FX Backtest Diary

Unbreakable EA "v1.5.0": The Secret to Its Unwavering Strength Revealed!

Mon, 29 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Unbreakable EA “v1.5.0”: The Secret to Its Unwavering Strength Revealed!”.

We’ve just wrapped up a comprehensive verification of our algorithmic FX trading system, v1.5.0, and the results are looking great! When it comes to automated trading, it’s not enough for a system to look good on paper; it needs to be robust, meaning it can handle various market conditions and slight changes to its internal settings without falling apart. That’s exactly what we put v1.5.0 through.

EA "v1.5.0": Is It Truly Robust? We Put Its Profitability to the Test!

Sun, 28 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA “v1.5.0”: Is It Truly Robust? We Put Its Profitability to the Test!”.

What’s the Big Idea? Testing Our EA’s Stamina!

When we build an Expert Advisor (EA) for FX trading, it’s not enough for it to look good on paper. We need to know it’s robust – meaning it can handle different market conditions and isn’t just a flash in the pan. Think of it like a champion athlete: can they perform consistently, year after year, not just win one big game? That’s exactly what we set out to test with our EA, version 1.5.0. We put it through two rigorous checks to see if it truly has staying power.

New EA Strategies: Why Weekly Average Regression Might Be a Trap, Not an Edge.

Sat, 27 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “New EA Strategies: Why Weekly Average Regression Might Be a Trap, Not an Edge.”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

3 Mean Reversion EAs Failed! But We Uncovered Crucial Insights

Fri, 26 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “3 Mean Reversion EAs Failed! But We Uncovered Crucial Insights”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

4 New FX EA Strategies Crumbled! What Failure Taught Us About Winning

Thu, 25 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “4 New FX EA Strategies Crumbled! What Failure Taught Us About Winning”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

About this site and the testing framework — why I built it

Wed, 24 Jun 2026 00:00:00 +0000

This site, “FX Backtest Diary,” is where I test automated FX trading (EA) ideas one by one and record the results. But it isn’t just a “did it make money or not” log. The real goal is to tell, as strictly as possible, whether an idea is a genuine edge or just happened to fit past data.

The Overnight Drift: A Real Anomaly Our EA Couldn't Profit From!

Wed, 24 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Overnight Drift: A Real Anomaly Our EA Couldn’t Profit From!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

How the testing works — why can you trust these results?

Tue, 23 Jun 2026 00:00:00 +0000

Across this site you’ll see phrases like “vanished in forward testing,” “failed the M1 intraday check,” or “Monte Carlo pass rate of X%.” Here’s what each of those means, as plainly as I can put it. They’re all gates designed to keep us from being fooled by a fake edge.

The Limits of Mean Reversion: Why Our EA Found No Edge With RSI14!

Tue, 23 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Limits of Mean Reversion: Why Our EA Found No Edge With RSI14!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

About the test data — where it comes from and how it's cleaned

Mon, 22 Jun 2026 00:00:00 +0000

Testing lives and dies by its data. Here’s an honest look at what powers the verifications on this site.

What data is used

The main feed is 1-minute (M1) OHLCV data for major FX pairs, gold, and so on. OHLCV means each bar’s open, high, low, close and volume. With 1-minute bars I can rebuild any higher timeframe I want — 5-minute, hourly, daily.

Market Regime Switching: Our EA's Smart Strategy Hit a Wall!

Mon, 22 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Market Regime Switching: Our EA’s Smart Strategy Hit a Wall!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

EA v1.5.0: The Ultimate Balance of Monthly Profit and Risk Revealed!

Sun, 21 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA v1.5.0: The Ultimate Balance of Monthly Profit and Risk Revealed!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

I built a feature to verify trading logic from YouTube videos

Sun, 21 Jun 2026 00:00:00 +0000

YouTube is full of “you can win with this method!” videos. They’re tempting, but verifying each one by hand is a lot of work. So I built a tool that extracts the trading rules from a video and feeds them straight into the testing framework.

How it works

Roughly, the flow is:

Feature list — what the framework can do

Sat, 20 Jun 2026 00:00:00 +0000

Here’s what the home-grown engine btengine and its tools can do. In one line: “prepare data → run strategies → reject fake edges → analyze → publish,” all wired together in-house.

The cards below are the main features. If something catches your eye, check out the related research articles too.

Market-Wide Oversold: Our EA Found a Real Mean Reversion Insight!

Sat, 20 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Market-Wide Oversold: Our EA Found a Real Mean Reversion Insight!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

The Nampin Trap: Our EA Reveals How Averaging Down Amplifies Losses!

Fri, 19 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Nampin Trap: Our EA Reveals How Averaging Down Amplifies Losses!”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the big idea?

Today, we’re diving into a trading concept called Nampin, or “averaging down.” You might have heard of it, and it sounds appealing on the surface. The basic idea is this: if you open a trade and the market moves against you, putting you in a floating loss, you open another position in the same direction at the new, worse price. The goal? To lower your overall average entry price. If the market then recovers even slightly, you reach breakeven or profit faster than if you’d just waited for your initial entry price to be hit. It’s the opposite of pyramiding (which we looked at in Research 53), where you add to a winning position. With Nampin, you’re essentially buying more when your trade is already losing, hoping for a bounce. It’s a common strategy, especially among manual traders, but can it work for an Expert Advisor (EA)? Let’s find out!

Conquering Weak Spots: Our Search for Uncorrelated EA Logic Continues!

Thu, 18 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Conquering Weak Spots: Our Search for Uncorrelated EA Logic Continues!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

EA Awakens! New "Connors RSI2" Logic Brings Real Profit Power!

Wed, 17 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA Awakens! New “Connors RSI2” Logic Brings Real Profit Power!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

The Golden Logic of Exit: Our EA Found the Perfect Profit-Taking Sweet Spot!

Tue, 16 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Golden Logic of Exit: Our EA Found the Perfect Profit-Taking Sweet Spot!”.

What’s the idea?

When you’re running an Expert Advisor (EA) in FX trading, how you decide to exit a trade is just as crucial as how you enter it. You want to let your winners run, but you also don’t want to hold on for so long that your profits evaporate when the trend reverses. It’s a tricky balance! In previous research (Study 121), we looked at Take Profit (TP) levels, which are essentially hard limits on how much profit you’ll take. For that, we found a pretty straightforward relationship: generally, the looser the TP, the better. But what about more dynamic exit strategies, like using an “exit channel”? That’s what this study was all about. We wanted to see if simply widening the exit channel would always lead to better results, or if there was a sweet spot.

The Take Profit Trap: Why Our EA Found "No TP" is Actually Best!

Mon, 15 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Take Profit Trap: Why Our EA Found “No TP” is Actually Best!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

Sometimes, the simplest approach is the best. Today, we’re diving into an experiment that looked at whether adding a fixed Take Profit (TP) could improve one of our trend-following EAs, “BreakoutLong.” The existing strategy doesn’t use a fixed TP; instead, it closes trades when the price exits a channel. We wanted to see if setting a specific profit target would make it even more robust.

Can "Uncorrelated EAs" Slash Drawdown? Why Our Dream Failed!

Sun, 14 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Can “Uncorrelated EAs” Slash Drawdown? Why Our Dream Failed!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’re always looking for ways to make our algorithmic trading systems (EAs) more robust and profitable. One of the biggest enemies of consistent profits is “drawdown” (DD) – that’s when your trading capital shrinks from its peak before it recovers. A common belief is that high drawdown often comes from having too many trades that move in lockstep, or are “correlated.” So, we had a hypothesis: if we could build the core of our trading system using currency pairs that have very low correlation with each other, we could significantly reduce system drawdown. Lower drawdown means we can safely use more leverage, which in turn could lead to higher profits. It’s like building a diversified investment portfolio; you don’t want all your assets to tank at the same time. If some go down, others might go up or stay stable, smoothing out the overall ride.

The 1166 Losing Trades: What Our EA Taught Us About Failure

Sat, 13 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The 1166 Losing Trades: What Our EA Taught Us About Failure”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the Idea?

We’re always looking for ways to make our algorithmic trading systems (EAs) better, right? One common approach is to identify what makes a trade a loser and then try to avoid those situations. If we can filter out the bad trades, theoretically, our overall performance should improve. That was the core idea behind this research: to dig into the losing trades of our “robust5 FX/H1/HTF” trend-following EA and see if they shared any common characteristics at the moment of entry.

Can We Predict Market Crashes Sooner? The Hunt for New Risk-Off Signals!

Fri, 12 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Can We Predict Market Crashes Sooner? The Hunt for New Risk-Off Signals!”.

Trying to predict market downturns before they hit is a holy grail for any automated trading system (EA). Our previous research (Study 117) showed that simply looking at the magnitude of correlation between different assets wasn’t enough to reduce drawdowns. So, we shifted our focus: what if we could find a directional signal that specifically warns us of impending market drops? Our current system uses a basic filter: it reduces risk when the US500 stock index dips below its 200-day Simple Moving Average (SMA). This is like a general “market health check.” But we wanted something better, something that could act as an earlier, more precise storm warning. We explored a few candidates:

Can Currency Correlation Cut Drawdown? Our Surprising Failure!

