🤫 Why Hedge Funds Love When You Trade Manually [Market Theory]
Recently, Bitcoin was stuck in a narrow range.
Then, out of nowhere, one of the biggest crypto hacks in history occurred — ByBit was breached, losing over $1.4 billion in ETH.
This was the largest hack ever recorded. Yet, even Ethereum barely reacted, reversing strongly the next day.
This should have been an obvious signal: the market was refusing to drop despite catastrophic news.
Any rational observer would assume that shorting crypto was the wrong move.
Yet, just two days later, both BTC and ETH tumbled significantly — for no apparent reason.
Why?
Because market moves are driven by liquidity, not news.
A quantitative explanation can be found in market microstructure theory, as discussed in The Market Microstructure Theory by Maureen O’Hara.
Prices don’t simply react to news — they shift based on how liquidity providers and large traders adjust their positions in response to order flows, not facts.
Why BTC and ETH didn’t react heavily to the ByBit hack?
No major liquidity providers or leveraged traders were forced to unwind their positions.
But two days later, a seemingly random price drop may have been caused by a cascading deleveraging event, where a large player was forced to exit for completely unrelated reasons.
The market isn’t rational—it’s an ecosystem of traders, funds, and algorithms reacting to liquidity imbalances.
Here's why price moves often have hidden causes:
If we analyze on-chain data and order book imbalances, we would find that the major moves correlate with liquidation levels, not fundamental catalysts.
Historically, large crypto moves have far stronger ties to forced liquidations and hidden leverage than to traditional economic events.
Another explanation comes from market efficiency theory, as outlined in A Random Walk Down Wall Street by Burton Malkiel.
Markets absorb information almost instantly, meaning that any predictable response to news is immediately arbitraged away by high-frequency trading algorithms.
How retail traders are outmatched by algorithms:
Market-making firms and hedge funds deploy machine learning models trained on vast historical datasets.
These models don’t trade based on whether news is “good” or “bad”— they trade on statistical inefficiencies, liquidity flows, and execution strategies.
A retail trader trying to “front-run” the ByBit hack was competing against HFT algorithms pricing in this news in milliseconds.
Some Algocrat AI clients attempted to outthink the market by manually buying BTC at what seemed like a perfect moment — only to see the price collapse days later.
Why manual trading Is a losing game:
Humans are simply too slow.
By the time an event seems to warrant a rational response, market makers, hedge funds, and algorithms have already acted.
The price drop two days after the hack could have been caused by internal portfolio rebalancing by a major institution or a coordinated move by market makers — factors entirely invisible to the retail trader.
The market isn’t irrational — it follows a logic inaccessible to traders who rely on human intuition instead of quantitative models.
What seems like “irrational” price action is actually rooted in complex factors like liquidity flows, hidden leverage, and instant arbitrage opportunities.
In such an environment, relying on human intuition or news-based trades is a high-risk gamble.
That’s why Algocrat AI doesn’t chase narratives:
Instead, it harnesses a momentum-based approach to trading crypto pairs, focusing on quantifiable inefficiencies where real edge lies.
By relying on data-driven signals rather than headlines, it seeks to navigate the market’s hidden logic,
And capitalize on opportunities beyond the grasp of manual strategies,
Delivering "too good to be true" results, for 6 years in a row.
Best regards,
The Algocrat AI Team
PS - What are you waiting to capitalize on such opportunities yourself? Click here to apply now
Recently, Bitcoin was stuck in a narrow range.
Then, out of nowhere, one of the biggest crypto hacks in history occurred — ByBit was breached, losing over $1.4 billion in ETH.
This was the largest hack ever recorded. Yet, even Ethereum barely reacted, reversing strongly the next day.
This should have been an obvious signal: the market was refusing to drop despite catastrophic news.
Any rational observer would assume that shorting crypto was the wrong move.
Yet, just two days later, both BTC and ETH tumbled significantly — for no apparent reason.
