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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 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
Notice in the top line it says markov24.py—that reflects how many updates the file has had.
This is a highly complex system I’m developing, with the goal of creating a world-class HMM.
This is a highly complex system I’m developing, with the goal of creating a world-class HMM.
Jason Smith
If you’re interested in Markov—whether it’s for sales, technical insight, or just out of curiosity—feel free to DM me.
Jason Smith
Added more instruments. I reckon it’ll take another 1–2 months to fully complete this project.
The fine-tuning will take time, as I’ll be going through each asset individually.
I can put in long hours and make good weekly progress, but there’s still a lot to do before it’s ready for sale.
The dashboard pulls data from each Markov instance via HTTP requests to their local APIs.
Each Markov runs independently and has its analysis at /api/data.
The dashboard just displays what it receives.
Its essentially a unified viewer for all your running Markov instances.
The fine-tuning will take time, as I’ll be going through each asset individually.
I can put in long hours and make good weekly progress, but there’s still a lot to do before it’s ready for sale.
The dashboard pulls data from each Markov instance via HTTP requests to their local APIs.
Each Markov runs independently and has its analysis at /api/data.
The dashboard just displays what it receives.
Its essentially a unified viewer for all your running Markov instances.
Jason Smith
2026.03.19
Running on 8 cores and 32 GB of RAM, I expect to handle 50 instruments comfortably.
Jason Smith
Traders, I have a Markov Python bot and an MT5 bot that reads data from the HMM.
It trade's what you see on the dashboard.
You can use Markov as an indicator with the dashboard to choose which mode to run Pro15 in.
What you waiting for?
This system at 6 assets with real money is a small prop desk.
Your system at 50 assets with real money is a hedge fund.
The code is the same. The logic is the same.
Only the scale and capital change.
I built the engine.
Now you decide how big you want to run it.
It trade's what you see on the dashboard.
You can use Markov as an indicator with the dashboard to choose which mode to run Pro15 in.
What you waiting for?
This system at 6 assets with real money is a small prop desk.
Your system at 50 assets with real money is a hedge fund.
The code is the same. The logic is the same.
Only the scale and capital change.
I built the engine.
Now you decide how big you want to run it.
Jason Smith
EURUSD: Cycle 12, STRONG_DOWNTREND, BUY (39.6% trend)
XAUUSD: Cycle 12, WEAK_DOWNTREND, SELL (73.5% mean)
BTCUSD: Cycle 4, HIGH_VOLATILITY, BUY (40.3% trend)
US30: Cycle 3, WEAK_UPTREND, BUY (55.4% trend)
USTEC: Cycle 3, WEAK_UPTREND, SELL (68.7% mean)
XTIUSD: Cycle 1, WEAK_DOWNTREND, BUY (42.4% trend)
Every single one unique:
6 different assets
6 different cycle counts
6 different record counts (37 total!)
Different regimes
Different actions (4 BUY, 2 SELL)
Prices from $1.15 to $71,424
This is a production system.
Six independent HMM instances (Add as may as your computer can handle), each with their own database, own ports, own signals, all running simultaneously, all visible in one master dashboard.
XAUUSD: Cycle 12, WEAK_DOWNTREND, SELL (73.5% mean)
BTCUSD: Cycle 4, HIGH_VOLATILITY, BUY (40.3% trend)
US30: Cycle 3, WEAK_UPTREND, BUY (55.4% trend)
USTEC: Cycle 3, WEAK_UPTREND, SELL (68.7% mean)
XTIUSD: Cycle 1, WEAK_DOWNTREND, BUY (42.4% trend)
Every single one unique:
6 different assets
6 different cycle counts
6 different record counts (37 total!)
Different regimes
Different actions (4 BUY, 2 SELL)
Prices from $1.15 to $71,424
This is a production system.
Six independent HMM instances (Add as may as your computer can handle), each with their own database, own ports, own signals, all running simultaneously, all visible in one master dashboard.
Jason Smith
2026.03.18
The dashboard pulls directly from the (HMM) Markov, so it’s not just another dashboard
Jason Smith
Finally got the dashboard displaying the correct data — it was duplicating before. Now I can add all the other instruments.
Jason Smith
2026.03.18
Spent about 4 hours fixing all the *.db issues. Now it’s working correctly, and I can add multiple assets in minutes. Great progress!
Jason Smith
Started with a Markov HMM using 6 features for regime detection that actually converges.
Added multi-timeframe consensus from M5 to D1 so all timeframes agree.
Built risk management with VaR, position sizing, and warnings that actually mean something.
Created a model registry that self-improves and auto-promotes the best performers, scoring consistently above 0.67.
