Weekly Updated Galileo FX Settings: Conservative, Moderate & Aggressive

[Donnie Azoff]

Some of the bots smell very grid-like in nature, which should be a cause for concern. Does anyone know, or can anyone provide information on whether these bots implement exponential lot sizes in any situations, showing martingale tendencies? This can be very risky.


Also, I’m wondering how anyone picks their stop loss when they don’t know what the strategy actually does. For example, if you don’t know whether it uses ATR, if it’s volatility-based, or how dynamic the bot’s entry and exit logic is, how do you choose take profits or stop losses? These all seem like areas of concern to me.


For any machine learning portions, are there different model weights provided in the backtests? For example, are there backtests using different parameters to ensure the model is robust? I don’t believe this system is truly adaptive due to the complexity required to implement real-time updates. If it’s a static machine learning setup, can anyone provide tests showing performance with different model weights? For instance, if it’s a static LSTM, are there backtests using multiple sets of weights to ensure robustness across different market conditions?

 

[Sanchay]

how do you decide your stop loss as well as your take profit on the bot f you don't know the algorithm.
Normally, in a discretionary or algorithmic strategy, SL/TP levels are chosen based on something logical:

• ATR (Average True Range): e.g. 1.5× ATR for stop, 3× ATR for take profit.
• Support/resistance levels: SL below support, TP near resistance.
• Volatility filters: SL/TP adapt dynamically to changing volatility.

Am I just very dense? is there something I am missing? is there a setting like with the lot size being set to 0 which automatically adjusts for these issues? Please let me know as I am very curious into how to make this thing potentially profitable.

Hi @Donnie Azoff , Thanks for posting your questions.
You are not dense, you are asking the right questions and I am here to answer them for you. Unlike traditional black-box systems that either hard-code their SL/TP logic or bury it deep in proprietary models, Galileo FX works differently. It pushes the decision-making into your hands, but in a way that’s far more curated than it seems.

The entry logic is based on our proprietary trend reversal algorithms, but you have the control over its sensitivity via Consecutive Bullish / Bearish Signals , for example, If you set Consecutive Bullish Signal to 3 it means that after 3 consecutive Bullish Signals it will take one Bearish trade and vice versa for Bullish trade.

Setting Lot Size = 0 activates Auto Lot Mode. The bot then calculates position size proportionally to your account balance. It does not dynamically adjust SL/TP based on volatility like an ATR filter, but it does scale exposure. That’s the closest it comes to "automatic" sizing relative to your risk.

Some of the bots smell very grid-like in nature, which should be a cause for concern. Does anyone know, or can anyone provide information on whether these bots implement exponential lot sizes in any situations, showing martingale tendencies? This can be very risky.


Also, I’m wondering how anyone picks their stop loss when they don’t know what the strategy actually does. For example, if you don’t know whether it uses ATR, if it’s volatility-based, or how dynamic the bot’s entry and exit logic is, how do you choose take profits or stop losses? These all seem like areas of concern to me.


For any machine learning portions, are there different model weights provided in the backtests? For example, are there backtests using different parameters to ensure the model is robust? I don’t believe this system is truly adaptive due to the complexity required to implement real-time updates. If it’s a static machine learning setup, can anyone provide tests showing performance with different model weights? For instance, if it’s a static LSTM, are there backtests using multiple sets of weights to ensure robustness across different market conditions?

You're absolutely right to raise these concerns—many bots on the market do rely on grid strategies, martingale logic, or opaque models that make it nearly impossible to assess risk. Fortunately, Galileo FX does not fall into that category.