Why?
Because market moves are driven by liquidity, not news.
A quantitative explanation can be found in market microstructure theory, as discussed in The Market Microstructure Theory by Maureen O’Hara.
Prices don’t simply react to news — they shift based on how liquidity providers and large traders adjust their positions in response to order flows, not facts.
Why BTC and ETH didn’t react heavily to the ByBit hack?
No major liquidity providers or leveraged traders were forced to unwind their positions.
But two days later, a seemingly random price drop may have been caused by a cascading deleveraging event, where a large player was forced to exit for completely unrelated reasons.
The market isn’t rational—it’s an ecosystem of traders, funds, and algorithms reacting to liquidity imbalances.
Here's why price moves often have hidden causes:
If we analyze on-chain data and order book imbalances, we would find that the major moves correlate with liquidation levels, not fundamental catalysts.
Historically, large crypto moves have far stronger ties to forced liquidations and hidden leverage than to traditional economic events.
Another explanation comes from market efficiency theory, as outlined in A Random Walk Down Wall Street by Burton Malkiel.
Markets absorb information almost instantly, meaning that any predictable response to news is immediately arbitraged away by high-frequency trading algorithms.
How retail traders are outmatched by algorithms:
Market-making firms and hedge funds deploy machine learning models trained on vast historical datasets.
These models don’t trade based on whether news is “good” or “bad”— they trade on statistical inefficiencies, liquidity flows, and execution strategies.
A retail trader trying to “front-run” the ByBit hack was competing against HFT algorithms pricing in this news in milliseconds.
Some Algocrat AI clients attempted to outthink the market by manually buying BTC at what seemed like a perfect moment — only to see the price collapse days later.
Why manual trading Is a losing game:
Humans are simply too slow.
By the time an event seems to warrant a rational response, market makers, hedge funds, and algorithms have already acted.
The price drop two days after the hack could have been caused by internal portfolio rebalancing by a major institution or a coordinated move by market makers — factors entirely invisible to the retail trader.
The market isn’t irrational — it follows a logic inaccessible to traders who rely on human intuition instead of quantitative models.
What seems like “irrational” price action is actually rooted in complex factors like liquidity flows, hidden leverage, and instant arbitrage opportunities.
In such an environment, relying on human intuition or news-based trades is a high-risk gamble.
That’s why Algocrat AI doesn’t chase narratives:
Instead, it harnesses a momentum-based approach to trading crypto pairs, focusing on quantifiable inefficiencies where real edge lies.
By relying on data-driven signals rather than headlines, it seeks to navigate the market’s hidden logic,
And capitalize on opportunities beyond the grasp of manual strategies,
Delivering "too good to be true" results, for 6 years in a row.
Best regards,
The Algocrat AI Team
PS - What are you waiting to capitalize on such opportunities yourself? Click here to apply now
🔬 Here's A Detailed Comparison of Results Across Different Brokers [Strategy Analysis]
One of the most frequently discussed topics in the Algocrat AI community is why trading results vary across different brokers.
While we are working on a scalable copytrading solution to ensure identical trades for all users (for better and for worse), we decided to conduct a long-term comparison, analyzing several different brokers head-to-head with the public Pepperstone account.
But before diving into the results, let’s cover some fundamentals,
Why do results differ across brokers?
There are two main reasons why trading outcomes vary between brokers:
1. Execution and Expenses – The better a broker's execution and the lower its expenses, the better the results
2. Quote Differences – Since Algocrat AI's trading algorithms operate on individual accounts, variations in quotes between brokers influence trade execution. These differences are random and, unlike execution discrepancies, cannot be predicted in advance. However, over longer periods, these random variations tend to cancel out, making results more comparable.
Pepperstone: A benchmark for comparison
Among the brokers we work with, Pepperstone stands out for its excellent execution and competitive expenses.
