Maker of Winners
at
Trading
I don't sell dreams, shortcuts, or nonsense. If you're still losing money in the market, the problem isn't the bots or the charts—it's you. But don't worry, we don't judge here; we fix.
🤖 ALL OUR EXPERT ADVISORS: https://www.mql5.com/en/users/envex/seller
⚠️ NEW MQL5 GROUP: https://www.mql5.com/en/messages/01c72081307dda01
🔵 TELEGRAM: https://t.me/+Jwdm825813I1Nzk0
If you feel that you don't know even the most basic things, start here: https://www.mql5.com/en/blogs/post/761104
🤖 ALL OUR EXPERT ADVISORS: https://www.mql5.com/en/users/envex/seller
⚠️ NEW MQL5 GROUP: https://www.mql5.com/en/messages/01c72081307dda01
🔵 TELEGRAM: https://t.me/+Jwdm825813I1Nzk0
If you feel that you don't know even the most basic things, start here: https://www.mql5.com/en/blogs/post/761104
Enrique Enguix
You know what's worse than a losing strategy?
One that looks like a winner... until you tweak one setting and it collapses like a house of cards.
Plenty of so-called Expert Advisors shine in backtests.
Sure… as long as the market behaves and doesn’t sneeze too hard.
We don’t play that game.
Here’s how we validate the configuration sets that power our EAs (like Nexus) — and why they’re nothing like the .set files you find floating around the internet.
---
1. Modified Environments of the Same Asset
We don’t test a EUR/USD set on GBP/USD. That’s noise.
Instead, we create alternate realities of EUR/USD by adding subtle random distortions to:
- Open prices
- Highs and lows
- Close prices
Just enough chaos to break poorly built strategies.
Why?
To check if the set survives when the market behaves almost the same… but not quite.
If it does, we know it’s not a fluke.
---
2. Monte Carlo Stress Testing
Maybe your strategy just got lucky with the trade sequence?
We flip the deck:
- Shuffle trades
- Alter spreads
- Simulate slippage
- Generate stochastic market noise
If it still holds up — then we’re getting somewhere.
---
3. Parameter Permutation
What if those stellar results came from hitting the magic numbers?
We don’t buy it.
We test similar input combinations to see if the good performance comes from a wide, stable range… or a narrow, fragile spike.
No robustness? No deal.
---
4. Out-of-Sample Validation
We split the data: part for training, part for testing.
If the set only works in its training period — it’s out.
Only those that hold up in the unknown future make the cut.
---
5. Expected Degradation Analysis
Sure, a little performance drop outside of training is expected.
But how much?
- Minimal degradation? Acceptable.
- Massive drop? Toss it.
Simple as that.
---
6. Statistical Robustness Analysis
It’s not enough to be profitable.
We want stability across all key metrics:
- Profit factor
- Trade count
- Smooth equity curve
- Controlled drawdown
- Reasonable trade duration
- No fake-out spikes
---
Bottom line:
We don’t optimize for the prettiest curve.
Classic optimization equals overfitting and false confidence.
We aim for something harder:
Performance that survives chaos.
That’s why the sets we deliver with products like Nexus are battle-tested to hell and back.
Because we’d rather spend weeks fine-tuning… than hand you something shiny but brittle.
---
Do what you want with this info.
But if you’re running an EA and have no idea what’s behind the parameters…
You’re gambling. Not trading.
🧠
One that looks like a winner... until you tweak one setting and it collapses like a house of cards.
Plenty of so-called Expert Advisors shine in backtests.
Sure… as long as the market behaves and doesn’t sneeze too hard.
We don’t play that game.
Here’s how we validate the configuration sets that power our EAs (like Nexus) — and why they’re nothing like the .set files you find floating around the internet.
---
1. Modified Environments of the Same Asset
We don’t test a EUR/USD set on GBP/USD. That’s noise.
Instead, we create alternate realities of EUR/USD by adding subtle random distortions to:
- Open prices
- Highs and lows
- Close prices
Just enough chaos to break poorly built strategies.
Why?
To check if the set survives when the market behaves almost the same… but not quite.
If it does, we know it’s not a fluke.
