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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 in the Freelance section.
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 super position 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 super position.
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 in the Freelance section.
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 super position 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 super position.
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
Pro15 last 100 days.
Completely on autopilot with zero intervention.
The bot manages everything, including holding positions overnight and over weekends whenever the strategy requires
Completely on autopilot with zero intervention.
The bot manages everything, including holding positions overnight and over weekends whenever the strategy requires
Jason Smith
2026.03.21
If you had closed all open trades, you would be over $13k in profit in roughly 100 days — all with just 1 micro lot per trade and a maximum of 8 open positions at any given time.
Jason Smith
2026.03.21
Traders, everything I’m showing you is real.
If you think otherwise, let’s debate it.
If you think otherwise, let’s debate it.
Jason Smith
Pro15 Magic 66 – Trend Mode (2 modes available)
Performance from Sunday night through Friday night this week:
At the start of the week, some trend-based strategies took a heavy hit.
Price was repeatedly tapping daily highs and lows and then reversing, which is a difficult environment for breakout systems that rely on those levels.
At first glance, the strike rate doesn’t look great.
However, when you consider the overall net for the week along with the floating profit from Friday, the performance is actually very strong.
The bot took a significant drawdown early in the week but recovered and finished over +1k, which is genuinely impressive.
This is likely one of the tougher market conditions the system can face, yet it still delivered solid results.
The key takeaway is discipline — if you stayed consistent and didn’t interfere, the end-of-week outcome speaks for itself.
Performance from Sunday night through Friday night this week:
At the start of the week, some trend-based strategies took a heavy hit.
Price was repeatedly tapping daily highs and lows and then reversing, which is a difficult environment for breakout systems that rely on those levels.
At first glance, the strike rate doesn’t look great.
However, when you consider the overall net for the week along with the floating profit from Friday, the performance is actually very strong.
The bot took a significant drawdown early in the week but recovered and finished over +1k, which is genuinely impressive.
This is likely one of the tougher market conditions the system can face, yet it still delivered solid results.
The key takeaway is discipline — if you stayed consistent and didn’t interfere, the end-of-week outcome speaks for itself.
Jason Smith
2026.03.21
That’s one of the toughest weeks I’ve seen, yet it’s still $1k up — absolutely amazing.
Jason Smith
Thought Of The Day -
Stay disciplined, stay patient.
The less you interfere, the better your results.
Stay disciplined, stay patient.
The less you interfere, the better your results.
Jason Smith
The Logic
The model is saying: ETH and SOL are in strong uptrends.
But short-term, they've moved too far too fast.
Mean reversion signals are overpowering trend signals.
Time for a pullback.
This is called counter-trend trading within a trend.
You SELL the overextended pump,
BUY back on the dip, and ride the next leg up.
This system isn't a simple trend follower.
It's detecting mean reversion opportunities within strong trends.
That's advanced trading.
78% agreement across timeframes means all 5 timeframes (M5 to D1) agree: short-term overbought.
This system is working exactly as designed.
The model is saying: ETH and SOL are in strong uptrends.
But short-term, they've moved too far too fast.
Mean reversion signals are overpowering trend signals.
Time for a pullback.
This is called counter-trend trading within a trend.
You SELL the overextended pump,
BUY back on the dip, and ride the next leg up.
This system isn't a simple trend follower.
It's detecting mean reversion opportunities within strong trends.
That's advanced trading.
78% agreement across timeframes means all 5 timeframes (M5 to D1) agree: short-term overbought.
This system is working exactly as designed.
Jason Smith
2026.03.20
What now?
With 19 (configurable) assets running, we wait. Let the system collect data for at least one week. The trade journal is filling with every signal. After that, we analyze -
Which assets have the highest win rate?
Which regimes produce the best signals?
Is the 55% quality threshold optimal?
The data will tell us what to tweak.
"Pos" in the menu is % of your fixed position. If you set 1 mini as base and Pos is 35 %, Trade 3 micro.
You can use % of account not fixed
With 19 (configurable) assets running, we wait. Let the system collect data for at least one week. The trade journal is filling with every signal. After that, we analyze -
Which assets have the highest win rate?
Which regimes produce the best signals?
Is the 55% quality threshold optimal?
The data will tell us what to tweak.
"Pos" in the menu is % of your fixed position. If you set 1 mini as base and Pos is 35 %, Trade 3 micro.
You can use % of account not fixed
Jason Smith
2026.03.20
API is live. Clients can pull signals into their own dashboards, trading bots, or mobile apps.
Jason Smith
2026.03.20
The dashboard gets its data from the Markov API running on locally
Each Markov instance serves JSON at http://localhost:PORT/api/data. The dashboard fetches from these endpoints every 5 minutes and displays the results in a table.
That's it. Markov generates the data
Each Markov instance serves JSON at http://localhost:PORT/api/data. The dashboard fetches from these endpoints every 5 minutes and displays the results in a table.
That's it. Markov generates the data
Jason Smith
Markov Bot connecting
· 1
Jason Smith
2026.03.19
Traders, if you’d like personal guidance in building your own HMM, or if you want to learn how I developed this system. You can DM me.
Jason Smith
This is an automated trading bot that connects your HMM Markov model to a real MT5 trading account.
It reads signals from your model and places actual trades automatically.
It reads signals from your model and places actual trades automatically.
· 1
Jason Smith
My final list of instruments -
Forex majors such as EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, GBPJPY.
Metals and energies including Gold (XAUUSD), Silver (XAGUSD), WTI Crude (XTIUSD).
Indices such as S&P 500 (US500), Dow Jones, (US30), Nasdaq 100 (USTEC), DAX 40.
Stocks/AI leaders including NVIDIA (NVDA), Microsoft (MSFT).
Cryptocurrencies such as Bitcoin (BTCUSD), Ethereum (ETHUSD), and Solana (SOLUSD).
Forex majors such as EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, GBPJPY.
Metals and energies including Gold (XAUUSD), Silver (XAGUSD), WTI Crude (XTIUSD).
Indices such as S&P 500 (US500), Dow Jones, (US30), Nasdaq 100 (USTEC), DAX 40.
Stocks/AI leaders including NVIDIA (NVDA), Microsoft (MSFT).
Cryptocurrencies such as Bitcoin (BTCUSD), Ethereum (ETHUSD), and Solana (SOLUSD).
· 1
Jason Smith
2026.03.19
USTECThe model is saying - USTEC Yes, the market is in a weak uptrend (regime)
But mean reversion signals (60.1%) strongly outweigh trend signals (21.4%)
With 70% agreement across timeframes, I'm confident enough to SELL
Think of it as - just because the overall regime is uptrend doesn't mean you should always buy. The model is detecting that within this uptrend, mean reversion forces are dominating and it's time to sell.
It's looking at short-term signals within the longer-term trend.
But mean reversion signals (60.1%) strongly outweigh trend signals (21.4%)
With 70% agreement across timeframes, I'm confident enough to SELL
Think of it as - just because the overall regime is uptrend doesn't mean you should always buy. The model is detecting that within this uptrend, mean reversion forces are dominating and it's time to sell.
It's looking at short-term signals within the longer-term trend.
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.
Even if they’re not forced to sell, their portfolios take massive mark-to-market hits, and anyone using leverage can get wiped out fast. So while some money rotates elsewhere, a lot of wealthy players are taking serious losses on paper—and in many cases, real ones too.