While all brokers undergo a rigorous selection process before we integrate them, not all are created equal. Pepperstone ranks among the best, making it a logical benchmark for our comparison.
IC Markets: slightly better
As shown in the screenshots, IC Markets' performance has been slightly better than Pepperstone's from mid-April 2024 to March 2025. Long-term results are practically the same, with occasional slight variations due to differences in quotes and execution.
Fusion Markets: a close contender
Fusion is one of the most popular brokers among Algocrat AI users and frequently discussed in relation to result differences. We examined accounts that have been trading with Fusion since April 2024 and compared their performance to Pepperstone.
• Fusion slightly outperformed Pepperstone, delivering a 10.17% return compared to 10.00% on Pepperstone – a 1.7% difference in Fusion’s favor.
• If we shift the comparison start date to mid-May 2024, Fusion’s advantage increases to approximately 5%.
• However, for certain periods, Pepperstone performs better. The key takeaway? Over long periods, results between the two brokers are nearly identical.
Why does Fusion's performance spark debate?
If Fusion outperforms Pepperstone for the last 10.5 months, why is it a source of frustration for some traders? Short-term performance swings.
• Imagine joining in January 2024, only to see a -6% loss on Fusion while Pepperstone gained +6%. That’s frustrating.
• Yet, in February 2024, Fusion rebounded with a +6-7% profit, while Pepperstone hovered around 0%.
• This is how trading works: the longer the timeframe, the less significant quotes-based differences become.
Vantage: a shorter trading history, a similar story
Our trading history with Vantage began in September 2024, making long-term comparisons more challenging. However:
• So far, Vantage has slightly underperformed Pepperstone. Yet, Vantage’s highest equity peak exceeded Pepperstone’s. If we had compared results up to early February, the conclusion would have flipped, favoring Vantage instead
• Again, long-term results remain close, with short-term differences based on specific start dates
What about OX Securities?
Unlike Fusion or Vantage, OX Securities stands out for having noticeably weaker execution than most brokers.
However, this doesn’t mean results are drastically worse. OX Securities lags behind Pepperstone by approximately 2.89% (9.99/9.71-1), making results almost equal.
• Over seven months, the difference remains relatively small, proving once again that execution plays a role but does not make or break long-term profitability.
• Right now, OX Securities sits at an all-time high with 8% made in February, while Pepperstone is in a drawdown with close to zero result in February.
One of the most frequently discussed topics in the Algocrat AI community is why trading results vary across different brokers.
While we are working on a scalable copytrading solution to ensure identical trades for all users (for better and for worse), we decided to conduct a long-term comparison, analyzing several different brokers head-to-head with the public Pepperstone account.
But before diving into the results, let’s cover some fundamentals,
Why do results differ across brokers?
There are two main reasons why trading outcomes vary between brokers:
1. Execution and Expenses – The better a broker's execution and the lower its expenses, the better the results
2. Quote Differences – Since Algocrat AI's trading algorithms operate on individual accounts, variations in quotes between brokers influence trade execution. These differences are random and, unlike execution discrepancies, cannot be predicted in advance. However, over longer periods, these random variations tend to cancel out, making results more comparable.
Pepperstone: A benchmark for comparison
Among the brokers we work with, Pepperstone stands out for its excellent execution and competitive expenses.
While all brokers undergo a rigorous selection process before we integrate them, not all are created equal. Pepperstone ranks among the best, making it a logical benchmark for our comparison.
IC Markets: slightly better
As shown in the screenshots, IC Markets' performance has been slightly better than Pepperstone's from mid-April 2024 to March 2025. Long-term results are practically the same, with occasional slight variations due to differences in quotes and execution.
Fusion Markets: a close contender
Fusion is one of the most popular brokers among Algocrat AI users and frequently discussed in relation to result differences. We examined accounts that have been trading with Fusion since April 2024 and compared their performance to Pepperstone.
• Fusion slightly outperformed Pepperstone, delivering a 10.17% return compared to 10.00% on Pepperstone – a 1.7% difference in Fusion’s favor.