---
2. Monte Carlo Stress Testing
Maybe your strategy just got lucky with the trade sequence?
We flip the deck:
- Shuffle trades
- Alter spreads
- Simulate slippage
- Generate stochastic market noise
If it still holds up — then we’re getting somewhere.
---
3. Parameter Permutation
What if those stellar results came from hitting the magic numbers?
We don’t buy it.
We test similar input combinations to see if the good performance comes from a wide, stable range… or a narrow, fragile spike.
No robustness? No deal.
---
4. Out-of-Sample Validation
We split the data: part for training, part for testing.
If the set only works in its training period — it’s out.
Only those that hold up in the unknown future make the cut.
---
5. Expected Degradation Analysis
Sure, a little performance drop outside of training is expected.
But how much?
- Minimal degradation? Acceptable.
- Massive drop? Toss it.
Simple as that.
---
6. Statistical Robustness Analysis
It’s not enough to be profitable.
We want stability across all key metrics:
- Profit factor
- Trade count
- Smooth equity curve
- Controlled drawdown
- Reasonable trade duration
- No fake-out spikes
---
Bottom line:
We don’t optimize for the prettiest curve.
Classic optimization equals overfitting and false confidence.
We aim for something harder:
Performance that survives chaos.
That’s why the sets we deliver with products like Nexus are battle-tested to hell and back.
Because we’d rather spend weeks fine-tuning… than hand you something shiny but brittle.
---
Do what you want with this info.
But if you’re running an EA and have no idea what’s behind the parameters…
You’re gambling. Not trading.
🧠
· 2
Enrique Enguix
Enrique Enguix:
🚧 Why Haven’t I Released the New EA Yet? ⚙️📊
A few weeks ago, I shared a chart showing some highly promising backtest results from a new Expert Advisor (EA) I've been developing. At first glance, the EA looked ready for market—but I decided to delay its launch to continue improving its robustness.
Today, I'd like to explain clearly why I haven't yet published it and the rigorous processes I'm following to ensure maximum robustness before release.
---
### 📌 Why a Good Backtest is Not Enough
A backtest is just the first step: A strategy can look fantastic when tested against historical data. However, real market conditions differ dramatically. Real spreads, slippage, sudden volatility, and unexpected market behaviors can't always be accurately replicated by historical tests alone.
The real challenge—and what truly matters—is how the strategy performs in unseen future scenarios.
---
### 🔍 Robustness Tests: The Key to Reliable EAs
My evaluation process uses advanced methodologies to ensure strategy robustness:
- Walk-Forward Cross-Validation
I begin with thousands of different parameter combinations (around 5000), dividing data into multiple segments. Each configuration is trained on historical data (*Backtest*) and then rigorously tested on new, unseen data periods (*Forward Test*).
Goal: Identify setups that perform reliably in diverse scenarios—not just in ideal historical conditions.
- Pattern Bagging (Voting among Patterns)
Rather than relying on a single technical rule, I combine multiple technical patterns (RSI, MACD, ADX, ATR, Bollinger Bands, volume, among others) through a method known as "Bagging". Each pattern casts a weighted "vote" for trade entries, creating robust signals and minimizing overfitting.
- Dynamic and Advanced Features
I've recently added more advanced, adaptive technical indicators, such as:
- Volatility-based patterns (ATR-driven adaptive ranges).
- Extreme volatility and volume spike filters, avoiding unstable market conditions.
- Dynamic EMA crossovers (adaptive exponential moving averages).
- Bollinger Band breakout strategies conditioned on compression phases (squeezes).
These dynamic features enhance adaptability but also increase complexity, requiring careful validation to ensure they genuinely enhance robustness rather than just adding unnecessary complexity.
- Adaptive Time and Volatility Filters
I'm also testing sophisticated, statistically-driven filters to avoid trading during historically unfavorable market hours or conditions. This goes far beyond simple "time filters," using real statistical insights to exclude disadvantageous trading periods.
---
### 🚨 Why Am I Discarding Many Good-Looking Ideas?
Because my ultimate criterion is performance degradation, meaning how much the EA’s performance worsens from historical (backtest) to unseen (forward) market conditions. Any feature or strategy that causes performance instability—no matter how attractive it initially seems—is quickly discarded.
