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Desarrollador de sistemas automatizados de trading en FX enfocado en estrategias robustas y basadas en reglas.
Mi trabajo se centra en sistemas automatizados basados en rangos y conscientes del régimen de mercado, diseñados con énfasis en la preservación de capital, la consistencia estadística y la supervivencia a largo plazo en condiciones reales de mercado.
Priorizo la ejecución selectiva, la exposición al riesgo controlada y un diseño conservador por encima de enfoques agresivos o de alta frecuencia. Todos los sistemas se construyen utilizando lógica transparente, filtros de seguridad estrictos y una extensa evaluación histórica a través de distintos regímenes de mercado.
El soporte, la claridad y el perfeccionamiento continuo son principios fundamentales detrás de cada lanzamiento.
Mi trabajo se centra en sistemas automatizados basados en rangos y conscientes del régimen de mercado, diseñados con énfasis en la preservación de capital, la consistencia estadística y la supervivencia a largo plazo en condiciones reales de mercado.
Priorizo la ejecución selectiva, la exposición al riesgo controlada y un diseño conservador por encima de enfoques agresivos o de alta frecuencia. Todos los sistemas se construyen utilizando lógica transparente, filtros de seguridad estrictos y una extensa evaluación histórica a través de distintos regímenes de mercado.
El soporte, la claridad y el perfeccionamiento continuo son principios fundamentales detrás de cada lanzamiento.
Amigos
Si añade a sus amigos a través de sus perfiles o la búsqueda, podrá comunicarse y monitorear fácilmente su presencia en el sitio web.
Ignacio Rubio Bustos Fierro
What Does a Small Edge Actually Look Like?
I ran 1,000 Monte Carlo simulations for two simple systems:
System A
R:R = 1:1
Win rate = 50%
No edge
System B
R:R = 1:1
Win rate = 55%
Positive edge
Each simulation:
1,000 trades
Fixed risk per trade
Initial capital: $10,000
What happens?
Breakeven system:
Final balance distribution centers around initial capital
Outcomes are purely variance-driven
Positive edge system:
Entire distribution shifts to the right
Median outcome increases materially
But dispersion remains high
Even with a real edge, not every path wins.
Conclusion
Edge does not eliminate volatility.
It shifts probability mass.
Trade by trade, the difference looks small.
Across 1,000 trades, the distribution changes completely.
Expected value determines direction.
Variance determines experience.
I ran 1,000 Monte Carlo simulations for two simple systems:
System A
R:R = 1:1
Win rate = 50%
No edge
System B
R:R = 1:1
Win rate = 55%
Positive edge
Each simulation:
1,000 trades
Fixed risk per trade
Initial capital: $10,000
What happens?
Breakeven system:
Final balance distribution centers around initial capital
Outcomes are purely variance-driven
Positive edge system:
Entire distribution shifts to the right
Median outcome increases materially
But dispersion remains high
Even with a real edge, not every path wins.
Conclusion
Edge does not eliminate volatility.
It shifts probability mass.
Trade by trade, the difference looks small.
Across 1,000 trades, the distribution changes completely.
Expected value determines direction.
Variance determines experience.
Ignacio Rubio Bustos Fierro
Sharpe Does Not Eliminate Path Risk
A systematic strategy showing:
Sharpe Ratio: 2.02
CAGR: 69.5%
Max Drawdown (realized): −15.6%
1,059 trades
Expectancy: 0.13R
From a classical perspective, this is statistically robust.
However, Sharpe measures return efficiency relative to volatility.
It does not measure sequencing risk.
What Happens If We Reshuffle the Same Trades?
I ran a bootstrap Monte Carlo simulation:
1,000 reshuffled trade sequences
Fixed 1% risk per trade
Initial capital: $50,000
No parameter changes
Same edge, same expectancy
Only trade order randomized
Results
Final Balance Distribution:
Mean: $197,755
Median: $182,512
5th percentile: $97,235
Probability of finishing below initial capital: 0.2%
The statistical edge clearly shifts the distribution to the right.
