Strategy. The Fund is an actively managed non-diversified exchange-traded fund (ETF) that seeks to achieve its investment objective by investing in asset classes that Draco Evolution Corp., the Funds sub-adviser (the Sub-Adviser) believes offer the most attractive combined risk/return opportunities. The term asset classes generally includes, among others, U.S. equities of small-, mid- and large- capitalization companies, gold, fixed-income securities (including U.S. government and corporate bonds (including high-yield or junk bonds) of varying maturities and credit qualities and international dollar-denominated bonds of varying maturities and credit qualities), mortgage-backed securities and asset-backed securities, foreign securities and currencies. The Funds portfolio construction typically includes investments across a variety of these asset classes. For each asset class in which the Fund invests, the Sub-Adviser … Show more… The Fund is an actively managed non-diversified exchange-traded fund (ETF) that seeks to achieve its investment objective by investing in asset classes that Draco Evolution Corp., the Funds sub-adviser (the Sub-Adviser) believes offer the most attractive combined risk/return opportunities. The term asset classes generally includes, among others, U.S. equities of small-, mid- and large- capitalization companies, gold, fixed-income securities (including U.S. government and corporate bonds (including high-yield or junk bonds) of varying maturities and credit qualities and international dollar-denominated bonds of varying maturities and credit qualities), mortgage-backed securities and asset-backed securities, foreign securities and currencies. The Funds portfolio construction typically includes investments across a variety of these asset classes. For each asset class in which the Fund invests, the Sub-Adviser will typically recommend to the Adviser ETFs, leveraged ETFs, inverse ETFs, inverse leveraged ETFs and exchange-traded notes (collectively referred to as ETFs that provide the desired asset class exposure). The Sub-Adviser anticipates the Fund will hold 10-20 different ETF positions across a broad spectrum of asset classes as dictated by its proprietary AI-driven model (the Draco Model). The Sub-Advisers investment selection criteria for ETFs includes a review of its structure, relative performance among its peer group and total operating expense ratio. The Sub-Adviser will typically invest in ETFs that have strong performance records relative to peers, have lower operating expenses, and have a demonstrated expertise in executing the respective ETFs investment strategy. The Fund may take larger positions in certain asset classes and/or industries as dictated by the Draco Model. However, the Funds systematic investment process focuses on diversification across asset classes while attempting to minimize volatility and produce attractive risk-adjusted returns (i.e., returns made relative to the amount of risk taken). The Funds use of a systematic investment process does not guarantee that such risk-adjusted returns will be achieved. The Draco Model consists of an AI model and macroeconomic quantitative model. The AI model is trained with historical pricing data and technical indicators (e.g., moving average and momentum) of the selected market securities which enables the Draco Model to make short-term regime predictions (bearish or bullish). Moving averages (MA) of a period of trading days and closing price of the same period are used to train the model to predict trends, while ATR (Average True Range) and Week range are used to assess momentum and train the model to predict both short-term and long-term volatility, respectively. The AI model was trained using machine learning which refers to a type of technology where computers are programmed to learn from past data and improve their performance over time without being explicitly programmed for every task. The machine learning process is designed to teach a computer to learn from examples and adapt its behavior based on the information it receives. During training, the AI model is exposed to a dataset containing examples with known inputs and corresponding outputs (labels). The AI model adjusts its model structure and input training data through an optimization algorithm in order to minimize the difference between its predictions and the actual outputs in the training data. This process allows the model to learn patterns and relationships within the data, enabling it to better inform its predictions on new, unseen data. Training is a crucial step in the development of machine learning models and typically involves iteratively adjusting the model's parameters until it achieves satisfactory performance on the training dataset. In making investment decisions, the AI model generates a market regime prediction (AI output) by assessing the daily market conditions, which is later used to determine the Funds asset class selections and allocations. The AI model is responsible for determining about 70% of the asset selections and allocation weights. The AI output is then augmented by a quantitative model. The Sub-Adviser leverages the quantitative model to analyze lower frequency macroeconomic data and predict the long-term market outlook. The macroeconomic data used includes, among other items, average weekly hours worked, applications for unemployment insurance, new orders for consumer goods and materials, new orders for capital goods, new building permits, spread between long and short interest rates, inflation adjusted money supply and average consumer expectations for business conditions, to predict future market conditions. The macroeconomic quantitative model attempts to decipher the long-term economic environment and further refine the asset