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Innovating the Future of Trading with Precision and Strategy
Welcome to my MQL5 profile! I’m TraderSmith, a passionate and results-driven trader committed to developing Expert Advisors (EAs) that transform complex market data into simple, actionable trading decisions.
Through developing trading bots, I aim to eliminate the emotional biases that often lead to impulsive decisions.
Every strategy I design is rooted in robust market analysis, precise risk management, and an understanding of market dynamics, ensuring that each trade has a solid foundation.
I focus on creating tools that are adaptable to a wide range of trading styles and risk profiles.
From high-frequency scalping to trend-following strategies, my EAs are built to execute trades with speed, accuracy, and discipline—freeing you from emotional reactions and enabling you to trade with confidence.
Every Expert Advisor I develop is designed with a clear trading philosophy.
I avoid random, “black-box” approaches and instead focus on precise, rule-based systems that are backtested, validated, and optimized to adapt to current market conditions.
Whether you’re executing strategies with pending orders, focusing on ATR-based risk management, or leveraging complex thresholds, my EAs are built to excel in dynamic market environments.
Comprehensive Support and Education
Trading is a journey, and I’m here to support you along the way.
Not only do I provide customer support to help you with any setup or technical questions, but I also offer educational resources and guidance on how to fully leverage the bots in various market conditions.
My goal is to ensure you not only use the tools effectively but also understand the underlying strategies that drive them.
My Commitment to You
As an active trader, I am constantly analyzing market trends and refining my tools to keep up with the latest advancements in algorithmic trading.
My mission is to give you the edge you need—whether you’re managing a prop account, building a portfolio, or just getting started.
I’m committed to creating robust, high-performance solutions that allow you to trade smarter, not harder.
I believe in building long-term relationships with my clients, so your feedback and success are always important to me.
If you have any questions, need advice, or simply want to discuss strategies, don’t hesitate to reach out. I’m here to ensure your trading journey is as successful as possible.
Thank you for considering my tools for your trading endeavors. I look forward to helping you achieve your trading goals!
Through algorithmic trading, you will develop discipline and patience—core traits for consistent performance. These bots will enforce rule-based execution, removing emotional bias and promoting data-driven decision-making. Over time, you'll learn that edge comes from systematic testing, risk management, and long-term consistency. Each trade, whether a win or a loss, becomes part of a feedback loop that sharpens both your strategy and your mindset. Once you’ve set up the bot, let it work.
Avoid the urge to micromanage every trade. Trust in the strategy and parameters you’ve defined.
Welcome to my MQL5 profile! I’m TraderSmith, a passionate and results-driven trader committed to developing Expert Advisors (EAs) that transform complex market data into simple, actionable trading decisions.
Through developing trading bots, I aim to eliminate the emotional biases that often lead to impulsive decisions.
Every strategy I design is rooted in robust market analysis, precise risk management, and an understanding of market dynamics, ensuring that each trade has a solid foundation.
I focus on creating tools that are adaptable to a wide range of trading styles and risk profiles.
From high-frequency scalping to trend-following strategies, my EAs are built to execute trades with speed, accuracy, and discipline—freeing you from emotional reactions and enabling you to trade with confidence.
Every Expert Advisor I develop is designed with a clear trading philosophy.
I avoid random, “black-box” approaches and instead focus on precise, rule-based systems that are backtested, validated, and optimized to adapt to current market conditions.
Whether you’re executing strategies with pending orders, focusing on ATR-based risk management, or leveraging complex thresholds, my EAs are built to excel in dynamic market environments.
Comprehensive Support and Education
Trading is a journey, and I’m here to support you along the way.
Not only do I provide customer support to help you with any setup or technical questions, but I also offer educational resources and guidance on how to fully leverage the bots in various market conditions.
My goal is to ensure you not only use the tools effectively but also understand the underlying strategies that drive them.
My Commitment to You
As an active trader, I am constantly analyzing market trends and refining my tools to keep up with the latest advancements in algorithmic trading.
My mission is to give you the edge you need—whether you’re managing a prop account, building a portfolio, or just getting started.
I’m committed to creating robust, high-performance solutions that allow you to trade smarter, not harder.
I believe in building long-term relationships with my clients, so your feedback and success are always important to me.
If you have any questions, need advice, or simply want to discuss strategies, don’t hesitate to reach out. I’m here to ensure your trading journey is as successful as possible.
Thank you for considering my tools for your trading endeavors. I look forward to helping you achieve your trading goals!