Many initially promising ideas have fallen away at this stage because they failed the robustness test.
---
### 🛠️ Next Steps: Toward a Truly Robust EA
Currently, I have clearly identified which elements truly maintain long-term robustness and which undermine it:
- Strategic Simplification: Eliminating unnecessary complexity and retaining only the most reliable technical rules.
- Real Adaptability: Prioritizing signals driven by dynamic volatility (ATR) and actual market conditions rather than static parameters.
- Stricter Statistical Validation: Only retaining patterns and conditions that pass rigorous statistical validations consistently.
---
### ✅ Conclusion: Quality and Robustness over Speed
I could have launched this EA weeks ago, driven solely by appealing backtest graphs. It likely would have generated immediate sales. But my commitment to you (and myself) is to release products that truly perform well and adapt consistently to real market conditions over time.
This is why—even though it takes a bit longer—I prefer delivering a product that not only looks good at first glance but genuinely demonstrates consistency and adaptive capabilities over time.
What to do in the meantime?
If you're looking for an already robust and battle-tested solution, I highly recommend taking a look at our flagship product: Nexus EA Forex MT5. It's built on proven technology and is designed precisely for stable, long-term performance in real trading conditions.
Thanks for your patience and continuous support. Let’s keep advancing on the path to intelligent, technical, and robust trading. 📈🔬
🚧 Why Haven’t I Released the New EA Yet? ⚙️📊
A few weeks ago, I shared a chart showing some highly promising backtest results from a new Expert Advisor (EA) I've been developing. At first glance, the EA looked ready for market—but I decided to delay its launch to continue improving its robustness.
Today, I'd like to explain clearly why I haven't yet published it and the rigorous processes I'm following to ensure maximum robustness before release.
---
### 📌 Why a Good Backtest is Not Enough
A backtest is just the first step: A strategy can look fantastic when tested against historical data. However, real market conditions differ dramatically. Real spreads, slippage, sudden volatility, and unexpected market behaviors can't always be accurately replicated by historical tests alone.
The real challenge—and what truly matters—is how the strategy performs in unseen future scenarios.
---
### 🔍 Robustness Tests: The Key to Reliable EAs
My evaluation process uses advanced methodologies to ensure strategy robustness:
- Walk-Forward Cross-Validation
I begin with thousands of different parameter combinations (around 5000), dividing data into multiple segments. Each configuration is trained on historical data (*Backtest*) and then rigorously tested on new, unseen data periods (*Forward Test*).
Goal: Identify setups that perform reliably in diverse scenarios—not just in ideal historical conditions.
- Pattern Bagging (Voting among Patterns)
Rather than relying on a single technical rule, I combine multiple technical patterns (RSI, MACD, ADX, ATR, Bollinger Bands, volume, among others) through a method known as "Bagging". Each pattern casts a weighted "vote" for trade entries, creating robust signals and minimizing overfitting.
- Dynamic and Advanced Features
I've recently added more advanced, adaptive technical indicators, such as:
- Volatility-based patterns (ATR-driven adaptive ranges).
- Extreme volatility and volume spike filters, avoiding unstable market conditions.
- Dynamic EMA crossovers (adaptive exponential moving averages).
- Bollinger Band breakout strategies conditioned on compression phases (squeezes).
These dynamic features enhance adaptability but also increase complexity, requiring careful validation to ensure they genuinely enhance robustness rather than just adding unnecessary complexity.
- Adaptive Time and Volatility Filters
I'm also testing sophisticated, statistically-driven filters to avoid trading during historically unfavorable market hours or conditions. This goes far beyond simple "time filters," using real statistical insights to exclude disadvantageous trading periods.
---
### 🚨 Why Am I Discarding Many Good-Looking Ideas?
Because my ultimate criterion is performance degradation, meaning how much the EA’s performance worsens from historical (backtest) to unseen (forward) market conditions. Any feature or strategy that causes performance instability—no matter how attractive it initially seems—is quickly discarded.
Many initially promising ideas have fallen away at this stage because they failed the robustness test.
