An equity curve simulator is mainly used to test the effectiveness and profitability of a trading strategy before it is used in real trading. This process is referred to as backtesting. It allows users to generate important metrics and statistical information, including profits and losses, Sharpe ratio, drawdown, and other important performance indicators.

The simulations generated by the Equity Curve Simulator are highly informative in that they provide a detailed idea of the expected performance of a strategy. They can help identify risks, optimize performance, and make informed decisions about strategy application.

However, it is important to note that simulator results are based on historical data. Therefore, they are not necessarily a reliable indicator of future results. Market conditions can change and unforeseen events can occur that affect the actual performance of a strategy. Therefore, an equity curve simulator should always be used as one part of a comprehensive risk management system and not as the sole basis for decision-making.

Here are some examples of how a strategy might determine these points:

A strategy could use technical indicators such as Moving Average Crossovers, RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), Bollinger Bands, etc. to determine the optimal entry and exit points. For example, an entry point could be when the price of a stock rises above its moving average, while an exit point could be when the price of the stock falls below its moving average.

Another strategy could use fundamental indicators such as earnings reports, economic indicators, etc. For example, an entry point could be when a company's earnings exceed expectations, while an exit point could be when a company's earnings are below expectations.

Strategies could be based on corporate actions or events, such as mergers, acquisitions, IPOs, earnings announcements, etc. For example, an entry point could be when a company announces a merger, while an exit point could be after the merger is completed.

Strategies based on statistical properties of price series can also be used. For example, the strategy might take a position betting that the price will revert to the average (mean-reversion strategy) when the stock price deviates significantly from the historical average.

Strategies could also be based on machine learning and algorithms trained to recognize patterns in historical data and predict future price movements. These algorithms could use a mix of the above strategies or employ entirely novel patterns identified in the data.

In all of these cases, it is important to note that the simulation is based on past data and its results are not guaranteed to accurately predict future performance. It is also necessary to consider transaction costs, slippage, and other real-world trading factors when designing and testing a strategy.