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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
Markov Trades
Jason Smith
2026.04.01
We have a few trades set at 1 mini, as that’s the broker’s minimum for those symbols. It defaults to 1 mini, and the Markov model applies its percentage on top—but since 1 mini is already the minimum, the percentage has no effect. So I need to increase the base size to 5 mini for those instruments.
Jason Smith
Notice: Quant-Level HMM Trading System (MQL5)
I’ve developed a Hidden Markov Model (HMM) trading system with a Python backend, ready for MetaTrader 5 integration.
This system operates at quant fund-level sophistication, detecting market regimes and generating probabilistic entry/exit signals across multiple symbols and timeframes.
I’m looking for someone experienced in marketing, promotion, or a trading channels to help advertise and sell this system.
I’ve developed a Hidden Markov Model (HMM) trading system with a Python backend, ready for MetaTrader 5 integration.
This system operates at quant fund-level sophistication, detecting market regimes and generating probabilistic entry/exit signals across multiple symbols and timeframes.
I’m looking for someone experienced in marketing, promotion, or a trading channels to help advertise and sell this system.
Jason Smith
Brad Pickett is a retired English mixed martial artist best known for competing in the bantamweight division of the Ultimate Fighting Championship (UFC).
He fought professionally from the mid‑2000s through 2017 and earned a reputation as a tough, fan‑friendly fighter with the nickname “One Punch.”
He fought professionally from the mid‑2000s through 2017 and earned a reputation as a tough, fan‑friendly fighter with the nickname “One Punch.”
Jason Smith
2026.04.01
A rose that grew from a crack in the concrete.🌹
Even in the toughest circumstances, something can grow. It’s about overcoming struggle, thriving despite adversity, and turning hardship into strength.
Even in the toughest circumstances, something can grow. It’s about overcoming struggle, thriving despite adversity, and turning hardship into strength.
Jason Smith
Bitcoin - Ive spent more than eight weeks waiting for a clean double bottom to buy some coins.
I placed an order at 46 k GBP / 62 k USD.
Bottom yellow line.
Trader's this is almost a no lose gamble.
I placed an order at 46 k GBP / 62 k USD.
Bottom yellow line.
Trader's this is almost a no lose gamble.
Show all comments (5)
Thomas Eduard Van Der Jagt
2026.04.01
no your insights looks correct i always looking by this kind of charts the RSI to make a divergence also
Jason Smith
2026.04.01
Spot trading = actively buying and selling coins to profit from short-term moves.
Buying coins = simply holding long-term and waiting for price to rise. I used to trade divergences a few years ago, when i was Forex trading mainly using MFI and RSI.
Buying coins = simply holding long-term and waiting for price to rise. I used to trade divergences a few years ago, when i was Forex trading mainly using MFI and RSI.
Jason Smith
Markov Model — Upgraded to canary in this cycle, while another model was promoted to production.
This is a typical canary deployment pattern:
A new model (Bitcoin, ff83e501) is deployed to canary for small-scale traffic validation
If performance holds, it may later be promoted to production in a future cycle
Meanwhile, the existing canary (75b1a2ca) was validated and promoted to production in this cycle
This is a typical canary deployment pattern:
A new model (Bitcoin, ff83e501) is deployed to canary for small-scale traffic validation
If performance holds, it may later be promoted to production in a future cycle
Meanwhile, the existing canary (75b1a2ca) was validated and promoted to production in this cycle
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.
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.