---
### 🛠️ Next Steps: Toward a Truly Robust EA
Currently, I have clearly identified which elements truly maintain long-term robustness and which undermine it:
- Strategic Simplification: Eliminating unnecessary complexity and retaining only the most reliable technical rules.
- Real Adaptability: Prioritizing signals driven by dynamic volatility (ATR) and actual market conditions rather than static parameters.
- Stricter Statistical Validation: Only retaining patterns and conditions that pass rigorous statistical validations consistently.
---
### ✅ Conclusion: Quality and Robustness over Speed
I could have launched this EA weeks ago, driven solely by appealing backtest graphs. It likely would have generated immediate sales. But my commitment to you (and myself) is to release products that truly perform well and adapt consistently to real market conditions over time.
This is why—even though it takes a bit longer—I prefer delivering a product that not only looks good at first glance but genuinely demonstrates consistency and adaptive capabilities over time.
What to do in the meantime?
If you're looking for an already robust and battle-tested solution, I highly recommend taking a look at our flagship product: Nexus EA Forex MT5. It's built on proven technology and is designed precisely for stable, long-term performance in real trading conditions.
Thanks for your patience and continuous support. Let’s keep advancing on the path to intelligent, technical, and robust trading. 📈🔬
· 2
Enrique Enguix
# Evaluating the Quality of Historical Forex Data in MetaTrader 5
A technical study on the reliability of broker-provided data for algorithmic backtesting.
---
## Summary
The accuracy of any backtest in MetaTrader 5 (MT5) depends on the integrity of the historical tick data downloaded from the broker’s server. This study identifies typical deficiencies in retail feeds—temporal lag, missing ticks, bid/ask inconsistencies, and file corruption—and compares them with professional reference sources like Dukascopy. Objective quality metrics are proposed, and practical recommendations are made for EA developers and quantitative researchers.
---
## 1. How is historical data generated and stored in MT5?
- Each broker provides their own feed—there is no central standard.
- Historical ticks are stored in .tkc compressed files, located at:
/terminal_directory/bases/{server}/ticks/{symbol}/
- Many brokers do not log every tick. Instead, they rebuild prices from M1 candles.
- MT5 users have no direct indication of data quality unless they measure it manually.
---
## 2. Key Quality Metrics
These are the four core variables used to assess tick data integrity:
Tick Coverage (TC)
> % of seconds during trading hours that contain at least one tick.
> Lower values indicate gaps and structural bias in volatility modeling.
Average Gap Length (AGL)
> Average duration (in seconds) of gaps between consecutive ticks.
> Affects drawdown estimates and scalping strategy validity.
Bid-Ask Sanity (BAS)
> % of ticks where Ask ≥ Bid and spread is within the 99th percentile.
> Anomalies here suggest feed corruption or artificial pricing.
Timestamp Jitter (TSJ)
> Standard deviation (ms) between tick timestamps and NTP-aligned server time.
> Essential for evaluating event-based and high-frequency strategies.
---
## 3. Methodology
- Brokers selected: 8 MT5 servers, both ECN and market makers.
- Symbols analyzed: EURUSD, period 2024-01-01 to 2024-12-31.
- Data extraction: MQL5 script using CopyTicksRange() to download full raw tick data.
- Data storage: TKC files parsed using Python and restructured for analysis.
- Reference feed: Dukascopy tick-level data imported via Tick Data Suite with 99.9% model quality.
---
## 4. Results (summary)
Broker A (ECN)
- Tick Coverage: 91.2%
- Avg. Gap: 8.4 seconds
- Bid/Ask Sanity: 97.6%
- Timestamp Jitter: 43 ms
Broker B (Market Maker)
- Tick Coverage: 78.5%
- Avg. Gap: 21.7 seconds
- Bid/Ask Sanity: 94.1%
- Timestamp Jitter: 112 ms
Dukascopy (reference)
- Tick Coverage: 99.8%
- Avg. Gap: 0.5 seconds
- Bid/Ask Sanity: 99.9%
- Timestamp Jitter: 9 ms
---
## 5. Discussion
- A scalping EA tested with TC < 85% showed an inflated profit/risk ratio by +27% compared to reference-grade data.
- MT5 TKC files lack checksums or integrity validation—partial downloads or corruption often go unnoticed.