Through algorithmic trading, you will develop discipline and patience—core traits for consistent performance. These bots will enforce rule-based execution, removing emotional bias and promoting data-driven decision-making. Over time, you'll learn that edge comes from systematic testing, risk management, and long-term consistency. Each trade, whether a win or a loss, becomes part of a feedback loop that sharpens both your strategy and your mindset. Once you’ve set up the bot, let it work.
Avoid the urge to micromanage every trade. Trust in the strategy and parameters you’ve defined.
Jason Smith
“until the world blows, we will excel”
No matter how long things go on or how difficult life becomes, we will keep pushing forward and continue to succeed.
Progress and self-improvement won’t stop under any circumstances.
Resilience, persistence, and confidence
No matter how long things go on or how difficult life becomes, we will keep pushing forward and continue to succeed.
Progress and self-improvement won’t stop under any circumstances.
Resilience, persistence, and confidence
· 2
Amr Abdelhay Yousef Kamel
Dün
love it
Jason Smith
Thought of the day:
To live is to suffer, to survive is to find some meaning in the suffering.
Friedrich Nietzsche explored the idea that suffering is an inherent part of life and that meaning must be created rather than discovered.
He suggests that human beings transform suffering into growth through interpretation and strength of will.
It reflects his belief that suffering is fundamental to existence, and that individuals must create meaning through their response to it.
To live is to suffer, to survive is to find some meaning in the suffering.
Friedrich Nietzsche explored the idea that suffering is an inherent part of life and that meaning must be created rather than discovered.
He suggests that human beings transform suffering into growth through interpretation and strength of will.
It reflects his belief that suffering is fundamental to existence, and that individuals must create meaning through their response to it.
· 2
Jason Smith
Monte Carlo simulation with GPU acceleration and CPU fallback.
The system automatically uses the GPU via PyTorch when available, enabling fully vectorised, high-speed simulations.
If a compatible GPU is not detected, it seamlessly falls back to CPU execution, ensuring reliability across all environments without breaking functionality.
This approach combines performance and portability—leveraging GPU parallelism for large-scale simulations while maintaining full compatibility on standard CPU-only systems.
The system automatically uses the GPU via PyTorch when available, enabling fully vectorised, high-speed simulations.
If a compatible GPU is not detected, it seamlessly falls back to CPU execution, ensuring reliability across all environments without breaking functionality.
This approach combines performance and portability—leveraging GPU parallelism for large-scale simulations while maintaining full compatibility on standard CPU-only systems.
Jason Smith
28 bots (Forex pairs) are loaded and waiting for the HMM signal.
Deployment is handled via batch files, so startup is quick and simple.
One click launches all 28 Markov instances, and another click starts the bots—just two files: markov.bat and bot.bat
The image below is just the bots
Deployment is handled via batch files, so startup is quick and simple.
One click launches all 28 Markov instances, and another click starts the bots—just two files: markov.bat and bot.bat
The image below is just the bots
Jason Smith
Dün
Still in beta and nearly ready for sale. The bot doesn’t even have a proper name yet. It originally started as a gold-only system, which is where the name came from, and the version number reflects how many iterations, upgrades, and debug cycles it’s gone through since those early “goldbot” builds. What I have now could be the final version, but I still need to finish testing all the CLI flags before confirming that.
Jason Smith
Hidden Markov Model.
Note the different position sizes -
The HMM applies a multiplier such as 38%, 54%, 75%, or 95%.
Base position: 1 mini lot (fixed maximum configuarable).
Final position: Base × Multiplier (then rounded to micro lots).
NZDJPY: 95% → 1 mini × 0.95 = 0.95 mini → 9 micro
EURJPY: 75% → 1 mini × 0.75 = 0.75 mini → 7 micro
CADJPY: 54% → 1 mini × 0.54 = 0.54 mini → 5.4 micro → 5 micro
EURGBP: 38% → 1 mini × 0.38 = 0.38 mini → 3.8 micro → 4 micro
Drift cap (when active): 50% maximum.
Example: NZDJPY at 95% would be reduced to 50% → 5 micro.
The HMM is your position sizing engine.
Confidence determines risk, which determines the multiplier, which determines the micro lots.
1 mini lot is the risk ceiling.
The model decides how far below that ceiling each trade should be placed.
Note the different position sizes -
The HMM applies a multiplier such as 38%, 54%, 75%, or 95%.
Base position: 1 mini lot (fixed maximum configuarable).
