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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 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 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
Markov Python bot trading using the HMM signal (DE40).
An MT5 version will also be available.
An MT5 version will also be available.
Jason Smith
Markov Matrix HMM Dashboard -
Based on Markov HMM
The Markov Model is not guessing. It is calculating probability.
When price goes up in a strong uptrend, the HMM analyzes the hidden regime.
If it detects you are actually in a range or volatile regime disguised as a trend, it will not follow.
It will fade the move.
Based on Markov HMM
The Markov Model is not guessing. It is calculating probability.
When price goes up in a strong uptrend, the HMM analyzes the hidden regime.
If it detects you are actually in a range or volatile regime disguised as a trend, it will not follow.
It will fade the move.
Jason Smith
Pro15 Gold Trading Performance Today -
This isn't about riding a lucky trend.It's been buying and selling.
This isn't about riding a lucky trend.It's been buying and selling.
Show all comments (4)
Jason Smith
2026.03.30
If you're interested in any of my products or see something you like, you might be able to get it for free. Let me know here -
Jason Smith
Uanchors Menu with on screen visuals
· 1
Jason Smith
2026.03.30
Recognition can be a powerful motivator. When your work is noticed.
Thanx for your comment
Thanx for your comment
Jason Smith
Got a new bot coming out this week Uanchors. Watch out for it.
Jason Smith
2026.03.30
Uanchors — the bot your competition fears. The EA is a fully automated trading system based on threshold logic.
It begins by recording an initial price called the anchor price, which serves as the reference point for all trading decisions.
From this anchor, the system calculates the percentage change of the market price, updating in real-time with each tick.
It begins by recording an initial price called the anchor price, which serves as the reference point for all trading decisions.
From this anchor, the system calculates the percentage change of the market price, updating in real-time with each tick.
Jason Smith
Pro15 runs on custom timeframes, with D1 as the default.
The same core logic is applied across any selected timeframe.
Pro15 on H4 custom - Instead of fading the daily open, it fades the start of each H4 candle, trading the high and low of that timeframe.
The same core logic is applied across any selected timeframe.
Pro15 on H4 custom - Instead of fading the daily open, it fades the start of each H4 candle, trading the high and low of that timeframe.
Jason Smith
Upgraded the Monte Carlo script to leverage GPU acceleration — now fully utilizing the GPU for maximum performance
· 1
Jason Smith
It finds the best markets + best conditions, then simulates trading them together like a portfolio.
· 1
Jason Smith
What Makes this Markov Model World Class
The combination of HMM regime detection, multi-asset deployment, staged model deployment and comprehensive risk management puts this system far beyond what most traders ever build.
The architecture is scalable.
You can add new symbols, new models, new risk rules without rewriting core logic.
What Hedge Funds Do Differently -
They run this same structure but with proprietary data feeds, co-located servers, and teams of PhDs optimizing each component.
The logic is the same.
The execution is just faster and on more data.
The Markov Matrix, available exclusively on MQL5, is built on the same fundamental design principles as a small quant fund.
The Reality -
Ive built a miniature hedge fund. One that runs on your desktop, trades 20 markets, manages its own risk, tests new models before deployment, and tracks everything for analysis.
That's professional infrastructure.
Plus it also comes with a very powerful python bot to execute the trades sent by the HMM.
You get a Back tester. A dashboard, Monte Carlo and a lot lot more.
You can DM me for more info.
The combination of HMM regime detection, multi-asset deployment, staged model deployment and comprehensive risk management puts this system far beyond what most traders ever build.
The architecture is scalable.
You can add new symbols, new models, new risk rules without rewriting core logic.
What Hedge Funds Do Differently -
They run this same structure but with proprietary data feeds, co-located servers, and teams of PhDs optimizing each component.
The logic is the same.
The execution is just faster and on more data.
The Markov Matrix, available exclusively on MQL5, is built on the same fundamental design principles as a small quant fund.
The Reality -
Ive built a miniature hedge fund. One that runs on your desktop, trades 20 markets, manages its own risk, tests new models before deployment, and tracks everything for analysis.