Thu, 11 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Can Currency Correlation Cut Drawdown? Our Surprising Failure!”.

What’s the idea?

We’re always looking for ways to make our algorithmic FX trading systems (EAs) more robust, and a big part of that is managing drawdowns. A drawdown (DD) is simply the peak-to-trough decline in your trading account balance – basically, how much your equity dropped from its highest point before recovering. Lower drawdowns mean a smoother ride and less stress! My hypothesis for this experiment was that a significant portion of our system’s drawdown, especially in our robust5 setup (which focuses on all-yen cross pairs like EURJPY, GBPJPY, etc.), comes from what I call “correlation drawdown.” This happens when multiple currency pairs move in the same unfavorable direction simultaneously. Think of it like putting all your eggs in one basket – if all your trades are highly correlated, a single market event can hit them all at once, leading to concentrated risk and a bigger DD. So, the bright idea was this: what if we could dynamically adjust our leverage based on the average correlation between these currency pairs? If the average correlation was high, we’d reduce our leverage. This would hopefully shrink the concentrated risk and, in turn, reduce our drawdown. Then, when correlations were lower, we’d have room to increase leverage again (since DD is often the main constraint on how much leverage we can use). To test this, I took our core FX system (version 1.4.1, which already incorporates volume-targeting, a stock market filter, and higher timeframe analysis) and added a “correlation overlay.” This overlay calculated an adjustment factor (corr_lev) based on the current pair correlations, clipping it between 0.6 and 1.4 to keep the leverage adjustments within reasonable bounds. Crucially, this was designed to be applied without any “hindsight” – meaning the system wouldn’t know future correlations when making decisions.

Scale-Out vs. Pyramiding: Which Strategy Actually Boosts Your EA?

Wed, 10 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Scale-Out vs. Pyramiding: Which Strategy Actually Boosts Your EA?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Ever wondered if you could squeeze more out of your winning trades, or protect profits better during volatile periods? That’s exactly what we explored in this research, looking at two common trading concepts: Pyramiding (also known as scaling in) and Scale-out (or partial profit-taking). Pyramiding is the idea of adding to a winning position. Imagine you’re in a trade that’s going your way, and you decide to buy more, effectively “doubling down” on what you believe is a strong trend. The hope is to compound your gains as the market continues in your favor. Scale-out, on the other hand, is about taking some profit off the table while letting the rest of your position run. Think of it like a poker player who pockets some chips after a big win but keeps a portion in play. You secure some gains, reducing your risk, but still give yourself a chance for more profit if the trend continues. For this study, we integrated these concepts into our robust v1.4.1 core FX trading system, which operates on a Higher Time Frame (H1) logic. Specifically, for scale-out, we modeled it as “half profit-take, half runner” using a take-profit target based on the Average True Range (ATR) – a common volatility indicator. Our goal was to see if either of these strategies could genuinely improve performance or risk management without fundamentally altering the core engine.

Can More Filters Guarantee Better Trades? The “Magic” We Uncovered!

Tue, 09 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Can More Filters Guarantee Better Trades? The “Magic” We Uncovered!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We often get requests from traders asking if we can make an Expert Advisor (EA) even better by adding more filters. The idea is simple: if we stack several filters on top of each other, can we boost the win rate and Profit Factor (PF), even if it means taking fewer trades? It sounds logical, right? More conditions should mean higher quality trades! However, our past research (studies 62, 86, 89, and 35) has given us a healthy dose of skepticism. We’ve often seen that filters might look great on the data they were developed on (what we call “In-Sample” or IS data), but their magic tends to disappear when tested on fresh, unseen data (“Out-of-Sample” or OOS). This time, we wanted to put that idea to a really tough test. The goal wasn’t just to make more money, but to improve the quality and consistency of the trades, even if it meant a slight dip in overall trade volume.

Do Stronger Support/Resistance Lines Mean More Wins? We Tested It!

Mon, 08 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Do Stronger Support/Resistance Lines Mean More Wins? We Tested It!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Wouldn’t it be great if we could spot market conditions that almost guarantee higher win rates for our Expert Advisors (EAs)? The thinking goes like this: imagine the market as a landscape with support and resistance levels – those invisible lines where prices tend to bounce or break. Some areas might have many strong, clear levels stacked up, while others are a bit “flatter” or less defined. The user hypothesis we wanted to test was simple: if a market looks “structurally strong” – meaning lots of clear support/resistance levels (what we call “level intensity” or “level strength”) – then our trading strategy should perform better there. And if it performs better, why not increase our risk a bit to capitalize on those supposedly “safer” or higher-probability trades? More strength, more profit, right? We wanted to see if this “strength-linked sizing” strategy made sense.

14% More Profit? Why This “Aggregated Vol-Target” Idea Was Shelved (Again)

Sun, 07 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “14% More Profit? Why This “Aggregated Vol-Target” Idea Was Shelved (Again)”.

What’s the Big Idea?

Sometimes, an idea for improving our algorithmic trading (EA) systems seems brilliant on paper, but faces practical roadblocks. That’s exactly what happened with our “aggregated volatility targeting” concept a while back. Here’s the backstory: In an earlier research project (Research 88), we explored a way to manage risk and position sizing across multiple trading strategies (which we often call “sleeves”). Instead of each sleeve adjusting its own position based on its individual volatility, we tried combining all their equity into one big pool. Then, we’d adjust positions for all sleeves based on this aggregated equity’s volatility. The results were impressive: a +14% improvement over the “distributed” approach, where each sleeve acts independently. So, why didn’t we use it? The catch was complexity. Implementing this “aggregated” system required a central “master orchestrator” to manage everything. This created a single point of failure and made the whole setup much more complicated to operate for our users. So, despite the performance boost, we shelved it. Fast forward to today: our deployment method has evolved. We now primarily use a single MT5 Expert Advisor (EA) per trading account. Crucially, this EA already has access to the account’s total equity. This change meant that implementing the aggregated volatility targeting would now be almost free in terms of additional cost or complexity! The original reason for rejection was gone. Given this new reality, it was time for a fresh look. Could this “aggregated” idea finally deliver its promised performance boost with our latest system, v1.4.0 (which includes our new “stock filter” and vt_cap3.0 improvements)? We decided to re-evaluate it with a rigorous, two-pass iterative testing method, ensuring no “hindsight” was used – meaning we only used past market data to make decisions, just like a real EA would.

Is v1.4.0 REALLY Optimal? We Tested 2 New Ideas to Find Out!

Sat, 06 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Is v1.4.0 REALLY Optimal? We Tested 2 New Ideas to Find Out!”.

What’s the idea?

We’re always on the hunt for ways to make our algorithmic trading systems (EAs) even better. This time, we put our Core System v1.4.0 under the microscope to test two specific ideas:

Crisis Alpha: Our “Amulet” EA for Stock Crashes Collapsed. Why?

Fri, 05 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Crisis Alpha: Our “Amulet” EA for Stock Crashes Collapsed. Why?”.

What’s the Idea? Betting Against the Market

We’re always looking for clever ways to make our algorithmic trading strategies (EAs) more robust, especially during market turmoil. One exciting idea that’s often floated is “crisis alpha” – a strategy that actually profits when the rest of the market is crashing. The hypothesis for this experiment was simple: Stock market indices, like the S&P 500 or Nasdaq, often experience sharp, persistent downtrends. Think back to late 2018, early 2020, or much of 2022. During these periods, if we could consistently “short” these indices (meaning, bet on their price going down) using a trend-following approach, we might not only make money but also have a valuable hedge against our main portfolio. In other words, when our main long-only strategies are struggling, this short-biased strategy could potentially kick in and save the day! This concept is different from a previous attempt (Research 104) where we tried to use the Japanese Yen as a safe haven in FX. That didn’t work out, so we wanted to see if shorting indices directly would be a more effective way to capture crisis alpha.

Silver Trends & AI Filter: Why Both Our New EA Ideas Were Rejected

Thu, 04 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Silver Trends & AI Filter: Why Both Our New EA Ideas Were Rejected”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

We’re always looking for new ways to refine our algorithmic trading strategies (EAs) and find that elusive “edge” in the market. This time, I dived into two interesting areas: the trend-following potential of Silver (XAGUSD) and the utility of the Kaufman Efficiency Ratio as a trend filter.

Aggressive EA Strategy: Why Our Core+Per-Pair Crumbled Under Costs

Wed, 03 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Aggressive EA Strategy: Why Our Core+Per-Pair Crumbled Under Costs”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

Index CFD Trading: Unmasking The Real Costs for Our EA Strategy

Tue, 02 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Index CFD Trading: Unmasking The Real Costs for Our EA Strategy”.

What’s the idea?