Final position: Base × Multiplier (then rounded to micro lots).
NZDJPY: 95% → 1 mini × 0.95 = 0.95 mini → 9 micro
EURJPY: 75% → 1 mini × 0.75 = 0.75 mini → 7 micro
CADJPY: 54% → 1 mini × 0.54 = 0.54 mini → 5.4 micro → 5 micro
EURGBP: 38% → 1 mini × 0.38 = 0.38 mini → 3.8 micro → 4 micro
Drift cap (when active): 50% maximum.
Example: NZDJPY at 95% would be reduced to 50% → 5 micro.
The HMM is your position sizing engine.
Confidence determines risk, which determines the multiplier, which determines the micro lots.
1 mini lot is the risk ceiling.
The model decides how far below that ceiling each trade should be placed.
Jason Smith
“Canary Drift” meaning
Drift refers to the degradation of a model’s performance over time as market conditions change.
A strategy that once worked well can begin to fail as the underlying data patterns shift in ways the model was not trained on.
The canary model is designed to detect this drift early, before the production system begins to lose money.
It acts as a real-time monitoring layer, running alongside live trading models to identify early signs of instability.
The idea is similar to the “canary in a coal mine.” Miners once used canaries underground as an early warning system for toxic gas.
If dangerous conditions appeared, the canary would be affected first, giving miners time to evacuate before the situation became fatal.
In the same way, the canary model is exposed to the same market data as the production system but is used purely for monitoring rather than trading.
Your production model is the one placing real trades, while the canary continuously “tests the air” on the same inputs.
If the canary’s performance begins to degrade, it signals that market conditions may have shifted.
This allows you to pause trading or roll back to a more stable champion model before real losses occur.
This matters because markets are constantly evolving.
A model that performs well in one regime may fail in another without obvious warning signs in individual predictions.
Drift can take different forms, including data drift, where the input distributions change due to shifts like volatility spikes or new correlations between assets.
It can also appear as concept drift, where the relationship between inputs and outputs changes, such as when previously reliable support and resistance levels break down.
In other cases, prediction drift can occur, where the model’s outputs become systematically biased, for example repeatedly holding positions when trades should actually be taken.
In this setup, the canary is showing “monitoring drift” at the same age as your production and champion models, which indicates they were all trained from the same batch.
The canary is actively checking for any performance degradation.
The alert does not necessarily confirm failure, but it is a warning that performance divergence has begun and should be investigated.
Overall, the canary acts as an early warning system for model decay.
It does not execute trades itself but instead monitors behaviour continuously.
If it detects drift, it provides the signal to pause or roll back the production system, protecting capital before meaningful losses occur.
Drift refers to the degradation of a model’s performance over time as market conditions change.
A strategy that once worked well can begin to fail as the underlying data patterns shift in ways the model was not trained on.
The canary model is designed to detect this drift early, before the production system begins to lose money.
It acts as a real-time monitoring layer, running alongside live trading models to identify early signs of instability.
The idea is similar to the “canary in a coal mine.” Miners once used canaries underground as an early warning system for toxic gas.
If dangerous conditions appeared, the canary would be affected first, giving miners time to evacuate before the situation became fatal.
In the same way, the canary model is exposed to the same market data as the production system but is used purely for monitoring rather than trading.
Your production model is the one placing real trades, while the canary continuously “tests the air” on the same inputs.
If the canary’s performance begins to degrade, it signals that market conditions may have shifted.
This allows you to pause trading or roll back to a more stable champion model before real losses occur.
This matters because markets are constantly evolving.
A model that performs well in one regime may fail in another without obvious warning signs in individual predictions.
Drift can take different forms, including data drift, where the input distributions change due to shifts like volatility spikes or new correlations between assets.
It can also appear as concept drift, where the relationship between inputs and outputs changes, such as when previously reliable support and resistance levels break down.
In other cases, prediction drift can occur, where the model’s outputs become systematically biased, for example repeatedly holding positions when trades should actually be taken.
In this setup, the canary is showing “monitoring drift” at the same age as your production and champion models, which indicates they were all trained from the same batch.
The canary is actively checking for any performance degradation.
The alert does not necessarily confirm failure, but it is a warning that performance divergence has begun and should be investigated.
Overall, the canary acts as an early warning system for model decay.
It does not execute trades itself but instead monitors behaviour continuously.
If it detects drift, it provides the signal to pause or roll back the production system, protecting capital before meaningful losses occur.
Jason Smith
Empiricism: Knowledge comes from experience and observation.