class selections and allocation weights set by the AI model. Based on the Draco Models asset allocation selections and allocations, the Sub-Adviser will instruct the Fund to invest in the selected assets accordingly. Typically, the Sub-Adviser will not override the Draco Models suggestion of asset selections and allocations, while theres some extreme circumstances when the defined threshold is met, the Sub-Adviser may override the Draco Model and the investment committee will decide when the decision-making process will be back to the Draco Model. Such threshold is defined below and discussed further under the additional information about the Funds investment objectives and principal strategies. Training is a crucial step in the development of machine learning models and typically involves iteratively adjusting the model's parameters until it achieves satisfactory performance on the training dataset. Subsequently, the quantitative model analyzes macroeconomic data, which includes, among other items, average weekly hours worked, applications for unemployment insurance, new orders for consumer goods and materials, new orders for capital goods, new building permits, spread between long and short interest rates, inflation adjusted money supply and average consumer expectations for business conditions, to predict future market conditions. From this prediction, the Sub-Advisers model assesses the strength of the macro prediction, resulting in either a more aggressive or more conservative asset allocation for the Fund. When the model predicts a macro bearish market, the Fund will typically invest in ETFs that provide exposure to U.S. treasury bonds, gold, and the U.S. dollar and, to a lesser extent, in one or more inverse and/or leveraged ETFs that seek to replicate or provide the inverse (or multiple) of the daily performance of an index (e.g. S&P 500, Nasdaq 100 and Dow 30) and asset class (e.g., small capitalization companies). During periods of bullish market conditions, the Sub-Advisers model will typically instruct the Fund to invest in ETFs that provide exposure to U.S. equities, high yield bonds (i.e., junk bonds), investment grade bonds and U.S. treasuries and, to a lesser extent, in leveraged ETFs that seek to enhance the daily performance (e.g., 2x or 3x) of an index (e.g., S&P 500, Nasdaq 100 and Dow 30), asset class (e.g., small capitalization companies), sector (e.g., information technology) or industry (e.g., semiconductors). The exposure to each of the noted asset classes will vary based on the strength of the AI models signal regarding future market conditions. Leveraged ETFs seek to provide investment results that match a multiple of the performance of an underlying index (e.g., three times the performance). Inverse ETFs seek to provide investment results that match a negative (i.e., the opposite) of the performance of an underlying index. Leveraged inverse ETFs seek to provide investment results that match a negative multiple of the performance of an underlying index. Leveraged, inverse, and inverse leveraged ETFs often reset daily, meaning that they are designed to achieve their stated objectives on a daily basis. Due to the effect of compounding, their performance over longer periods of time can differ significantly from the performance (or inverse of the performance) of their underlying index or benchmark during the same period of time. The Fund will generally invest in inverse and/or inverse leveraged ETFs to obtain its desired downside protection. The Sub-Advisers model will automatically examine the market conditions, predict the market direction and rebalance the Funds portfolio allocation, as well as provide real-time systematic risk management. The AI model assesses market conditions on a daily basis to determine if the Funds asset allocations need to be adjusted which may result in frequent trading in the Fund. The model parameters the Sub-Adviser may optimize include security selection criteria, weighting, diversification, rebalancing frequency, and cash allocation. The Sub-Adviser has full discretion to override the system at any time, but it is unlikely the Sub-Adviser will do so on a regular basis. This would generally occur when the Funds intraday drawdown exceeds 10% or when the Funds drawdown exceeds 20% from its all-time high. Drawdown is a measure of how much the Funds net asset value has declined from its highest point during a specific time period. At times, the Fund may invest in futures contracts to provide exposure to an asset class. The Sub-Adviser anticipates that futures contracts will be primarily used to provide downside protection for the Fund. Futures contracts will be used when the Sub-Adviser believes they provide the desired protection more cost efficiently than inverse and leverage inverse ETFs. Investments in derivative instruments, like futures, have the economic effect of creating financial leverage in the Funds portfolio because those investments may give rise to losses that exceed the amount the Fund has invested in those instruments. Financial leverage will magnify, sometimes significantly, the Funds exposure to any increase or decrease in prices associated with a particular reference asset resulting in increased volatility in the value of the Funds portfolio. To the extent the Fund invests in derivative instruments, the value of the Funds portfolio is likely to experience greater volatility over short-term periods. While financial leverage has the potential to produce greater gains, it also may result in greater losses, which in some cases may cause the Fund to liquidate other portfolio investments at a loss to comply with limits on leverage requirements imposed by the Investment Company Act of 1940, as amended (the 1940 Act) or to meet redemption requests. Show less