Getting an Expert Advisor (EA) ready for live trading isn’t just about crafting a brilliant strategy; it’s also about ensuring it can survive the real-world costs of trading. This research dive, “Research 108,” was all about making sure a specific strategy — what I call an “index sleeve” — wouldn’t be eaten alive by trading costs, especially those associated with Index CFDs. An Index CFD (Contract for Difference) is a popular way to trade the price movements of a stock market index, like the S&P 500, without actually buying all the underlying stocks. It’s flexible, but comes with its own set of costs. Our “index sleeve” strategy is designed to capture big, long-term trends, making very few trades over many years. Think of it like a patient hunter waiting for the perfect, massive game.

FX Swap Shock: Is Your EA Losing Money While You Sleep?

Mon, 01 Jun 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “FX Swap Shock: Is Your EA Losing Money While You Sleep?”.

What’s the idea?

When we talk about taking an Expert Advisor (EA) from backtesting to live trading, it’s easy to focus just on the raw profit/loss numbers. But there are crucial “hidden” costs and benefits that can make or break a strategy in the real world. Today, we’re diving into one of those big ones: swap costs. What are swaps? In FX trading, a swap is basically the interest you either pay or receive for holding a position overnight. It’s determined by the interest rate differential between the two currencies in a pair. If you’re holding a currency with a higher interest rate against one with a lower rate, you might earn swap (a “positive carry”). If it’s the other way around, you’ll pay swap (a “negative carry”). For our particular EA, we found that trades are held for an average of 6.8 days. That’s long enough for swap costs and benefits to really add up and impact overall profitability. So, understanding them isn’t just an academic exercise – it’s vital for knowing what to expect when we go live.

Real-World FX: Can Our EA Survive the Hidden Costs of Trading?

Sun, 31 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Real-World FX: Can Our EA Survive the Hidden Costs of Trading?”.

This time, we’re diving into a crucial topic for any automated trading strategy: how well it stands up to the real-world costs of trading. Our focus today is on Core v1.4.0, a low-frequency Expert Advisor (EA) that holds trades for an average of 6.8 days. Over 11 years, it’s executed 6168 trades, which gives us a solid dataset to stress test its resilience.

The AI Sizing Challenge: Why Contextual Bandit Couldn't Optimize Leverage

Sat, 30 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The AI Sizing Challenge: Why Contextual Bandit Couldn’t Optimize Leverage”.

What’s the idea?

We’ve explored various ways to make our algorithmic FX trading systems (EAs) smarter, especially when it comes to deciding how much to trade – what we call “position sizing.” This is like a chef adjusting the amount of spice based on the ingredients and the diners’ preferences. Get it right, and you could boost returns while managing risk. Get it wrong, and… well, let’s just say it’s not good! Previously, we tried using Machine Learning (ML) for this (Research 100), aiming to dynamically adjust leverage (how much borrowed money we use for trades) based on market conditions. But that experiment didn’t pan out; the ML system performed no better than a “placebo” (a shuffled version of its own output). It seemed the underlying market structure it was trying to learn was either too complex or already captured by our existing, simpler methods. This time, we’re trying a different flavor of AI: a contextual bandit. Think of it as a simpler, more focused type of reinforcement learning (RL). Instead of learning a complex sequence of actions, a contextual bandit learns the best single action to take in a given “context” or market state. Our goal was to see if this approach could learn to pick optimal leverage levels (0.5x, 1.0x, or 1.5x) based on different market scenarios, outperforming our existing, hand-coded sizing logic.

Crisis Alpha: Why Our Safe Haven EA Strategy Didn't Deliver

Fri, 29 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Crisis Alpha: Why Our Safe Haven EA Strategy Didn’t Deliver”.

What’s the idea?

Sometimes, even the most robust Expert Advisors (EAs) have downtime. Our v1.4.0 system, for instance, is designed to reduce its core positions when the stock market signals “risk-off” – that’s when investors are nervous, stocks are falling, and they’re looking for safer places to put their money. This makes sense for capital preservation, but it also leaves a bit of an “empty slot” in our trading activity. So, we wondered: could we fill that slot? The hypothesis was to introduce a “crisis alpha” strategy. This meant actively trading traditional safe-haven assets (like buying Japanese Yen or Gold) specifically during these risk-off periods. The goal? To generate profit when our main system is taking a cautious stance, essentially acting as a complementary hedge. Think of it like a smart backup generator that kicks in exactly when the main power grid gets shaky!

Range Bar Breakouts: Why Our "Discovery" Was Just a Placebo Effect

Thu, 28 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Range Bar Breakouts: Why Our “Discovery” Was Just a Placebo Effect”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Today, we’re diving into a fascinating experiment with a common concept in algorithmic trading: range bars. Unlike traditional time-based bars (like a 15-minute bar, or M15, which always takes 15 minutes to form), range bars are built based on price movement. Think of it like this: instead of a camera taking a picture every 15 minutes, it only takes one after a certain amount of movement has occurred. This is often used for “denoising” – trying to filter out the irrelevant wiggles and focus on significant price action. Our strategy was a “long-only Donchian break” using these range bars. We took a standard M15 timeframe and converted it into “ATR bricks” – a specific type of range bar where each “brick” represents a fixed multiple of the Average True Range (ATR), a measure of market volatility. The Donchian break part simply means we’d enter a long trade when the price broke above the highest high of the last ‘N’ range bars, expecting a continuation of the trend.

YouTube's DEG Method: Our EA Reveals Its Hidden Trend Power!

Wed, 27 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “YouTube’s DEG Method: Our EA Reveals Its Hidden Trend Power!”.

What’s the Idea Behind This Strategy?

We’re always on the hunt for robust trading strategies, especially those that promise to combine powerful concepts. This time, we dove into a fascinating approach that merges three giants of technical analysis: Dow Theory, Elliott Wave, and Granville’s Rules. The goal? To build an Expert Advisor (EA) that can identify strong trends and capitalize on them. Here’s how the strategy was designed to work:

Beyond One EA: How Blending Core & Per-Pair Redefined FX Automation

Tue, 26 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Beyond One EA: How Blending Core & Per-Pair Redefined FX Automation”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the Big Idea? Blending EAs for Better Returns!

Ever wondered if combining different automated trading strategies, or Expert Advisors (EAs), could lead to even better results than running them individually? That’s exactly what we set out to explore in our latest research! The core idea was to blend the daily signals from three distinct EAs to see if their combined strength could create a more powerful and profitable system.

AI's Risk Prediction: Was Its Trading Logic a Placebo?

Mon, 25 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “AI’s Risk Prediction: Was Its Trading Logic a Placebo?”.

What’s the idea?

Most algorithmic trading strategies (EAs) focus on predicting which way the market will go – up or down. But what if we shifted our focus from direction to risk? That was the core idea behind this experiment. Instead of trying to predict future price movements, we aimed to predict future volatility (how much prices are likely to swing). If we know how risky the market is likely to be, we can adjust our trade size, or “leverage,” accordingly. High predicted risk? We take a smaller position. Low predicted risk? Maybe we can safely increase our leverage. This is called “leverage optimization,” and the goal is to smooth out returns and improve risk-adjusted performance. To do this, we turned to Machine Learning (ML), specifically an algorithm called LightGBM (a powerful and efficient gradient boosting framework). Think of it like a super-smart pattern recognition tool, designed to find hidden relationships in data.

The Unstoppable EA Evolution: Our Quest for New Trading Logic!

Sun, 24 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Unstoppable EA Evolution: Our Quest for New Trading Logic!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

FX Revolution! How Stock Market Signals Unlocked a New Edge!

Sat, 23 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “FX Revolution! How Stock Market Signals Unlocked a New Edge!”.

What if the Stock Market Held a Secret for Your FX Trades?

We’ve always been on the hunt for new ways to improve our algorithmic FX trading strategies (EAs). Today, we’re diving into a fascinating new approach: using the stock market as a kind of “weather forecast” for our FX trades! This is the first time we’ve tried using an external index as a signal, and the results are pretty exciting.

Simplicity Wins: Why Fixed Weighting is Best for Your EA Portfolio!

Fri, 22 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Simplicity Wins: Why Fixed Weighting is Best for Your EA Portfolio!”.

What’s the idea?

When you’re running multiple Expert Advisors (EAs) or trading various currency pairs, a big question is: how much capital should you allocate to each? Should you give every EA an equal slice of the pie, or try to dynamically adjust the weights based on market conditions, risk, or recent performance? This research project aimed to find out if more sophisticated, dynamic portfolio allocation methods could beat the simplest approach: fixed, equal weighting. We put several popular dynamic allocation strategies to the test:

Are Popular Indicators Always King? What Our EA Test Revealed!

Thu, 21 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Are Popular Indicators Always King? What Our EA Test Revealed!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

Unleashing the Beast: The Secret Behind Core v1.3's Evolution!

Wed, 20 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Unleashing the Beast: The Secret Behind Core v1.3’s Evolution!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

Decoding the Market: Finding the Best Logic and Realistic Monthly Profits!