That's professional infrastructure.
Plus it also comes with a very powerful python bot to execute the trades sent by the HMM.
You get a Back tester. A dashboard, Monte Carlo and a lot lot more.
You can DM me for more info.
Jason Smith
Markov Matrix HMM
This system tracks multiple HMM models to ensure you never deploy a bad one without testing.
Production is the live model trading. It's currently 11 days old.
Canary is a test model running alongside production but not trading.
You compare its performance to decide if it should replace production.
Champion and Challenger are for comparing historical models against new candidates.
The database stores all trading decisions from the production model.
The registry stores all trained model files.
This setup lets you upgrade models safely.
The canary catches problems before they hit your live account.
You always have a backup to roll back to if something fails.
This system tracks multiple HMM models to ensure you never deploy a bad one without testing.
Production is the live model trading. It's currently 11 days old.
Canary is a test model running alongside production but not trading.
You compare its performance to decide if it should replace production.
Champion and Challenger are for comparing historical models against new candidates.
The database stores all trading decisions from the production model.
The registry stores all trained model files.
This setup lets you upgrade models safely.
The canary catches problems before they hit your live account.
You always have a backup to roll back to if something fails.
Jason Smith
The image below is not the bot its the actual HMM -
Strong Uptrends Can Be Sells.
The HMM balances two opposing forces.
One wants to follow the trend and buy strength.
The other expects prices to revert and sells when they get too high.
When price is strongly rising, the mean reversion side triggers a sell signal because it sees the move as extreme and expects a pullback.
This is intentional -
It's designed to catch reversals.
It means you sometimes get sell signals during strong uptrends when the mean reversion side wins the battle.
The agreement score tells you if both sides agree.
Low agreement means they're fighting each other.
Strong Uptrends Can Be Sells.
The HMM balances two opposing forces.
One wants to follow the trend and buy strength.
The other expects prices to revert and sells when they get too high.
When price is strongly rising, the mean reversion side triggers a sell signal because it sees the move as extreme and expects a pullback.
This is intentional -
It's designed to catch reversals.
It means you sometimes get sell signals during strong uptrends when the mean reversion side wins the battle.
The agreement score tells you if both sides agree.
Low agreement means they're fighting each other.
Jason Smith
Hidden Markov Model (HMM) -
A mathematical model that detects invisible market conditions (trending, ranging, volatile) by analyzing visible data like price moves and volatility.
These are the signals that the Python bot uses to execute trades.
A mathematical model that detects invisible market conditions (trending, ranging, volatile) by analyzing visible data like price moves and volatility.
These are the signals that the Python bot uses to execute trades.
Jason Smith
What the Bot Is
This python bot is running a multi-asset automated trading system powered by Hidden Markov Models (HMM) .
This python bot is running a multi-asset automated trading system powered by Hidden Markov Models (HMM) .
Jason Smith
Thought of the day -
If you build it they will come
If you build it they will come
Jason Smith
2026.03.27
Not sure where Im going with this blog but its going to be built around the Markov Model
Jason Smith
Using a batch file to run multiple commands makes things much easier.
The dashboard, Markov processes, and bots require launching 39 separate shells, each with its own setup, which is very tedious to do manually.
With a batch file, you can simply click once and run everything automatically.This file is for launching the bots.
I have a separate one for running the Markov processes.
The dashboard, Markov processes, and bots require launching 39 separate shells, each with its own setup, which is very tedious to do manually.
With a batch file, you can simply click once and run everything automatically.This file is for launching the bots.
I have a separate one for running the Markov processes.
Jason Smith
2026.03.27
Everything I’m working on is still in beta, so I’m continuously testing, refining, and making improvements as I go.
Jason Smith
Creating Monte Carlo Simulation in Python - Imagine you have a trading strategy.
You backtested it and it made $10,000 over a year.
What if trades happened in a different order?
What if you hit your losing streak first instead of last?
Would you still make $10,000? Would you blow up?
