Performance

Overview

Lune AI Signals provides precise, data-driven trading signals for stocks and ETFs, leveraging advanced machine learning algorithms. This documentation outlines the performance of these signals through backtest results and historical performance reports. By analyzing these metrics, users can gain insights into the effectiveness and reliability of Lune AI Signals, helping them make informed trading decisions.

Backtesting

Backtest Results

Backtesting is a crucial part of evaluating the effectiveness of trading signals. Lune AI Signals employs advanced algorithms and machine learning models to generate these signals. Backtest results are derived from historical market data to simulate how the signals would have performed in the past. This process helps in understanding the potential profitability and risk associated with the trading strategies.

Historical Performance Reports

Overview

Historical performance reports provide a detailed view of how the signals have performed over different periods. These reports are essential for understanding the consistency and reliability of the signals over time.

Key Features of Historical Performance Reports

  • Hypothetical Daily Performance: This shows the day-to-day performance of the trading signals, providing insights into short-term trends and fluctuations.

  • Hypothetical Monthly Performance: A month-by-month breakdown of the signal performance, which helps in assessing the long-term viability and stability of the trading strategies.

  • Net Return per Fund (ETF): Distribution of returns across different funds and ETFs, indicating which assets have contributed most to the overall performance.

Raw Backtests & Performance Data

We provide access to the full Raw Backtests & Performance Data for all of our performance reports. You can review every single trade and every metric for complete transparency.

Additional Information & Transparency

  • The backtest results are generated using Lune Trading’s proprietary algorithms and systems, which utilize machine learning models and quantitative analysis.

  • The backtesting process involves simulating the execution of trades based on historical market data to evaluate the performance of the trading signals.

  • Historical data is sourced from reputable financial data providers. Slippage, transaction costs, and other market factors are not accounted for in the results.

Key Metrics in Performance Reports

  • Net Returns: The total profit or loss from an investment.

  • Net Returns %: The percentage gain or loss of an investment relative to the initial investment amount.

  • Compound Annual Growth % (CAGR): The annual growth rate of an investment assuming profits are reinvested each year.

  • Win Rate: The percentage of trades or investments that result in a profit.

  • Sharpe Ratio: A measure of risk-adjusted return, indicating how much excess return is received for the extra volatility endured.

  • Sortino Ratio: A measure of risk-adjusted return that differentiates harmful volatility from total volatility by using downside deviation.

  • Max Drawdown %: The maximum observed loss from a peak to a trough before a new peak is attained.

  • Strategy Began on: The starting date of the trading or investment strategy.

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Copyright Β© Lune Trading. Disclaimer: Past performance is not indicative of future results. The content on our site is educational and reflects our own opinions. We advise everyone to know the risks involved with investing and trading.