User manual for reBalance Expert bot [PRO]

CryptoInsider

This trading robot implements a position rebalancing strategy during market declines. This approach helps shorten the distance to the profit fixation level, increasing trading efficiency when the price returns to favorable levels.
https://jt-lab.com/en/store/rebalance-expert-pro

Key Parameters of the Robot

Symbols - List of Cryptocurrencies
Description: A set of trading pairs the robot works with.
Example:
By default, the robot trades the following assets:
BCH/USDT, BTC/USDT, ADA/USDT, ETH/USDT, XRP/USDT, TRX/USDT, SOL/USDT, LTC/USDT, BNB/USDT, DOGE/USDT.
runMode - Operating Mode
Description: Determines which funds the robot operates with:
demo — Simulated trading ("paper money"), no real orders are placed on the account.
real — Real funds, the robot trades using your actual account.
Example:
To test the strategy risk-free, use demo mode. After successful testing, switch to real for live trading.
aggressionLevel - Aggression Level
Description: Influences position sizes and risk levels:
Very Conservative — Minimal risk.
Conservative — Low risk.
Balanced — Moderate risk (default).
Aggressive — High risk.
Very Aggressive — Maximum risk.
Example:
At the Balanced level, the robot calculates positions moderately, minimizing risks. At the Aggressive level, position sizes increase, offering higher potential profits but with greater risk.
sizeUsd - Initial Position Size
Description: The base position size managed by the robot.
Formula: sizeUsd = (balanceUsd * riskFactor) / count(symbols)

Where:
balanceUsd — Total account balance.
riskFactor — Risk coefficient defined by the developer (default: 0.3).
count (symbols) — Number of cryptocurrencies in the list.
Example:
Balance: $1000.
Number of cryptocurrencies: 10.
riskFactor = 0.3.
sizeUsd = (1000 * 0.3) / 10 = $30

rbPercent - Price Change for Averaging
Description: The percentage price change at which the robot opens a new position to average down.
Example:
If rbPercent = 7 and the cryptocurrency price drops by 7%, the robot opens an additional position.
rbSizeFactor - Coefficient for New Position
Description: Determines how much larger the new position is compared to the previous one.
If rbSizeFactor = 1, each subsequent position equals the previous one.
Calculation Example:
Initial position: $30.
Level 1: $30 + ($30 × 1) = $60.
Level 2: $60 + ($60 × 1) = $120.
Level 3: $120 + ($120 × 1) = $240.
basketProfit - Minimum Profit Percentage for Fixation
Description: Sets the minimum profit percentage at which the robot fixes the profit and starts a new trading cycle.
Example:
If basketProfit = 0.5, the robot fixes the profit once the total profit reaches 0.5% and returns to the starting position (sizeUsd).

Example Settings

  symbols: 'BTC/USDT, ETH/USDT, ADA/USDT, XRP/USDT',
  runMode: 'demo',
  aggressionLevel: 'Balanced',
  sizeUsd: 30,
  rbPercent: 7,
  rbSizeFactor: 1,
  basketProfit: 0.5,

In this example:
The robot operates in demo mode with a moderate aggression level.
The starting position size is $30.
A new position is opened when the price decreases by 7%.
Each subsequent position equals the previous one.
Profit fixation occurs when the total profit reaches 0.5%.

Recommendations
Start with demo mode to test the strategy.
Optimize the aggressionLevel and rbPercent parameters to suit your goals.
Monitor the list of cryptocurrencies (symbols) to avoid trading assets with low liquidity.
Happy trading!

CryptoInsider

Optimizing trading robot parameters is a crucial step in developing effective strategies for automated trading. Each parameter directly impacts the balance between risk and profitability, as well as the overall behavior of the robot in the market. During the analysis, various values of key parameters such as rbPercent, basketProfit, and rbSizeFactor were tested. The goal of this study was to identify the optimal settings that deliver maximum profit with minimal possible risk.

Below are the optimization results, demonstrating how parameter adjustments affect the robot's key performance metrics, including profit, drawdown, and risk factor.

Optimization of rbPercent
Highest profit is achieved at lower rbPercent values (4 and 6), but these come with high risk (Max Drawdown up to -51.34).
Risk factor (RF) improves as rbPercent increases, making strategies more stable but with reduced profit.
Recommendation: Optimal values are in the range of rbPercent = 8–10, providing a balance between profit and risk.
Optimization of basketProfit
Maximum profit is observed at lower basketProfit values (0.5–1.5), but this comes with a higher number of orders.
As basketProfit increases, the number of orders decreases, which helps reduce commission costs but also leads to lower profit.
Risk factor (RF) remains stable across different basketProfit values, indicating consistent risk distribution.
Recommendation: A balanced approach suggests using basketProfit = 1.5–2.5.
Optimization of rbSizeFactor
Increasing rbSizeFactor to 1.25–1.5 leads to higher profits but also significantly increases the maximum drawdown (up to -30.7).
Risk factor (RF) is significantly better at lower rbSizeFactor values (0.5–0.75).
Recommendation: Optimal values are in the range of rbSizeFactor = 0.75–1, providing acceptable profit with moderate risks.
Overall Conclusions:
To reduce risks, it is advisable to use strategies with moderate aggressiveness:
rbPercent: 8–10
basketProfit: 1.5–2.5
rbSizeFactor: 0.75–1
For maximum profit, more aggressive parameters (rbPercent = 4–6, basketProfit = 0.5–1.5, rbSizeFactor = 1.25–1.5) can be used, but these come with higher risk.

These results will help you select a strategy based on your preferences for profitability and risk level.