Algobee
(crypto futures)

Algobee utilizes machine learning algorithms known as genetic algorithms (GA), a powerful optimization technique inspired by natural selection, to create robust trading strategies. By mimicking the process of evolution, Algobee identifies and refines optimal trading parameters for your selected trading pair.

Genetic Algorithms: A genetic algorithm is a search and optimization method that evolves solutions through selection, crossover, and mutation. In Algobee, this approach enables the creation of multiple trading strategies (populations), which evolve over time to maximize profitability and minimize drawdown.

Key Components: 

1. 1. Population: A set of trading strategies, each with unique parameters (e.g., entry/exit thresholds, stop-loss levels, etc.). 
   2. Fitness: Measures the performance of each strategy, focusing on metrics such as profit, drawdown, and risk-reward ratio. 
   3. Selection: The best-performing strategies (with the highest fitness) are selected for reproduction. 
   4. Crossover: Combines the parameters of selected strategies to create new ones, inheriting strengths from their “parents.” 
   5. Mutation: Introduces random variations in parameters to explore new possibilities and prevent stagnation. 
   6. Evolution: Over multiple generations, the population evolves, and Algobee selects the fittest strategy for live trading.

How Algobee Works:

Initialization: When you create an instance, Algobee generates an initial population of strategies tailored to your selected trading pair. Each strategy is tested on historical data to evaluate its fitness.

Evolution Process:

1. Trend Detection:

l Algobee identifies the current market trend using advanced statistical methods.

l Genetic algorithms optimize strategy parameters for each trend type (uptrend, downtrend, sideways).

2. Live Trading Rules:

l After evolving through multiple generations, the fittest strategy is deployed for live trading.

l Algobee constantly monitors market conditions and adapts parameters in real time.

Entry Rules:

1. Uptrend (Buy Signal): Optimized parameters suggest a high-probability buy entry.

l Entry triggers when the strategy identifies oversold conditions in an uptrend.

l Algobee executes a buy position.

2. Downtrend (Sell Signal):

l Optimized parameters suggest a high-probability sell entry.

l Entry triggers when the strategy identifies overbought conditions in a downtrend.

l Algobee executes a sell position.

Exit Rules:

l Positions are closed or reverse when:

1. The fitness evaluation indicates a reversal in trend.

2. The evolved parameters suggest a loss-minimizing exit.

Key Points to Note:

1. Manual Position Closure Guidelines for Parent and Offspring Strategies:

l    In some cases, there will be both a parent and an offspring strategy, each operating as an independent bot trading a specific symbol. The parent strategy may hold hedged positions alongside the offspring strategy. In such scenarios, manually closing one side of the position is strictly prohibited, as it alters the bot’s intended functionality and may lead to unintended losses.

l    Generally, manually closing bot positions is not recommended, as the system is fully automated. However, if you choose to close positions manually—perhaps because the net PNL is positive—you must do so in pairs. This means closing both the long and short positions of either the parent or the offspring strategy simultaneously.

2. Evolution Takes Time:

l  The first few days after activation are a learning phase for Algobee. It takes up to 5 days  to evolve and sync with the market, refining its parameters through real-time data.

l  During this period, some losses may occur. This is part of the optimization process.

3. Ratios

• The instance-to-balance ratio is 1:100.

  This means that for every 100 USDT in your balance, you only need one bot instance.Additionally, the recommended trade size for each instance is 2% of your balance, ensuring optimal risk management while maximizing trading opportunities.

4. Market Direction Prediction:

l  Algobee’s genetic algorithms excel at adapting to dynamic market conditions, aiming to:

l  Identify the point where markets are likely to turn bullish.

l  Identify the point where markets are likely to turn bearish.

l  If the prediction is incorrect, Algobee quickly re-evaluates and adjusts its strategy, reducing exposure to losses.

5. Continuous Adaptation:

l  Unlike static strategies, Algobee evolves continuously, adapting to market volatility and trends.

MOMENTUM STRATEGY:

The Momentum Strategy targets the top losing tokens. It executes SHORT or LONG positions for these tokens according to the number of positions set by the user. Once the market hits the overall take-profit percentage (e.g., 2% of the user’s account size), the bot automatically closes all positions and retain profits.

AlgoBee indicate that the momentum should be used during the period the crypto market just change from Bullish to Bearish and vise versa.