Tue, 19 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Decoding the Market: Finding the Best Logic and Realistic Monthly Profits!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

The Counter-Trend Genius: Why Our Reverse Golden Cross Strategy Failed!

Mon, 18 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Counter-Trend Genius: Why Our Reverse Golden Cross Strategy Failed!”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the idea?

You know the “Golden Cross,” right? It’s a classic bullish signal in technical analysis, where a shorter-term moving average (like the 50-day Simple Moving Average, or SMA) crosses above a longer-term one (like the 200-day SMA). It’s often seen as a sign that an upward trend is starting or strengthening. We also looked for an additional confirmation: an upward shift in the market’s “Dow structure,” which basically means higher highs and higher lows, reinforcing the bullish momentum. Now, most traders would see these combined signals and think, “Time to buy!” But what if that obvious bullish signal is actually a trap? What if all the smart money has already priced it in, or even worse, it’s a “bull trap” designed to sucker in late buyers before the market reverses? This is what we call a “contrarian” hypothesis – betting against the crowd. So, for this experiment, we mechanized a strategy called GoldenCrossFade. The idea was simple: when we saw both a Golden Cross and a confirmed upward shift in Dow structure, we’d take a short position, betting that the market would fall. We wanted to see if fading (betting against) this seemingly strong bullish signal actually had an edge. For comparison, we also tested a follow strategy, which would simply go long (buy) on the same signal, as most people would.

The Trend Continuation Illusion? What Our EA Test Really Revealed!

Sun, 17 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Trend Continuation Illusion? What Our EA Test Really Revealed!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What Was the Idea?

We’re always looking for clever ways to spot profitable trading opportunities, especially with automated strategies (EAs). This time, we explored a “Continuation Breakout” idea. Imagine a market that’s already in an uptrend, then takes a little breather, consolidating in a tight range, before finally breaking out and continuing its original upward journey. That’s the core concept! Our goal was to mechanize this intuition, hoping to filter out lower-quality trades from a basic “plain breakout” strategy. Here’s how we defined it:

Beyond the Limits: How Volatility Targeting Unlocked Real FX Profit Growth!

Sat, 16 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Beyond the Limits: How Volatility Targeting Unlocked Real FX Profit Growth!”.

What’s the Big Idea: Taming Volatility Over Time

We’ve been hard at work trying to squeeze more performance out of our algorithmic trading systems (EAs). In our previous research (Study 87), we found that simply adding more “sleeves” (diversifying across different EAs) had hit a ceiling. Our Core System v1.2.0 was already performing well, with a Calmar Ratio around 1.4 and a monthly return of +1.17% (normalized for a 10% drawdown). But we knew there had to be another way to improve. Instead of just diversifying across different EAs, we decided to try something entirely different: diversifying over time. The core idea is called Portfolio Volatility Targeting. Imagine you’re sailing a boat: when the seas are rough and choppy (high volatility), you “reef” your sails, reducing your exposure to the wind. When the waters are calm, you unfurl more sail, expanding your exposure. That’s exactly what Volatility Targeting does for our portfolio. It dynamically adjusts our daily exposure – in other words, how much risk we’re taking – to keep the realized volatility (how much our portfolio’s value actually swings up and down) constant. If the market is experiencing high volatility or choppy sideways movement, we shrink our exposure. If things are quiet and calm, we expand it. Crucially, to avoid any “peeking into the future,” we always determine today’s leverage (how much capital we’re using relative to our equity) based only on yesterday’s volatility. This is a big departure from older methods that reacted to our portfolio’s performance (e.g., cutting lot sizes if our equity dipped below a moving average). Those methods are reactive and often lag behind. Volatility Targeting, however, connects directly to the actual market risk we’re experiencing, making it much more potent.

Core System Hit Its Ceiling? Why Index Diversification Failed!

Fri, 15 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Core System Hit Its Ceiling? Why Index Diversification Failed!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

EA Enhancement Fails: "Level Importance" Couldn't Boost BreakoutLong!

Thu, 14 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA Enhancement Fails: “Level Importance” Couldn’t Boost BreakoutLong!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Alright, let’s dive into another experiment with our Expert Advisors (EAs). This time, the goal was to improve our “BreakoutLong” strategy, an EA that aims to profit when prices break above a significant resistance level. The idea was to make it smarter by only taking “real” breakouts, not just any little wiggle above a line. We had previously developed a “level importance” scoring system (levels.py) that assigns a score to horizontal support and resistance levels based on how strong or significant they are. This system worked wonders for our yosuga EA, which is a pullback strategy (meaning it buys when price dips to strong support). So, the thinking was: if it works for pullbacks, maybe it can also filter breakouts? The hypothesis was simple: if we only allow the BreakoutLong EA to trade when it breaks through a highly important horizontal level, we could filter out the “noisy small breakouts” that often lead to losses. Our hope was to reduce the correlated drawdown (DD) – that painful period when multiple strategies lose money at the same time – which had hit about -38% in a previous study (Research 85). We wanted to make breakouts more robust by focusing on higher-quality entries.

Supercharging "Breakout_h1": Our EA Platform's Enhancement Test!

Wed, 13 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Supercharging “Breakout_h1”: Our EA Platform’s Enhancement Test!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

Welcome back to the blog! Today, we’re diving deep into a fascinating strategy called breakout_h1. It’s a prime example of how rigorous testing can reveal hidden flaws and lead to a much stronger trading system.

EA Line Recognition: How "Tolerance Band Width" Changes Performance!

Tue, 12 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA Line Recognition: How “Tolerance Band Width” Changes Performance!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

The Case for “Wobbly” Lines: Why Precision Isn’t Always Your Friend in FX Trading

Ever looked at an FX chart and drawn a perfect trendline, only for the price to “touch” it, “bounce” off it, or “slightly break” it before reversing? If you’re using an Expert Advisor (EA) to trade these lines, you know how frustrating it can be when the market doesn’t respect your perfectly drawn geometry. Our community brought up a great point: charts aren’t always precise. There’s often a slight delay in reaction, a “touch” that isn’t quite exact, or a “fakeout” where price seems to break a level only to snap back. This suggests our lines need some “wiggle room” – a tolerance band around them. We’ve actually had this concept of “width” or “tolerance bands” built into our EA engine for a while, dynamically linked to Average True Range (ATR). (Quick jargon check: Average True Range, or ATR, is a measure of market volatility. It helps our EA adjust these bands dynamically, so they get wider in choppy markets and narrower in calm ones.) We use it for various line types: merge_atr for touch zones, break_atr for fakeout zones, and level_atr for general proximity for channels, plus retest_atr for trendlines. However, while we’d thoroughly tested the robustness of horizontal levels by sweeping their level_atr width (that was Research 73), we hadn’t done the same for the actual widths of channels and trendlines themselves. It was time to put them to the test! We decided to systematically “sweep” (test a range of values for) these band widths, from narrow to wide, and see how it impacted our forward test results.

YouTube's Granville Law: Our EA Test Found No Edge!

Mon, 11 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “YouTube’s Granville Law: Our EA Test Found No Edge!”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

Have you ever stumbled upon a YouTube video promising to unlock the secrets of FX trading with a simple indicator? Many of us have! This time, we put a popular strategy based on Granville’s Law and the 200-period Exponential Moving Average (EMA) to the test. The specific goal? To reliably catch the elusive “Wave 3” in a trend.

YouTube FX Strategy Exposed: Our EA Test Found "False Positives"!

Sun, 10 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “YouTube FX Strategy Exposed: Our EA Test Found “False Positives”!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

Unmasking YouTube FX Strategies: The EMA Cross & Pullback Test

We’ve all seen those captivating YouTube videos promising easy profits with a simple trading strategy. As your EA verification blog editor, I’m always curious to put these claims to the test. This time, we tackled a popular approach: the 4-hour (H4) EMA cross with a pullback entry. Was it the holy grail, or just another mirage? Let’s dive in!

The +37% Monthly Profit Secret: Blending EAs for Maximum Gain!

Sat, 09 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The +37% Monthly Profit Secret: Blending EAs for Maximum Gain!”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the Big Idea?

Ever thought about combining different trading strategies to get even better results? That’s exactly what we explored here! We took three of our already-verified algorithmic trading systems (also known as Expert Advisors or EAs) – core_system_v1.1.0, satellite, and satellite2 – and tried to blend their daily returns. Our goal was simple: find the perfect mix that maximizes monthly profit while keeping the maximum drawdown (DD) to a sensible 10% or less. Think of it like building a balanced investment portfolio. You don’t just put all your money into one stock; you diversify. We’re doing the same with EAs, hoping that by combining them, we can smooth out returns and boost overall performance. We also looked at how these systems correlate with each other: core and satellite had a moderate correlation of 0.43, core and satellite2 were also moderately correlated at 0.47, but satellite and satellite2 had a much lower correlation of 0.19. Lower correlation often means better diversification!

Give Your EA Eyes: Automating Candlestick & Chart Patterns!

