returns to $10,000. The drawdown is recorded once the account rises above $10,000.

Loss and decline are not always synonymous. Losses often refer to the purchase price in relation to the current or exit price, but most traders consider a drawdown as a peak-to-trough indicator.


In the financial and investment sectors, correlation is a statistic that gauges how closely two assets move in tandem. Advanced portfolio management makes use of correlations, which are calculated as the correlation coefficient, whose value must fall between the range of -1.0 and +1.0.

When there is a complete positive correlation, the correlation coefficient is 1. This means that the secondary security moves in lockstep, in the same direction, as the first security moves, whether up or down. A zero correlation suggests that there is no linear link at all, whereas a perfect negative correlation indicates that two assets move in opposite directions.

In investment, a diverse portfolio is where correlation is most crucial. By purchasing non-correlated assets, investors can reduce their risk. Consider an investor who owns stock in a technology company. The investor may decide to invest in an FMCG company if it is determined that there is little link between the technology and consumer sectors, knowing that a bad outcome for one sector might not harm the other.

Bayesian Structural Time Series

By dividing time-series data into its trend, seasonality, and irregular components, the statistical modeling method known as Bayesian Structural Time Series (BSTS) enables the analysis of time-series data. It is founded on the Bayesian paradigm, which makes use of subjective prior beliefs and involves updating them in light of new information.

In instances when the data is noisy and has several sources of change, BSTS is very helpful in forecasting time series data. This method concurrently estimates the time series model's parameters and forecasts future values using a hierarchical Bayesian model. Many sources of uncertainty, including measurement error and parameter uncertainty, can be included because of the hierarchical structure.

The capacity of BSTS to manage missing data and data points with irregular spacing distinguishes it from other time series modeling methods. This is accomplished by employing a state-space model, which enables the estimation of the time series' unobserved components. By adding outside variables to the model, the approach can also be used to estimate causal effects.

A potential drawback of BSTS is the requirement for prior knowledge of the underlying process being mimicked. By using informative priors, which take into account prior information or professional judgment, this issue might be resolved. The prior selection, however, should be well thought out as it might significantly affect the outcomes.

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