- Timestamp drift was especially pronounced on weekend restarts and low-liquidity sessions.
- Previous research confirms that reconstructing ticks from candles introduces heteroscedastic second-order noise.
---
## 6. Practical Recommendations
1. Use external tick data feeds (e.g. Dukascopy, TrueFX) instead of relying solely on your broker.
2. Inject professional data via tools like Tick Data Suite or QuantDataManager.
3. Run checksum or SHA-256 validation on downloaded data to detect corruption.
4. Always compare multiple sources and note variations in spread and coverage.
5. Publish your backtesting conditions and data quality metrics to ensure reproducibility.
---
## 7. Conclusion
> A trading system tested on poor data is worse than untested.
> Most retail broker feeds are not reliable for robust statistical analysis.
> High-quality, validated tick data is essential if your EA is meant to survive live markets.
---
This is not marketing. This is science.
A technical study on the reliability of broker-provided data for algorithmic backtesting.
---
## Summary
The accuracy of any backtest in MetaTrader 5 (MT5) depends on the integrity of the historical tick data downloaded from the broker’s server. This study identifies typical deficiencies in retail feeds—temporal lag, missing ticks, bid/ask inconsistencies, and file corruption—and compares them with professional reference sources like Dukascopy. Objective quality metrics are proposed, and practical recommendations are made for EA developers and quantitative researchers.
---
## 1. How is historical data generated and stored in MT5?
- Each broker provides their own feed—there is no central standard.
- Historical ticks are stored in .tkc compressed files, located at:
/terminal_directory/bases/{server}/ticks/{symbol}/
- Many brokers do not log every tick. Instead, they rebuild prices from M1 candles.
- MT5 users have no direct indication of data quality unless they measure it manually.
---
## 2. Key Quality Metrics
These are the four core variables used to assess tick data integrity:
Tick Coverage (TC)
> % of seconds during trading hours that contain at least one tick.
> Lower values indicate gaps and structural bias in volatility modeling.
Average Gap Length (AGL)
> Average duration (in seconds) of gaps between consecutive ticks.
> Affects drawdown estimates and scalping strategy validity.
Bid-Ask Sanity (BAS)
> % of ticks where Ask ≥ Bid and spread is within the 99th percentile.
> Anomalies here suggest feed corruption or artificial pricing.
Timestamp Jitter (TSJ)
> Standard deviation (ms) between tick timestamps and NTP-aligned server time.
> Essential for evaluating event-based and high-frequency strategies.
---
## 3. Methodology
- Brokers selected: 8 MT5 servers, both ECN and market makers.
- Symbols analyzed: EURUSD, period 2024-01-01 to 2024-12-31.
- Data extraction: MQL5 script using CopyTicksRange() to download full raw tick data.
- Data storage: TKC files parsed using Python and restructured for analysis.
- Reference feed: Dukascopy tick-level data imported via Tick Data Suite with 99.9% model quality.
---
## 4. Results (summary)
Broker A (ECN)
- Tick Coverage: 91.2%
- Avg. Gap: 8.4 seconds
- Bid/Ask Sanity: 97.6%
- Timestamp Jitter: 43 ms
Broker B (Market Maker)
- Tick Coverage: 78.5%
- Avg. Gap: 21.7 seconds
- Bid/Ask Sanity: 94.1%
- Timestamp Jitter: 112 ms
Dukascopy (reference)
- Tick Coverage: 99.8%
- Avg. Gap: 0.5 seconds
- Bid/Ask Sanity: 99.9%
- Timestamp Jitter: 9 ms
---
## 5. Discussion
- A scalping EA tested with TC < 85% showed an inflated profit/risk ratio by +27% compared to reference-grade data.
- MT5 TKC files lack checksums or integrity validation—partial downloads or corruption often go unnoticed.
- Timestamp drift was especially pronounced on weekend restarts and low-liquidity sessions.
- Previous research confirms that reconstructing ticks from candles introduces heteroscedastic second-order noise.