• If we shift the comparison start date to mid-May 2024, Fusion’s advantage increases to approximately 5%.
• However, for certain periods, Pepperstone performs better. The key takeaway? Over long periods, results between the two brokers are nearly identical.
Why does Fusion's performance spark debate?
If Fusion outperforms Pepperstone for the last 10.5 months, why is it a source of frustration for some traders? Short-term performance swings.
• Imagine joining in January 2024, only to see a -6% loss on Fusion while Pepperstone gained +6%. That’s frustrating.
• Yet, in February 2024, Fusion rebounded with a +6-7% profit, while Pepperstone hovered around 0%.
• This is how trading works: the longer the timeframe, the less significant quotes-based differences become.
Vantage: a shorter trading history, a similar story
Our trading history with Vantage began in September 2024, making long-term comparisons more challenging. However:
• So far, Vantage has slightly underperformed Pepperstone. Yet, Vantage’s highest equity peak exceeded Pepperstone’s. If we had compared results up to early February, the conclusion would have flipped, favoring Vantage instead
• Again, long-term results remain close, with short-term differences based on specific start dates
What about OX Securities?
Unlike Fusion or Vantage, OX Securities stands out for having noticeably weaker execution than most brokers.
However, this doesn’t mean results are drastically worse. OX Securities lags behind Pepperstone by approximately 2.89% (9.99/9.71-1), making results almost equal.
• Over seven months, the difference remains relatively small, proving once again that execution plays a role but does not make or break long-term profitability.
• Right now, OX Securities sits at an all-time high with 8% made in February, while Pepperstone is in a drawdown with close to zero result in February.
Since long-term results across brokers remain close while short-term fluctuations can be significant, the best approach is to diversify funds across multiple brokers with similar trading conditions. This strategy provides additional diversification and minimizes the impact of temporary performance differences.
How can you get the best possible results?
We also see that, over the long run, Binance has slightly better results than most other brokers, including Pepperstone. If you have access to Binance, it offers some of the best trading conditions overall
What does this tell us?
• Execution and expenses matter, but long-term results between good brokers tend to converge.
• Short-term differences are inevitable and can lead to frustration, but zooming out reveals the bigger picture.
• Random quote variations exist and affect short-term results but balance out over time.
• Diversifying across multiple brokers can help mitigate short-term fluctuations.
The takeaway is clear:
Focusing on long-term strategy rather than short-term fluctuations is the key to success. No broker will always be the best, but choosing one with strong execution and reasonable expenses ensures consistency over time.
Best,
The Algocrat AI Team
How can you get the best possible results?
We also see that, over the long run, Binance has slightly better results than most other brokers, including Pepperstone. If you have access to Binance, it offers some of the best trading conditions overall
What does this tell us?
• Execution and expenses matter, but long-term results between good brokers tend to converge.
• Short-term differences are inevitable and can lead to frustration, but zooming out reveals the bigger picture.
• Random quote variations exist and affect short-term results but balance out over time.
• Diversifying across multiple brokers can help mitigate short-term fluctuations.
The takeaway is clear:
Focusing on long-term strategy rather than short-term fluctuations is the key to success. No broker will always be the best, but choosing one with strong execution and reasonable expenses ensures consistency over time.
Best,
The Algocrat AI Team
📊 +5.60% / March 2025 [Monthly Performance]
Hi,
Despite challenging market conditions, Algocrat AI delivered a solid performance in March, showcasing the system’s resilience and adaptability.
Here are the key stats:
📈 Account Growth: +5.60%
📉 Maximum Drawdown: 4.95%
In a month where many strategies struggled, Algocrat AI stayed focused, navigating volatility and managing risk effectively.
It’s another reminder of the strength behind a data-driven, well-tested system built for long-term consistency.