Drawdown Behavior Under Reordering
Drawdowns across simulations:
Mean max DD: 18.7%
95th percentile max DD: 28.3%
Extreme worst case observed: 40.2%
The historical max DD was −15.6%.
Most alternative paths remain within a statistically reasonable drawdown range.
Occasional deeper drawdowns are structurally possible due to trade clustering.
Same edge.
Different paths.
Takeaway
Sharpe summarizes the realized trajectory.
It does not describe the full distribution of plausible outcomes.
Performance should be evaluated as a probability distribution — not a single equity curve.
A systematic strategy showing:
Sharpe Ratio: 2.02
CAGR: 69.5%
Max Drawdown (realized): −15.6%
1,059 trades
Expectancy: 0.13R
From a classical perspective, this is statistically robust.
However, Sharpe measures return efficiency relative to volatility.
It does not measure sequencing risk.
What Happens If We Reshuffle the Same Trades?
I ran a bootstrap Monte Carlo simulation:
1,000 reshuffled trade sequences
Fixed 1% risk per trade
Initial capital: $50,000
No parameter changes
Same edge, same expectancy
Only trade order randomized
Results
Final Balance Distribution:
Mean: $197,755
Median: $182,512
5th percentile: $97,235
Probability of finishing below initial capital: 0.2%
The statistical edge clearly shifts the distribution to the right.
Drawdown Behavior Under Reordering
Drawdowns across simulations:
Mean max DD: 18.7%
95th percentile max DD: 28.3%
Extreme worst case observed: 40.2%
The historical max DD was −15.6%.
Most alternative paths remain within a statistically reasonable drawdown range.
Occasional deeper drawdowns are structurally possible due to trade clustering.
Same edge.
Different paths.
Takeaway
Sharpe summarizes the realized trajectory.
It does not describe the full distribution of plausible outcomes.
Performance should be evaluated as a probability distribution — not a single equity curve.
Ignacio Rubio Bustos Fierro
Development update — capital management refinement in progress.
Current research is focused on implementing a new capital management framework designed to preserve balance stability while selectively exploiting performance peaks.
The objective is to maximize long-term returns without increasing overall market exposure, maintaining the system’s conservative and regime-aware philosophy.
This work is part of the ongoing effort to strengthen robustness, improve survivability in live conditions, and enhance risk-adjusted performance over time.
SteadyRange M5:
https://www.mql5.com/es/market/product/157077?source=Site+Profile+Seller
Current research is focused on implementing a new capital management framework designed to preserve balance stability while selectively exploiting performance peaks.
The objective is to maximize long-term returns without increasing overall market exposure, maintaining the system’s conservative and regime-aware philosophy.
This work is part of the ongoing effort to strengthen robustness, improve survivability in live conditions, and enhance risk-adjusted performance over time.
SteadyRange M5:
https://www.mql5.com/es/market/product/157077?source=Site+Profile+Seller
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Ignacio Rubio Bustos Fierro
We are currently working on expanding the bot’s operational range, with the goal of progressively extending it to cover the 1.122 – 1.188 price zone.
This update is part of an ongoing effort to improve robustness and adaptability across broader market conditions.
This update is part of an ongoing effort to improve robustness and adaptability across broader market conditions.
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Ignacio Rubio Bustos Fierro
Ha publicado el producto
STEADYRANGE M5 Sistema Algorítmico Profesional de Trading en Rango para EURUSD (M5) Enfoque del Sistema SteadyRange M5 es un Expert Advisor profesional diseñado exclusivamente para EURUSD en timeframe M5 , enfocado en: participación selectiva control estricto del riesgo consistencia operativa a largo plazo El sistema opera dentro de una zona intradía estructuralmente definida (1.1450 – 1.2050) , priorizando la calidad de ejecución por encima de la frecuencia de operaciones. No utiliza
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