Monte Carlo answers that by replaying your strategy thousands of times, each time shuffling the trades randomly.
Some simulations you get lucky and make $20,000.Some you get unlucky and lose $5,000.
Some you just grind to $12,000.After running all these simulations, you see the full range of what could happen.
You learn your average outcome, your worst case, and most importantly, your risk of going broke.A strategy that looks great in backtest might fail in 30% of Monte Carlo simulations.
That tells you it's too risky. You need to size down or improve the edge.
It's simply a reality check. It strips away the "if everything goes perfectly" fantasy and shows you what will probably happen, what could go wrong, and how bad it might get.No bullshit.
Just probability telling you if your strategy is actually safe to trade.
Monte Carlo Simulation Named after the famous Monte Carlo Casino in Monaco because it relies on randomness and probability.
What It Actually Is Monte Carlo simulation is mathematical risk analysis, not gambling.
Gambling is betting without knowing the odds, while Monte Carlo is calculating the odds before you bet.
Gamblers hope for luck, but traders using Monte Carlo know exactly what bad luck looks like before it happens.
It removes hope and replaces it with probability.
You don’t wonder, “What if I hit a losing streak?” You know, “There’s a 5% chance I lose 10 trades in a row.” You don’t panic when a drawdown hits.
You expected it.
You sized your positions so you survive it.
Despite the casino name, Monte Carlo is one of the most serious, scientific tools in finance.
Professionals use it specifically to avoid gambling with their money.
You backtested it and it made $10,000 over a year.
What if trades happened in a different order?
What if you hit your losing streak first instead of last?
Would you still make $10,000? Would you blow up?
Monte Carlo answers that by replaying your strategy thousands of times, each time shuffling the trades randomly.
Some simulations you get lucky and make $20,000.Some you get unlucky and lose $5,000.
Some you just grind to $12,000.After running all these simulations, you see the full range of what could happen.
You learn your average outcome, your worst case, and most importantly, your risk of going broke.A strategy that looks great in backtest might fail in 30% of Monte Carlo simulations.
That tells you it's too risky. You need to size down or improve the edge.
It's simply a reality check. It strips away the "if everything goes perfectly" fantasy and shows you what will probably happen, what could go wrong, and how bad it might get.No bullshit.
Just probability telling you if your strategy is actually safe to trade.
Monte Carlo Simulation Named after the famous Monte Carlo Casino in Monaco because it relies on randomness and probability.
What It Actually Is Monte Carlo simulation is mathematical risk analysis, not gambling.
Gambling is betting without knowing the odds, while Monte Carlo is calculating the odds before you bet.
Gamblers hope for luck, but traders using Monte Carlo know exactly what bad luck looks like before it happens.
It removes hope and replaces it with probability.
You don’t wonder, “What if I hit a losing streak?” You know, “There’s a 5% chance I lose 10 trades in a row.” You don’t panic when a drawdown hits.
You expected it.
You sized your positions so you survive it.
Despite the casino name, Monte Carlo is one of the most serious, scientific tools in finance.
Professionals use it specifically to avoid gambling with their money.
Jason Smith
I’m available for projects.You can ask for me directly on Freelancer.
I can turn trading strategies into fully functional systems.
I can turn trading strategies into fully functional systems.
Jason Smith
2026.03.26
Monte Carlo Simulation Named after the famous Monte Carlo Casino in Monaco because it relies on randomness and probability. What It Actually Is Monte Carlo simulation is mathematical risk analysis, not gambling. Gambling is betting without knowing the odds, while Monte Carlo is calculating the odds before you bet. Gamblers hope for luck, but traders using Monte Carlo know exactly what bad luck looks like before it happens. It removes hope and replaces it with probability. You don’t wonder, “What if I hit a losing streak?” You know, “There’s a 5% chance I lose 10 trades in a row.” You don’t panic when a drawdown hits. You expected it. You sized your positions so you survive it. Despite the casino name, Monte Carlo is one of the most serious, scientific tools in finance. Professionals use it specifically to avoid gambling with their money.