As always, for a full performance breakdown, you can visit our verified MyFxBook track record:
🔗 Click Here To Access Our MyFxBook Track Record
Best regards,
The Algocrat AI Team
Hi,
Despite challenging market conditions, Algocrat AI delivered a solid performance in March, showcasing the system’s resilience and adaptability.
Here are the key stats:
📈 Account Growth: +5.60%
📉 Maximum Drawdown: 4.95%
In a month where many strategies struggled, Algocrat AI stayed focused, navigating volatility and managing risk effectively.
It’s another reminder of the strength behind a data-driven, well-tested system built for long-term consistency.
As always, for a full performance breakdown, you can visit our verified MyFxBook track record:
🔗 Click Here To Access Our MyFxBook Track Record
Best regards,
The Algocrat AI Team
🔍 Small Gains, Big Regret: How Early Exits Kill Momentum [Strategy Analysis]
Over the past few weeks, we re-examined whether tighter take-profit (TP) levels or trailing stops could boost performance.
We’ve run this research before, but a string of near-miss TPs prompted a fresh, data-heavy look.
Back-testing shows that whenever we introduce early profit-taking, long-term results deteriorate.
Cutting winners short sacrifices the outsized moves that account for a disproportionate share of momentum profits.
Why is that?
Big moves drive most of the profits.
In momentum-driven crypto markets, missing just a handful of major breakouts can erase months of gains.
By introducing smaller TP levels or trailing stops, we miss these big gains, making drawdowns longer and profits smaller.
However, there are indeed some market periods where this approach delivers suboptimal results.
Our advanced filtering systems filter out most such periods, but no filtering is perfect.
The only way to avoid all such periods is not to trade at all.
Here's what you should know:
We continuously refine our multi-factor filters and portfolio overlays to sidestep most adverse conditions.
Diversification across pairs further smooths equity curves, though it can’t replace the payoff from fully capturing big market moves.
We’ll keep iterating on filters and execution logic, but the data draws a clear line: beyond a point, tighter exits undercut the very edge momentum trading depends on.
Understanding and respecting that boundary is how we protect long-run alpha.
Hope you find this valuable.
Best,
The Algocrat AI Team
P.S. The best trades come without warning. Join us today so you don’t miss them — sign up now.
Over the past few weeks, we re-examined whether tighter take-profit (TP) levels or trailing stops could boost performance.
We’ve run this research before, but a string of near-miss TPs prompted a fresh, data-heavy look.
Back-testing shows that whenever we introduce early profit-taking, long-term results deteriorate.
Cutting winners short sacrifices the outsized moves that account for a disproportionate share of momentum profits.
Why is that?
Big moves drive most of the profits.
In momentum-driven crypto markets, missing just a handful of major breakouts can erase months of gains.
By introducing smaller TP levels or trailing stops, we miss these big gains, making drawdowns longer and profits smaller.
However, there are indeed some market periods where this approach delivers suboptimal results.
Our advanced filtering systems filter out most such periods, but no filtering is perfect.
The only way to avoid all such periods is not to trade at all.
Here's what you should know:
We continuously refine our multi-factor filters and portfolio overlays to sidestep most adverse conditions.
Diversification across pairs further smooths equity curves, though it can’t replace the payoff from fully capturing big market moves.
We’ll keep iterating on filters and execution logic, but the data draws a clear line: beyond a point, tighter exits undercut the very edge momentum trading depends on.
Understanding and respecting that boundary is how we protect long-run alpha.
Hope you find this valuable.
Best,
The Algocrat AI Team
P.S. The best trades come without warning. Join us today so you don’t miss them — sign up now.
📊 -9.34% / April 2025 [Monthly Performance]
Hi,
April brought challenging market conditions, and as a result, Algocrat AI closed the month with a loss of -9.34% and a maximum drawdown of 19.96%.
While these periods are never easy, they are a natural part of any high-performance strategy.
Over the past years, we've seen that drawdowns—though uncomfortable—are temporary, and staying the course through volatility is what allows the strategy to capture outsized returns over time.
