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1 year
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The best algorithmic developers aren't just programmers - they're traders who code.
I develop and code custom trading strategies, automation tools and custom software across multiple platforms and languages, including TradingView (Pine Script), MetaTrader 5 (MQL5), Python, C, C++, PHP, JavaScript, Java, and other modern development frameworks.
Looking for a quantitative developer role.
I can turn trading strategies into fully functional systems.
Extensive experience with Linux (Gentoo, Debian) and Unix systems (FreeBSD, OpenBSD)
I’m available for projects.You can ask for me directly on Freelancer
How Observation Changes Outcomes :
In quantum mechanics, when light (or electrons) passes through two slits, it creates an interference pattern on the screen behind them.
Each particle seems to go through both slits at once, existing in a superposition of all possible paths and the resulting pattern reflects the probabilities of where the particle could land.
If you try to measure which slit the particle goes through, the interference pattern disappears.
Observing the particle forces it into a definite state - it goes through one slit or the other.
The act of measurement collapses the wave function and fundamentally changes the outcome.
Before you check a trade, it’s in superposition.
From a statistical perspective, your trade has a probability of winning or losing based on your system but you don’t yet know the outcome.
The trade is evolving naturally, just like a quantum system.
The moment you look at it, your observation collapses the “trade wave function” into a definite state - good or bad, winning or losing.
That observation triggers an emotional reaction — stress, fear, or overconfidence—which can cause you to break your plan, over-leverage, or revenge trade.
Just like in quantum mechanics, the act of measurement affects the system.
If you hadn’t looked, the system would have continued evolving naturally and you would have followed your plan without emotional interference.
This analogy mirrors the quantum concept perfectly - observation changes the outcome, not because the market changed, but because your interaction with it changed your behavior.
In other words, checking too often destroys the natural probabilistic outcome of your system, just like measuring the slit destroys the interference pattern.
The trade itself hasn’t changed; your observation changed how you interact with it, which changes the outcome.
Final Thoughts:
Traders, you know what I’m talking about — in a demo, you can leave your strategy untouched for days, weeks, even months.
The moment it goes live, you start checking too often, micromanaging your trades, and suddenly your observation is affecting the outcome.
I develop and code custom trading strategies, automation tools and custom software across multiple platforms and languages, including TradingView (Pine Script), MetaTrader 5 (MQL5), Python, C, C++, PHP, JavaScript, Java, and other modern development frameworks.
Looking for a quantitative developer role.
I can turn trading strategies into fully functional systems.
Extensive experience with Linux (Gentoo, Debian) and Unix systems (FreeBSD, OpenBSD)
I’m available for projects.You can ask for me directly on Freelancer
How Observation Changes Outcomes :
In quantum mechanics, when light (or electrons) passes through two slits, it creates an interference pattern on the screen behind them.
Each particle seems to go through both slits at once, existing in a superposition of all possible paths and the resulting pattern reflects the probabilities of where the particle could land.
If you try to measure which slit the particle goes through, the interference pattern disappears.
Observing the particle forces it into a definite state - it goes through one slit or the other.
The act of measurement collapses the wave function and fundamentally changes the outcome.
Before you check a trade, it’s in superposition.
From a statistical perspective, your trade has a probability of winning or losing based on your system but you don’t yet know the outcome.
The trade is evolving naturally, just like a quantum system.
The moment you look at it, your observation collapses the “trade wave function” into a definite state - good or bad, winning or losing.
That observation triggers an emotional reaction — stress, fear, or overconfidence—which can cause you to break your plan, over-leverage, or revenge trade.
Just like in quantum mechanics, the act of measurement affects the system.
If you hadn’t looked, the system would have continued evolving naturally and you would have followed your plan without emotional interference.
This analogy mirrors the quantum concept perfectly - observation changes the outcome, not because the market changed, but because your interaction with it changed your behavior.
In other words, checking too often destroys the natural probabilistic outcome of your system, just like measuring the slit destroys the interference pattern.
The trade itself hasn’t changed; your observation changed how you interact with it, which changes the outcome.
Final Thoughts:
Traders, you know what I’m talking about — in a demo, you can leave your strategy untouched for days, weeks, even months.
The moment it goes live, you start checking too often, micromanaging your trades, and suddenly your observation is affecting the outcome.
Jason Smith
📁 HMM Allocator Python Package (mt5 version of bot)
├── markov5.py # Main trading engine
├── trading_bot.py # Automated execution bot
├── config.yaml # All settings
├── saved_models/ # Trained HMM models
├── hmm_allocator_history.db # All trade data
└── api_keys.json # Secure key storage
├── markov5.py # Main trading engine
├── trading_bot.py # Automated execution bot
├── config.yaml # All settings
├── saved_models/ # Trained HMM models
├── hmm_allocator_history.db # All trade data
└── api_keys.json # Secure key storage
Jason Smith
Hidden Markov Matrix (HMM) is available for sale, but it has not yet been uploaded and no advert has been published.