Fri, 08 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Give Your EA Eyes: Automating Candlestick & Chart Patterns!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

It’s no secret that traders are always looking for an “edge” – that consistent statistical advantage that helps them make profitable trades. For us here, building robust algorithmic trading systems (EAs, or Expert Advisors) means constantly expanding our toolkit to quantify what many discretionary traders already use. This time, we’ve focused on bringing the classic wisdom of candlestick and chart patterns into our automated analysis!

Volume Profile vs. Swing Levels: Which Gives Your EA The Edge?

Thu, 07 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Volume Profile vs. Swing Levels: Which Gives Your EA The Edge?”.

What’s the Big Idea? A New Level-Finding Tool for Our EAs!

Here in the lab, we’re always on the hunt for ways to make our algorithmic trading robots (EAs) smarter and more robust. Our current champion framework, built on the solid foundations of Dow Theory, Multi-Timeframe Analysis (MTF), and a trusty pullback strategy, has proven itself quite effective. It uses what we call “swing levels” (from our Research 73) to pinpoint key areas where price is likely to react. But what if there’s another powerful way to identify these crucial levels? That’s exactly what we explored in this study. We wanted to see if Volume Profile – a fascinating tool that shows where the most trading activity has occurred – could be integrated into our proven framework as an even more effective “level filter.” Our goal was simple: Could it outperform or at least complement our existing swing levels, especially for FX trading?

The Ultimate EA Toolkit: Quantifying Pro Trader Wisdom!

Wed, 06 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Ultimate EA Toolkit: Quantifying Pro Trader Wisdom!”.

What’s the Big Idea?

This isn’t about revealing a groundbreaking new trading strategy today. Instead, it’s about making our Expert Advisor (EA) verification process much stronger and more robust! Think of it like a chef upgrading their kitchen with better tools and ingredients – not to cook a new dish immediately, but to be able to cook any dish better in the future. Our goal with Research 77 was to take as many measurable elements as possible, even those often left to human discretion, and turn them into automated, quantifiable “engines” for our testing toolkit. We scoured the web for useful, quantifiable methods that we hadn’t implemented yet, and then we got to work!

Revealed: Satellite-2 EA! The Culmination of Yosuga's Dow Method.

Tue, 05 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Revealed: Satellite-2 EA! The Culmination of Yosuga’s Dow Method.”.

What’s the Big Idea?

Have you ever wondered if the “magic” of a skilled human trader could be bottled up and turned into an automated system? That’s exactly what we set out to explore with our latest venture, the Satellite-2 System. This isn’t just another EA; it’s our first confirmed success in proving that a discretionary (human-led) trading method can be logically quantified and mechanized into a robust algorithm. Specifically, we tackled the “Yosuga-style Dow method,” a classic trend-following strategy that focuses on identifying pullbacks after a trend reversal. Many believe these kinds of human-intuition-based strategies are impossible to automate effectively, especially when put to the test in real-world forward testing. But we challenged that assumption! Our approach, which we’ve dubbed “level-filtered YosugaDow,” zeros in on these specific pullback opportunities. It combines the core Dow theory principles with Multiple Time Frame (MTF) analysis – essentially looking at the market from different time perspectives (e.g., hourly and daily charts) – and critically, it incorporates “effective horizontal levels.” These are like invisible lines on your chart (support and resistance) that the market tends to react to. We gave these levels a numerical “score” (requiring a min_level_score=20) to filter for only the most significant ones. This system is designed to run independently, a “satellite” to our main core_system_v1 and another existing satellite.

Price Action for EA: The Unsolved Challenge! Why This Automation Attempt Failed.

Mon, 04 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Price Action for EA: The Unsolved Challenge! Why This Automation Attempt Failed.”.

What Was the Idea? Quantifying Price Action

We’ve all seen those popular trading textbooks and courses that talk about “Price Action” (PA). Things like a long wick (or “shadow”) on a candlestick signaling price rejection, or an engulfing candle suggesting a reversal. These patterns are often touted as powerful tools for discretionary traders. But can we actually turn this intuitive “reading” of candlesticks into hard, mechanical rules for an Expert Advisor (EA)? That was the big question for this research! Our goal was to take these common PA patterns – specifically, long wicks indicating rejection and engulfing candles suggesting a turnaround – and quantify them at the individual candlestick level. Think of it as trying to teach a computer to “see” what a human trader sees when they spot these patterns. We then wanted to see if adding this quantified Price Action as a “confirmation” filter would improve our already robust trading system. We’d previously identified genuinely strong horizontal levels (from Research 73, specifically those we rated level >= 20 for their historical significance) as a fantastic edge. So, the hypothesis was: if price hits one of these strong levels AND we see a specific PA pattern, would that make for an even better trade signal?

Diagonal Lines for EA: Can "Channels" Be Quantified? Why This Idea Failed!

Sun, 03 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Diagonal Lines for EA: Can “Channels” Be Quantified? Why This Idea Failed!”.

This time, we tried to automate and verify the effectiveness of diagonal channels – those trendlines you often draw on charts – and the results were a clear “no go.”

What’s the idea?

In our journey to verify the components of a popular discretionary trading approach (let’s call it the “Yosuga style”), we’ve been tackling its core elements one by one. Previously, we had great success quantifying horizontal support and resistance levels (which we discussed in Research 73). These levels proved robust and profitable in our automated tests. Now, it was time to turn our attention to the other major line element in this style: diagonal channels. The goal was simple: could we take the concept of drawing a channel between two swing points, score how price reacts to those lines (touches, bounces), and use that information to generate profitable trades? We wanted to see if these diagonal lines, often used by manual traders, could hold up under rigorous automated testing.

Unlocked: Powerful Trading Edge Integrated into EA! Will Performance Skyrocket?

Sat, 02 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Unlocked: Powerful Trading Edge Integrated into EA! Will Performance Skyrocket?”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

EA's New Vision: Quantifying "Effective" Lines! A Game-Changer for Winning Logic?

Fri, 01 May 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA’s New Vision: Quantifying “Effective” Lines! A Game-Changer for Winning Logic?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

The “Logic of Lines”: Can We Code Discretionary Trading?

Have you ever wondered if the “gut feeling” a seasoned trader uses to spot support and resistance lines could actually be turned into code? One of our sharp-eyed users suggested that discretionary trading isn’t just intuition; it’s based on logical lines that, if quantified with low discretion, could statistically win. In other words, if we can objectively define what makes a line “effective,” we might find a real edge, distinct from the kind of overfitting you sometimes see in machine learning. This idea was too good to pass up, and it led us to significantly upgrade our testing platform.

Yosuga EA's Secret: Limiting Entries to "Line Vicinity" Boosts Performance!

Thu, 30 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Yosuga EA’s Secret: Limiting Entries to “Line Vicinity” Boosts Performance!”.

This post wraps up our deep dive into the ‘Yosuga-style’ trading method, specifically testing a key discretionary filter. The goal was to see if a particular rule, claimed to boost profitability, could be successfully turned into a mechanical Expert Advisor (EA).

Yosuga's Dow Method: Can This Trend-Following Secret Be Automated by EA?

Wed, 29 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Yosuga’s Dow Method: Can This Trend-Following Secret Be Automated by EA?”.

What’s the Idea Behind This EA?

We recently got our hands on some intriguing educational materials from a trader known as Yosuga (dating back to 2022). His core philosophy really resonated with us: “Trend following is the only way to win in FX,” and “FX is all about catching the very beginning of a trend.” Guess what? This aligns perfectly with what we’ve discovered in our own research here on the blog! It’s always great when independent conclusions point in the same direction. So, the goal for this project was to take Yosuga’s Dow Theory-based trend trading strategy and see if we could turn it into an Expert Advisor (EA) – basically, automate it. Could we capture his successful insights in a machine-driven trading system?

Monthly 3% in FX EA: Dream or Reality? Unveiling the True Cost & Limits!

Tue, 28 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Monthly 3% in FX EA: Dream or Reality? Unveiling the True Cost & Limits!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

Index EA vs. Corona Shock: Did It Survive Intraday Crashes? The Ultimate Test!

Mon, 27 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Index EA vs. Corona Shock: Did It Survive Intraday Crashes? The Ultimate Test!”.

What’s the idea?

We’re always looking for robust algorithmic trading strategies (EAs) that can handle whatever the market throws at them. This time, we focused on a specific “sleeve” of our portfolio: trading major stock indices like the S&P 500 (USA500), Nasdaq 100 (USATECH), and Dow Jones Industrial Average (USA30). The core idea is simple: it’s a long-only strategy. This means we only ever buy, betting that these indices will generally trend upwards over time. We use an exponential trend indicator, like a 200-period Simple Moving Average (SMA200), on daily charts to identify when to be in the market. If the price is above the SMA200, we’re looking to buy; if it falls below, we exit. The big question, though, is how such a strategy would perform during sudden, violent market crashes. While daily charts might look fine, what happens within those crash days? Could we get wiped out by a massive intra-day drop, even if the daily signal eventually gets us out? That’s what this research aimed to find out.

