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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
No matter what system you use in trading, the edge usually isn’t just strategy. It’s how you control the downside when you’re wrong.
A lot of trader's get stuck obsessing over the perfect strategy, indicators, or timing entries.
But the one who last tend to focus more on how much they risk per trade, how they handle losing streaks, their position sizing, and staying consistent over time.
Even a mediocre strategy can survive, and sometimes even perform well, with solid risk management.
A great strategy can fall apart quickly if risk isn’t controlled.
A simple way to think about it is that the strategy gives you potential edge, while risk management is what allows you to survive and actually compound results over time.
Survival is everything.
You can’t benefit from a good system if you’re wiped out before it has a chance to play out.
A lot of trader's get stuck obsessing over the perfect strategy, indicators, or timing entries.
But the one who last tend to focus more on how much they risk per trade, how they handle losing streaks, their position sizing, and staying consistent over time.
Even a mediocre strategy can survive, and sometimes even perform well, with solid risk management.
A great strategy can fall apart quickly if risk isn’t controlled.
A simple way to think about it is that the strategy gives you potential edge, while risk management is what allows you to survive and actually compound results over time.
Survival is everything.
You can’t benefit from a good system if you’re wiped out before it has a chance to play out.
Jason Smith
MarkovMatrix>python BOT.py --symbol EURUSD --fixed-lots 0.1 --journal eurobot.db --magic 3 --auto-reconnect --mt5-path "C:\Program Files\MetaTrader 7 IC Markets Global\terminal64.exe" --max-positions 2
Jason Smith
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 30 (was 5) minutes and displays the results in a table.
Each Markov instance serves JSON at http://localhost:PORT/api/data.
The dashboard fetches from these endpoints every 30 (was 5) minutes and displays the results in a table.
Jason Smith
2026.03.24
This full system will be available for sale soon.Not sure when.
If you’re interested, feel free to DM me.
If you’re interested, feel free to DM me.
Jason Smith
Python Bot trading the HMM signals.
Jason Smith
2026.03.24
Position sizes are different because of the Markov signal size multiplier.
The Markov model assigns higher size multipliers to signals it's more confident in.Stronger signals get larger positions. Weaker signals get smaller positions.
That's exactly how it should work. The bot scales risk based on signal confidence.
The Markov model assigns higher size multipliers to signals it's more confident in.Stronger signals get larger positions. Weaker signals get smaller positions.
That's exactly how it should work. The bot scales risk based on signal confidence.
Jason Smith
2026.03.24
If you run with --fixed-lots 0.1 (1 mini lot = 0.1), the Markov size multiplier scales it:
Symbol Pos % Fixed Lots Calculated Lots Rounded Lots
USDJPY 80% 0.1 × 0.80 = 0.080 0.08
BTCUSD 77% 0.1 × 0.77 = 0.077 0.08
XAUUSD 76% 0.1 × 0.76 = 0.076 0.08
EURUSD 69% 0.1 × 0.69 = 0.069 0.07
AUDUSD 52% 0.1 × 0.52 = 0.052 0.05
Symbol Pos % Fixed Lots Calculated Lots Rounded Lots
USDJPY 80% 0.1 × 0.80 = 0.080 0.08
BTCUSD 77% 0.1 × 0.77 = 0.077 0.08
XAUUSD 76% 0.1 × 0.76 = 0.076 0.08
EURUSD 69% 0.1 × 0.69 = 0.069 0.07
AUDUSD 52% 0.1 × 0.52 = 0.052 0.05
Jason Smith
Thought of the day:
Just because something is different doesn’t mean it isn’t better.
Just because something is different doesn’t mean it isn’t better.
· 1
Jason Smith
2026.03.24
It reflects Linus Torvalds (Linux Creator) philosophy on innovation and technology—especially relevant to open-source development—where unconventional approaches can often outperform traditional ones.
Jason Smith
New cli Flags for the Markov Bot (Part 1)
Jason Smith
2026.03.24
The command-line help looks intimidating, but it’s actually much simpler once you break it down. Think of it like a menu of options for running a trading bot. You don’t have to use all of them—just the ones you need.
Jason Smith
2026.03.24
ONE SIGNAL, TWO CHOICES
The HMM Python script generates the signals. Those signals are saved to a text file.
You have two different bots that can read that file:
Python Bot – Runs outside MT5. Connects through MT5 API. Places trades. Manages risk. Logs to SQLite.
MT5 EA – Runs inside MT5. Places trades. Does the same job.
Both bots read the same signal file. Both execute the same logic. The results are identical.
You can use either. The HMM doesn't care. The signals are the same. The trades are the same. Pick the bot that fits your workflow.
The HMM Python script generates the signals. Those signals are saved to a text file.
You have two different bots that can read that file:
Python Bot – Runs outside MT5. Connects through MT5 API. Places trades. Manages risk. Logs to SQLite.
MT5 EA – Runs inside MT5. Places trades. Does the same job.
Both bots read the same signal file. Both execute the same logic. The results are identical.
You can use either. The HMM doesn't care. The signals are the same. The trades are the same. Pick the bot that fits your workflow.
Jason Smith
Gold is bleeding billions and Pro15 is right in the middle, turning the sell-off into massive gains.
Jason Smith
2026.03.23
If you can’t afford to buy it right away, you can always rent it for a month to get started, then purchase it later without any time limitations.
DM me for this or any anything else
DM me for this or any anything else
Jason Smith
This looks like a large-scale sell-off of gold from Gulf state players.
Jason Smith
2026.03.23
Rich investors are getting hit hard by this move. A drop from a recent high $5,600 to $4,100 means huge positions lose billions very quickly, especially for funds and large holders who were heavily exposed to gold.
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.
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.
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.
By placing the stop loss at the previous H4 (configurable) candle’s low - high, you’re using a level that the market has already respected. If price breaks that level, it’s a reasonable signal that your trade direction is wrong, so you’re getting out based on logic rather than guesswork.