As always, our systems remained disciplined and fully operational throughout the month.
We continue to monitor performance closely and refine where needed without compromising the integrity of the long-term approach.
For a detailed breakdown of our results, feel free to visit our verified MyFxBook track record:
🔗 Click Here To Access Our MyFxBook Track Record
Best regards,
The Algocrat AI Team
Hi,
April brought challenging market conditions, and as a result, Algocrat AI closed the month with a loss of -9.34% and a maximum drawdown of 19.96%.
While these periods are never easy, they are a natural part of any high-performance strategy.
Over the past years, we've seen that drawdowns—though uncomfortable—are temporary, and staying the course through volatility is what allows the strategy to capture outsized returns over time.
As always, our systems remained disciplined and fully operational throughout the month.
We continue to monitor performance closely and refine where needed without compromising the integrity of the long-term approach.
For a detailed breakdown of our results, feel free to visit our verified MyFxBook track record:
🔗 Click Here To Access Our MyFxBook Track Record
Best regards,
The Algocrat AI Team
🛠 Breakouts, Rebalances, and Refinements: Portfolio Update Now Live [Strategy Update]
We recently rolled out a major update to the Algocrat AI portfolio.
Although improvements are an ongoing effort, any change applied to live accounts must pass a rigorous validation process, so updates are infrequent and implemented with great care.
Here is a summary of what’s new:
- New Ethereum trading system – we added a breakout strategy that has already executed several trades. Its low correlation with our existing systems should enhance overall portfolio performance
- Portfolio rebalancing – we reduced the allocation to strategies that have underperformed in recent years and increased exposure to those that have been delivering stronger results
- System-by-system review – we thoroughly reassessed every strategy in the mix, refining entry and exit rules and adjusting risk-mitigation features
Most of this work is technical and not immediately visible, but we expect these adjustments to improve long-term performance.
Best,
The Algocrat AI Team
We recently rolled out a major update to the Algocrat AI portfolio.
Although improvements are an ongoing effort, any change applied to live accounts must pass a rigorous validation process, so updates are infrequent and implemented with great care.
Here is a summary of what’s new:
- New Ethereum trading system – we added a breakout strategy that has already executed several trades. Its low correlation with our existing systems should enhance overall portfolio performance
- Portfolio rebalancing – we reduced the allocation to strategies that have underperformed in recent years and increased exposure to those that have been delivering stronger results
- System-by-system review – we thoroughly reassessed every strategy in the mix, refining entry and exit rules and adjusting risk-mitigation features
Most of this work is technical and not immediately visible, but we expect these adjustments to improve long-term performance.
Best,
The Algocrat AI Team
📊 Binance vs. MetaTrader Brokers: Who’s Delivering the Best Results in 2025? [Market Analysis]
It’s no secret that at Algocrat AI, we keep a close eye not only on the markets and our systems but also on the trading venues we use.
Yet decisions can’t rest on impressions alone, so we pull live data from each venue and track how conditions change over time.
Because meaningful statistics require a decent sample size, we run this analysis roughly once a year.
We carried it out in early 2024 - just before Algocrat AI’s public launch - and repeated this recently.
Here’s a brief summary of what we found:
In our 2023-24 review (made in early 2024), Binance was the clear leader, offering the highest liquidity, best execution, and lowest overall costs of all venues we used.
Since then, two things have changed: we started working with new venues, while Binance’s market share has slipped as competitors closed the gap.
Both trends call for a fresh assessment.
How the landscape looks now:
Binance remains one of the strongest crypto exchanges, but competitors have caught up:
Forex brokers now deliver markedly better execution for crypto and virtually zero downtime, several have recorded near 100 % uptime in 2024.
Crypto pairs that once suffered sporadic outages have been upgraded to a technical standard on par with FX pairs at many brokers.
Because downtime and slippage translate into higher costs, these improvements matter.