A Priori: Knowledge comes from reason and logic, independent of experience.
Imagine a child kept artificially alive from birth, but without any senses — no sight, no hearing, no touch, taste, or smell.
For years, this person grows, completely cut off from the world.
Then, at maturity, the five senses are suddenly given.
The question is striking - would this person have a single thought in their head?
It asks whether all knowledge comes from experience, whether the mind could hold any innate ideas independent of that experience, and how much of thought depends on the senses versus reasoning alone.
It’s a simple yet profound way to reflect on how we become who we are — shaped through experience, perception, and the constant interaction between mind and world.
An empiricist would say the child has no thoughts at first, because all knowledge comes from experience — the mind is a blank slate until the senses provide input.
An a Priori thinker like Immanuel Kant would argue that while the child gains knowledge through experience, certain structures of understanding or concepts are innate, so the mind isn’t completely empty.
We are born with an inherent capacity for logical thinking that is not derived from observation, but is realised through experience.
A Priori: Knowledge comes from reason and logic, independent of experience.
Imagine a child kept artificially alive from birth, but without any senses — no sight, no hearing, no touch, taste, or smell.
For years, this person grows, completely cut off from the world.
Then, at maturity, the five senses are suddenly given.
The question is striking - would this person have a single thought in their head?
It asks whether all knowledge comes from experience, whether the mind could hold any innate ideas independent of that experience, and how much of thought depends on the senses versus reasoning alone.
It’s a simple yet profound way to reflect on how we become who we are — shaped through experience, perception, and the constant interaction between mind and world.
An empiricist would say the child has no thoughts at first, because all knowledge comes from experience — the mind is a blank slate until the senses provide input.
An a Priori thinker like Immanuel Kant would argue that while the child gains knowledge through experience, certain structures of understanding or concepts are innate, so the mind isn’t completely empty.
We are born with an inherent capacity for logical thinking that is not derived from observation, but is realised through experience.
Jason Smith
For sale: the complete HMM (Hidden Markov Model) Allocator package, including the Markov Matrix Python bot, interactive dashboard, source files, and full copyright ownership.
Perfect for anyone looking to deploy advanced algorithmic trading tools immediately.
This complete package is available exclusively on the MQL5 Marketplace.
DM me for more info
Perfect for anyone looking to deploy advanced algorithmic trading tools immediately.
This complete package is available exclusively on the MQL5 Marketplace.
DM me for more info
Jason Smith
Pazartesi
Includes full instructions and ongoing support, along with a comprehensive PDF guide explaining how to use the system and customise it to trade a wide range of assets, including indices, oil, gold, Bitcoin, stocks, and more.
Jason Smith
Markov Matrix HMM bot start up screen (part 1).
A Hmm allocator. (Hidden Markov Model)
A dashboard.
Python bot and an mql5 one.
This complete package is available exclusively on the MQL5 Marketplace. Coming soon !
A Hmm allocator. (Hidden Markov Model)
A dashboard.
Python bot and an mql5 one.
This complete package is available exclusively on the MQL5 Marketplace. Coming soon !
Jason Smith
HMM allocator analyzes market regimes and outputs BUY/SELL/HOLD signals with quality scores and position sizing.
Jason Smith
You’ve already seen the HMM dashboard.
The Python bot that receives signals from Marko also has its own dashboard.
The dashboard is a real-time web interface that lets you monitor and control your trading bot from any browser.
It displays your account balance, equity, daily profit and loss, all open positions with current profit or loss, the most recent HMM signal received, and a live log of bot activity.
You can also pause trading, resume trading, or manually close any open position with a single click.
When you run the bot with the --dashboard flag, it starts a web server on port 5000 (or a port you specify with --dashboard-port), and you access it by opening http://localhost:5000 in your browser.
It updates automatically every two seconds, giving you full visibility and control without needing to touch the command line.
The Python bot that receives signals from Marko also has its own dashboard.
The dashboard is a real-time web interface that lets you monitor and control your trading bot from any browser.
It displays your account balance, equity, daily profit and loss, all open positions with current profit or loss, the most recent HMM signal received, and a live log of bot activity.
You can also pause trading, resume trading, or manually close any open position with a single click.
When you run the bot with the --dashboard flag, it starts a web server on port 5000 (or a port you specify with --dashboard-port), and you access it by opening http://localhost:5000 in your browser.
It updates automatically every two seconds, giving you full visibility and control without needing to touch the command line.