1. No Grid, No Martingale
   Galileo FX does not implement any grid-based strategy and absolutely does not use exponential lot sizing or martingale logic. You will never see the bot double down on losses or increase exposure as a form of recovery. Instead, it offers manual control over the number of trades through a Max Orders setting, allowing you to specify a fixed number of positions per chart or symbol. Each trade is executed based on independent signal criteria, not cumulative loss recovery.
2. Fixed Lot Sizes & Full Control
   Users define the exact lot size per trade—there’s no hidden multiplier logic. You choose the amount, and the bot executes that size consistently unless you activate Auto Lot mode (by setting Lot Size = 0), which scales the position size relative to your account balance. Even then, it’s linear, not exponential.
3. Defined, Transparent Strategy Logic
   Galileo FX is built on a proprietary trend-reversal model based on technical indicators. While the full algorithm isn't publicly disclosed for IP reasons, the core logic centers around a specific number of consecutive bullish or bearish signals. These parameters are fully visible and customizable by the user.
4. Stop Loss and Take Profit = Your Control
   Because the bot doesn’t hide risk parameters, users set their own Stop Loss and Take Profit levels. If you're unsure how to align them with the underlying strategy, the best approach is to test several .set files provided on the Performance Page, which include SL/TP presets based on historical performance. You can refine these in demo mode first, and adjust to suit your risk tolerance and market conditions.
5. No Machine Learning or Black Box Weights
   Galileo FX does not use any machine learning model like LSTM or neural nets. There are no “model weights,” and thus no risk of performance degrading due to outdated ML training. The system is rules-based, not AI-driven. This means its behavior is deterministic and reproducible. While this may seem limiting compared to adaptive systems, it also means you won’t be trading on a model you can’t verify or interpret.

In short, Galileo FX is intentionally built without complexity that would mask risk. It's customizable, disciplined, and transparent—designed for those who care about having control over their strategy, not outsourcing it blindly.


Hope this helps clarify things.

 

[Sanchay]

Hey everyone here are this week's setting examples:
1. Kinetic Arbitrage Raider

A breakout-driven USDCHF M30 system that channels kinetic energy into sharp directional moves, eclipsing market noise with fast execution and precise trailing logic. With balanced bullish-bearish inputs and tight stop-to-profit dynamics, it thrives in intraday volatility, capturing bursts before momentum fades into shadow.

Trading Style: Day Trading
Trading Mode: Aggressive

Lots: 0
Risk: 2.5%
Max Orders: 2

Stop Loss: 156
Take Profit: 220
Trailing Start: 175
Trailing Step: 10

Bullish Signals: 6
Bearish Signals: 9

2. Rogue Crater Singularity

A precision-tuned EURUSD M30 setup that thrives on rogue delta shifts—those sudden liquidity cracks that collapse into singular momentum bursts. With high bullish signal weight and adaptive trailing steps, it seeks to capture directional drives while balancing risk across volatile intraday cycles.

Trading Style: Day Trading
Trading Mode: Aggressive

Lots: 0
Risk: 2.5%
Max Orders: 2

Stop Loss: 247
Take Profit: 333
Trailing Start: 280
Trailing Step: 35

Bullish Signals: 9
Bearish Signals: 3

Thanks

 

[Sanchay]

Hey everyone here are this week's setting examples:

1. Rogue Impulse Nexus

A momentum-sensitive USDCAD M30 strategy that exploits synthetic impulse waves across the market’s nexus points. With asymmetric bearish bias and adaptive trailing, it locks onto breakout surges while preserving capital during corrections. Its balance of precision stops and extended take profits makes it thrive in intraday volatility streams.

Trading Style: Day Trading
Trading Mode: Aggressive

Lots: 0
Risk: 2.5%
Max Orders: 2

Stop Loss: 155
Take Profit: 365
Trailing Start: 125
Trailing Step: 30

Bullish Signals: 5
Bearish Signals: 7

2. Quantum Spread Rogue

A precision-tuned AUDUSD M30 setup that operates like a helix coil, compressing and releasing energy from quantum-level spread imbalances. With a dual trailing engine and tight stop–profit alignment, it thrives in controlled volatility, exploiting liquidity pockets for consistent intraday opportunities.

Trading Style: Day Trading
Trading Mode: Conservative

Lots: 0
Risk: 2.5%
Max Orders: 2

Stop Loss: 580
Take Profit: 300
Trailing Start: 175
Trailing Step: 10

Bullish Signals: 5
Bearish Signals: 3

Thank you all