Execution quality and cost: what was once Binance’s undisputed edge is now merely a competitive offering. On some metrics, other brokers even outperform it
We also examined Binance’s global share of Bitcoin and Ethereum perpetual futures.
The exchange’s slice of that pie has shrunk from 65 % in 2022 to 41 % in 2024, a shift that aligns with what we see in live trading accounts.
So, which one is the “best” option?
There is no single winner.
IC Markets, Pepperstone, Vantage, Binance, and Exness all deliver execution quality and cost structures so close that the differences sit within statistical noise.
That’s why Algocrat AI trades across all of them and treats them as broadly interchangeable.
If you haven't seen our comparison post previously, we highly recommend revisiting it.
There, we have compared actual live results of accounts working with different venues.
And if you haven't signed up yet.. what are you waiting for?
🔗 Click here to Sign Up Now
Best,
The Algocrat AI Team
It’s no secret that at Algocrat AI, we keep a close eye not only on the markets and our systems but also on the trading venues we use.
Yet decisions can’t rest on impressions alone, so we pull live data from each venue and track how conditions change over time.
Because meaningful statistics require a decent sample size, we run this analysis roughly once a year.
We carried it out in early 2024 - just before Algocrat AI’s public launch - and repeated this recently.
Here’s a brief summary of what we found:
In our 2023-24 review (made in early 2024), Binance was the clear leader, offering the highest liquidity, best execution, and lowest overall costs of all venues we used.
Since then, two things have changed: we started working with new venues, while Binance’s market share has slipped as competitors closed the gap.
Both trends call for a fresh assessment.
How the landscape looks now:
Binance remains one of the strongest crypto exchanges, but competitors have caught up:
Forex brokers now deliver markedly better execution for crypto and virtually zero downtime, several have recorded near 100 % uptime in 2024.
Crypto pairs that once suffered sporadic outages have been upgraded to a technical standard on par with FX pairs at many brokers.
Because downtime and slippage translate into higher costs, these improvements matter.
Execution quality and cost: what was once Binance’s undisputed edge is now merely a competitive offering. On some metrics, other brokers even outperform it
We also examined Binance’s global share of Bitcoin and Ethereum perpetual futures.
The exchange’s slice of that pie has shrunk from 65 % in 2022 to 41 % in 2024, a shift that aligns with what we see in live trading accounts.
So, which one is the “best” option?
There is no single winner.
IC Markets, Pepperstone, Vantage, Binance, and Exness all deliver execution quality and cost structures so close that the differences sit within statistical noise.
That’s why Algocrat AI trades across all of them and treats them as broadly interchangeable.
If you haven't seen our comparison post previously, we highly recommend revisiting it.
There, we have compared actual live results of accounts working with different venues.
And if you haven't signed up yet.. what are you waiting for?
🔗 Click here to Sign Up Now
Best,
The Algocrat AI Team
😱 “Has Algocrat AI Lost Its Edge?” Here's What the Data Actually Says [Performance Analysis]
The feedback about Algocrat AI we've received recently is that our current results are underwhelming.
And it's true: our performance this year is below average, especially compared to last year’s results.
However, it's quite common for Algocrat AI to experience lower-than-average results.
By definition, the average value means “roughly half the time we are below it”.
So, we decided to provide a more thorough look into the performance metrics of Algocrat AI throughout the years to see if what we’re experiencing right now is normal.
Here's how it works:
All the calculations and charts below were produced using the QuantStats library.
It's one of the most well-known quant libraries used to assess the performance of algorithmic trading strategies and various assets all across the globe.
It’s fully open-source, and the data we use is public, pulled from our third-party verified Myfxbook track records, which means anyone could reproduce the results we’re showing here.
Without further ado, let’s dive straight into the metrics.
First, let’s assess year-to-year performance:
Algocrat AI has been trading publicly since 2020, as you can check in our Myfxbook account.