---
## 6. Practical Recommendations
1. Use external tick data feeds (e.g. Dukascopy, TrueFX) instead of relying solely on your broker.
2. Inject professional data via tools like Tick Data Suite or QuantDataManager.
3. Run checksum or SHA-256 validation on downloaded data to detect corruption.
4. Always compare multiple sources and note variations in spread and coverage.
5. Publish your backtesting conditions and data quality metrics to ensure reproducibility.
---
## 7. Conclusion
> A trading system tested on poor data is worse than untested.
> Most retail broker feeds are not reliable for robust statistical analysis.
> High-quality, validated tick data is essential if your EA is meant to survive live markets.
---
This is not marketing. This is science.
· 4
Enrique Enguix
After several weeks of development, I’m thrilled to share the core idea of our new EA in simple terms:
---
1. Real-Time “Mining” of Patterns
Imagine the EA as a treasure hunter: it reads every new candle on the chart and extracts tiny “details” (bullish or bearish, big body, long wicks, high/low volume…). This is what we call mining patterns.
2. Grouping & Historical Testing
Once those details are collected, the EA combines them in dozens of ways (pairs, triplets…) and back-tests each combination over past data to see which ones would have made money. Only the winners survive.
3. Dual Validation: Past & Recent
To avoid “old-fashioned” signals, we apply two filters:
- Old Window: Did it work months ago?
- Recent Window: Is it still working today?
Only rules that pass both filters stay active.
4. Automatic, Controlled Trading
When a winning pattern is detected, the EA opens a trade with:
- Stop-Loss and Take-Profit calculated automatically.
- Position size set by your chosen risk percentage (e.g. 1 % of your account).
- Only one trade per candle, avoiding order overload.
5. Continuous Evolution
Every X days (configurable), the EA re-mines, re-validates all rules, and updates its signals. It always trades what works now, not what worked yesterday.
---
In short, it’s like having a mini-lab inside MetaTrader that:
- Miners patterns right off the chart.
- Tests their profitability.
- Validates their robustness.
- Trades them safely and efficiently.
This system is intuitive (you choose which details to mine and how often) and fully automated. You’ll be able to test and tweak it 100 % in your platform very soon.
Due to its mining capabilities, we’ve noticed it performs exceptionally well on GOLD, DAX, S&P 500, and Forex in general. Over the next few weeks we’ll focus on:
1. Ease of Use: building presets and UI so anyone can set up in under a minute.
2. Profitability & Robustness: ensuring rules hold up across markets and conditions, avoiding overfitting.
3. Adaptability: offering customization options for your needs while refining our internal methodology.
In the image: a precision backtest on gold.
---
1. Real-Time “Mining” of Patterns
Imagine the EA as a treasure hunter: it reads every new candle on the chart and extracts tiny “details” (bullish or bearish, big body, long wicks, high/low volume…). This is what we call mining patterns.
2. Grouping & Historical Testing
Once those details are collected, the EA combines them in dozens of ways (pairs, triplets…) and back-tests each combination over past data to see which ones would have made money. Only the winners survive.
3. Dual Validation: Past & Recent
To avoid “old-fashioned” signals, we apply two filters:
- Old Window: Did it work months ago?
- Recent Window: Is it still working today?
Only rules that pass both filters stay active.
4. Automatic, Controlled Trading
When a winning pattern is detected, the EA opens a trade with:
- Stop-Loss and Take-Profit calculated automatically.
- Position size set by your chosen risk percentage (e.g. 1 % of your account).
- Only one trade per candle, avoiding order overload.
5. Continuous Evolution
Every X days (configurable), the EA re-mines, re-validates all rules, and updates its signals. It always trades what works now, not what worked yesterday.
---
In short, it’s like having a mini-lab inside MetaTrader that:
- Miners patterns right off the chart.
- Tests their profitability.
- Validates their robustness.
- Trades them safely and efficiently.
This system is intuitive (you choose which details to mine and how often) and fully automated. You’ll be able to test and tweak it 100 % in your platform very soon.
Due to its mining capabilities, we’ve noticed it performs exceptionally well on GOLD, DAX, S&P 500, and Forex in general. Over the next few weeks we’ll focus on:
1. Ease of Use: building presets and UI so anyone can set up in under a minute.
2. Profitability & Robustness: ensuring rules hold up across markets and conditions, avoiding overfitting.
3. Adaptability: offering customization options for your needs while refining our internal methodology.
In the image: a precision backtest on gold.