Index EA on M1: Can It Survive Intraday Shocks? Unveiling Hidden Risks!

Sun, 26 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Index EA on M1: Can It Survive Intraday Shocks? Unveiling Hidden Risks!”.

What’s the idea?

You know how sometimes a trading strategy looks amazing on paper, or when you backtest it on daily charts, but you worry about what happens when things get really fast and furious during the trading day? That’s exactly the challenge we tackled recently! We’ve been working on an exciting strategy (let’s call it “Research 59”) that showed great potential for trading stock indices. But there was one big question mark, a “blocker” preventing us from fully adopting it: we’d only tested it on daily (D1) data. We hadn’t checked how it would handle the minute-by-minute (M1) ups and downs, especially those sudden gaps or sharp drops that can happen intraday. Our main concern was whether these rapid moves could cause the strategy to hit our internal “proprietary daily -5% loss limit.” It’s like having a race car that’s proven on the highway, but you need to see if it can handle the tight turns and sudden stops of a city race track!

Our EA Gets a 3rd Engine: Uncorrelated Strategies for Maximum Stability!

Sat, 25 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Our EA Gets a 3rd Engine: Uncorrelated Strategies for Maximum Stability!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

Short-Term EA Upgrade: Can Higher Timeframe Filters Boost Profit Quality?

Fri, 24 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Short-Term EA Upgrade: Can Higher Timeframe Filters Boost Profit Quality?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’re continuing our exploration from Research 61, where we’re always looking for ways to make our algorithmic trading systems (also known as Expert Advisors, or EAs) perform better. This time, our focus was on “BreakoutLong,” an EA that typically looks for price breakouts to enter long trades. The big question was: can we improve its performance by adding some smart “filters” to help it pick only the best trades? Think of filters like a bouncer at a club, letting in only the VIPs. We wanted our EA to be pickier, avoiding low-quality trades that often just eat into profits. We added two main types of filters:

Existing Trend EAs on Shorter Timeframes: Will More Trades Boost Profits?

Thu, 23 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Existing Trend EAs on Shorter Timeframes: Will More Trades Boost Profits?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Ever wondered if an Expert Advisor (EA) – that’s an automated trading program – that performs beautifully on, say, an hourly chart could do even better if you just sped it up and ran it on a 5-minute chart? It’s a common thought: more trades, more opportunities, right? Well, we took a couple of our established, proven trend-following systems, BreakoutLong (which only takes long trades) and DonchianBreakout (which trades both long and short), and put this idea to the test. The goal was to see what happens when you take a system designed for slower, longer-term trends and force it into the fast lane of shorter timeframes (TF), specifically going from H1 (1-hour charts) all the way down to M5 (5-minute charts). Crucially, we kept all the internal parameters of the EAs exactly the same – no re-optimization for the shorter TFs, because we wanted to see the inherent performance change.

Trendline Break Strategy: Can This Unique EA Catch the First Wave?

Wed, 22 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Trendline Break Strategy: Can This Unique EA Catch the First Wave?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

Today, we’re diving into a fascinating trading concept: capturing the “first wave” after a trendline break and retest. The idea is simple yet compelling, and we put it through our rigorous testing grinder.

Our EA Just Broke the Monthly Profit Ceiling! Find Out How.

Tue, 21 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Our EA Just Broke the Monthly Profit Ceiling! Find Out How.”.

This time around, we explored a fascinating way to potentially boost our algorithmic trading systems (EAs) beyond what we thought was possible: by combining an existing, reliable strategy with a completely new one. The goal was simple but ambitious: break through a perceived ceiling on our monthly profits.

The Missing Piece? Stock Indices Could Be Your EA Portfolio's New Edge!

Mon, 20 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Missing Piece? Stock Indices Could Be Your EA Portfolio’s New Edge!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’re always on the hunt for trading strategies that don’t just make money, but also play nicely together. Imagine having a bunch of different investments, and when one zigs, another zags – that’s the power of diversification! For a long time, we’ve been trying to find a “true uncorrelated edge” within FX and gold markets, something that genuinely moves independently from our core strategies. And guess what? We finally found a strong candidate: stock market indices! The core idea here was to see if a simple trend-following strategy on major stock indices could offer a robust, profitable edge that was uncorrelated with our existing FX and gold strategies. If it worked, it could be a game-changer for building a more stable, higher-performing portfolio.

Optimizing EA Exits: Is There a Hidden Winning Logic, Or Just a Wall?

Sun, 19 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Optimizing EA Exits: Is There a Hidden Winning Logic, Or Just a Wall?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

Sometimes, the best optimization is no optimization at all. We’ve often focused on making our Expert Advisors (EAs) smarter about when to enter a trade. But what about when to leave? This time, we set our sights on optimizing the exit strategy for our core trend-following system, BreakoutLong. The idea behind focusing on exits is that improving how you manage an already established “edge” (your trading advantage) might carry less risk of overfitting compared to tweaking entries. It’s like improving your car’s braking system versus trying to make it accelerate faster – both matter, but one might be a safer bet for overall performance.

Ichimoku Kinko Hyo & Supertrend: Can They Deliver a Winning EA Logic?

Sat, 18 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Ichimoku Kinko Hyo & Supertrend: Can They Deliver a Winning EA Logic?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Alright, let’s talk about trend following! We’re always on the hunt for new “edges” in the market – those little statistical advantages that can help our algorithmic trading systems (EAs) make money. This time, I decided to put two well-known trend indicators, Ichimoku Kinko Hyo and Supertrend, to the test. Could they offer a fresh perspective on capturing trends, or even replace parts of our existing, proven strategies? My goal was simple: see if these indicators could identify profitable long (buy) trends on their own, and then, if they showed promise, see how they’d perform when integrated into my core multi-timeframe system.

Japan's "10 Billion Yen Trade": Can We Replicate Its Winning Edge in EA?

Fri, 17 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Japan’s “10 Billion Yen Trade”: Can We Replicate Its Winning Edge in EA?”.

Today, we’re diving into the popular FX trading style of a well-known Japanese educator, Ishin-no-suke, to see if its core principles can be successfully automated as an Expert Advisor (EA).

Who is Ishin-no-suke?

Ishin-no-suke is a prominent Japanese FX educator based in Bangkok. He’s quite well-known for his books (like “Ishin-ryu Trade-jutsu”), blogs (multimillionaire-trade.com, fx-lifeschool.info), and YouTube channel, where he teaches his approach to trading. His focus is primarily on discretionary day trading, meaning he relies on human judgment and skill rather than rigid, automated rules.

Pyramiding Strategy: Did Chasing Wins Bring Max Profit or Risk?

Thu, 16 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Pyramiding Strategy: Did Chasing Wins Bring Max Profit or Risk?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’ve all heard the trading adage, “Let your winners run,” or “add to a winning position.” It sounds great in theory, right? You jump on a trend, and as it keeps going your way, you add more fuel to the fire, riding that “winning horse” for maximum profit. This is called pyramiding, and it’s what we wanted to explore in this research. Specifically, we looked at a classic Turtle-style pyramiding strategy. Imagine you enter a trade when the price breaks out of a certain range. If the trend continues positively, you add another “unit” (another portion of your trade) for every X amount of price movement (often based on ATR, or Average True Range, which measures market volatility). You keep doing this up to a maximum number of units, with each unit having its own ATR stop-loss to protect profits. The whole position is then exited if the price breaks out of the trend in the opposite direction.

Unlock Explosive Monthly Profits: The 3 Secrets to Boosting Your EA!

Wed, 15 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Unlock Explosive Monthly Profits: The 3 Secrets to Boosting Your EA!”.

This research dives into how we can squeeze more monthly profit out of our algorithmic FX trading systems, or EAs. There are fundamentally three ways to do this:

Increase your risk appetite: This means being willing to dip further into your “drawdown budget” – the maximum loss you’re comfortable with.
Improve your system’s efficiency: Specifically, enhance the ratio of your returns to your drawdowns, which then allows you to safely use more leverage.
Pyramiding: This is about adding to winning positions, essentially compounding your gains when a trend is strong. This is a powerful technique but often requires significant changes to the core trading engine itself. For this study, we focused on the first two approaches, as pyramiding is a much larger undertaking.

How I Tested It

We looked at two main areas to see if we could boost those monthly returns.

Gold EA Breakthrough: Our Final System's New XAUUSD Strategy!

Tue, 14 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Gold EA Breakthrough: Our Final System’s New XAUUSD Strategy!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’ve been working on our flagship algorithmic trading system, which we call final_system. It’s designed to trade multiple assets, specifically Gold (XAUUSD) and four other major FX pairs. Our goal is always to make it more robust, profitable, and safer. This time, we focused on Gold. Previously, Gold was using a Breakout long strategy – essentially, buying when prices broke above a certain level, hoping for a trend to continue. We had a hunch that a different strategy, ATRcandle long, might perform better for Gold. The ATRcandle long strategy likely uses the Average True Range (ATR) to gauge volatility and identify entries based on candle characteristics, aiming to catch strong moves. The plan was simple: swap out the Breakout long for ATRcandle long only for Gold, while keeping the other four FX pairs on their existing Breakout long strategy. We then re-evaluated the entire system with a consistent risk setting of 0.003 (meaning a very small percentage of our capital risked per trade).