The overall return reached a phenomenal 15,000%, leaving Bitcoin in the dust. Moreover, the worst full year we’ve had to date is 2022, where we made 173.74%.
Pretty good for the worst year we’ve had so far.
Next, let’s take a look at a 6-month rolling Sortino chart, as one of the best available risk-adjusted performance metrics.
What we see is that the 2024 performance was well above average (at some point, Sortino reached an unrealistic 10.0), while the last 6 months are closer to average.
We’ve had such results many times before.
What’s interesting to note is that the 2024 performance was abnormal, almost twice the average year we observed before it.
So, it’s natural to see some regression to the mean afterward, since we do not usually make 300%+ per year.
This means that there is no statistically significant change from the past dynamics of the portfolio.
What we see now has been observed before on real, third-party verified public accounts.
So, what’s next?
After more than a decade of real-world experience in algorithmic trading, we’ve come to the inevitable conclusion:
Almost all attempts to predict the market’s medium-term dynamics (“Bitcoin is going to the moon, meet you at $200k!”, “Oh no, it’s falling all the way down to zero again…”) are pointless.
Nobody can do this with precision - it’s a losing game.
That’s why we don’t play it.
The best traders in the world can predict short-term price dynamics with maybe up to 60% precision at some points.
That’s what we do:
Boring exploitation of statistically significant market effects we can observe and measure.
Since 2018 with world-class success
And that’s what we intend to continue doing.
If you'd like to come along:
🔗 Click here to Sign Up Now
Best,
The Algocrat AI Team
The feedback about Algocrat AI we've received recently is that our current results are underwhelming.
And it's true: our performance this year is below average, especially compared to last year’s results.
However, it's quite common for Algocrat AI to experience lower-than-average results.
By definition, the average value means “roughly half the time we are below it”.
So, we decided to provide a more thorough look into the performance metrics of Algocrat AI throughout the years to see if what we’re experiencing right now is normal.
Here's how it works:
All the calculations and charts below were produced using the QuantStats library.
It's one of the most well-known quant libraries used to assess the performance of algorithmic trading strategies and various assets all across the globe.
It’s fully open-source, and the data we use is public, pulled from our third-party verified Myfxbook track records, which means anyone could reproduce the results we’re showing here.
Without further ado, let’s dive straight into the metrics.
First, let’s assess year-to-year performance:
Algocrat AI has been trading publicly since 2020, as you can check in our Myfxbook account.
The overall return reached a phenomenal 15,000%, leaving Bitcoin in the dust. Moreover, the worst full year we’ve had to date is 2022, where we made 173.74%.
Pretty good for the worst year we’ve had so far.
Next, let’s take a look at a 6-month rolling Sortino chart, as one of the best available risk-adjusted performance metrics.
What we see is that the 2024 performance was well above average (at some point, Sortino reached an unrealistic 10.0), while the last 6 months are closer to average.
We’ve had such results many times before.
What’s interesting to note is that the 2024 performance was abnormal, almost twice the average year we observed before it.
So, it’s natural to see some regression to the mean afterward, since we do not usually make 300%+ per year.
This means that there is no statistically significant change from the past dynamics of the portfolio.
What we see now has been observed before on real, third-party verified public accounts.
So, what’s next?
After more than a decade of real-world experience in algorithmic trading, we’ve come to the inevitable conclusion:
Almost all attempts to predict the market’s medium-term dynamics (“Bitcoin is going to the moon, meet you at $200k!”, “Oh no, it’s falling all the way down to zero again…”) are pointless.
Nobody can do this with precision - it’s a losing game.
That’s why we don’t play it.
The best traders in the world can predict short-term price dynamics with maybe up to 60% precision at some points.
That’s what we do:
Boring exploitation of statistically significant market effects we can observe and measure.
Since 2018 with world-class success
And that’s what we intend to continue doing.
If you'd like to come along:
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Best,
The Algocrat AI Team