· 3
Enrique Enguix
My neighbor just got a 30-year mortgage… for a barbecue.
I swear.
He doesn’t care about the house. What he really wanted was a patch of fake grass, a shiny chrome grill, and a table where he could host Sunday BBQs with his friends.
I knew it the second he said:
“Man, I can already see myself there with a beer, watching those ribs cook…”
Not a word about the variable interest rate, notary fees, or the 300,000 euros he just signed for like it was pocket change.
And it got me thinking.
Because he’s not the only one.
Most people don’t buy homes. They buy the fantasy of what they think they’ll feel once they have it.
Like the guy who signs up for the gym because he imagines his six-pack… but never actually shows up.
Or the trader who enters the market dreaming of that epic winning trade… but panics the second things go south.
You know what they all have in common?
They’re not ready for what it *really* takes to get what they say they want.
And in trading, that’s deadly.
Because a lot of people get in for the idea of freedom, of “working from home,” of “being your own boss”…
But they can’t survive even a week when the system hits drawdown.
They start second-guessing. Tinkering. Overtrading. “Trying something I saw on YouTube.”
They become the guy with the barbecue dream… but no house. No plan. No structure. Just vibes and smoke.
I used to be that guy too.
I fell in love with the idea. But I didn’t understand the cycles, the stats, the risk control… or the value of sticking to a system with logic, even when the weeks got tough.
It took time to get it.
Now I know: the ones who make it in this game aren’t the smartest. They’re the ones who can go through the process without betraying it every time it gets uncomfortable.
The ones who don’t need a barbecue fantasy to justify the investment.
That’s why I talk so much about systems that aren’t driven by emotion. That don’t bend every time the wind changes.
I don’t sell dreams.
I share what lets me show up every day without having a heart attack every time the market throws a tantrum.
Automated strategies that work with rules. With logic. With control.
No magic. No promises. Just *operational reality*.
Because if you’re going to mortgage your future for something, make damn sure it’s not just a fantasy.
My neighbor still hasn’t used the barbecue.
Not once.
But guess what… he’s already complaining about the mortgage.
Up to you what you invest in.
I swear.
He doesn’t care about the house. What he really wanted was a patch of fake grass, a shiny chrome grill, and a table where he could host Sunday BBQs with his friends.
I knew it the second he said:
“Man, I can already see myself there with a beer, watching those ribs cook…”
Not a word about the variable interest rate, notary fees, or the 300,000 euros he just signed for like it was pocket change.
And it got me thinking.
Because he’s not the only one.
Most people don’t buy homes. They buy the fantasy of what they think they’ll feel once they have it.
Like the guy who signs up for the gym because he imagines his six-pack… but never actually shows up.
Or the trader who enters the market dreaming of that epic winning trade… but panics the second things go south.
You know what they all have in common?
They’re not ready for what it *really* takes to get what they say they want.
And in trading, that’s deadly.
Because a lot of people get in for the idea of freedom, of “working from home,” of “being your own boss”…
But they can’t survive even a week when the system hits drawdown.
They start second-guessing. Tinkering. Overtrading. “Trying something I saw on YouTube.”
They become the guy with the barbecue dream… but no house. No plan. No structure. Just vibes and smoke.
I used to be that guy too.
I fell in love with the idea. But I didn’t understand the cycles, the stats, the risk control… or the value of sticking to a system with logic, even when the weeks got tough.
It took time to get it.
Now I know: the ones who make it in this game aren’t the smartest. They’re the ones who can go through the process without betraying it every time it gets uncomfortable.
The ones who don’t need a barbecue fantasy to justify the investment.
That’s why I talk so much about systems that aren’t driven by emotion. That don’t bend every time the wind changes.
I don’t sell dreams.
I share what lets me show up every day without having a heart attack every time the market throws a tantrum.
Automated strategies that work with rules. With logic. With control.
No magic. No promises. Just *operational reality*.
Because if you’re going to mortgage your future for something, make damn sure it’s not just a fantasy.
My neighbor still hasn’t used the barbecue.
Not once.
But guess what… he’s already complaining about the mortgage.
Up to you what you invest in.
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