Keep an eye out for it. If you would like some information before the release, you can request details via DM.
Keep an eye out for it. If you would like some information before the release, you can request details via DM.
Jason Smith
2026.03.13
You will receive three core files - The Hidden Markov Python source, the Python trading bot source, and an MT5 version of the python bot written in MQL5. Everything is fully configurable.
Jason Smith
2026.03.13
The system also includes a web interface that allows you to interact with and monitor the model more easily. Through the interface you can view model outputs, monitor signals, and adjust configuration settings without needing to edit the code directly. It provides a simple dashboard-style environment where you can run the model, review data, and manage how the bot behaves.
Jason Smith
UAnchor with Inverse Magic is a fully automated trading system built around threshold-based market logic.
The EA starts by capturing an initial market level known as the anchor price.
This anchor acts as the core reference point from which all trading decisions are derived.
The system continuously measures the percentage change in price, recalculating the distance from the anchor on every market tick.
As price moves away from the anchor, predefined thresholds trigger trading logic designed to react dynamically to market expansion and reversal.
The Inverse Magic component adds an additional layer of strategy control, allowing the system to manage and distinguish trade logic internally for more advanced position handling.
Status: Currently in development and active testing.
Availability: Not available for sale at this time.
The EA starts by capturing an initial market level known as the anchor price.
This anchor acts as the core reference point from which all trading decisions are derived.
The system continuously measures the percentage change in price, recalculating the distance from the anchor on every market tick.
As price moves away from the anchor, predefined thresholds trigger trading logic designed to react dynamically to market expansion and reversal.
The Inverse Magic component adds an additional layer of strategy control, allowing the system to manage and distinguish trade logic internally for more advanced position handling.
Status: Currently in development and active testing.
Availability: Not available for sale at this time.
Jason Smith
Pro15 converted to Python with Hidden Markov Model (HMM) — Beta Stage, Currently Under Testing
Jason Smith
Pro15 – No intervention. 95 days untouched. Completely on autopilot.
· 1
Jason Smith
2026.03.04
You’re welcome to review the MT5 logs if you wish. Traders, everything I show you is real and fully transparent. I’ve had two sales so far — the first received a 5-star review, and the second is currently awaiting review.
Jason Smith
Introducing a new Pro15 feature - Integrated Markov functionality.
Python creates the regimes for Pro15.
Python creates the regimes for Pro15.
· 1
Jason Smith
2026.02.28
This feature is currently in its beta stage, meaning it is live and functional but still undergoing testing and refinement.
Jason Smith
I was experiencing some issues with the Python code earlier.
This is the latest version of the Markov model, fully integrated to work with Pro15 and the gold market.
It can also be purchased without Pro15 integration, allowing you to connect your own bots.
However, when combined with Pro15, it delivers institutional-grade performance.
This will be on sale soon.Don't know when but look out for it.Elite trading bot.
This is the latest version of the Markov model, fully integrated to work with Pro15 and the gold market.
It can also be purchased without Pro15 integration, allowing you to connect your own bots.
However, when combined with Pro15, it delivers institutional-grade performance.
This will be on sale soon.Don't know when but look out for it.Elite trading bot.
· 1
Jason Smith
Pro15 now integrates a Markov Matrix framework.
This version runs in Python and operates with two EAs.
I may need to distribute the EAs through the Marketplace and source the Python integration separately via a Freelancer job.
This version runs in Python and operates with two EAs.
I may need to distribute the EAs through the Marketplace and source the Python integration separately via a Freelancer job.
Jason Smith
2026.02.27
Might have to dump whole project and start again. Good learning curve though. Take a break for a week.
Jason Smith
BEAUTIFUL Look at that 8:15 AM - 40% trend / 60% mean (slight mean bias)
9:15 AM: 0% trend / 100% mean (strong mean regime detected!)
9:15 AM: 0% trend / 100% mean (strong mean regime detected!)
Jason Smith
2026.02.27
This is EXACTLY how it should work.
The HMM detected a strong mean-reversion environment and is now betting fully on the mean bot.
The HMM detected a strong mean-reversion environment and is now betting fully on the mean bot.
Jason Smith
Watch out for Markov. Testing on Pro15. Trend bot and Mean bot is just a renamed pro15
Jason Smith
2026.02.27
Exciting new project in Alpha phase at present - Beta - stable (released to the public)
Jason Smith
2026.02.27
input string PythonFilePath = "C:\\Users\\YourName\\AppData\\Roaming\\MetaQuotes\\Terminal\\Common\\"; // Path to Python files
8 market regimes. 4 timeframes. 1 decision. HMM knows what you don't.
Your trading bot is only as good as its risk management. This has 5 layers.
From M15 to D1 - see the whole picture before you trade.
The only HMM-based system that actually places the trades for you.