Double Your Gold Profits? New Dual-Entry Strategy for XAUUSD!

Mon, 13 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Double Your Gold Profits? New Dual-Entry Strategy for XAUUSD!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’re always on the lookout for ways to make our algorithmic trading systems (EAs) even better, and one common approach is diversification. The theory goes that if you combine multiple strategies, especially ones that don’t always move in lockstep, you can smooth out returns and potentially reduce overall risk. This time, we explored combining two different “long” entry strategies for Gold (XAUUSD) to see if we could create a super-strategy. Specifically, we looked at:

Can This External EA Deliver? "ATR Candle Breakout" Put to the Test!

Sun, 12 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Can This External EA Deliver? “ATR Candle Breakout” Put to the Test!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’re always on the hunt for promising algorithmic trading strategies (EAs), and this time we turned our attention to bmtrading.de, an external vendor. They offer a range of EAs for MT5, and after a quick scan, one caught our eye: the ATR Candle Breakout EA. This strategy targets Gold (XAUUSD) on H1 (1-hour) and H4 (4-hour) timeframes. The core logic behind the ATR Candle Breakout is pretty neat: it looks for a “breakout” candle whose range (high minus low) is significantly larger than the average true range (ATR), which is a measure of volatility. If such a candle closes near its extreme (either high for a bullish candle or low for a bearish one), the EA assumes momentum is kicking in and enters a trade in that direction. It then sizes the trade based on volatility and uses an ATR-based trailing stop to protect profits. The vendor’s public backtest claims for this EA were quite attractive (using real tick data from 2015-2026): a total profit of +39%, a Profit Factor (PF) of 1.15, a maximum drawdown (DD) of around 9.6%, with 1552 trades and a win rate of approximately 23%. (For those new to the terms: Profit Factor (PF) is simply your gross profit divided by your gross loss; anything over 1.0 means the strategy is profitable. Drawdown (DD) is the peak-to-trough decline in your capital, a key measure of risk.)

The Adaptive EA Dream DIES? Why Auto-Strategy Switching Fell Flat.

Sat, 11 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Adaptive EA Dream DIES? Why Auto-Strategy Switching Fell Flat.”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Ever wonder if your trading robot (or EA, Expert Advisor) could be smarter? Instead of sticking to one strategy, what if it could adapt its approach based on what the market is doing right now? That’s the core concept behind what we call a “Regime Router” EA. The idea is simple: market conditions (or “regimes”) change all the time – sometimes we’re trending up, sometimes down, sometimes just bouncing in a range. A smart EA, theoretically, could switch to the best strategy for the current regime. For this research, we designed an adaptive EA that could switch between three main sub-logics (which we internally call “sleeves”):

Market Weather Forecast: MTF RSI+SMA — The Ultimate Trend Detector?

Fri, 10 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Market Weather Forecast: MTF RSI+SMA — The Ultimate Trend Detector?”.

What’s the idea?

Today, we’re diving into a common question among EA developers: Can we combine popular indicators across different timeframes to build a truly robust and profitable trading strategy? Specifically, we explored a strategy using Multiple Time Frame (MTF) analysis with the Relative Strength Index (RSI) and Simple Moving Average (SMA). Here’s the core concept: Imagine you’re trying to figure out if it’s a good time to buy. Instead of just looking at one chart, you “zoom out” to a higher timeframe for the big picture, then “zoom in” to a lower timeframe for the perfect entry.

Gold Rush: Can a Dedicated Scalping EA Tame XAUUSD's Wild Swings?

Thu, 09 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Gold Rush: Can a Dedicated Scalping EA Tame XAUUSD’s Wild Swings?”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Today, we’re diving into a quest many EA traders dream of: finding a scalping strategy that consistently wins only on Gold (XAUUSD). Gold is a notoriously volatile and exciting instrument, and the idea of snatching small, frequent profits from its movements is very appealing! The specific request was for a “scalping-ish logic that can win only on Gold.” To tackle this, I decided to explore two popular types of trading strategies:

Fractals Evolved: Can Combining Indicators Create the Ultimate EA?

Wed, 08 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Fractals Evolved: Can Combining Indicators Create the Ultimate EA?”.

What’s the idea?

We’re always on the hunt for robust trading strategies, especially those based on common technical indicators. This time, we wanted to see if we could take a “FractalBreakout” strategy – which essentially looks for price breaking above or below a recent fractal (a specific high or low point) – and make it more reliable by adding other popular indicators as filters. Think of it like this: a fractal breakout can give you a signal, but maybe it’s often a false alarm. Could we use other indicators to confirm that signal, like a second opinion, before entering a trade? We chose three well-known indicators to combine with the fractal breakout:

Unlock Hidden Trends: Bill Williams' Fractal Breakout Strategy Exposed!

Tue, 07 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Unlock Hidden Trends: Bill Williams’ Fractal Breakout Strategy Exposed!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

Today we’re diving into Bill Williams Fractals, a popular technical indicator, to see if they hold the key to a profitable algorithmic FX trading strategy (EA). The idea is simple: can we make money by trading when price breaks out from these fractal highs and lows?

Your EA's Emergency Brake? How Auto-Risk Adjustments Save Your Trades!

Mon, 06 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Your EA’s Emergency Brake? How Auto-Risk Adjustments Save Your Trades!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the big idea here?

Today, we’re diving into a strategy that combines a core trend-following approach with a clever risk management layer I call an “equity overlay.” The main goal of this particular experiment was to see if we could reduce the dreaded drawdown – that temporary decline in your trading account balance – without sacrificing too much profit. The core of the strategy is built on identifying long-term trends, a classic “edge” in trading. But the twist comes from the “equity overlay.” Here’s how it works: if your trading account’s equity (your current balance) drops below its 60-day Moving Average (MA) – essentially, if your account performance has been weaker than its recent average – the EA automatically halves your risk per trade. Think of it like tightening your belt when your finances are a bit lean. The idea is to protect capital during rough patches, so you’re still around when the good times return.

Satellite System Unveiled: The Secret to More Stable EA Profits!

Sun, 05 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Satellite System Unveiled: The Secret to More Stable EA Profits!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

We’re always looking for ways to make our algorithmic trading (EA) portfolio more robust and less risky. Our main “core” systems, like breakout strategies, might be great, but they often move in similar ways. What if we could add a second system that behaves differently, even if it only offers small gains? That’s the idea behind this “satellite” system: to find tiny, uncorrelated “edges” in the market and bundle them together to diversify our overall portfolio. This particular satellite system is built around a few specific concepts:

AI Unlocks FX Secrets: Machine Learning Discovers Hidden Trading Edge!

Sat, 04 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “AI Unlocks FX Secrets: Machine Learning Discovers Hidden Trading Edge!”.

What’s the idea?

We’re always on the hunt for those elusive “edges” in algorithmic trading – those persistent, profitable advantages that give our trading strategies an upper hand. Lately, I’ve been diving deep into Machine Learning (ML) to see if these sophisticated algorithms could uncover new, hidden patterns in FX price data that traditional indicators might miss. The goal was ambitious: I wanted to give an ML model a bunch of different inputs (what we call “features” in ML) and let it find its own way. We fed it 19 distinct features, ranging from simple moving averages to more complex momentum and volatility indicators. We then set it loose across a massive dataset covering all 20 major currency pairs. Could ML, with its incredible flexibility, find something truly novel in the markets?

Core System v1.5.0 Unleashed: New Strategy Promises Explosive Gains!

Fri, 03 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Core System v1.5.0 Unleashed: New Strategy Promises Explosive Gains!”.

Connors RSI2 entry example (USDJPY daily, real data): buy the dip when price is above the 200-day SMA and RSI(2) falls below 10.

Uncovering Hidden Treasures: Our Deep Dive for Separate Profit Sources

Thu, 02 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Uncovering Hidden Treasures: Our Deep Dive for Separate Profit Sources”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

It’s always exciting to hunt for new edges in algorithmic trading. We’re constantly looking for “alternative” or “separate” profit sources – things that can add a little extra juice to our strategies, especially to hit a consistent 2% monthly return with a safe drawdown. Think of it like trying to find small, independent streams that can feed into a larger river of profit.

Beyond Our Main Strategy: Hunting for the Elusive "Second Edge

Wed, 01 Apr 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Beyond Our Main Strategy: Hunting for the Elusive “Second Edge”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the Big Idea?

When you’re trading with an Expert Advisor (EA), having one profitable strategy – your “core” edge – is fantastic. But what if you could find a second edge? Something that zigs when your main strategy zags, smoothing out your equity curve and potentially boosting overall returns? That’s what I set out to explore in my latest round of research. The goal was to find a truly independent, price-based strategy that could diversify and strengthen our core long trend-following approach.