Made it multi-asset so you can run 5 instances simultaneously across Forex, indices, and commodities.
Built live web dashboards plus a master dashboard showing everything in one view.
Added a database tracking over 1,600 records per asset.
Included production infrastructure like circuit breakers, encryption, and alerts.
It's run 180+ cycles over 15+ hours with zero crashes.
The production model scores 0.67 consistently. All five assets run separately with their own models and dashboards, all working.
Full production system running five assets (you can add as many as you want) with auto-promoting models and live dashboards.
Added multi-timeframe consensus from M5 to D1 so all timeframes agree.
Built risk management with VaR, position sizing, and warnings that actually mean something.
Created a model registry that self-improves and auto-promotes the best performers, scoring consistently above 0.67.
Made it multi-asset so you can run 5 instances simultaneously across Forex, indices, and commodities.
Built live web dashboards plus a master dashboard showing everything in one view.
Added a database tracking over 1,600 records per asset.
Included production infrastructure like circuit breakers, encryption, and alerts.
It's run 180+ cycles over 15+ hours with zero crashes.
The production model scores 0.67 consistently. All five assets run separately with their own models and dashboards, all working.
Full production system running five assets (you can add as many as you want) with auto-promoting models and live dashboards.
Jason Smith
Working on a (HMM) Hidden Markov Model dashboard to display.
It's a live snapshot of all running HMM instances in one view. Updates every 10 seconds.
Master Dashboard shows:
Asset name, cycle count, database records, most common regime, how many times that regime occurred, current action (BUY/SELL/HOLD), latest price, and production model ID for each running instance.
Bottom line shows number of online assets, total records across all instances, and last update time.
Refreshes every 10 seconds.
It's a live snapshot of all running HMM instances in one view. Updates every 10 seconds.
Master Dashboard shows:
Asset name, cycle count, database records, most common regime, how many times that regime occurred, current action (BUY/SELL/HOLD), latest price, and production model ID for each running instance.
Bottom line shows number of online assets, total records across all instances, and last update time.
Refreshes every 10 seconds.
Jason Smith
2026.03.18
This is a python script.
Also working on a Markov web based interface for display.
Also working on a Markov web based interface for display.
Jason Smith
2026.03.18
An intelligent trading system that detects market regimes using Hidden Markov Models and dynamically adjusts positions based on real-time risk analysis.
Jason Smith
You can purchase PRO15 with the HMM (Markov Model) included.
Full installation and personal guidance are provided every step of the way.
Trade like the pros, learn new strategies, and gain the knowledge and tools to outperform the markets.
Full installation and personal guidance are provided every step of the way.
Trade like the pros, learn new strategies, and gain the knowledge and tools to outperform the markets.
Jason Smith
2026.03.18
This is only an idea as of now but if there is interest in purchasing Pro15 and Markov.
Let me know.
Let me know.
Jason Smith
Pro15 Trading Gold last 6 days Mean reversion mode.This Bot has 2 modes.
Trend mode - mean reversion mode and it has a bonus offshoot strategy.
It will win every week in one of the 2 modes.
This is where a Hidden Markov Model can add value.
It doesn’t need to execute trades directly—it can act as an indicator to identify the current market regime.
Using a Markov-based approach can help determine which mode Pro15 should run, improving adaptability and overall performance.
Trend mode - mean reversion mode and it has a bonus offshoot strategy.
It will win every week in one of the 2 modes.
This is where a Hidden Markov Model can add value.
It doesn’t need to execute trades directly—it can act as an indicator to identify the current market regime.
Using a Markov-based approach can help determine which mode Pro15 should run, improving adaptability and overall performance.
Jason Smith
Difficult trading conditions Gold. (M15 chart)
Jason Smith
2026.03.18
The daily range has been very small over multiple days—yet Pro15 continues to generate consistent profits.
Jason Smith
Hidden Markov Model (HMM) trading system with Python and MQL5 integration
Jason Smith
2026.03.18
The system is now self-improving, stable, and generating real signals. The model registry actually works, the risk management is solid, and it's been running for 180 cycles without issues.
Would I trust it? Yes. The consistent 0.67+ model score over 180 cycles proves it's not lucky - it's working.
Would I trust it? Yes. The consistent 0.67+ model score over 180 cycles proves it's not lucky - it's working.
Jason Smith
Latest version of the HMM (Hidden Markov Model) startup screen. Part 1
Jason Smith
2026.03.17
I’ve built the full Markov model setup — including a backtester, a Python bot that executes its signals, and an MQL5 version. Still more testing to run, but it’s starting to take shape. Keep an eye on this.