Conclusion Corrected: Long-Only Trend Following Is a REAL Edge!

Tue, 31 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Conclusion Corrected: Long-Only Trend Following Is a REAL Edge!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

We’ve just had a breakthrough in our EA research! For a while, we’ve been on the hunt for genuine algorithmic edges, and we’ve finally confirmed something significant about long-only trend following.

Was It Just Hindsight? Our Forward Validation Delivers a Decisive Conclusion

Mon, 30 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Was It Just Hindsight? Our Forward Validation Delivers a Decisive Conclusion”.

What’s the idea?

Today, we’re diving into a crucial experiment to see if we can find a truly sustainable edge in algorithmic FX trading. The big question is: can we pick a winning portfolio of currency pairs for the coming year solely based on their past performance, without any “peeking” into the future? To answer this, I designed what’s called a complete Out-of-Sample (OOS) testing approach. Think of it like this: imagine you’re picking a fantasy sports team for the upcoming season. You’re only allowed to look at player stats from previous seasons. You pick your best team, and then you see how they perform in the new, unseen season. That’s exactly what we did, but with currency pairs and trading strategies.

Power in Numbers: How Combining Weak EAs Boosts Success & Stability

Sun, 29 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Power in Numbers: How Combining Weak EAs Boosts Success & Stability”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

Today, we’re diving into a fascinating approach to building robust trading systems: combining multiple “weak edges” that don’t necessarily move in lockstep. Think of it like having several small, consistent fishing spots that are unlikely to all go dry at the same time. Individually, each spot might not yield a huge catch, but together, they create a much more reliable and stable supply. The goal here is to create a diversified portfolio of uncorrelated strategies, each with a slight advantage, to achieve low drawdown (DD) and high stability – a real path to consistent withdrawals.

Weak Alone, Strong Together? Unlocking Stability with Combined Micro-Edges

Sat, 28 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Weak Alone, Strong Together? Unlocking Stability with Combined Micro-Edges”.

What’s the Idea?

Ever wonder if you need a single, super-powerful trading strategy to succeed, or if combining smaller, less flashy ones could be the secret? That’s exactly what I’ve been exploring! My latest research dives into the concept of building a robust trading system by stacking up multiple “weak” but uncorrelated trading edges. The goal isn’t to hit a home run, but to create a highly stable, low-risk portfolio that consistently generates profits, making it ideal for things like prop firm challenges where capital preservation is key.

Our Promising EA Vanished: How Future Data Exposed a False Edge

Fri, 27 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Our Promising EA Vanished: How Future Data Exposed a False Edge”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

What’s the idea?

You know how exciting it is to find an Expert Advisor (EA) strategy that looks incredibly profitable in backtests? Well, we’ve been digging deep into some of those promising candidates, especially one involving a Donchian channel strategy on Gold, which initially showed a whopping +21.8% profit. The big question we’re always trying to answer is: Is this a real “edge” – a genuine statistical advantage that will keep making money – or just a fluke? To truly answer that, we need to work with “clean data.” Think of it like a chef needing fresh, unadulterated ingredients. In trading, “clean data” means making sure our historical price information is free from any errors or, crucially, any “future data leakage” (also known as look-ahead bias). This is where information from after a trade would have occurred accidentally creeps into the data used to decide on the trade, making the strategy look much better than it ever could in real life. It’s like seeing tomorrow’s newspaper headlines today – you’d make a killing, but it’s not a realistic scenario!

Gold Data Alert: Critical Anomalies Found, Threatening EA Validation!

Thu, 26 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Gold Data Alert: Critical Anomalies Found, Threatening EA Validation!”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

The Gold Rush That Wasn’t: Unmasking a Sneaky Data Quality Problem

You know how crucial good data is when you’re backtesting EAs (Expert Advisors) for FX trading. We spend so much time worrying about overfitting – making sure our strategies aren’t just “lucky” on past data. But what if the data itself is lying to us? That’s the messy, fascinating problem I recently stumbled upon, and it completely changed how I approach all my testing.

Gold EA Failure: Why Our Donchian Strategy Hit Intraday Limits

Wed, 25 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Gold EA Failure: Why Our Donchian Strategy Hit Intraday Limits”.

Here’s an interesting case study from our recent research, where a significant daily loss wasn’t what it seemed on the surface.

What’s the idea?

We were testing a gold trading Expert Advisor (EA) based on the Donchian Channel strategy, operating on the M1 (1-minute) timeframe. For those new to EAs, it’s basically an automated system that executes trades for you. The Donchian Channel is a classic trend-following indicator, designed to help us catch and ride big market moves. The core idea is to let profits run as long as the trend continues.

Gold Trading: We Uncovered the Edge of Donchian Trend Following

Tue, 24 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Gold Trading: We Uncovered the Edge of Donchian Trend Following”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

We’ve just found what might be a truly robust trading strategy for Gold (XAUUSD) using Donchian Channels – a first for our research, ticking all three critical boxes for a promising EA!

The Ultimate EA Hunt: Scanning Every FX Pair for Untapped Profit

Mon, 23 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “The Ultimate EA Hunt: Scanning Every FX Pair for Untapped Profit”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

We just wrapped up a massive scanning project for algorithmic trading strategies (Expert Advisors, or EAs) across various currency pairs, and we’ve found some really promising, specialized candidates!

Ride the FX Wave: Our EA Finds the Strongest & Weakest Currency Pairs

Sun, 22 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Ride the FX Wave: Our EA Finds the Strongest & Weakest Currency Pairs”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

Market Adaptation: Can Our EA Switch Strategies for Superior Returns?

Sat, 21 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Market Adaptation: Can Our EA Switch Strategies for Superior Returns?”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

We’ve been exploring various algorithmic trading ideas for FX, and today we’re diving into a composite strategy designed to adapt to different market conditions. The core idea was to switch between trend-following and mean-reversion strategies depending on whether the market was trending or ranging.

Hidden Dangers: Our EA's True Intraday Risk Finally Revealed!

Fri, 20 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Hidden Dangers: Our EA’s True Intraday Risk Finally Revealed!”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the big idea?

When we talk about verifying algorithmic FX trading strategies (EAs), especially those designed for longer timeframes like daily (D1), it’s easy to get a false sense of security. A D1 strategy only looks at the market once a day, usually at the close of the daily candle. But what if something wild happens during the day? You could hit your daily loss limit and not even know it until the day is over! That’s why we’ve been working on a new approach. The core idea is to take D1 trades and, while they are open, monitor their profit and loss minute-by-minute (M1). This lets us reconstruct our account equity second-by-second and, crucially, detect if we’ve breached our daily or maximum loss limits intraday. Think of it like putting a high-speed camera on your EA’s performance, rather than just a single snapshot at the end of the day.

Prop Firm Challenge: Unmasking Our EA's True Expected Value

Thu, 19 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Prop Firm Challenge: Unmasking Our EA’s True Expected Value”.

What’s the idea?

We’ve been looking into the Fintokei Quartz challenge, a popular prop firm offering where traders aim to prove their strategy on a simulated account to eventually manage real capital. The specific challenge we focused on was for a ¥1,000,000 account, with a ¥12,500 participation fee and a 1% risk set for each trade. Our goal was to calculate the “overall Expected Value” (EV) of taking on this challenge with a specific EA (Expert Advisor, or automated trading strategy). In simple terms, EV tells us, on average, how much profit or loss we expect to make each time we attempt the challenge, factoring in the costs and potential payouts.

Can Our EA Pass Prop Firm Challenges? We Simulated the Odds!

Wed, 18 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “Can Our EA Pass Prop Firm Challenges? We Simulated the Odds!”.

Mean-reversion (RSI) signal example (EURUSD daily, real data): look for a bounce when RSI is oversold.

What’s the Big Idea?

Ever wondered if your algorithmic trading strategy (EA) has what it takes to pass a prop firm challenge? That’s exactly what we set out to explore with our latest research! We wanted to figure out the probability of success for a given strategy, not just in theory, but by simulating the actual rules of a prop firm. Imagine you have a trading strategy, and you’ve seen how it performs day-to-day. We take that history of daily returns and essentially shuffle and re-deal it many, many times. This process is called block bootstrap resampling – it helps us create thousands of potential future scenarios based on your strategy’s past performance, but with enough randomness to feel realistic. Then, for each of these simulated futures, we apply the prop firm’s specific rules to see how often our strategy would “pass” the challenge.

EA Research Unveiled: The Honest Truth About Simple Strategies & What's Next

Tue, 17 Mar 2026 00:00:00 +0000

A beginner-friendly summary of the verification: “EA Research Unveiled: The Honest Truth About Simple Strategies & What’s Next”.

Breakout entry example (XAUUSD daily, real data): buy when price breaks above the recent high.

This post is a wrap-up of our recent deep dive into verifying algorithmic FX trading strategies, specifically focusing on a range of simpler Expert Advisors (EAs).

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